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The representation learning of speech, without textual resources, is an area
of significant interest for many low resource speech applications. In this
paper, we describe an approach to self-supervised representation learning from
raw audio using a hidden unit clustering (HUC) framework. The input to the
model consists of audio samples that are windowed and processed with 1-D
convolutional layers. The learned "time-frequency" representations from the
convolutional neural network (CNN) module are further processed with long short
term memory (LSTM) layers which generate a contextual vector representation for
every windowed segment. The HUC framework, allowing the categorization of the
representations into a small number of phoneme-like units, is used to train the
model for learning semantically rich speech representations. The targets
consist of phoneme-like pseudo labels for each audio segment and these are
generated with an iterative k-means algorithm. We explore techniques that
improve the speaker invariance of the learned representations and illustrate
the effectiveness of the proposed approach on two settings, i) completely
unsupervised speech applications on the sub-tasks described as part of the
ZeroSpeech 2021 challenge and ii) semi-supervised automatic speech recognition
(ASR) applications on the TIMIT dataset and on the GramVaani challenge Hindi
dataset. In these experiments, we achieve state-of-art results for various
ZeroSpeech tasks. Further, on the ASR experiments, the HUC representations are
shown to improve significantly over other established benchmarks based on
Wav2vec, HuBERT and Best-RQ. | [
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] |
Real-time music information retrieval (RT-MIR) has much potential to augment
the capabilities of traditional acoustic instruments. We develop RT-MIR
techniques aimed at augmenting percussive fingerstyle, which blends acoustic
guitar playing with guitar body percussion. We formulate several design
objectives for RT-MIR systems for augmented instrument performance: (i) causal
constraint, (ii) perceptually negligible action-to-sound latency, (iii) control
intimacy support, (iv) synthesis control support. We present and evaluate
real-time guitar body percussion recognition and embedding learning techniques
based on convolutional neural networks (CNNs) and CNNs jointly trained with
variational autoencoders (VAEs). We introduce a taxonomy of guitar body
percussion based on hand part and location. We follow a cross-dataset
evaluation approach by collecting three datasets labelled according to the
taxonomy. The embedding quality of the models is assessed using KL-Divergence
across distributions corresponding to different taxonomic classes. Results
indicate that the networks are strong classifiers especially in a simplified
2-class recognition task, and the VAEs yield improved class separation compared
to CNNs as evidenced by increased KL-Divergence across distributions. We argue
that the VAE embedding quality could support control intimacy and rich
interaction when the latent space's parameters are used to control an external
synthesis engine. Further design challenges around generalisation to different
datasets have been identified. | [
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] |
The dynamic vehicle dispatching problem corresponds to deciding which
vehicles to assign to requests that arise stochastically over time and space.
It emerges in diverse areas, such as in the assignment of trucks to loads to be
transported; in emergency systems; and in ride-hailing services. In this paper,
we model the problem as a semi-Markov decision process, which allows us to
treat time as continuous. In this setting, decision epochs coincide with
discrete events whose time intervals are random. We argue that an event-based
approach substantially reduces the combinatorial complexity of the decision
space and overcomes other limitations of discrete-time models often proposed in
the literature. In order to test our approach, we develop a new discrete-event
simulator and use double deep q-learning to train our decision agents.
Numerical experiments are carried out in realistic scenarios using data from
New York City. We compare the policies obtained through our approach with
heuristic policies often used in practice. Results show that our policies
exhibit better average waiting times, cancellation rates and total service
times, with reduction in average waiting times of up to 50% relative to the
other tested heuristic policies. | [
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] |
Source-free domain adaptation (SFDA) aims to adapt a pretrained model from a
labeled source domain to an unlabeled target domain without access to the
source domain data, preserving source domain privacy. Despite its prevalence in
visual applications, SFDA is largely unexplored in time series applications.
The existing SFDA methods that are mainly designed for visual applications may
fail to handle the temporal dynamics in time series, leading to impaired
adaptation performance. To address this challenge, this paper presents a simple
yet effective approach for source-free domain adaptation on time series data,
namely MAsk and imPUte (MAPU). First, to capture temporal information of the
source domain, our method performs random masking on the time series signals
while leveraging a novel temporal imputer to recover the original signal from a
masked version in the embedding space. Second, in the adaptation step, the
imputer network is leveraged to guide the target model to produce target
features that are temporally consistent with the source features. To this end,
our MAPU can explicitly account for temporal dependency during the adaptation
while avoiding the imputation in the noisy input space. Our method is the first
to handle temporal consistency in SFDA for time series data and can be
seamlessly equipped with other existing SFDA methods. Extensive experiments
conducted on three real-world time series datasets demonstrate that our MAPU
achieves significant performance gain over existing methods. Our code is
available at \url{https://github.com/mohamedr002/MAPU_SFDA_TS}. | [
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] |
In this study, we present a novel dataset for training machine learning
models translating between OpenMP Fortran and C++ code. To ensure reliability
and applicability, the dataset is created from a range of representative
open-source OpenMP benchmarks. It is also refined using a meticulous code
similarity test. The effectiveness of our dataset is assessed using both
quantitative (CodeBLEU) and qualitative (human evaluation) methods. We showcase
how this dataset significantly elevates the translation competencies of large
language models (LLMs). Specifically, models without prior coding knowledge
experienced a boost of $\mathbf{\times~5.1}$ in their CodeBLEU scores, while
models with some coding familiarity saw an impressive
$\mathbf{\times~9.9}$-fold increase. The best fine-tuned model using our
dataset outperforms GPT-4. It is also reaching human-level accuracy. This work
underscores the immense potential of our dataset in propelling advancements in
the domain of code translation for high-performance computing. The dataset is
accessible at
\href{https://github.com/bin123apple/Fortran-CPP-HPC-code-translation-dataset}{OpenMP-Fortran-CPP-Translation}. | [
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] |
Continuous-time nonlinear optimal control problems hold great promise in
real-world applications. After decades of development, reinforcement learning
(RL) has achieved some of the greatest successes as a general nonlinear control
design method. However, a recent comprehensive analysis of state-of-the-art
continuous-time RL (CT-RL) methods, namely, adaptive dynamic programming
(ADP)-based CT-RL algorithms, reveals they face significant design challenges
due to their complexity, numerical conditioning, and dimensional scaling
issues. Despite advanced theoretical results, existing ADP CT-RL synthesis
methods are inadequate in solving even small, academic problems. The goal of
this work is thus to introduce a suite of new CT-RL algorithms for control of
affine nonlinear systems. Our design approach relies on two important factors.
First, our methods are applicable to physical systems that can be partitioned
into smaller subproblems. This constructive consideration results in reduced
dimensionality and greatly improved intuitiveness of design. Second, we
introduce a new excitation framework to improve persistence of excitation (PE)
and numerical conditioning performance via classical input/output insights.
Such a design-centric approach is the first of its kind in the ADP CT-RL
community. In this paper, we progressively introduce a suite of (decentralized)
excitable integral reinforcement learning (EIRL) algorithms. We provide
convergence and closed-loop stability guarantees, and we demonstrate these
guarantees on a significant application problem of controlling an unstable,
nonminimum phase hypersonic vehicle (HSV). | [
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] |
In recent years, advances in deep learning have resulted in a plethora of
successes in the use of reinforcement learning (RL) to solve complex sequential
decision tasks with high-dimensional inputs. However, existing systems lack the
necessary mechanisms to provide humans with a holistic view of their
competence, presenting an impediment to their adoption, particularly in
critical applications where the decisions an agent makes can have significant
consequences. Yet, existing RL-based systems are essentially competency-unaware
in that they lack the necessary interpretation mechanisms to allow human
operators to have an insightful, holistic view of their competency. Towards
more explainable Deep RL (xDRL), we propose a new framework based on analyses
of interestingness. Our tool provides various measures of RL agent competence
stemming from interestingness analysis and is applicable to a wide range of RL
algorithms, natively supporting the popular RLLib toolkit. We showcase the use
of our framework by applying the proposed pipeline in a set of scenarios of
varying complexity. We empirically assess the capability of the approach in
identifying agent behavior patterns and competency-controlling conditions, and
the task elements mostly responsible for an agent's competence, based on global
and local analyses of interestingness. Overall, we show that our framework can
provide agent designers with insights about RL agent competence, both their
capabilities and limitations, enabling more informed decisions about
interventions, additional training, and other interactions in collaborative
human-machine settings. | [
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] |
When re-structuring patient cohorts into so-called population graphs,
initially independent data points can be incorporated into one interconnected
graph structure. This population graph can then be used for medical downstream
tasks using graph neural networks (GNNs). The construction of a suitable graph
structure is a challenging step in the learning pipeline that can have severe
impact on model performance. To this end, different graph assessment metrics
have been introduced to evaluate graph structures. However, these metrics are
limited to classification tasks and discrete adjacency matrices, only covering
a small subset of real-world applications. In this work, we introduce extended
graph assessment metrics (GAMs) for regression tasks and continuous adjacency
matrices. We focus on two GAMs in specific: \textit{homophily} and
\textit{cross-class neighbourhood similarity} (CCNS). We extend the notion of
GAMs to more than one hop, define homophily for regression tasks, as well as
continuous adjacency matrices, and propose a light-weight CCNS distance for
discrete and continuous adjacency matrices. We show the correlation of these
metrics with model performance on different medical population graphs and under
different learning settings. | [
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] |
Backdoor attacks pose serious security threats to deep neural networks
(DNNs). Backdoored models make arbitrarily (targeted) incorrect predictions on
inputs embedded with well-designed triggers while behaving normally on clean
inputs. Many works have explored the invisibility of backdoor triggers to
improve attack stealthiness. However, most of them only consider the
invisibility in the spatial domain without explicitly accounting for the
generation of invisible triggers in the frequency domain, making the generated
poisoned images be easily detected by recent defense methods. To address this
issue, in this paper, we propose a DUal stealthy BAckdoor attack method named
DUBA, which simultaneously considers the invisibility of triggers in both the
spatial and frequency domains, to achieve desirable attack performance, while
ensuring strong stealthiness. Specifically, we first use Discrete Wavelet
Transform to embed the high-frequency information of the trigger image into the
clean image to ensure attack effectiveness. Then, to attain strong
stealthiness, we incorporate Fourier Transform and Discrete Cosine Transform to
mix the poisoned image and clean image in the frequency domain. Moreover, the
proposed DUBA adopts a novel attack strategy, in which the model is trained
with weak triggers and attacked with strong triggers to further enhance the
attack performance and stealthiness. We extensively evaluate DUBA against
popular image classifiers on four datasets. The results demonstrate that it
significantly outperforms the state-of-the-art backdoor attacks in terms of the
attack success rate and stealthiness | [
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] |
In the contemporary digital landscape, online reviews have become an
indispensable tool for promoting products and services across various
businesses. Marketers, advertisers, and online businesses have found incentives
to create deceptive positive reviews for their products and negative reviews
for their competitors' offerings. As a result, the writing of deceptive reviews
has become an unavoidable practice for businesses seeking to promote themselves
or undermine their rivals. Detecting such deceptive reviews has become an
intense and ongoing area of research. This research paper proposes a machine
learning model to identify deceptive reviews, with a particular focus on
restaurants. This study delves into the performance of numerous experiments
conducted on a dataset of restaurant reviews known as the Deceptive Opinion
Spam Corpus. To accomplish this, an n-gram model and max features are developed
to effectively identify deceptive content, particularly focusing on fake
reviews. A benchmark study is undertaken to explore the performance of two
different feature extraction techniques, which are then coupled with five
distinct machine learning classification algorithms. The experimental results
reveal that the passive aggressive classifier stands out among the various
algorithms, showcasing the highest accuracy not only in text classification but
also in identifying fake reviews. Moreover, the research delves into data
augmentation and implements various deep learning techniques to further enhance
the process of detecting deceptive reviews. The findings shed light on the
efficacy of the proposed machine learning approach and offer valuable insights
into dealing with deceptive reviews in the realm of online businesses. | [
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] |
This paper proposes a novel communication-efficient split learning (SL)
framework, named SplitFC, which reduces the communication overhead required for
transmitting intermediate feature and gradient vectors during the SL training
process. The key idea of SplitFC is to leverage different dispersion degrees
exhibited in the columns of the matrices. SplitFC incorporates two compression
strategies: (i) adaptive feature-wise dropout and (ii) adaptive feature-wise
quantization. In the first strategy, the intermediate feature vectors are
dropped with adaptive dropout probabilities determined based on the standard
deviation of these vectors. Then, by the chain rule, the intermediate gradient
vectors associated with the dropped feature vectors are also dropped. In the
second strategy, the non-dropped intermediate feature and gradient vectors are
quantized using adaptive quantization levels determined based on the ranges of
the vectors. To minimize the quantization error, the optimal quantization
levels of this strategy are derived in a closed-form expression. Simulation
results on the MNIST, CIFAR-10, and CelebA datasets demonstrate that SplitFC
provides more than a 5.6% increase in classification accuracy compared to
state-of-the-art SL frameworks, while they require 320 times less communication
overhead compared to the vanilla SL framework without compression. | [
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] |
Network-based intrusion detection system (NIDS) monitors network traffic for
malicious activities, forming the frontline defense against increasing attacks
over information infrastructures. Although promising, our quantitative analysis
shows that existing methods perform inconsistently in declaring various unknown
attacks (e.g., 9% and 35% F1 respectively for two distinct unknown threats for
an SVM-based method) or detecting diverse known attacks (e.g., 31% F1 for the
Backdoor and 93% F1 for DDoS by a GCN-based state-of-the-art method), and
reveals that the underlying cause is entangled distributions of flow features.
This motivates us to propose 3D-IDS, a novel method that aims to tackle the
above issues through two-step feature disentanglements and a dynamic graph
diffusion scheme. Specifically, we first disentangle traffic features by a
non-parameterized optimization based on mutual information, automatically
differentiating tens and hundreds of complex features of various attacks. Such
differentiated features will be fed into a memory model to generate
representations, which are further disentangled to highlight the
attack-specific features. Finally, we use a novel graph diffusion method that
dynamically fuses the network topology for spatial-temporal aggregation in
evolving data streams. By doing so, we can effectively identify various attacks
in encrypted traffics, including unknown threats and known ones that are not
easily detected. Experiments show the superiority of our 3D-IDS. We also
demonstrate that our two-step feature disentanglements benefit the
explainability of NIDS. | [
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] |
The interaction between elephants and their environment has profound
implications for both ecology and conservation strategies. This study presents
an analytical approach to decipher the intricate patterns of elephant movement
in Sub-Saharan Africa, concentrating on key ecological drivers such as seasonal
variations and rainfall patterns. Despite the complexities surrounding these
influential factors, our analysis provides a holistic view of elephant
migratory behavior in the context of the dynamic African landscape. Our
comprehensive approach enables us to predict the potential impact of these
ecological determinants on elephant migration, a critical step in establishing
informed conservation strategies. This projection is particularly crucial given
the impacts of global climate change on seasonal and rainfall patterns, which
could substantially influence elephant movements in the future. The findings of
our work aim to not only advance the understanding of movement ecology but also
foster a sustainable coexistence of humans and elephants in Sub-Saharan Africa.
