pwc-archive/papers-with-abstracts huggingface dataset:
A Ferroelectric Tunnel Junction-based Integrate-and-Fire Neuron
Authors: Paolo Gibertini, Luca Fehlings, Suzanne Lancaster, Quang Duong, Thomas Mikolajick, Catherine Dubourdieu, Stefan Slesazeck, Erika Covi, Veeresh Deshpande
Date: 2022-11-04 00:00:00
Tasks: Edge-computing

Event-based neuromorphic systems provide a low-power solution by using artificial neurons and synapses to process data asynchronously in the form of spikes. Ferroelectric Tunnel Junctions (FTJs) are ultra low-power memory devices and are well-suited to be integrated in these systems. Here, we present a hybrid FTJ-CMOS Integrate-and-Fire neuron which constitutes a fundamental building block for new-generation neuromorphic networks for edge computing. We demonstrate electrically tunable neural dynamics achievable by tuning the switching of the FTJ device.
Collaborative Multiobjective Evolutionary Algorithms in search of better Pareto Fronts. An application to trading systems
Authors: Francisco J. Soltero, Pablo Fernández-Blanco, J. Ignacio Hidalgo
Date: 2022-11-04 00:00:00
Tasks: Evolutionary Algorithms, Time Series, Time Series Analysis

Technical indicators use graphic representations of data sets by applying various mathematical formulas to financial time series of prices. These formulas comprise a set of rules and parameters whose values are not necessarily known and depend on many factors: the market in which it operates, the size of the time window, and others. This paper focuses on the real-time optimization of the parameters applied for analyzing time series of data. In particular, we optimize the parameters of technical and financial indicators and propose other applications, such as glucose time series. We propose the combination of several Multi-objective Evolutionary Algorithms (MOEAs). Unlike other approaches, this paper applies a set of different MOEAs, collaborating to construct a global Pareto Set of solutions. Solutions for financial problems seek high returns with minimal risk. The optimization process is continuous and occurs at the same frequency as the investment time interval. This technique permits the application of non-dominated solutions obtained with different MOEAs simultaneously. Experimental results show that this technique increases the returns of the commonly used Buy \& Hold strategy and other multi-objective strategies, even for daily operations.
Hardware/Software co-design with ADC-Less In-memory Computing Hardware for Spiking Neural Networks
Authors: Marco Paul E. Apolinario, Adarsh Kumar Kosta, Utkarsh Saxena, Kaushik Roy
Date: 2022-11-03 00:00:00
Tasks: Gesture Recognition, image-classification, Image Classification, Optical Flow Estimation

Spiking Neural Networks (SNNs) are bio-plausible models that hold great potential for realizing energy-efficient implementations of sequential tasks on resource-constrained edge devices. However, commercial edge platforms based on standard GPUs are not optimized to deploy SNNs, resulting in high energy and latency. While analog In-Memory Computing (IMC) platforms can serve as energy-efficient inference engines, they are accursed by the immense energy, latency, and area requirements of high-precision ADCs (HP-ADC), overshadowing the benefits of in-memory computations. We propose a hardware/software co-design methodology to deploy SNNs into an ADC-Less IMC architecture using sense-amplifiers as 1-bit ADCs replacing conventional HP-ADCs and alleviating the above issues. Our proposed framework incurs minimal accuracy degradation by performing hardware-aware training and is able to scale beyond simple image classification tasks to more complex sequential regression tasks. Experiments on complex tasks of optical flow estimation and gesture recognition show that progressively increasing the hardware awareness during SNN training allows the model to adapt and learn the errors due to the non-idealities associated with ADC-Less IMC. Also, the proposed ADC-Less IMC offers significant energy and latency improvements, $2-7\times$ and $8.9-24.6\times$, respectively, depending on the SNN model and the workload, compared to HP-ADC IMC.
Sports Camera Pose Refinement Using an Evolution Strategy
Authors: Grzegorz Rypeść, Grzegorz Kurzejamski, Jacek Komorowski
Date: 2022-11-03 00:00:00

