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Aligning users across networks using graph representation learning has been
found effective where the alignment is accomplished in a low-dimensional
embedding space. Yet, achieving highly precise alignment is still challenging,
especially when nodes with long-range connectivity to the labeled anchors are
encountered. To alleviate this limitation, we purposefully designed WL-Align
which adopts a regularized representation learning framework to learn
distinctive node representations. It extends the Weisfeiler-Lehman Isormorphism
Test and learns the alignment in alternating phases of "across-network
Weisfeiler-Lehman relabeling" and "proximity-preserving representation
learning". The across-network Weisfeiler-Lehman relabeling is achieved through
iterating the anchor-based label propagation and a similarity-based hashing to
exploit the known anchors' connectivity to different nodes in an efficient and
robust manner. The representation learning module preserves the second-order
proximity within individual networks and is regularized by the across-network
Weisfeiler-Lehman hash labels. Extensive experiments on real-world and
synthetic datasets have demonstrated that our proposed WL-Align outperforms the
state-of-the-art methods, achieving significant performance improvements in the
"exact matching" scenario. Data and code of WL-Align are available at
https://github.com/ChenPengGang/WLAlignCode. | [
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] |
In the era of noisy intermediate scale quantum devices, variational quantum
circuits (VQCs) are currently one of the main strategies for building quantum
machine learning models. These models are made up of a quantum part and a
classical part. The quantum part is given by a parametrization $U$, which, in
general, is obtained from the product of different quantum gates. By its turn,
the classical part corresponds to an optimizer that updates the parameters of
$U$ in order to minimize a cost function $C$. However, despite the many
applications of VQCs, there are still questions to be answered, such as for
example: What is the best sequence of gates to be used? How to optimize their
parameters? Which cost function to use? How the architecture of the quantum
chips influences the final results? In this article, we focus on answering the
last question. We will show that, in general, the cost function will tend to a
typical average value the closer the parameterization used is from a
$2$-design. Therefore, the closer this parameterization is to a $2$-design, the
less the result of the quantum neural network model will depend on its
parametrization. As a consequence, we can use the own architecture of the
quantum chips to defined the VQC parametrization, avoiding the use of
additional swap gates and thus diminishing the VQC depth and the associated
errors. | [
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] |
Designing an intelligent volume-weighted average price (VWAP) strategy is a
critical concern for brokers, since traditional rule-based strategies are
relatively static that cannot achieve a lower transaction cost in a dynamic
market. Many studies have tried to minimize the cost via reinforcement
learning, but there are bottlenecks in improvement, especially for
long-duration strategies such as the VWAP strategy. To address this issue, we
propose a deep learning and hierarchical reinforcement learning jointed
architecture termed Macro-Meta-Micro Trader (M3T) to capture market patterns
and execute orders from different temporal scales. The Macro Trader first
allocates a parent order into tranches based on volume profiles as the
traditional VWAP strategy does, but a long short-term memory neural network is
used to improve the forecasting accuracy. Then the Meta Trader selects a
short-term subgoal appropriate to instant liquidity within each tranche to form
a mini-tranche. The Micro Trader consequently extracts the instant market state
and fulfils the subgoal with the lowest transaction cost. Our experiments over
stocks listed on the Shanghai stock exchange demonstrate that our approach
outperforms baselines in terms of VWAP slippage, with an average cost saving of
1.16 base points compared to the optimal baseline. | [
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] |
Collaboration among industrial Internet of Things (IoT) devices and edge
networks is essential to support computation-intensive deep neural network
(DNN) inference services which require low delay and high accuracy. Sampling
rate adaption which dynamically configures the sampling rates of industrial IoT
devices according to network conditions, is the key in minimizing the service
delay. In this paper, we investigate the collaborative DNN inference problem in
industrial IoT networks. To capture the channel variation and task arrival
randomness, we formulate the problem as a constrained Markov decision process
(CMDP). Specifically, sampling rate adaption, inference task offloading and
edge computing resource allocation are jointly considered to minimize the
average service delay while guaranteeing the long-term accuracy requirements of
different inference services. Since CMDP cannot be directly solved by general
reinforcement learning (RL) algorithms due to the intractable long-term
constraints, we first transform the CMDP into an MDP by leveraging the Lyapunov
optimization technique. Then, a deep RL-based algorithm is proposed to solve
the MDP. To expedite the training process, an optimization subroutine is
embedded in the proposed algorithm to directly obtain the optimal edge
computing resource allocation. Extensive simulation results are provided to
demonstrate that the proposed RL-based algorithm can significantly reduce the
average service delay while preserving long-term inference accuracy with a high
probability. | [
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] |
Despite some successful applications of goal-driven navigation, existing deep
reinforcement learning (DRL)-based approaches notoriously suffers from poor
data efficiency issue. One of the reasons is that the goal information is
decoupled from the perception module and directly introduced as a condition of
decision-making, resulting in the goal-irrelevant features of the scene
representation playing an adversary role during the learning process. In light
of this, we present a novel Goal-guided Transformer-enabled reinforcement
learning (GTRL) approach by considering the physical goal states as an input of
the scene encoder for guiding the scene representation to couple with the goal
information and realizing efficient autonomous navigation. More specifically,
we propose a novel variant of the Vision Transformer as the backbone of the
perception system, namely Goal-guided Transformer (GoT), and pre-train it with
expert priors to boost the data efficiency. Subsequently, a reinforcement
learning algorithm is instantiated for the decision-making system, taking the
goal-oriented scene representation from the GoT as the input and generating
decision commands. As a result, our approach motivates the scene representation
to concentrate mainly on goal-relevant features, which substantially enhances
the data efficiency of the DRL learning process, leading to superior navigation
performance. Both simulation and real-world experimental results manifest the
superiority of our approach in terms of data efficiency, performance,
robustness, and sim-to-real generalization, compared with other
state-of-the-art (SOTA) baselines. The demonstration video
(https://www.youtube.com/watch?v=aqJCHcsj4w0) and the source code
(https://github.com/OscarHuangWind/DRL-Transformer-SimtoReal-Navigation) are
also provided. | [
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] |
Federated learning has recently been applied to recommendation systems to
protect user privacy. In federated learning settings, recommendation systems
can train recommendation models only collecting the intermediate parameters
instead of the real user data, which greatly enhances the user privacy. Beside,
federated recommendation systems enable to collaborate with other data
platforms to improve recommended model performance while meeting the regulation
and privacy constraints. However, federated recommendation systems faces many
new challenges such as privacy, security, heterogeneity and communication
costs. While significant research has been conducted in these areas, gaps in
the surveying literature still exist. In this survey, we-(1) summarize some
common privacy mechanisms used in federated recommendation systems and discuss
the advantages and limitations of each mechanism; (2) review some robust
aggregation strategies and several novel attacks against security; (3)
summarize some approaches to address heterogeneity and communication costs
problems; (4)introduce some open source platforms that can be used to build
federated recommendation systems; (5) present some prospective research
directions in the future. This survey can guide researchers and practitioners
understand the research progress in these areas. | [
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] |
For Prognostics and Health Management (PHM) of Lithium-ion (Li-ion)
batteries, many models have been established to characterize their degradation
process. The existing empirical or physical models can reveal important
information regarding the degradation dynamics. However, there are no general
and flexible methods to fuse the information represented by those models.
Physics-Informed Neural Network (PINN) is an efficient tool to fuse empirical
or physical dynamic models with data-driven models. To take full advantage of
various information sources, we propose a model fusion scheme based on PINN. It
is implemented by developing a semi-empirical semi-physical Partial
Differential Equation (PDE) to model the degradation dynamics of Li-ion
batteries. When there is little prior knowledge about the dynamics, we leverage
the data-driven Deep Hidden Physics Model (DeepHPM) to discover the underlying
governing dynamic models. The uncovered dynamics information is then fused with
that mined by the surrogate neural network in the PINN framework. Moreover, an
uncertainty-based adaptive weighting method is employed to balance the multiple
learning tasks when training the PINN. The proposed methods are verified on a
public dataset of Li-ion Phosphate (LFP)/graphite batteries. | [
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] |
We consider infinite horizon Markov decision processes (MDPs) with fast-slow
structure, meaning that certain parts of the state space move "fast" (and in a
sense, are more influential) while other parts transition more "slowly." Such
structure is common in real-world problems where sequential decisions need to
be made at high frequencies, yet information that varies at a slower timescale
also influences the optimal policy. Examples include: (1) service allocation
for a multi-class queue with (slowly varying) stochastic costs, (2) a restless
multi-armed bandit with an environmental state, and (3) energy demand response,
where both day-ahead and real-time prices play a role in the firm's revenue.
Models that fully capture these problems often result in MDPs with large state
spaces and large effective time horizons (due to frequent decisions), rendering
them computationally intractable. We propose an approximate dynamic programming
algorithmic framework based on the idea of "freezing" the slow states, solving
a set of simpler finite-horizon MDPs (the lower-level MDPs), and applying value
iteration (VI) to an auxiliary MDP that transitions on a slower timescale (the
upper-level MDP). We also extend the technique to a function approximation
setting, where a feature-based linear architecture is used. On the theoretical
side, we analyze the regret incurred by each variant of our frozen-state
approach. Finally, we give empirical evidence that the frozen-state approach
generates effective policies using just a fraction of the computational cost,
while illustrating that simply omitting slow states from the decision modeling
is often not a viable heuristic. | [
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] |
In this paper, we study the problem of MOOC quality evaluation which is
essential for improving the course materials, promoting students' learning
efficiency, and benefiting user services. While achieving promising
performances, current works still suffer from the complicated interactions and
relationships of entities in MOOC platforms. To tackle the challenges, we
formulate the problem as a course representation learning task-based and
develop an Information-aware Graph Representation Learning(IaGRL) for
multi-view MOOC quality evaluation. Specifically, We first build a MOOC
Heterogeneous Network (HIN) to represent the interactions and relationships
among entities in MOOC platforms. And then we decompose the MOOC HIN into
multiple single-relation graphs based on meta-paths to depict the multi-view
semantics of courses. The course representation learning can be further
converted to a multi-view graph representation task. Different from traditional
graph representation learning, the learned course representations are expected
to match the following three types of validity: (1) the agreement on
expressiveness between the raw course portfolio and the learned course
representations; (2) the consistency between the representations in each view
and the unified representations; (3) the alignment between the course and MOOC
platform representations. Therefore, we propose to exploit mutual information
for preserving the validity of course representations. We conduct extensive
experiments over real-world MOOC datasets to demonstrate the effectiveness of
our proposed method. | [
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] |
Reverse engineering binaries is required to understand and analyse programs
for which the source code is unavailable. Decompilers can transform the largely
unreadable binaries into a more readable source code-like representation.
However, reverse engineering is time-consuming, much of which is taken up by
labelling the functions with semantic information.
While the automated summarisation of decompiled code can help Reverse
Engineers understand and analyse binaries, current work mainly focuses on
summarising source code, and no suitable dataset exists for this task.
In this work, we extend large pre-trained language models of source code to
summarise decompiled binary functions. Furthermore, we investigate the impact
of input and data properties on the performance of such models. Our approach
consists of two main components; the data and the model.
We first build CAPYBARA, a dataset of 214K decompiled function-documentation
pairs across various compiler optimisations. We extend CAPYBARA further by
generating synthetic datasets and deduplicating the data.
Next, we fine-tune the CodeT5 base model with CAPYBARA to create BinT5. BinT5
achieves the state-of-the-art BLEU-4 score of 60.83, 58.82, and 44.21 for
summarising source, decompiled, and synthetically stripped decompiled code,
respectively. This indicates that these models can be extended to decompiled
binaries successfully.
Finally, we found that the performance of BinT5 is not heavily dependent on
the dataset size and compiler optimisation level. We recommend future research
to further investigate transferring knowledge when working with less expressive
input formats such as stripped binaries. | [
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] |
Recent engineering developments have seen the emergence of Machine Learning
(ML) as a powerful form of data analysis with widespread applicability beyond
its historical roots in the design of autonomous agents. However, relatively
little attention has been paid to the interaction between people and ML
systems. Recent developments on Explainable ML address this by providing visual
and textual information on how the ML system arrived at a conclusion. In this
paper we view the interaction between humans and ML systems within the broader
context of interaction between agents capable of learning and explanation.
Within this setting, we argue that it is more helpful to view the interaction
as characterised by two-way intelligibility of information rather than once-off
explanation of a prediction. We formulate two-way intelligibility as a property
of a communication protocol. Development of the protocol is motivated by a set
of `Intelligibility Axioms' for decision-support systems that use ML with a
human-in-the-loop. The axioms are intended as sufficient criteria to claim
that: (a) information provided by a human is intelligible to an ML system; and
(b) information provided by an ML system is intelligible to a human. The axioms
inform the design of a general synchronous interaction model between agents
capable of learning and explanation. We identify conditions of compatibility
between agents that result in bounded communication, and define Weak and Strong
Two-Way Intelligibility between agents as properties of the communication
protocol. | [
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The recent spike in certified Artificial Intelligence (AI) tools for
healthcare has renewed the debate around adoption of this technology. One
thread of such debate concerns Explainable AI (XAI) and its promise to render
AI devices more transparent and trustworthy. A few voices active in the medical
AI space have expressed concerns on the reliability of Explainable AI
techniques and especially feature attribution methods, questioning their use
and inclusion in guidelines and standards. Despite valid concerns, we argue
that existing criticism on the viability of post-hoc local explainability
methods throws away the baby with the bathwater by generalizing a problem that
is specific to image data. We begin by characterizing the problem as a lack of
semantic match between explanations and human understanding. To understand when
feature importance can be used reliably, we introduce a distinction between
feature importance of low- and high-level features. We argue that for data
types where low-level features come endowed with a clear semantics, such as
tabular data like Electronic Health Records (EHRs), semantic match can be
obtained, and thus feature attribution methods can still be employed in a
meaningful and useful way. Finally, we sketch a procedure to test whether
semantic match has been achieved. | [
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] |
This paper presents two wireless measurement campaigns in industrial
testbeds: industrial Vehicle-to-vehicle (iV2V) and industrial
Vehicle-to-infrastructure plus Sensor (iV2I+), together with detailed
information about the two captured datasets. iV2V covers sidelink communication
scenarios between Automated Guided Vehicles (AGVs), while iV2I+ is conducted at
an industrial setting where an autonomous cleaning robot is connected to a
private cellular network. The combination of different communication
technologies within a common measurement methodology provides insights that can
be exploited by Machine Learning (ML) for tasks such as fingerprinting,
line-of-sight detection, prediction of quality of service or link selection.
