pwc-archive/papers-with-abstracts huggingface dataset:
On the vibrational free energy of hydrated proteins
Authors: Yves-Henri Sanejouand
Date: 2020-10-20 00:00:00

When the hydration shell of a protein is filled with at least 0.6 gram of water per gram of protein, a significant anti-correlation between the vibrational free energy and the potential energy of energy-minimized conformers is observed. This means that low potential energy, well-hydrated, protein conformers tend to be more rigid than high-energy ones. On the other hand, in the case of CASP target 624, when its hydration shell is filled, a significant average energy gap is observed between the crystal structure and the best conformers proposed during the prediction experiment, strongly suggesting that including explicit water molecules may help identifying unlikely conformers among good-looking ones.
Can Steering Wheel Detect Your Driving Fatigue?
Authors: Jianchao Lu, Xi Zheng, Tianyi Zhang, Michael Sheng, Chen Wang, Jiong Jin, Shui Yu, Wanlei Zhou
Date: 2020-10-18 00:00:00

Automated Driving System (ADS) has attracted increasing attention from both industrial and academic communities due to its potential for increasing the safety, mobility and efficiency of existing transportation systems. The state-of-the-art ADS follows the human-in-the-loop (HITL) design, where the driver's anomalous behaviour is closely monitored by the system. Though many approaches have been proposed for detecting driver fatigue, they largely depend on vehicle driving parameters and facial features, which lacks reliability. Approaches using physiological based sensors (e.g., electroencephalogram or electrocardiogram) are either too clumsy to wear or impractical to install. In this paper, we propose a novel driver fatigue detection method by embedding surface electromyography (sEMG) sensors on a steering wheel. Compared with the existing methods, our approach is able to collect bio-signals in a non-intrusive way and detect driver fatigue at an earlier stage. The experimental results show that our approach outperforms existing methods with the weighted average F1 scores about 90%. We also propose promising future directions to deploy this approach in real-life settings, such as applying multimodal learning using several supplementary sensors.
1.23-Tb/s per Wavelength Single-Waveguide On-Chip Optical Interconnect Enabled by Mode-division Multiplexing
Authors: Hanzi Huang, Yetian Huang, Yu He, Haoshuo Chen, Yong Zhang, Qianwu Zhang, Nicolas K. Fontaine, Roland Ryf, Yingxiong Song, Yikai Su
Date: 2020-10-17 00:00:00
Tasks: Decoder

We experimentally demonstrate a record net capacity per wavelength of 1.23~Tb/s over a single silicon-on-insulator (SOI) multimode waveguide for optical interconnects employing on-chip mode-division multiplexing and 11$\times$11 multiple-in-multiple-out (MIMO) digital signal processing.
End-to-end Wireless Path Deployment with Intelligent Surfaces Using Interpretable Neural Networks
Authors: Christos Liaskos, Shuai Niez, Ageliki Tsioliaridou, Andreas Pitsillides, Sotiris Ioannidis, Ian F. Akyildiz
Date: 2020-10-16 00:00:00

Intelligent surfaces exert deterministic control over the wireless propagation phenomenon, enabling novel capabilities in performance, security and wireless power transfer. Such surfaces come in the form of rectangular tiles that cascade to cover large surfaces such as walls, ceilings or building facades. Each tile is addressable and can receive software commands from a controller, manipulating an impinging electromagnetic wave upon it by customizing its reflection direction, focus, polarization and phase. A new problem arises concerning the orchestration of a set of tiles towards serving end-to-end communication objectives. Towards that end, we propose a novel intelligent surface networking algorithm based on interpretable neural networks. Tiles are mapped to neural network nodes and any tile line-of-sight connectivity is expressed as a neural network link. Tile wave manipulation functionalities are captured via geometric reflection with virtually rotatable tile surface norm, thus being able to tunable distribute power impinging upon a tile over the corresponding neural network links, with the corresponding power parts acting as the link weights. A feedforward/backpropagate process optimizes these weights to match ideal propagation outcomes (normalized network power outputs) to wireless user emissions (normalized network power inputs). An interpretation process translates these weights to the corresponding tile wave manipulation functionalities.
Online adaptive group-wise sparse NPLS for ECoG neural signal decoding
Authors: Alexandre Moly, Alexandre Aksenov, Alim Louis Benabid, Tetiana Aksenova
Date: 2020-10-14 00:00:00
Tasks: Decoder