By predicting potential elephant routes, our work can inform strategies to
minimize human-elephant conflict, effectively manage land use, and enhance
anti-poaching efforts. This research underscores the importance of integrating
movement ecology and climatic variables for effective wildlife management and
conservation planning. | [
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] |
Predicting synergistic drug combinations can help accelerate discovery of
cancer treatments, particularly therapies personalized to a patient's specific
tumor via biopsied cells. In this paper, we propose a novel setting and models
for in-context drug synergy learning. We are given a small "personalized
dataset" of 10-20 drug synergy relationships in the context of specific cancer
cell targets. Our goal is to predict additional drug synergy relationships in
that context. Inspired by recent work that pre-trains a GPT language model (LM)
to "in-context learn" common function classes, we devise novel pre-training
schemes that enable a GPT model to in-context learn "drug synergy functions".
Our model -- which does not use any textual corpora, molecular fingerprints,
protein interaction or any other domain-specific knowledge -- is able to
achieve competitive results. We further integrate our in-context approach with
a genetic algorithm to optimize model prompts and select synergy candidates to
test after conducting a patient biopsy. Finally, we explore a novel task of
inverse drug design which can potentially enable the design of drugs that
synergize specifically to target a given patient's "personalized dataset". Our
findings can potentially have an important impact on precision cancer medicine,
and also raise intriguing questions on non-textual pre-training for LMs. | [
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] |
This empirical study proposes a novel methodology to measure users' perceived
trust in an Explainable Artificial Intelligence (XAI) model. To do so, users'
mental models are elicited using Fuzzy Cognitive Maps (FCMs). First, we exploit
an interpretable Machine Learning (ML) model to classify suspected COVID-19
patients into positive or negative cases. Then, Medical Experts' (MEs) conduct
a diagnostic decision-making task based on their knowledge and then prediction
and interpretations provided by the XAI model. In order to evaluate the impact
of interpretations on perceived trust, explanation satisfaction attributes are
rated by MEs through a survey. Then, they are considered as FCM's concepts to
determine their influences on each other and, ultimately, on the perceived
trust. Moreover, to consider MEs' mental subjectivity, fuzzy linguistic
variables are used to determine the strength of influences. After reaching the
steady state of FCMs, a quantified value is obtained to measure the perceived
trust of each ME. The results show that the quantified values can determine
whether MEs trust or distrust the XAI model. We analyze this behavior by
comparing the quantified values with MEs' performance in completing diagnostic
tasks. | [
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] |
Robot learning is often difficult due to the expense of gathering data. The
need for large amounts of data can, and should, be tackled with effective
algorithms and leveraging expert information on robot dynamics. Bayesian
reinforcement learning (BRL), thanks to its sample efficiency and ability to
exploit prior knowledge, is uniquely positioned as such a solution method.
Unfortunately, the application of BRL has been limited due to the difficulties
of representing expert knowledge as well as solving the subsequent inference
problem. This paper advances BRL for robotics by proposing a specialized
framework for physical systems. In particular, we capture this knowledge in a
factored representation, then demonstrate the posterior factorizes in a similar
shape, and ultimately formalize the model in a Bayesian framework. We then
introduce a sample-based online solution method, based on Monte-Carlo tree
search and particle filtering, specialized to solve the resulting model. This
approach can, for example, utilize typical low-level robot simulators and
handle uncertainty over unknown dynamics of the environment. We empirically
demonstrate its efficiency by performing on-robot learning in two human-robot
interaction tasks with uncertainty about human behavior, achieving near-optimal
performance after only a handful of real-world episodes. A video of learned
policies is at https://youtu.be/H9xp60ngOes. | [
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] |
The 2022 Russian invasion of Ukraine was accompanied by a large-scale,
pro-Russian propaganda campaign on social media. However, the strategy behind
the dissemination of propaganda has remained unclear, particularly how the
online discourse was strategically shaped by the propagandists' community.
Here, we analyze the strategy of the Twitter community using an inverse
reinforcement learning (IRL) approach. Specifically, IRL allows us to model
online behavior as a Markov decision process, where the goal is to infer the
underlying reward structure that guides propagandists when interacting with
users with a supporting or opposing stance toward the invasion. Thereby, we aim
to understand empirically whether and how between-user interactions are
strategically used to promote the proliferation of Russian propaganda. For
this, we leverage a large-scale dataset with 349,455 posts with pro-Russian
propaganda from 132,131 users. We show that bots and humans follow a different
strategy: bots respond predominantly to pro-invasion messages, suggesting that
they seek to drive virality; while messages indicating opposition primarily
elicit responses from humans, suggesting that they tend to engage in critical
discussions. To the best of our knowledge, this is the first study analyzing
the strategy behind propaganda from the 2022 Russian invasion of Ukraine
through the lens of IRL. | [
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] |
Automated program analysis is a pivotal research domain in many areas of
Computer Science -- Formal Methods and Artificial Intelligence, in particular.
Due to the undecidability of the problem of program equivalence, comparing two
programs is highly challenging. Typically, in order to compare two programs, a
relation between both programs' sets of variables is required. Thus, mapping
variables between two programs is useful for a panoply of tasks such as program
equivalence, program analysis, program repair, and clone detection. In this
work, we propose using graph neural networks (GNNs) to map the set of variables
between two programs based on both programs' abstract syntax trees (ASTs). To
demonstrate the strength of variable mappings, we present three use-cases of
these mappings on the task of program repair to fix well-studied and recurrent
bugs among novice programmers in introductory programming assignments (IPAs).
Experimental results on a dataset of 4166 pairs of incorrect/correct programs
show that our approach correctly maps 83% of the evaluation dataset. Moreover,
our experiments show that the current state-of-the-art on program repair,
greatly dependent on the programs' structure, can only repair about 72% of the
incorrect programs. In contrast, our approach, which is solely based on
variable mappings, can repair around 88.5%. | [
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] |
Quantum devices should operate in adherence to quantum physics principles.
Quantum random access memory (QRAM), a fundamental component of many essential
quantum algorithms for tasks such as linear algebra, data search, and machine
learning, is often proposed to offer $\mathcal{O}(\log N)$ circuit depth for
$\mathcal{O}(N)$ data size, given $N$ qubits. However, this claim appears to
breach the principle of relativity when dealing with a large number of qubits
in quantum materials interacting locally. In our study we critically explore
the intrinsic bounds of rapid quantum memories based on causality, employing
the relativistic quantum field theory and Lieb-Robinson bounds in quantum
many-body systems. In this paper, we consider a hardware-efficient QRAM design
in hybrid quantum acoustic systems. Assuming clock cycle times of approximately
$10^{-3}$ seconds and a lattice spacing of about 1 micrometer, we show that
QRAM can accommodate up to $\mathcal{O}(10^7)$ logical qubits in 1 dimension,
$\mathcal{O}(10^{15})$ to $\mathcal{O}(10^{20})$ in various 2D architectures,
and $\mathcal{O}(10^{24})$ in 3 dimensions. We contend that this causality
bound broadly applies to other quantum hardware systems. Our findings highlight
the impact of fundamental quantum physics constraints on the long-term
performance of quantum computing applications in data science and suggest
potential quantum memory designs for performance enhancement. | [
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] |
Learned cardinality estimation methods have achieved high precision compared
to traditional methods. Among learned methods, query-driven approaches face the
data and workload drift problem for a long time. Although both query-driven and
hybrid methods are proposed to avoid this problem, even the state-of-the-art of
them suffer from high training and estimation costs, limited scalability,
instability, and long-tailed distribution problem on high cardinality and
high-dimensional tables, which seriously affects the practical application of
learned cardinality estimators. In this paper, we prove that most of these
problems are directly caused by the widely used progressive sampling. We solve
this problem by introducing predicates information into the autoregressive
model and propose Duet, a stable, efficient, and scalable hybrid method to
estimate cardinality directly without sampling or any non-differentiable
process, which can not only reduces the inference complexity from O(n) to O(1)
compared to Naru and UAE but also achieve higher accuracy on high cardinality
and high-dimensional tables. Experimental results show that Duet can achieve
all the design goals above and be much more practical and even has a lower
inference cost on CPU than that of most learned methods on GPU. | [
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] |
Cricket is the second most popular sport after soccer in terms of viewership.
However, the assessment of individual player performance, a fundamental task in
team sports, is currently primarily based on aggregate performance statistics,
including average runs and wickets taken. We propose Context-Aware Metric of
player Performance, CAMP, to quantify individual players' contributions toward
a cricket match outcome. CAMP employs data mining methods and enables effective
data-driven decision-making for selection and drafting, coaching and training,
team line-ups, and strategy development. CAMP incorporates the exact context of
performance, such as opponents' strengths and specific circumstances of games,
such as pressure situations. We empirically evaluate CAMP on data of
limited-over cricket matches between 2001 and 2019. In every match, a committee
of experts declares one player as the best player, called Man of the M}atch
(MoM). The top two rated players by CAMP match with MoM in 83\% of the 961
games. Thus, the CAMP rating of the best player closely matches that of the
domain experts. By this measure, CAMP significantly outperforms the current
best-known players' contribution measure based on the Duckworth-Lewis-Stern
(DLS) method. | [
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] |
Preserving training dynamics across batch sizes is an important tool for
practical machine learning as it enables the trade-off between batch size and
wall-clock time. This trade-off is typically enabled by a scaling rule, for
example, in stochastic gradient descent, one should scale the learning rate
linearly with the batch size. Another important machine learning tool is the
model EMA, a functional copy of a target model, whose parameters move towards
those of its target model according to an Exponential Moving Average (EMA) at a
rate parameterized by a momentum hyperparameter. This model EMA can improve the
robustness and generalization of supervised learning, stabilize
pseudo-labeling, and provide a learning signal for Self-Supervised Learning
(SSL). Prior works have not considered the optimization of the model EMA when
performing scaling, leading to different training dynamics across batch sizes
and lower model performance. In this work, we provide a scaling rule for
optimization in the presence of a model EMA and demonstrate the rule's validity
across a range of architectures, optimizers, and data modalities. We also show
the rule's validity where the model EMA contributes to the optimization of the
target model, enabling us to train EMA-based pseudo-labeling and SSL methods at
small and large batch sizes. For SSL, we enable training of BYOL up to batch
size 24,576 without sacrificing performance, a 6$\times$ wall-clock time
reduction under idealized hardware settings. | [
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] |
The isomorphism problem is a fundamental problem in network analysis, which
involves capturing both low-order and high-order structural information. In
terms of extracting low-order structural information, graph isomorphism
algorithms analyze the structural equivalence to reduce the solver space
dimension, which demonstrates its power in many applications, such as protein
design, chemical pathways, and community detection. For the more commonly
occurring high-order relationships in real-life scenarios, the problem of
hypergraph isomorphism, which effectively captures these high-order structural
relationships, cannot be straightforwardly addressed using graph isomorphism
methods. Besides, the existing hypergraph kernel methods may suffer from high
memory consumption or inaccurate sub-structure identification, thus yielding
sub-optimal performance. In this paper, to address the abovementioned problems,
we first propose the hypergraph Weisfiler-Lehman test algorithm for the
hypergraph isomorphism test problem by generalizing the Weisfiler-Lehman test
algorithm from graphs to hypergraphs. Secondly, based on the presented
algorithm, we propose a general hypergraph Weisfieler-Lehman kernel framework
and implement two instances, which are Hypergraph Weisfeiler-Lehamn Subtree
Kernel and Hypergraph Weisfeiler-Lehamn Hyperedge Kernel. In order to fulfill
our research objectives, a comprehensive set of experiments was meticulously
designed, including seven graph classification datasets and 12 hypergraph
classification datasets. Results on hypergraph classification datasets show
significant improvements compared to other typical kernel-based methods, which
demonstrates the effectiveness of the proposed methods. In our evaluation, we
found that our proposed methods outperform the second-best method in terms of
runtime, running over 80 times faster when handling complex hypergraph
structures. | [
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] |
Big data and machine learning tools have jointly empowered humans in making
data-driven decisions. However, many of them capture empirical associations
that might be spurious due to confounding factors and subgroup heterogeneity.
The famous Simpson's paradox is such a phenomenon where aggregated and
subgroup-level associations contradict with each other, causing cognitive
confusions and difficulty in making adequate interpretations and decisions.
Existing tools provide little insights for humans to locate, reason about, and
prevent pitfalls of spurious association in practice. We propose VISPUR, a
visual analytic system that provides a causal analysis framework and a
human-centric workflow for tackling spurious associations. These include a
CONFOUNDER DASHBOARD, which can automatically identify possible confounding
factors, and a SUBGROUP VIEWER, which allows for the visualization and
comparison of diverse subgroup patterns that likely or potentially result in a
misinterpretation of causality. Additionally, we propose a REASONING
STORYBOARD, which uses a flow-based approach to illustrate paradoxical
phenomena, as well as an interactive DECISION DIAGNOSIS panel that helps ensure
accountable decision-making. Through an expert interview and a controlled user
experiment, our qualitative and quantitative results demonstrate that the
proposed "de-paradox" workflow and the designed visual analytic system are
effective in helping human users to identify and understand spurious
associations, as well as to make accountable causal decisions. | [
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] |
Reinforcement learning is still struggling with solving long-horizon surgical
robot tasks which involve multiple steps over an extended duration of time due
to the policy exploration challenge. Recent methods try to tackle this problem
by skill chaining, in which the long-horizon task is decomposed into multiple
subtasks for easing the exploration burden and subtask policies are temporally
connected to complete the whole long-horizon task. However, smoothly connecting
all subtask policies is difficult for surgical robot scenarios. Not all states
are equally suitable for connecting two adjacent subtasks. An undesired
terminate state of the previous subtask would make the current subtask policy
unstable and result in a failed execution. In this work, we introduce
value-informed skill chaining (ViSkill), a novel reinforcement learning
framework for long-horizon surgical robot tasks. The core idea is to
distinguish which terminal state is suitable for starting all the following
subtask policies. To achieve this target, we introduce a state value function
that estimates the expected success probability of the entire task given a
state. Based on this value function, a chaining policy is learned to instruct
subtask policies to terminate at the state with the highest value so that all
subsequent policies are more likely to be connected for accomplishing the task.