Moreover, the datasets are publicly available, labelled and prefiltered for
fast on-boarding and applicability. | [
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] |
Computational discovery of ideal lead compounds is a critical process for
modern drug discovery. It comprises multiple stages: hit screening, molecular
property prediction, and molecule optimization. Current efforts are disparate,
involving the establishment of models for each stage, followed by multi-stage
multi-model integration. However, this is non-ideal, as clumsy integration of
incompatible models increases research overheads, and may even reduce success
rates in drug discovery. Facilitating compatibilities requires establishing
inherent model consistencies across lead discovery stages. Towards that effect,
we propose an open deep graph learning (DGL) based pipeline: generative
adversarial feature subspace enhancement (GAFSE), which first unifies the
modeling of these stages into one learning framework. GAFSE also offers
standardized modular design and streamlined interfaces for future expansions
and community support. GAFSE combines adversarial/generative learning, graph
attention network, graph reconstruction network, and optimizes the
classification/regression loss, adversarial/generative loss, and reconstruction
loss simultaneously. Convergence analysis theoretically guarantees model
generalization performance. Exhaustive benchmarking demonstrates that the GAFSE
pipeline achieves excellent performance across almost all lead discovery
stages, while also providing valuable model interpretability. Hence, we believe
this tool will enhance the efficiency and productivity of drug discovery
researchers. | [
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] |
Epilepsy is one of the most common neurological disorders, typically observed
via seizure episodes. Epileptic seizures are commonly monitored through
electroencephalogram (EEG) recordings due to their routine and low expense
collection. The stochastic nature of EEG makes seizure identification via
manual inspections performed by highly-trained experts a tedious endeavor,
motivating the use of automated identification. The literature on automated
identification focuses mostly on supervised learning methods requiring expert
labels of EEG segments that contain seizures, which are difficult to obtain.
Motivated by these observations, we pose seizure identification as an
unsupervised anomaly detection problem. To this end, we employ the first
unsupervised transformer-based model for seizure identification on raw EEG. We
train an autoencoder involving a transformer encoder via an unsupervised loss
function, incorporating a novel masking strategy uniquely designed for
multivariate time-series data such as EEG. Training employs EEG recordings that
do not contain any seizures, while seizures are identified with respect to
reconstruction errors at inference time. We evaluate our method on three
publicly available benchmark EEG datasets for distinguishing seizure vs.
non-seizure windows. Our method leads to significantly better seizure
identification performance than supervised learning counterparts, by up to 16%
recall, 9% accuracy, and 9% Area under the Receiver Operating Characteristics
Curve (AUC), establishing particular benefits on highly imbalanced data.
Through accurate seizure identification, our method could facilitate widely
accessible and early detection of epilepsy development, without needing
expensive label collection or manual feature extraction. | [
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] |
Methods based on Deep Learning have recently been applied on astrophysical
parameter recovery thanks to their ability to capture information from complex
data. One of these methods is the approximate Bayesian Neural Networks (BNNs)
which have demonstrated to yield consistent posterior distribution into the
parameter space, helpful for uncertainty quantification. However, as any modern
neural networks, they tend to produce overly confident uncertainty estimates
and can introduce bias when BNNs are applied to data. In this work, we
implement multiplicative normalizing flows (MNFs), a family of approximate
posteriors for the parameters of BNNs with the purpose of enhancing the
flexibility of the variational posterior distribution, to extract $\Omega_m$,
$h$, and $\sigma_8$ from the QUIJOTE simulations. We have compared this method
with respect to the standard BNNs, and the flipout estimator. We found that
MNFs combined with BNNs outperform the other models obtaining predictive
performance with almost one order of magnitude larger that standard BNNs,
$\sigma_8$ extracted with high accuracy ($r^2=0.99$), and precise uncertainty
estimates. The latter implies that MNFs provide more realistic predictive
distribution closer to the true posterior mitigating the bias introduced by the
variational approximation and allowing to work with well-calibrated networks. | [
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] |
Finding the optimal pass sequence of compilation can lead to a significant
reduction in program size and/or improvement in program efficiency. Prior works
on compilation pass ordering have two major drawbacks. They either require an
excessive budget (in terms of compilation steps) at compile time or fail to
generalize to unseen programs. In this paper, for code-size reduction tasks, we
propose a novel pipeline to find program-dependent pass sequences within 45
compilation calls. It first identifies a coreset of 50 pass sequences via
greedy optimization of a submodular function, and then learns a policy with
Graph Neural Network (GNN) to pick the optimal sequence by predicting the
normalized values of the pass sequences in the coreset. Despite its simplicity,
our pipeline outperforms the default -Oz flag by an average of 4.7% over a
large collection (4683) of unseen code repositories from diverse domains across
14 datasets. In comparison, previous approaches like reinforcement learning on
the raw pass sequence space may take days to train due to sparse reward, and
may not generalize well in held-out ones from different domains. Our results
demonstrate that existing human-designed compiler flags can be improved with a
simple yet effective technique that transforms the raw action space into a
small one with denser rewards. | [
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As a practical alternative of speech separation, target speaker extraction
(TSE) aims to extract the speech from the desired speaker using additional
speaker cue extracted from the speaker. Its main challenge lies in how to
properly extract and leverage the speaker cue to benefit the extracted speech
quality. The cue extraction method adopted in majority existing TSE studies is
to directly utilize discriminative speaker embedding, which is extracted from
the pre-trained models for speaker verification. Although the high speaker
discriminability is a most desirable property for speaker verification task, we
argue that it may be too sophisticated for TSE. In this study, we propose that
a simplified speaker cue with clear class separability might be preferred for
TSE. To verify our proposal, we introduce several forms of speaker cues,
including naive speaker embedding (such as, x-vector and xi-vector) and new
speaker embeddings produced from sparse LDA-transform. Corresponding TSE models
are built by integrating these speaker cues with SepFormer (one SOTA speech
separation model). Performances of these TSE models are examined on the
benchmark WSJ0-2mix dataset. Experimental results validate the effectiveness
and generalizability of our proposal, showing up to 9.9% relative improvement
in SI-SDRi. Moreover, with SI-SDRi of 19.4 dB and PESQ of 3.78, our best TSE
system significantly outperforms the current SOTA systems and offers the top
TSE results reported till date on the WSJ0-2mix. | [
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] |
Open-world novelty--a sudden change in the mechanics or properties of an
environment--is a common occurrence in the real world. Novelty adaptation is an
agent's ability to improve its policy performance post-novelty. Most
reinforcement learning (RL) methods assume that the world is a closed, fixed
process. Consequentially, RL policies adapt inefficiently to novelties. To
address this, we introduce WorldCloner, an end-to-end trainable neuro-symbolic
world model for rapid novelty adaptation. WorldCloner learns an efficient
symbolic representation of the pre-novelty environment transitions, and uses
this transition model to detect novelty and efficiently adapt to novelty in a
single-shot fashion. Additionally, WorldCloner augments the policy learning
process using imagination-based adaptation, where the world model simulates
transitions of the post-novelty environment to help the policy adapt. By
blending ''imagined'' transitions with interactions in the post-novelty
environment, performance can be recovered with fewer total environment
interactions. Using environments designed for studying novelty in sequential
decision-making problems, we show that the symbolic world model helps its
neural policy adapt more efficiently than model-based and model-based
neural-only reinforcement learning methods. | [
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] |
Quantifying cause and effect relationships is an important problem in many
domains. The gold standard solution is to conduct a randomised controlled
trial. However, in many situations such trials cannot be performed. In the
absence of such trials, many methods have been devised to quantify the causal
impact of an intervention from observational data given certain assumptions.
One widely used method are synthetic control models. While identifiability of
the causal estimand in such models has been obtained from a range of
assumptions, it is widely and implicitly assumed that the underlying
assumptions are satisfied for all time periods both pre- and post-intervention.
This is a strong assumption, as synthetic control models can only be learned in
pre-intervention period. In this paper we address this challenge, and prove
identifiability can be obtained without the need for this assumption, by
showing it follows from the principle of invariant causal mechanisms. Moreover,
for the first time, we formulate and study synthetic control models in Pearl's
structural causal model framework. Importantly, we provide a general framework
for sensitivity analysis of synthetic control causal inference to violations of
the assumptions underlying non-parametric identifiability. We end by providing
an empirical demonstration of our sensitivity analysis framework on simulated
and real data in the widely-used linear synthetic control framework. | [
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] |
Learning feature interactions is the key to success for the large-scale CTR
prediction in Ads ranking and recommender systems. In industry, deep neural
network-based models are widely adopted for modeling such problems. Researchers
proposed various neural network architectures for searching and modeling the
feature interactions in an end-to-end fashion. However, most methods only learn
static feature interactions and have not fully leveraged deep CTR models'
representation capacity. In this paper, we propose a new model: DynInt. By
extending Polynomial-Interaction-Network (PIN), which learns higher-order
interactions recursively to be dynamic and data-dependent, DynInt further
derived two modes for modeling dynamic higher-order interactions: dynamic
activation and dynamic parameter. In dynamic activation mode, we adaptively
adjust the strength of learned interactions by instance-aware activation gating
networks. In dynamic parameter mode, we re-parameterize the parameters by
different formulations and dynamically generate the parameters by
instance-aware parameter generation networks. Through instance-aware gating
mechanism and dynamic parameter generation, we enable the PIN to model dynamic
interaction for potential industry applications. We implement the proposed
model and evaluate the model performance on real-world datasets. Extensive
experiment results demonstrate the efficiency and effectiveness of DynInt over
state-of-the-art models. | [
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] |
Federated recommendation is a new Internet service architecture that aims to
provide privacy-preserving recommendation services in federated settings.
Existing solutions are used to combine distributed recommendation algorithms
and privacy-preserving mechanisms. Thus it inherently takes the form of
heavyweight models at the server and hinders the deployment of on-device
intelligent models to end-users. This paper proposes a novel Personalized
Federated Recommendation (PFedRec) framework to learn many user-specific
lightweight models to be deployed on smart devices rather than a heavyweight
model on a server. Moreover, we propose a new dual personalization mechanism to
effectively learn fine-grained personalization on both users and items. The
overall learning process is formulated into a unified federated optimization
framework. Specifically, unlike previous methods that share exactly the same
item embeddings across users in a federated system, dual personalization allows
mild finetuning of item embeddings for each user to generate user-specific
views for item representations which can be integrated into existing federated
recommendation methods to gain improvements immediately. Experiments on
multiple benchmark datasets have demonstrated the effectiveness of PFedRec and
the dual personalization mechanism. Moreover, we provide visualizations and
in-depth analysis of the personalization techniques in item embedding, which
shed novel insights on the design of recommender systems in federated settings.
The code is available. | [
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] |
Learning feature interactions is crucial to success for large-scale CTR
prediction in recommender systems and Ads ranking. Researchers and
practitioners extensively proposed various neural network architectures for
searching and modeling feature interactions. However, we observe that different
datasets favor different neural network architectures and feature interaction
types, suggesting that different feature interaction learning methods may have
their own unique advantages. Inspired by this observation, we propose
AdaEnsemble: a Sparsely-Gated Mixture-of-Experts (SparseMoE) architecture that
can leverage the strengths of heterogeneous feature interaction experts and
adaptively learns the routing to a sparse combination of experts for each
example, allowing us to build a dynamic hierarchy of the feature interactions
of different types and orders. To further improve the prediction accuracy and
inference efficiency, we incorporate the dynamic early exiting mechanism for
feature interaction depth selection. The AdaEnsemble can adaptively choose the
feature interaction depth and find the corresponding SparseMoE stacking layer
to exit and compute prediction from. Therefore, our proposed architecture
inherits the advantages of the exponential combinations of sparsely gated
experts within SparseMoE layers and further dynamically selects the optimal
feature interaction depth without executing deeper layers. We implement the
proposed AdaEnsemble and evaluate its performance on real-world datasets.
