pwc-archive/papers-with-abstracts huggingface dataset:
Bayesian Learning of Parameterised Quantum Circuits
Authors: Samuel Duffield, Marcello Benedetti, Matthias Rosenkranz
Date: 2022-06-15 00:00:00
Tasks: Dimensionality Reduction

Currently available quantum computers suffer from constraints including hardware noise and a limited number of qubits. As such, variational quantum algorithms that utilise a classical optimiser in order to train a parameterised quantum circuit have drawn significant attention for near-term practical applications of quantum technology. In this work, we take a probabilistic point of view and reformulate the classical optimisation as an approximation of a Bayesian posterior. The posterior is induced by combining the cost function to be minimised with a prior distribution over the parameters of the quantum circuit. We describe a dimension reduction strategy based on a maximum a posteriori point estimate with a Laplace prior. Experiments on the Quantinuum H1-2 computer show that the resulting circuits are faster to execute and less noisy than the circuits trained without the dimension reduction strategy. We subsequently describe a posterior sampling strategy based on stochastic gradient Langevin dynamics. Numerical simulations on three different problems show that the strategy is capable of generating samples from the full posterior and avoiding local optima.
Local Identifiability of Deep ReLU Neural Networks: the Theory
Authors: Joachim Bona-Pellissier, François Malgouyres, François Bachoc
Date: 2022-06-15 00:00:00

Is a sample rich enough to determine, at least locally, the parameters of a neural network? To answer this question, we introduce a new local parameterization of a given deep ReLU neural network by fixing the values of some of its weights. This allows us to define local lifting operators whose inverses are charts of a smooth manifold of a high dimensional space. The function implemented by the deep ReLU neural network composes the local lifting with a linear operator which depends on the sample. We derive from this convenient representation a geometrical necessary and sufficient condition of local identifiability. Looking at tangent spaces, the geometrical condition provides: 1/ a sharp and testable necessary condition of identifiability and 2/ a sharp and testable sufficient condition of local identifiability. The validity of the conditions can be tested numerically using backpropagation and matrix rank computations.
Noise Covariance Estimation in Multi-Task High-dimensional Linear Models
Authors: Kai Tan, Gabriel Romon, Pierre C Bellec
Date: 2022-06-15 00:00:00
Tasks: regression, Vocal Bursts Intensity Prediction

This paper studies the multi-task high-dimensional linear regression models where the noise among different tasks is correlated, in the moderately high dimensional regime where sample size $n$ and dimension $p$ are of the same order. Our goal is to estimate the covariance matrix of the noise random vectors, or equivalently the correlation of the noise variables on any pair of two tasks. Treating the regression coefficients as a nuisance parameter, we leverage the multi-task elastic-net and multi-task lasso estimators to estimate the nuisance. By precisely understanding the bias of the squared residual matrix and by correcting this bias, we develop a novel estimator of the noise covariance that converges in Frobenius norm at the rate $n^{-1/2}$ when the covariates are Gaussian. This novel estimator is efficiently computable. Under suitable conditions, the proposed estimator of the noise covariance attains the same rate of convergence as the "oracle" estimator that knows in advance the regression coefficients of the multi-task model. The Frobenius error bounds obtained in this paper also illustrate the advantage of this new estimator compared to a method-of-moments estimator that does not attempt to estimate the nuisance. As a byproduct of our techniques, we obtain an estimate of the generalization error of the multi-task elastic-net and multi-task lasso estimators. Extensive simulation studies are carried out to illustrate the numerical performance of the proposed method.
Unbiased Estimation using Underdamped Langevin Dynamics
Authors: Hamza Ruzayqat, Neil K. Chada, Ajay Jasra
Date: 2022-06-14 00:00:00