This paper presents a robust end-to-end method for sports cameras extrinsic parameters optimization using a novel evolution strategy. First, we developed a neural network architecture for an edge or area-based segmentation of a sports field. Secondly, we implemented the evolution strategy, which purpose is to refine extrinsic camera parameters given a single, segmented sports field image. Experimental comparison with state-of-the-art camera pose refinement methods on real-world data demonstrates the superiority of the proposed algorithm. We also perform an ablation study and propose a way to generalize the method to additionally refine the intrinsic camera matrix.
Multi-output Gaussian processes for inverse uncertainty quantification in neutron noise analysis
Authors: Paul Lartaud, Philippe Humbert, Josselin Garnier
Date: 2022-11-04 00:00:00
Tasks: Gaussian Processes, Uncertainty Quantification

In a fissile material, the inherent multiplicity of neutrons born through induced fissions leads to correlations in their detection statistics. The correlations between neutrons can be used to trace back some characteristics of the fissile material. This technique known as neutron noise analysis has applications in nuclear safeguards or waste identification. It provides a non-destructive examination method for an unknown fissile material. This is an example of an inverse problem where the cause is inferred from observations of the consequences. However, neutron correlation measurements are often noisy because of the stochastic nature of the underlying processes. This makes the resolution of the inverse problem more complex since the measurements are strongly dependent on the material characteristics. A minor change in the material properties can lead to very different outputs. Such an inverse problem is said to be ill-posed. For an ill-posed inverse problem the inverse uncertainty quantification is crucial. Indeed, seemingly low noise in the data can lead to strong uncertainties in the estimation of the material properties. Moreover, the analytical framework commonly used to describe neutron correlations relies on strong physical assumptions and is thus inherently biased. This paper addresses dual goals. Firstly, surrogate models are used to improve neutron correlations predictions and quantify the errors on those predictions. Then, the inverse uncertainty quantification is performed to include the impact of measurement error alongside the residual model bias.
Time series quantile regression using random forests
Authors: Hiroshi Shiraishi, Tomoshige Nakamura, Ryotato Shibuki
Date: 2022-11-04 00:00:00
Tasks: quantile regression, regression, Time Series, Time Series Analysis

We discuss an application of Generalized Random Forests (GRF) proposed by Athey et al.(2019) to quantile regression for time series data. We extracted the theoretical results of the GRF consistency for i.i.d. data to time series data. In particular, in the main theorem, based only on the general assumptions for time series data in Davis and Nielsen (2020), and trees in Athey et al.(2019), we show that the tsQRF (time series Quantile Regression Forests) estimator is consistent. Davis and Nielsen (2020) also discussed the estimation problem using Random Forests (RF) for time series data, but the construction procedure of the RF treated by the GRF is essentially different, and different ideas are used throughout the theoretical proof. In addition, a simulation and real data analysis were conducted.In the simulation, the accuracy of the conditional quantile estimation was evaluated under time series models. In the real data using the Nikkei Stock Average, our estimator is demonstrated to be more sensitive than the others in terms of volatility, thus preventing underestimation of risk.
Material Named Entity Recognition (MNER) for Knowledge-driven Materials Using Deep Learning Approach
Authors: M. Saef Ullah Miah, Junaida Sulaiman
Date: 2022-11-04 00:00:00
Tasks: Articles, named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER)

The scientific literature contains a wealth of cutting-edge knowledge in the field of materials science, as well as useful data (e.g., numerical data from experimental results, material properties and structure). These data are critical for data-driven machine learning (ML) and deep learning (DL) methods to accelerate material discovery. Due to the large and growing number of publications, it is difficult for humans to manually retrieve and retain this knowledge. In this context, we investigate a deep neural network model based on Bi-LSTM to retrieve knowledge from published scientific articles. The proposed deep neural network-based model achieves an f-1 score of \~97\% for the Material Named Entity Recognition (MNER) task. The study addresses motivation, relevant work, methodology, hyperparameters, and overall performance evaluation. The analysis provides insight into the results of the experiment and points to future directions for current research.
Explainable Information Retrieval: A Survey
Authors: Avishek Anand, Lijun Lyu, Maximilian Idahl, Yumeng Wang, Jonas Wallat, Zijian Zhang
Date: 2022-11-04 00:00:00
Tasks: Information Retrieval, Retrieval, Survey