We demonstrate the effectiveness of our method on three complex surgical robot
tasks from SurRoL, a comprehensive surgical simulation platform, achieving high
task success rates and execution efficiency. Code is available at
$\href{https://github.com/med-air/ViSkill}{\text{https://github.com/med-air/ViSkill}}$. | [
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] |
A majority of recent advancements related to the fault diagnosis of
electrical motors are based on the assumption that training and testing data
are drawn from the same distribution. However, the data distribution can vary
across different operating conditions during real-world operating scenarios of
electrical motors. Consequently, this assumption limits the practical
implementation of existing studies for fault diagnosis, as they rely on fully
labelled training data spanning all operating conditions and assume a
consistent distribution. This is because obtaining a large number of labelled
samples for several machines across different fault cases and operating
scenarios may be unfeasible. In order to overcome the aforementioned
limitations, this work proposes a framework to develop a foundational model for
fault diagnosis of electrical motors. It involves building a neural
network-based backbone to learn high-level features using self-supervised
learning, and then fine-tuning the backbone to achieve specific objectives. The
primary advantage of such an approach is that the backbone can be fine-tuned to
achieve a wide variety of target tasks using very less amount of training data
as compared to traditional supervised learning methodologies. The empirical
evaluation demonstrates the effectiveness of the proposed approach by obtaining
more than 90\% classification accuracy by fine-tuning the backbone not only
across different types of fault scenarios or operating conditions, but also
across different machines. This illustrates the promising potential of the
proposed approach for cross-machine fault diagnosis tasks in real-world
applications. | [
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] |
This paper presents novel technology and methodology aimed at enhancing crowd
management in both the planning and operational phases. The approach
encompasses innovative data collection techniques, data integration, and
visualization using a 3D Digital Twin, along with the incorporation of
artificial intelligence (AI) tools for risk identification. The paper
introduces the Bowtie model, a comprehensive framework designed to assess and
predict risk levels. The model combines objective estimations and predictions,
such as traffic flow operations and crowdedness levels, with various
aggravating factors like weather conditions, sentiments, and the purpose of
visitors, to evaluate the expected risk of incidents. The proposed framework is
applied to the Crowd Safety Manager project in Scheveningen, where the DigiTwin
is developed based on a wealth of real-time data sources. One noteworthy data
source is Resono, offering insights into the number of visitors and their
movements, leveraging a mobile phone panel of over 2 million users in the
Netherlands. Particular attention is given to the left-hand side of the Bowtie,
which includes state estimation, prediction, and forecasting. Notably, the
focus is on generating multi-day ahead forecasts for event-planning purposes
using Resono data. Advanced machine learning techniques, including the XGBoost
framework, are compared, with XGBoost demonstrating the most accurate
forecasts. The results indicate that the predictions are adequately accurate.
However, certain locations may benefit from additional input data to further
enhance prediction quality. Despite these limitations, this work contributes to
a more effective crowd management system and opens avenues for further
advancements in this critical field. | [
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] |
Deep neural networks (DNNs) are increasingly being used as controllers in
reactive systems. However, DNNs are highly opaque, which renders it difficult
to explain and justify their actions. To mitigate this issue, there has been a
surge of interest in explainable AI (XAI) techniques, capable of pinpointing
the input features that caused the DNN to act as it did. Existing XAI
techniques typically face two limitations: (i) they are heuristic, and do not
provide formal guarantees that the explanations are correct; and (ii) they
often apply to ``one-shot'' systems, where the DNN is invoked independently of
past invocations, as opposed to reactive systems. Here, we begin bridging this
gap, and propose a formal DNN-verification-based XAI technique for reasoning
about multi-step, reactive systems. We suggest methods for efficiently
calculating succinct explanations, by exploiting the system's transition
constraints in order to curtail the search space explored by the underlying
verifier. We evaluate our approach on two popular benchmarks from the domain of
automated navigation; and observe that our methods allow the efficient
computation of minimal and minimum explanations, significantly outperforming
the state of the art. We also demonstrate that our methods produce formal
explanations that are more reliable than competing, non-verification-based XAI
techniques. | [
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] |
Finding a high-quality feasible solution to a combinatorial optimization (CO)
problem in a limited time is challenging due to its discrete nature. Recently,
there has been an increasing number of machine learning (ML) methods for
addressing CO problems. Neural diving (ND) is one of the learning-based
approaches to generating partial discrete variable assignments in Mixed Integer
Programs (MIP), a framework for modeling CO problems. However, a major drawback
of ND is a large discrepancy between the ML and MIP objectives, i.e., variable
value classification accuracy over primal bound. Our study investigates that a
specific range of variable assignment rates (coverage) yields high-quality
feasible solutions, where we suggest optimizing the coverage bridges the gap
between the learning and MIP objectives. Consequently, we introduce a post-hoc
method and a learning-based approach for optimizing the coverage. A key idea of
our approach is to jointly learn to restrict the coverage search space and to
predict the coverage in the learned search space. Experimental results
demonstrate that learning a deep neural network to estimate the coverage for
finding high-quality feasible solutions achieves state-of-the-art performance
in NeurIPS ML4CO datasets. In particular, our method shows outstanding
performance in the workload apportionment dataset, achieving the optimality gap
of 0.45%, a ten-fold improvement over SCIP within the one-minute time limit. | [
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Autonomous Vehicles (AVs) have the potential to provide numerous societal
benefits, such as decreased road accidents and increased overall transportation
efficiency. However, quantifying the risk associated with AVs is challenging
due to the lack of historical data and the rapidly evolving technology. This
paper presents a data-driven framework for comparing the risk of different AVs'
behaviors in various operational design domains (ODDs), based on counterfactual
simulations of "misbehaving" road users. We introduce the concept of
counterfactual safety margin, which represents the minimum deviation from
normal behavior that could lead to a collision. This concept helps to find the
most critical scenarios but also to assess the frequency and severity of risk
of AVs. We show that the proposed methodology is applicable even when the AV's
behavioral policy is unknown -- through worst- and best-case analyses -- making
the method useful also to external third-party risk assessors. Our experimental
results demonstrate the correlation between the safety margin, the driving
policy quality, and the ODD shedding light on the relative risk associated with
different AV providers. This work contributes to AV safety assessment and aids
in addressing legislative and insurance concerns surrounding this emerging
technology. | [
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] |
Buy It Again (BIA) recommendations are crucial to retailers to help improve
user experience and site engagement by suggesting items that customers are
likely to buy again based on their own repeat purchasing patterns. Most
existing BIA studies analyze guests personalized behavior at item granularity.
A category-based model may be more appropriate in such scenarios. We propose a
recommendation system called a hierarchical PCIC model that consists of a
personalized category model (PC model) and a personalized item model within
categories (IC model). PC model generates a personalized list of categories
that customers are likely to purchase again. IC model ranks items within
categories that guests are likely to consume within a category. The
hierarchical PCIC model captures the general consumption rate of products using
survival models. Trends in consumption are captured using time series models.
Features derived from these models are used in training a category-grained
neural network. We compare PCIC to twelve existing baselines on four standard
open datasets. PCIC improves NDCG up to 16 percent while improving recall by
around 2 percent. We were able to scale and train (over 8 hours) PCIC on a
large dataset of 100M guests and 3M items where repeat categories of a guest
out number repeat items. PCIC was deployed and AB tested on the site of a major
retailer, leading to significant gains in guest engagement. | [
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] |
Fast screening of drug molecules based on the ligand binding affinity is an
important step in the drug discovery pipeline. Graph neural fingerprint is a
promising method for developing molecular docking surrogates with high
throughput and great fidelity. In this study, we built a COVID-19 drug docking
dataset of about 300,000 drug candidates on 23 coronavirus protein targets.
With this dataset, we trained graph neural fingerprint docking models for
high-throughput virtual COVID-19 drug screening. The graph neural fingerprint
models yield high prediction accuracy on docking scores with the mean squared
error lower than $0.21$ kcal/mol for most of the docking targets, showing
significant improvement over conventional circular fingerprint methods. To make
the neural fingerprints transferable for unknown targets, we also propose a
transferable graph neural fingerprint method trained on multiple targets. With
comparable accuracy to target-specific graph neural fingerprint models, the
transferable model exhibits superb training and data efficiency. We highlight
that the impact of this study extends beyond COVID-19 dataset, as our approach
for fast virtual ligand screening can be easily adapted and integrated into a
general machine learning-accelerated pipeline to battle future bio-threats. | [
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We present our work on electric bicycles (e-bikes) and their implications for
policymakers in the United States. E-bikes have gained significant popularity
as a fast and eco-friendly transportation option. As we strive for a
sustainable energy plan, understanding the growth and impact of e-bikes is
crucial for policymakers. Our mathematical modeling offers insights into the
value of e-bikes and their role in the future. Using an ARIMA model, a
supervised machine-learning algorithm, we predicted the growth of e-bike sales
in the U.S. Our model, trained on historical sales data from January 2006 to
December 2022, projected sales of 1.3 million units in 2025 and 2.113 million
units in 2028. To assess the factors contributing to e-bike usage, we employed
a Random Forest regression model. The most significant factors influencing
e-bike sales growth were disposable personal income and popularity.
Furthermore, we examined the environmental and health impacts of e-bikes.
Through Monte Carlo simulations, we estimated the reduction in carbon emissions
due to e-bike use and the calories burned through e-biking. Our findings
revealed that e-bike usage in the U.S. resulted in a reduction of 15,737.82
kilograms of CO2 emissions in 2022. Additionally, e-bike users burned
approximately 716,630.727 kilocalories through their activities in the same
year. Our research provides valuable insights for policymakers, emphasizing the
potential of e-bikes as a sustainable transportation solution. By understanding
the growth factors and quantifying the environmental and health benefits,
policymakers can make informed decisions about integrating e-bikes into future
energy and transportation strategies. | [
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] |
Deep learning (DL) has been successfully applied to encrypted network traffic
classification in experimental settings. However, in production use, it has
been shown that a DL classifier's performance inevitably decays over time.
Re-training the model on newer datasets has been shown to only partially
improve its performance. Manually re-tuning the model architecture to meet the
performance expectations on newer datasets is time-consuming and requires
domain expertise. We propose AutoML4ETC, a novel tool to automatically design
efficient and high-performing neural architectures for encrypted traffic
classification. We define a novel, powerful search space tailored specifically
for the early classification of encrypted traffic using packet header bytes. We
show that with different search strategies over our search space, AutoML4ETC
generates neural architectures that outperform the state-of-the-art encrypted
traffic classifiers on several datasets, including public benchmark datasets
and real-world TLS and QUIC traffic collected from the Orange mobile network.
In addition to being more accurate, AutoML4ETC's architectures are
significantly more efficient and lighter in terms of the number of parameters.
Finally, we make AutoML4ETC publicly available for future research. | [
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] |
Electroencephalography (EEG) decoding is a challenging task due to the
limited availability of labelled data. While transfer learning is a promising
technique to address this challenge, it assumes that transferable data domains
and task are known, which is not the case in this setting. This study
investigates the transferability of deep learning representations between
different EEG decoding tasks. We conduct extensive experiments using
state-of-the-art decoding models on two recently released EEG datasets, ERP
CORE and M$^3$CV, containing over 140 subjects and 11 distinct cognitive tasks.
We measure the transferability of learned representations by pre-training deep
neural networks on one task and assessing their ability to decode subsequent
tasks. Our experiments demonstrate that, even with linear probing transfer,
significant improvements in decoding performance can be obtained, with gains of
up to 28% compare with the pure supervised approach. Additionally, we discover
evidence that certain decoding paradigms elicit specific and narrow brain
activities, while others benefit from pre-training on a broad range of
representations. By revealing which tasks transfer well and demonstrating the
benefits of transfer learning for EEG decoding, our findings have practical
implications for mitigating data scarcity in this setting. The transfer maps
generated also provide insights into the hierarchical relations between
cognitive tasks, hence enhancing our understanding of how these tasks are
connected from a neuroscientific standpoint. | [
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] |
Recently, with the advancement of the Internet of Things (IoT), WiFi
CSI-based HAR has gained increasing attention from academic and industry
communities. By integrating the deep learning technology with CSI-based HAR,
researchers achieve state-of-the-art performance without the need of expert
knowledge. However, the scarcity of labeled CSI data remains the most prominent
challenge when applying deep learning models in the context of CSI-based HAR
due to the privacy and incomprehensibility of CSI-based HAR data. On the other
hand, SSL has emerged as a promising approach for learning meaningful
representations from data without heavy reliance on labeled examples.
Therefore, considerable efforts have been made to address the challenge of
insufficient data in deep learning by leveraging SSL algorithms. In this paper,
we undertake a comprehensive inventory and analysis of the potential held by
different categories of SSL algorithms, including those that have been
previously studied and those that have not yet been explored, within the field.