In this work we consider the unbiased estimation of expectations w.r.t.~probability measures that have non-negative Lebesgue density, and which are known point-wise up-to a normalizing constant. We focus upon developing an unbiased method via the underdamped Langevin dynamics, which has proven to be popular of late due to applications in statistics and machine learning. Specifically in continuous-time, the dynamics can be constructed {so that as the time goes to infinity they} admit the probability of interest as a stationary measure. {In many cases, time-discretized versions of the underdamped Langevin dynamics are used in practice which are run only with a fixed number of iterations.} We develop a novel scheme based upon doubly randomized estimation as in \cite{ub_grad,disc_model}, which requires access only to time-discretized versions of the dynamics. {The proposed scheme aims to remove the dicretization bias and the bias resulting from running the dynamics for a finite number of iterations}. We prove, under standard assumptions, that our estimator is of finite variance and either has finite expected cost, or has finite cost with a high probability. To illustrate our theoretical findings we provide numerical experiments which verify our theory, which include challenging examples from Bayesian statistics and statistical physics.
Rethinking Reinforcement Learning for Recommendation: A Prompt Perspective
Authors: Xin Xin, Tiago Pimentel, Alexandros Karatzoglou, Pengjie Ren, Konstantina Christakopoulou, Zhaochun Ren
Date: 2022-06-15 00:00:00
Tasks: Recommendation Systems, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Modern recommender systems aim to improve user experience. As reinforcement learning (RL) naturally fits this objective -- maximizing an user's reward per session -- it has become an emerging topic in recommender systems. Developing RL-based recommendation methods, however, is not trivial due to the \emph{offline training challenge}. Specifically, the keystone of traditional RL is to train an agent with large amounts of online exploration making lots of `errors' in the process. In the recommendation setting, though, we cannot afford the price of making `errors' online. As a result, the agent needs to be trained through offline historical implicit feedback, collected under different recommendation policies; traditional RL algorithms may lead to sub-optimal policies under these offline training settings. Here we propose a new learning paradigm -- namely Prompt-Based Reinforcement Learning (PRL) -- for the offline training of RL-based recommendation agents. While traditional RL algorithms attempt to map state-action input pairs to their expected rewards (e.g., Q-values), PRL directly infers actions (i.e., recommended items) from state-reward inputs. In short, the agents are trained to predict a recommended item given the prior interactions and an observed reward value -- with simple supervised learning. At deployment time, this historical (training) data acts as a knowledge base, while the state-reward pairs are used as a prompt. The agents are thus used to answer the question: \emph{ Which item should be recommended given the prior interactions \& the prompted reward value}? We implement PRL with four notable recommendation models and conduct experiments on two real-world e-commerce datasets. Experimental results demonstrate the superior performance of our proposed methods.
RecBole 2.0: Towards a More Up-to-Date Recommendation Library
Authors: Wayne Xin Zhao, Yupeng Hou, Xingyu Pan, Chen Yang, Zeyu Zhang, Zihan Lin, Jingsen Zhang, Shuqing Bian, Jiakai Tang, Wenqi Sun, Yushuo Chen, Lanling Xu, Gaowei Zhang, Zhen Tian, Changxin Tian, Shanlei Mu, Xinyan Fan, Xu Chen, Ji-Rong Wen
Date: 2022-06-15 00:00:00
Tasks: Benchmarking, Data Augmentation, Fairness, Graph Neural Network, Meta-Learning, Recommendation Systems

In order to support the study of recent advances in recommender systems, this paper presents an extended recommendation library consisting of eight packages for up-to-date topics and architectures. First of all, from a data perspective, we consider three important topics related to data issues (i.e., sparsity, bias and distribution shift), and develop five packages accordingly: meta-learning, data augmentation, debiasing, fairness and cross-domain recommendation. Furthermore, from a model perspective, we develop two benchmarking packages for Transformer-based and graph neural network (GNN)-based models, respectively. All the packages (consisting of 65 new models) are developed based on a popular recommendation framework RecBole, ensuring that both the implementation and interface are unified. For each package, we provide complete implementations from data loading, experimental setup, evaluation and algorithm implementation. This library provides a valuable resource to facilitate the up-to-date research in recommender systems. The project is released at the link: https://github.com/RUCAIBox/RecBole2.0.
ParamNet: A Multi-Layer Parametric Network for Joint Channel Estimation and Symbol Detection
Authors: Vincent Choqueuse, Alexandru Frunza, Adel Belouchrani, Stéphane Azou, Pascal Morel
Date: 2022-06-15 00:00:00