Extensive experiment results demonstrate the efficiency and effectiveness of
AdaEnsemble over state-of-the-art models. | [
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With the increasing availability of data for Prognostics and Health
Management (PHM), Deep Learning (DL) techniques are now the subject of
considerable attention for this application, often achieving more accurate
Remaining Useful Life (RUL) predictions. However, one of the major challenges
for DL techniques resides in the difficulty of obtaining large amounts of
labelled data on industrial systems. To overcome this lack of labelled data, an
emerging learning technique is considered in our work: Self-Supervised
Learning, a sub-category of unsupervised learning approaches. This paper aims
to investigate whether pre-training DL models in a self-supervised way on
unlabelled sensors data can be useful for RUL estimation with only Few-Shots
Learning, i.e. with scarce labelled data. In this research, a fatigue damage
prognostics problem is addressed, through the estimation of the RUL of aluminum
alloy panels (typical of aerospace structures) subject to fatigue cracks from
strain gauge data. Synthetic datasets composed of strain data are used allowing
to extensively investigate the influence of the dataset size on the predictive
performance. Results show that the self-supervised pre-trained models are able
to significantly outperform the non-pre-trained models in downstream RUL
prediction task, and with less computational expense, showing promising results
in prognostic tasks when only limited labelled data is available. | [
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] |
Constraint-based causal discovery algorithms learn part of the causal graph
structure by systematically testing conditional independences observed in the
data. These algorithms, such as the PC algorithm and its variants, rely on
graphical characterizations of the so-called equivalence class of causal graphs
proposed by Pearl. However, constraint-based causal discovery algorithms
struggle when data is limited since conditional independence tests quickly lose
their statistical power, especially when the conditioning set is large. To
address this, we propose using conditional independence tests where the size of
the conditioning set is upper bounded by some integer $k$ for robust causal
discovery. The existing graphical characterizations of the equivalence classes
of causal graphs are not applicable when we cannot leverage all the conditional
independence statements. We first define the notion of $k$-Markov equivalence:
Two causal graphs are $k$-Markov equivalent if they entail the same conditional
independence constraints where the conditioning set size is upper bounded by
$k$. We propose a novel representation that allows us to graphically
characterize $k$-Markov equivalence between two causal graphs. We propose a
sound constraint-based algorithm called the $k$-PC algorithm for learning this
equivalence class. Finally, we conduct synthetic, and semi-synthetic
experiments to demonstrate that the $k$-PC algorithm enables more robust causal
discovery in the small sample regime compared to the baseline algorithms. | [
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] |
In their seminal work, Nayyar et al. (2013) showed that imperfect information
can be abstracted away from common-payoff games by having players publicly
announce their policies as they play. This insight underpins sound solvers and
decision-time planning algorithms for common-payoff games. Unfortunately, a
naive application of the same insight to two-player zero-sum games fails
because Nash equilibria of the game with public policy announcements may not
correspond to Nash equilibria of the original game. As a consequence, existing
sound decision-time planning algorithms require complicated additional
mechanisms that have unappealing properties. The main contribution of this work
is showing that certain regularized equilibria do not possess the
aforementioned non-correspondence problem -- thus, computing them can be
treated as perfect-information problems. Because these regularized equilibria
can be made arbitrarily close to Nash equilibria, our result opens the door to
a new perspective to solving two-player zero-sum games and yields a simplified
framework for decision-time planning in two-player zero-sum games, void of the
unappealing properties that plague existing decision-time planning approaches. | [
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] |
We introduce a modality-agnostic neural compression algorithm based on a
functional view of data and parameterised as an Implicit Neural Representation
(INR). Bridging the gap between latent coding and sparsity, we obtain compact
latent representations non-linearly mapped to a soft gating mechanism. This
allows the specialisation of a shared INR network to each data item through
subnetwork selection. After obtaining a dataset of such latent representations,
we directly optimise the rate/distortion trade-off in a modality-agnostic space
using neural compression. Variational Compression of Implicit Neural
Representations (VC-INR) shows improved performance given the same
representational capacity pre quantisation while also outperforming previous
quantisation schemes used for other INR techniques. Our experiments demonstrate
strong results over a large set of diverse modalities using the same algorithm
without any modality-specific inductive biases. We show results on images,
climate data, 3D shapes and scenes as well as audio and video, introducing
VC-INR as the first INR-based method to outperform codecs as well-known and
diverse as JPEG 2000, MP3 and AVC/HEVC on their respective modalities. | [
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] |
Have you ever been troubled by the complexity and computational cost of SE(3)
protein structure modeling and been amazed by the simplicity and power of
language modeling? Recent work has shown promise in simplifying protein
structures as sequences of protein angles; therefore, language models could be
used for unconstrained protein backbone generation. Unfortunately, such
simplification is unsuitable for the constrained protein inpainting problem,
where the model needs to recover masked structures conditioned on unmasked
ones, as it dramatically increases the computing cost of geometric constraints.
To overcome this dilemma, we suggest inserting a hidden \textbf{a}tomic
\textbf{d}irection \textbf{s}pace (\textbf{ADS}) upon the language model,
converting invariant backbone angles into equivalent direction vectors and
preserving the simplicity, called Seq2Direct encoder ($\text{Enc}_{s2d}$).
Geometric constraints could be efficiently imposed on the newly introduced
direction space. A Direct2Seq decoder ($\text{Dec}_{d2s}$) with mathematical
guarantees is also introduced to develop a \textbf{SDS}
($\text{Enc}_{s2d}$+$\text{Dec}_{d2s}$) model. We apply the SDS model as the
denoising neural network during the conditional diffusion process, resulting in
a constrained generative model--\textbf{DiffSDS}. Extensive experiments show
that the plug-and-play ADS could transform the language model into a strong
structural model without loss of simplicity. More importantly, the proposed
DiffSDS outperforms previous strong baselines by a large margin on the task of
protein inpainting. | [
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] |
Existing learning approaches to dexterous manipulation use demonstrations or
interactions with the environment to train black-box neural networks that
provide little control over how the robot learns the skills or how it would
perform post training. These approaches pose significant challenges when
implemented on physical platforms given that, during initial stages of
training, the robot's behavior could be erratic and potentially harmful to its
own hardware, the environment, or any humans in the vicinity. A potential way
to address these limitations is to add constraints during learning that
restrict and guide the robot's behavior during training as well as roll outs.
Inspired by the success of constrained approaches in other domains, we
investigate the effects of adding position-based constraints to a 24-DOF robot
hand learning to perform object relocation using Constrained Policy
Optimization. We find that a simple geometric constraint can ensure the robot
learns to move towards the object sooner than without constraints. Further,
training with this constraint requires a similar number of samples as its
unconstrained counterpart to master the skill. These findings shed light on how
simple constraints can help robots achieve sensible and safe behavior quickly
and ease concerns surrounding hardware deployment. We also investigate the
effects of the strictness of these constraints and report findings that provide
insights into how different degrees of strictness affect learning outcomes. Our
code is available at
https://github.com/GT-STAR-Lab/constrained-rl-dexterous-manipulation. | [
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] |
This paper presents a new approach which uses the tools within Artificial
Intelligence (AI) software libraries as an alternative way of solving partial
differential equations (PDEs) that have been discretised using standard
numerical methods. In particular, we describe how to represent numerical
discretisations arising from the finite volume and finite element methods by
pre-determining the weights of convolutional layers within a neural network. As
the weights are defined by the discretisation scheme, no training of the
network is required and the solutions obtained are identical (accounting for
solver tolerances) to those obtained with standard codes often written in
Fortran or C++. We also explain how to implement the Jacobi method and a
multigrid solver using the functions available in AI libraries. For the latter,
we use a U-Net architecture which is able to represent a sawtooth multigrid
method. A benefit of using AI libraries in this way is that one can exploit
their power and their built-in technologies. For example, their executions are
already optimised for different computer architectures, whether it be CPUs,
GPUs or new-generation AI processors. In this article, we apply the proposed
approach to eigenvalue problems in reactor physics where neutron transport is
described by diffusion theory. For a fuel assembly benchmark, we demonstrate
that the solution obtained from our new approach is the same (accounting for
solver tolerances) as that obtained from the same discretisation coded in a
standard way using Fortran. We then proceed to solve a reactor core benchmark
using the new approach. | [
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] |
When creating multi-channel time-series datasets for Human Activity
Recognition (HAR), researchers are faced with the issue of subject selection
criteria. It is unknown what physical characteristics and/or soft-biometrics,
such as age, height, and weight, need to be taken into account to train a
classifier to achieve robustness towards heterogeneous populations in the
training and testing data. This contribution statistically curates the training
data to assess to what degree the physical characteristics of humans influence
HAR performance. We evaluate the performance of a state-of-the-art
convolutional neural network on two HAR datasets that vary in the sensors,
activities, and recording for time-series HAR. The training data is
intentionally biased with respect to human characteristics to determine the
features that impact motion behaviour. The evaluations brought forth the impact
of the subjects' characteristics on HAR. Thus, providing insights regarding the
robustness of the classifier with respect to heterogeneous populations. The
study is a step forward in the direction of fair and trustworthy artificial
intelligence by attempting to quantify representation bias in multi-channel
time series HAR data. | [
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] |
While artificial intelligence has made remarkable strides in revealing the
relationship between biological macromolecules' primary sequence and tertiary
structure, designing RNA sequences based on specified tertiary structures
remains challenging. Though existing approaches in protein design have
thoroughly explored structure-to-sequence dependencies in proteins, RNA design
still confronts difficulties due to structural complexity and data scarcity.
Adding to the problem, direct transplantation of protein design methodologies
into RNA design fails to achieve satisfactory outcomes although sharing similar
structural components. In this study, we aim to systematically construct a
data-driven RNA design pipeline. We crafted a large, well-curated benchmark
dataset and designed a comprehensive structural modeling approach to represent
the complex RNA tertiary structure. More importantly, we proposed a
hierarchical data-efficient representation learning framework that learns
structural representations through contrastive learning at both cluster-level
and sample-level to fully leverage the limited data. By constraining data
representations within a limited hyperspherical space, the intrinsic
relationships between data points could be explicitly imposed. Moreover, we
incorporated extracted secondary structures with base pairs as prior knowledge
to facilitate the RNA design process. Extensive experiments demonstrate the
effectiveness of our proposed method, providing a reliable baseline for future
RNA design tasks. The source code and benchmark dataset will be released
publicly. | [
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The Bitcoin cryptocurrency has received much attention recently. In the
network of Bitcoin, transactions are recorded in a ledger. In this network, the
process of recording transactions depends on some nodes called miners that
execute a protocol known as mining protocol. One of the significant aspects of
mining protocol is incentive compatibility. However, literature has shown that
Bitcoin mining's protocol is not incentive-compatible. Some nodes with high
computational power can obtain more revenue than their fair share by adopting a
type of attack called the selfish mining attack. In this paper, we propose an
artificial intelligence-based defense against selfish mining attacks by
applying the theory of learning automata. The proposed defense mechanism
ignores private blocks by assigning weight based on block discovery time and
changes current Bitcoin's fork resolving policy by evaluating branches' height
difference in a self-adaptive manner utilizing learning automata. To the best
of our knowledge, the proposed protocol is the literature's first
learning-based defense mechanism. Simulation results have shown the superiority
of the proposed mechanism against tie-breaking mechanism, which is a well-known
defense. The simulation results have shown that the suggested defense mechanism
increases the profit threshold up to 40\% and decreases the revenue of selfish
attackers. | [
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As global digitization continues to grow, technology becomes more affordable
and easier to use, and social media platforms thrive, becoming the new means of
spreading information and news. Communities are built around sharing and
discussing current events. Within these communities, users are enabled to share
their opinions about each event. Using Sentiment Analysis to understand the
polarity of each message belonging to an event, as well as the entire event,
can help to better understand the general and individual feelings of
significant trends and the dynamics on online social networks. In this context,
we propose a new ensemble architecture, EDSA-Ensemble (Event Detection
Sentiment Analysis Ensemble), that uses Event Detection and Sentiment Analysis
to improve the detection of the polarity for current events from Social Media.
For Event Detection, we use techniques based on Information Diffusion taking
into account both the time span and the topics. To detect the polarity of each
event, we preprocess the text and employ several Machine and Deep Learning
models to create an ensemble model. The preprocessing step includes several
word representation models, i.e., raw frequency, TFIDF, Word2Vec, and
Transformers. The proposed EDSA-Ensemble architecture improves the event
sentiment classification over the individual Machine and Deep Learning models. | [
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] |
If $A$ and $B$ are sets such that $A \subset B$, generalisation may be
understood as the inference from $A$ of a hypothesis sufficient to construct
$B$. One might infer any number of hypotheses from $A$, yet only some of those
may generalise to $B$. How can one know which are likely to generalise? One
strategy is to choose the shortest, equating the ability to compress
information with the ability to generalise (a proxy for intelligence). We
examine this in the context of a mathematical formalism of enactive cognition.
We show that compression is neither necessary nor sufficient to maximise
performance (measured in terms of the probability of a hypothesis
generalising). We formulate a proxy unrelated to length or simplicity, called
weakness. We show that if tasks are uniformly distributed, then there is no
choice of proxy that performs at least as well as weakness maximisation in all
tasks while performing strictly better in at least one. In experiments
comparing maximum weakness and minimum description length in the context of
binary arithmetic, the former generalised at between $1.1$ and $5$ times the
rate of the latter. We argue this demonstrates that weakness is a far better
proxy, and explains why Deepmind's Apperception Engine is able to generalise
effectively. | [
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The Internet of Things (IoT) faces tremendous security challenges. Machine
learning models can be used to tackle the growing number of cyber-attack
variations targeting IoT systems, but the increasing threat posed by
adversarial attacks restates the need for reliable defense strategies. This
work describes the types of constraints required for a realistic adversarial
cyber-attack example and proposes a methodology for a trustworthy adversarial
robustness analysis with a realistic adversarial evasion attack vector. The
proposed methodology was used to evaluate three supervised algorithms, Random
Forest (RF), Extreme Gradient Boosting (XGB), and Light Gradient Boosting
Machine (LGBM), and one unsupervised algorithm, Isolation Forest (IFOR).
Constrained adversarial examples were generated with the Adaptative
Perturbation Pattern Method (A2PM), and evasion attacks were performed against
models created with regular and adversarial training. Even though RF was the
least affected in binary classification, XGB consistently achieved the highest
accuracy in multi-class classification. The obtained results evidence the
inherent susceptibility of tree-based algorithms and ensembles to adversarial
evasion attacks and demonstrates the benefits of adversarial training and a
security by design approach for a more robust IoT network intrusion detection
and cyber-attack classification. | [
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] |
Numerical approximations of partial differential equations (PDEs) are
routinely employed to formulate the solution of physics, engineering and
mathematical problems involving functions of several variables, such as the
propagation of heat or sound, fluid flow, elasticity, electrostatics,
electrodynamics, and more. While this has led to solving many complex
phenomena, there are some limitations. Conventional approaches such as Finite
Element Methods (FEMs) and Finite Differential Methods (FDMs) require
considerable time and are computationally expensive. In contrast, data driven
machine learning-based methods such as neural networks provide a faster, fairly
accurate alternative, and have certain advantages such as discretization
invariance and resolution invariance. This article aims to provide a
comprehensive insight into how data-driven approaches can complement
conventional techniques to solve engineering and physics problems, while also
noting some of the major pitfalls of machine learning-based approaches.
Furthermore, we highlight, a novel and fast machine learning-based approach
(~1000x) to learning the solution operator of a PDE operator learning. We will
note how these new computational approaches can bring immense advantages in
tackling many problems in fundamental and applied physics. | [
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] |
Software and System logs record runtime information about processes executing
within a system. These logs have become the most critical and ubiquitous forms
of observability data that help developers understand system behavior, monitor
system health and resolve issues. However, the volume of logs generated can be
humongous (of the order of petabytes per day) especially for complex
distributed systems, such as cloud, search engine, social media, etc. This has
propelled a lot of research on developing AI-based log based analytics and
intelligence solutions that can process huge volume of raw logs and generate
insights. In order to enable users to perform multiple types of AI-based log
analysis tasks in a uniform manner, we introduce LogAI
(https://github.com/salesforce/logai), a one-stop open source library for log
analytics and intelligence. LogAI supports tasks such as log summarization, log
clustering and log anomaly detection. It adopts the OpenTelemetry data model,
to enable compatibility with different log management platforms. LogAI provides
a unified model interface and provides popular time-series, statistical
learning and deep learning models. Alongside this, LogAI also provides an
out-of-the-box GUI for users to conduct interactive analysis. With LogAI, we
can also easily benchmark popular deep learning algorithms for log anomaly
detection without putting in redundant effort to process the logs. We have
opensourced LogAI to cater to a wide range of applications benefiting both
academic research and industrial prototyping. | [
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-0.0275099016726017
] |
This paper presents a hierarchical classification system that automatically
categorizes a scholarly publication using its abstract into a three-tier
hierarchical label set (discipline, field, subfield) in a multi-class setting.
