pwc-archive/papers-with-abstracts huggingface dataset:
Physical Layer Security for V2I Communications: Reflecting Surfaces Vs. Relaying
Authors: Neji Mensi, Danda B. Rawat, Elyes Balti
Date: 2020-10-14 00:00:00

Wireless vehicular network (WVN) is exponentially gaining attention from industries and researchers since it is the Keystone of intelligent transportation systems (ITS). Despite the sophisticated features and services that it can offer, it is susceptible to networking attacks such as eavesdropping threats where the confidential transmitted signal could be overheard by a malicious entity. In this paper, we intend to study the physical layer security (PLS) where we consider the eavesdropping attack for vehicle-to-infrastructure (V2I) communications. We analyze the average secrecy capacity, under different scenarios by comparing the performances of employing the decode-and-forward (D relay, the amplify-and-forward fixed gain (AFFG) relay, and the intelligent reflecting surface (IRS). Actually, this comparison investigates the efficiency of IRS comparing to the traditional relaying systems, since it was introduced as a novel paradigm in wireless technology with highly promising potential, especially in 5G and 6G.
Joint SO(3)-Spectral Domain Filtering of Spherical Signals in the Presence of Anisotropic Noise
Authors: Adeem Aslam, Zubair Khalid
Date: 2020-10-14 00:00:00

We present a joint SO(3)-spectral domain filtering framework using directional spatially localized spherical harmonic transform (DSLSHT), for the estimation and enhancement of random anisotropic signals on the sphere contaminated by random anisotropic noise. We design an optimal filter for filtering the DSLSHT representation of the noise-contaminated signal in the joint SO(3)-spectral domain. The filter is optimal in the sense that the filtered representation in the joint domain is the minimum mean square error estimate of the DSLSHT representation of the underlying (noise-free) source signal. We also derive a least-square solution for the estimate of the source signal from the filtered representation in the joint domain. We demonstrate the capability of the proposed filtering framework using the Earth topography map in the presence of anisotropic, zero-mean, uncorrelated Gaussian noise, and compare its performance with the joint spatial-spectral domain filtering framework.
Spatial-Slepian Transform on the Sphere
Authors: Adeem Aslam, Zubair Khalid
Date: 2020-10-14 00:00:00

We present spatial-Slepian transform~(SST) for the representation of signals on the sphere to support localized signal analysis. We use well-optimally concentrated Slepian functions, obtained by solving the Slepian spatial-spectral concentration problem of finding bandlimited and spatially optimally concentrated functions on the sphere, to formulate the proposed transform and obtain the joint spatial-Slepian domain representation of the signal. Due to the optimal energy concentration of the Slepian functions in the spatial domain, the proposed spatial-Slepian transform allows us to probe spatially localized content of the signal. Furthermore, we present an inverse transform to recover the signal from the spatial-Slepian coefficients, and show that well-optimally concentrated rotated Slepian functions form a tight frame on the sphere. We develop an algorithm for the fast computation of the spatial-Slepian transform and carry out computational complexity analysis. We present the formulation of SST for zonal Slepian functions, which are spatially optimally concentrated in the polar cap~(axisymmetric) region, and provide an illustration using the Earth topography map. To demonstrate the utility of the proposed transform, we carry out localized variation analysis; employing SST for detecting hidden localized variations in the signal.
Optimal Insurance under Maxmin Expected Utility
Authors: Corina Birghila, Tim J. Boonen, Mario Ghossoub
Date: 2020-10-14 00:00:00

We examine a problem of demand for insurance indemnification, when the insured is sensitive to ambiguity and behaves according to the Maxmin-Expected Utility model of Gilboa and Schmeidler (1989), whereas the insurer is a (risk-averse or risk-neutral) Expected-Utility maximizer. We characterize optimal indemnity functions both with and without the customary ex ante no-sabotage requirement on feasible indemnities, and for both concave and linear utility functions for the two agents. This allows us to provide a unifying framework in which we examine the effects of the no-sabotage condition, marginal utility of wealth, belief heterogeneity, as well as ambiguity (multiplicity of priors) on the structure of optimal indemnity functions. In particular, we show how the singularity in beliefs leads to an optimal indemnity function that involves full insurance on an event to which the insurer assigns zero probability, while the decision maker assigns a positive probability. We examine several illustrative examples, and we provide numerical studies for the case of a Wasserstein and a Renyi ambiguity set.
Decomposing non-stationary signals with time-varying wave-shape functions
Authors: Marcelo A. Colominas, Hau-Tieng Wu
Date: 2020-10-14 00:00:00
Tasks: Computational Efficiency, regression, Time Series, Time Series Analysis

