pwc-archive/papers-with-abstracts huggingface dataset:
Self-supervised learning with bi-label masked speech prediction for streaming multi-talker speech recognition
Authors: Zili Huang, Zhuo Chen, Naoyuki Kanda, Jian Wu, Yiming Wang, Jinyu Li, Takuya Yoshioka, Xiaofei Wang, Peidong Wang
Date: 2022-11-10 00:00:00
Tasks: Representation Learning, Self-Supervised Learning, speech-recognition, Speech Recognition

Self-supervised learning (SSL), which utilizes the input data itself for representation learning, has achieved state-of-the-art results for various downstream speech tasks. However, most of the previous studies focused on offline single-talker applications, with limited investigations in multi-talker cases, especially for streaming scenarios. In this paper, we investigate SSL for streaming multi-talker speech recognition, which generates transcriptions of overlapping speakers in a streaming fashion. We first observe that conventional SSL techniques do not work well on this task due to the poor representation of overlapping speech. We then propose a novel SSL training objective, referred to as bi-label masked speech prediction, which explicitly preserves representations of all speakers in overlapping speech. We investigate various aspects of the proposed system including data configuration and quantizer selection. The proposed SSL setup achieves substantially better word error rates on the LibriSpeechMix dataset.
Self-supervised learning of audio representations using angular contrastive loss
Authors: Shanshan Wang, Soumya Tripathy, Annamaria Mesaros
Date: 2022-11-10 00:00:00
Tasks: Contrastive Learning, Self-Supervised Learning

In Self-Supervised Learning (SSL), various pretext tasks are designed for learning feature representations through contrastive loss. However, previous studies have shown that this loss is less tolerant to semantically similar samples due to the inherent defect of instance discrimination objectives, which may harm the quality of learned feature embeddings used in downstream tasks. To improve the discriminative ability of feature embeddings in SSL, we propose a new loss function called Angular Contrastive Loss (ACL), a linear combination of angular margin and contrastive loss. ACL improves contrastive learning by explicitly adding an angular margin between positive and negative augmented pairs in SSL. Experimental results show that using ACL for both supervised and unsupervised learning significantly improves performance. We validated our new loss function using the FSDnoisy18k dataset, where we achieved 73.6% and 77.1% accuracy in sound event classification using supervised and self-supervised learning, respectively.
A Python library for nonlinear system identification using Multi-Gene Genetic Programming algorithm
Authors: Henrique Carvalho de Castro, Bruno Henrique Groenner Barbosa
Date: 2022-11-10 00:00:00
Tasks: parameter estimation

Models can be built directly from input and output data trough a process known as system identification. The Nonlinear AutoRegressive with eXogenous inputs (NARMAX) models are among the most used mathematical representations in the area and has many successful applications on data-driven modeling in different fields. Such models become extremely large when they have high degree of non-linearity and long-term dependencies. Hence, a structure selection process must be performed to make them parsimonious. In the present paper, it is introduced a toolbox in Python that performs the structure selection process using the evolutionary algorithm named Multi-Gene Genetic Programming (MGGP). The toolbox encapsulates basic tools for parameter estimation, simulation and validation, and it allows the users to customize their evaluation function including prior knowledge and constraints in the individual structure (gray-box identification).
A sufficient condition for $k$-contraction in Lurie systems
Authors: Ron Ofir, Alexander Ovseevich, Michael Margaliot
Date: 2022-11-10 00:00:00
Tasks: LEMMA

We consider a Lurie system obtained via a connection of a linear time-invariant system and a nonlinear feedback function. Such systems often have more than a single equilibrium and are thus not contractive with respect to any norm. We derive a new sufficient condition for $k$-contraction of a Lurie system. For $k=1$, our sufficient condition reduces to the standard stability condition based on the bounded real lemma and a small gain condition. For $k=2$, our condition guarantees well-ordered asymptotic behaviour of the closed-loop system: every bounded solution converges to an equilibrium, which is not necessarily unique. We apply our results to derive a sufficient condition for $k$-contractivity of a networked system.
Secure State Estimation against Sparse Attacks on a Time-varying Set of Sensors
Authors: Zishuo Li, Muhammad Umar B. Niazi, Changxin Liu, Yilin Mo, Karl H. Johansson
Date: 2022-11-10 00:00:00
Tasks: State Estimation

