pwc-archive/papers-with-abstracts huggingface dataset:
Robust-MBDL: A Robust Multi-branch Deep Learning Based Model for Remaining Useful Life Prediction and Operational Condition Identification of Rotating Machines
Authors: Khoa Tran, Hai-Canh Vu, Lam Pham, Nassim Boudaoud
Date: 2023-09-12 00:00:00
Tasks: Deep Learning

In this paper, a Robust Multi-branch Deep learning-based system for remaining useful life (RUL) prediction and condition operations (CO) identification of rotating machines is proposed. In particular, the proposed system comprises main components: (1) an LSTM-Autoencoder to denoise the vibration data; (2) a feature extraction to generate time-domain, frequency-domain, and time-frequency based features from the denoised data; (3) a novel and robust multi-branch deep learning network architecture to exploit the multiple features. The performance of our proposed system was evaluated and compared to the state-of-the-art systems on two benchmark datasets of XJTU-SY and PRONOSTIA. The experimental results prove that our proposed system outperforms the state-of-the-art systems and presents potential for real-life applications on bearing machines.
Naturalistic Tasks Can Benefit Psychiatry
Authors: Assia Chericoni, Benjamin Yost Hayden
Date: 2023-09-07 00:00:00

Maselli et al. do an excellent job highlighting how studying rich, complex, naturalistic behaviors can help neuroscientists and psychologists understand neural and mental phenomena. We fully concur with their arguments, as well as with somewhat similar ones made by other scholars. Indeed, we believe that the potential value of naturalistic paradigms is even greater than the authors state. We are particularly sanguine about the power of the naturalistic approach to help with understanding psychiatric diseases.
Multimodal Integration of Olfactory and Visual Processing through DCM analysis: Contextual Modulation of Facial Perception
Authors: Gianluca Rho, Alejandro Luis Callara, Francesco Bossi, Dimitri Ognibene, Cinzia Cecchetto, Tommaso Lomonaco, Enzo Pasquale Scilingo, Alberto Greco
Date: 2023-09-07 00:00:00
Tasks: ERP

This study examines the modulatory effect of contextual hedonic olfactory stimuli on the visual processing of neutral faces using event-related potentials (ERPs) and effective connectivity analysis. The aim is to investigate how odors' valence influences the cortical connectivity underlying face processing, and the role arousal enhanced by faces plays on such visual-odor multimodal integration. To this goal, a novel methodological approach combining electrodermal activity (EDA) and dynamic causal modeling (DCM) was proposed to examine cortico-cortical interactions changes. The results revealed that EDA sympathetic responses were associated with an increase of the N170 amplitude, which may be suggested as a marker of heightened arousal to faces. Hedonic odors had an impact on early visual ERP components, with increased N1 amplitude during the administration of unpleasant odor and decreased vertex positive potential (VPP) amplitude during the administration of both unpleasant and neutral odors. On the connectivity side, unpleasant odors strengthened the forward connection from the inferior temporal gyrus (ITG) to the middle temporal gyrus (MTG), involved in processing changeable facial features. Conversely, the occurrence of sympathetic responses was correlated with an inhibition of the same connection, and with an enhancement of the backward connection from ITG to the fusiform face gyrus. These findings suggest that negative odors may enhance the interpretation of emotional expressions and mental states, while faces capable of enhancing sympathetic arousal prioritize the processing of identity. The proposed methodology provides insights into the neural mechanisms underlying the integration of visual and olfactory stimuli in face processing.
Measuring vagueness and subjectivity in texts: from symbolic to neural VAGO
Authors: Benjamin Icard, Vincent Claveau, Ghislain Atemezing, Paul Égré
Date: 2023-09-12 00:00:00