This paper proposes a parametric-based network architecture for joint channel estimation and data detection in communications systems with hardware impairments. This architecture is composed of a data-augmented layer, a custom soft thresholding function, and several linear layers modeling the effect of channel effects and hardware impairments. In the proposed network, the soft thresholding function softly constrains the detected data to be within the considered constellation. The latter depends only on one one parameter that is optimized during training. The benefit of the proposed approach is illustrated through a communication chain corrupted by multiple impairments and noises.
Electrochemically stable lithium-ion and electron insulators (LEIs) for solid-state batteries
Authors: Kai Pei, So Yeon Kim, Ju Li
Date: 2021-08-17 00:00:00
Proceeding: Nano Research 2021 8

Rechargeable solid-state Li metal batteries demand ordered flows of Li-ions and electrons in and out of solid structures, with repeated waxing and waning of LiBCC phase near contact interfaces which gives rise to various electro-chemo-mechanical challenges. There have been approaches that adopt three-dimensional (3D) nanoporous architectures consisting of mixed ion-electron conductors (MIECs) to combat these challenges. However, there has remained an issue of LiBCC nucleation at the interfaces between different solid components (e.g., solid electrolyte/MIEC interface), which could undermine the interfacial bonding, thereby leading to the evolution of mechanical instability and the loss of ionic/electronic percolation. In this regard, the present work shows that the Li-ion and electron insulators (LEIs) that are thermodynamically stable against LiBCC could combat such challenges by blocking transportation of charge carriers on the interfaces, analogous to dielectric layers in transistors. We searched the ab initio database and have identified 48 crystalline compounds to be LEI candidates (46 experimentally reported compounds and 2 hypothetical compounds predicted to be stable) with a band gap greater than 3 eV and vanishing Li solubility. Among these compounds, those with good adhesion to solid electrolyte and mixed ion-electron conductor of interest, but are lithiophobic, are expected to be the most useful. We also extended the search to Na or K metal compatible alkali-ion and electron insulators, and identified some crystalline compounds with a property to resist corresponding alkali-ions and electrons.
Is Synthetic Voice Detection Research Going Into the Right Direction?
Authors: Stefano Borzì, Oliver Giudice, Filippo Stanco, Dario Allegra
Date: 2022-06-19 00:00:00
Proceeding: IEEE/CVF Conference on Computer Vision and Pattern Recognition 2022 6
Tasks: DeepFake Detection, fake voice detection

Machine Learning, and in general Artificial Intelligence approaches, brought a great advance in each and every field of Computer Science increasing accuracy levels of predictors in any known problem. Indeed, this evolution enabled the construction of effective frameworks and solutions able to be used in investigative and forensics scenarios for detection of fakes and, in general, manipulations in multimedia contents. On the other hand, can we trust these systems? Is research activity going in the right direction? Are we just taking the low-hanging fruit without taking into account many real-case-in-the-wild situations? The purpose of this paper is to raise an alert to the research community in the specific context of synthetic voice detection, where data available for training is not big enough to give sufficient trust in the techniques available in the literature. To this aim, an exploratory investigation of the most common voice spoofing dataset was carried out and it was surprisingly easy to build simple classifiers without any Deep Learning techniques. Simple considerations on bitrate were sufficient to achieve an effective detection performance.
UST/DME: A Clock Tree Router For General Skew Constraints
Authors: CHUNG-WEN ALBERT TSAO, CHENG-KOK KOH
Date: 2002-07-03 00:00:00
Proceeding: 2002/07/03 2002 7
Tasks: Scheduling