This paper studies the problem of secure state estimation of a linear time-invariant (LTI) system with bounded noise in the presence of sparse attacks on an unknown, time-varying set of sensors. In other words, at each time, the attacker has the freedom to choose an arbitrary set of no more that $p$ sensors and manipulate their measurements without restraint. To this end, we propose a secure state estimation scheme and guarantee a bounded estimation error subject to $2p$-sparse observability and a mild, technical assumption that the system matrix has no degenerate eigenvalues. The proposed scheme comprises a design of decentralized observer for each sensor based on the local observable subspace decomposition. At each time step, the local estimates of sensors are fused by solving an optimization problem to obtain a secure estimation, which is then followed by a local detection-and-resetting process of the decentralized observers. The estimation error is shown to be upper-bounded by a constant which is determined only by the system parameters and noise magnitudes. Moreover, we optimize the detector threshold to ensure that the benign sensors do not trigger the detector. The efficacy of the proposed algorithm is demonstrated by its application on a benchmark example of IEEE 14-bus system.
Data-based Transfer Stabilization in Linear Systems
Authors: Lidong Li, Claudio De Persis, Pietro Tesi, Nima Monshizadeh
Date: 2022-11-10 00:00:00

We present a novel framework for transferring the knowledge from one system (source) to design a stabilizing controller for a second system (target). Our motivation stems from the hypothesis that abundant data can be collected from the source system, whereas the data from the target system is scarce. We consider both cases where data collected from the source system is noiseless and noisy. For each case, by leveraging the data collected from the source system and a priori knowledge on the maximum distance of the two systems, we find a suitable, and relatively small, compact set of systems that contains the actual target system, and then provide a controller that stabilizes the compact set. In particular, the controller can be obtained by solving a set of linear matrix inequalities (LMIs). Feasibility of those LMIs is discussed in details. We complement our theoretical findings by two numerical case studies of low-order and high-order systems.
Effect of Device Mismatches in Differential Oscillatory Neural Networks
Authors: Jafar Shamsi, María José Avedillo, Bernabé Linares-Barranco, Teresa Serrano-Gotarredona
Date: 2022-11-10 00:00:00

Analog implementation of Oscillatory Neural Networks (ONNs) has the potential to implement fast and ultra-low-power computing capabilities. One of the drawbacks of analog implementation is component mismatches which cause desynchronization and instability in ONNs. Emerging devices like memristors and VO2 are particularly prone to variations. In this paper, we study the effect of component mismatches on the performance of differential ONNs (DONNs). Mismatches were considered in two main blocks: differential oscillatory neurons and synaptic circuits. To measure DONN tolerance to mismatches in each block, performance was evaluated with mismatches being present separately in each block. Memristor-bridge circuits with four memristors were used as the synaptic circuits. The differential oscillatory neurons were based on VO2-devices. The simulation results showed that DONN performance was more vulnerable to mismatches in the components of the differential oscillatory neurons than to mismatches in the synaptic circuits. DONNs were found to tolerate up to 20% mismatches in the memristance of the synaptic circuits. However, mismatches in the differential oscillatory neurons resulted in non-uniformity of the natural frequencies, causing desynchronization and instability. Simulations showed that 0.5% relative standard deviation (RSD) in natural frequencies can reduce DONN performance dramatically. In addition, sensitivity analyses showed that the high threshold voltage of VO2-devices is the most sensitive parameter for frequency non-uniformity and desynchronization.
Event-Triggered Intermittent Prescribed Performance Control for Spacecraft Attitude Reorientation
Authors: Jiakun Lei, Tao Meng, Kun Wang, Weijia Wang, Zhonghe Jin
Date: 2022-11-10 00:00:00