This system enables a holistic categorization of research activities in the
mentioned hierarchy in terms of knowledge production through articles and
impact through citations, permitting those activities to fall into multiple
categories. The classification system distinguishes 44 disciplines, 718 fields
and 1,485 subfields among 160 million abstract snippets in Microsoft Academic
Graph (version 2018-05-17). We used batch training in a modularized and
distributed fashion to address and allow for interdisciplinary and interfield
classifications in single-label and multi-label settings. In total, we have
conducted 3,140 experiments in all considered models (Convolutional Neural
Networks, Recurrent Neural Networks, Transformers). The classification accuracy
is > 90% in 77.13% and 78.19% of the single-label and multi-label
classifications, respectively. We examine the advantages of our classification
by its ability to better align research texts and output with disciplines, to
adequately classify them in an automated way, and to capture the degree of
interdisciplinarity. The proposed system (a set of pre-trained models) can
serve as a backbone to an interactive system for indexing scientific
publications in the future. | [
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] |
The financial markets, which involve more than $90 trillion market capitals,
attract the attention of innumerable investors around the world. Recently,
reinforcement learning in financial markets (FinRL) has emerged as a promising
direction to train agents for making profitable investment decisions. However,
the evaluation of most FinRL methods only focuses on profit-related measures
and ignores many critical axes, which are far from satisfactory for financial
practitioners to deploy these methods into real-world financial markets.
Therefore, we introduce PRUDEX-Compass, which has 6 axes, i.e., Profitability,
Risk-control, Universality, Diversity, rEliability, and eXplainability, with a
total of 17 measures for a systematic evaluation. Specifically, i) we propose
AlphaMix+ as a strong FinRL baseline, which leverages mixture-of-experts (MoE)
and risk-sensitive approaches to make diversified risk-aware investment
decisions, ii) we evaluate 8 FinRL methods in 4 long-term real-world datasets
of influential financial markets to demonstrate the usage of our
PRUDEX-Compass, iii) PRUDEX-Compass together with 4 real-world datasets,
standard implementation of 8 FinRL methods and a portfolio management
environment is released as public resources to facilitate the design and
comparison of new FinRL methods. We hope that PRUDEX-Compass can not only shed
light on future FinRL research to prevent untrustworthy results from stagnating
FinRL into successful industry deployment but also provide a new challenging
algorithm evaluation scenario for the reinforcement learning (RL) community. | [
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] |
Movement is how people interact with and affect their environment. For
realistic character animation, it is necessary to synthesize such interactions
between virtual characters and their surroundings. Despite recent progress in
character animation using machine learning, most systems focus on controlling
an agent's movements in fairly simple and homogeneous environments, with
limited interactions with other objects. Furthermore, many previous approaches
that synthesize human-scene interactions require significant manual labeling of
the training data. In contrast, we present a system that uses adversarial
imitation learning and reinforcement learning to train physically-simulated
characters that perform scene interaction tasks in a natural and life-like
manner. Our method learns scene interaction behaviors from large unstructured
motion datasets, without manual annotation of the motion data. These scene
interactions are learned using an adversarial discriminator that evaluates the
realism of a motion within the context of a scene. The key novelty involves
conditioning both the discriminator and the policy networks on scene context.
We demonstrate the effectiveness of our approach through three challenging
scene interaction tasks: carrying, sitting, and lying down, which require
coordination of a character's movements in relation to objects in the
environment. Our policies learn to seamlessly transition between different
behaviors like idling, walking, and sitting. By randomizing the properties of
the objects and their placements during training, our method is able to
generalize beyond the objects and scenarios depicted in the training dataset,
producing natural character-scene interactions for a wide variety of object
shapes and placements. The approach takes physics-based character motion
generation a step closer to broad applicability. | [
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The rapid ascent in carbon dioxide emissions is a major cause of global
warming and climate change, which pose a huge threat to human survival and
impose far-reaching influence on the global ecosystem. Therefore, it is very
necessary to effectively control carbon dioxide emissions by accurately
predicting and analyzing the change trend timely, so as to provide a reference
for carbon dioxide emissions mitigation measures. This paper is aiming to
select a suitable model to predict the near-real-time daily emissions based on
univariate daily time-series data from January 1st, 2020 to September 30st,
2022 of all sectors (Power, Industry, Ground Transport, Residential, Domestic
Aviation, International Aviation) in China. We proposed six prediction models,
which including three statistical models: Grey prediction (GM(1,1)),
autoregressive integrated moving average (ARIMA) and seasonal autoregressive
integrated moving average with exogenous factors (SARIMAX); three machine
learning models: artificial neural network (ANN), random forest (RF) and long
short term memory (LSTM). To evaluate the performance of these models, five
criteria: Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean
Absolute Error (MAE), Mean Absolute Percentage Error (MAPE) and Coefficient of
Determination () are imported and discussed in detail. In the results, three
machine learning models perform better than that three statistical models, in
which LSTM model performs the best on five criteria values for daily emissions
prediction with the 3.5179e-04 MSE value, 0.0187 RMSE value, 0.0140 MAE value,
14.8291% MAPE value and 0.9844 value. | [
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] |
Despite the prevalence and many successes of deep learning applications in de
novo molecular design, the problem of peptide generation targeting specific
proteins remains unsolved. A main barrier for this is the scarcity of the
high-quality training data. To tackle the issue, we propose a novel machine
learning based peptide design architecture, called Latent Space Approximate
Trajectory Collector (LSATC). It consists of a series of samplers on an
optimization trajectory on a highly non-convex energy landscape that
approximates the distributions of peptides with desired properties in a latent
space. The process involves little human intervention and can be implemented in
an end-to-end manner. We demonstrate the model by the design of peptide
extensions targeting Beta-catenin, a key nuclear effector protein involved in
canonical Wnt signalling. When compared with a random sampler, LSATC can sample
peptides with $36\%$ lower binding scores in a $16$ times smaller interquartile
range (IQR) and $284\%$ less hydrophobicity with a $1.4$ times smaller IQR.
LSATC also largely outperforms other common generative models. Finally, we
utilized a clustering algorithm to select 4 peptides from the 100 LSATC
designed peptides for experimental validation. The result confirms that all the
four peptides extended by LSATC show improved Beta-catenin binding by at least
$20.0\%$, and two of the peptides show a $3$ fold increase in binding affinity
as compared to the base peptide. | [
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] |
The Metaverse is emerging as maturing technologies are empowering the
different facets. Virtual Reality (VR) technologies serve as the backbone of
the virtual universe within the Metaverse to offer a highly immersive user
experience. As mobility is emphasized in the Metaverse context, VR devices
reduce their weights at the sacrifice of local computation abilities. In this
paper, for a system consisting of a Metaverse server and multiple VR users, we
consider two cases of (i) the server generating frames and transmitting them to
users, and (ii) users generating frames locally and thus consuming device
energy. Moreover, in our multi-user VR scenario for the Metaverse, users have
different characteristics and demands for Frames Per Second (FPS). Then the
channel access arrangement (including the decisions on frame generation
location), and transmission powers for the downlink communications from the
server to the users are jointly optimized to improve the utilities of users.
This joint optimization is addressed by deep reinforcement learning (DRL) with
heterogeneous actions. Our proposed user-centric DRL algorithm is called
User-centric Critic with Heterogenous Actors (UCHA). Extensive experiments
demonstrate that our UCHA algorithm leads to remarkable results under various
requirements and constraints. | [
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] |
Cross-domain Sequential Recommendation (CSR) is an emerging yet challenging
task that depicts the evolution of behavior patterns for overlapped users by
modeling their interactions from multiple domains. Existing studies on CSR
mainly focus on using composite or in-depth structures that achieve significant
improvement in accuracy but bring a huge burden to the model training.
Moreover, to learn the user-specific sequence representations, existing works
usually adopt the global relevance weighting strategy (e.g., self-attention
mechanism), which has quadratic computational complexity. In this work, we
introduce a lightweight external attention-enhanced GCN-based framework to
solve the above challenges, namely LEA-GCN. Specifically, by only keeping the
neighborhood aggregation component and using the Single-Layer Aggregating
Protocol (SLAP), our lightweight GCN encoder performs more efficiently to
capture the collaborative filtering signals of the items from both domains. To
further alleviate the framework structure and aggregate the user-specific
sequential pattern, we devise a novel dual-channel External Attention (EA)
component, which calculates the correlation among all items via a lightweight
linear structure. Extensive experiments are conducted on two real-world
datasets, demonstrating that LEA-GCN requires a smaller volume and less
training time without affecting the accuracy compared with several
state-of-the-art methods. | [
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] |
Neural Language Models of Code, or Neural Code Models (NCMs), are rapidly
progressing from research prototypes to commercial developer tools. As such,
understanding the capabilities and limitations of such models is becoming
critical. However, the abilities of these models are typically measured using
automated metrics that often only reveal a portion of their real-world
performance. While, in general, the performance of NCMs appears promising,
currently much is unknown about how such models arrive at decisions. To this
end, this paper introduces $do_{code}$, a post-hoc interpretability methodology
specific to NCMs that is capable of explaining model predictions. $do_{code}$
is based upon causal inference to enable programming language-oriented
explanations. While the theoretical underpinnings of $do_{code}$ are extensible
to exploring different model properties, we provide a concrete instantiation
that aims to mitigate the impact of spurious correlations by grounding
explanations of model behavior in properties of programming languages. To
demonstrate the practical benefit of $do_{code}$, we illustrate the insights
that our framework can provide by performing a case study on two popular deep
learning architectures and nine NCMs. The results of this case study illustrate
that our studied NCMs are sensitive to changes in code syntax and statistically
learn to predict tokens related to blocks of code (e.g., brackets, parenthesis,
semicolon) with less confounding bias as compared to other programming language
constructs. These insights demonstrate the potential of $do_{code}$ as a useful
model debugging mechanism that may aid in discovering biases and limitations in
NCMs. | [
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] |
We give the first algorithm that maintains an approximate decision tree over
an arbitrary sequence of insertions and deletions of labeled examples, with
strong guarantees on the worst-case running time per update request. For
instance, we show how to maintain a decision tree where every vertex has Gini
gain within an additive $\alpha$ of the optimum by performing
$O\Big(\frac{d\,(\log n)^4}{\alpha^3}\Big)$ elementary operations per update,
where $d$ is the number of features and $n$ the maximum size of the active set
(the net result of the update requests). We give similar bounds for the
information gain and the variance gain. In fact, all these bounds are
corollaries of a more general result, stated in terms of decision rules --
functions that, given a set $S$ of labeled examples, decide whether to split
$S$ or predict a label. Decision rules give a unified view of greedy decision
tree algorithms regardless of the example and label domains, and lead to a
general notion of $\epsilon$-approximate decision trees that, for natural
decision rules such as those used by ID3 or C4.5, implies the gain
approximation guarantees above. The heart of our work provides a deterministic
algorithm that, given any decision rule and any $\epsilon > 0$, maintains an
$\epsilon$-approximate tree using $O\!\left(\frac{d\, f(n)}{n}
\operatorname{poly}\frac{h}{\epsilon}\right)$ operations per update, where
$f(n)$ is the complexity of evaluating the rule over a set of $n$ examples and
$h$ is the maximum height of the maintained tree. | [
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] |
Machine Learning models are being deployed as parts of real-world systems
with the upsurge of interest in artificial intelligence. The design,
implementation, and maintenance of such systems are challenged by real-world
environments that produce larger amounts of heterogeneous data and users
requiring increasingly faster responses with efficient resource consumption.
These requirements push prevalent software architectures to the limit when
deploying ML-based systems. Data-oriented Architecture (DOA) is an emerging
concept that equips systems better for integrating ML models. DOA extends
current architectures to create data-driven, loosely coupled, decentralised,
open systems. Even though papers on deployed ML-based systems do not mention
DOA, their authors made design decisions that implicitly follow DOA. The
reasons why, how, and the extent to which DOA is adopted in these systems are
unclear. Implicit design decisions limit the practitioners' knowledge of DOA to
design ML-based systems in the real world. This paper answers these questions
by surveying real-world deployments of ML-based systems. The survey shows the
design decisions of the systems and the requirements these satisfy. Based on
the survey findings, we also formulate practical advice to facilitate the
deployment of ML-based systems. Finally, we outline open challenges to
deploying DOA-based systems that integrate ML models. | [
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] |
This article explores the concepts of online protocol synthesis using
Reinforcement Learning (RL). The study is performed in the context of sensor
and IoT networks with ultra low complexity wireless transceivers. The paper
introduces the use of RL and Multi Armed Bandit (MAB), a specific type of RL,
for Medium Access Control (MAC) under different network and traffic conditions.