Explainable information retrieval is an emerging research area aiming to make transparent and trustworthy information retrieval systems. Given the increasing use of complex machine learning models in search systems, explainability is essential in building and auditing responsible information retrieval models. This survey fills a vital gap in the otherwise topically diverse literature of explainable information retrieval. It categorizes and discusses recent explainability methods developed for different application domains in information retrieval, providing a common framework and unifying perspectives. In addition, it reflects on the common concern of evaluating explanations and highlights open challenges and opportunities.
Few-Shot Model Agnostic Federated Learning
Authors: Wenke Huang, Mang Ye, Bo Du, Xiang Gao
Date: 2022-10-10 00:00:00
Proceeding: Proceedings of the 30th ACM International Conference on Multimedia 2022 10
Tasks: Federated Learning, model

Federated learning has received increasing attention for its ability to collaborative learning without leaking privacy. Promising advances have been achieved under the assumption that participants share the same model structure. However, when participants independently customize their models, models suffer communication barriers, which leads the model heterogeneity problem. Moreover, in real scenarios, the data held by participants is often limited, making the local models trained only on private data present poor performance. Consequently, this paper studies a new challenging problem, namely few-shot model agnostic federated learning, where the local participants design their independent models from their limited private datasets. Considering the scarcity of the private data, we propose to utilize the abundant public available datasets for bridging the gap between local private participants. However, its usage also brings in two problems: inconsistent labels and large domain gap between the public and private datasets. To address these issues, this paper presents a novel framework with two main parts: 1) model agnostic federated learning, it performs public-private communication by unifying the model prediction outputs on the shared public datasets; 2) latent embedding adaptation, it addresses the domain gap with an adversarial learning scheme to discriminate the public and private domains. Together with theoretical generalization bound analysis, comprehensive experiments under various settings have verified our advantage over existing methods. It provides a simple but effective baseline for future advancement. The code is available at https://github.com/WenkeHuang/FSFL.
Self-supervised Sparse Representation for Video Anomaly Detection
Authors: Jhih-Ciang Wu*, He-Yen Hsieh*, Ding-Jie Chen, Chiou-Shann Fuh, Tyng-Luh Liu
Date: 2022-10-23 00:00:00
Proceeding: ECCV 2022 2022 10
Tasks: Anomaly Detection, Anomaly Detection In Surveillance Videos, Self-Supervised Learning, Weakly-supervised Video Anomaly Detection

Video anomaly detection (VAD) aims at localizing unexpected actions or activities in a video sequence. Existing mainstream VAD techniques are based on either the one-class formulation, which assumes all training data are normal, or weakly-supervised, which requires only video-level normal/anomaly labels. To establish a unified approach to solving the two VAD settings, we introduce a self-supervised sparse representation (S3R) framework that models the concept of anomaly at feature level by exploring the synergy between dictionary-based representation and self-supervised learning. With the learned dictionary, S3R facilitates two coupled modules, en-Normal and de-Normal, to reconstruct snippet-level features and filter out normal-event features. The self-supervised techniques also enable generating samples of pseudo normal/anomaly to train the anomaly detector. We demonstrate with extensive experiments that S3R achieves new state-of-the-art performances on popular benchmark datasets for both one-class and weakly-supervised VAD tasks. Our code is publicly available at https://github.com/louisYen/S3R.
A weak MLMC scheme for Lévy-copula-driven SDEs with applications to the pricing of credit, equity and interest rate derivatives
Authors: Aleksandar Mijatović, Romain Palfray
Date: 2022-11-04 00:00:00
Tasks: Point Processes