We provide an in-depth investigation of SSL algorithms in the context of WiFi
CSI-based HAR. We evaluate four categories of SSL algorithms using three
publicly available CSI HAR datasets, each encompassing different tasks and
environmental settings. To ensure relevance to real-world applications, we
design performance metrics that align with specific requirements. Furthermore,
our experimental findings uncover several limitations and blind spots in
existing work, highlighting the barriers that need to be addressed before SSL
can be effectively deployed in real-world WiFi-based HAR applications. Our
results also serve as a practical guideline for industry practitioners and
provide valuable insights for future research endeavors in this field. | [
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] |
Developing robot controllers capable of achieving dexterous nonprehensile
manipulation, such as pushing an object on a table, is challenging. The
underactuated and hybrid-dynamics nature of the problem, further complicated by
the uncertainty resulting from the frictional interactions, requires
sophisticated control behaviors. Reinforcement Learning (RL) is a powerful
framework for developing such robot controllers. However, previous RL
literature addressing the nonprehensile pushing task achieves low accuracy,
non-smooth trajectories, and only simple motions, i.e. without rotation of the
manipulated object. We conjecture that previously used unimodal exploration
strategies fail to capture the inherent hybrid-dynamics of the task, arising
from the different possible contact interaction modes between the robot and the
object, such as sticking, sliding, and separation. In this work, we propose a
multimodal exploration approach through categorical distributions, which
enables us to train planar pushing RL policies for arbitrary starting and
target object poses, i.e. positions and orientations, and with improved
accuracy. We show that the learned policies are robust to external disturbances
and observation noise, and scale to tasks with multiple pushers. Furthermore,
we validate the transferability of the learned policies, trained entirely in
simulation, to a physical robot hardware using the KUKA iiwa robot arm. See our
supplemental video: https://youtu.be/vTdva1mgrk4. | [
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] |
Ocean modeling is a powerful tool for simulating the physical, chemical, and
biological processes of the ocean, which is the foundation for marine science
research and operational oceanography. Modern numerical ocean modeling mainly
consists of governing equations and numerical algorithms. Nonlinear
instability, computational expense, low reusability efficiency and high
coupling costs have gradually become the main bottlenecks for the further
development of numerical ocean modeling. Recently, artificial
intelligence-based modeling in scientific computing has shown revolutionary
potential for digital twins and scientific simulations, but the bottlenecks of
numerical ocean modeling have not been further solved. Here, we present
AI-GOMS, a large AI-driven global ocean modeling system, for accurate and
efficient global ocean daily prediction. AI-GOMS consists of a backbone model
with the Fourier-based Masked Autoencoder structure for basic ocean variable
prediction and lightweight fine-tuning models incorporating regional
downscaling, wave decoding, and biochemistry coupling modules. AI-GOMS has
achieved the best performance in 30 days of prediction for the global ocean
basic variables with 15 depth layers at 1/4{\deg} spatial resolution. Beyond
the good performance in statistical metrics, AI-GOMS realizes the simulation of
mesoscale eddies in the Kuroshio region at 1/12{\deg} spatial resolution and
ocean stratification in the tropical Pacific Ocean. AI-GOMS provides a new
backbone-downstream paradigm for Earth system modeling, which makes the system
transferable, scalable and reusable. | [
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Consider a setting in which devices and a server share a pre-trained model.
The server wishes to make an inference on a new input given the model. Devices
have access to data, previously not used for training, and can communicate to
the server over a common wireless channel. If the devices have no access to the
new input, can communication from devices to the server enhance the quality of
the inference decision at the server? Recent work has introduced federated
conformal prediction (CP), which leverages devices-to-server communication to
improve the reliability of the server's decision. With federated CP, devices
communicate to the server information about the loss accrued by the shared
pre-trained model on the local data, and the server leverages this information
to calibrate a decision interval, or set, so that it is guaranteed to contain
the correct answer with a pre-defined target reliability level. Previous work
assumed noise-free communication, whereby devices can communicate a single real
number to the server. In this paper, we study for the first time federated CP
in a wireless setting. We introduce a novel protocol, termed wireless federated
conformal prediction (WFCP), which builds on type-based multiple access (TBMA)
and on a novel quantile correction strategy. WFCP is proved to provide formal
reliability guarantees in terms of coverage of the predicted set produced by
the server. Using numerical results, we demonstrate the significant advantages
of WFCP against digital implementations of existing federated CP schemes,
especially in regimes with limited communication resources and/or large number
of devices. | [
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] |
Artificial intelligence (AI) is increasingly being considered to assist human
decision-making in high-stake domains (e.g. health). However, researchers have
discussed an issue that humans can over-rely on wrong suggestions of the AI
model instead of achieving human AI complementary performance. In this work, we
utilized salient feature explanations along with what-if, counterfactual
explanations to make humans review AI suggestions more analytically to reduce
overreliance on AI and explored the effect of these explanations on trust and
reliance on AI during clinical decision-making. We conducted an experiment with
seven therapists and ten laypersons on the task of assessing post-stroke
survivors' quality of motion, and analyzed their performance, agreement level
on the task, and reliance on AI without and with two types of AI explanations.
Our results showed that the AI model with both salient features and
counterfactual explanations assisted therapists and laypersons to improve their
performance and agreement level on the task when `right' AI outputs are
presented. While both therapists and laypersons over-relied on `wrong' AI
outputs, counterfactual explanations assisted both therapists and laypersons to
reduce their over-reliance on `wrong' AI outputs by 21\% compared to salient
feature explanations. Specifically, laypersons had higher performance degrades
by 18.0 f1-score with salient feature explanations and 14.0 f1-score with
counterfactual explanations than therapists with performance degrades of 8.6
and 2.8 f1-scores respectively. Our work discusses the potential of
counterfactual explanations to better estimate the accuracy of an AI model and
reduce over-reliance on `wrong' AI outputs and implications for improving
human-AI collaborative decision-making. | [
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] |
Generative Artificial Intelligence (AI) has seen mainstream adoption lately,
especially in the form of consumer-facing, open-ended, text and image
generating models. However, the use of such systems raises significant ethical
and safety concerns, including privacy violations, misinformation and
intellectual property theft. The potential for generative AI to displace human
creativity and livelihoods has also been under intense scrutiny. To mitigate
these risks, there is an urgent need of policies and regulations responsible
and ethical development in the field of generative AI. Existing and proposed
centralized regulations by governments to rein in AI face criticisms such as
not having sufficient clarity or uniformity, lack of interoperability across
lines of jurisdictions, restricting innovation, and hindering free market
competition. Decentralized protections via crowdsourced safety tools and
mechanisms are a potential alternative. However, they have clear deficiencies
in terms of lack of adequacy of oversight and difficulty of enforcement of
ethical and safety standards, and are thus not enough by themselves as a
regulation mechanism. We propose a marriage of these two strategies via a
framework we call Dual Governance. This framework proposes a cooperative
synergy between centralized government regulations in a U.S. specific context
and safety mechanisms developed by the community to protect stakeholders from
the harms of generative AI. By implementing the Dual Governance framework, we
posit that innovation and creativity can be promoted while ensuring safe and
ethical deployment of generative AI. | [
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] |
Arctic amplification has altered the climate patterns both regionally and
globally, resulting in more frequent and more intense extreme weather events in
the past few decades. The essential part of Arctic amplification is the
unprecedented sea ice loss as demonstrated by satellite observations.
Accurately forecasting Arctic sea ice from sub-seasonal to seasonal scales has
been a major research question with fundamental challenges at play. In addition
to physics-based Earth system models, researchers have been applying multiple
statistical and machine learning models for sea ice forecasting. Looking at the
potential of data-driven approaches to study sea ice variations, we propose
MT-IceNet - a UNet based spatial and multi-temporal (MT) deep learning model
for forecasting Arctic sea ice concentration (SIC). The model uses an
encoder-decoder architecture with skip connections and processes multi-temporal
input streams to regenerate spatial maps at future timesteps. Using bi-monthly
and monthly satellite retrieved sea ice data from NSIDC as well as atmospheric
and oceanic variables from ERA5 reanalysis product during 1979-2021, we show
that our proposed model provides promising predictive performance for per-pixel
SIC forecasting with up to 60% decrease in prediction error for a lead time of
6 months as compared to its state-of-the-art counterparts. | [
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] |
Predicting stock prices presents a challenging research problem due to the
inherent volatility and non-linear nature of the stock market. In recent years,
knowledge-enhanced stock price prediction methods have shown groundbreaking
results by utilizing external knowledge to understand the stock market. Despite
the importance of these methods, there is a scarcity of scholarly works that
systematically synthesize previous studies from the perspective of external
knowledge types. Specifically, the external knowledge can be modeled in
different data structures, which we group into non-graph-based formats and
graph-based formats: 1) non-graph-based knowledge captures contextual
information and multimedia descriptions specifically associated with an
individual stock; 2) graph-based knowledge captures interconnected and
interdependent information in the stock market. This survey paper aims to
provide a systematic and comprehensive description of methods for acquiring
external knowledge from various unstructured data sources and then
incorporating it into stock price prediction models. We also explore fusion
methods for combining external knowledge with historical price features.
Moreover, this paper includes a compilation of relevant datasets and delves
into potential future research directions in this domain. | [
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] |
Epilepsy is a prevalent neurological disorder characterized by recurrent and
unpredictable seizures, necessitating accurate prediction for effective
management and patient care. Application of machine learning (ML) on
electroencephalogram (EEG) recordings, along with its ability to provide
valuable insights into brain activity during seizures, is able to make accurate
and robust seizure prediction an indispensable component in relevant studies.
In this research, we present a comprehensive comparative analysis of five
machine learning models - Random Forest (RF), Decision Tree (DT), Extra Trees
(ET), Logistic Regression (LR), and Gradient Boosting (GB) - for the prediction
of epileptic seizures using EEG data. The dataset underwent meticulous
preprocessing, including cleaning, normalization, outlier handling, and
oversampling, ensuring data quality and facilitating accurate model training.
These preprocessing techniques played a crucial role in enhancing the models'
performance. The results of our analysis demonstrate the performance of each
model in terms of accuracy. The LR classifier achieved an accuracy of 56.95%,
while GB and DT both attained 97.17% accuracy. RT achieved a higher accuracy of
98.99%, while the ET model exhibited the best performance with an accuracy of
99.29%. Our findings reveal that the ET model outperformed not only the other
models in the comparative analysis but also surpassed the state-of-the-art
results from previous research. The superior performance of the ET model makes
it a compelling choice for accurate and robust epileptic seizure prediction
using EEG data. | [
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Sleep is a crucial aspect of our overall health and well-being. It plays a
vital role in regulating our mental and physical health, impacting our mood,
memory, and cognitive function to our physical resilience and immune system.
The classification of sleep stages is a mandatory step to assess sleep quality,
providing the metrics to estimate the quality of sleep and how well our body is
functioning during this essential period of rest. Photoplethysmography (PPG)
has been demonstrated to be an effective signal for sleep stage inference,
meaning it can be used on its own or in a combination with others signals to
determine sleep stage. This information is valuable in identifying potential
sleep issues and developing strategies to improve sleep quality and overall
health. In this work, we present a machine learning sleep-wake classification
model based on the eXtreme Gradient Boosting (XGBoost) algorithm and features
extracted from PPG signal and activity counts. The performance of our method
was comparable to current state-of-the-art methods with a Sensitivity of 91.15
$\pm$ 1.16%, Specificity of 53.66 $\pm$ 1.12%, F1-score of 83.88 $\pm$ 0.56%,
and Kappa of 48.0 $\pm$ 0.86%. Our method offers a significant improvement over
other approaches as it uses a reduced number of features, making it suitable
for implementation in wearable devices that have limited computational power. | [
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] |
Collaborative filtering (CF) is a pivotal technique in modern recommender
systems. The learning process of CF models typically consists of three
components: interaction encoder, loss function, and negative sampling. Although
many existing studies have proposed various CF models to design sophisticated
interaction encoders, recent work shows that simply reformulating the loss
functions can achieve significant performance gains. This paper delves into
analyzing the relationship among existing loss functions. Our mathematical
analysis reveals that the previous loss functions can be interpreted as
alignment and uniformity functions: (i) the alignment matches user and item
representations, and (ii) the uniformity disperses user and item distributions.
Inspired by this analysis, we propose a novel loss function that improves the
design of alignment and uniformity considering the unique patterns of datasets
called Margin-aware Alignment and Weighted Uniformity (MAWU). The key novelty
of MAWU is two-fold: (i) margin-aware alignment (MA) mitigates
user/item-specific popularity biases, and (ii) weighted uniformity (WU) adjusts
the significance between user and item uniformities to reflect the inherent
characteristics of datasets. Extensive experimental results show that MF and
LightGCN equipped with MAWU are comparable or superior to state-of-the-art CF
models with various loss functions on three public datasets. | [
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] |
A recommender system that optimizes its recommendations solely to fit a
user's history of ratings for consumed items can create a filter bubble,
wherein the user does not get to experience items from novel, unseen
categories. One approach to mitigate this undesired behavior is to recommend
items with high potential for serendipity, namely surprising items that are
likely to be highly rated. In this paper, we propose a content-based
formulation of serendipity that is rooted in Bayesian surprise and use it to
measure the serendipity of items after they are consumed and rated by the user.
When coupled with a collaborative-filtering component that identifies similar
users, this enables recommending items with high potential for serendipity. To
facilitate the evaluation of topic-level models for surprise and serendipity,
we introduce a dataset of book reading histories extracted from Goodreads,
containing over 26 thousand users and close to 1.3 million books, where we
manually annotate 449 books read by 4 users in terms of their time-dependent,
topic-level surprise. Experimental evaluations show that models that use
Bayesian surprise correlate much better with the manual annotations of
topic-level surprise than distance-based heuristics, and also obtain better
serendipitous item recommendation performance. | [
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Over the past few years, deep learning has firmly established its prowess
across various domains, including computer vision, speech recognition, and
natural language processing. Motivated by its outstanding success, researchers
have been directing their efforts towards applying deep learning techniques to
recommender systems. Neural collaborative filtering (NCF) and Neural Matrix
Factorization (NeuMF) refreshes the traditional inner product in matrix
factorization with a neural architecture capable of learning complex and
data-driven functions. While these models effectively capture user-item
interactions, they overlook the specific attributes of both users and items.
This can lead to robustness issues, especially for items and users that belong
to the "long tail". Such challenges are commonly recognized in recommender
systems as a part of the cold-start problem. A direct and intuitive approach to
address this issue is by leveraging the features and attributes of the items
and users themselves. In this paper, we introduce a refined NeuMF model that
considers not only the interaction between users and items, but also acrossing
associated attributes. Moreover, our proposed architecture features a shared
user embedding, seamlessly integrating with user embeddings to imporve the
robustness and effectively address the cold-start problem. Rigorous experiments
on both the Movielens and Pinterest datasets demonstrate the superiority of our
Cross-Attribute Matrix Factorization model, particularly in scenarios
characterized by higher dataset sparsity. | [
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] |
The goal of Causal Discovery is to find automated search methods for learning
causal structures from observational data. In some cases all variables of the
interested causal mechanism are measured, and the task is to predict the
effects one measured variable has on another. In contrast, sometimes the
variables of primary interest are not directly observable but instead inferred
from their manifestations in the data. These are referred to as latent
variables. One commonly known example is the psychological construct of
intelligence, which cannot directly measured so researchers try to assess
through various indicators such as IQ tests. In this case, casual discovery
algorithms can uncover underlying patterns and structures to reveal the causal
connections between the latent variables and between the latent and observed
variables. This thesis focuses on two questions in causal discovery: providing
an alternative definition of k-Triangle Faithfulness that (i) is weaker than
strong faithfulness when applied to the Gaussian family of distributions, (ii)
can be applied to non-Gaussian families of distributions, and (iii) under the
assumption that the modified version of Strong Faithfulness holds, can be used
to show the uniform consistency of a modified causal discovery algorithm;
relaxing the sufficiency assumption to learn causal structures with latent
variables. Given the importance of inferring cause-and-effect relationships for
understanding and forecasting complex systems, the work in this thesis of
relaxing various simplification assumptions is expected to extend the causal
discovery method to be applicable in a wider range with diversified causal
mechanism and statistical phenomena. | [
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Recommender systems have become fundamental building blocks of modern online
products and services, and have a substantial impact on user experience. In the
past few years, deep learning methods have attracted a lot of research, and are
now heavily used in modern real-world recommender systems. Nevertheless,
dealing with recommendations in the cold-start setting, e.g., when a user has
done limited interactions in the system, is a problem that remains far from
solved. Meta-learning techniques, and in particular optimization-based
meta-learning, have recently become the most popular approaches in the academic
research literature for tackling the cold-start problem in deep learning models
for recommender systems. However, current meta-learning approaches are not
practical for real-world recommender systems, which have billions of users and
items, and strict latency requirements. In this paper we show that it is
possible to obtaining similar, or higher, performance on commonly used
benchmarks for the cold-start problem without using meta-learning techniques.
