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We propose a framework for verifiable and compositional reinforcement
learning (RL) in which a collection of RL subsystems, each of which learns to
accomplish a separate subtask, are composed to achieve an overall task. The
framework consists of a high-level model, represented as a parametric Markov
decision process, which is used to plan and analyze compositions of subsystems,
and of the collection of low-level subsystems themselves. The subsystems are
implemented as deep RL agents operating under partial observability. By
defining interfaces between the subsystems, the framework enables automatic
decompositions of task specifications, e.g., reach a target set of states with
a probability of at least 0.95, into individual subtask specifications, i.e.
achieve the subsystem's exit conditions with at least some minimum probability,
given that its entry conditions are met. This in turn allows for the
independent training and testing of the subsystems. We present theoretical
results guaranteeing that if each subsystem learns a policy satisfying its
subtask specification, then their composition is guaranteed to satisfy the
overall task specification. Conversely, if the subtask specifications cannot
all be satisfied by the learned policies, we present a method, formulated as
the problem of finding an optimal set of parameters in the high-level model, to
automatically update the subtask specifications to account for the observed
shortcomings. The result is an iterative procedure for defining subtask
specifications, and for training the subsystems to meet them. Experimental
results demonstrate the presented framework's novel capabilities in
environments with both full and partial observability, discrete and continuous
state and action spaces, as well as deterministic and stochastic dynamics. | [
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] |
Semantic and Cross-language code clone generation may be useful for code
reuse, code comprehension, refactoring and benchmarking. OpenAI's GPT model has
potential in such clone generation as GPT is used for text generation. When
developers copy/paste codes from Stack Overflow (SO) or within a system, there
might be inconsistent changes leading to unexpected behaviours. Similarly, if
someone possesses a code snippet in a particular programming language but seeks
equivalent functionality in a different language, a semantic cross-language
code clone generation approach could provide valuable assistance. In this
study, using SemanticCloneBench as a vehicle, we evaluated how well the GPT-3
model could help generate semantic and cross-language clone variants for a
given fragment.We have comprised a diverse set of code fragments and assessed
GPT-3s performance in generating code variants.Through extensive
experimentation and analysis, where 9 judges spent 158 hours to validate, we
investigate the model's ability to produce accurate and semantically correct
variants. Our findings shed light on GPT-3's strengths in code generation,
offering insights into the potential applications and challenges of using
advanced language models in software development. Our quantitative analysis
yields compelling results. In the realm of semantic clones, GPT-3 attains an
impressive accuracy of 62.14% and 0.55 BLEU score, achieved through few-shot
prompt engineering. Furthermore, the model shines in transcending linguistic
confines, boasting an exceptional 91.25% accuracy in generating cross-language
clones | [
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While session-based recommender systems (SBRSs) have shown superior
recommendation performance, multi-task learning (MTL) has been adopted by SBRSs
to enhance their prediction accuracy and generalizability further. Hierarchical
MTL (H-MTL) sets a hierarchical structure between prediction tasks and feeds
outputs from auxiliary tasks to main tasks. This hierarchy leads to richer
input features for main tasks and higher interpretability of predictions,
compared to existing MTL frameworks. However, the H-MTL framework has not been
investigated in SBRSs yet. In this paper, we propose HierSRec which
incorporates the H-MTL architecture into SBRSs. HierSRec encodes a given
session with a metadata-aware Transformer and performs next-category prediction
(i.e., auxiliary task) with the session encoding. Next, HierSRec conducts
next-item prediction (i.e., main task) with the category prediction result and
session encoding. For scalable inference, HierSRec creates a compact set of
candidate items (e.g., 4% of total items) per test example using the category
prediction. Experiments show that HierSRec outperforms existing SBRSs as per
next-item prediction accuracy on two session-based recommendation datasets. The
accuracy of HierSRec measured with the carefully-curated candidate items aligns
with the accuracy of HierSRec calculated with all items, which validates the
usefulness of our candidate generation scheme via H-MTL. | [
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] |
By and large, the professional handling of huge data collections is regarded
as a fundamental ingredient of the progress of machine learning and of its
spectacular results in related disciplines, with a growing agreement on risks
connected to the centralization of such data collections. This paper sustains
the position that the time has come for thinking of new learning protocols
where machines conquer cognitive skills in a truly human-like context centered
on environmental interactions. This comes with specific restrictions on the
learning protocol according to the collectionless principle, which states that,
at each time instant, data acquired from the environment is processed with the
purpose of contributing to update the current internal representation of the
environment, and that the agent is not given the privilege of recording the
temporal stream. Basically, there is neither permission to store the temporal
information coming from the sensors, thus promoting the development of
self-organized memorization skills at a more abstract level, instead of relying
on bare storage to simulate learning dynamics that are typical of offline
learning algorithms. This purposely extreme position is intended to stimulate
the development of machines that learn to dynamically organize the information
by following human-based schemes. The proposition of this challenge suggests
developing new foundations on computational processes of learning and reasoning
that might open the doors to a truly orthogonal competitive track on AI
technologies that avoid data accumulation by design, thus offering a framework
which is better suited concerning privacy issues, control and customizability.
Finally, pushing towards massively distributed computation, the collectionless
approach to AI will likely reduce the concentration of power in companies and
governments, thus better facing geopolitical issues. | [
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] |
DNA exhibits remarkable potential as a data storage solution due to its
impressive storage density and long-term stability, stemming from its inherent
biomolecular structure. However, developing this novel medium comes with its
own set of challenges, particularly in addressing errors arising from storage
and biological manipulations. These challenges are further conditioned by the
structural constraints of DNA sequences and cost considerations. In response to
these limitations, we have pioneered a novel compression scheme and a
cutting-edge Multiple Description Coding (MDC) technique utilizing neural
networks for DNA data storage. Our MDC method introduces an innovative approach
to encoding data into DNA, specifically designed to withstand errors
effectively. Notably, our new compression scheme overperforms classic image
compression methods for DNA-data storage. Furthermore, our approach exhibits
superiority over conventional MDC methods reliant on auto-encoders. Its
distinctive strengths lie in its ability to bypass the need for extensive model
training and its enhanced adaptability for fine-tuning redundancy levels.
Experimental results demonstrate that our solution competes favorably with the
latest DNA data storage methods in the field, offering superior compression
rates and robust noise resilience. | [
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Despite their promise to facilitate new scientific discoveries, the
opaqueness of neural networks presents a challenge in interpreting the logic
behind their findings. Here, we use a eXplainable-AI (XAI) technique called
$inception$ or $deep$ $dreaming$, which has been invented in machine learning
for computer vision. We use this technique to explore what neural networks
learn about quantum optics experiments. Our story begins by training deep
neural networks on the properties of quantum systems. Once trained, we "invert"
the neural network -- effectively asking how it imagines a quantum system with
a specific property, and how it would continuously modify the quantum system to
change a property. We find that the network can shift the initial distribution
of properties of the quantum system, and we can conceptualize the learned
strategies of the neural network. Interestingly, we find that, in the first
layers, the neural network identifies simple properties, while in the deeper
ones, it can identify complex quantum structures and even quantum entanglement.
This is in reminiscence of long-understood properties known in computer vision,
which we now identify in a complex natural science task. Our approach could be
useful in a more interpretable way to develop new advanced AI-based scientific
discovery techniques in quantum physics. | [
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] |
With the rapid development of electronic science and technology, the research
on wearable devices is constantly updated, but for now, it is not comprehensive
for wearable devices to recognize and analyze the movement of specific sports.
Based on this, this paper improves wearable devices of table tennis sport, and
realizes the pattern recognition and evaluation of table tennis players' motor
skills through artificial intelligence. Firstly, a device is designed to
collect the movement information of table tennis players and the actual
movement data is processed. Secondly, a sliding window is made to divide the
collected motion data into a characteristic database of six table tennis
benchmark movements. Thirdly, motion features were constructed based on feature
engineering, and motor skills were identified for different models after
dimensionality reduction. Finally, the hierarchical evaluation system of motor
skills is established with the loss functions of different evaluation indexes.
The results show that in the recognition of table tennis players' motor skills,
the feature-based BP neural network proposed in this paper has higher
recognition accuracy and stronger generalization ability than the traditional
convolutional neural network. | [
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] |
Decoding visual representations from human brain activity has emerged as a
thriving research domain, particularly in the context of brain-computer
interfaces. Our study presents an innovative method that employs to classify
and reconstruct images from the ImageNet dataset using electroencephalography
(EEG) data from subjects that had viewed the images themselves (i.e. "brain
decoding"). We analyzed EEG recordings from 6 participants, each exposed to 50
images spanning 40 unique semantic categories. These EEG readings were
converted into spectrograms, which were then used to train a convolutional
neural network (CNN), integrated with a knowledge distillation procedure based
on a pre-trained Contrastive Language-Image Pre-Training (CLIP)-based image
classification teacher network. This strategy allowed our model to attain a
top-5 accuracy of 80%, significantly outperforming a standard CNN and various
RNN-based benchmarks. Additionally, we incorporated an image reconstruction
mechanism based on pre-trained latent diffusion models, which allowed us to
generate an estimate of the images which had elicited EEG activity. Therefore,
our architecture not only decodes images from neural activity but also offers a
credible image reconstruction from EEG only, paving the way for e.g. swift,
individualized feedback experiments. Our research represents a significant step
forward in connecting neural signals with visual cognition. | [
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] |
Nuclear Magnetic Resonance (NMR) spectroscopy has served as a powerful
analytical tool for studying molecular structure and dynamics in chemistry and
biology. However, the processing of raw data acquired from NMR spectrometers
and subsequent quantitative analysis involves various specialized tools, which
necessitates comprehensive knowledge in programming and NMR. Particularly, the
emerging deep learning tools is hard to be widely used in NMR due to the
sophisticated setup of computation. Thus, NMR processing is not an easy task
for chemist and biologists. In this work, we present CloudBrain-NMR, an
intelligent online cloud computing platform designed for NMR data reading,
processing, reconstruction, and quantitative analysis. The platform is
conveniently accessed through a web browser, eliminating the need for any
program installation on the user side. CloudBrain-NMR uses parallel computing
with graphics processing units and central processing units, resulting in
significantly shortened computation time. Furthermore, it incorporates
state-of-the-art deep learning-based algorithms offering comprehensive
functionalities that allow users to complete the entire processing procedure
without relying on additional software. This platform has empowered NMR
applications with advanced artificial intelligence processing. CloudBrain-NMR
is openly accessible for free usage at https://csrc.xmu.edu.cn/CloudBrain.html | [
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] |
Ball bearings find widespread use in various manufacturing and mechanical
domains, and methods based on machine learning have been widely adopted in the
field to monitor wear and spot defects before they lead to failures. Few
studies, however, have addressed the problem of censored data, in which failure
is not observed. In this paper, we propose a novel approach to predict the time
to failure in ball bearings using survival analysis. First, we analyze bearing
data in the frequency domain and annotate when a bearing fails by comparing the
Kullback-Leibler divergence and the standard deviation between its break-in
frequency bins and its break-out frequency bins. Second, we train several
survival models to estimate the time to failure based on the annotated data and
covariates extracted from the time domain, such as skewness, kurtosis and
entropy. The models give a probabilistic prediction of risk over time and allow
us to compare the survival function between groups of bearings. We demonstrate
our approach on the XJTU and PRONOSTIA datasets. On XJTU, the best result is a
0.70 concordance-index and 0.21 integrated Brier score. On PRONOSTIA, the best
is a 0.76 concordance-index and 0.19 integrated Brier score. Our work motivates
further work on incorporating censored data in models for predictive
maintenance. | [
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] |
Quantum Support Vector Machines (QSVM) play a vital role in using quantum
resources for supervised machine learning tasks, such as classification.
However, current methods are strongly limited in terms of scalability on Noisy
Intermediate Scale Quantum (NISQ) devices. In this work, we propose a novel
approach called the Variational Quantum Linear Solver (VQLS) enhanced QSVM.
This is built upon our idea of utilizing the variational quantum linear solver
to solve system of linear equations of a least squares-SVM on a NISQ device.
The implementation of our approach is evaluated by an extensive series of
numerical experiments with the Iris dataset, which consists of three distinct
iris plant species. Based on this, we explore the practicality and
effectiveness of our algorithm by constructing a classifier capable of
classification in a feature space ranging from one to seven dimensions.
Furthermore, by strategically exploiting both classical and quantum computing
for various subroutines of our algorithm, we effectively mitigate practical
challenges associated with the implementation. These include significant
improvement in the trainability of the variational ansatz and notable
reductions in run-time for cost calculations. Based on the numerical
experiments, our approach exhibits the capability of identifying a separating
hyperplane in an 8-dimensional feature space. Moreover, it consistently
demonstrated strong performance across various instances with the same dataset. | [
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] |
Optimizing traffic dynamics in an evolving transportation landscape is
crucial, particularly in scenarios where autonomous vehicles (AVs) with varying
levels of autonomy coexist with human-driven cars. This paper presents a novel
approach to optimizing choices of AVs using Proximal Policy Optimization (PPO),
a reinforcement learning algorithm. We learned a policy to minimize traffic
jams (i.e., minimize the time to cross the scenario) and to minimize pollution
in a roundabout in Milan, Italy. Through empirical analysis, we demonstrate
that our approach can reduce time and pollution levels. Furthermore, we
qualitatively evaluate the learned policy using a cutting-edge cockpit to
assess its performance in near-real-world conditions. To gauge the practicality
and acceptability of the policy, we conducted evaluations with human
participants using the simulator, focusing on a range of metrics like traffic
smoothness and safety perception. In general, our findings show that
human-driven vehicles benefit from optimizing AVs dynamics. Also, participants
in the study highlighted that the scenario with 80\% AVs is perceived as safer
than the scenario with 20\%. The same result is obtained for traffic smoothness
perception. | [
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] |
Addressing causal queries across the Pearl Causal Hierarchy (PCH) (i.e.,
associational, interventional and counterfactual), which is formalized as
\Layer{} Valuations, is a central task in contemporary causal inference
research. Counterfactual questions, in particular, pose a significant challenge
as they often necessitate a complete knowledge of structural equations. This
paper identifies \textbf{the degeneracy problem} caused by the consistency
rule. To tackle this, the \textit{Distribution-consistency Structural Causal
Models} (DiscoSCMs) is introduced, which extends both the structural causal
models (SCM) and the potential outcome framework. The correlation pattern of
potential outcomes in personalized incentive scenarios, described by $P(y_x,
y'_{x'})$, is used as a case study for elucidation. Although counterfactuals
are no longer degenerate, they remain indeterminable. As a result, the
condition of independent potential noise is incorporated into DiscoSCM. It is
found that by adeptly using homogeneity, counterfactuals can be identified.