Objective. Brain-computer interfaces (BCIs) create a new communication pathway between the brain and an effector without neuromuscular activation. BCI experiments highlighted high intra and inter-subjects variability in the BCI decoders. Although BCI model is generally relying on neurological markers generalizable on the majority of subjects, it requires to generate a wide range of neural features to include possible neurophysiological patterns. However, the processing of noisy and high dimensional features, such as brain signals, brings several challenges to overcome such as model calibration issues, model generalization and interpretation problems and hardware related obstacles. Approach. An online adaptive group-wise sparse decoder named Lp-Penalized REW-NPLS algorithm (PREW-NPLS) is presented to reduce the feature space dimension employed for BCI decoding. The proposed decoder was designed to create BCI systems with low computational cost suited for portable applications and tested during offline pseudo-online study based on online closed-loop BCI control of the left and right 3D arm movements of a virtual avatar from the ECoG recordings of a tetraplegic patient. Main results. PREW-NPLS algorithm highlight at least as good decoding performance as REW-NPLS algorithm. However, the decoding performance obtained with PREW-NPLS were achieved thanks to sparse models with up to 64% and 75% of the electrodes set to 0 for the left and right hand models respectively using L1-PREW-NPLS. Significance. The designed solution proposed an online incremental adaptive algorithm suitable for online adaptive decoder calibration which estimate sparse decoding solutions. The PREW-NPLS models are suited for portable applications with low computational power using only small number of electrodes with degrading the decoding performance.
Time-domain Representation of Passband Scattering Parameters
Authors: Justin B. King
Date: 2020-10-14 00:00:00

This paper presents a simple and accurate method for the inclusion of linear, time-invariant (LTI) networks, described by RF frequency-domain data, within equivalent baseband time-domain simulations. The time-domain representation is formulated as an equivalent baseband discrete-time impulse response, which may be convolved with the equivalent baseband form of the input signal, to obtain the corresponding equivalent baseband output. This allows networks which are most accurately described in the frequency domain, such as frequency-dispersive transmission lines, to be efficiently included as part of a transient time-domain simulation.
A Novel Self-Packaged DBBPF With Multiple TZs for 5G Applications
Authors: Hao Zhang
Date: 2020-10-14 00:00:00

A self-packaged dual-band bandpass filter (DBBPF) with high isolation and low insertion loss (IL) for 5G applications is proposed in this paper. To get high stopband suppression, multiple and controllable transmission zeros (TZs) are produced. This novel DBBPF is designed with a pair of quarter-wavelength stepped-impedance resonators (QSIRs) and a half-wavelength hairpin resonator (HWHR). This DBBPF is excited by a pair of U-shape feed lines, which are designed on G6 to fully excite the resonators and to introduce source/load TZs at the same time. In this letter, the generation of two passbands and TZs will be discussed by separate electric and magnetic coupling paths (SEMCP) and mixed EM coupling analysis. This DBBPF achieves a low IL of 0.85/1.15 dB with the fractional bandwidths (FBW) of 11.0% and 6.9% at the center frequencies of 3.45 GHz and 4.9 GHz for 5G application, respectively. The total size is 0.32{\lambda}g*0.45{\lambda}g. Especially, three controllable TZs are introduced between two passbands.
Monitoring Large Crowds With WiFi: A Privacy-Preserving Approach
Authors: Jean-François Determe, Sophia Azzagnuni, Utkarsh Singh, François Horlin, Philippe De Doncker
Date: 2020-10-20 00:00:00
Tasks: Privacy Preserving