It then introduces a novel learning based protocol synthesis framework that
addresses specific difficulties and limitations in medium access for both
random access and time slotted networks. The mechanism does not rely on carrier
sensing, network time-synchronization, collision detection, and other low level
complex operations, thus making it ideal for ultra simple transceiver hardware
used in resource constrained sensor and IoT networks. Additionally, the ability
of independent protocol learning by the nodes makes the system robust and
adaptive to the changes in network and traffic conditions. It is shown that the
nodes can be trained to learn to avoid collisions, and to achieve network
throughputs that are comparable to ALOHA based access protocols in sensor and
IoT networks with simplest transceiver hardware. It is also shown that using
RL, it is feasible to synthesize access protocols that can sustain network
throughput at high traffic loads, which is not feasible in the ALOHA-based
systems. The ability of the system to provide throughput fairness under network
and traffic heterogeneities are also experimentally demonstrated. | [
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] |
This paper proposes a Time Division Multiple Access (TDMA) MAC slot
allocation protocol with efficient bandwidth usage in wireless sensor networks
and Internet of Things (IoTs). The developed protocol has two primary
components: a Multi-Armed Bandits (MAB)-based slot allocation mechanism for
collision free transmission, and a Decentralized Defragmented Slot Backshift
(DDSB) operation for improving bandwidth usage efficiency. The proposed
framework is decentralized in that each node finds its transmission schedule
independently without the control of any centralized arbitrator. The developed
mechanism is suitable for networks with or without time synchronization, thus,
making it suitable for low-complexity wireless transceivers for wireless sensor
and IoT nodes. This framework is able to manage the trade-off between learning
convergence time and bandwidth. In addition, it allows the nodes to adapt to
topological changes while maintaining efficient bandwidth usage. The developed
logic is tested for both fully-connected and arbitrary mesh networks with
extensive simulation experiments. It is shown how the nodes can learn to select
collision-free transmission slots using MAB. Moreover, the nodes learn to
self-adjust their transmission schedules using a novel DDSB framework in order
to reduce bandwidth usage. | [
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] |
Summarizing text-rich documents has been long studied in the literature, but
most of the existing efforts have been made to summarize a static and
predefined multi-document set. With the rapid development of online platforms
for generating and distributing text-rich documents, there arises an urgent
need for continuously summarizing dynamically evolving multi-document sets
where the composition of documents and sets is changing over time. This is
especially challenging as the summarization should be not only effective in
incorporating relevant, novel, and distinctive information from each concurrent
multi-document set, but also efficient in serving online applications. In this
work, we propose a new summarization problem, Evolving Multi-Document sets
stream Summarization (EMDS), and introduce a novel unsupervised algorithm PDSum
with the idea of prototype-driven continuous summarization. PDSum builds a
lightweight prototype of each multi-document set and exploits it to adapt to
new documents while preserving accumulated knowledge from previous documents.
To update new summaries, the most representative sentences for each
multi-document set are extracted by measuring their similarities to the
prototypes. A thorough evaluation with real multi-document sets streams
demonstrates that PDSum outperforms state-of-the-art unsupervised
multi-document summarization algorithms in EMDS in terms of relevance, novelty,
and distinctiveness and is also robust to various evaluation settings. | [
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] |
With the continuous increment of maritime applications, the development of
marine networks for data offloading becomes necessary. However, the limited
maritime network resources are very difficult to satisfy real-time demands.
Besides, how to effectively handle multiple compute-intensive tasks becomes
another intractable issue. Hence, in this paper, we focus on the decision of
maritime task offloading by the cooperation of unmanned aerial vehicles (UAVs)
and vessels. Specifically, we first propose a cooperative offloading framework,
including the demands from marine Internet of Things (MIoTs) devices and
resource providers from UAVs and vessels. Due to the limited energy and
computation ability of UAVs, it is necessary to help better apply the vessels
to computation offloading. Then, we formulate the studied problem into a Markov
decision process, aiming to minimize the total execution time and energy cost.
Then, we leverage Lyapunov optimization to convert the long-term constraints of
the total execution time and energy cost into their short-term constraints,
further yielding a set of per-time-slot optimization problems. Furthermore, we
propose a Q-learning based approach to solve the short-term problem
efficiently. Finally, simulation results are conducted to verify the
correctness and effectiveness of the proposed algorithm. | [
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] |
Modeling difficulty, time-varying model, and uncertain external inputs are
the main challenges for energy management of fuel cell hybrid electric
vehicles. In the paper, a fuzzy reinforcement learning-based energy management
strategy for fuel cell hybrid electric vehicles is proposed to reduce fuel
consumption, maintain the batteries' long-term operation, and extend the
lifetime of the fuel cells system. Fuzzy Q-learning is a model-free
reinforcement learning that can learn itself by interacting with the
environment, so there is no need for modeling the fuel cells system. In
addition, frequent startup of the fuel cells will reduce the remaining useful
life of the fuel cells system. The proposed method suppresses frequent fuel
cells startup by considering the penalty for the times of fuel cell startups in
the reward of reinforcement learning. Moreover, applying fuzzy logic to
approximate the value function in Q-Learning can solve continuous state and
action space problems. Finally, a python-based training and testing platform
verify the effectiveness and self-learning improvement of the proposed method
under conditions of initial state change, model change and driving condition
change. | [
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] |
With the rapid development of the World Wide Web (WWW), heterogeneous graphs
(HG) have explosive growth. Recently, heterogeneous graph neural network (HGNN)
has shown great potential in learning on HG. Current studies of HGNN mainly
focus on some HGs with strong homophily properties (nodes connected by
meta-path tend to have the same labels), while few discussions are made in
those that are less homophilous. Recently, there have been many works on
homogeneous graphs with heterophily. However, due to heterogeneity, it is
non-trivial to extend their approach to deal with HGs with heterophily. In this
work, based on empirical observations, we propose a meta-path-induced metric to
measure the homophily degree of a HG. We also find that current HGNNs may have
degenerated performance when handling HGs with less homophilous properties.
Thus it is essential to increase the generalization ability of HGNNs on
non-homophilous HGs. To this end, we propose HDHGR, a homophily-oriented deep
heterogeneous graph rewiring approach that modifies the HG structure to
increase the performance of HGNN. We theoretically verify HDHGR. In addition,
experiments on real-world HGs demonstrate the effectiveness of HDHGR, which
brings at most more than 10% relative gain. | [
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] |
We propose schemas as a model for abstractions that can be used for rapid
transfer learning, inference, and planning. Common structured representations
of concepts and behaviors -- schemas -- have been proposed as a powerful way to
encode abstractions. Latent graph learning is emerging as a new computational
model of the hippocampus to explain map learning and transitive inference. We
build on this work to show that learned latent graphs in these models have a
slot structure -- schemas -- that allow for quick knowledge transfer across
environments. In a new environment, an agent can rapidly learn new bindings
between the sensory stream to multiple latent schemas and select the best
fitting one to guide behavior. To evaluate these graph schemas, we use two
previously published challenging tasks: the memory & planning game and one-shot
StreetLearn, that are designed to test rapid task solving in novel
environments. Graph schemas can be learned in far fewer episodes than previous
baselines, and can model and plan in a few steps in novel variations of these
tasks. We further demonstrate learning, matching, and reusing graph schemas in
navigation tasks in more challenging environments with aliased observations and
size variations, and show how different schemas can be composed to model larger
2D and 3D environments. | [
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] |
The ongoing transition to renewable energy is increasing the share of
fluctuating power sources like wind and solar, raising power grid volatility
and making grid operation increasingly complex and costly. In our prior work,
we have introduced a congestion management approach consisting of a
redispatching optimizer combined with a machine learning-based topology
optimization agent. Compared to a typical redispatching-only agent, it was able
to keep a simulated grid in operation longer while at the same time reducing
operational cost. Our approach also ranked 1st in the L2RPN 2022 competition
initiated by RTE, Europe's largest grid operator. The aim of this paper is to
bring this promising technology closer to the real world of power grid
operation. We deploy RL-based agents in two settings resembling established
workflows, AI-assisted day-ahead planning and realtime control, in an attempt
to show the benefits and caveats of this new technology. We then analyse
congestion, redispatching and switching profiles, and elementary sensitivity
analysis providing a glimpse of operation robustness. While there is still a
long way to a real control room, we believe that this paper and the associated
prototypes help to narrow the gap and pave the way for a safe deployment of RL
agents in tomorrow's power grids. | [
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] |
Deep learning-based grasp prediction models have become an industry standard
for robotic bin-picking systems. To maximize pick success, production
environments are often equipped with several end-effector tools that can be
swapped on-the-fly, based on the target object. Tool-change, however, takes
time. Choosing the order of grasps to perform, and corresponding tool-change
actions, can improve system throughput; this is the topic of our work. The main
challenge in planning tool change is uncertainty - we typically cannot see
objects in the bin that are currently occluded. Inspired by queuing and
admission control problems, we model the problem as a Markov Decision Process
(MDP), where the goal is to maximize expected throughput, and we pursue an
approximate solution based on model predictive control, where at each time step
we plan based only on the currently visible objects. Special to our method is
the idea of void zones, which are geometrical boundaries in which an unknown
object will be present, and therefore cannot be accounted for during planning.
Our planning problem can be solved using integer linear programming (ILP).
However, we find that an approximate solution based on sparse tree search
yields near optimal performance at a fraction of the time. Another question
that we explore is how to measure the performance of tool-change planning: we
find that throughput alone can fail to capture delicate and smooth behavior,
and propose a principled alternative. Finally, we demonstrate our algorithms on
both synthetic and real world bin picking tasks. | [
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] |
The use of AI in healthcare is designed to improve care delivery and augment
the decisions of providers to enhance patient outcomes. When deployed in
clinical settings, the interaction between providers and AI is a critical
component for measuring and understanding the effectiveness of these digital
tools on broader health outcomes. Even in cases where AI algorithms have high
diagnostic accuracy, healthcare providers often still rely on their experience
and sometimes gut feeling to make a final decision. Other times, providers rely
unquestioningly on the outputs of the AI models, which leads to a concern about
over-reliance on the technology. The purpose of this research was to understand
how reliant drug shop dispensers were on AI-powered technologies when
determining a differential diagnosis for a presented clinical case vignette. We
explored how the drug dispensers responded to technology that is framed as
always correct in an attempt to measure whether they begin to rely on it
without any critical thought of their own. We found that dispensers relied on
the decision made by the AI 25 percent of the time, even when the AI provided
no explanation for its decision. | [
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] |
Task automation of surgical robot has the potentials to improve surgical
efficiency. Recent reinforcement learning (RL) based approaches provide
scalable solutions to surgical automation, but typically require extensive data
collection to solve a task if no prior knowledge is given. This issue is known
as the exploration challenge, which can be alleviated by providing expert
demonstrations to an RL agent. Yet, how to make effective use of demonstration
data to improve exploration efficiency still remains an open challenge. In this
work, we introduce Demonstration-guided EXploration (DEX), an efficient
reinforcement learning algorithm that aims to overcome the exploration problem
with expert demonstrations for surgical automation. To effectively exploit
demonstrations, our method estimates expert-like behaviors with higher values
to facilitate productive interactions, and adopts non-parametric regression to
enable such guidance at states unobserved in demonstration data. Extensive
experiments on $10$ surgical manipulation tasks from SurRoL, a comprehensive
surgical simulation platform, demonstrate significant improvements in the
exploration efficiency and task success rates of our method. Moreover, we also
deploy the learned policies to the da Vinci Research Kit (dVRK) platform to
show the effectiveness on the real robot. Code is available at
https://github.com/med-air/DEX. | [
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] |
An accurate and explainable automatic monitoring system is critical for the
safety of high efficiency energy conversion plants that operate under extreme
working condition. Nonetheless, currently available data-driven monitoring
systems often fall short in meeting the requirements for either high-accuracy
or interpretability, which hinders their application in practice. To overcome
this limitation, a data-driven approach, AttentionMixer, is proposed under a
generalized message passing framework, with the goal of establishing an
accurate and interpretable radiation monitoring framework for energy conversion
plants. To improve the model accuracy, the first technical contribution
involves the development of spatial and temporal adaptive message passing
blocks, which enable the capture of spatial and temporal correlations,
respectively; the two blocks are cascaded through a mixing operator. To enhance
the model interpretability, the second technical contribution involves the
implementation of a sparse message passing regularizer, which eliminates
spurious and noisy message passing routes. The effectiveness of the
AttentionMixer approach is validated through extensive evaluations on a
monitoring benchmark collected from the national radiation monitoring network
for nuclear power plants, resulting in enhanced monitoring accuracy and
interpretability in practice. | [
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] |
Object rearranging is one of the most common deformable manipulation tasks,
where the robot needs to rearrange a deformable object into a goal
configuration. Previous studies focus on designing an expert system for each
specific task by model-based or data-driven approaches and the application
scenarios are therefore limited. Some research has been attempting to design a
general framework to obtain more advanced manipulation capabilities for
deformable rearranging tasks, with lots of progress achieved in simulation.
However, transferring from simulation to reality is difficult due to the
limitation of the end-to-end CNN architecture. To address these challenges, we
design a local GNN (Graph Neural Network) based learning method, which utilizes
two representation graphs to encode keypoints detected from images.
Self-attention is applied for graph updating and cross-attention is applied for
generating manipulation actions. Extensive experiments have been conducted to
demonstrate that our framework is effective in multiple 1-D (rope, rope ring)
and 2-D (cloth) rearranging tasks in simulation and can be easily transferred
to a real robot by fine-tuning a keypoint detector. | [
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] |
While quantum reinforcement learning (RL) has attracted a surge of attention
recently, its theoretical understanding is limited. In particular, it remains
elusive how to design provably efficient quantum RL algorithms that can address
the exploration-exploitation trade-off. To this end, we propose a novel
UCRL-style algorithm that takes advantage of quantum computing for tabular
Markov decision processes (MDPs) with $S$ states, $A$ actions, and horizon $H$,
and establish an $\mathcal{O}(\mathrm{poly}(S, A, H, \log T))$ worst-case
regret for it, where $T$ is the number of episodes. Furthermore, we extend our
results to quantum RL with linear function approximation, which is capable of
handling problems with large state spaces. Specifically, we develop a quantum
algorithm based on value target regression (VTR) for linear mixture MDPs with
$d$-dimensional linear representation and prove that it enjoys
$\mathcal{O}(\mathrm{poly}(d, H, \log T))$ regret. Our algorithms are variants
of UCRL/UCRL-VTR algorithms in classical RL, which also leverage a novel
combination of lazy updating mechanisms and quantum estimation subroutines.