In more detail, we show that, when tuned correctly, standard and widely adopted
deep learning models perform just as well as newer meta-learning models. We
further show that an extremely simple modular approach using common
representation learning techniques, can perform comparably to meta-learning
techniques specifically designed for the cold-start setting while being much
more easily deployable in real-world applications. | [
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0.00008529041224392131,
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0.06397079676389694,
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0.038932692259550095,
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] |
Transformers have demonstrated remarkable success in natural language
processing; however, their potential remains mostly unexplored for problems
arising in dynamical systems. In this work, we investigate the optimal output
estimation problem using transformers, which generate output predictions using
all the past ones. We train the transformer using various systems drawn from a
prior distribution and then evaluate its performance on previously unseen
systems from the same distribution. As a result, the obtained transformer acts
like a prediction algorithm that learns in-context and quickly adapts to and
predicts well for different systems - thus we call it meta-output-predictor
(MOP). MOP matches the performance of the optimal output estimator, based on
Kalman filter, for most linear dynamical systems even though it does not have
access to a model. We observe via extensive numerical experiments that MOP also
performs well in challenging scenarios with non-i.i.d. noise, time-varying
dynamics, and nonlinear dynamics like a quadrotor system with unknown
parameters. To further support this observation, in the second part of the
paper, we provide statistical guarantees on the performance of MOP and quantify
the required amount of training to achieve a desired excess risk during
test-time. Finally, we point out some limitations of MOP by identifying two
classes of problems MOP fails to perform well, highlighting the need for
caution when using transformers for control and estimation. | [
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] |
Cryptocurrencies have become a popular and widely researched topic of
interest in recent years for investors and scholars. In order to make informed
investment decisions, it is essential to comprehend the factors that impact
cryptocurrency prices and to identify risky cryptocurrencies. This paper
focuses on analyzing historical data and using artificial intelligence
algorithms on on-chain parameters to identify the factors affecting a
cryptocurrency's price and to find risky cryptocurrencies. We conducted an
analysis of historical cryptocurrencies' on-chain data and measured the
correlation between the price and other parameters. In addition, we used
clustering and classification in order to get a better understanding of a
cryptocurrency and classify it as risky or not. The analysis revealed that a
significant proportion of cryptocurrencies (39%) disappeared from the market,
while only a small fraction (10%) survived for more than 1000 days. Our
analysis revealed a significant negative correlation between cryptocurrency
price and maximum and total supply, as well as a weak positive correlation
between price and 24-hour trading volume. Moreover, we clustered
cryptocurrencies into five distinct groups using their on-chain parameters,
which provides investors with a more comprehensive understanding of a
cryptocurrency when compared to those clustered with it. Finally, by
implementing multiple classifiers to predict whether a cryptocurrency is risky
or not, we obtained the best f1-score of 76% using K-Nearest Neighbor. | [
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] |
The Research & Development (R&D) phase of drug development is a lengthy and
costly process. To revolutionize this process, we introduce our new concept
QMLS to shorten the whole R&D phase to three to six months and decrease the
cost to merely fifty to eighty thousand USD. For Hit Generation, Machine
Learning Molecule Generation (MLMG) generates possible hits according to the
molecular structure of the target protein while the Quantum Simulation (QS)
filters molecules from the primary essay based on the reaction and binding
effectiveness with the target protein. Then, For Lead Optimization, the
resultant molecules generated and filtered from MLMG and QS are compared, and
molecules that appear as a result of both processes will be made into dozens of
molecular variations through Machine Learning Molecule Variation (MLMV), while
others will only be made into a few variations. Lastly, all optimized molecules
would undergo multiple rounds of QS filtering with a high standard for reaction
effectiveness and safety, creating a few dozen pre-clinical-trail-ready drugs.
This paper is based on our first paper, where we pitched the concept of machine
learning combined with quantum simulations. In this paper we will go over the
detailed design and framework of QMLS, including MLMG, MLMV, and QS. | [
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] |
With the exponential increase of the protein sequence databases over time,
multiple-sequence alignment (MSA) methods, like PSI-BLAST, perform exhaustive
and time-consuming database search to retrieve evolutionary information. The
resulting position-specific scoring matrices (PSSMs) of such search engines
represent a crucial input to many machine learning (ML) models in the field of
bioinformatics and computational biology. A protein sequence is a collection of
contiguous tokens or characters called amino acids (AAs). The analogy to
natural language allowed us to exploit the recent advancements in the field of
Natural Language Processing (NLP) and therefore transfer NLP state-of-the-art
algorithms to bioinformatics. This research presents an Embedding Language
Model (ELMo), converting a protein sequence to a numerical vector
representation. While the original ELMo trained a 2-layer bidirectional Long
Short-Term Memory (LSTMs) network following a two-path architecture, one for
the forward and the second for the backward pass, by merging the idea of PSSMs
with the concept of transfer-learning, this work introduces a novel
bidirectional language model (bi-LM) with four times less free parameters and
using rather a single path for both passes. The model was trained not only on
predicting the next AA but also on the probability distribution of the next AA
derived from similar, yet different sequences as summarized in a PSSM,
simultaneously for multi-task learning, hence learning evolutionary information
of protein sequences as well. The network architecture and the pre-trained
model are made available as open source under the permissive MIT license on
GitHub at https://github.com/issararab/PEvoLM. | [
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] |
Whether current or near-term AI systems could be conscious is a topic of
scientific interest and increasing public concern. This report argues for, and
exemplifies, a rigorous and empirically grounded approach to AI consciousness:
assessing existing AI systems in detail, in light of our best-supported
neuroscientific theories of consciousness. We survey several prominent
scientific theories of consciousness, including recurrent processing theory,
global workspace theory, higher-order theories, predictive processing, and
attention schema theory. From these theories we derive "indicator properties"
of consciousness, elucidated in computational terms that allow us to assess AI
systems for these properties. We use these indicator properties to assess
several recent AI systems, and we discuss how future systems might implement
them. Our analysis suggests that no current AI systems are conscious, but also
suggests that there are no obvious technical barriers to building AI systems
which satisfy these indicators. | [
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] |
Robots have been successfully used to perform tasks with high precision. In
real-world environments with sparse rewards and multiple goals, learning is
still a major challenge and Reinforcement Learning (RL) algorithms fail to
learn good policies. Training in simulation environments and then fine-tuning
in the real world is a common approach. However, adapting to the real-world
setting is a challenge. In this paper, we present a method named Ready for
Production Hierarchical RL (ReProHRL) that divides tasks with hierarchical
multi-goal navigation guided by reinforcement learning. We also use object
detectors as a pre-processing step to learn multi-goal navigation and transfer
it to the real world. Empirical results show that the proposed ReProHRL method
outperforms the state-of-the-art baseline in simulation and real-world
environments in terms of both training time and performance. Although both
methods achieve a 100% success rate in a simple environment for single
goal-based navigation, in a more complex environment and multi-goal setting,
the proposed method outperforms the baseline by 18% and 5%, respectively. For
the real-world implementation and proof of concept demonstration, we deploy the
proposed method on a nano-drone named Crazyflie with a front camera to perform
multi-goal navigation experiments. | [
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] |
Recommender systems have been gaining increasing research attention over the
years. Most existing recommendation methods focus on capturing users'
personalized preferences through historical user-item interactions, which may
potentially violate user privacy. Additionally, these approaches often overlook
the significance of the temporal fluctuation in item popularity that can sway
users' decision-making. To bridge this gap, we propose Popularity-Aware
Recommender (PARE), which makes non-personalized recommendations by predicting
the items that will attain the highest popularity. PARE consists of four
modules, each focusing on a different aspect: popularity history, temporal
impact, periodic impact, and side information. Finally, an attention layer is
leveraged to fuse the outputs of four modules. To our knowledge, this is the
first work to explicitly model item popularity in recommendation systems.
Extensive experiments show that PARE performs on par or even better than
sophisticated state-of-the-art recommendation methods. Since PARE prioritizes
item popularity over personalized user preferences, it can enhance existing
recommendation methods as a complementary component. Our experiments
demonstrate that integrating PARE with existing recommendation methods
significantly surpasses the performance of standalone models, highlighting
PARE's potential as a complement to existing recommendation methods.
Furthermore, the simplicity of PARE makes it immensely practical for industrial
applications and a valuable baseline for future research. | [
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] |
Modern large-scale scientific discovery requires multidisciplinary
collaboration across diverse computing facilities, including High Performance
Computing (HPC) machines and the Edge-to-Cloud continuum. Integrated data
analysis plays a crucial role in scientific discovery, especially in the
current AI era, by enabling Responsible AI development, FAIR, Reproducibility,
and User Steering. However, the heterogeneous nature of science poses
challenges such as dealing with multiple supporting tools, cross-facility
environments, and efficient HPC execution. Building on data observability,
adapter system design, and provenance, we propose MIDA: an approach for
lightweight runtime Multi-workflow Integrated Data Analysis. MIDA defines data
observability strategies and adaptability methods for various parallel systems
and machine learning tools. With observability, it intercepts the dataflows in
the background without requiring instrumentation while integrating domain,
provenance, and telemetry data at runtime into a unified database ready for
user steering queries. We conduct experiments showing end-to-end multi-workflow
analysis integrating data from Dask and MLFlow in a real distributed deep
learning use case for materials science that runs on multiple environments with
up to 276 GPUs in parallel. We show near-zero overhead running up to 100,000
tasks on 1,680 CPU cores on the Summit supercomputer. | [
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] |
In the prevailing convergence of traditional infrastructure-based deployment
(i.e., Telco and industry operational networks) towards evolving deployments
enabled by 5G and virtualization, there is a keen interest in elaborating
effective security controls to protect these deployments in-depth. By
considering key enabling technologies like 5G and virtualization, evolving
networks are democratized, facilitating the establishment of point presences
integrating different business models ranging from media, dynamic web content,
gaming, and a plethora of IoT use cases. Despite the increasing services
provided by evolving networks, many cybercrimes and attacks have been launched
in evolving networks to perform malicious activities. Due to the limitations of
traditional security artifacts (e.g., firewalls and intrusion detection
systems), the research on digital forensic data analytics has attracted more
attention. Digital forensic analytics enables people to derive detailed
information and comprehensive conclusions from different perspectives of
cybercrimes to assist in convicting criminals and preventing future crimes.
This chapter presents a digital analytics framework for network anomaly
detection, including multi-perspective feature engineering, unsupervised
anomaly detection, and comprehensive result correction procedures. Experiments
on real-world evolving network data show the effectiveness of the proposed
forensic data analytics solution. | [
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] |
Transformer has been successfully used in practical applications, such as
ChatGPT, due to its powerful advantages. However, users' input is leaked to the
model provider during the service. With people's attention to privacy,
privacy-preserving Transformer inference is on the demand of such services.
Secure protocols for non-linear functions are crucial in privacy-preserving
Transformer inference, which are not well studied. Thus, designing practical
secure protocols for non-linear functions is hard but significant to model
performance. In this work, we propose a framework \emph{East} to enable
efficient and accurate secure Transformer inference. Firstly, we propose a new
oblivious piecewise polynomial evaluation algorithm and apply it to the
activation functions, which reduces the runtime and communication of GELU by
over 1.5$\times$ and 2.5$\times$, compared to prior arts. Secondly, the secure
protocols for softmax and layer normalization are carefully designed to
faithfully maintain the desired functionality. Thirdly, several optimizations
are conducted in detail to enhance the overall efficiency. We applied
\emph{East} to BERT and the results show that the inference accuracy remains
consistent with the plaintext inference without fine-tuning. Compared to Iron,
we achieve about 1.8$\times$ lower communication within 1.2$\times$ lower
runtime. | [
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] |
Complex decision-making systems rarely have direct access to the current
state of the world and they instead rely on opinions to form an understanding
of what the ground truth could be. Even in problems where experts provide
opinions without any intention to manipulate the decision maker, it is
challenging to decide which expert's opinion is more reliable -- a challenge
that is further amplified when decision-maker has limited, delayed, or no
access to the ground truth after the fact. This paper explores a statistical
approach to infer the competence of each expert based on their opinions without
any need for the ground truth. Echoing the logic behind what is commonly
referred to as \textit{the wisdom of crowds}, we propose measuring the
competence of each expert by their likeliness to agree with their peers. We
further show that the more reliable an expert is the more likely it is that
they agree with their peers. We leverage this fact to propose a completely
unsupervised version of the na\"{i}ve Bayes classifier and show that the
proposed technique is asymptotically optimal for a large class of problems. In
addition to aggregating a large block of opinions, we further apply our
technique for online opinion aggregation and for decision-making based on a
limited the number of opinions. | [
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] |
Influence estimation aims to predict the total influence spread in social
networks and has received surged attention in recent years. Most current
studies focus on estimating the total number of influenced users in a social
network, and neglect susceptibility estimation that aims to predict the
probability of each user being influenced from the individual perspective. As a
more fine-grained estimation task, susceptibility estimation is full of
attractiveness and practical value. Based on the significance of susceptibility
estimation and dynamic properties of social networks, we propose a task, called
susceptibility estimation in dynamic social networks, which is even more
realistic and valuable in real-world applications. Susceptibility estimation in
dynamic networks has yet to be explored so far and is computationally
intractable to naively adopt Monte Carlo simulation to obtain the results. To
this end, we propose a novel end-to-end framework DySuse based on dynamic graph
embedding technology. Specifically, we leverage a structural feature module to
independently capture the structural information of influence diffusion on each
single graph snapshot. Besides, {we propose the progressive mechanism according
to the property of influence diffusion,} to couple the structural and temporal
information during diffusion tightly. Moreover, a self-attention block {is
designed to} further capture temporal dependency by flexibly weighting
historical timestamps. Experimental results show that our framework is superior
to the existing dynamic graph embedding models and has satisfactory prediction
performance in multiple influence diffusion models. | [
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] |
In recent years, countless research papers have addressed the topics of
knowledge graph creation, extension, or completion in order to create knowledge
graphs that are larger, more correct, or more diverse. This research is
typically motivated by the argumentation that using such enhanced knowledge
graphs to solve downstream tasks will improve performance. Nonetheless, this is
hardly ever evaluated. Instead, the predominant evaluation metrics - aiming at
correctness and completeness - are undoubtedly valuable but fail to capture the
complete picture, i.e., how useful the created or enhanced knowledge graph
actually is. Further, the accessibility of such a knowledge graph is rarely
considered (e.g., whether it contains expressive labels, descriptions, and
sufficient context information to link textual mentions to the entities of the
knowledge graph). To better judge how well knowledge graphs perform on actual
tasks, we present KGrEaT - a framework to estimate the quality of knowledge
graphs via actual downstream tasks like classification, clustering, or
recommendation. Instead of comparing different methods of processing knowledge
graphs with respect to a single task, the purpose of KGrEaT is to compare
various knowledge graphs as such by evaluating them on a fixed task setup. The
framework takes a knowledge graph as input, automatically maps it to the
datasets to be evaluated on, and computes performance metrics for the defined
tasks. It is built in a modular way to be easily extendable with additional
tasks and datasets. | [
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] |
Understanding the dynamics of large quantum systems is hindered by the curse
of dimensionality. Statistical learning offers new possibilities in this regime
by neural-network protocols and classical shadows, while both methods have
limitations: the former is plagued by the predictive uncertainty and the latter
lacks the generalization ability. Here we propose a data-centric learning
paradigm combining the strength of these two approaches to facilitate diverse
quantum system learning (QSL) tasks. Particularly, our paradigm utilizes
classical shadows along with other easily obtainable information of quantum
systems to create the training dataset, which is then learnt by neural networks
to unveil the underlying mapping rule of the explored QSL problem. Capitalizing
on the generalization power of neural networks, this paradigm can be trained
offline and excel at predicting previously unseen systems at the inference
stage, even with few state copies. Besides, it inherits the characteristic of
classical shadows, enabling memory-efficient storage and faithful prediction.