Furthermore, more refined results are achieved in the unit problem scenario. In
simpler terms, when modeling counterfactuals, one should contemplate: "Consider
a person with average ability who takes a test and, due to good luck, achieves
an exceptionally high score. If this person were to retake the test under
identical external conditions, what score will he obtain? An exceptionally high
score or an average score?" If your choose is predicting an average score, then
you are essentially choosing DiscoSCM over the traditional frameworks based on
the consistency rule. | [
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] |
Understanding the decision-making process of a machine/deep learning model is
crucial, particularly in security-sensitive applications. In this study, we
introduce a neural network framework that combines the global and exact
interpretability properties of rule-based models with the high performance of
deep neural networks.
Our proposed framework, called $\textit{Truth Table rules}$ (TT-rules), is
built upon $\textit{Truth Table nets}$ (TTnets), a family of deep neural
networks initially developed for formal verification. By extracting the set of
necessary and sufficient rules $\mathcal{R}$ from the trained TTnet model
(global interpretability), yielding the same output as the TTnet (exact
interpretability), TT-rules effectively transforms the neural network into a
rule-based model. This rule-based model supports binary classification,
multi-label classification, and regression tasks for tabular datasets.
Furthermore, our TT-rules framework optimizes the rule set $\mathcal{R}$ into
$\mathcal{R}_{opt}$ by reducing the number and size of the rules. To enhance
model interpretation, we leverage Reduced Ordered Binary Decision Diagrams
(ROBDDs) to visualize these rules effectively.
After outlining the framework, we evaluate the performance of TT-rules on
seven tabular datasets from finance, healthcare, and justice domains. We also
compare the TT-rules framework to state-of-the-art rule-based methods. Our
results demonstrate that TT-rules achieves equal or higher performance compared
to other interpretable methods while maintaining a balance between performance
and complexity. Notably, TT-rules presents the first accurate rule-based model
capable of fitting large tabular datasets, including two real-life DNA datasets
with over 20K features. Finally, we extensively investigate a rule-based model
derived from TT-rules using the Adult dataset. | [
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] |
Diffusion models have shown promising results in speech enhancement, using a
task-adapted diffusion process for the conditional generation of clean speech
given a noisy mixture. However, at test time, the neural network used for score
estimation is called multiple times to solve the iterative reverse process.
This results in a slow inference process and causes discretization errors that
accumulate over the sampling trajectory. In this paper, we address these
limitations through a two-stage training approach. In the first stage, we train
the diffusion model the usual way using the generative denoising score matching
loss. In the second stage, we compute the enhanced signal by solving the
reverse process and compare the resulting estimate to the clean speech target
using a predictive loss. We show that using this second training stage enables
achieving the same performance as the baseline model using only 5 function
evaluations instead of 60 function evaluations. While the performance of usual
generative diffusion algorithms drops dramatically when lowering the number of
function evaluations (NFEs) to obtain single-step diffusion, we show that our
proposed method keeps a steady performance and therefore largely outperforms
the diffusion baseline in this setting and also generalizes better than its
predictive counterpart. | [
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] |
The rapidly evolving nature of Android apps poses a significant challenge to
static batch machine learning algorithms employed in malware detection systems,
as they quickly become obsolete. Despite this challenge, the existing
literature pays limited attention to addressing this issue, with many advanced
Android malware detection approaches, such as Drebin, DroidDet and MaMaDroid,
relying on static models. In this work, we show how retraining techniques are
able to maintain detector capabilities over time. Particularly, we analyze the
effect of two aspects in the efficiency and performance of the detectors: 1)
the frequency with which the models are retrained, and 2) the data used for
retraining. In the first experiment, we compare periodic retraining with a more
advanced concept drift detection method that triggers retraining only when
necessary. In the second experiment, we analyze sampling methods to reduce the
amount of data used to retrain models. Specifically, we compare fixed sized
windows of recent data and state-of-the-art active learning methods that select
those apps that help keep the training dataset small but diverse. Our
experiments show that concept drift detection and sample selection mechanisms
result in very efficient retraining strategies which can be successfully used
to maintain the performance of the static Android malware state-of-the-art
detectors in changing environments. | [
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] |
Autonomous mobility tasks such as lastmile delivery require reasoning about
operator indicated preferences over terrains on which the robot should navigate
to ensure both robot safety and mission success. However, coping with out of
distribution data from novel terrains or appearance changes due to lighting
variations remains a fundamental problem in visual terrain adaptive navigation.
Existing solutions either require labor intensive manual data recollection and
labeling or use handcoded reward functions that may not align with operator
preferences. In this work, we posit that operator preferences for visually
novel terrains, which the robot should adhere to, can often be extrapolated
from established terrain references within the inertial, proprioceptive, and
tactile domain. Leveraging this insight, we introduce Preference extrApolation
for Terrain awarE Robot Navigation, PATERN, a novel framework for extrapolating
operator terrain preferences for visual navigation. PATERN learns to map
inertial, proprioceptive, tactile measurements from the robots observations to
a representation space and performs nearest neighbor search in this space to
estimate operator preferences over novel terrains. Through physical robot
experiments in outdoor environments, we assess PATERNs capability to
extrapolate preferences and generalize to novel terrains and challenging
lighting conditions. Compared to baseline approaches, our findings indicate
that PATERN robustly generalizes to diverse terrains and varied lighting
conditions, while navigating in a preference aligned manner. | [
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] |
In this work, we present a scalable reinforcement learning method for
training multi-task policies from large offline datasets that can leverage both
human demonstrations and autonomously collected data. Our method uses a
Transformer to provide a scalable representation for Q-functions trained via
offline temporal difference backups. We therefore refer to the method as
Q-Transformer. By discretizing each action dimension and representing the
Q-value of each action dimension as separate tokens, we can apply effective
high-capacity sequence modeling techniques for Q-learning. We present several
design decisions that enable good performance with offline RL training, and
show that Q-Transformer outperforms prior offline RL algorithms and imitation
learning techniques on a large diverse real-world robotic manipulation task
suite. The project's website and videos can be found at
https://qtransformer.github.io | [
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] |
The problem of coordinated data collection is studied for a mobile
crowdsensing (MCS) system. A mobile crowdsensing platform (MCSP) sequentially
publishes sensing tasks to the available mobile units (MUs) that signal their
willingness to participate in a task by sending sensing offers back to the
MCSP. From the received offers, the MCSP decides the task assignment. A stable
task assignment must address two challenges: the MCSP's and MUs' conflicting
goals, and the uncertainty about the MUs' required efforts and preferences. To
overcome these challenges a novel decentralized approach combining matching
theory and online learning, called collision-avoidance multi-armed bandit with
strategic free sensing (CA-MAB-SFS), is proposed. The task assignment problem
is modeled as a matching game considering the MCSP's and MUs' individual goals
while the MUs learn their efforts online. Our innovative "free-sensing"
mechanism significantly improves the MU's learning process while reducing
collisions during task allocation. The stable regret of CA-MAB-SFS, i.e., the
loss of learning, is analytically shown to be bounded by a sublinear function,
ensuring the convergence to a stable optimal solution. Simulation results show
that CA-MAB-SFS increases the MUs' and the MCSP's satisfaction compared to
state-of-the-art methods while reducing the average task completion time by at
least 16%. | [
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] |
Classical motion planning for robotic manipulation includes a set of general
algorithms that aim to minimize a scene-specific cost of executing a given
plan. This approach offers remarkable adaptability, as they can be directly
used off-the-shelf for any new scene without needing specific training
datasets. However, without a prior understanding of what diverse valid
trajectories are and without specially designed cost functions for a given
scene, the overall solutions tend to have low success rates. While
deep-learning-based algorithms tremendously improve success rates, they are
much harder to adopt without specialized training datasets. We propose EDMP, an
Ensemble-of-costs-guided Diffusion for Motion Planning that aims to combine the
strengths of classical and deep-learning-based motion planning. Our
diffusion-based network is trained on a set of diverse kinematically valid
trajectories. Like classical planning, for any new scene at the time of
inference, we compute scene-specific costs such as "collision cost" and guide
the diffusion to generate valid trajectories that satisfy the scene-specific
constraints. Further, instead of a single cost function that may be
insufficient in capturing diversity across scenes, we use an ensemble of costs
to guide the diffusion process, significantly improving the success rate
compared to classical planners. EDMP performs comparably with SOTA
deep-learning-based methods while retaining the generalization capabilities
primarily associated with classical planners. | [
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] |
There is a correlation between adjacent channels of electroencephalogram
(EEG), and how to represent this correlation is an issue that is currently
being explored. In addition, due to inter-individual differences in EEG
signals, this discrepancy results in new subjects need spend a amount of
calibration time for EEG-based motor imagery brain-computer interface. In order
to solve the above problems, we propose a Dynamic Domain Adaptation Based Deep
Learning Network (DADL-Net). First, the EEG data is mapped to the
three-dimensional geometric space and its temporal-spatial features are learned
through the 3D convolution module, and then the spatial-channel attention
mechanism is used to strengthen the features, and the final convolution module
can further learn the spatial-temporal information of the features. Finally, to
account for inter-subject and cross-sessions differences, we employ a dynamic
domain-adaptive strategy, the distance between features is reduced by
introducing a Maximum Mean Discrepancy loss function, and the classification
layer is fine-tuned by using part of the target domain data. We verify the
performance of the proposed method on BCI competition IV 2a and OpenBMI
datasets. Under the intra-subject experiment, the accuracy rates of 70.42% and
73.91% were achieved on the OpenBMI and BCIC IV 2a datasets. | [
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] |
Today's AI systems for medical decision support often succeed on benchmark
datasets in research papers but fail in real-world deployment. This work
focuses on the decision making of sepsis, an acute life-threatening systematic
infection that requires an early diagnosis with high uncertainty from the
clinician. Our aim is to explore the design requirements for AI systems that
can support clinical experts in making better decisions for the early diagnosis
of sepsis. The study begins with a formative study investigating why clinical
experts abandon an existing AI-powered Sepsis predictive module in their
electrical health record (EHR) system. We argue that a human-centered AI system
needs to support human experts in the intermediate stages of a medical
decision-making process (e.g., generating hypotheses or gathering data),
instead of focusing only on the final decision. Therefore, we build SepsisLab
based on a state-of-the-art AI algorithm and extend it to predict the future
projection of sepsis development, visualize the prediction uncertainty, and
propose actionable suggestions (i.e., which additional laboratory tests can be
collected) to reduce such uncertainty. Through heuristic evaluation with six
clinicians using our prototype system, we demonstrate that SepsisLab enables a
promising human-AI collaboration paradigm for the future of AI-assisted sepsis
diagnosis and other high-stakes medical decision making. | [
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] |
The rising usage of AI and ML-based processing across application domains has
exacerbated the need for low-cost ML implementation, specifically for
resource-constrained embedded systems. To this end, approximate computing, an
approach that explores the power, performance, area (PPA), and behavioral
accuracy (BEHAV) trade-offs, has emerged as a possible solution for
implementing embedded machine learning. Due to the predominance of MAC
operations in ML, designing platform-specific approximate arithmetic operators
forms one of the major research problems in approximate computing. Recently
there has been a rising usage of AI/ML-based design space exploration
techniques for implementing approximate operators. However, most of these
approaches are limited to using ML-based surrogate functions for predicting the
PPA and BEHAV impact of a set of related design decisions. While this approach
leverages the regression capabilities of ML methods, it does not exploit the
more advanced approaches in ML. To this end, we propose AxOCS, a methodology
for designing approximate arithmetic operators through ML-based supersampling.
Specifically, we present a method to leverage the correlation of PPA and BEHAV
metrics across operators of varying bit-widths for generating larger bit-width
operators. The proposed approach involves traversing the relatively smaller
design space of smaller bit-width operators and employing its associated
Design-PPA-BEHAV relationship to generate initial solutions for
metaheuristics-based optimization for larger operators. The experimental
evaluation of AxOCS for FPGA-optimized approximate operators shows that the
proposed approach significantly improves the quality-resulting hypervolume for
multi-objective optimization-of 8x8 signed approximate multipliers. | [
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] |
Effective learning strategies based on principles like personalization,
retrieval practice, and spaced repetition are often challenging to implement
due to practical constraints. Here we explore the integration of AI tutors to
complement learning programs in accordance with learning sciences. A
semester-long study was conducted at UniDistance Suisse, where an AI tutor app
was provided to psychology students taking a neuroscience course (N=51). After
automatically generating microlearning questions from existing course materials
using GPT-3, the AI tutor developed a dynamic neural-network model of each
student's grasp of key concepts. This enabled the implementation of distributed
retrieval practice, personalized to each student's individual level and
abilities. The results indicate that students who actively engaged with the AI
tutor achieved significantly higher grades. Moreover, active engagement led to
an average improvement of up to 15 percentile points compared to a parallel
course without AI tutor. Additionally, the grasp strongly correlated with the
exam grade, thus validating the relevance of neural-network predictions. This
research demonstrates the ability of personal AI tutors to model human learning
processes and effectively enhance academic performance. By integrating AI
tutors into their programs, educators can offer students personalized learning
experiences grounded in the principles of learning sciences, thereby addressing
the challenges associated with implementing effective learning strategies.
These findings contribute to the growing body of knowledge on the
transformative potential of AI in education. | [
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] |
Individual differences in personality determine our preferences, traits and
values, which should similarly hold for the way we express ourselves. With
current advancements and transformations of technology and society, text-based
communication has become ordinary and often even surpasses natural voice
conversations -- with distinct challenges and opportunities. In this
exploratory work, we investigate the impact of personality on the tendency how
players of a team-based collaborative alternate reality game express themselves
affectively. We collected chat logs from eleven players over two weeks, labeled
them according to their affective state, and assessed the connection between
them and the five-factor personality domains and facets. After applying
multi-linear regression, we found a series of reasonable correlations between
(combinations of) personality variables and expressed affect -- as increased
confusion could be predicted by lower self-competence (C1), personal annoyance
by vulnerability to stress (N6) and expressing anger occured more often in
players that are prone to anxiety (N1), less humble and modest (A5), think less
carefully before they act (C6) and have higher neuroticism (N). Expanding the
data set, sample size and input modalities in subsequent work, we aim to
confirm these findings and reveal even more interesting connections that could
inform affective computing and games user research equally. | [
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] |
A kidney exchange program, also called a kidney paired donation program, can
be viewed as a repeated, dynamic trading and allocation mechanism. This
suggests that a dynamic algorithm for transplant exchange selection may have
superior performance in comparison to the repeated use of a static algorithm.