This is the key to breaking the $\Omega(\sqrt{T})$-regret barrier in classical
RL. To the best of our knowledge, this is the first work studying the online
exploration in quantum RL with provable logarithmic worst-case regret. | [
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] |
Embedding-based Retrieval (EBR) in e-commerce search is a powerful search
retrieval technique to address semantic matches between search queries and
products. However, commercial search engines like Facebook Marketplace Search
are complex multi-stage systems optimized for multiple business objectives. At
Facebook Marketplace, search retrieval focuses on matching search queries with
relevant products, while search ranking puts more emphasis on contextual
signals to up-rank the more engaging products. As a result, the end-to-end
searcher experience is a function of both relevance and engagement, and the
interaction between different stages of the system. This presents challenges to
EBR systems in order to optimize for better searcher experiences. In this paper
we presents Que2Engage, a search EBR system built towards bridging the gap
between retrieval and ranking for end-to-end optimizations. Que2Engage takes a
multimodal & multitask approach to infuse contextual information into the
retrieval stage and to balance different business objectives. We show the
effectiveness of our approach via a multitask evaluation framework and thorough
baseline comparisons and ablation studies. Que2Engage is deployed on Facebook
Marketplace Search and shows significant improvements in searcher engagement in
two weeks of A/B testing. | [
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] |
Recently, many convolutional neural networks (CNNs) for classification by
time domain data of multisignals have been developed. Although some signals are
important for correct classification, others are not. When data that do not
include important signals for classification are taken as the CNN input layer,
the calculation, memory, and data collection costs increase. Therefore,
identifying and eliminating nonimportant signals from the input layer are
important. In this study, we proposed features gradient-based signals selection
algorithm (FG-SSA), which can be used for finding and removing nonimportant
signals for classification by utilizing features gradient obtained by the
calculation process of grad-CAM. When we define N as the number of signals, the
computational complexity of the proposed algorithm is linear time O(N), that
is, it has a low calculation cost. We verified the effectiveness of the
algorithm using the OPPORTUNITY Activity Recognition dataset, which is an open
dataset comprising acceleration signals of human activities. In addition, we
checked the average 6.55 signals from a total of 15 acceleration signals (five
triaxial sensors) that were removed by FG-SSA while maintaining high
generalization scores of classification. Therefore, the proposed algorithm
FG-SSA has an effect on finding and removing signals that are not important for
CNN-based classification. | [
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Robotics research has been focusing on cooperative multi-agent problems,
where agents must work together and communicate to achieve a shared objective.
To tackle this challenge, we explore imitation learning algorithms. These
methods learn a controller by observing demonstrations of an expert, such as
the behaviour of a centralised omniscient controller, which can perceive the
entire environment, including the state and observations of all agents.
Performing tasks with complete knowledge of the state of a system is
relatively easy, but centralised solutions might not be feasible in real
scenarios since agents do not have direct access to the state but only to their
observations. To overcome this issue, we train end-to-end Neural Networks that
take as input local observations obtained from an omniscient centralised
controller, i.e., the agents' sensor readings and the communications received,
producing as output the action to be performed and the communication to be
transmitted.
This study concentrates on two cooperative tasks using a distributed
controller: distributing the robots evenly in space and colouring them based on
their position relative to others. While an explicit exchange of messages
between the agents is required to solve the second task, in the first one, a
communication protocol is unnecessary, although it may increase performance.
The experiments are run in Enki, a high-performance open-source simulator for
planar robots, which provides collision detection and limited physics support
for robots evolving on a flat surface. Moreover, it can simulate groups of
robots hundreds of times faster than real-time.
The results show how applying a communication strategy improves the
performance of the distributed model, letting it decide which actions to take
almost as precisely and quickly as the expert controller. | [
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] |
In recent years, Reinforcement Learning (RL) has emerged as a powerful tool
for solving a wide range of problems, including decision-making and genomics.
The exponential growth of raw genomic data over the past two decades has
exceeded the capacity of manual analysis, leading to a growing interest in
automatic data analysis and processing. RL algorithms are capable of learning
from experience with minimal human supervision, making them well-suited for
genomic data analysis and interpretation. One of the key benefits of using RL
is the reduced cost associated with collecting labeled training data, which is
required for supervised learning. While there have been numerous studies
examining the applications of Machine Learning (ML) in genomics, this survey
focuses exclusively on the use of RL in various genomics research fields,
including gene regulatory networks (GRNs), genome assembly, and sequence
alignment. We present a comprehensive technical overview of existing studies on
the application of RL in genomics, highlighting the strengths and limitations
of these approaches. We then discuss potential research directions that are
worthy of future exploration, including the development of more sophisticated
reward functions as RL heavily depends on the accuracy of the reward function,
the integration of RL with other machine learning techniques, and the
application of RL to new and emerging areas in genomics research. Finally, we
present our findings and conclude by summarizing the current state of the field
and the future outlook for RL in genomics. | [
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] |
Cross-domain recommendation has attracted increasing attention from industry
and academia recently. However, most existing methods do not exploit the
interest invariance between domains, which would yield sub-optimal solutions.
In this paper, we propose a cross-domain recommendation method: Self-supervised
Interest Transfer Network (SITN), which can effectively transfer invariant
knowledge between domains via prototypical contrastive learning. Specifically,
we perform two levels of cross-domain contrastive learning: 1)
instance-to-instance contrastive learning, 2) instance-to-cluster contrastive
learning. Not only that, we also take into account users' multi-granularity and
multi-view interests. With this paradigm, SITN can explicitly learn the
invariant knowledge of interest clusters between domains and accurately capture
users' intents and preferences. We conducted extensive experiments on a public
dataset and a large-scale industrial dataset collected from one of the world's
leading e-commerce corporations. The experimental results indicate that SITN
achieves significant improvements over state-of-the-art recommendation methods.
Additionally, SITN has been deployed on a micro-video recommendation platform,
and the online A/B testing results further demonstrate its practical value.
Supplement is available at: https://github.com/fanqieCoffee/SITN-Supplement. | [
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] |
In this paper, the performance optimization of federated learning (FL), when
deployed over a realistic wireless multiple-input multiple-output (MIMO)
communication system with digital modulation and over-the-air computation
(AirComp) is studied. In particular, an MIMO system is considered in which edge
devices transmit their local FL models (trained using their locally collected
data) to a parameter server (PS) using beamforming to maximize the number of
devices scheduled for transmission. The PS, acting as a central controller,
generates a global FL model using the received local FL models and broadcasts
it back to all devices. Due to the limited bandwidth in a wireless network,
AirComp is adopted to enable efficient wireless data aggregation. However,
fading of wireless channels can produce aggregate distortions in an
AirComp-based FL scheme. To tackle this challenge, we propose a modified
federated averaging (FedAvg) algorithm that combines digital modulation with
AirComp to mitigate wireless fading while ensuring the communication
efficiency. This is achieved by a joint transmit and receive beamforming
design, which is formulated as a optimization problem to dynamically adjust the
beamforming matrices based on current FL model parameters so as to minimize the
transmitting error and ensure the FL performance. To achieve this goal, we
first analytically characterize how the beamforming matrices affect the
performance of the FedAvg in different iterations. Based on this relationship,
an artificial neural network (ANN) is used to estimate the local FL models of
all devices and adjust the beamforming matrices at the PS for future model
transmission. The algorithmic advantages and improved performance of the
proposed methodologies are demonstrated through extensive numerical
experiments. | [
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] |
The problem of integrating high-level task planning in the execution loop of
a real-world robot architecture remains challenging, as the planning times of
traditional symbolic planners explode combinatorially with the number of
symbols to plan upon. In this paper, we present Teriyaki, a framework for
training Large Language Models (LLMs), and in particular the now well-known
GPT-3 model, into neurosymbolic planners compatible with the Planning Domain
Definition Language (PDDL). Unlike symbolic approaches, LLMs require a training
process. However, their response time scales with the combined length of the
input and the output. Hence, LLM-based planners can potentially provide
significant performance gains on complex planning problems as the technology
matures and becomes more accessible. In this preliminary work, which to our
knowledge is the first using LLMs for planning in robotics, we (i) outline a
methodology for training LLMs as PDDL solvers, (ii) generate PDDL-compliant
planners for two challenging PDDL domains, and (iii) test the planning times
and the plan quality associated with the obtained planners, while also
comparing them to a state-of-the-art PDDL planner, namely Probe. Results
confirm the viability of the approach, with Teriyaki-based planners being able
to solve 95.5% of problems in a test data set of 1000 samples, and even
generating plans up to 13.5% shorter on average than the employed traditional
planner, depending on the domain. | [
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] |
Cellular traffic prediction is an indispensable part for intelligent
telecommunication networks. Nevertheless, due to the frequent user mobility and
complex network scheduling mechanisms, cellular traffic often inherits
complicated spatial-temporal patterns, making the prediction incredibly
challenging. Although recent advanced algorithms such as graph-based prediction
approaches have been proposed, they frequently model spatial dependencies based
on static or dynamic graphs and neglect the coexisting multiple spatial
correlations induced by traffic generation. Meanwhile, some works lack the
consideration of the diverse cellular traffic patterns, result in suboptimal
prediction results. In this paper, we propose a novel deep learning network
architecture, Adaptive Hybrid Spatial-Temporal Graph Neural Network (AHSTGNN),
to tackle the cellular traffic prediction problem. First, we apply adaptive
hybrid graph learning to learn the compound spatial correlations among cell
towers. Second, we implement a Temporal Convolution Module with multi-periodic
temporal data input to capture the nonlinear temporal dependencies. In
addition, we introduce an extra Spatial-Temporal Adaptive Module to conquer the
heterogeneity lying in cell towers. Our experiments on two real-world cellular
traffic datasets show AHSTGNN outperforms the state-of-the-art by a significant
margin, illustrating the superior scalability of our method for
spatial-temporal cellular traffic prediction. | [
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In many studies, we want to determine the influence of certain features on a
dependent variable. More specifically, we are interested in the strength of the
influence -- i.e., is the feature relevant? -- and, if so, how the feature
influences the dependent variable. Recently, data-driven approaches such as
\emph{random forest regression} have found their way into applications
(Boulesteix et al., 2012). These models allow to directly derive measures of
feature importance, which are a natural indicator of the strength of the
influence. For the relevant features, the correlation or rank correlation
between the feature and the dependent variable has typically been used to
determine the nature of the influence. More recent methods, some of which can
also measure interactions between features, are based on a modeling approach.
In particular, when machine learning models are used, SHAP scores are a recent
and prominent method to determine these trends (Lundberg et al., 2017).
In this paper, we introduce a novel notion of feature importance based on the
well-studied Gram-Schmidt decorrelation method. Furthermore, we propose two
estimators for identifying trends in the data using random forest regression,
the so-called absolute and relative transversal rate. We empirically compare
the properties of our estimators with those of well-established estimators on a
variety of synthetic and real-world datasets. | [
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] |
Training Graph Neural Networks (GNNs) on large graphs is challenging due to
the conflict between the high memory demand and limited GPU memory. Recently,
distributed full-graph GNN training has been widely adopted to tackle this
problem. However, the substantial inter-GPU communication overhead can cause
severe throughput degradation. Existing communication compression techniques
mainly focus on traditional DNN training, whose bottleneck lies in
synchronizing gradients and parameters. We find they do not work well in
distributed GNN training as the barrier is the layer-wise communication of
features during the forward pass & feature gradients during the backward pass.
To this end, we propose an efficient distributed GNN training framework Sylvie,
which employs one-bit quantization technique in GNNs and further pipelines the
curtailed communication with computation to enormously shrink the overhead
while maintaining the model quality. In detail, Sylvie provides a lightweight
Low-bit Module to quantize the sent data and dequantize the received data back
to full precision values in each layer. Additionally, we propose a Bounded
Staleness Adaptor to control the introduced staleness to achieve further
performance enhancement. We conduct theoretical convergence analysis and
extensive experiments on various models & datasets to demonstrate Sylvie can
considerably boost the training throughput by up to 28.1x. | [
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] |
In imitation and reinforcement learning, the cost of human supervision limits
the amount of data that robots can be trained on. An aspirational goal is to
construct self-improving robots: robots that can learn and improve on their
own, from autonomous interaction with minimal human supervision or oversight.
Such robots could collect and train on much larger datasets, and thus learn
more robust and performant policies. While reinforcement learning offers a
framework for such autonomous learning via trial-and-error, practical
realizations end up requiring extensive human supervision for reward function
design and repeated resetting of the environment between episodes of
interactions. In this work, we propose MEDAL++, a novel design for
self-improving robotic systems: given a small set of expert demonstrations at
the start, the robot autonomously practices the task by learning to both do and
undo the task, simultaneously inferring the reward function from the
demonstrations. The policy and reward function are learned end-to-end from
high-dimensional visual inputs, bypassing the need for explicit state
estimation or task-specific pre-training for visual encoders used in prior
work. We first evaluate our proposed algorithm on a simulated non-episodic
benchmark EARL, finding that MEDAL++ is both more data efficient and gets up to
30% better final performance compared to state-of-the-art vision-based methods.
Our real-robot experiments show that MEDAL++ can be applied to manipulation
problems in larger environments than those considered in prior work, and
autonomous self-improvement can improve the success rate by 30-70% over
behavior cloning on just the expert data. Code, training and evaluation videos
along with a brief overview is available at:
https://architsharma97.github.io/self-improving-robots/ | [
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Concept learning deals with learning description logic concepts from a
background knowledge and input examples. The goal is to learn a concept that
covers all positive examples, while not covering any negative examples. This
non-trivial task is often formulated as a search problem within an infinite
quasi-ordered concept space. Although state-of-the-art models have been
successfully applied to tackle this problem, their large-scale applications
have been severely hindered due to their excessive exploration incurring
impractical runtimes. Here, we propose a remedy for this limitation. We
reformulate the learning problem as a multi-label classification problem and
propose a neural embedding model (NERO) that learns permutation-invariant
embeddings for sets of examples tailored towards predicting $F_1$ scores of
pre-selected description logic concepts. By ranking such concepts in descending
order of predicted scores, a possible goal concept can be detected within few
retrieval operations, i.e., no excessive exploration. Importantly, top-ranked
concepts can be used to start the search procedure of state-of-the-art symbolic
models in multiple advantageous regions of a concept space, rather than
starting it in the most general concept $\top$. Our experiments on 5 benchmark
datasets with 770 learning problems firmly suggest that NERO significantly
(p-value <1%) outperforms the state-of-the-art models in terms of $F_1$ score,
the number of explored concepts, and the total runtime. We provide an
open-source implementation of our approach. | [
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] |
This vision paper proposes KGNet, an on-demand graph machine learning (GML)
as a service on top of RDF engines to support GML-enabled SPARQL queries. KGNet
automates the training of GML models on a KG by identifying a task-specific
subgraph. This helps reduce the task-irrelevant KG structure and properties for
better scalability and accuracy. While training a GML model on KG, KGNet
collects metadata of trained models in the form of an RDF graph called KGMeta,
which is interlinked with the relevant subgraphs in KG. Finally, all trained
models are accessible via a SPARQL-like query. We call it a GML-enabled query
and refer to it as SPARQLML. KGNet supports SPARQLML on top of existing RDF
engines as an interface for querying and inferencing over KGs using GML models.