This paper focuses on the issue of how to realize spacecraft attitude control with guaranteed performance while conspicuously reducing the actuator acting frequency simultaneously. The prescribed performance control (PPC) scheme is often employed for the control with guaranteed performance. However, conventional PPC controllers are designed from the perspective of continuous system, which contradicts the "discrete" control logic in actual spacecraft control system, and such a problem limited the application value of PPC scheme in actual applications. In order to significantly lower the actuator acting frequency while still maintaining the desired performance, a composite event-trigger mechanism is proposed for this issue, turning off the actuator and eliminating unnecessary control output under appropriate conditions. Further, the proposed composite event-trigger mechanism is combined with a prescribed performance control scheme, constructing a complete controller structure for solving the presented issue. Meanwhile, a special performance function is designed to provide mild transition process. Based on the proposed scheme, a specific backstepping controller is further developed as a validation. Finally, numerical simulation results are presented to validate the effectiveness of the proposed scheme.
Generic Cryo-CMOS Device Modeling and EDACompatible Platform for Reliable Cryogenic IC Design
Authors: Zhidong Tang, Zewei Wang, Yumeng Yuan, Chang He, Xin Luo, Ao Guo, Renhe Chen, Yongqi Hu, Longfei Yang, Chengwei Cao, Linlin Liu, Liujiang Yu, Ganbing Shang, Yongfeng Cao, Shoumian Chen, Yuhang Zhao, Shaojian Hu, Xufeng Kou
Date: 2022-11-10 00:00:00

This paper outlines the establishment of a generic cryogenic CMOS database in which key electrical parameters and transfer characteristics of the MOSFETs are quantified as functions of device size, temperature/frequency responses. Meanwhile, comprehensive device statistical study is conducted to evaluate the influence of variation and mismatch effects at low temperatures. Furthermore, by incorporating the Cryo-CMOS compact model into the process design kit (PDK), the cryogenic 4 Kb SRAM, 5-bit flash ADC and 8-bit current steering DAC are designed, and their performance is readily investigated and optimized on the EDA-compatible platform, hence laying a solid foundation for large-scale cryogenic IC design.
Distributed State Estimation for Linear Time-invariant Systems with Aperiodic Sampled Measurement
Authors: Shimin Wang, Ya-Jun Pan, Martin Guay
Date: 2022-11-09 00:00:00
Tasks: State Estimation

This paper deals with the state estimation of linear time-invariant systems using distributed observers with local sampled-data measurement and aperiodic communication. Each observer agent perceives partial information of the system to be observed but does not satisfy the observability condition. Consequently, distributed observers are designed to exponentially estimate the state of the system to be observed by time-varying sampling and asynchronous communication. Additionally, explicit upper bounds on allowable sampling periods for convergent estimation errors are given. Finally, a numerical example is provided to demonstrate the validity of the theoretical results
Data-driven Cyberattack Synthesis against Network Control Systems
Authors: Omanshu Thapliyal, Inseok Hwang
Date: 2022-11-09 00:00:00

Network Control Systems (NCSs) pose unique vulnerabilities to cyberattacks due to a heavy reliance on communication channels. These channels can be susceptible to eavesdropping, false data injection (FDI), and denial of service (DoS). As a result, smarter cyberattacks can employ a combination of techniques to cause degradation of the considered NCS performance. We consider a white-box cyberattack synthesis technique in which the attacker initially eavesdrops to gather system data, and constructs equivalent system model. We utilize the equivalent model to synthesize hybrid cyberattacks -- a combination of FDI and DoS attacks against the NCS. Reachable sets for the equivalent NCS model provide rapid, real-time directives towards selecting NCS agents to be attacked. The devised method provides a significantly more realistic approach toward cyberattack synthesis against NCSs with unknown parameters. We demonstrate the proposed method using a multi-aerial vehicle formation control scenario.
Generalization of generative model for neuronal ensemble inference method
Authors: Shun Kimura, Koujin Takeda
Date: 2022-11-07 00:00:00
Tasks: Bayesian Inference