This paper presents a crowd monitoring system based on the passive detection of probe requests. The system meets strict privacy requirements and is suited to monitoring events or buildings with a least a few hundreds of attendees. We present our counting process and an associated mathematical model. From this model, we derive a concentration inequality that highlights the accuracy of our crowd count estimator. Then, we describe our system. We present and discuss our sensor hardware, our computing system architecture, and an efficient implementation of our counting algorithm -- as well as its space and time complexity. We also show how our system ensures the privacy of people in the monitored area. Finally, we validate our system using nine weeks of data from a public library endowed with a camera-based counting system, which generates counts against which we compare those of our counting system. This comparison empirically quantifies the accuracy of our counting system, thereby showing it to be suitable for monitoring public areas. Similarly, the concentration inequality provides a theoretical validation of the system.
Distributed Radio Frequency Cooperation at the Wavelength Level Using Wireless Phase Synchronization
Authors: Serge R. Mghabghab, Sean M. Ellison, Jeffrey A. Nanzer
Date: 2020-10-20 00:00:00

Coordinating the operations of separate wireless systems at the wavelength level can lead to significant improvements in wireless capabilities. We address a fundamental challenge in distributed radio frequency system cooperation - inter-node phase alignment - which must be accomplished wirelessly, and is particularly challenging when the nodes are in relative motion. We present a solution to this problem that is based on a novel combined high-accuracy ranging and frequency transfer technique. Using this approach, we present the design of the first fully wireless distributed system operating at the wavelength level. We demonstrate the system in the first open-loop coherent distributed beamforming experiment. Internode range estimation to support phase alignment was performed using a two-tone stepped frequency waveform with a single pulse, while a two-tone waveform was used for frequency synchronization, where the oscillator of a secondary node was disciplined to the primary node. In this concept, secondary nodes are equipped with an adjunct self-mixing circuit that is able to extract the reference frequency from the captured synchronization waveform. The approach was implemented on a two-node dynamic system using Ettus X310 software-defined radios, with coherent beamforming at 1.5 GHz. We demonstrate distributed beamforming with greater than 90% of the maximum possible coherent gain throughout the displacement of the secondary node over one full cycle of the beamforming frequency.
Diversity in immunogenomics: the value and the challenge
Authors: Kerui Peng, Yana Safonova, Mikhail Shugay, Alice Popejoy, Oscar Rodriguez, Felix Breden, Petter Brodin, Amanda M. Burkhardt, Carlos Bustamante, Van-Mai Cao-Lormeau, Martin M. Corcoran, Darragh Duffy, Macarena Fuentes Guajardo, Ricardo Fujita, Victor Greiff, Vanessa D. Jonsson, Xiao Liu, Lluis Quintana-Murci, Maura Rossetti, Jianming Xie, Gur Yaari, Wei zhang, Malak S. Abedalthagafi, Khalid O. Adekoya, Rahaman A. Ahmed, Wei-Chiao Chang, Clive Gray, Yusuke Nakamura, William D. Lees, Purvesh Khatri, Houda Alachkar, Cathrine Scheepers, Corey T. Watson, Gunilla B. Karlsson Hedestam, Serghei Mangul
Date: 2020-10-20 00:00:00
Tasks: Diversity

With the advent of high-throughput sequencing technologies, the fields of immunogenomics and adaptive immune receptor repertoire research are facing both opportunities and challenges. Adaptive immune receptor repertoire sequencing (AIRR-seq) has become an increasingly important tool to characterize T and B cell responses in settings of interest. However, the majority of AIRR-seq studies conducted so far were performed in individuals of European ancestry, restricting the ability to identify variation in human adaptive immune responses across populations and limiting their applications. As AIRR-seq studies depend on the ability to assign VDJ sequence reads to the correct germline gene segments, efforts to characterize the genomic loci that encode adaptive immune receptor genes in different populations are urgently needed. The availability of comprehensive germline gene databases and further applications of AIRR-seq studies to individuals of non-European ancestry will substantially enhance our understanding of human adaptive immune responses, promote the development of effective diagnostics and treatments, and eventually advance precision medicine.
Evolutionary dynamics in an SI epidemic model with phenotype-structured susceptible compartment
Authors: Tommaso Lorenzi, Andrea Pugliese, Mattia Sensi, Agnese Zardini
Date: 2020-10-20 00:00:00