We confirm this hypothesis using a full scale simulation of the Canadian Kidney
Paired Donation Program: learning algorithms, that attempt to learn optimal
patient-donor weights in advance via dynamic simulations, do lead to improved
outcomes. Specifically, our learning algorithms, designed with the objective of
fairness (that is, equity in terms of transplant accessibility across cPRA
groups), also lead to an increased number of transplants and shorter average
waiting times. Indeed, our highest performing learning algorithm improves
egalitarian fairness by 10% whilst also increasing the number of transplants by
6% and decreasing waiting times by 24%. However, our main result is much more
surprising. We find that the most critical factor in determining the
performance of a kidney exchange program is not the judicious assignment of
positive weights (rewards) to patient-donor pairs. Rather, the key factor in
increasing the number of transplants, decreasing waiting times and improving
group fairness is the judicious assignment of a negative weight (penalty) to
the small number of non-directed donors in the kidney exchange program. | [
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] |
Graph Neural Networks (GNNs) have achieved promising performance in a variety
of graph-focused tasks. Despite their success, existing GNNs suffer from two
significant limitations: a lack of interpretability in results due to their
black-box nature, and an inability to learn representations of varying orders.
To tackle these issues, we propose a novel Model-agnostic Graph Neural Network
(MaGNet) framework, which is able to sequentially integrate information of
various orders, extract knowledge from high-order neighbors, and provide
meaningful and interpretable results by identifying influential compact graph
structures. In particular, MaGNet consists of two components: an estimation
model for the latent representation of complex relationships under graph
topology, and an interpretation model that identifies influential nodes, edges,
and important node features. Theoretically, we establish the generalization
error bound for MaGNet via empirical Rademacher complexity, and showcase its
power to represent layer-wise neighborhood mixing. We conduct comprehensive
numerical studies using simulated data to demonstrate the superior performance
of MaGNet in comparison to several state-of-the-art alternatives. Furthermore,
we apply MaGNet to a real-world case study aimed at extracting task-critical
information from brain activity data, thereby highlighting its effectiveness in
advancing scientific research. | [
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] |
The automatic discovery of a model to represent the history of encounters of
a group of patients with the healthcare system -- the so-called "pathway of
patients" -- is a new field of research that supports clinical and
organisational decisions to improve the quality and efficiency of the treatment
provided. The pathways of patients with chronic conditions tend to vary
significantly from one person to another, have repetitive tasks, and demand the
analysis of multiple perspectives (interventions, diagnoses, medical
specialities, among others) influencing the results. Therefore, modelling and
mining those pathways is still a challenging task. In this work, we propose a
framework comprising: (i) a pathway model based on a multi-aspect graph, (ii) a
novel dissimilarity measurement to compare pathways taking the elapsed time
into account, and (iii) a mining method based on traditional centrality
measures to discover the most relevant steps of the pathways. We evaluated the
framework using the study cases of pregnancy and diabetes, which revealed its
usefulness in finding clusters of similar pathways, representing them in an
easy-to-interpret way, and highlighting the most significant patterns according
to multiple perspectives. | [
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] |
Cough acoustics contain multitudes of vital information about
pathomorphological alterations in the respiratory system. Reliable and accurate
detection of cough events by investigating the underlying cough latent features
and disease diagnosis can play an indispensable role in revitalizing the
healthcare practices. The recent application of Artificial Intelligence (AI)
and advances of ubiquitous computing for respiratory disease prediction has
created an auspicious trend and myriad of future possibilities in the medical
domain. In particular, there is an expeditiously emerging trend of Machine
learning (ML) and Deep Learning (DL)-based diagnostic algorithms exploiting
cough signatures. The enormous body of literature on cough-based AI algorithms
demonstrate that these models can play a significant role for detecting the
onset of a specific respiratory disease. However, it is pertinent to collect
the information from all relevant studies in an exhaustive manner for the
medical experts and AI scientists to analyze the decisive role of AI/ML. This
survey offers a comprehensive overview of the cough data-driven ML/DL detection
and preliminary diagnosis frameworks, along with a detailed list of significant
features. We investigate the mechanism that causes cough and the latent cough
features of the respiratory modalities. We also analyze the customized cough
monitoring application, and their AI-powered recognition algorithms. Challenges
and prospective future research directions to develop practical, robust, and
ubiquitous solutions are also discussed in detail. | [
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The application of Unbiased Learning to Rank (ULTR) is widespread in modern
systems for training unbiased ranking models from biased click logs. The key is
to explicitly model a generation process for user behavior and fit click data
based on examination hypothesis. Previous research found empirically that the
true latent relevance can be recovered in most cases as long as the clicks are
perfectly fitted. However, we demonstrate that this is not always achievable,
resulting in a significant reduction in ranking performance. In this work, we
aim to answer if or when the true relevance can be recovered from click data,
which is a foundation issue for ULTR field. We first define a ranking model as
identifiable if it can recover the true relevance up to a scaling
transformation, which is enough for pairwise ranking objective. Then we explore
an equivalent condition for identifiability that can be novely expressed as a
graph connectivity test problem: if and only if a graph (namely identifiability
graph, or IG) constructed on the underlying structure of the dataset is
connected, we can guarantee that the relevance can be correctly recovered. When
the IG is not connected, there may be bad cases leading to poor ranking
performance. To address this issue, we propose two methods, namely node
intervention and node merging, to modify the dataset and restore connectivity
of the IG. Empirical results obtained on a simulation dataset and two LTR
benchmark datasets confirm the validity of our proposed theorems and show the
effectiveness of our methods in mitigating data bias when the relevance model
is unidentifiable. | [
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] |
A wireless communications system usually consists of a transmitter which
transmits the information and a receiver which recovers the original
information from the received distorted signal. Deep learning (DL) has been
used to improve the performance of the receiver in complicated channel
environments and state-of-the-art (SOTA) performance has been achieved.
However, its robustness has not been investigated. In order to evaluate the
robustness of DL-based information recovery models under adversarial
circumstances, we investigate adversarial attacks on the SOTA DL-based
information recovery model, i.e., DeepReceiver. We formulate the problem as an
optimization problem with power and peak-to-average power ratio (PAPR)
constraints. We design different adversarial attack methods according to the
adversary's knowledge of DeepReceiver's model and/or testing samples. Extensive
experiments show that the DeepReceiver is vulnerable to the designed attack
methods in all of the considered scenarios. Even in the scenario of both model
and test sample restricted, the adversary can attack the DeepReceiver and
increase its bit error rate (BER) above 10%. It can also be found that the
DeepReceiver is vulnerable to adversarial perturbations even with very low
power and limited PAPR. These results suggest that defense measures should be
taken to enhance the robustness of DeepReceiver. | [
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] |
Due to the large state space of the two-qubit system, and the adoption of
ladder reward function in the existing quantum state preparation methods, the
convergence speed is slow and it is difficult to prepare the desired target
quantum state with high fidelity under limited conditions. To solve the above
problems, a difference-driven reinforcement learning (RL) algorithm for quantum
state preparation of two-qubit system is proposed by improving the reward
function and action selection strategy. Firstly, a model is constructed for the
problem of preparing quantum states of a two-qubit system, with restrictions on
the type of quantum gates and the time for quantum state evolution. In the
preparation process, a weighted differential dynamic reward function is
designed to assist the algorithm quickly obtain the maximum expected cumulative
reward. Then, an adaptive e-greedy action selection strategy is adopted to
achieve a balance between exploration and utilization to a certain extent,
thereby improving the fidelity of the final quantum state. The simulation
results show that the proposed algorithm can prepare quantum state with high
fidelity under limited conditions. Compared with other algorithms, it has
different degrees of improvement in convergence speed and fidelity of the final
quantum state. | [
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] |
Reliability quantification of deep reinforcement learning (DRL)-based control
is a significant challenge for the practical application of artificial
intelligence (AI) in safety-critical systems. This study proposes a method for
quantifying the reliability of DRL-based control. First, an existing method,
random noise distillation, was applied to the reliability evaluation to clarify
the issues to be solved. Second, a novel method for reliability quantification
was proposed to solve these issues. The reliability is quantified using two
neural networks: reference and evaluator. They have the same structure with the
same initial parameters. The outputs of the two networks were the same before
training. During training, the evaluator network parameters were updated to
maximize the difference between the reference and evaluator networks for
trained data. Thus, the reliability of the DRL-based control for a state can be
evaluated based on the difference in output between the two networks. The
proposed method was applied to DQN-based control as an example of a simple
task, and its effectiveness was demonstrated. Finally, the proposed method was
applied to the problem of switching trained models depending on the state.
Con-sequently, the performance of the DRL-based control was improved by
switching the trained models according to their reliability. | [
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] |
From the last decade, researchers in the field of machine learning (ML) and
assistive developmental robotics (ADR) have taken an interest in artificial
empathy (AE) as a possible future paradigm for human-robot interaction (HRI).
Humans learn empathy since birth, therefore, it is challenging to instill this
sense in robots and intelligent machines. Nevertheless, by training over a vast
amount of data and time, imitating empathy, to a certain extent, can be
possible for robots. Training techniques for AE, along with findings from the
field of empathetic AI research, are ever-evolving. The standard workflow for
artificial empathy consists of three stages: 1) Emotion Recognition (ER) using
the retrieved features from video or textual data, 2) analyzing the perceived
emotion or degree of empathy to choose the best course of action, and 3)
carrying out a response action. Recent studies that show AE being used with
virtual agents or robots often include Deep Learning (DL) techniques. For
instance, models like VGGFace are used to conduct ER. Semi-supervised models
like Autoencoders generate the corresponding emotional states and behavioral
responses. However, there has not been any study that presents an independent
approach for evaluating AE, or the degree to which a reaction was empathetic.
This paper aims to investigate and evaluate existing works for measuring and
evaluating empathy, as well as the datasets that have been collected and used
so far. Our goal is to highlight and facilitate the use of state-of-the-art
methods in the area of AE by comparing their performance. This will aid
researchers in the area of AE in selecting their approaches with precision. | [
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Exploring alternative ideas by rewriting text is integral to the writing
process. State-of-the-art large language models (LLMs) can simplify writing
variation generation. However, current interfaces pose challenges for
simultaneous consideration of multiple variations: creating new versions
without overwriting text can be difficult, and pasting them sequentially can
clutter documents, increasing workload and disrupting writers' flow. To tackle
this, we present ABScribe, an interface that supports rapid, yet visually
structured, exploration of writing variations in human-AI co-writing tasks.
With ABScribe, users can swiftly produce multiple variations using LLM prompts,
which are auto-converted into reusable buttons. Variations are stored
adjacently within text segments for rapid in-place comparisons using mouse-over
interactions on a context toolbar. Our user study with 12 writers shows that
ABScribe significantly reduces task workload (d = 1.20, p < 0.001), enhances
user perceptions of the revision process (d = 2.41, p < 0.001) compared to a
popular baseline workflow, and provides insights into how writers explore
variations using LLMs. | [
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] |
Motivated by humans' ability to adapt skills in the learning of new ones,
this paper presents AdaptNet, an approach for modifying the latent space of
existing policies to allow new behaviors to be quickly learned from like tasks
in comparison to learning from scratch. Building on top of a given
reinforcement learning controller, AdaptNet uses a two-tier hierarchy that
augments the original state embedding to support modest changes in a behavior
and further modifies the policy network layers to make more substantive
changes. The technique is shown to be effective for adapting existing
physics-based controllers to a wide range of new styles for locomotion, new
task targets, changes in character morphology and extensive changes in
environment. Furthermore, it exhibits significant increase in learning
efficiency, as indicated by greatly reduced training times when compared to
training from scratch or using other approaches that modify existing policies.
Code is available at https://motion-lab.github.io/AdaptNet. | [
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] |
The agility of animals, particularly in complex activities such as running,
turning, jumping, and backflipping, stands as an exemplar for robotic system
design. Transferring this suite of behaviors to legged robotic systems
introduces essential inquiries: How can a robot be trained to learn multiple
locomotion behaviors simultaneously? How can the robot execute these tasks with
a smooth transition? And what strategies allow for the integrated application
of these skills? This paper introduces the Versatile Instructable Motion prior
(VIM) - a Reinforcement Learning framework designed to incorporate a range of
agile locomotion tasks suitable for advanced robotic applications. Our
framework enables legged robots to learn diverse agile low-level skills by
imitating animal motions and manually designed motions with Functionality
reward and Stylization reward. While the Functionality reward guides the
robot's ability to adopt varied skills, the Stylization reward ensures
performance alignment with reference motions. Our evaluations of the VIM
framework span both simulation environments and real-world deployment. To our
understanding, this is the first work that allows a robot to concurrently learn
diverse agile locomotion tasks using a singular controller. Further details and
supportive media can be found at our project site:
https://rchalyang.github.io/VIM . | [
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] |
Discovering mathematical equations that govern physical and biological
systems from observed data is a fundamental challenge in scientific research.
We present a new physics-informed framework for parameter estimation and
missing physics identification (gray-box) in the field of Systems Biology. The
proposed framework -- named AI-Aristotle -- combines eXtreme Theory of
Functional Connections (X-TFC) domain-decomposition and Physics-Informed Neural
Networks (PINNs) with symbolic regression (SR) techniques for parameter
discovery and gray-box identification. We test the accuracy, speed, flexibility
and robustness of AI-Aristotle based on two benchmark problems in Systems
Biology: a pharmacokinetics drug absorption model, and an ultradian endocrine
model for glucose-insulin interactions. We compare the two machine learning
methods (X-TFC and PINNs), and moreover, we employ two different symbolic
regression techniques to cross-verify our results. While the current work
focuses on the performance of AI-Aristotle based on synthetic data, it can
equally handle noisy experimental data and can even be used for black-box
identification in just a few minutes on a laptop. More broadly, our work
provides insights into the accuracy, cost, scalability, and robustness of
integrating neural networks with symbolic regressors, offering a comprehensive
guide for researchers tackling gray-box identification challenges in complex
dynamical systems in biomedicine and beyond. | [
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] |
Maintaining normal blood glucose levels through lifestyle behaviors is
central to maintaining health and preventing disease. Frequent exposure to
dysglycemia (i.e., abnormal glucose events such as hyperlycemia and
hypoglycemia) leads to chronic complications including diabetes, kidney disease
and need for dialysis, myocardial infarction, stroke, amputation, and death.
Therefore, a tool capable of predicting dysglycemia and offering users
actionable feedback about how to make changes in their diet, exercise, and
medication to prevent abnormal glycemic events could have significant societal
impacts. Counterfactual explanations can provide insights into why a model made
a particular prediction by generating hypothetical instances that are similar
to the original input but lead to a different prediction outcome. Therefore,
counterfactuals can be viewed as a means to design AI-driven health
interventions to prevent adverse health outcomes such as dysglycemia. In this
paper, we design GlyCoach, a framework for generating counterfactual
explanations for glucose control. Leveraging insights from adversarial
learning, GlyCoach characterizes the decision boundary for high-dimensional
health data and performs a grid search to generate actionable interventions.