In this article, we propose new approaches for solving the useful-skew tree (UST) routing problem [Xi and Dai 1997]: clock routing subject to general skew constraints. The clock layout synthesis engine of our UST algorithms is based on the deferred-merge embedding (DME) paradigm for the zero-skew tree (ZST) [Edahiro 1992; Chao et al. 1992] and bounded-skew tree (BST) [Cong and Koh 1995; Huang et al. 1995; Kahng and Tsao 1997; Cong et al. 1998] routings; hence, the names UST/DME and Greedy-UST/DME for our UST algorithms. Our novel contribution is that we simultaneously perform skew scheduling and tree routing so that each local skew range is incrementally refined to a skew value that minimizes the wirelength increase during the bottomup merging phase of DME. As a result, not only is the skew schedule feasible, but also the wirelength increase is minimized at each merging step of clock tree construction. The experimental results show very encouraging improvement over the previous BST/DME algorithm on three ISCAS89 benchmarks under general skew constraints in terms of total routing wirelength.
No-reference Stereoscopic Image Quality Predictor using Deep Features from Cyclopean Image
Authors: Oussama Messai, Aladine Chetouani, Fella Hachouf, Zianou Ahmed seghir
Date: 2021-01-01 00:00:00
Proceeding: Electronic Imaging 2021 1
Tasks: Image Quality Assessment, No-Reference Image Quality Assessment, Stereoscopic image quality assessment

With the expanding use of stereoscopic imaging for 3D applications, no-reference perceptual quality evaluation has become important to provide good viewing experience. The effect of the quality distortion is related to the scene’s spatial details. Taking this into account, this paper introduces a blind stereoscopic image quality measurement using synthesized cyclopean image and deep feature extraction. The proposed method is based on Human Visual System (HVS) modeling and quality-aware indicators. First, the cyclopean image is formed, taking on the existence of binocular rivalry / suppression that includes the asymmetric distortion case. Second, the cyclopean image is decomposed into four equivalent parts. Then, four Convolutional Neural Network (CNN) models are deployed to automatically extract quality feature sets. Finally, a feature bank is then created from the four patches and mapped to quality score using a Support Vector Regression (SVR) model. The best known 3D LIVE phase I and phase II databases were used to evaluate the efficiency of our technique. Compared with the state-of-the-art stereoscopic image quality measurement metrics, the proposed method has shown competitive outcomes and achieved good performance.
Smart Meter Data Anomaly Detection using Variational Recurrent Autoencoders with Attention
Authors: Wenjing Dai, Xiufeng Liu, Alfred Heller, Per Sieverts Nielsen
Date: 2022-06-08 00:00:00
Tasks: Anomaly Detection, Management, Missing Values, Time Series Analysis, Unsupervised Anomaly Detection

In the digitization of energy systems, sensors and smart meters are increasingly being used to monitor production, operation and demand. Detection of anomalies based on smart meter data is crucial to identify potential risks and unusual events at an early stage, which can serve as a reference for timely initiation of appropriate actions and improving management. However, smart meter data from energy systems often lack labels and contain noise and various patterns without distinctively cyclical. Meanwhile, the vague definition of anomalies in different energy scenarios and highly complex temporal correlations pose a great challenge for anomaly detection. Many traditional unsupervised anomaly detection algorithms such as cluster-based or distance-based models are not robust to noise and not fully exploit the temporal dependency in a time series as well as other dependencies amongst multiple variables (sensors). This paper proposes an unsupervised anomaly detection method based on a Variational Recurrent Autoencoder with attention mechanism. with "dirty" data from smart meters, our method pre-detects missing values and global anomalies to shrink their contribution while training. This paper makes a quantitative comparison with the VAE-based baseline approach and four other unsupervised learning methods, demonstrating its effectiveness and superiority. This paper further validates the proposed method by a real case study of detecting the anomalies of water supply temperature from an industrial heating plant.
Principal Trade-off Analysis
Authors: Alexander Strang, David SeWell, Alexander Kim, Kevin Alcedo, David Rosenbluth
Date: 2022-06-09 00:00:00