We present a hybrid approach to the automated measurement of vagueness and subjectivity in texts. We first introduce the expert system VAGO, we illustrate it on a small benchmark of fact vs. opinion sentences, and then test it on the larger French press corpus FreSaDa to confirm the higher prevalence of subjective markers in satirical vs. regular texts. We then build a neural clone of VAGO, based on a BERT-like architecture, trained on the symbolic VAGO scores obtained on FreSaDa. Using explainability tools (LIME), we show the interest of this neural version for the enrichment of the lexicons of the symbolic version, and for the production of versions in other languages.
JOADAA: joint online action detection and action anticipation
Authors: Mohammed Guermal, Francois Bremond, Rui Dai, Abid Ali
Date: 2023-09-12 00:00:00
Tasks: Action Anticipation, Action Detection, Online Action Detection

Action anticipation involves forecasting future actions by connecting past events to future ones. However, this reasoning ignores the real-life hierarchy of events which is considered to be composed of three main parts: past, present, and future. We argue that considering these three main parts and their dependencies could improve performance. On the other hand, online action detection is the task of predicting actions in a streaming manner. In this case, one has access only to the past and present information. Therefore, in online action detection (OAD) the existing approaches miss semantics or future information which limits their performance. To sum up, for both of these tasks, the complete set of knowledge (past-present-future) is missing, which makes it challenging to infer action dependencies, therefore having low performances. To address this limitation, we propose to fuse both tasks into a single uniform architecture. By combining action anticipation and online action detection, our approach can cover the missing dependencies of future information in online action detection. This method referred to as JOADAA, presents a uniform model that jointly performs action anticipation and online action detection. We validate our proposed model on three challenging datasets: THUMOS'14, which is a sparsely annotated dataset with one action per time step, CHARADES, and Multi-THUMOS, two densely annotated datasets with more complex scenarios. JOADAA achieves SOTA results on these benchmarks for both tasks.
LEyes: A Lightweight Framework for Deep Learning-Based Eye Tracking using Synthetic Eye Images
Authors: Sean Anthony Byrne, Virmarie Maquiling, Marcus Nyström, Enkelejda Kasneci, Diederick C. Niehorster
Date: 2023-09-12 00:00:00
Tasks: Gaze Estimation, Synthetic Data Generation

Deep learning has bolstered gaze estimation techniques, but real-world deployment has been impeded by inadequate training datasets. This problem is exacerbated by both hardware-induced variations in eye images and inherent biological differences across the recorded participants, leading to both feature and pixel-level variance that hinders the generalizability of models trained on specific datasets. While synthetic datasets can be a solution, their creation is both time and resource-intensive. To address this problem, we present a framework called Light Eyes or "LEyes" which, unlike conventional photorealistic methods, only models key image features required for video-based eye tracking using simple light distributions. LEyes facilitates easy configuration for training neural networks across diverse gaze-estimation tasks. We demonstrate that models trained using LEyes are consistently on-par or outperform other state-of-the-art algorithms in terms of pupil and CR localization across well-known datasets. In addition, a LEyes trained model outperforms the industry standard eye tracker using significantly more cost-effective hardware. Going forward, we are confident that LEyes will revolutionize synthetic data generation for gaze estimation models, and lead to significant improvements of the next generation video-based eye trackers.
Signatures of Bayesian inference emerge from energy efficient synapses
Authors: James Malkin, Cian O'Donnell, Conor Houghton, Laurence Aitchison
Date: 2023-09-06 00:00:00
Tasks: Bayesian Inference, image-classification, Image Classification

Biological synaptic transmission is unreliable, and this unreliability likely degrades neural circuit performance. While there are biophysical mechanisms that can increase reliability, for instance by increasing vesicle release probability, these mechanisms cost energy. We examined four such mechanisms along with the associated scaling of the energetic costs. We then embedded these energetic costs for reliability in artificial neural networks (ANN) with trainable stochastic synapses, and trained these networks on standard image classification tasks. The resulting networks revealed a tradeoff between circuit performance and the energetic cost of synaptic reliability. Additionally, the optimised networks exhibited two testable predictions consistent with pre-existing experimental data. Specifically, synapses with lower variability tended to have 1) higher input firing rates and 2) lower learning rates. Surprisingly, these predictions also arise when synapse statistics are inferred through Bayesian inference. Indeed, we were able to find a formal, theoretical link between the performance-reliability cost tradeoff and Bayesian inference. This connection suggests two incompatible possibilities: evolution may have chanced upon a scheme for implementing Bayesian inference by optimising energy efficiency, or alternatively, energy efficient synapses may display signatures of Bayesian inference without actually using Bayes to reason about uncertainty.
Fidelity-Induced Interpretable Policy Extraction for Reinforcement Learning
Authors: Xiao Liu, Wubing Chen, Mao Tan
Date: 2023-09-12 00:00:00
Tasks: Decision Making, Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Sequential Decision Making, Starcraft, Starcraft II