GlyCoach is unique in integrating prior knowledge about user preferences of
plausible explanations into the process of counterfactual generation. We
evaluate GlyCoach extensively using two real-world datasets and external
simulators from prior studies that predict glucose response. GlyCoach achieves
87\% sensitivity in the simulation-aided validation, surpassing the
state-of-the-art techniques for generating counterfactual explanations by at
least $10\%$. Besides, counterfactuals from GlyCoach exhibit a $32\%$ improved
normalized distance compared to previous research. | [
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] |
Transformer-based large language models (LLMs) are constrained by the fixed
context window of the underlying transformer architecture, hindering their
ability to produce long and logically consistent code. Memory-augmented LLMs
are a promising solution, but current approaches cannot handle long code
generation tasks since they (1) only focus on reading memory and reduce its
evolution to the concatenation of new memories or (2) use very specialized
memories that cannot adapt to other domains. This paper presents L2MAC, the
first practical LLM-based stored-program automatic computer for long and
consistent code generation. Its memory has two components: the instruction
registry, which is populated with a prompt program to solve the user-given
task, and a file store, which will contain the final and intermediate outputs.
Each instruction is executed by a separate LLM instance, whose context is
managed by a control unit capable of precise memory reading and writing to
ensure effective interaction with the file store. These components enable L2MAC
to generate virtually unbounded code structures, bypassing the constraints of
the finite context window while producing code that fulfills complex
user-specified requirements. We empirically show that L2MAC succeeds in
generating large code bases for system design tasks where other coding methods
fall short in implementing user requirements and provide insight into the
reasons for this performance gap. | [
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] |
Quantum Neural Networks (QNNs) are suggested as one of the quantum algorithms
which can be efficiently simulated with a low depth on near-term quantum
hardware in the presence of noises. However, their performance highly relies on
choosing the most suitable architecture of Variational Quantum Algorithms
(VQAs), and the problem-agnostic models often suffer issues regarding
trainability and generalization power. As a solution, the most recent works
explore Geometric Quantum Machine Learning (GQML) using QNNs equivariant with
respect to the underlying symmetry of the dataset. GQML adds an inductive bias
to the model by incorporating the prior knowledge on the given dataset and
leads to enhancing the optimization performance while constraining the search
space. This work proposes equivariant Quantum Convolutional Neural Networks
(EquivQCNNs) for image classification under planar $p4m$ symmetry, including
reflectional and $90^\circ$ rotational symmetry. We present the results tested
in different use cases, such as phase detection of the 2D Ising model and
classification of the extended MNIST dataset, and compare them with those
obtained with the non-equivariant model, proving that the equivariance fosters
better generalization of the model. | [
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Federated Learning (FL) is a machine learning paradigm, which enables
multiple and decentralized clients to collaboratively train a model under the
orchestration of a central aggregator. Traditional FL solutions rely on the
trust assumption of the centralized aggregator, which forms cohorts of clients
in a fair and honest manner. However, a malicious aggregator, in reality, could
abandon and replace the client's training models, or launch Sybil attacks to
insert fake clients. Such malicious behaviors give the aggregator more power to
control clients in the FL setting and determine the final training results. In
this work, we introduce zkFL, which leverages zero-knowledge proofs (ZKPs) to
tackle the issue of a malicious aggregator during the training model
aggregation process. To guarantee the correct aggregation results, the
aggregator needs to provide a proof per round. The proof can demonstrate to the
clients that the aggregator executes the intended behavior faithfully. To
further reduce the verification cost of clients, we employ a blockchain to
handle the proof in a zero-knowledge way, where miners (i.e., the nodes
validating and maintaining the blockchain data) can verify the proof without
knowing the clients' local and aggregated models. The theoretical analysis and
empirical results show that zkFL can achieve better security and privacy than
traditional FL, without modifying the underlying FL network structure or
heavily compromising the training speed. | [
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] |
The authors are concerned about the safety, health, and rights of the
European citizens due to inadequate measures and procedures required by the
current draft of the EU Artificial Intelligence (AI) Act for the conformity
assessment of AI systems. We observe that not only the current draft of the EU
AI Act, but also the accompanying standardization efforts in CEN/CENELEC, have
resorted to the position that real functional guarantees of AI systems
supposedly would be unrealistic and too complex anyways. Yet enacting a
conformity assessment procedure that creates the false illusion of trust in
insufficiently assessed AI systems is at best naive and at worst grossly
negligent. The EU AI Act thus misses the point of ensuring quality by
functional trustworthiness and correctly attributing responsibilities.
The trustworthiness of an AI decision system lies first and foremost in the
correct statistical testing on randomly selected samples and in the precision
of the definition of the application domain, which enables drawing samples in
the first place. We will subsequently call this testable quality functional
trustworthiness. It includes a design, development, and deployment that enables
correct statistical testing of all relevant functions.
We are firmly convinced and advocate that a reliable assessment of the
statistical functional properties of an AI system has to be the indispensable,
mandatory nucleus of the conformity assessment. In this paper, we describe the
three necessary elements to establish a reliable functional trustworthiness,
i.e., (1) the definition of the technical distribution of the application, (2)
the risk-based minimum performance requirements, and (3) the statistically
valid testing based on independent random samples. | [
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] |
Molecular property prediction is a critical task in computational drug
discovery. While recent advances in Graph Neural Networks (GNNs) and
Transformers have shown to be effective and promising, they face the following
limitations: Transformer self-attention does not explicitly consider the
underlying molecule structure while GNN feature representation alone is not
sufficient to capture granular and hidden interactions and characteristics that
distinguish similar molecules. To address these limitations, we propose SYN-
FUSION, a novel approach that synergistically combines pre-trained features
from GNNs and Transformers. This approach provides a comprehensive molecular
representation, capturing both the global molecule structure and the individual
atom characteristics. Experimental results on MoleculeNet benchmarks
demonstrate superior performance, surpassing previous models in 5 out of 7
classification datasets and 4 out of 6 regression datasets. The performance of
SYN-FUSION has been compared with other Graph-Transformer models that have been
jointly trained using a combination of transformer and graph features, and it
is found that our approach is on par with those models in terms of performance.
Extensive analysis of the learned fusion model across aspects such as loss,
latent space, and weight distribution further validates the effectiveness of
SYN-FUSION. Finally, an ablation study unequivocally demonstrates that the
synergy achieved by SYN-FUSION surpasses the performance of its individual
model components and their ensemble, offering a substantial improvement in
predicting molecular properties. | [
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] |
Machine learning, and representation learning in particular, has the
potential to facilitate drug discovery by screening a large chemical space in
silico. A successful approach for representing molecules is to treat them as a
graph and utilize graph neural networks. One of the key limitations of such
methods is the necessity to represent compounds with different numbers of
atoms, which requires aggregating the atom's information. Common aggregation
operators, such as averaging, result in loss of information at the atom level.
In this work, we propose a novel aggregating approach where each atom is
weighted non-linearly using the Boltzmann distribution with a hyperparameter
analogous to temperature. We show that using this weighted aggregation improves
the ability of the gold standard message-passing neural network to predict
antibiotic activity. Moreover, by changing the temperature hyperparameter, our
approach can reveal the atoms that are important for activity prediction in a
smooth and consistent way, thus providing a novel, regulated attention
mechanism for graph neural networks. We further validate our method by showing
that it recapitulates the functional group in beta-Lactam antibiotics. The
ability of our approach to rank the atoms' importance for a desired function
can be used within any graph neural network to provide interpretability of the
results and predictions at the node level. | [
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] |
This paper investigates new families of compositional optimization problems,
called $\underline{\bf n}$on-$\underline{\bf s}$mooth $\underline{\bf
w}$eakly-$\underline{\bf c}$onvex $\underline{\bf f}$inite-sum $\underline{\bf
c}$oupled $\underline{\bf c}$ompositional $\underline{\bf o}$ptimization (NSWC
FCCO). There has been a growing interest in FCCO due to its wide-ranging
applications in machine learning and AI, as well as its ability to address the
shortcomings of stochastic algorithms based on empirical risk minimization.
However, current research on FCCO presumes that both the inner and outer
functions are smooth, limiting their potential to tackle a more diverse set of
problems. Our research expands on this area by examining non-smooth
weakly-convex FCCO, where the outer function is weakly convex and
non-decreasing, and the inner function is weakly-convex. We analyze a
single-loop algorithm and establish its complexity for finding an
$\epsilon$-stationary point of the Moreau envelop of the objective function.
Additionally, we also extend the algorithm to solving novel non-smooth
weakly-convex tri-level finite-sum coupled compositional optimization problems,
which feature a nested arrangement of three functions. Lastly, we explore the
applications of our algorithms in deep learning for two-way partial AUC
maximization and multi-instance two-way partial AUC maximization, using
empirical studies to showcase the effectiveness of the proposed algorithms. | [
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] |
Being able to identify functions of interest in cross-architecture software
is useful whether you are analysing for malware, securing the software supply
chain or conducting vulnerability research. Cross-Architecture Binary Code
Similarity Search has been explored in numerous studies and has used a wide
range of different data sources to achieve its goals. The data sources
typically used draw on common structures derived from binaries such as function
control flow graphs or binary level call graphs, the output of the disassembly
process or the outputs of a dynamic analysis approach. One data source which
has received less attention is binary intermediate representations. Binary
Intermediate representations possess two interesting properties: they are cross
architecture by their very nature and encode the semantics of a function
explicitly to support downstream usage. Within this paper we propose Function
as a String Encoded Representation (FASER) which combines long document
transformers with the use of intermediate representations to create a model
capable of cross architecture function search without the need for manual
feature engineering, pre-training or a dynamic analysis step. We compare our
approach against a series of baseline approaches for two tasks; A general
function search task and a targeted vulnerability search task. Our approach
demonstrates strong performance across both tasks, performing better than all
baseline approaches. | [
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] |
Due to the abundant neurophysiological information in the
electroencephalogram (EEG) signal, EEG signals integrated with deep learning
methods have gained substantial traction across numerous real-world tasks.
However, the development of supervised learning methods based on EEG signals
has been hindered by the high cost and significant label discrepancies to
manually label large-scale EEG datasets. Self-supervised frameworks are adopted
in vision and language fields to solve this issue, but the lack of EEG-specific
theoretical foundations hampers their applicability across various tasks. To
solve these challenges, this paper proposes a knowledge-driven cross-view
contrastive learning framework (KDC2), which integrates neurological theory to
extract effective representations from EEG with limited labels. The KDC2 method
creates scalp and neural views of EEG signals, simulating the internal and
external representation of brain activity. Sequentially, inter-view and
cross-view contrastive learning pipelines in combination with various
augmentation methods are applied to capture neural features from different
views. By modeling prior neural knowledge based on homologous neural
information consistency theory, the proposed method extracts invariant and
complementary neural knowledge to generate combined representations.
Experimental results on different downstream tasks demonstrate that our method
outperforms state-of-the-art methods, highlighting the superior generalization
of neural knowledge-supported EEG representations across various brain tasks. | [
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] |
This paper presents a generic approach for applying the cognitive workload
recognizer by exploiting common electroencephalogram (EEG) patterns across
different human-machine tasks and individual sets. We propose a neural network
called SCVCNet, which eliminates task- and individual-set-related interferences
in EEGs by analyzing finer-grained frequency structures in the power spectral
densities. The SCVCNet utilizes a sliding cross-vector convolution (SCVC)
operation, where paired input layers representing the theta and alpha power are
employed. By extracting the weights from a kernel matrix's central row and
column, we compute the weighted sum of the two vectors around a specified scalp
location. Next, we introduce an inter-frequency-point feature integration
module to fuse the SCVC feature maps. Finally, we combined the two modules with
the output-channel pooling and classification layers to construct the model. To
train the SCVCNet, we employ the regularized least-square method with ridge
regression and the extreme learning machine theory. We validate its performance
using three databases, each consisting of distinct tasks performed by
independent participant groups. The average accuracy (0.6813 and 0.6229) and F1
score (0.6743 and 0.6076) achieved in two different validation paradigms show
partially higher performance than the previous works. All features and
algorithms are available on website:https://github.com/7ohnKeats/SCVCNet. | [
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] |
Electrocardiography (ECG) signal generation has been heavily explored using
generative adversarial networks (GAN) because the implementation of 12-lead
ECGs is not always feasible. The GAN models have achieved remarkable results in
reproducing ECG signals but are only designed for multiple lead inputs and the
features the GAN model preserves have not been identified-limiting the
generated signals use in cardiovascular disease (CVD)-predictive models. This
paper presents ECGNet which is a procedure that generates a complete set of
12-lead ECG signals from any single lead input using a GAN framework with a
bidirectional long short-term memory (LSTM) generator and a convolutional
neural network (CNN) discriminator. Cross and auto-correlation analysis
performed on the generated signals identifies features conserved during the
signal generation-i.e., features that can characterize the unique-nature of
each signal and thus likely indicators of CVD. Finally, by using ECG signals
annotated with the CVD-indicative features detailed by the correlation analysis
as inputs for a CVD-onset-predictive CNN model, we overcome challenges
preventing the prediction of multiple-CVD targets. Our models are experimented
on 15s 12-lead ECG dataset recorded using MyoVista's wavECG. Functional outcome
data for each patient is recorded and used in the CVD-predictive model. Our
best GAN model achieves state-of-the-art accuracy with Frechet Distance (FD)
scores of 4.73, 4.89, 5.18, 4.77, 4.71, and 5.55 on the V1-V6 pre-cordial leads
respectively and shows strength in preserving the P-Q segments and R-peaks in
the generated signals. To the best of our knowledge, ECGNet is the first to
predict all of the remaining eleven leads from the input of any single lead. | [
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] |
Myoelectric control is an area of electromyography of increasing interest
nowadays, particularly in applications such as Hand Gesture Recognition (HGR)
for bionic prostheses. Today's focus is on pattern recognition using Machine
Learning and, more recently, Deep Learning methods. Despite achieving good
results on sparse sEMG signals, the latter models typically require large
datasets and training times. Furthermore, due to the nature of stochastic sEMG
signals, traditional models fail to generalize samples for atypical or noisy
values. In this paper, we propose the design of a Vision Transformer (ViT)
based architecture with a Fuzzy Neural Block (FNB) called EMGTFNet to perform
Hand Gesture Recognition from surface electromyography (sEMG) signals. The
proposed EMGTFNet architecture can accurately classify a variety of hand
gestures without any need for data augmentation techniques, transfer learning
or a significant increase in the number of parameters in the network. The
accuracy of the proposed model is tested using the publicly available NinaPro
database consisting of 49 different hand gestures. Experiments yield an average
test accuracy of 83.57\% \& 3.5\% using a 200 ms window size and only 56,793
trainable parameters. Our results outperform the ViT without FNB, thus
demonstrating that including FNB improves its performance. Our proposal
framework EMGTFNet reported the significant potential for its practical
application for prosthetic control. | [
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] |
A Content-based Time Series Retrieval (CTSR) system is an information
retrieval system for users to interact with time series emerged from multiple
domains, such as finance, healthcare, and manufacturing. For example, users
seeking to learn more about the source of a time series can submit the time
series as a query to the CTSR system and retrieve a list of relevant time
series with associated metadata. By analyzing the retrieved metadata, users can
gather more information about the source of the time series. Because the CTSR
system is required to work with time series data from diverse domains, it needs
a high-capacity model to effectively measure the similarity between different
time series. On top of that, the model within the CTSR system has to compute
the similarity scores in an efficient manner as the users interact with the
system in real-time. In this paper, we propose an effective and efficient CTSR
model that outperforms alternative models, while still providing reasonable
inference runtimes. To demonstrate the capability of the proposed method in
solving business problems, we compare it against alternative models using our
in-house transaction data. Our findings reveal that the proposed model is the
most suitable solution compared to others for our transaction data problem. | [
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Causal DAGs (also known as Bayesian networks) are a popular tool for encoding
conditional dependencies between random variables. In a causal DAG, the random
variables are modeled as vertices in the DAG, and it is stipulated that every
random variable is independent of its ancestors conditioned on its parents. It
is possible, however, for two different causal DAGs on the same set of random
variables to encode exactly the same set of conditional dependencies. Such
causal DAGs are said to be Markov equivalent, and equivalence classes of Markov
equivalent DAGs are known as Markov Equivalent Classes (MECs). Beautiful
combinatorial characterizations of MECs have been developed in the past few
decades, and it is known, in particular that all DAGs in the same MEC must have
the same ''skeleton'' (underlying undirected graph) and v-structures (induced
subgraph of the form $a\rightarrow b \leftarrow c$).