How are the advantage relations between a set of agents playing a game organized and how do they reflect the structure of the game? In this paper, we illustrate "Principal Trade-off Analysis" (PTA), a decomposition method that embeds games into a low-dimensional feature space. We argue that the embeddings are more revealing than previously demonstrated by developing an analogy to Principal Component Analysis (PCA). PTA represents an arbitrary two-player zero-sum game as the weighted sum of pairs of orthogonal 2D feature planes. We show that the feature planes represent unique strategic trade-offs and truncation of the sequence provides insightful model reduction. We demonstrate the validity of PTA on a quartet of games (Kuhn poker, RPS+2, Blotto, and Pokemon). In Kuhn poker, PTA clearly identifies the trade-off between bluffing and calling. In Blotto, PTA identifies game symmetries, and specifies strategic trade-offs associated with distinct win conditions. These symmetries reveal limitations of PTA unaddressed in previous work. For Pokemon, PTA recovers clusters that naturally correspond to Pokemon types, correctly identifies the designed trade-off between those types, and discovers a rock-paper-scissor (RPS) cycle in the Pokemon generation type - all absent any specific information except game outcomes.
Binary Single-dimensional Convolutional Neural Network for Seizure Prediction
Authors: Shiqi Zhao, Jie Yang, Yankun Xu, Mohamad Sawan
Date: 2022-06-08 00:00:00
Tasks: EEG, Electroencephalogram (EEG), Prediction, Seizure prediction

Nowadays, several deep learning methods are proposed to tackle the challenge of epileptic seizure prediction. However, these methods still cannot be implemented as part of implantable or efficient wearable devices due to their large hardware and corresponding high-power consumption. They usually require complex feature extraction process, large memory for storing high precision parameters and complex arithmetic computation, which greatly increases required hardware resources. Moreover, available yield poor prediction performance, because they adopt network architecture directly from image recognition applications fails to accurately consider the characteristics of EEG signals. We propose in this paper a hardware-friendly network called Binary Single-dimensional Convolutional Neural Network (BSDCNN) intended for epileptic seizure prediction. BSDCNN utilizes 1D convolutional kernels to improve prediction performance. All parameters are binarized to reduce the required computation and storage, except the first layer. Overall area under curve, sensitivity, and false prediction rate reaches 0.915, 89.26%, 0.117/h and 0.970, 94.69%, 0.095/h on American Epilepsy Society Seizure Prediction Challenge (AES) dataset and the CHB-MIT one respectively. The proposed architecture outperforms recent works while offering 7.2 and 25.5 times reductions on the size of parameter and computation, respectively.
Human Activity Recognition on Time Series Accelerometer Sensor Data using LSTM Recurrent Neural Networks
Authors: Chrisogonas O. Odhiambo, Sanjoy Saha, Corby K. Martin, Homayoun Valafar
Date: 2022-06-03 00:00:00
Tasks: Activity Recognition, Human Activity Recognition, Time Series, Time Series Analysis

The use of sensors available through smart devices has pervaded everyday life in several applications including human activity monitoring, healthcare, and social networks. In this study, we focus on the use of smartwatch accelerometer sensors to recognize eating activity. More specifically, we collected sensor data from 10 participants while consuming pizza. Using this information, and other comparable data available for similar events such as smoking and medication-taking, and dissimilar activities of jogging, we developed a LSTM-ANN architecture that has demonstrated 90% success in identifying individual bites compared to a puff, medication-taking or jogging activities.
Beamforming in Integrated Sensing and Communication Systems with Reconfigurable Intelligent Surfaces
Authors: R. S. Prasobh Sankar, Sundeep Prabhakar Chepuri, Yonina C. Eldar
Date: 2022-06-15 00:00:00
Tasks: Integrated sensing and communication, ISAC

We consider transmit beamforming and reflection pattern design in reconfigurable intelligent surface (RIS)-assisted integrated sensing and communication (ISAC) systems to jointly precode communication symbols and radar waveforms. We treat two settings of multiple users and targets. In the first, we use a single RIS to enhance the communication performance of the ISAC system and design beams with good cross-correlation properties to match a desired beam pattern while guaranteeing a desired signal-to-interference plus noise ratio (SINR) for each user. In the second setting, we use two dedicated RISs to aid the ISAC system, wherein the beams are designed to maximize the worst-case target illumination power while guaranteeing a desired SINR for each user. We propose solvers based on alternating optimization as the design problems in both cases are non-convex optimization problems. Through a number of numerical simulations, we demonstrate the advantages of RIS-assisted ISAC systems. In particular, we show that the proposed single-RIS assisted ISAC system improves the minimum user SINR while suffering from a moderate loss in radar target illumination power. On the other hand, the dual-RIS assisted ISAC system improves both minimum user SINR as well as worst-case target illumination power at the targets, especially when the users and targets are not directly visible.