These features underscore the immense potential of the proposed data-centric
approach in discovering novel and large-scale quantum systems. For
concreteness, we present the instantiation of our paradigm in quantum state
tomography and direct fidelity estimation tasks and conduct numerical analysis
up to 60 qubits. Our work showcases the profound prospects of data-centric
artificial intelligence to advance QSL in a faithful and generalizable manner. | [
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] |
Recently, deep learning has shown to be effective for Electroencephalography
(EEG) decoding tasks. Yet, its performance can be negatively influenced by two
key factors: 1) the high variance and different types of corruption that are
inherent in the signal, 2) the EEG datasets are usually relatively small given
the acquisition cost, annotation cost and amount of effort needed. Data
augmentation approaches for alleviation of this problem have been empirically
studied, with augmentation operations on spatial domain, time domain or
frequency domain handcrafted based on expertise of domain knowledge. In this
work, we propose a principled approach to perform dynamic evolution on the data
for improvement of decoding robustness. The approach is based on
distributionally robust optimization and achieves robustness by optimizing on a
family of evolved data distributions instead of the single training data
distribution. We derived a general data evolution framework based on
Wasserstein gradient flow (WGF) and provides two different forms of evolution
within the framework. Intuitively, the evolution process helps the EEG decoder
to learn more robust and diverse features. It is worth mentioning that the
proposed approach can be readily integrated with other data augmentation
approaches for further improvements. We performed extensive experiments on the
proposed approach and tested its performance on different types of corrupted
EEG signals. The model significantly outperforms competitive baselines on
challenging decoding scenarios. | [
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] |
Pre-trained large transformer models have achieved remarkable performance in
the fields of natural language processing and computer vision. Since the
magnitude of available labeled electroencephalogram (EEG) data is much lower
than that of text and image data, it is difficult for transformer models
pre-trained from EEG to be developed as large as GPT-4 100T to fully unleash
the potential of this architecture. In this paper, we show that transformers
pre-trained from images as well as text can be directly fine-tuned for
EEG-based prediction tasks. We design AdaCE, plug-and-play Adapters for
Converting EEG data into image as well as text forms, to fine-tune pre-trained
vision and language transformers. The proposed AdaCE module is highly effective
for fine-tuning pre-trained transformers while achieving state-of-the-art
performance on diverse EEG-based prediction tasks. For example, AdaCE on the
pre-trained Swin-Transformer achieves 99.6%, an absolute improvement of 9.2%,
on the EEG-decoding task of human activity recognition (UCI HAR). Furthermore,
we empirically show that applying the proposed AdaCE to fine-tune larger
pre-trained models can achieve better performance on EEG-based predicting
tasks, indicating the potential of our adapters for even larger transformers.
The plug-and-play AdaCE module can be applied to fine-tuning most of the
popular pre-trained transformers on many other time-series data with multiple
channels, not limited to EEG data and the models we use. Our code will be
available at https://github.com/wangbxj1234/AdaCE. | [
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] |
The Potential Outcome Framework (POF) plays a prominent role in the field of
causal inference. Most causal inference models based on the POF (CIMs-POF) are
designed for eliminating confounding bias and default to an underlying
assumption of Confounding Covariates. This assumption posits that the
covariates consist solely of confounders. However, the assumption of
Confounding Covariates is challenging to maintain in practice, particularly
when dealing with high-dimensional covariates. While certain methods have been
proposed to differentiate the distinct components of covariates prior to
conducting causal inference, the consequences of treating non-confounding
covariates as confounders remain unclear. This ambiguity poses a potential risk
when conducting causal inference in practical scenarios. In this paper, we
present a unified graphical framework for the CIMs-POF, which greatly enhances
the comprehension of these models' underlying principles. Using this graphical
framework, we quantitatively analyze the extent to which the inference
performance of CIMs-POF is influenced when incorporating various types of
non-confounding covariates, such as instrumental variables, mediators,
colliders, and adjustment variables. The key findings are: in the task of
eliminating confounding bias, the optimal scenario is for the covariates to
exclusively encompass confounders; in the subsequent task of inferring
counterfactual outcomes, the adjustment variables contribute to more accurate
inferences. Furthermore, extensive experiments conducted on synthetic datasets
consistently validate these theoretical conclusions. | [
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Early brain development is characterized by the formation of a highly
organized structural connectome. The interconnected nature of this connectome
underlies the brain's cognitive abilities and influences its response to
diseases and environmental factors. Hence, quantitative assessment of
structural connectivity in the perinatal stage is useful for studying normal
and abnormal neurodevelopment. However, estimation of the connectome from
diffusion MRI data involves complex computations. For the perinatal period,
these computations are further challenged by the rapid brain development and
imaging difficulties. Combined with high inter-subject variability, these
factors make it difficult to chart the normal development of the structural
connectome. As a result, there is a lack of reliable normative baselines of
structural connectivity metrics at this critical stage in brain development. In
this study, we developed a computational framework, based on spatio-temporal
averaging, for determining such baselines. We used this framework to analyze
the structural connectivity between 33 and 44 postmenstrual weeks using data
from 166 subjects. Our results unveiled clear and strong trends in the
development of structural connectivity in perinatal stage. Connection weighting
based on fractional anisotropy and neurite density produced the most consistent
results. We observed increases in global and local efficiency, a decrease in
characteristic path length, and widespread strengthening of the connections
within and across brain lobes and hemispheres. We also observed asymmetry
patterns that were consistent between different connection weighting
approaches. The new computational method and results are useful for assessing
normal and abnormal development of the structural connectome early in life. | [
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] |
The driving style of an Autonomous Vehicle (AV) refers to how it behaves and
interacts with other AVs. In a multi-vehicle autonomous driving system, an AV
capable of identifying the driving styles of its nearby AVs can reliably
evaluate the risk of collisions and make more reasonable driving decisions.
However, there has not been a consistent definition of driving styles for an AV
in the literature, although it is considered that the driving style is encoded
in the AV's trajectories and can be identified using Maximum Entropy Inverse
Reinforcement Learning (ME-IRL) methods as a cost function. Nevertheless, an
important indicator of the driving style, i.e., how an AV reacts to its nearby
AVs, is not fully incorporated in the feature design of previous ME-IRL
methods. In this paper, we describe the driving style as a cost function of a
series of weighted features. We design additional novel features to capture the
AV's reaction-aware characteristics. Then, we identify the driving styles from
the demonstration trajectories generated by the Stochastic Model Predictive
Control (SMPC) using a modified ME-IRL method with our newly proposed features.
The proposed method is validated using MATLAB simulation and an off-the-shelf
experiment. | [
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] |
Several earlier studies have shown impressive control performance in complex
robotic systems by designing the controller using a neural network and training
it with model-free reinforcement learning. However, these outstanding
controllers with natural motion style and high task performance are developed
through extensive reward engineering, which is a highly laborious and
time-consuming process of designing numerous reward terms and determining
suitable reward coefficients. In this work, we propose a novel reinforcement
learning framework for training neural network controllers for complex robotic
systems consisting of both rewards and constraints. To let the engineers
appropriately reflect their intent to constraints and handle them with minimal
computation overhead, two constraint types and an efficient policy optimization
algorithm are suggested. The learning framework is applied to train locomotion
controllers for several legged robots with different morphology and physical
attributes to traverse challenging terrains. Extensive simulation and
real-world experiments demonstrate that performant controllers can be trained
with significantly less reward engineering, by tuning only a single reward
coefficient. Furthermore, a more straightforward and intuitive engineering
process can be utilized, thanks to the interpretability and generalizability of
constraints. The summary video is available at https://youtu.be/KAlm3yskhvM. | [
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] |
Federated learning (FL) is an emerging approach for training machine learning
models collaboratively while preserving data privacy. The need for privacy
protection makes it difficult for FL models to achieve global transparency and
explainability. To address this limitation, we incorporate logic-based
explanations into FL by proposing the Logical Reasoning-based eXplainable
Federated Learning (LR-XFL) approach. Under LR-XFL, FL clients create local
logic rules based on their local data and send them, along with model updates,
to the FL server. The FL server connects the local logic rules through a proper
logical connector that is derived based on properties of client data, without
requiring access to the raw data. In addition, the server also aggregates the
local model updates with weight values determined by the quality of the
clients' local data as reflected by their uploaded logic rules. The results
show that LR-XFL outperforms the most relevant baseline by 1.19%, 5.81% and
5.41% in terms of classification accuracy, rule accuracy and rule fidelity,
respectively. The explicit rule evaluation and expression under LR-XFL enable
human experts to validate and correct the rules on the server side, hence
improving the global FL model's robustness to errors. It has the potential to
enhance the transparency of FL models for areas like healthcare and finance
where both data privacy and explainability are important. | [
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] |
Dynamic Difficulty Adjustment (DDA) is a viable approach to enhance a
player's experience in video games. Recently, Reinforcement Learning (RL)
methods have been employed for DDA in non-competitive games; nevertheless, they
rely solely on discrete state-action space with a small search space. In this
paper, we propose a continuous RL-based DDA methodology for a visual working
memory (VWM) game to handle the complex search space for the difficulty of
memorization. The proposed RL-based DDA tailors game difficulty based on the
player's score and game difficulty in the last trial. We defined a continuous
metric for the difficulty of memorization. Then, we consider the task
difficulty and the vector of difficulty-score as the RL's action and state,
respectively. We evaluated the proposed method through a within-subject
experiment involving 52 subjects. The proposed approach was compared with two
rule-based difficulty adjustment methods in terms of player's score and game
experience measured by a questionnaire. The proposed RL-based approach resulted
in a significantly better game experience in terms of competence, tension, and
negative and positive affect. Players also achieved higher scores and win
rates. Furthermore, the proposed RL-based DDA led to a significantly less
decline in the score in a 20-trial session. | [
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] |
An important application of Synthetic Biology is the engineering of the host
cell system to yield useful products. However, an increase in the scale of the
host system leads to huge design space and requires a large number of
validation trials with high experimental costs. A comprehensible machine
learning approach that efficiently explores the hypothesis space and guides
experimental design is urgently needed for the Design-Build-Test-Learn (DBTL)
cycle of the host cell system. We introduce a novel machine learning framework
ILP-iML1515 based on Inductive Logic Programming (ILP) that performs abductive
logical reasoning and actively learns from training examples. In contrast to
numerical models, ILP-iML1515 is built on comprehensible logical
representations of a genome-scale metabolic model and can update the model by
learning new logical structures from auxotrophic mutant trials. The ILP-iML1515
framework 1) allows high-throughput simulations and 2) actively selects
experiments that reduce the experimental cost of learning gene functions in
comparison to randomly selected experiments. | [
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] |
This paper introduces a novel data-driven motion in-betweening system to
reach target poses of characters by making use of phases variables learned by a
Periodic Autoencoder. Our approach utilizes a mixture-of-experts neural network
model, in which the phases cluster movements in both space and time with
different expert weights. Each generated set of weights then produces a
sequence of poses in an autoregressive manner between the current and target
state of the character. In addition, to satisfy poses which are manually
modified by the animators or where certain end effectors serve as constraints
to be reached by the animation, a learned bi-directional control scheme is
implemented to satisfy such constraints. The results demonstrate that using
phases for motion in-betweening tasks sharpen the interpolated movements, and
furthermore stabilizes the learning process. Moreover, using phases for motion
in-betweening tasks can also synthesize more challenging movements beyond
locomotion behaviors. Additionally, style control is enabled between given
target keyframes. Our proposed framework can compete with popular
state-of-the-art methods for motion in-betweening in terms of motion quality
and generalization, especially in the existence of long transition durations.
Our framework contributes to faster prototyping workflows for creating animated
character sequences, which is of enormous interest for the game and film
industry. | [
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] |
The atmosphere affects humans in a multitude of ways, from loss of life due
to adverse weather effects to long-term social and economic impacts on
societies. Computer simulations of atmospheric dynamics are, therefore, of
great importance for the well-being of our and future generations. Here, we
propose AtmoRep, a novel, task-independent stochastic computer model of
atmospheric dynamics that can provide skillful results for a wide range of
applications. AtmoRep uses large-scale representation learning from artificial
intelligence to determine a general description of the highly complex,
stochastic dynamics of the atmosphere from the best available estimate of the
system's historical trajectory as constrained by observations. This is enabled
by a novel self-supervised learning objective and a unique ensemble that
samples from the stochastic model with a variability informed by the one in the
historical record. The task-independent nature of AtmoRep enables skillful
results for a diverse set of applications without specifically training for
them and we demonstrate this for nowcasting, temporal interpolation, model
correction, and counterfactuals. We also show that AtmoRep can be improved with
additional data, for example radar observations, and that it can be extended to
tasks such as downscaling. Our work establishes that large-scale neural
networks can provide skillful, task-independent models of atmospheric dynamics.