These combinatorial characterizations also suggest several natural
algorithmic questions. One of these is: given an undirected graph $G$ as input,
how many distinct Markov equivalence classes have the skeleton $G$? Much work
has been devoted in the last few years to this and other closely related
problems. However, to the best of our knowledge, a polynomial time algorithm
for the problem remains unknown.
In this paper, we make progress towards this goal by giving a fixed parameter
tractable algorithm for the above problem, with the parameters being the
treewidth and the maximum degree of the input graph $G$. The main technical
ingredient in our work is a construction we refer to as shadow, which lets us
create a "local description'' of long-range constraints imposed by the
combinatorial characterizations of MECs. | [
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] |
Robust optimization provides a mathematical framework for modeling and
solving decision-making problems under worst-case uncertainty. This work
addresses two-stage robust optimization (2RO) problems (also called adjustable
robust optimization), wherein first-stage and second-stage decisions are made
before and after uncertainty is realized, respectively. This results in a
nested min-max-min optimization problem which is extremely challenging
computationally, especially when the decisions are discrete. We propose
Neur2RO, an efficient machine learning-driven instantiation of
column-and-constraint generation (CCG), a classical iterative algorithm for
2RO. Specifically, we learn to estimate the value function of the second-stage
problem via a novel neural network architecture that is easy to optimize over
by design. Embedding our neural network into CCG yields high-quality solutions
quickly as evidenced by experiments on two 2RO benchmarks, knapsack and capital
budgeting. For knapsack, Neur2RO finds solutions that are within roughly $2\%$
of the best-known values in a few seconds compared to the three hours of the
state-of-the-art exact branch-and-price algorithm; for larger and more complex
instances, Neur2RO finds even better solutions. For capital budgeting, Neur2RO
outperforms three variants of the $k$-adaptability algorithm, particularly on
the largest instances, with a 5 to 10-fold reduction in solution time. Our code
and data are available at https://github.com/khalil-research/Neur2RO. | [
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] |
In the past decade, Artificial Intelligence driven drug design and discovery
has been a hot research topic, where an important branch is molecule generation
by generative models, from GAN-based models and VAE-based models to the latest
diffusion-based models. However, most existing models pursue only the basic
properties like validity and uniqueness of the generated molecules, a few go
further to explicitly optimize one single important molecular property (e.g.
QED or PlogP), which makes most generated molecules little usefulness in
practice. In this paper, we present a novel approach to generating molecules
with desirable properties, which expands the diffusion model framework with
multiple innovative designs. The novelty is two-fold. On the one hand,
considering that the structures of molecules are complex and diverse, and
molecular properties are usually determined by some substructures (e.g.
pharmacophores), we propose to perform diffusion on two structural levels:
molecules and molecular fragments respectively, with which a mixed Gaussian
distribution is obtained for the reverse diffusion process. To get desirable
molecular fragments, we develop a novel electronic effect based fragmentation
method. On the other hand, we introduce two ways to explicitly optimize
multiple molecular properties under the diffusion model framework. First, as
potential drug molecules must be chemically valid, we optimize molecular
validity by an energy-guidance function. Second, since potential drug molecules
should be desirable in various properties, we employ a multi-objective
mechanism to optimize multiple molecular properties simultaneously. Extensive
experiments with two benchmark datasets QM9 and ZINC250k show that the
molecules generated by our proposed method have better validity, uniqueness,
novelty, Fr\'echet ChemNet Distance (FCD), QED, and PlogP than those generated
by current SOTA models. | [
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] |
Artificial intelligence (AI) methods have become critical in scientific
applications to help accelerate scientific discovery. Large language models
(LLMs) are being considered as a promising approach to address some of the
challenging problems because of their superior generalization capabilities
across domains. The effectiveness of the models and the accuracy of the
applications is contingent upon their efficient execution on the underlying
hardware infrastructure. Specialized AI accelerator hardware systems have
recently become available for accelerating AI applications. However, the
comparative performance of these AI accelerators on large language models has
not been previously studied. In this paper, we systematically study LLMs on
multiple AI accelerators and GPUs and evaluate their performance
characteristics for these models. We evaluate these systems with (i) a
micro-benchmark using a core transformer block, (ii) a GPT- 2 model, and (iii)
an LLM-driven science use case, GenSLM. We present our findings and analyses of
the models' performance to better understand the intrinsic capabilities of AI
accelerators. Furthermore, our analysis takes into account key factors such as
sequence lengths, scaling behavior, sparsity, and sensitivity to gradient
accumulation steps. | [
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] |
Battery life prediction holds significant practical value for battery
research and development. Currently, many data-driven models rely on early
electrical signals from specific target batteries to predict their lifespan. A
common shortfall is that most existing methods are developed based on specific
aging conditions, which not only limits their model's capability but also
diminishes their effectiveness in predicting degradation under varied
conditions. As a result, these models often miss out on fully benefiting from
the rich historical data available under other conditions. Here, to address
above, we introduce an approach that explicitly captures differences between
electrical signals of a target battery and a reference battery, irrespective of
their materials and aging conditions, to forecast the target battery life.
Through this inter-cell difference, we not only enhance the feature space but
also pave the way for a universal battery life prediction framework.
Remarkably, our model that combines the inter- and intra-cell differences
shines across diverse conditions, standing out in its efficiency and accuracy
using all accessible datasets. An essential application of our approach is its
capability to leverage data from older batteries effectively, enabling newer
batteries to capitalize on insights gained from past batteries. This work not
only enriches the battery data utilization strategy but also sets the stage for
smarter battery management system in the future. | [
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] |
Autonomous driving systems (ADSs) are capable of sensing the environment and
making driving decisions autonomously. These systems are safety-critical, and
testing them is one of the important approaches to ensure their safety.
However, due to the inherent complexity of ADSs and the high dimensionality of
their operating environment, the number of possible test scenarios for ADSs is
infinite. Besides, the operating environment of ADSs is dynamic, continuously
evolving, and full of uncertainties, which requires a testing approach adaptive
to the environment. In addition, existing ADS testing techniques have limited
effectiveness in ensuring the realism of test scenarios, especially the realism
of weather conditions and their changes over time. Recently, reinforcement
learning (RL) has demonstrated great potential in addressing challenging
problems, especially those requiring constant adaptations to dynamic
environments. To this end, we present DeepQTest, a novel ADS testing approach
that uses RL to learn environment configurations with a high chance of
revealing abnormal ADS behaviors. Specifically, DeepQTest employs Deep
Q-Learning and adopts three safety and comfort measures to construct the reward
functions. To ensure the realism of generated scenarios, DeepQTest defines a
set of realistic constraints and introduces real-world weather conditions into
the simulated environment. We employed three comparison baselines, i.e.,
random, greedy, and a state-of-the-art RL-based approach DeepCOllision, for
evaluating DeepQTest on an industrial-scale ADS. Evaluation results show that
DeepQTest demonstrated significantly better effectiveness in terms of
generating scenarios leading to collisions and ensuring scenario realism
compared with the baselines. In addition, among the three reward functions
implemented in DeepQTest, Time-To-Collision is recommended as the best design
according to our study. | [
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] |
AI companies are attempting to create AI systems that outperform humans at
most economically valuable work. Current AI models are already automating away
the livelihoods of some artists, actors, and writers. But there is infighting
between those who prioritize current harms and future harms. We construct a
game-theoretic model of conflict to study the causes and consequences of this
disunity. Our model also helps explain why throughout history, stakeholders
sharing a common threat have found it advantageous to unite against it, and why
the common threat has in turn found it advantageous to divide and conquer.
Under realistic parameter assumptions, our model makes several predictions
that find preliminary corroboration in the historical-empirical record. First,
current victims of AI-driven disempowerment need the future victims to realize
that their interests are also under serious and imminent threat, so that future
victims are incentivized to support current victims in solidarity. Second, the
movement against AI-driven disempowerment can become more united, and thereby
more likely to prevail, if members believe that their efforts will be
successful as opposed to futile. Finally, the movement can better unite and
prevail if its members are less myopic. Myopic members prioritize their future
well-being less than their present well-being, and are thus disinclined to
solidarily support current victims today at personal cost, even if this is
necessary to counter the shared threat of AI-driven disempowerment. | [
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] |
In recent years, artificial intelligence (AI) and machine learning (ML) are
reshaping society's production methods and productivity, and also changing the
paradigm of scientific research. Among them, the AI language model represented
by ChatGPT has made great progress. Such large language models (LLMs) serve
people in the form of AI-generated content (AIGC) and are widely used in
consulting, healthcare, and education. However, it is difficult to guarantee
the authenticity and reliability of AIGC learning data. In addition, there are
also hidden dangers of privacy disclosure in distributed AI training. Moreover,
the content generated by LLMs is difficult to identify and trace, and it is
difficult to cross-platform mutual recognition. The above information security
issues in the coming era of AI powered by LLMs will be infinitely amplified and
affect everyone's life. Therefore, we consider empowering LLMs using blockchain
technology with superior security features to propose a vision for trusted AI.
This paper mainly introduces the motivation and technical route of blockchain
for LLM (BC4LLM), including reliable learning corpus, secure training process,
and identifiable generated content. Meanwhile, this paper also reviews the
potential applications and future challenges, especially in the frontier
communication networks field, including network resource allocation, dynamic
spectrum sharing, and semantic communication. Based on the above work combined
and the prospect of blockchain and LLMs, it is expected to help the early
realization of trusted AI and provide guidance for the academic community. | [
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] |
Federated learning enables multiple clients to collaboratively contribute to
the learning of a global model orchestrated by a central server. This learning
scheme promotes clients' data privacy and requires reduced communication
overheads. In an application like network traffic classification, this helps
hide the network vulnerabilities and weakness points. However, federated
learning is susceptible to backdoor attacks, in which adversaries inject
manipulated model updates into the global model. These updates inject a salient
functionality in the global model that can be launched with specific input
patterns. Nonetheless, the vulnerability of network traffic classification
models based on federated learning to these attacks remains unexplored. In this
paper, we propose GABAttack, a novel genetic algorithm-based backdoor attack
against federated learning for network traffic classification. GABAttack
utilizes a genetic algorithm to optimize the values and locations of backdoor
trigger patterns, ensuring a better fit with the input and the model. This
input-tailored dynamic attack is promising for improved attack evasiveness
while being effective. Extensive experiments conducted over real-world network
datasets validate the success of the proposed GABAttack in various situations
while maintaining almost invisible activity. This research serves as an
alarming call for network security experts and practitioners to develop robust
defense measures against such attacks. | [
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] |
Innovative foundation models, such as GPT-3 and stable diffusion models, have
made a paradigm shift in the realm of artificial intelligence (AI) towards
generative AI-based systems. In unison, from data communication and networking
perspective, AI and machine learning (AI/ML) algorithms are envisioned to be
pervasively incorporated into the future generations of wireless communications
systems, highlighting the need for novel AI-native solutions for the emergent
communication scenarios. In this article, we outline the applications of
generative AI in wireless communication systems to lay the foundations for
research in this field. Diffusion-based generative models, as the new
state-of-the-art paradigm of generative models, are introduced, and their
applications in wireless communication systems are discussed. Two case studies
are also presented to showcase how diffusion models can be exploited for the
development of resilient AI-native communication systems. Specifically, we
propose denoising diffusion probabilistic models (DDPM) for a wireless
communication scheme with non-ideal transceivers, where 30% improvement is
achieved in terms of bit error rate. As the second application, DDPMs are
employed at the transmitter to shape the constellation symbols, highlighting a
robust out-of-distribution performance. Finally, future directions and open
issues for the development of generative AI-based wireless systems are
discussed to promote future research endeavors towards wireless generative AI
(WiGenAI). | [
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Humans often acquire new skills through observation and imitation. For
robotic agents, learning from the plethora of unlabeled video demonstration
data available on the Internet necessitates imitating the expert without access
to its action, presenting a challenge known as Imitation Learning from
Observations (ILfO). A common approach to tackle ILfO problems is to convert
them into inverse reinforcement learning problems, utilizing a proxy reward
computed from the agent's and the expert's observations. Nonetheless, we
identify that tasks characterized by a progress dependency property pose
significant challenges for such approaches; in these tasks, the agent needs to
initially learn the expert's preceding behaviors before mastering the
subsequent ones. Our investigation reveals that the main cause is that the
reward signals assigned to later steps hinder the learning of initial
behaviors. To address this challenge, we present a novel ILfO framework that
enables the agent to master earlier behaviors before advancing to later ones.