We present an SI epidemic model whereby a continuous variable captures variability in proliferative potential and resistance to infection among susceptibles. The occurrence of heritable, spontaneous changes in these phenotype and the presence of a fitness trade-off between resistance to infection and proliferative potential are incorporated into the model. The model comprises an ODE for the number of infected individuals that is coupled with a partial integrodifferential equation for the population density of susceptibles through an integral term. The expression for the basic reproduction number $\mathcal{R}_0$ is derived, the disease-free and endemic equilibrium of the model are characterised and a threshold theorem is proved. Analytical results are integrated with numerical simulations of a calibrated version of the model based on the results of artificial selection experiments in a host-parasite system. The results of our mathematical study disentangle the impact of different evolutionary parameters on the spread of infectious diseases and the consequent phenotypic adaption of susceptible individuals. In particular, these results provide a theoretical basis for the observation that infectious diseases exerting stronger selective pressures on susceptibles and being characterised by higher infection rates are more likely to spread. Moreover, our results indicate that spontaneous phenotypic changes in proliferative potential and resistance to infection can either promote or prevent the spread of diseases depending on the strength of selection acting on susceptible individuals prior to infection. Finally, we demonstrate that, when an endemic equilibrium is established, higher levels of resistance to infection and lower degrees of phenotypic heterogeneity are to be expected in the presence of infections which are characterised by lower rates of death and exert stronger selective pressures.
Robust State of Health Estimation of Lithium-ion Batteries Using Convolutional Neural Network and Random Forest
Authors: Niankai Yang, Ziyou Song, Heath Hofmann, Jing Sun
Date: 2020-10-20 00:00:00

The State of Health (SOH) of lithium-ion batteries is directly related to their safety and efficiency, yet effective assessment of SOH remains challenging for real-world applications (e.g., electric vehicle). In this paper, the estimation of SOH (i.e., capacity fading) under partial discharge with different starting and final State of Charge (SOC) levels is investigated. The challenge lies in the fact that partial discharge truncates the data available for SOH estimation, thereby leading to the loss or distortion of common SOH indicators. To address this challenge associated with partial discharge, we explore the convolutional neural network (CNN) to extract indicators for both SOH and changes in SOH ($\Delta$SOH) between two successive charge/discharge cycles. The random forest algorithm is then adopted to produce the final SOH estimate by exploiting the indicators from the CNNs. Performance evaluation is conducted using the partial discharge data with different SOC ranges created from a fast-discharging dataset. The proposed approach is compared with i) a differential analysis-based approach and ii) two CNN-based approaches using only SOH and $\Delta$SOH indicators, respectively. Through comparison, the proposed approach demonstrates improved estimation accuracy and robustness. Sensitivity analysis of the CNN and random forest models further validates that the proposed approach makes better use of the available partial discharge data for SOH estimation.
Balancing quarantine and self-distancing measures in adaptive epidemic networks
Authors: Leonhard Horstmeyer, Christian Kuehn, Stefan Thurner
Date: 2020-10-20 00:00:00

We study the relative importance of two key control measures for epidemic spreading: endogenous social self-distancing and exogenous imposed quarantine. We use the framework of adaptive networks, moment-closure, and ordinary differential equations (ODEs) to introduce several novel models based upon susceptible-infected-recovered (SIR) dynamics. First, we compare computationally expensive, adaptive network simulations with their corresponding computationally highly efficient ODE equivalents and find excellent agreement. Second, we discover that there exists a relatively simple critical curve in parameter space for the epidemic threshold, which strongly suggests that there is a mutual compensation effect between the two mitigation strategies: as long as social distancing and quarantine measures are both sufficiently strong, large outbreaks are prevented. Third, we study the total number of infected and the maximum peak during large outbreaks using a combination of analytical estimates and numerical simulations. Also for large outbreaks we find a similar compensation effect as for the epidemic threshold. This suggests that if there is little incentive for social distancing within a population, drastic quarantining is required, and vice versa. Both pure scenarios are unrealistic in practice. Our models show that only a combination of measures is likely to succeed to control epidemic spreading. Fourth, we analytically compute an upper bound for the total number of infected on adaptive networks, using integral estimates in combination with the moment-closure approximation on the level of an observable. This is a methodological innovation. Our method allows us to elegantly and quickly check and cross-validate various conjectures about the relevance of different network control measures.
An Optimization-and-Simulation Framework for Redesigning University Campus Bus System with Social Distancing
Authors: Gongyu Chen, Xinyu Fei, Huiwen Jia, Xian Yu, Siqian Shen
Date: 2020-10-20 00:00:00