The development of KGNet poses research opportunities in several areas,
including meta-sampling for identifying task-specific subgraphs, GML pipeline
automation with computational constraints, such as limited time and memory
budget, and SPARQLML query optimization. KGNet supports different GML tasks,
such as node classification, link prediction, and semantic entity matching. We
evaluated KGNet using two real KGs of different application domains. Compared
to training on the entire KG, KGNet significantly reduced training time and
memory usage while maintaining comparable or improved accuracy. The KGNet
source-code is available for further study | [
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] |
Fixed-point (FXP) inference has proven suitable for embedded devices with
limited computational resources, and yet model training is continually
performed in floating-point (FLP). FXP training has not been fully explored and
the non-trivial conversion from FLP to FXP presents unavoidable performance
drop. We propose a novel method to train and obtain FXP convolutional
keyword-spotting (KWS) models. We combine our methodology with two
quantization-aware-training (QAT) techniques - squashed weight distribution and
absolute cosine regularization for model parameters, and propose techniques for
extending QAT over transient variables, otherwise neglected by previous
paradigms. Experimental results on the Google Speech Commands v2 dataset show
that we can reduce model precision up to 4-bit with no loss in accuracy.
Furthermore, on an in-house KWS dataset, we show that our 8-bit FXP-QAT models
have a 4-6% improvement in relative false discovery rate at fixed false reject
rate compared to full precision FLP models. During inference we argue that
FXP-QAT eliminates q-format normalization and enables the use of low-bit
accumulators while maximizing SIMD throughput to reduce user perceived latency.
We demonstrate that we can reduce execution time by 68% without compromising
KWS model's predictive performance or requiring model architectural changes.
Our work provides novel findings that aid future research in this area and
enable accurate and efficient models. | [
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Humans manipulate various kinds of fluids in their everyday life: creating
latte art, scooping floating objects from water, rolling an ice cream cone,
etc. Using robots to augment or replace human labors in these daily settings
remain as a challenging task due to the multifaceted complexities of fluids.
Previous research in robotic fluid manipulation mostly consider fluids governed
by an ideal, Newtonian model in simple task settings (e.g., pouring). However,
the vast majority of real-world fluid systems manifest their complexities in
terms of the fluid's complex material behaviors and multi-component
interactions, both of which were well beyond the scope of the current
literature. To evaluate robot learning algorithms on understanding and
interacting with such complex fluid systems, a comprehensive virtual platform
with versatile simulation capabilities and well-established tasks is needed. In
this work, we introduce FluidLab, a simulation environment with a diverse set
of manipulation tasks involving complex fluid dynamics. These tasks address
interactions between solid and fluid as well as among multiple fluids. At the
heart of our platform is a fully differentiable physics simulator, FluidEngine,
providing GPU-accelerated simulations and gradient calculations for various
material types and their couplings. We identify several challenges for fluid
manipulation learning by evaluating a set of reinforcement learning and
trajectory optimization methods on our platform. To address these challenges,
we propose several domain-specific optimization schemes coupled with
differentiable physics, which are empirically shown to be effective in tackling
optimization problems featured by fluid system's non-convex and non-smooth
properties. Furthermore, we demonstrate reasonable sim-to-real transfer by
deploying optimized trajectories in real-world settings. | [
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] |
Training deep neural networks (DNNs) takes signifcant time and resources. A
practice for expedited deployment is to use pre-trained deep neural networks
(PTNNs), often from model zoos -- collections of PTNNs; yet, the reliability of
model zoos remains unexamined. In the absence of an industry standard for the
implementation and performance of PTNNs, engineers cannot confidently
incorporate them into production systems. As a first step, discovering
potential discrepancies between PTNNs across model zoos would reveal a threat
to model zoo reliability. Prior works indicated existing variances in deep
learning systems in terms of accuracy. However, broader measures of reliability
for PTNNs from model zoos are unexplored. This work measures notable
discrepancies between accuracy, latency, and architecture of 36 PTNNs across
four model zoos. Among the top 10 discrepancies, we find differences of
1.23%-2.62% in accuracy and 9%-131% in latency. We also fnd mismatches in
architecture for well-known DNN architectures (e.g., ResNet and AlexNet). Our
findings call for future works on empirical validation, automated tools for
measurement, and best practices for implementation. | [
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] |
Deep Neural Networks (DNNs) are being adopted as components in software
systems. Creating and specializing DNNs from scratch has grown increasingly
difficult as state-of-the-art architectures grow more complex. Following the
path of traditional software engineering, machine learning engineers have begun
to reuse large-scale pre-trained models (PTMs) and fine-tune these models for
downstream tasks. Prior works have studied reuse practices for traditional
software packages to guide software engineers towards better package
maintenance and dependency management. We lack a similar foundation of
knowledge to guide behaviors in pre-trained model ecosystems.
In this work, we present the first empirical investigation of PTM reuse. We
interviewed 12 practitioners from the most popular PTM ecosystem, Hugging Face,
to learn the practices and challenges of PTM reuse. From this data, we model
the decision-making process for PTM reuse. Based on the identified practices,
we describe useful attributes for model reuse, including provenance,
reproducibility, and portability. Three challenges for PTM reuse are missing
attributes, discrepancies between claimed and actual performance, and model
risks. We substantiate these identified challenges with systematic measurements
in the Hugging Face ecosystem. Our work informs future directions on optimizing
deep learning ecosystems by automated measuring useful attributes and potential
attacks, and envision future research on infrastructure and standardization for
model registries. | [
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] |
We present a metaheuristic conditional neural-network-based method aimed at
identifying physically interesting metastable states in a potential energy
surface of high rugosity. To demonstrate how this method works, we identify and
analyze spin textures with topological charge $Q$ ranging from 1 to $-13$
(where antiskyrmions have $Q<0$) in the Pd/Fe/Ir(111) system, which we model
using a classical atomistic spin Hamiltonian based on parameters computed from
density functional theory. To facilitate the harvest of relevant spin textures,
we make use of the newly developed Segment Anything Model (SAM). Spin textures
with $Q$ ranging from $-3$ to $-6$ are further analyzed using
finite-temperature spin-dynamics simulations. We observe that for temperatures
up to around 20\,K, lifetimes longer than 200\,ps are predicted, and that when
these textures decay, new topological spin textures are formed. We also find
that the relative stability of the spin textures depend linearly on the
topological charge, but only when comparing the most stable antiskyrmions for
each topological charge. In general, the number of holes (i.e.,
non-self-intersecting curves that define closed domain walls in the structure)
in the spin texture is an important predictor of stability -- the more holes,
the less stable is the texture. Methods for systematic identification and
characterization of complex metastable skyrmionic textures -- such as the one
demonstrated here -- are highly relevant for advancements in the field of
topological spintronics. | [
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] |
The artificial intelligence (AI) system designer for thermal comfort faces
insufficient data recorded from the current user or overfitting due to
unreliable training data. This work introduces the reliable data set for
training the AI subsystem for thermal comfort. This paper presents the control
algorithm based on shallow supervised learning, which is simple enough to be
implemented in the Internet of Things (IoT) system for residential usage using
ASHRAE RP-884 and ASHRAE Global Thermal Comfort Database II. No training data
for thermal comfort is available as reliable as this dataset, but the direct
use of this data can lead to overfitting. This work offers the algorithm for
data filtering and semantic data augmentation for the ASHRAE database for the
supervised learning process. Overfitting always becomes a problem due to the
psychological aspect involved in the thermal comfort decision. The method to
check the AI system based on the psychrometric chart against overfitting is
presented. This paper also assesses the most important parameters needed to
achieve human thermal comfort. This method can support the development of
reinforced learning for thermal comfort. | [
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] |
AI-supported programming has arrived, as shown by the introduction and
successes of large language models for code, such as Copilot/Codex
(Github/OpenAI) and AlphaCode (DeepMind). Above human average performance on
programming challenges is now possible. However, software engineering is much
more than solving programming contests. Moving beyond code completion to
AI-supported software engineering will require an AI system that can, among
other things, understand how to avoid code smells, to follow language idioms,
and eventually (maybe!) propose rational software designs. In this study, we
explore the current limitations of AI-supported code completion tools like
Copilot and offer a simple taxonomy for understanding the classification of
AI-supported code completion tools in this space. We first perform an
exploratory study on Copilot's code suggestions for language idioms and code
smells. Copilot does not follow language idioms and avoid code smells in most
of our test scenarios. We then conduct additional investigation to determine
the current boundaries of AI-supported code completion tools like Copilot by
introducing a taxonomy of software abstraction hierarchies where 'basic
programming functionality' such as code compilation and syntax checking is at
the least abstract level, software architecture analysis and design are at the
most abstract level. We conclude by providing a discussion on challenges for
future development of AI-supported code completion tools to reach the design
level of abstraction in our taxonomy. | [
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Rewrite systems [6, 10, 12] have been widely employing equality saturation
[9], which is an optimisation methodology that uses a saturated e-graph to
represent all possible sequences of rewrite simultaneously, and then extracts
the optimal one. As such, optimal results can be achieved by avoiding the
phase-ordering problem. However, we observe that when the e-graph is not
saturated, it cannot represent all possible rewrite opportunities and therefore
the phase-ordering problem is re-introduced during the construction phase of
the e-graph. To address this problem, we propose MCTS-GEB, a domain-general
rewrite system that applies reinforcement learning (RL) to e-graph
construction. At its core, MCTS-GEB uses a Monte Carlo Tree Search (MCTS) [3]
to efficiently plan for the optimal e-graph construction, and therefore it can
effectively eliminate the phase-ordering problem at the construction phase and
achieve better performance within a reasonable time. Evaluation in two
different domains shows MCTS-GEB can outperform the state-of-the-art rewrite
systems by up to 49x, while the optimisation can generally take less than an
hour, indicating MCTS-GEB is a promising building block for the future
generation of rewrite systems. | [
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] |
State abstraction is an effective technique for planning in robotics
environments with continuous states and actions, long task horizons, and sparse
feedback. In object-oriented environments, predicates are a particularly useful
form of state abstraction because of their compatibility with symbolic planners
and their capacity for relational generalization. However, to plan with
predicates, the agent must be able to interpret them in continuous environment
states (i.e., ground the symbols). Manually programming predicate
interpretations can be difficult, so we would instead like to learn them from
data. We propose an embodied active learning paradigm where the agent learns
predicate interpretations through online interaction with an expert. For
example, after taking actions in a block stacking environment, the agent may
ask the expert: "Is On(block1, block2) true?" From this experience, the agent
learns to plan: it learns neural predicate interpretations, symbolic planning
operators, and neural samplers that can be used for bilevel planning. During
exploration, the agent plans to learn: it uses its current models to select
actions towards generating informative expert queries. We learn predicate
interpretations as ensembles of neural networks and use their entropy to
measure the informativeness of potential queries. We evaluate this approach in
three robotic environments and find that it consistently outperforms six
baselines while exhibiting sample efficiency in two key metrics: number of
environment interactions, and number of queries to the expert. Code:
https://tinyurl.com/active-predicates | [
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] |
In this work, we first formulate the problem of robotic water scooping using
goal-conditioned reinforcement learning. This task is particularly challenging
due to the complex dynamics of fluids and the need to achieve multi-modal
goals. The policy is required to successfully reach both position goals and
water amount goals, which leads to a large convoluted goal state space. To
overcome these challenges, we introduce Goal Sampling Adaptation for Scooping
(GOATS), a curriculum reinforcement learning method that can learn an effective
and generalizable policy for robot scooping tasks. Specifically, we use a
goal-factorized reward formulation and interpolate position goal distributions
and amount goal distributions to create curriculum throughout the learning
process. As a result, our proposed method can outperform the baselines in
simulation and achieves 5.46% and 8.71% amount errors on bowl scooping and
bucket scooping tasks, respectively, under 1000 variations of initial water
states in the tank and a large goal state space. Besides being effective in
simulation environments, our method can efficiently adapt to noisy real-robot
water-scooping scenarios with diverse physical configurations and unseen
settings, demonstrating superior efficacy and generalizability. The videos of
this work are available on our project page:
https://sites.google.com/view/goatscooping. | [
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The growing renewable energy sources have posed significant challenges to
traditional power scheduling. It is difficult for operators to obtain accurate
day-ahead forecasts of renewable generation, thereby requiring the future
scheduling system to make real-time scheduling decisions aligning with
ultra-short-term forecasts. Restricted by the computation speed, traditional
optimization-based methods can not solve this problem. Recent developments in
reinforcement learning (RL) have demonstrated the potential to solve this
challenge. However, the existing RL methods are inadequate in terms of
constraint complexity, algorithm performance, and environment fidelity. We are
the first to propose a systematic solution based on the state-of-the-art
reinforcement learning algorithm and the real power grid environment. The
proposed approach enables planning and finer time resolution adjustments of
power generators, including unit commitment and economic dispatch, thus
increasing the grid's ability to admit more renewable energy. The well-trained
scheduling agent significantly reduces renewable curtailment and load shedding,
which are issues arising from traditional scheduling's reliance on inaccurate
day-ahead forecasts. High-frequency control decisions exploit the existing
units' flexibility, reducing the power grid's dependence on hardware
transformations and saving investment and operating costs, as demonstrated in
experimental results. This research exhibits the potential of reinforcement
learning in promoting low-carbon and intelligent power systems and represents a
solid step toward sustainable electricity generation. | [
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] |
Program synthesis aims to automatically construct human-readable programs
that satisfy given task specifications, such as input/output pairs or
demonstrations. Recent works have demonstrated encouraging results in a variety
of domains, such as string transformation, tensor manipulation, and describing
behaviors of embodied agents. Most existing program synthesis methods are
designed to synthesize programs from scratch, generating a program token by
token, line by line. This fundamentally prevents these methods from scaling up
to synthesize programs that are longer or more complex. In this work, we
present a scalable program synthesis framework that instead synthesizes a
program by hierarchically composing programs. Specifically, we first learn a
task embedding space and a program decoder that can decode a task embedding
into a program. Then, we train a high-level module to comprehend the task
specification (e.g., input/output pairs or demonstrations) from long programs
and produce a sequence of task embeddings, which are then decoded by the
program decoder and composed to yield the synthesized program. We extensively
evaluate our proposed framework in a string transformation domain with
input/output pairs. The experimental results demonstrate that the proposed
framework can synthesize programs that are significantly longer and more
complex than the programs considered in prior program synthesis works. Website
at https://thoughtp0lice.github.io/hnps_web/ | [
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] |
Humans rely on touch and tactile sensing for a lot of dexterous manipulation
tasks. Our tactile sensing provides us with a lot of information regarding
contact formations as well as geometric information about objects during any
interaction. With this motivation, vision-based tactile sensors are being
widely used for various robotic perception and control tasks. In this paper, we
present a method for interactive perception using vision-based tactile sensors
for a part mating task, where a robot can use tactile sensors and a feedback
mechanism using a particle filter to incrementally improve its estimate of
objects (pegs and holes) that fit together. To do this, we first train a deep
neural network that makes use of tactile images to predict the probabilistic
correspondence between arbitrarily shaped objects that fit together. The
trained model is used to design a particle filter which is used twofold. First,
given one partial (or non-unique) observation of the hole, it incrementally
improves the estimate of the correct peg by sampling more tactile observations.