We introduce an Automatic Discount Scheduling (ADS) mechanism that adaptively
alters the discount factor in reinforcement learning during the training phase,
prioritizing earlier rewards initially and gradually engaging later rewards
only when the earlier behaviors have been mastered. Our experiments, conducted
on nine Meta-World tasks, demonstrate that our method significantly outperforms
state-of-the-art methods across all tasks, including those that are unsolvable
by them. | [
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] |
The challenge of navigation in environments with dynamic objects continues to
be a central issue in the study of autonomous agents. While predictive methods
hold promise, their reliance on precise state information makes them less
practical for real-world implementation. This study presents visual forecasting
as an innovative alternative. By introducing intuitive visual cues, this
approach projects the future trajectories of dynamic objects to improve agent
perception and enable anticipatory actions. Our research explores two distinct
strategies for conveying predictive information through visual forecasting: (1)
sequences of bounding boxes, and (2) augmented paths. To validate the proposed
visual forecasting strategies, we initiate evaluations in simulated
environments using the Unity engine and then extend these evaluations to
real-world scenarios to assess both practicality and effectiveness. The results
confirm the viability of visual forecasting as a promising solution for
navigation and obstacle avoidance in dynamic environments. | [
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] |
Content Delivery Networks carry the majority of Internet traffic, and the
increasing demand for video content as a major IP traffic across the Internet
highlights the importance of caching and prefetching optimization algorithms.
Prefetching aims to make data available in the cache before the requester
places its request to reduce access time and improve the Quality of Experience
on the user side. Prefetching is well investigated in operating systems,
compiler instructions, in-memory cache, local storage systems, high-speed
networks, and cloud systems. Traditional prefetching techniques are well
adapted to a particular access pattern, but fail to adapt to sudden variations
or randomization in workloads. This paper explores the use of reinforcement
learning to tackle the changes in user access patterns and automatically adapt
over time. To this end, we propose, DeePref, a Deep Reinforcement Learning
agent for online video content prefetching in Content Delivery Networks.
DeePref is a prefetcher implemented on edge networks and is agnostic to
hardware design, operating systems, and applications. Our results show that
DeePref DRQN, using a real-world dataset, achieves a 17% increase in
prefetching accuracy and a 28% increase in prefetching coverage on average
compared to baseline approaches that use video content popularity as a building
block to statically or dynamically make prefetching decisions. We also study
the possibility of transfer learning of statistical models from one edge
network into another, where unseen user requests from unknown distribution are
observed. In terms of transfer learning, the increase in prefetching accuracy
and prefetching coverage are [$30%$, $10%$], respectively. Our source code will
be available on Github. | [
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] |
Industrial systems such as recommender systems and online advertising, have
been widely equipped with multi-stage architectures, which are divided into
several cascaded modules, including matching, pre-ranking, ranking and
re-ranking. As a critical bridge between matching and ranking, existing
pre-ranking approaches mainly endure sample selection bias (SSB) problem owing
to ignoring the entire-chain data dependence, resulting in sub-optimal
performances. In this paper, we rethink pre-ranking system from the perspective
of the entire sample space, and propose Entire-chain Cross-domain Models (ECM),
which leverage samples from the whole cascaded stages to effectively alleviate
SSB problem. Besides, we design a fine-grained neural structure named ECMM to
further improve the pre-ranking accuracy. Specifically, we propose a
cross-domain multi-tower neural network to comprehensively predict for each
stage result, and introduce the sub-networking routing strategy with $L0$
regularization to reduce computational costs. Evaluations on real-world
large-scale traffic logs demonstrate that our pre-ranking models outperform
SOTA methods while time consumption is maintained within an acceptable level,
which achieves better trade-off between efficiency and effectiveness. | [
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] |
Bayesian brain theory suggests that the brain employs generative models to
understand the external world. The sampling-based perspective posits that the
brain infers the posterior distribution through samples of stochastic neuronal
responses. Additionally, the brain continually updates its generative model to
approach the true distribution of the external world. In this study, we
introduce the Hierarchical Exponential-family Energy-based (HEE) model, which
captures the dynamics of inference and learning. In the HEE model, we decompose
the partition function into individual layers and leverage a group of neurons
with shorter time constants to sample the gradient of the decomposed
normalization term. This allows our model to estimate the partition function
and perform inference simultaneously, circumventing the negative phase
encountered in conventional energy-based models (EBMs). As a result, the
learning process is localized both in time and space, and the model is easy to
converge. To match the brain's rapid computation, we demonstrate that neural
adaptation can serve as a momentum term, significantly accelerating the
inference process. On natural image datasets, our model exhibits
representations akin to those observed in the biological visual system.
Furthermore, for the machine learning community, our model can generate
observations through joint or marginal generation. We show that marginal
generation outperforms joint generation and achieves performance on par with
other EBMs. | [
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] |
A critical goal of autonomy and artificial intelligence is enabling
autonomous robots to rapidly adapt in dynamic and uncertain environments.
Classic adaptive control and safe control provide stability and safety
guarantees but are limited to specific system classes. In contrast, policy
adaptation based on reinforcement learning (RL) offers versatility and
generalizability but presents safety and robustness challenges. We propose
SafeDPA, a novel RL and control framework that simultaneously tackles the
problems of policy adaptation and safe reinforcement learning. SafeDPA jointly
learns adaptive policy and dynamics models in simulation, predicts environment
configurations, and fine-tunes dynamics models with few-shot real-world data. A
safety filter based on the Control Barrier Function (CBF) on top of the RL
policy is introduced to ensure safety during real-world deployment. We provide
theoretical safety guarantees of SafeDPA and show the robustness of SafeDPA
against learning errors and extra perturbations. Comprehensive experiments on
(1) classic control problems (Inverted Pendulum), (2) simulation benchmarks
(Safety Gym), and (3) a real-world agile robotics platform (RC Car) demonstrate
great superiority of SafeDPA in both safety and task performance, over
state-of-the-art baselines. Particularly, SafeDPA demonstrates notable
generalizability, achieving a 300% increase in safety rate compared to the
baselines, under unseen disturbances in real-world experiments. | [
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] |
In this paper, we investigate the convergence properties of a wide class of
Adam-family methods for minimizing quadratically regularized nonsmooth
nonconvex optimization problems, especially in the context of training
nonsmooth neural networks with weight decay. Motivated by the AdamW method, we
propose a novel framework for Adam-family methods with decoupled weight decay.
Within our framework, the estimators for the first-order and second-order
moments of stochastic subgradients are updated independently of the weight
decay term. Under mild assumptions and with non-diminishing stepsizes for
updating the primary optimization variables, we establish the convergence
properties of our proposed framework. In addition, we show that our proposed
framework encompasses a wide variety of well-known Adam-family methods, hence
offering convergence guarantees for these methods in the training of nonsmooth
neural networks. More importantly, we show that our proposed framework
asymptotically approximates the SGD method, thereby providing an explanation
for the empirical observation that decoupled weight decay enhances
generalization performance for Adam-family methods. As a practical application
of our proposed framework, we propose a novel Adam-family method named Adam
with Decoupled Weight Decay (AdamD), and establish its convergence properties
under mild conditions. Numerical experiments demonstrate that AdamD outperforms
Adam and is comparable to AdamW, in the aspects of both generalization
performance and efficiency. | [
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] |
In the ever-evolving realm of network security, the swift and accurate
identification of diverse attack classes within network traffic is of paramount
importance. This paper introduces "ByteStack-ID," a pioneering approach
tailored for packet-level intrusion detection. At its core, ByteStack-ID
leverages grayscale images generated from the frequency distributions of
payload data, a groundbreaking technique that greatly enhances the model's
ability to discern intricate data patterns. Notably, our approach is
exclusively grounded in packet-level information, a departure from conventional
Network Intrusion Detection Systems (NIDS) that predominantly rely on
flow-based data. While building upon the fundamental concept of stacking
methodology, ByteStack-ID diverges from traditional stacking approaches. It
seamlessly integrates additional meta learner layers into the concatenated base
learners, creating a highly optimized, unified model. Empirical results
unequivocally confirm the outstanding effectiveness of the ByteStack-ID
framework, consistently outperforming baseline models and state-of-the-art
approaches across pivotal performance metrics, including precision, recall, and
F1-score. Impressively, our proposed approach achieves an exceptional 81\%
macro F1-score in multiclass classification tasks. In a landscape marked by the
continuous evolution of network threats, ByteStack-ID emerges as a robust and
versatile security solution, relying solely on packet-level information
extracted from network traffic data. | [
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] |
We propose BOSS, an approach that automatically learns to solve new
long-horizon, complex, and meaningful tasks by growing a learned skill library
with minimal supervision. Prior work in reinforcement learning require expert
supervision, in the form of demonstrations or rich reward functions, to learn
long-horizon tasks. Instead, our approach BOSS (BOotStrapping your own Skills)
learns to accomplish new tasks by performing "skill bootstrapping," where an
agent with a set of primitive skills interacts with the environment to practice
new skills without receiving reward feedback for tasks outside of the initial
skill set. This bootstrapping phase is guided by large language models (LLMs)
that inform the agent of meaningful skills to chain together. Through this
process, BOSS builds a wide range of complex and useful behaviors from a basic
set of primitive skills. We demonstrate through experiments in realistic
household environments that agents trained with our LLM-guided bootstrapping
procedure outperform those trained with naive bootstrapping as well as prior
unsupervised skill acquisition methods on zero-shot execution of unseen,
long-horizon tasks in new environments. Website at clvrai.com/boss. | [
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] |
In many real-world problems, predictions are leveraged to monitor and control
cyber-physical systems, demanding guarantees on the satisfaction of reliability
and safety requirements. However, predictions are inherently uncertain, and
managing prediction uncertainty presents significant challenges in environments
characterized by complex dynamics and forking trajectories. In this work, we
assume access to a pre-designed probabilistic implicit or explicit sequence
model, which may have been obtained using model-based or model-free methods. We
introduce probabilistic time series-conformal risk prediction (PTS-CRC), a
novel post-hoc calibration procedure that operates on the predictions produced
by any pre-designed probabilistic forecaster to yield reliable error bars. In
contrast to existing art, PTS-CRC produces predictive sets based on an ensemble
of multiple prototype trajectories sampled from the sequence model, supporting
the efficient representation of forking uncertainties. Furthermore, unlike the
state of the art, PTS-CRC can satisfy reliability definitions beyond coverage.
This property is leveraged to devise a novel model predictive control (MPC)
framework that addresses open-loop and closed-loop control problems under
general average constraints on the quality or safety of the control policy. We
experimentally validate the performance of PTS-CRC prediction and control by
studying a number of use cases in the context of wireless networking. Across
all the considered tasks, PTS-CRC predictors are shown to provide more
informative predictive sets, as well as safe control policies with larger
returns. | [
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] |
Our ultimate goal is to build robust policies for robots that assist people.
What makes this hard is that people can behave unexpectedly at test time,
potentially interacting with the robot outside its training distribution and
leading to failures. Even just measuring robustness is a challenge. Adversarial
perturbations are the default, but they can paint the wrong picture: they can
correspond to human motions that are unlikely to occur during natural
interactions with people. A robot policy might fail under small adversarial
perturbations but work under large natural perturbations. We propose that
capturing robustness in these interactive settings requires constructing and
analyzing the entire natural-adversarial frontier: the Pareto-frontier of human
policies that are the best trade-offs between naturalness and low robot
performance. We introduce RIGID, a method for constructing this frontier by
training adversarial human policies that trade off between minimizing robot
reward and acting human-like (as measured by a discriminator). On an Assistive
Gym task, we use RIGID to analyze the performance of standard collaborative
Reinforcement Learning, as well as the performance of existing methods meant to
increase robustness. We also compare the frontier RIGID identifies with the
failures identified in expert adversarial interaction, and with
naturally-occurring failures during user interaction. Overall, we find evidence
that RIGID can provide a meaningful measure of robustness predictive of
deployment performance, and uncover failure cases in human-robot interaction
that are difficult to find manually. https://ood-human.github.io. | [
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] |
Deploying Machine Learning as a Service gives rise to model plagiarism,
leading to copyright infringement. Ownership testing techniques are designed to
identify model fingerprints for verifying plagiarism. However, previous works
often rely on overfitting or robustness features as fingerprints, lacking
theoretical guarantees and exhibiting under-performance on generalized models.
In this paper, we propose a novel ownership testing method called VeriDIP,
which verifies a DNN model's intellectual property. VeriDIP makes two major
contributions. (1) It utilizes membership inference attacks to estimate the
lower bound of privacy leakage, which reflects the fingerprint of a given
model. The privacy leakage fingerprints highlight the unique patterns through
which the models memorize sensitive training datasets. (2) We introduce a novel
approach using less private samples to enhance the performance of ownership
testing.
Extensive experimental results confirm that VeriDIP is effective and
efficient in validating the ownership of deep learning models trained on both
image and tabular datasets. VeriDIP achieves comparable performance to
state-of-the-art methods on image datasets while significantly reducing
computation and communication costs. Enhanced VeriDIP demonstrates superior
verification performance on generalized deep learning models, particularly on
table-trained models. Additionally, VeriDIP exhibits similar effectiveness on
utility-preserving differentially private models compared to non-differentially
private baselines. | [
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] |
The metaverse is a nascent concept that envisions a virtual universe, a
collaborative space where individuals can interact, create, and participate in
a wide range of activities. Privacy in the metaverse is a critical concern as
the concept evolves and immersive virtual experiences become more prevalent.
The metaverse privacy problem refers to the challenges and concerns surrounding
the privacy of personal information and data within Virtual Reality (VR)
environments as the concept of a shared VR space becomes more accessible.