The outbreak of coronavirus disease 2019 (COVID-19) has led to significant challenges for schools, workplaces and communities to return to operations during the pandemic, requiring policymakers to balance individuals' safety and operational efficiency. In this paper, we present our work using mixed-integer programming and simulation for redesigning routes and bus schedules for University of Michigan (UM)'s campus bus system during the COVID-19 pandemic. We propose a hub-and-spoke design and utilize real data of student activities to identify hub locations and bus stops to be used in the new routes. Using the same total number of buses to operate, each new bus route has 50\% or fewer seats being used and takes maximumly 15 minutes, to reduce disease transmission through expiratory aerosol. We sample a variety of scenarios that cover variations of peak demand, social-distancing requirements, and break-down buses, to demonstrate the system resiliency of the new routes and schedules via simulation. The new bus routes are implemented and used by all UM campuses during the academic year 2020-2021, to ensure social distancing and short travel time. Our approach can be generalized to redesign public transit systems with social distancing requirement during the pandemic to reduce passengers' infection risk.
On Consensusability of Linear Interconnected Multi-Agent Systems and Simultaneous Stabilization
Authors: Mustafa Sahin Turan, Liang Xu, Giancarlo Ferrari-Trecate
Date: 2020-10-20 00:00:00

Consensusability of multi-agent systems (MASs) certifies the existence of a distributed controller capable of driving the states of each subsystem to a consensus value. We study the consensusability of linear interconnected MASs (LIMASs) where, as in several real-world applications, subsystems are physically coupled. We show that consensusability is related to the simultaneous stabilizability of multiple LTI systems, and present a novel sufficient condition in form of a linear program for verifying this property. We also derive several necessary and sufficient consensusability conditions for LIMASs in terms of parameters of the subsystem matrices and the eigenvalues of the physical and communication graph Laplacians. The results show that weak physical couplings among subsystems and densely-connected physical and communication graphs are favorable for consensusability. Finally, we validate our results through simulations of networks of supercapacitors and DC microgrids.
Statistical Analysis of the LMS Algorithm for Proper and Improper Gaussian Processes
Authors: Enrique T. R. Pinto, Leonardo S. Resende
Date: 2020-10-20 00:00:00
Tasks: Gaussian Processes

The LMS algorithm is one of the most widely used techniques in adaptive filtering. Accurate modeling of the algorithm in various circumstances is paramount to achieving an efficient adaptive Wiener filter design process. In the recent decades, concerns have been raised on studying improper signals and providing an accurate model of the LMS algorithm for both proper and improper signals. Other models for the LMS algorithm for improper signals available in the scientific literature either make use of the independence assumptions regarding the desired signal and the input signal vector, or are exclusive to proper signals; it is shown that by not considering these assumptions a more general model can be derived. In the presented simulations it is possible to verify that the model introduced in this paper outperforms the other available models.
Real-time Speech Frequency Bandwidth Extension
Date: 2020-10-21 00:00:00
Tasks: Bandwidth Extension, CPU

In this paper we propose a lightweight model for frequency bandwidth extension of speech signals, increasing the sampling frequency from 8kHz to 16kHz while restoring the high frequency content to a level almost indistinguishable from the 16kHz ground truth. The model architecture is based on SEANet (Sound EnhAncement Network), a wave-to-wave fully convolutional model, which uses a combination of feature losses and adversarial losses to reconstruct an enhanced version of the input speech. In addition, we propose a variant of SEANet that can be deployed on-device in streaming mode, achieving an architectural latency of 16ms. When profiled on a single core of a mobile CPU, processing one 16ms frame takes only 1.5ms. The low latency makes it viable for bi-directional voice communication systems.