Various brain functions that are necessary to maintain life activities materialize through the interaction of countless neurons. Therefore, it is important to analyze functional neuronal network. To elucidate the mechanism of brain function, many studies are being actively conducted on functional neuronal ensemble and hub, including all areas of neuroscience. In addition, recent study suggests that the existence of functional neuronal ensembles and hubs contributes to the efficiency of information processing. For these reasons, there is a demand for methods to infer functional neuronal ensembles from neuronal activity data, and methods based on Bayesian inference have been proposed. However, there is a problem in modeling the activity in Bayesian inference. The features of each neuron's activity have non-stationarity depending on physiological experimental conditions. As a result, the assumption of stationarity in Bayesian inference model impedes inference, which leads to destabilization of inference results and degradation of inference accuracy. In this study, we extend the range of the variable for expressing the neuronal state, and generalize the likelihood of the model for extended variables. By comparing with the previous study, our model can express the neuronal state in larger space. This generalization without restriction of the binary input enables us to perform soft clustering and apply the method to non-stationary neuroactivity data. In addition, for the effectiveness of the method, we apply the developed method to multiple synthetic fluorescence data generated from the electrical potential data in leaky integrated-and-fire model.
Plastic neural network with transmission delays promotes equivalence between function and structure
Authors: P. R. Protachevicz, F. S. Borges, A. M. Batista, M. S. Baptista, I. L. Caldas, E. E. N. Macau, E. L. Lameu
Date: 2022-11-10 00:00:00

The brain is formed by cortical regions that are associated with different cognitive functions. Neurons within the same region are more likely to connect than neurons in distinct regions, making the brain network to have characteristics of a network of subnetworks. The values of synaptic delays between neurons of different subnetworks are greater than those of the same subnetworks. This difference in communication time between neurons has consequences on the firing patterns observed in the brain, which is directly related to changes in neural connectivity, known as synaptic plasticity. In this work, we build a plastic network of Hodgkin-Huxley neurons in which the connectivity modifications follow a spike-time dependent rule. We define an internal-delay among neurons communicating within the same subnetwork, an external-delay for neurons belonging to distinct subnetworks, and study how these communicating delays affect the entire network dynamics. We observe that the neuronal network exhibits a specific connectivity configuration for each synchronised pattern. Our results show how synaptic delays and plasticity work together to promote the formation of structural coupling among the neuronal subnetworks. We conclude that plastic neuronal networks are able to promote equivalence between function and structure meaning that topology emerges from behaviour and behaviour emerges from topology, creating a complex dynamical process where topology adapts to conform with the plastic rules and firing patterns reflect the changes on the synaptic weights.
A Benchmarking Dataset with 2440 Organic Molecules for Volume Distribution at Steady State
Authors: Wenwen Liu, Cheng Luo, Hecheng Wang, Fanwang Meng
Date: 2022-11-10 00:00:00
Tasks: Benchmarking, feature selection, regression