With this, they provide a novel means to make the large record of atmospheric
observations accessible for applications and for scientific inquiry,
complementing existing simulations based on first principles. | [
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] |
Financial exchanges across the world use limit order books (LOBs) to process
orders and match trades. For research purposes it is important to have large
scale efficient simulators of LOB dynamics. LOB simulators have previously been
implemented in the context of agent-based models (ABMs), reinforcement learning
(RL) environments, and generative models, processing order flows from
historical data sets and hand-crafted agents alike. For many applications,
there is a requirement for processing multiple books, either for the
calibration of ABMs or for the training of RL agents. We showcase the first
GPU-enabled LOB simulator designed to process thousands of books in parallel,
with a notably reduced per-message processing time. The implementation of our
simulator - JAX-LOB - is based on design choices that aim to best exploit the
powers of JAX without compromising on the realism of LOB-related mechanisms. We
integrate JAX-LOB with other JAX packages, to provide an example of how one may
address an optimal execution problem with reinforcement learning, and to share
some preliminary results from end-to-end RL training on GPUs. | [
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There has been a growing interest in creating intelligent diagnostic systems
to assist medical professionals in analyzing and processing big data for the
treatment of incurable diseases. One of the key challenges in this field is
detecting thyroid cancer, where advancements have been made using machine
learning (ML) and big data analytics to evaluate thyroid cancer prognosis and
determine a patient's risk of malignancy. This review paper summarizes a large
collection of articles related to artificial intelligence (AI)-based techniques
used in the diagnosis of thyroid cancer. Accordingly, a new classification was
introduced to classify these techniques based on the AI algorithms used, the
purpose of the framework, and the computing platforms used. Additionally, this
study compares existing thyroid cancer datasets based on their features. The
focus of this study is on how AI-based tools can support the diagnosis and
treatment of thyroid cancer, through supervised, unsupervised, or hybrid
techniques. It also highlights the progress made and the unresolved challenges
in this field. Finally, the future trends and areas of focus in this field are
discussed. | [
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] |
Neurons in early sensory areas rapidly adapt to changing sensory statistics,
both by normalizing the variance of their individual responses and by reducing
correlations between their responses. Together, these transformations may be
viewed as an adaptive form of statistical whitening. Existing mechanistic
models of adaptive whitening exclusively use either synaptic plasticity or gain
modulation as the biological substrate for adaptation; however, on their own,
each of these models has significant limitations. In this work, we unify these
approaches in a normative multi-timescale mechanistic model that adaptively
whitens its responses with complementary computational roles for synaptic
plasticity and gain modulation. Gains are modified on a fast timescale to adapt
to the current statistical context, whereas synapses are modified on a slow
timescale to match structural properties of the input statistics that are
invariant across contexts. Our model is derived from a novel multi-timescale
whitening objective that factorizes the inverse whitening matrix into basis
vectors, which correspond to synaptic weights, and a diagonal matrix, which
corresponds to neuronal gains. We test our model on synthetic and natural
datasets and find that the synapses learn optimal configurations over long
timescales that enable adaptive whitening on short timescales using gain
modulation. | [
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] |
Quadratic Unconstrained Binary Optimization (QUBO) is a generic technique to
model various NP-hard combinatorial optimization problems in the form of binary
variables. The Hamiltonian function is often used to formulate QUBO problems
where it is used as the objective function in the context of optimization.
Recently, PI-GNN, a generic scalable framework, has been proposed to address
the Combinatorial Optimization (CO) problems over graphs based on a simple
Graph Neural Network (GNN) architecture. Their novel contribution was a generic
QUBO-formulated Hamiltonian-inspired loss function that was optimized using
GNN. In this study, we address a crucial issue related to the aforementioned
setup especially observed in denser graphs. The reinforcement learning-based
paradigm has also been widely used to address numerous CO problems. Here we
also formulate and empirically evaluate the compatibility of the
QUBO-formulated Hamiltonian as the generic reward function in the Reinforcement
Learning paradigm to directly integrate the actual node projection status
during training as the form of rewards. In our experiments, we observed up to
44% improvement in the RL-based setup compared to the PI-GNN algorithm. Our
implementation can be found in
https://github.com/rizveeredwan/learning-graph-structure. | [
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] |
Developing a thorough understanding of the target audience (and/or single
individuals) is a key factor for success - which is exceptionally important and
powerful for the domain of video games that can not only benefit from informed
decision making during development, but ideally even tailor game content,
difficulty and player experience while playing. The granular assessment of
individual personality and differences across players is a particularly
difficult endeavor, given the highly variant human nature, disagreement in
psychological background models and because of the effortful data collection
that most often builds upon long, time-consuming and deterrent questionnaires.
In this work, we explore possibilities to predict a series of player
personality questionnaire metrics from recorded in-game behavior and extend
related work by explicitly adding affective dialog decisions to the game
environment which could elevate the model's accuracy. Using random forest
regression, we predicted a wide variety of personality metrics from seven
established questionnaires across 62 players over 60 minute gameplay of a
customized version of the role-playing game Fallout: New Vegas. While some
personality variables could already be identified from reasonable underlying
in-game actions and affective expressions, we did not find ways to predict
others or encountered questionable correlations that could not be justified by
theoretical background literature. Yet, building on the initial opportunities
of this explorative study, we are striving to massively enlarge our data set to
players from an ecologically valid industrial game environment and investigate
the performance of more sophisticated machine learning approaches. | [
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] |
Recent works have shown the promise of learning pre-trained models for 3D
molecular representation. However, existing pre-training models focus
predominantly on equilibrium data and largely overlook off-equilibrium
conformations. It is challenging to extend these methods to off-equilibrium
data because their training objective relies on assumptions of conformations
being the local energy minima. We address this gap by proposing a force-centric
pretraining model for 3D molecular conformations covering both equilibrium and
off-equilibrium data. For off-equilibrium data, our model learns directly from
their atomic forces. For equilibrium data, we introduce zero-force
regularization and forced-based denoising techniques to approximate
near-equilibrium forces. We obtain a unified pre-trained model for 3D molecular
representation with over 15 million diverse conformations. Experiments show
that, with our pre-training objective, we increase forces accuracy by around 3
times compared to the un-pre-trained Equivariant Transformer model. By
incorporating regularizations on equilibrium data, we solved the problem of
unstable MD simulations in vanilla Equivariant Transformers, achieving
state-of-the-art simulation performance with 2.45 times faster inference time
than NequIP. As a powerful molecular encoder, our pre-trained model achieves
on-par performance with state-of-the-art property prediction tasks. | [
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] |
Recommender systems play an essential role in the choices people make in
domains such as entertainment, shopping, food, news, employment, and education.
The machine learning models underlying these recommender systems are often
enormously large and black-box in nature for users, content providers, and
system developers alike. It is often crucial for all stakeholders to understand
the model's rationale behind making certain predictions and recommendations.
This is especially true for the content providers whose livelihoods depend on
the recommender system. Drawing motivation from the practitioners' need, in
this work, we propose a recourse framework for recommender systems, targeted
towards the content providers. Algorithmic recourse in the recommendation
setting is a set of actions that, if executed, would modify the recommendations
(or ranking) of an item in the desired manner. A recourse suggests actions of
the form: "if a feature changes X to Y, then the ranking of that item for a set
of users will change to Z." Furthermore, we demonstrate that RecRec is highly
effective in generating valid, sparse, and actionable recourses through an
empirical evaluation of recommender systems trained on three real-world
datasets. To the best of our knowledge, this work is the first to conceptualize
and empirically test a generalized framework for generating recourses for
recommender systems. | [
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] |
Given the prevalence of rolling bearing fault diagnosis as a practical issue
across various working conditions, the limited availability of samples
compounds the challenge. Additionally, the complexity of the external
environment and the structure of rolling bearings often manifests faults
characterized by randomness and fuzziness, hindering the effective extraction
of fault characteristics and restricting the accuracy of fault diagnosis. To
overcome these problems, this paper presents a novel approach termed
constructive Incremental learning-based ensemble domain adaptation (CIL-EDA)
approach. Specifically, it is implemented on stochastic configuration networks
(SCN) to constructively improve its adaptive performance in multi-domains.
Concretely, a cloud feature extraction method is employed in conjunction with
wavelet packet decomposition (WPD) to capture the uncertainty of fault
information from multiple resolution aspects. Subsequently, constructive
Incremental learning-based domain adaptation (CIL-DA) is firstly developed to
enhance the cross-domain learning capability of each hidden node through domain
matching and construct a robust fault classifier by leveraging limited labeled
data from both target and source domains. Finally, fault diagnosis results are
obtained by a majority voting of CIL-EDA which integrates CIL-DA and parallel
ensemble learning. Experimental results demonstrate that our CIL-DA outperforms
several domain adaptation methods and CIL-EDA consistently outperforms
state-of-art fault diagnosis methods in few-shot scenarios. | [
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] |
This paper develops a Decentralized Multi-Agent Reinforcement Learning
(Dec-MARL) method to solve the SoC balancing problem in the distributed energy
storage system (DESS). First, the SoC balancing problem is formulated into a
finite Markov decision process with action constraints derived from demand
balance, which can be solved by Dec-MARL. Specifically, the first-order average
consensus algorithm is utilized to expand the observations of the DESS state in
a fully-decentralized way, and the initial actions (i.e., output power) are
decided by the agents (i.e., energy storage units) according to these
observations. In order to get the final actions in the allowable range, a
counterfactual demand balance algorithm is proposed to balance the total demand
and the initial actions. Next, the agents execute the final actions and get
local rewards from the environment, and the DESS steps into the next state.
Finally, through the first-order average consensus algorithm, the agents get
the average reward and the expended observation of the next state for later
training. By the above procedure, Dec-MARL reveals outstanding performance in a
fully-decentralized system without any expert experience or constructing any
complicated model. Besides, it is flexible and can be extended to other
decentralized multi-agent systems straightforwardly. Extensive simulations have
validated the effectiveness and efficiency of Dec-MARL. | [
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] |
A growing body of research has explored how to support humans in making
better use of AI-based decision support, including via training and onboarding.
Existing research has focused on decision-making tasks where it is possible to
evaluate "appropriate reliance" by comparing each decision against a ground
truth label that cleanly maps to both the AI's predictive target and the human
decision-maker's goals. However, this assumption does not hold in many
real-world settings where AI tools are deployed today (e.g., social work,
criminal justice, and healthcare). In this paper, we introduce a
process-oriented notion of appropriate reliance called critical use that
centers the human's ability to situate AI predictions against knowledge that is
uniquely available to them but unavailable to the AI model. To explore how
training can support critical use, we conduct a randomized online experiment in
a complex social decision-making setting: child maltreatment screening. We find
that, by providing participants with accelerated, low-stakes opportunities to
practice AI-assisted decision-making in this setting, novices came to exhibit
patterns of disagreement with AI that resemble those of experienced workers. A
qualitative examination of participants' explanations for their AI-assisted
decisions revealed that they drew upon qualitative case narratives, to which
the AI model did not have access, to learn when (not) to rely on AI
predictions. Our findings open new questions for the study and design of
training for real-world AI-assisted decision-making. | [
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Car-following models have made significant contributions to our understanding
of longitudinal driving behavior. However, they often exhibit limited accuracy
and flexibility, as they cannot fully capture the complexity inherent in
car-following processes, or may falter in unseen scenarios due to their
reliance on confined driving skills present in training data. It is worth
noting that each car-following model possesses its own strengths and weaknesses
depending on specific driving scenarios. Therefore, we propose
EnsembleFollower, a hierarchical planning framework for achieving advanced
human-like car-following. The EnsembleFollower framework involves a high-level
Reinforcement Learning-based agent responsible for judiciously managing
multiple low-level car-following models according to the current state, either
by selecting an appropriate low-level model to perform an action or by
allocating different weights across all low-level components. Moreover, we
propose a jerk-constrained kinematic model for more convincing car-following
simulations. We evaluate the proposed method based on real-world driving data
from the HighD dataset. The experimental results illustrate that
EnsembleFollower yields improved accuracy of human-like behavior and achieves
effectiveness in combining hybrid models, demonstrating that our proposed
framework can handle diverse car-following conditions by leveraging the
strengths of various low-level models. | [
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] |
Forecasting financial market trends through time series analysis and natural
language processing poses a complex and demanding undertaking, owing to the
numerous variables that can influence stock prices. These variables encompass a
spectrum of economic and political occurrences, as well as prevailing public
attitudes. Recent research has indicated that the expression of public
sentiments on social media platforms such as Twitter may have a noteworthy
impact on the determination of stock prices. The objective of this study was to
assess the viability of Twitter sentiments as a tool for predicting stock
prices of major corporations such as Tesla, Apple. Our study has revealed a
robust association between the emotions conveyed in tweets and fluctuations in
stock prices. Our findings indicate that positivity, negativity, and
subjectivity are the primary determinants of fluctuations in stock prices. The
data was analyzed utilizing the Long-Short Term Memory neural network (LSTM)
model, which is currently recognized as the leading methodology for predicting
stock prices by incorporating Twitter sentiments and historical stock prices
data. The models utilized in our study demonstrated a high degree of
reliability and yielded precise outcomes for the designated corporations. In
summary, this research emphasizes the significance of incorporating public
opinions into the prediction of stock prices. The application of Time Series
Analysis and Natural Language Processing methodologies can yield significant
scientific findings regarding financial market patterns, thereby facilitating
informed decision-making among investors. The results of our study indicate
that the utilization of Twitter sentiments can serve as a potent instrument for
forecasting stock prices, and ought to be factored in when formulating
investment strategies. | [
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] |
Developing a generative model of realistic order flow in financial markets is
a challenging open problem, with numerous applications for market participants.
Addressing this, we propose the first end-to-end autoregressive generative
model that generates tokenized limit order book (LOB) messages. These messages
are interpreted by a Jax-LOB simulator, which updates the LOB state. To handle
long sequences efficiently, the model employs simplified structured state-space
layers to process sequences of order book states and tokenized messages. Using
LOBSTER data of NASDAQ equity LOBs, we develop a custom tokenizer for message
data, converting groups of successive digits to tokens, similar to tokenization
in large language models. Out-of-sample results show promising performance in
approximating the data distribution, as evidenced by low model perplexity.