This paper develops a novel weak multilevel Monte-Carlo (MLMC) approximation scheme for L\'evy-driven Stochastic Differential Equations (SDEs). The scheme is based on the state space discretization (via a continuous-time Markov chain approximation) of the pure-jump component of the driving L\'evy process and is particularly suited if the multidimensional driver is given by a L\'evy copula. The multilevel version of the algorithm requires a new coupling of the approximate L\'evy drivers in the consecutive levels of the scheme, which is defined via a coupling of the corresponding Poisson point processes. The multilevel scheme is weak in the sense that the bound on the level variances is based on the coupling alone without requiring strong convergence. Moreover, the coupling is natural for the proposed discretization of jumps and is easy to simulate. The approximation scheme and its multilevel analogous are applied to examples taken from mathematical finance, including the pricing of credit, equity and interest rate derivatives.
Variable-Drift Diffusion Models of Pedestrian Road-Crossing Decisions
Authors: Pekkanen, J., Giles, O. T., Dr, Lee, Y., Madigan, R., Daimon, T., Merat, N., Markkula, G.
Date: 2021-04-14 00:00:00
Proceeding: Computational Brain & Behavior 2021 4
Tasks: Decision Making

Human behavior and interaction in road traffic is highly complex, with many open scientific questions of high applied importance, not least in relation to recent development efforts toward automated vehicles. In parallel, recent decades have seen major advances in cognitive neuroscience models of human decision-making, but these models have mainly been applied to simplified laboratory tasks. Here, we demonstrate how variable-drift extensions of drift diffusion (or evidence accumulation) models of decision-making can be adapted to the mundane yet non-trivial scenario of a pedestrian deciding if and when to cross a road with oncoming vehicle traffic. Our variable-drift diffusion models provide a mechanistic account of pedestrian road-crossing decisions, and how these are impacted by a variety of sensory cues: time and distance gaps in oncoming vehicle traffic, vehicle deceleration implicitly signaling intent to yield, as well as explicit communication of such yielding intentions. We conclude that variable-drift diffusion models not only hold great promise as mechanistic models of complex real-world decisions, but that they can also serve as applied tools for improving road traffic safety and efficiency.
YSF: A 6TiSCH Scheduling Function Minimizing Latency of Data Gathering in IIoT
Authors: Yasuyuki Tanaka, Pascale Minet, Mališa Vučinić, Xavier Vilajosana, and Thomas Watteyne
Date: 2022-10-06 00:00:00
Proceeding: IEEE Internet of Things Journal 2022 10
Tasks: Scheduling

Data gathering systems in the Industrial IoT require
an end-to-end latency as low as 1 s with coverage of a few
hundred meters. The 6TiSCH standard is well suited for these
types of applications. A 6TiSCH network is a multihop wireless
IPv6 network which uses time-slotted channel hopping (TSCH).
TSCH is a medium access mode of IEEE802.15.4 which pro-
vides deterministic properties, and increases robustness against
external interference and multipath fading. A key component of
TSCH is its scheduling function that builds the communication
schedule, which greatly impacts network performance. Although
there are several proposed TSCH scheduling solutions in the lit-
erature, most of them are not directly applicable to 6TiSCH for
real-world deployments because they fail to take into consider-
ation the dynamics of a network. Some of them assume a fixed
routing topology, which does not match 6TiSCH where the rout-
ing topology dynamically changes with the radio environment. In
this article, we propose a full-featured 6TiSCH scheduling func-
tion called YSF, that autonomously takes into account all aspects
of network dynamics, including the network formation phase and
parent switching. YSF aims at minimizing latency and maximiz-
ing reliability for data gathering applications. We evaluate YSF
by simulation, and compare it to MSF, the state-of-art schedul-
ing function being standardized by the IETF 6TiSCH working
group.
Management and Detection System for Medical Surgical Equipment
Authors: Alexandra Hadar, Natan Levy, Michael Winokur
Date: 2022-11-04 00:00:00
Tasks: Management, Medical Procedure