Background: The volume of distribution at steady state (VDss) is a fundamental pharmacokinetics (PK) property of drugs, which measures how effectively a drug molecule is distributed throughout the body. Along with the clearance (CL), it determines the half-life and, therefore, the drug dosing interval. However, the molecular data size limits the generalizability of the reported machine learning models. Objective: This study aims to provide a clean and comprehensive dataset for human VDss as the benchmarking data source, fostering and benefiting future predictive studies. Moreover, several predictive models were also built with machine learning regression algorithms. Methods: The dataset was curated from 13 publicly accessible data sources and the DrugBank database entirely from intravenous drug administration and then underwent extensive data cleaning. The molecular descriptors were calculated with Mordred, and feature selection was conducted for constructing predictive models. Five machine learning methods were used to build regression models, grid search was used to optimize hyperparameters, and ten-fold cross-validation was used to evaluate the model. Results: An enriched dataset of VDss (https://github.com/da-wen-er/VDss) was constructed with 2440 molecules. Among the prediction models, the LightGBM model was the most stable and had the best internal prediction ability with Q2 = 0.837, R2=0.814 and for the other four models, Q2 was higher than 0.79. Conclusions: To the best of our knowledge, this is the largest dataset for VDss, which can be used as the benchmark for computational studies of VDss. Moreover, the regression models reported within this study can be of use for pharmacokinetic related studies.
Improved Prediction of Beta-Amyloid and Tau Burden Using Hippocampal Surface Multivariate Morphometry Statistics and Sparse Coding
Authors: Jianfeng Wu, Yi Su, Wenhui Zhu, Negar Jalili Mallak, Natasha Lepore, Eric M. Reiman, Richard J. Caselli, Paul M. Thompson, Kewei Chen, Yalin Wang
Date: 2022-10-28 00:00:00

Background: Beta-amyloid (A$\beta$) plaques and tau protein tangles in the brain are the defining 'A' and 'T' hallmarks of Alzheimer's disease (AD), and together with structural atrophy detectable on brain magnetic resonance imaging (MRI) scans as one of the neurodegenerative ('N') biomarkers comprise the ''ATN framework'' of AD. Current methods to detect A$\beta$/tau pathology include cerebrospinal fluid (CSF; invasive), positron emission tomography (PET; costly and not widely available), and blood-based biomarkers (BBBM; promising but mainly still in development). Objective: To develop a non-invasive and widely available structural MRI-based framework to quantitatively predict the amyloid and tau measurements. Methods: With MRI-based hippocampal multivariate morphometry statistics (MMS) features, we apply our Patch Analysis-based Surface Correntropy-induced Sparse coding and max-pooling (PASCS-MP) method combined with the ridge regression model to individual amyloid/tau measure prediction. Results: We evaluate our framework on amyloid PET/MRI and tau PET/MRI datasets from the Alzheimer's Disease Neuroimaging Initiative (ADNI). Each subject has one pair consisting of a PET image and MRI scan, collected at about the same time. Experimental results suggest that amyloid/tau measurements predicted with our PASCP-MP representations are closer to the real values than the measures derived from other approaches, such as hippocampal surface area, volume, and shape morphometry features based on spherical harmonics (SPHARM). Conclusion: The MMS-based PASCP-MP is an efficient tool that can bridge hippocampal atrophy with amyloid and tau pathology and thus help assess disease burden, progression, and treatment effects.
Unifying the communicable disease spreading paradigm with Gompertzian growth
Authors: Matz A. Haugen, Dorothea Gilbert
Date: 2022-11-10 00:00:00

A number of studies have shown that cumulative mortality followed a Gompertz curve in the initial Covid pandemic period, March-April 2020. We show that the Gompertz curve is incompatible with expected initial logistic growth curves as predicted by traditional Susceptible-Infected-Recovered (SIR) models, and propose a new theory which better explains the nature of the mortality characteristics based on a global biosphere disturbance. Second, we show that for the Gompertz curve to emerge, the disturbance has to act on everyone simultaneously, rejecting the possibility of a disease propagation stage. Third, we connect logistic growth with Gompertzian growth by augmenting the logistic growth equation with higher order interaction terms, and show that the SIR model family is compatible with Gompertzian growth only when all nodes in the transmission network communicate with infinite speed and interaction. Crucially, this augmentation must be accompanied by a causality-reversal where the source of growth is not the pool of infected but the pool of susceptible people. We thus find a novel bridge between logistic and Gompertzian growth, separate from the existing Richards model (also called $\theta$-logistic growth).