Furthermore, the mid-price returns calculated from the generated order flow
exhibit a significant correlation with the data, indicating impressive
conditional forecast performance. Due to the granularity of generated data, and
the accuracy of the model, it offers new application areas for future work
beyond forecasting, e.g. acting as a world model in high-frequency financial
reinforcement learning applications. Overall, our results invite the use and
extension of the model in the direction of autoregressive large financial
models for the generation of high-frequency financial data and we commit to
open-sourcing our code to facilitate future research. | [
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] |
Key to rich, dexterous manipulation in the real world is the ability to
coordinate control across two hands. However, while the promise afforded by
bimanual robotic systems is immense, constructing control policies for dual arm
autonomous systems brings inherent difficulties. One such difficulty is the
high-dimensionality of the bimanual action space, which adds complexity to both
model-based and data-driven methods. We counteract this challenge by drawing
inspiration from humans to propose a novel role assignment framework: a
stabilizing arm holds an object in place to simplify the environment while an
acting arm executes the task. We instantiate this framework with BimanUal
Dexterity from Stabilization (BUDS), which uses a learned restabilizing
classifier to alternate between updating a learned stabilization position to
keep the environment unchanged, and accomplishing the task with an acting
policy learned from demonstrations. We evaluate BUDS on four bimanual tasks of
varying complexities on real-world robots, such as zipping jackets and cutting
vegetables. Given only 20 demonstrations, BUDS achieves 76.9% task success
across our task suite, and generalizes to out-of-distribution objects within a
class with a 52.7% success rate. BUDS is 56.0% more successful than an
unstructured baseline that instead learns a BC stabilizing policy due to the
precision required of these complex tasks. Supplementary material and videos
can be found at https://sites.google.com/view/stabilizetoact . | [
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] |
The rapid growth of memory and computation requirements of large language
models (LLMs) has outpaced the development of hardware, hindering people who
lack large-scale high-end GPUs from training or deploying LLMs. However,
consumer-level GPUs, which constitute a larger market share, are typically
overlooked in LLM due to their weaker computing performance, smaller storage
capacity, and lower communication bandwidth. Additionally, users may have
privacy concerns when interacting with remote LLMs. In this paper, we envision
a decentralized system unlocking the potential vast untapped consumer-level
GPUs in pre-training, inference and fine-tuning of LLMs with privacy
protection. However, this system faces critical challenges, including limited
CPU and GPU memory, low network bandwidth, the variability of peer and device
heterogeneity. To address these challenges, our system design incorporates: 1)
a broker with backup pool to implement dynamic join and quit of computing
providers; 2) task scheduling with hardware performance to improve system
efficiency; 3) abstracting ML procedures into directed acyclic graphs (DAGs) to
achieve model and task universality; 4) abstracting intermediate represention
and execution planes to ensure compatibility of various devices and deep
learning (DL) frameworks. Our performance analysis demonstrates that 50 RTX
3080 GPUs can achieve throughputs comparable to those of 4 H100 GPUs, which are
significantly more expensive. | [
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] |
The design of modern recommender systems relies on understanding which parts
of the feature space are relevant for solving a given recommendation task.
However, real-world data sets in this domain are often characterized by their
large size, sparsity, and noise, making it challenging to identify meaningful
signals. Feature ranking represents an efficient branch of algorithms that can
help address these challenges by identifying the most informative features and
facilitating the automated search for more compact and better-performing models
(AutoML). We introduce OutRank, a system for versatile feature ranking and data
quality-related anomaly detection. OutRank was built with categorical data in
mind, utilizing a variant of mutual information that is normalized with regard
to the noise produced by features of the same cardinality. We further extend
the similarity measure by incorporating information on feature similarity and
combined relevance. The proposed approach's feasibility is demonstrated by
speeding up the state-of-the-art AutoML system on a synthetic data set with no
performance loss. Furthermore, we considered a real-life click-through-rate
prediction data set where it outperformed strong baselines such as random
forest-based approaches. The proposed approach enables exploration of up to
300% larger feature spaces compared to AutoML-only approaches, enabling faster
search for better models on off-the-shelf hardware. | [
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] |
The exponential growth in scholarly publications necessitates advanced tools
for efficient article retrieval, especially in interdisciplinary fields where
diverse terminologies are used to describe similar research. Traditional
keyword-based search engines often fall short in assisting users who may not be
familiar with specific terminologies. To address this, we present a knowledge
graph-based paper search engine for biomedical research to enhance the user
experience in discovering relevant queries and articles. The system, dubbed
DiscoverPath, employs Named Entity Recognition (NER) and part-of-speech (POS)
tagging to extract terminologies and relationships from article abstracts to
create a KG. To reduce information overload, DiscoverPath presents users with a
focused subgraph containing the queried entity and its neighboring nodes and
incorporates a query recommendation system, enabling users to iteratively
refine their queries. The system is equipped with an accessible Graphical User
Interface that provides an intuitive visualization of the KG, query
recommendations, and detailed article information, enabling efficient article
retrieval, thus fostering interdisciplinary knowledge exploration. DiscoverPath
is open-sourced at https://github.com/ynchuang/DiscoverPath. | [
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] |
Recent neuroimaging studies have highlighted the importance of
network-centric brain analysis, particularly with functional magnetic resonance
imaging. The emergence of Deep Neural Networks has fostered a substantial
interest in predicting clinical outcomes and categorizing individuals based on
brain networks. However, the conventional approach involving static brain
network analysis offers limited potential in capturing the dynamism of brain
function. Although recent studies have attempted to harness dynamic brain
networks, their high dimensionality and complexity present substantial
challenges. This paper proposes a novel methodology, Dynamic bRAin Transformer
(DART), which combines static and dynamic brain networks for more effective and
nuanced brain function analysis. Our model uses the static brain network as a
baseline, integrating dynamic brain networks to enhance performance against
traditional methods. We innovatively employ attention mechanisms, enhancing
model explainability and exploiting the dynamic brain network's temporal
variations. The proposed approach offers a robust solution to the low
signal-to-noise ratio of blood-oxygen-level-dependent signals, a recurring
issue in direct DNN modeling. It also provides valuable insights into which
brain circuits or dynamic networks contribute more to final predictions. As
such, DRAT shows a promising direction in neuroimaging studies, contributing to
the comprehensive understanding of brain organization and the role of neural
circuits. | [
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] |
Anomaly detection in medical imaging is a challenging task in contexts where
abnormalities are not annotated. This problem can be addressed through
unsupervised anomaly detection (UAD) methods, which identify features that do
not match with a reference model of normal profiles. Artificial neural networks
have been extensively used for UAD but they do not generally achieve an optimal
trade-o$\hookleftarrow$ between accuracy and computational demand. As an
alternative, we investigate mixtures of probability distributions whose
versatility has been widely recognized for a variety of data and tasks, while
not requiring excessive design e$\hookleftarrow$ort or tuning. Their
expressivity makes them good candidates to account for complex multivariate
reference models. Their much smaller number of parameters makes them more
amenable to interpretation and e cient learning. However, standard estimation
procedures, such as the Expectation-Maximization algorithm, do not scale well
to large data volumes as they require high memory usage. To address this issue,
we propose to incrementally compute inferential quantities. This online
approach is illustrated on the challenging detection of subtle abnormalities in
MR brain scans for the follow-up of newly diagnosed Parkinsonian patients. The
identified structural abnormalities are consistent with the disease
progression, as accounted by the Hoehn and Yahr scale. | [
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] |
Counterfactual Regret Minimization (CFR) and its variants are the best
algorithms so far for solving large-scale incomplete information games.
However, we believe that there are two problems with CFR: First, matrix
multiplication is required in CFR iteration, and the time complexity of one
iteration is too high; Secondly, the game characteristics in the real world are
different. Just using one CFR algorithm will not be perfectly suitable for all
game problems.
For these two problems, this paper proposes a new algorithm called Pure CFR
(PCFR) based on CFR. PCFR can be seen as a combination of CFR and Fictitious
Play (FP), inheriting the concept of counterfactual regret (value) from CFR,
and using the best response strategy instead of the regret matching strategy
for the next iteration. This algorithm has three advantages. First, PCFR can be
combined with any CFR variant. The resulting Pure MCCFR (PMCCFR) can
significantly reduce the time and space complexity of one iteration. Secondly,
our experiments show that the convergence speed of the PMCCFR is 2$\sim$3 times
that of the MCCFR. Finally, there is a type of game that is very suitable for
PCFR. We call this type of game clear-game, which is characterized by a high
proportion of dominated strategies. Experiments show that in clear-game, the
convergence rate of PMCCFR is two orders of magnitude higher than that of
MCCFR. | [
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] |
This paper investigates quantile regression in the presence of non-convex and
non-smooth sparse penalties, such as the minimax concave penalty (MCP) and
smoothly clipped absolute deviation (SCAD). The non-smooth and non-convex
nature of these problems often leads to convergence difficulties for many
algorithms. While iterative techniques like coordinate descent and local linear
approximation can facilitate convergence, the process is often slow. This
sluggish pace is primarily due to the need to run these approximation
techniques until full convergence at each step, a requirement we term as a
\emph{secondary convergence iteration}. To accelerate the convergence speed, we
employ the alternating direction method of multipliers (ADMM) and introduce a
novel single-loop smoothing ADMM algorithm with an increasing penalty
parameter, named SIAD, specifically tailored for sparse-penalized quantile
regression. We first delve into the convergence properties of the proposed SIAD
algorithm and establish the necessary conditions for convergence.
Theoretically, we confirm a convergence rate of $o\big({k^{-\frac{1}{4}}}\big)$
for the sub-gradient bound of augmented Lagrangian. Subsequently, we provide
numerical results to showcase the effectiveness of the SIAD algorithm. Our
findings highlight that the SIAD method outperforms existing approaches,
providing a faster and more stable solution for sparse-penalized quantile
regression. | [
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] |
Surveillance systems have emerged as crucial elements in upholding peace and
security in the modern world. Their ubiquity aids in monitoring suspicious
activities effectively. However, in densely populated environments, continuous
active monitoring becomes impractical, necessitating the development of
intelligent surveillance systems. AI integration in the surveillance domain was
a big revolution, however, speed issues have prevented its widespread
implementation in the field. It has been observed that quantum artificial
intelligence has led to a great breakthrough. Quantum artificial
intelligence-based surveillance systems have shown to be more accurate as well
as capable of performing well in real-time scenarios, which had never been seen
before. In this research, a RentinaNet model is integrated with Quantum CNN and
termed as Quantum-RetinaNet. By harnessing the Quantum capabilities of QCNN,
Quantum-RetinaNet strikes a balance between accuracy and speed. This innovative
integration positions it as a game-changer, addressing the challenges of active
monitoring in densely populated scenarios. As demand for efficient surveillance
solutions continues to grow, Quantum-RetinaNet offers a compelling alternative
to existing CNN models, upholding accuracy standards without sacrificing
real-time performance. The unique attributes of Quantum-RetinaNet have
far-reaching implications for the future of intelligent surveillance. With its
enhanced processing speed, it is poised to revolutionize the field, catering to
the pressing need for rapid yet precise monitoring. As Quantum-RetinaNet
becomes the new standard, it ensures public safety and security while pushing
the boundaries of AI in surveillance. | [
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] |
In many contact-rich tasks, force sensing plays an essential role in adapting
the motion to the physical properties of the manipulated object. To enable
robots to capture the underlying distribution of object properties necessary
for generalising learnt manipulation tasks to unseen objects, existing Learning
from Demonstration (LfD) approaches require a large number of costly human
demonstrations. Our proposed semi-supervised LfD approach decouples the learnt
model into an haptic representation encoder and a motion generation decoder.
This enables us to pre-train the first using large amount of unsupervised data,
easily accessible, while using few-shot LfD to train the second, leveraging the
benefits of learning skills from humans. We validate the approach on the wiping
task using sponges with different stiffness and surface friction. Our results
demonstrate that pre-training significantly improves the ability of the LfD
model to recognise physical properties and generate desired wiping motions for
unseen sponges, outperforming the LfD method without pre-training. We validate
the motion generated by our semi-supervised LfD model on the physical robot
hardware using the KUKA iiwa robot arm. We also validate that the haptic
representation encoder, pre-trained in simulation, captures the properties of
real objects, explaining its contribution to improving the generalisation of
the downstream task. | [
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] |
Cloud Computing (CC) is revolutionizing the way IT resources are delivered to
users, allowing them to access and manage their systems with increased
cost-effectiveness and simplified infrastructure. However, with the growth of
CC comes a host of security risks, including threats to availability,
integrity, and confidentiality. To address these challenges, Machine Learning
(ML) is increasingly being used by Cloud Service Providers (CSPs) to reduce the
need for human intervention in identifying and resolving security issues. With
the ability to analyze vast amounts of data, and make high-accuracy
predictions, ML can transform the way CSPs approach security. In this paper, we
will explore some of the most recent research in the field of ML-based security
in Cloud Computing. We will examine the features and effectiveness of a range
of ML algorithms, highlighting their unique strengths and potential
limitations. Our goal is to provide a comprehensive overview of the current
state of ML in cloud security and to shed light on the exciting possibilities
that this emerging field has to offer. | [
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Endowing robots with the human ability to learn a growing set of skills over
the course of a lifetime as opposed to mastering single tasks is an open
problem in robot learning. While multi-task learning approaches have been
proposed to address this problem, they pay little attention to task inference.
In order to continually learn new tasks, the robot first needs to infer the
task at hand without requiring predefined task representations. In this paper,
we propose a self-supervised task inference approach. Our approach learns
action and intention embeddings from self-organization of the observed movement
and effect parts of unlabeled demonstrations and a higher-level behavior
embedding from self-organization of the joint action-intention embeddings. We
construct a behavior-matching self-supervised learning objective to train a
novel Task Inference Network (TINet) to map an unlabeled demonstration to its
nearest behavior embedding, which we use as the task representation. A
multi-task policy is built on top of the TINet and trained with reinforcement
learning to optimize performance over tasks. We evaluate our approach in the
fixed-set and continual multi-task learning settings with a humanoid robot and
compare it to different multi-task learning baselines. The results show that
our approach outperforms the other baselines, with the difference being more
pronounced in the challenging continual learning setting, and can infer tasks
from incomplete demonstrations. Our approach is also shown to generalize to
unseen tasks based on a single demonstration in one-shot task generalization
experiments. | [
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