Metaverse will harness advancements from various technologies such as
Artificial Intelligence (AI), Extended Reality (XR), Mixed Reality (MR), and
5G/6G-based communication to provide personalized and immersive services to its
users. Moreover, to enable more personalized experiences, the metaverse relies
on the collection of fine-grained user data that leads to various privacy
issues. Therefore, before the potential of the metaverse can be fully realized,
privacy concerns related to personal information and data within VR
environments must be addressed. This includes safeguarding users' control over
their data, ensuring the security of their personal information, and protecting
in-world actions and interactions from unauthorized sharing. In this paper, we
explore various privacy challenges that future metaverses are expected to face,
given their reliance on AI for tracking users, creating XR and MR experiences,
and facilitating interactions. Moreover, we thoroughly analyze technical
solutions such as differential privacy, Homomorphic Encryption (HE), and
Federated Learning (FL) and discuss related sociotechnical issues regarding
privacy. | [
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] |
Cybersecurity has become a significant issue in the digital era as a result
of the growth in everyday computer use. Cybercriminals now engage in more than
virus distribution and computer hacking. Cyberwarfare has developed as a result
because it has become a threat to a nation's survival. Malware analysis serves
as the first line of defence against an attack and is a significant component
of cybercrime. Every day, malware attacks target a large number of computer
users, businesses, and governmental agencies, causing billions of dollars in
losses. Malware may evade multiple AV software with a very minor, cunning tweak
made by its designers, despite the fact that security experts have a variety of
tools at their disposal to identify it. To address this challenge, a new
dataset called the Obfuscated Malware Dataset (OMD) has been developed. This
dataset comprises 40 distinct malware families having 21924 samples, and it
incorporates obfuscation techniques that mimic the strategies employed by
malware creators to make their malware variations different from the original
samples. The purpose of this dataset is to provide a more realistic and
representative environment for evaluating the effectiveness of malware analysis
techniques. Different conventional machine learning algorithms including but
not limited to Support Vector Machine (SVM), Random Forrest (RF), Extreme
Gradient Boosting (XGBOOST) etc are applied and contrasted. The results
demonstrated that XGBoost outperformed the other algorithms, achieving an
accuracy of f 82%, precision of 88%, recall of 80%, and an F1-Score of 83%. | [
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] |
It is a long-standing challenge in modern recommender systems to effectively
make recommendations for new users, namely the cold-start problem. Cross-Domain
Recommendation (CDR) has been proposed to address this challenge, but current
ways to represent users' interests across systems are still severely limited.
We introduce Personal Knowledge Graph (PKG) as a domain-invariant interest
representation, and propose a novel CDR paradigm named MeKB-Rec. We first link
users and entities in a knowledge base to construct a PKG of users' interests,
named MeKB. Then we learn a semantic representation of MeKB for the
cross-domain recommendation. To efficiently utilize limited training data in
CDR, MeKB-Rec employs Pretrained Language Models to inject world knowledge into
understanding users' interests. Beyond most existing systems, our approach
builds a semantic mapping across domains which breaks the requirement for
in-domain user behaviors, enabling zero-shot recommendations for new users in a
low-resource domain. We experiment MeKB-Rec on well-established public CDR
datasets, and demonstrate that the new formulation % is more powerful than
previous approaches, achieves a new state-of-the-art that significantly
improves HR@10 and NDCG@10 metrics over best previous approaches by 24\%--91\%,
with a 105\% improvement for HR@10 of zero-shot users with no behavior in the
target domain. We deploy MeKB-Rec in WeiXin recommendation scenarios and
achieve significant gains in core online metrics. MeKB-Rec is now serving
hundreds of millions of users in real-world products. | [
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] |
The objective of this study is to investigate the application of various
channel attention mechanisms within the domain of brain-computer interface
(BCI) for motor imagery decoding. Channel attention mechanisms can be seen as a
powerful evolution of spatial filters traditionally used for motor imagery
decoding. This study systematically compares such mechanisms by integrating
them into a lightweight architecture framework to evaluate their impact. We
carefully construct a straightforward and lightweight baseline architecture
designed to seamlessly integrate different channel attention mechanisms. This
approach is contrary to previous works which only investigate one attention
mechanism and usually build a very complex, sometimes nested architecture. Our
framework allows us to evaluate and compare the impact of different attention
mechanisms under the same circumstances. The easy integration of different
channel attention mechanisms as well as the low computational complexity
enables us to conduct a wide range of experiments on three datasets to
thoroughly assess the effectiveness of the baseline model and the attention
mechanisms. Our experiments demonstrate the strength and generalizability of
our architecture framework as well as how channel attention mechanisms can
improve the performance while maintaining the small memory footprint and low
computational complexity of our baseline architecture. Our architecture
emphasizes simplicity, offering easy integration of channel attention
mechanisms, while maintaining a high degree of generalizability across
datasets, making it a versatile and efficient solution for EEG motor imagery
decoding within brain-computer interfaces. | [
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] |
The prevalent deployment of learning from human preferences through
reinforcement learning (RLHF) relies on two important approximations: the first
assumes that pairwise preferences can be substituted with pointwise rewards.
The second assumes that a reward model trained on these pointwise rewards can
generalize from collected data to out-of-distribution data sampled by the
policy. Recently, Direct Preference Optimisation (DPO) has been proposed as an
approach that bypasses the second approximation and learn directly a policy
from collected data without the reward modelling stage. However, this method
still heavily relies on the first approximation.
In this paper we try to gain a deeper theoretical understanding of these
practical algorithms. In particular we derive a new general objective called
$\Psi$PO for learning from human preferences that is expressed in terms of
pairwise preferences and therefore bypasses both approximations. This new
general objective allows us to perform an in-depth analysis of the behavior of
RLHF and DPO (as special cases of $\Psi$PO) and to identify their potential
pitfalls. We then consider another special case for $\Psi$PO by setting $\Psi$
simply to Identity, for which we can derive an efficient optimisation
procedure, prove performance guarantees and demonstrate its empirical
superiority to DPO on some illustrative examples. | [
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] |
This position paper explores the broad landscape of AI potentiality in the
context of cybersecurity, with a particular emphasis on its possible risk
factors with awareness, which can be managed by incorporating human experts in
the loop, i.e., "Human-AI" teaming. As artificial intelligence (AI)
technologies advance, they will provide unparalleled opportunities for attack
identification, incident response, and recovery. However, the successful
deployment of AI into cybersecurity measures necessitates an in-depth
understanding of its capabilities, challenges, and ethical and legal
implications to handle associated risk factors in real-world application areas.
Towards this, we emphasize the importance of a balanced approach that
incorporates AI's computational power with human expertise. AI systems may
proactively discover vulnerabilities and detect anomalies through pattern
recognition, and predictive modeling, significantly enhancing speed and
accuracy. Human experts can explain AI-generated decisions to stakeholders,
regulators, and end-users in critical situations, ensuring responsibility and
accountability, which helps establish trust in AI-driven security solutions.
Therefore, in this position paper, we argue that human-AI teaming is worthwhile
in cybersecurity, in which human expertise such as intuition, critical
thinking, or contextual understanding is combined with AI's computational power
to improve overall cyber defenses. | [
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] |
Randomized experimental comparisons of alternative pedagogical strategies
could provide useful empirical evidence in instructors' decision-making.
However, traditional experiments do not have a clear and simple pathway to
using data rapidly to try to increase the chances that students in an
experiment get the best conditions. Drawing inspiration from the use of machine
learning and experimentation in product development at leading technology
companies, we explore how adaptive experimentation might help in continuous
course improvement. In adaptive experiments, as different arms/conditions are
deployed to students, data is analyzed and used to change the experience for
future students. This can be done using machine learning algorithms to identify
which actions are more promising for improving student experience or outcomes.
This algorithm can then dynamically deploy the most effective conditions to
future students, resulting in better support for students' needs. We illustrate
the approach with a case study providing a side-by-side comparison of
traditional and adaptive experimentation of self-explanation prompts in online
homework problems in a CS1 course. This provides a first step in exploring the
future of how this methodology can be useful in bridging research and practice
in doing continuous improvement. | [
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] |
The growing adoption of declarative software specification languages, coupled
with their inherent difficulty in debugging, has underscored the need for
effective and automated repair techniques applicable to such languages.
Researchers have recently explored various methods to automatically repair
declarative software specifications, such as template-based repair,
feedback-driven iterative repair, and bounded exhaustive approaches. The latest
developments in large language models provide new opportunities for the
automatic repair of declarative specifications. In this study, we assess the
effectiveness of utilizing OpenAI's ChatGPT to repair software specifications
written in the Alloy declarative language. Unlike imperative languages,
specifications in Alloy are not executed but rather translated into logical
formulas and evaluated using backend constraint solvers to identify
specification instances and counterexamples to assertions. Our evaluation
focuses on ChatGPT's ability to improve the correctness and completeness of
Alloy declarative specifications through automatic repairs. We analyze the
results produced by ChatGPT and compare them with those of leading automatic
Alloy repair methods. Our study revealed that while ChatGPT falls short in
comparison to existing techniques, it was able to successfully repair bugs that
no other technique could address. Our analysis also identified errors in
ChatGPT's generated repairs, including improper operator usage, type errors,
higher-order logic misuse, and relational arity mismatches. Additionally, we
observed instances of hallucinations in ChatGPT-generated repairs and
inconsistency in its results. Our study provides valuable insights for software
practitioners, researchers, and tool builders considering ChatGPT for
declarative specification repairs. | [
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] |
Large Language Models (LLMs) have excelled as high-level semantic planners
for sequential decision-making tasks. However, harnessing them to learn complex
low-level manipulation tasks, such as dexterous pen spinning, remains an open
problem. We bridge this fundamental gap and present Eureka, a human-level
reward design algorithm powered by LLMs. Eureka exploits the remarkable
zero-shot generation, code-writing, and in-context improvement capabilities of
state-of-the-art LLMs, such as GPT-4, to perform evolutionary optimization over
reward code. The resulting rewards can then be used to acquire complex skills
via reinforcement learning. Without any task-specific prompting or pre-defined
reward templates, Eureka generates reward functions that outperform expert
human-engineered rewards. In a diverse suite of 29 open-source RL environments
that include 10 distinct robot morphologies, Eureka outperforms human experts
on 83% of the tasks, leading to an average normalized improvement of 52%. The
generality of Eureka also enables a new gradient-free in-context learning
approach to reinforcement learning from human feedback (RLHF), readily
incorporating human inputs to improve the quality and the safety of the
generated rewards without model updating. Finally, using Eureka rewards in a
curriculum learning setting, we demonstrate for the first time, a simulated
Shadow Hand capable of performing pen spinning tricks, adeptly manipulating a
pen in circles at rapid speed. | [
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] |
Tool use is a hallmark of advanced intelligence, exemplified in both animal
behavior and robotic capabilities. This paper investigates the feasibility of
imbuing robots with the ability to creatively use tools in tasks that involve
implicit physical constraints and long-term planning. Leveraging Large Language
Models (LLMs), we develop RoboTool, a system that accepts natural language
instructions and outputs executable code for controlling robots in both
simulated and real-world environments. RoboTool incorporates four pivotal
components: (i) an "Analyzer" that interprets natural language to discern key
task-related concepts, (ii) a "Planner" that generates comprehensive strategies
based on the language input and key concepts, (iii) a "Calculator" that
computes parameters for each skill, and (iv) a "Coder" that translates these
plans into executable Python code. Our results show that RoboTool can not only
comprehend explicit or implicit physical constraints and environmental factors
but also demonstrate creative tool use. Unlike traditional Task and Motion
Planning (TAMP) methods that rely on explicit optimization, our LLM-based
system offers a more flexible, efficient, and user-friendly solution for
complex robotics tasks. Through extensive experiments, we validate that
RoboTool is proficient in handling tasks that would otherwise be infeasible
without the creative use of tools, thereby expanding the capabilities of
robotic systems. Demos are available on our project page:
https://creative-robotool.github.io/. | [
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] |
Federated Learning (FL) is a promising distributed learning approach that
enables multiple clients to collaboratively train a shared global model.
However, recent studies show that FL is vulnerable to various poisoning
attacks, which can degrade the performance of global models or introduce
backdoors into them. In this paper, we first conduct a comprehensive study on
prior FL attacks and detection methods. The results show that all existing
detection methods are only effective against limited and specific attacks. Most
detection methods suffer from high false positives, which lead to significant
performance degradation, especially in not independent and identically
distributed (non-IID) settings. To address these issues, we propose FLTracer,
the first FL attack provenance framework to accurately detect various attacks
and trace the attack time, objective, type, and poisoned location of updates.
Different from existing methodologies that rely solely on cross-client anomaly
detection, we propose a Kalman filter-based cross-round detection to identify
adversaries by seeking the behavior changes before and after the attack. Thus,
this makes it resilient to data heterogeneity and is effective even in non-IID
settings. To further improve the accuracy of our detection method, we employ
four novel features and capture their anomalies with the joint decisions.
Extensive evaluations show that FLTracer achieves an average true positive rate
of over $96.88\%$ at an average false positive rate of less than $2.67\%$,
significantly outperforming SOTA detection methods. \footnote{Code is available
at \url{https://github.com/Eyr3/FLTracer}.} | [
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Measurement-based quantum computation (MBQC) offers a fundamentally unique
paradigm to design quantum algorithms. Indeed, due to the inherent randomness
of quantum measurements, the natural operations in MBQC are not deterministic
and unitary, but are rather augmented with probabilistic byproducts. Yet, the
main algorithmic use of MBQC so far has been to completely counteract this
probabilistic nature in order to simulate unitary computations expressed in the
circuit model. In this work, we propose designing MBQC algorithms that embrace
this inherent randomness and treat the random byproducts in MBQC as a resource
for computation. As a natural application where randomness can be beneficial,
we consider generative modeling, a task in machine learning centered around
generating complex probability distributions. To address this task, we propose
a variational MBQC algorithm equipped with control parameters that allow to
directly adjust the degree of randomness to be admitted in the computation. Our
numerical findings indicate that this additional randomness can lead to
significant gains in learning performance in certain generative modeling tasks.
These results highlight the potential advantages in exploiting the inherent
randomness of MBQC and motivate further research into MBQC-based algorithms. | [
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] |
Visuospatial neglect is a disorder characterised by impaired awareness for
visual stimuli located in regions of space and frames of reference. It is often
associated with stroke. Patients can struggle with all aspects of daily living
and community participation. Assessment methods are limited and show several
shortcomings, considering they are mainly performed on paper and do not
implement the complexity of daily life. Similarly, treatment options are sparse
and often show only small improvements. We present an artificial intelligence
solution designed to accurately assess a patient's visuospatial neglect in a
three-dimensional setting. We implement an active learning method based on
Gaussian process regression to reduce the effort it takes a patient to undergo
an assessment. Furthermore, we describe how this model can be utilised in
patient oriented treatment and how this opens the way to gamification,
tele-rehabilitation and personalised healthcare, providing a promising avenue
for improving patient engagement and rehabilitation outcomes. To validate our
assessment module, we conducted clinical trials involving patients in a
real-world setting. We compared the results obtained using our AI-based
assessment with the widely used conventional visuospatial neglect tests
currently employed in clinical practice. The validation process serves to
establish the accuracy and reliability of our model, confirming its potential
as a valuable tool for diagnosing and monitoring visuospatial neglect. Our VR
application proves to be more sensitive, while intra-rater reliability remains
high. | [
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] |
An amino acid insertion or deletion, or InDel, can have profound and varying
functional impacts on a protein's structure. InDel mutations in the
transmembrane conductor regulator protein for example give rise to cystic
fibrosis. Unfortunately performing InDel mutations on physical proteins and
studying their effects is a time prohibitive process. Consequently, modeling
InDels computationally can supplement and inform wet lab experiments. In this
work, we make use of our data sets of exhaustive double InDel mutations for
three proteins which we computationally generated using a robotics inspired
inverse kinematics approach available in Rosetta. We develop and train a neural
network, RoseNet, on several structural and energetic metrics output by Rosetta
during the mutant generation process. We explore and present how RoseNet is
able to emulate the exhaustive data set using deep learning methods, and show
to what extent it can predict Rosetta metrics for unseen mutant sequences with
two InDels. RoseNet achieves a Pearson correlation coefficient median accuracy
of 0.775 over all Rosetta scores for the largest protein. Furthermore, a
sensitivity analysis is performed to determine the necessary quantity of data
required to accurately emulate the structural scores for computationally
generated mutants. We show that the model can be trained on minimal data (<50%)
and still retain a high level of accuracy. | [
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The Versatile Video Coding (VVC) standard has been recently finalized by the
Joint Video Exploration Team (JVET). Compared to the High Efficiency Video
Coding (HEVC) standard, VVC offers about 50% compression efficiency gain, in
terms of Bjontegaard Delta-Rate (BD-rate), at the cost of a 10-fold increase in
encoding complexity. In this paper, we propose a method based on Convolutional
Neural Network (CNN) to speed up the inter partitioning process in VVC.