Deep Reinforcement Learning (DRL) has achieved remarkable success in sequential decision-making problems. However, existing DRL agents make decisions in an opaque fashion, hindering the user from establishing trust and scrutinizing weaknesses of the agents. While recent research has developed Interpretable Policy Extraction (IPE) methods for explaining how an agent takes actions, their explanations are often inconsistent with the agent's behavior and thus, frequently fail to explain. To tackle this issue, we propose a novel method, Fidelity-Induced Policy Extraction (FIPE). Specifically, we start by analyzing the optimization mechanism of existing IPE methods, elaborating on the issue of ignoring consistency while increasing cumulative rewards. We then design a fidelity-induced mechanism by integrate a fidelity measurement into the reinforcement learning feedback. We conduct experiments in the complex control environment of StarCraft II, an arena typically avoided by current IPE methods. The experiment results demonstrate that FIPE outperforms the baselines in terms of interaction performance and consistency, meanwhile easy to understand.
A Machine Learning Framework to Deconstruct the Primary Drivers for Electricity Market Price Events
Authors: Milan Jain, Xueqing Sun, Sohom Datta, Abhishek Somani
Date: 2023-09-12 00:00:00

Power grids are moving towards 100% renewable energy source bulk power grids, and the overall dynamics of power system operations and electricity markets are changing. The electricity markets are not only dispatching resources economically but also taking into account various controllable actions like renewable curtailment, transmission congestion mitigation, and energy storage optimization to ensure grid reliability. As a result, price formations in electricity markets have become quite complex. Traditional root cause analysis and statistical approaches are rendered inapplicable to analyze and infer the main drivers behind price formation in the modern grid and markets with variable renewable energy (VRE). In this paper, we propose a machine learning-based analysis framework to deconstruct the primary drivers for price spike events in modern electricity markets with high renewable energy. The outcomes can be utilized for various critical aspects of market design, renewable dispatch and curtailment, operations, and cyber-security applications. The framework can be applied to any ISO or market data; however, in this paper, it is applied to open-source publicly available datasets from California Independent System Operator (CAISO) and ISO New England (ISO-NE).
Characterization of Large-Scale Functional Brain Networks During Ketamine-Medetomidine Anesthetic Induction
Authors: Eduardo C. Padovani
Date: 2016-03-31 00:00:00
Tasks: Functional Connectivity

Several experiments provide evidence that specialized brain regions functionally interact and reveal that the brain processes and integrates information in a specific and structured manner. Networks can be applied to model brain functional activities, providing means to characterize and quantify this structured form of organization. Reports substantiate that different physiological states or diseases that affect the central nervous system may be associated with alterations in these networks, which might be reflected in graphs of different architectures. However, the relationship between their structure and the organism's distinct physiological conditions is poorly comprehended. Therefore, experiments that estimate the functional neural networks of subjects exposed to different controlled conditions are highly relevant. Within this context, this research has sought to model large-scale functional brain networks during an anesthetic induction process. The experiment was based on intra-cranial recordings of the neural activities of an old-world macaque of the species Macaca fuscata. Neural activity was recorded during a Ketamine-Medetomidine anesthetic induction process, and networks were estimated sequentially in five-second intervals. One and a half minutes after administering the anesthetics, changes occurred in various network properties, revealing a transition in the network architecture. During general anesthesia, functional connectivity and network integration capabilities were reduced at both local and global levels. Additionally, it has been verified that the brain shifted to a highly specific and dynamic state. The results provide empirical evidence and report the relationship between the induced state of anesthesia and functional network properties, contributing to the elucidation of novel aspects of the neural correlates of consciousness.
lfads-torch: A modular and extensible implementation of latent factor analysis via dynamical systems
Authors: Andrew R. Sedler, Chethan Pandarinath
Date: 2023-09-03 00:00:00
Tasks: Denoising