Retained surgical bodies (RSB) are any foreign bodies left inside the patient after a medical procedure. RSB is often caused by human mistakes or miscommunication between medical staff during the procedure. Infection, medical complications, and even death are possible consequences of RSB, and it is a significant risk for patients, hospitals, and surgical staff. In this paper. we describe the engineering process we have done to explore the design space, define a feasible solution, and simulate, verify, and validate a state-of-the-art Cyber-Physical System that can significantly decrease the incidence of RSB and thus increase patients' survivability rate. This system might save patients' suffering and lives and reduce medical staff negligence lawsuits while improving the hospital's reputation. The paper illustrates each step of the process with examples and describes the chosen solution in detail.
Multi-view Multi-label Fine-grained Emotion Decoding from Human Brain Activity
Authors: Kaicheng Fu, Changde Du, Shengpei Wang, Huiguang He
Date: 2022-10-26 00:00:00
Tasks: Multi-Label Classification, MUlTI-LABEL-ClASSIFICATION

Decoding emotional states from human brain activity plays an important role in brain-computer interfaces. Existing emotion decoding methods still have two main limitations: one is only decoding a single emotion category from a brain activity pattern and the decoded emotion categories are coarse-grained, which is inconsistent with the complex emotional expression of human; the other is ignoring the discrepancy of emotion expression between the left and right hemispheres of human brain. In this paper, we propose a novel multi-view multi-label hybrid model for fine-grained emotion decoding (up to 80 emotion categories) which can learn the expressive neural representations and predicting multiple emotional states simultaneously. Specifically, the generative component of our hybrid model is parametrized by a multi-view variational auto-encoder, in which we regard the brain activity of left and right hemispheres and their difference as three distinct views, and use the product of expert mechanism in its inference network. The discriminative component of our hybrid model is implemented by a multi-label classification network with an asymmetric focal loss. For more accurate emotion decoding, we first adopt a label-aware module for emotion-specific neural representations learning and then model the dependency of emotional states by a masked self-attention mechanism. Extensive experiments on two visually evoked emotional datasets show the superiority of our method.
Emotion Recognition With Temporarily Localized 'Emotional Events' in Naturalistic Context
Authors: Mohammad Asif, Sudhakar Mishra, Majithia Tejas Vinodbhai, Uma Shanker Tiwary
Date: 2022-10-25 00:00:00
Tasks: EEG, Electroencephalogram (EEG), Emotion Recognition

Emotion recognition using EEG signals is an emerging area of research due to its broad applicability in BCI. Emotional feelings are hard to stimulate in the lab. Emotions do not last long, yet they need enough context to be perceived and felt. However, most EEG-related emotion databases either suffer from emotionally irrelevant details (due to prolonged duration stimulus) or have minimal context doubting the feeling of any emotion using the stimulus. We tried to reduce the impact of this trade-off by designing an experiment in which participants are free to report their emotional feelings simultaneously watching the emotional stimulus. We called these reported emotional feelings "Emotional Events" in our Dataset on Emotion with Naturalistic Stimuli (DENS). We used EEG signals to classify emotional events on different combinations of Valence(V) and Arousal(A) dimensions and compared the results with benchmark datasets of DEAP and SEED. STFT is used for feature extraction and used in the classification model consisting of CNN-LSTM hybrid layers. We achieved significantly higher accuracy with our data compared to DEEP and SEED data. We conclude that having precise information about emotional feelings improves the classification accuracy compared to long-duration EEG signals which might be contaminated by mind-wandering.
Twenty-Five Years of Advances in Beamforming: From Convex and Nonconvex Optimization to Learning Techniques
Authors: Ahmet M. Elbir, Kumar Vijay Mishra, Sergiy A. Vorobyov, Robert W. Heath Jr
Date: 2022-11-03 00:00:00
Tasks: Astronomy

Beamforming is a signal processing technique to steer, shape, and focus an electromagnetic wave using an array of sensors toward a desired direction. It has been used in several engineering applications such as radar, sonar, acoustics, astronomy, seismology, medical imaging, and communications. With the advances in multi-antenna technologies largely for radar and communications, there has been a great interest on beamformer design mostly relying on convex/nonconvex optimization. Recently, machine learning is being leveraged for obtaining attractive solutions to more complex beamforming problems. This article captures the evolution of beamforming in the last twenty-five years from convex-to-nonconvex optimization and optimization-to-learning approaches. It provides a glimpse of this important signal processing technique into a variety of transmit-receive architectures, propagation zones, paths, and conventional/emerging applications.