Firstly, a novel representation for the quadtree with nested multi-type tree
(QTMT) partition is introduced, derived from the partition path. Secondly, we
develop a U-Net-based CNN taking a multi-scale motion vector field as input at
the Coding Tree Unit (CTU) level. The purpose of CNN inference is to predict
the optimal partition path during the Rate-Distortion Optimization (RDO)
process. To achieve this, we divide CTU into grids and predict the Quaternary
Tree (QT) depth and Multi-type Tree (MT) split decisions for each cell of the
grid. Thirdly, an efficient partition pruning algorithm is introduced to employ
the CNN predictions at each partitioning level to skip RDO evaluations of
unnecessary partition paths. Finally, an adaptive threshold selection scheme is
designed, making the trade-off between complexity and efficiency scalable.
Experiments show that the proposed method can achieve acceleration ranging from
16.5% to 60.2% under the RandomAccess Group Of Picture 32 (RAGOP32)
configuration with a reasonable efficiency drop ranging from 0.44% to 4.59% in
terms of BD-rate, which surpasses other state-of-the-art solutions.
Additionally, our method stands out as one of the lightest approaches in the
field, which ensures its applicability to other encoders. | [
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] |
Recent studies suggest that the existing neural models have difficulty
handling repeated items in sequential recommendation tasks. However, our
understanding of this difficulty is still limited. In this study, we
substantially advance this field by identifying a major source of the problem:
the single hidden state embedding and static item embeddings in the output
softmax layer. Specifically, the similarity structure of the global item
embeddings in the softmax layer sometimes forces the single hidden state
embedding to be close to new items when copying is a better choice, while
sometimes forcing the hidden state to be close to the items from the input
inappropriately. To alleviate the problem, we adapt the recently-proposed
softmax alternatives such as softmax-CPR to sequential recommendation tasks and
demonstrate that the new softmax architectures unleash the capability of the
neural encoder on learning when to copy and when to exclude the items from the
input sequence. By only making some simple modifications on the output softmax
layer for SASRec and GRU4Rec, softmax-CPR achieves consistent improvement in 12
datasets. With almost the same model size, our best method not only improves
the average NDCG@10 of GRU4Rec in 5 datasets with duplicated items by 10%
(4%-17% individually) but also improves 7 datasets without duplicated items by
24% (8%-39%)! | [
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] |
A deep autoencoder (DAE)-based structure for endto-end communication over the
two-user Z-interference channel (ZIC) with finite-alphabet inputs is designed
in this paper. The proposed structure jointly optimizes the two encoder/decoder
pairs and generates interference-aware constellations that dynamically adapt
their shape based on interference intensity to minimize the bit error rate
(BER). An in-phase/quadrature-phase (I/Q) power allocation layer is introduced
in the DAE to guarantee an average power constraint and enable the architecture
to generate constellations with nonuniform shapes. This brings further gain
compared to standard uniform constellations such as quadrature amplitude
modulation. The proposed structure is then extended to work with imperfect
channel state information (CSI). The CSI imperfection due to both the
estimation and quantization errors are examined. The performance of the DAEZIC
is compared with two baseline methods, i.e., standard and rotated
constellations. The proposed structure significantly enhances the performance
of the ZIC both for the perfect and imperfect CSI. Simulation results show that
the improvement is achieved in all interference regimes (weak, moderate, and
strong) and consistently increases with the signal-to-noise ratio (SNR). For
example, more than an order of magnitude BER reduction is obtained with respect
to the most competitive conventional method at weak interference when SNR>15dB
and two bits per symbol are transmitted. The improvements reach about two
orders of magnitude when quantization error exists, indicating that the DAE-ZIC
is more robust to the interference compared to the conventional methods. | [
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We introduce TeleQnA, the first benchmark dataset designed to evaluate the
knowledge of Large Language Models (LLMs) in telecommunications. Comprising
10,000 questions and answers, this dataset draws from diverse sources,
including standards and research articles. This paper outlines the automated
question generation framework responsible for creating this dataset, along with
how human input was integrated at various stages to ensure the quality of the
questions. Afterwards, using the provided dataset, an evaluation is conducted
to assess the capabilities of LLMs, including GPT-3.5 and GPT-4. The results
highlight that these models struggle with complex standards related questions
but exhibit proficiency in addressing general telecom-related inquiries.
Additionally, our results showcase how incorporating telecom knowledge context
significantly enhances their performance, thus shedding light on the need for a
specialized telecom foundation model. Finally, the dataset is shared with
active telecom professionals, whose performance is subsequently benchmarked
against that of the LLMs. The findings illustrate that LLMs can rival the
performance of active professionals in telecom knowledge, thanks to their
capacity to process vast amounts of information, underscoring the potential of
LLMs within this domain. The dataset has been made publicly accessible on
GitHub. | [
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] |
The pre-train and fine-tune paradigm in machine learning has had dramatic
success in a wide range of domains because the use of existing data or
pre-trained models on the internet enables quick and easy learning of new
tasks. We aim to enable this paradigm in robotic reinforcement learning,
allowing a robot to learn a new task with little human effort by leveraging
data and models from the Internet. However, reinforcement learning often
requires significant human effort in the form of manual reward specification or
environment resets, even if the policy is pre-trained. We introduce RoboFuME, a
reset-free fine-tuning system that pre-trains a multi-task manipulation policy
from diverse datasets of prior experiences and self-improves online to learn a
target task with minimal human intervention. Our insights are to utilize
calibrated offline reinforcement learning techniques to ensure efficient online
fine-tuning of a pre-trained policy in the presence of distribution shifts and
leverage pre-trained vision language models (VLMs) to build a robust reward
classifier for autonomously providing reward signals during the online
fine-tuning process. In a diverse set of five real robot manipulation tasks, we
show that our method can incorporate data from an existing robot dataset
collected at a different institution and improve on a target task within as
little as 3 hours of autonomous real-world experience. We also demonstrate in
simulation experiments that our method outperforms prior works that use
different RL algorithms or different approaches for predicting rewards. Project
website: https://robofume.github.io | [
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] |
Prediction of binding sites for transcription factors is important to
understand how they regulate gene expression and how this regulation can be
modulated for therapeutic purposes. Although in the past few years there are
significant works addressing this issue, there is still space for improvement.
In this regard, a transformer based capsule network viz. DNABERT-Cap is
proposed in this work to predict transcription factor binding sites mining
ChIP-seq datasets. DNABERT-Cap is a bidirectional encoder pre-trained with
large number of genomic DNA sequences, empowered with a capsule layer
responsible for the final prediction. The proposed model builds a predictor for
transcription factor binding sites using the joint optimisation of features
encompassing both bidirectional encoder and capsule layer, along with
convolutional and bidirectional long-short term memory layers. To evaluate the
efficiency of the proposed approach, we use a benchmark ChIP-seq datasets of
five cell lines viz. A549, GM12878, Hep-G2, H1-hESC and Hela, available in the
ENCODE repository. The results show that the average area under the receiver
operating characteristic curve score exceeds 0.91 for all such five cell lines.
DNABERT-Cap is also compared with existing state-of-the-art deep learning based
predictors viz. DeepARC, DeepTF, CNN-Zeng and DeepBind, and is seen to
outperform them. | [
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Natural language serves as the primary mode of communication when an
intelligent agent with a physical presence engages with human beings. While a
plethora of research focuses on natural language understanding (NLU),
encompassing endeavors such as sentiment analysis, intent prediction, question
answering, and summarization, the scope of NLU directed at situations
necessitating tangible actions by an embodied agent remains limited. The
inherent ambiguity and incompleteness inherent in natural language present
challenges for intelligent agents striving to decipher human intention. To
tackle this predicament head-on, we introduce a novel system known as task and
argument grounding for Embodied agents (tagE). At its core, our system employs
an inventive neural network model designed to extract a series of tasks from
complex task instructions expressed in natural language. Our proposed model
adopts an encoder-decoder framework enriched with nested decoding to
effectively extract tasks and their corresponding arguments from these
intricate instructions. These extracted tasks are then mapped (or grounded) to
the robot's established collection of skills, while the arguments find
grounding in objects present within the environment. To facilitate the training
and evaluation of our system, we have curated a dataset featuring complex
instructions. The results of our experiments underscore the prowess of our
approach, as it outperforms robust baseline models. | [
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] |
Artificial Intelligence (AI) systems have made remarkable progress, attaining
super-human performance across various domains. This presents us with an
opportunity to further human knowledge and improve human expert performance by
leveraging the hidden knowledge encoded within these highly performant AI
systems. Yet, this knowledge is often hard to extract, and may be hard to
understand or learn from. Here, we show that this is possible by proposing a
new method that allows us to extract new chess concepts in AlphaZero, an AI
system that mastered the game of chess via self-play without human supervision.
Our analysis indicates that AlphaZero may encode knowledge that extends beyond
the existing human knowledge, but knowledge that is ultimately not beyond human
grasp, and can be successfully learned from. In a human study, we show that
these concepts are learnable by top human experts, as four top chess
grandmasters show improvements in solving the presented concept prototype
positions. This marks an important first milestone in advancing the frontier of
human knowledge by leveraging AI; a development that could bear profound
implications and help us shape how we interact with AI systems across many AI
applications. | [
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] |
Biases with respect to socially-salient attributes of individuals have been
well documented in evaluation processes used in settings such as admissions and
hiring. We view such an evaluation process as a transformation of a
distribution of the true utility of an individual for a task to an observed
distribution and model it as a solution to a loss minimization problem subject
to an information constraint. Our model has two parameters that have been
identified as factors leading to biases: the resource-information trade-off
parameter in the information constraint and the risk-averseness parameter in
the loss function. We characterize the distributions that arise from our model
and study the effect of the parameters on the observed distribution. The
outputs of our model enrich the class of distributions that can be used to
capture variation across groups in the observed evaluations. We empirically
validate our model by fitting real-world datasets and use it to study the
effect of interventions in a downstream selection task. These results
contribute to an understanding of the emergence of bias in evaluation processes
and provide tools to guide the deployment of interventions to mitigate biases. | [
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] |
Robot learning methods have recently made great strides, but generalization
and robustness challenges still hinder their widespread deployment. Failing to
detect and address potential failures renders state-of-the-art learning systems
not combat-ready for high-stakes tasks. Recent advances in interactive
imitation learning have presented a promising framework for human-robot
teaming, enabling the robots to operate safely and continually improve their
performances over long-term deployments. Nonetheless, existing methods
typically require constant human supervision and preemptive feedback, limiting
their practicality in realistic domains. This work aims to endow a robot with
the ability to monitor and detect errors during task execution. We introduce a
model-based runtime monitoring algorithm that learns from deployment data to
detect system anomalies and anticipate failures. Unlike prior work that cannot
foresee future failures or requires failure experiences for training, our
method learns a latent-space dynamics model and a failure classifier, enabling
our method to simulate future action outcomes and detect out-of-distribution
and high-risk states preemptively. We train our method within an interactive
imitation learning framework, where it continually updates the model from the
experiences of the human-robot team collected using trustworthy deployments.
Consequently, our method reduces the human workload needed over time while
ensuring reliable task execution. Our method outperforms the baselines across
system-level and unit-test metrics, with 23% and 40% higher success rates in
simulation and on physical hardware, respectively. More information at
https://ut-austin-rpl.github.io/sirius-runtime-monitor/ | [
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] |
Inverse modelling with deep learning algorithms involves training deep
architecture to predict device's parameters from its static behaviour. Inverse
device modelling is suitable to reconstruct drifted physical parameters of
devices temporally degraded or to retrieve physical configuration. There are
many variables that can influence the performance of an inverse modelling
method. In this work the authors propose a deep learning method trained for
retrieving physical parameters of Level-3 model of Power Silicon-Carbide MOSFET
(SiC Power MOS). The SiC devices are used in applications where classical
silicon devices failed due to high-temperature or high switching capability.
The key application of SiC power devices is in the automotive field (i.e. in
the field of electrical vehicles). Due to physiological degradation or
high-stressing environment, SiC Power MOS shows a significant drift of physical
parameters which can be monitored by using inverse modelling. The aim of this
work is to provide a possible deep learning-based solution for retrieving
physical parameters of the SiC Power MOSFET. Preliminary results based on the
retrieving of channel length of the device are reported. Channel length of
power MOSFET is a key parameter involved in the static and dynamic behaviour of
the device. The experimental results reported in this work confirmed the
effectiveness of a multi-layer perceptron designed to retrieve this parameter. | [
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Generative AI applications are recently catering to a vast user base by
creating diverse and high-quality AI-generated content (AIGC). With the
proliferation of mobile devices and rapid growth of mobile traffic, providing
ubiquitous access to high-quality AIGC services via wireless communication
networks is becoming the future direction for AIGC products. However, it is
challenging to provide optimal AIGC services in wireless networks with unstable
channels, limited bandwidth resources, and unevenly distributed computational
resources. To tackle these challenges, we propose a semantic communication
(SemCom)-empowered AIGC (SemAIGC) generation and transmission framework, where
only semantic information of the content rather than all the binary bits should
be extracted and transmitted by using SemCom. Specifically, SemAIGC integrates
diffusion-based models within the semantic encoder and decoder for efficient
content generation and flexible adjustment of the computing workload of both
transmitter and receiver. Meanwhile, we devise a resource-aware workload
trade-off (ROOT) scheme into the SemAIGC framework to intelligently decide
transmitter/receiver workload, thus adjusting the utilization of computational
resource according to service requirements. Simulations verify the superiority
of our proposed SemAIGC framework in terms of latency and content quality
compared to conventional approaches. | [
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