Latent factor analysis via dynamical systems (LFADS) is an RNN-based variational sequential autoencoder that achieves state-of-the-art performance in denoising high-dimensional neural activity for downstream applications in science and engineering. Recently introduced variants and extensions continue to demonstrate the applicability of the architecture to a wide variety of problems in neuroscience. Since the development of the original implementation of LFADS, new technologies have emerged that use dynamic computation graphs, minimize boilerplate code, compose model configuration files, and simplify large-scale training. Building on these modern Python libraries, we introduce lfads-torch -- a new open-source implementation of LFADS that unifies existing variants and is designed to be easier to understand, configure, and extend. Documentation, source code, and issue tracking are available at https://github.com/arsedler9/lfads-torch .
BatMan-CLR: Making Few-shots Meta-Learners Resilient Against Label Noise
Authors: Jeroen M. Galjaard, Robert Birke, Juan Perez, Lydia Y. Chen
Date: 2023-09-12 00:00:00
Tasks: Meta-Learning

The negative impact of label noise is well studied in classical supervised learning yet remains an open research question in meta-learning. Meta-learners aim to adapt to unseen learning tasks by learning a good initial model in meta-training and consecutively fine-tuning it according to new tasks during meta-testing. In this paper, we present the first extensive analysis of the impact of varying levels of label noise on the performance of state-of-the-art meta-learners, specifically gradient-based $N$-way $K$-shot learners. We show that the accuracy of Reptile, iMAML, and foMAML drops by up to 42% on the Omniglot and CifarFS datasets when meta-training is affected by label noise. To strengthen the resilience against label noise, we propose two sampling techniques, namely manifold (Man) and batch manifold (BatMan), which transform the noisy supervised learners into semi-supervised ones to increase the utility of noisy labels. We first construct manifold samples of $N$-way $2$-contrastive-shot tasks through augmentation, learning the embedding via a contrastive loss in meta-training, and then perform classification through zeroing on the embedding in meta-testing. We show that our approach can effectively mitigate the impact of meta-training label noise. Even with 60% wrong labels \batman and \man can limit the meta-testing accuracy drop to ${2.5}$, ${9.4}$, ${1.1}$ percent points, respectively, with existing meta-learners across the Omniglot, CifarFS, and MiniImagenet datasets.
GraspGF: Learning Score-based Grasping Primitive for Human-assisting Dexterous Grasping
Authors: Tianhao Wu, Mingdong Wu, Jiyao Zhang, Yunchong Gan, Hao Dong
Date: 2023-09-12 00:00:00

The use of anthropomorphic robotic hands for assisting individuals in situations where human hands may be unavailable or unsuitable has gained significant importance. In this paper, we propose a novel task called human-assisting dexterous grasping that aims to train a policy for controlling a robotic hand's fingers to assist users in grasping objects. Unlike conventional dexterous grasping, this task presents a more complex challenge as the policy needs to adapt to diverse user intentions, in addition to the object's geometry. We address this challenge by proposing an approach consisting of two sub-modules: a hand-object-conditional grasping primitive called Grasping Gradient Field~(GraspGF), and a history-conditional residual policy. GraspGF learns `how' to grasp by estimating the gradient from a success grasping example set, while the residual policy determines `when' and at what speed the grasping action should be executed based on the trajectory history. Experimental results demonstrate the superiority of our proposed method compared to baselines, highlighting the user-awareness and practicality in real-world applications. The codes and demonstrations can be viewed at "https://sites.google.com/view/graspgf".
PRESTI: Predicting Repayment Effort of Self-Admitted Technical Debt Using Textual Information
Authors: Yikun Li, Mohamed Soliman, Paris Avgeriou
Date: 2023-09-12 00:00:00