Second, it selects the next action for the robot to sample the next touch (and
thus image) which results in maximum uncertainty reduction to minimize the
number of interactions during the perception task. We evaluate our method on
several part-mating tasks with novel objects using a robot equipped with a
vision-based tactile sensor. We also show the efficiency of the proposed action
selection method against a naive method. See supplementary video at
https://www.youtube.com/watch?v=jMVBg_e3gLw . | [
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] |
Electroencephalography (EEG) plays a significant role in the Brain Computer
Interface (BCI) domain, due to its non-invasive nature, low cost, and ease of
use, making it a highly desirable option for widespread adoption by the general
public. This technology is commonly used in conjunction with deep learning
techniques, the success of which is largely dependent on the quality and
quantity of data used for training. To address the challenge of obtaining
sufficient EEG data from individual participants while minimizing user effort
and maintaining accuracy, this study proposes an advanced methodology for data
augmentation: generating synthetic EEG data using denoising diffusion
probabilistic models. The synthetic data are generated from electrode-frequency
distribution maps (EFDMs) of emotionally labeled EEG recordings. To assess the
validity of the synthetic data generated, both a qualitative and a quantitative
comparison with real EEG data were successfully conducted. This study opens up
the possibility for an open\textendash source accessible and versatile toolbox
that can process and generate data in both time and frequency dimensions,
regardless of the number of channels involved. Finally, the proposed
methodology has potential implications for the broader field of neuroscience
research by enabling the creation of large, publicly available synthetic EEG
datasets without privacy concerns. | [
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] |
Current work on using visual analytics to determine causal relations among
variables has mostly been based on the concept of counterfactuals. As such the
derived static causal networks do not take into account the effect of time as
an indicator. However, knowing the time delay of a causal relation can be
crucial as it instructs how and when actions should be taken. Yet, similar to
static causality, deriving causal relations from observational time-series
data, as opposed to designed experiments, is not a straightforward process. It
can greatly benefit from human insight to break ties and resolve errors. We
hence propose a set of visual analytics methods that allow humans to
participate in the discovery of causal relations associated with windows of
time delay. Specifically, we leverage a well-established method, logic-based
causality, to enable analysts to test the significance of potential causes and
measure their influences toward a certain effect. Furthermore, since an effect
can be a cause of other effects, we allow users to aggregate different temporal
cause-effect relations found with our method into a visual flow diagram to
enable the discovery of temporal causal networks. To demonstrate the
effectiveness of our methods we constructed a prototype system named DOMINO and
showcase it via a number of case studies using real-world datasets. Finally, we
also used DOMINO to conduct several evaluations with human analysts from
different science domains in order to gain feedback on the utility of our
system in practical scenarios. | [
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] |
Deep neural networks (DNNs) are recently shown to be vulnerable to backdoor
attacks, where attackers embed hidden backdoors in the DNN model by injecting a
few poisoned examples into the training dataset. While extensive efforts have
been made to detect and remove backdoors from backdoored DNNs, it is still not
clear whether a backdoor-free clean model can be directly obtained from
poisoned datasets. In this paper, we first construct a causal graph to model
the generation process of poisoned data and find that the backdoor attack acts
as the confounder, which brings spurious associations between the input images
and target labels, making the model predictions less reliable. Inspired by the
causal understanding, we propose the Causality-inspired Backdoor Defense (CBD),
to learn deconfounded representations for reliable classification.
Specifically, a backdoored model is intentionally trained to capture the
confounding effects. The other clean model dedicates to capturing the desired
causal effects by minimizing the mutual information with the confounding
representations from the backdoored model and employing a sample-wise
re-weighting scheme. Extensive experiments on multiple benchmark datasets
against 6 state-of-the-art attacks verify that our proposed defense method is
effective in reducing backdoor threats while maintaining high accuracy in
predicting benign samples. Further analysis shows that CBD can also resist
potential adaptive attacks. The code is available at
\url{https://github.com/zaixizhang/CBD}. | [
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] |
The warming of the Arctic, also known as Arctic amplification, is led by
several atmospheric and oceanic drivers. However, the details of its underlying
thermodynamic causes are still unknown. Inferring the causal effects of
atmospheric processes on sea ice melt using fixed treatment effect strategies
leads to unrealistic counterfactual estimations. Such models are also prone to
bias due to time-varying confoundedness. Further, the complex non-linearity in
Earth science data makes it infeasible to perform causal inference using
existing marginal structural techniques. In order to tackle these challenges,
we propose TCINet - time-series causal inference model to infer causation under
continuous treatment using recurrent neural networks and a novel probabilistic
balancing technique. Through experiments on synthetic and observational data,
we show how our research can substantially improve the ability to quantify
leading causes of Arctic sea ice melt, further paving paths for causal
inference in observational Earth science. | [
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] |
Reinforcement learning-based policies for continuous control robotic
navigation tasks often fail to adapt to changes in the environment during
real-time deployment, which may result in catastrophic failures. To address
this limitation, we propose a novel approach called RE-MOVE (REquest help and
MOVE on) to adapt already trained policy to real-time changes in the
environment without re-training via utilizing a language-based feedback. The
proposed approach essentially boils down to addressing two main challenges of
(1) when to ask for feedback and, if received, (2) how to incorporate feedback
into trained policies. RE-MOVE incorporates an epistemic uncertainty-based
framework to determine the optimal time to request instructions-based feedback.
For the second challenge, we employ a zero-shot learning natural language
processing (NLP) paradigm with efficient, prompt design and leverage
state-of-the-art GPT-3.5, Llama-2 language models. To show the efficacy of the
proposed approach, we performed extensive synthetic and real-world evaluations
in several test-time dynamic navigation scenarios. Utilizing RE-MOVE result in
up to 80% enhancement in the attainment of successful goals, coupled with a
reduction of 13.50% in the normalized trajectory length, as compared to
alternative approaches, particularly in demanding real-world environments with
perceptual challenges. | [
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] |
Continued improvements in machine learning techniques offer exciting new
opportunities through the use of larger models and larger training datasets.
However, there is a growing need to offer these new capabilities on-board
low-powered devices such as smartphones, wearables and other embedded
environments where only low memory is available. Towards this, we consider
methods to reduce the model size of Conformer-based speech recognition models
which typically require models with greater than 100M parameters down to just
$5$M parameters while minimizing impact on model quality. Such a model allows
us to achieve always-on ambient speech recognition on edge devices with
low-memory neural processors. We propose model weight reuse at different levels
within our model architecture: (i) repeating full conformer block layers, (ii)
sharing specific conformer modules across layers, (iii) sharing sub-components
per conformer module, and (iv) sharing decomposed sub-component weights after
low-rank decomposition. By sharing weights at different levels of our model, we
can retain the full model in-memory while increasing the number of virtual
transformations applied to the input. Through a series of ablation studies and
evaluations, we find that with weight sharing and a low-rank architecture, we
can achieve a WER of 2.84 and 2.94 for Librispeech dev-clean and test-clean
respectively with a $5$M parameter model. | [
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] |
Generation of pseudo-code descriptions of legacy source code for software
maintenance is a manually intensive task. Recent encoder-decoder language
models have shown promise for automating pseudo-code generation for high
resource programming languages such as C++, but are heavily reliant on the
availability of a large code-pseudocode corpus. Soliciting such pseudocode
annotations for codes written in legacy programming languages (PL) is a time
consuming and costly affair requiring a thorough understanding of the source
PL. In this paper, we focus on transferring the knowledge acquired by the
code-to-pseudocode neural model trained on a high resource PL (C++) using
parallel code-pseudocode data. We aim to transfer this knowledge to a legacy PL
(C) with no PL-pseudocode parallel data for training. To achieve this, we
utilize an Iterative Back Translation (IBT) approach with a novel test-cases
based filtration strategy, to adapt the trained C++-to-pseudocode model to
C-to-pseudocode model. We observe an improvement of 23.27% in the success rate
of the generated C codes through back translation, over the successive IBT
iteration, illustrating the efficacy of our approach. | [
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] |
Partially Observable Monte Carlo Planning (POMCP) is an efficient solver for
Partially Observable Markov Decision Processes (POMDPs). It allows scaling to
large state spaces by computing an approximation of the optimal policy locally
and online, using a Monte Carlo Tree Search based strategy. However, POMCP
suffers from sparse reward function, namely, rewards achieved only when the
final goal is reached, particularly in environments with large state spaces and
long horizons. Recently, logic specifications have been integrated into POMCP
to guide exploration and to satisfy safety requirements. However, such
policy-related rules require manual definition by domain experts, especially in
real-world scenarios. In this paper, we use inductive logic programming to
learn logic specifications from traces of POMCP executions, i.e., sets of
belief-action pairs generated by the planner. Specifically, we learn rules
expressed in the paradigm of answer set programming. We then integrate them
inside POMCP to provide soft policy bias toward promising actions. In the
context of two benchmark scenarios, rocksample and battery, we show that the
integration of learned rules from small task instances can improve performance
with fewer Monte Carlo simulations and in larger task instances. We make our
modified version of POMCP publicly available at
https://github.com/GiuMaz/pomcp_clingo.git. | [
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] |
The impacts of link recommendations on social networks are challenging to
evaluate, and so far they have been studied in limited settings. Observational
studies are restricted in the kinds of causal questions they can answer and
naive A/B tests often lead to biased evaluations due to unaccounted network
interference. Furthermore, evaluations in simulation settings are often limited
to static network models that do not take into account the potential feedback
loops between link recommendation and organic network evolution. To this end,
we study the impacts of recommendations on social networks in dynamic settings.
Adopting a simulation-based approach, we consider an explicit dynamic formation
model -- an extension of the celebrated Jackson-Rogers model -- and investigate
how link recommendations affect network evolution over time. Empirically, we
find that link recommendations have surprising delayed and indirect effects on
the structural properties of networks. Specifically, we find that link
recommendations can exhibit considerably different impacts in the immediate
term and in the long term. For instance, we observe that friend-of-friend
recommendations can have an immediate effect in decreasing degree inequality,
but in the long term, they can make the degree distribution substantially more
unequal. Moreover, we show that the effects of recommendations can persist in
networks, in part due to their indirect impacts on natural dynamics even after
recommendations are turned off. We show that, in counterfactual simulations,
removing the indirect effects of link recommendations can make the network
trend faster toward what it would have been under natural growth dynamics. | [
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] |
Differentiable logics (DL) have recently been proposed as a method of
training neural networks to satisfy logical specifications. A DL consists of a
syntax in which specifications are stated and an interpretation function that
translates expressions in the syntax into loss functions. These loss functions
can then be used during training with standard gradient descent algorithms. The
variety of existing DLs and the differing levels of formality with which they
are treated makes a systematic comparative study of their properties and
implementations difficult. This paper remedies this problem by suggesting a
meta-language for defining DLs that we call the Logic of Differentiable Logics,
or LDL. Syntactically, it generalises the syntax of existing DLs to FOL, and
for the first time introduces the formalism for reasoning about vectors and
learners. Semantically, it introduces a general interpretation function that
can be instantiated to define loss functions arising from different existing
DLs. We use LDL to establish several theoretical properties of existing DLs,
and to conduct their empirical study in neural network verification. | [
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Tactile information plays a critical role in human dexterity. It reveals
useful contact information that may not be inferred directly from vision. In
fact, humans can even perform in-hand dexterous manipulation without using
vision. Can we enable the same ability for the multi-finger robot hand? In this
paper, we present Touch Dexterity, a new system that can perform in-hand object
rotation using only touching without seeing the object. Instead of relying on
precise tactile sensing in a small region, we introduce a new system design
using dense binary force sensors (touch or no touch) overlaying one side of the
whole robot hand (palm, finger links, fingertips). Such a design is low-cost,
giving a larger coverage of the object, and minimizing the Sim2Real gap at the
same time. We train an in-hand rotation policy using Reinforcement Learning on
diverse objects in simulation. Relying on touch-only sensing, we can directly
deploy the policy in a real robot hand and rotate novel objects that are not
presented in training. Extensive ablations are performed on how tactile
information help in-hand manipulation.Our project is available at
https://touchdexterity.github.io. | [
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] |
Quantum Machine Learning has the potential to improve traditional machine
learning methods and overcome some of the main limitations imposed by the
classical computing paradigm. However, the practical advantages of using
quantum resources to solve pattern recognition tasks are still to be
demonstrated.
This work proposes a universal, efficient framework that can reproduce the
output of a plethora of classical supervised machine learning algorithms
exploiting quantum computation's advantages. The proposed framework is named
Multiple Aggregator Quantum Algorithm (MAQA) due to its capability to combine
multiple and diverse functions to solve typical supervised learning problems.
In its general formulation, MAQA can be potentially adopted as the quantum
counterpart of all those models falling into the scheme of aggregation of
multiple functions, such as ensemble algorithms and neural networks. From a
computational point of view, the proposed framework allows generating an
exponentially large number of different transformations of the input at the
cost of increasing the depth of the corresponding quantum circuit linearly.
Thus, MAQA produces a model with substantial descriptive power to broaden the
horizon of possible applications of quantum machine learning with a
computational advantage over classical methods. As a second meaningful
addition, we discuss the adoption of the proposed framework as hybrid
quantum-classical and fault-tolerant quantum algorithm. | [
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