Technical debt refers to the consequences of sub-optimal decisions made during software development that prioritize short-term benefits over long-term maintainability. Self-Admitted Technical Debt (SATD) is a specific form of technical debt, explicitly documented by developers within software artifacts such as source code comments and commit messages. As SATD can hinder software development and maintenance, it is crucial to estimate the effort required to repay it so that we can effectively prioritize it. However, we currently lack an understanding of SATD repayment, and more importantly, we lack approaches that can automatically estimate the repayment effort of SATD based on its textual description. To bridge this gap, we have curated a comprehensive dataset of 341,740 SATD items from 2,568,728 commits across 1,060 Apache repositories and analyzed the repayment effort comparing SATD vs. non-SATD items, as well as different types of SATD items. Furthermore, we proposed an innovative approach for Predicting Repayment Effort of SATD using Textual Information, named PRESTI. Our findings show that different types of SATD require varying levels of repayment effort, with code/design, requirement, and test debt demanding greater effort compared to non-SATD items, while documentation debt requires less. We have evaluated our approaches, particularly BERT- and TextCNN-based models, which outperform traditional machine learning methods and the baseline in estimating repayment effort. Additionally, we summarize keywords associated with varying levels of repayment effort that occur during SATD repayment. Our work aims to enhance SATD repayment prioritization and resource allocation, thereby improving software development and maintainability.
DSLOT-NN: Digit-Serial Left-to-Right Neural Network Accelerator
Authors: Muhammad Sohail Ibrahim, Muhammad Usman, Malik Zohaib Nisar, Jeong-A Lee
Date: 2023-09-12 00:00:00

We propose a Digit-Serial Left-tO-righT (DSLOT) arithmetic based processing technique called DSLOT-NN with aim to accelerate inference of the convolution operation in the deep neural networks (DNNs). The proposed work has the ability to assess and terminate the ineffective convolutions which results in massive power and energy savings. The processing engine is comprised of low-latency most-significant-digit-first (MSDF) (also called online) multipliers and adders that processes data from left-to-right, allowing the execution of subsequent operations in digit-pipelined manner. Use of online operators eliminates the need for the development of complex mechanism of identifying the negative activation, as the output with highest weight value is generated first, and the sign of the result can be identified as soon as first non-zero digit is generated. The precision of the online operators can be tuned at run-time, making them extremely useful in situations where accuracy can be compromised for power and energy savings. The proposed design has been implemented on Xilinx Virtex-7 FPGA and is compared with state-of-the-art Stripes on various performance metrics. The results show the proposed design presents power savings, has shorter cycle time, and approximately 50% higher OPS per watt.
Through their eyes: multi-subject Brain Decoding with simple alignment techniques
Authors: Matteo Ferrante, Tommaso Boccato, Nicola Toschi
Date: 2023-08-01 00:00:00
Tasks: Brain Decoding, regression

Previous brain decoding research primarily involves single-subject studies, reconstructing stimuli via fMRI activity from the same subject. Our study aims to introduce a generalization technique for cross-subject brain decoding, facilitated by exploring data alignment methods. We utilized the NSD dataset, a comprehensive 7T fMRI vision experiment involving multiple subjects exposed to 9841 images, 982 of which were viewed by all. Our approach involved training a decoding model on one subject, aligning others' data to this space, and testing the decoding on the second subject. We compared ridge regression, hyper alignment, and anatomical alignment techniques for fMRI data alignment. We established that cross-subject brain decoding is feasible, even using around 10% of the total data, or 982 common images, with comparable performance to single-subject decoding. Ridge regression was the best method for functional alignment. Through subject alignment, we achieved superior brain decoding and a potential 90% reduction in scan time. This could pave the way for more efficient experiments and further advancements in the field, typically requiring an exorbitant 20-hour scan time per subject.