Modern time series are usually composed of multiple oscillatory components, with time-varying frequency and amplitude contaminated by noise. The signal processing mission is further challenged if each component has an oscillatory pattern, or the wave-shape function, far from a sinusoidal function, and the oscillatory pattern is even changing from time to time. In practice, if multiple components exist, it is desirable to robustly decompose the signal into each component for various purposes, and extract desired dynamics information. Such challenges have raised a significant amount of interest in the past decade, but a satisfactory solution is still lacking. We propose a novel {\em nonlinear regression scheme} to robustly decompose a signal into its constituting multiple oscillatory components with time-varying frequency, amplitude and wave-shape function. We coined the algorithm {\em shape-adaptive mode decomposition (SAMD)}. In addition to simulated signals, we apply SAMD to two physiological signals, impedance pneumography and electroencephalography. Comparison with existing solutions, including linear regression, recursive diffeomorphism-based regression and multiresolution mode decomposition, shows that our proposal can provide an accurate and meaningful decomposition with computational efficiency.
Nanopores -- a Versatile Tool to Study Protein Dynamics
Date: 2020-10-14 00:00:00

Proteins are the active working horses in our body. These biomolecules
perform all vital cellular functions from DNA replication and general
biosynthesis to metabolic signaling and environmental sensing. While static 3D
structures are now readily available, observing the functional cycle of
proteins - involving conformational changes and interactions - remains very
challenging, e.g., due to ensemble averaging. However, time-resolved
information is crucial to gain a mechanistic understanding of protein function.
Single-molecule techniques such as FRET and force spectroscopies provide
answers but can be limited by the required labelling, a narrow time bandwidth,
and more. Here, we describe electrical nanopore detection as a tool for probing
protein dynamics. With a time bandwidth ranging from microseconds to hours, it
covers an exceptionally wide range of timescales that is very relevant for
protein function. First, we discuss the working principle of label-free
nanopore experiments, various pore designs, instrumentation, and the
characteristics of nanopore signals. In the second part, we review a few
nanopore experiments that solved research questions in protein science, and we
compare nanopores to other single-molecule techniques. We hope to make
electrical nanopore sensing more accessible to the biochemical community, and
to inspire new creative solutions to resolve a variety of protein dynamics -
one molecule at a time.
Reconfigurable Intelligent Surface: Design the Channel -- a New Opportunity for Future Wireless Networks
Authors: Miguel Dajer, Zhengxiang Ma, Leonard Piazzi, Narayan Prasad, Xiao-Feng Qi, Baoling Sheen, Jin Yang, Guosen Yue
Date: 2020-10-14 00:00:00

In this paper, we survey state-of-the-art research outcomes in the burgeoning field of reconfigurable intelligent surface (RIS) in view of its potential for significant performance enhancement for next generation wireless communication networks by means of adapting the propagation environment. Emphasis has been placed on several aspects gating the commercially viability of a future network deployment. Comprehensive summaries are provided for practical hardware design considerations and broad implications of artificial intelligence techniques, so are in-depth outlooks on salient aspects of system models, use cases, and physical layer optimization techniques.
Comparison between instrumental variable and mediation-based methods for reconstructing causal gene networks in yeast
Authors: Adriaan-Alexander Ludl, Tom Michoel
Date: 2020-10-14 00:00:00
Tasks: Causal Inference

Causal gene networks model the flow of information within a cell, but reconstructing them from omics data is challenging because correlation does not imply causation. Combining genomics and transcriptomics data from a segregating population allows to orient the direction of causality between gene expression traits using genomic variants. Instrumental-variable methods (IV) use a local expression quantitative trait locus (eQTL) as a randomized instrument for a gene's expression level, and assign target genes based on distal eQTL associations. Mediation-based methods (ME) additionally require that distal eQTL associations are mediated by the source gene. Here we used Findr, a software providing uniform implementations of IV, ME, and coexpression-based methods, a recent dataset of 1,012 segregants from a cross between two budding yeast strains, and the YEASTRACT database of known transcriptional interactions to compare causal gene network inference methods. We found that causal inference methods result in a significant overlap with the ground-truth, whereas coexpression did not perform better than random. A subsampling analysis revealed that the performance of ME decreases at large sample sizes, due to a loss of sensitivity when residual correlations become significant. IV methods contain false positive predictions, due to genomic linkage between eQTL instruments. IV and ME methods also have complementary roles for identifying causal genes underlying transcriptional hotspots. IV methods correctly predicted STB5 targets for a hotspot centred on the transcription factor STB5, whereas ME failed due to Stb5p auto-regulating its own expression. ME suggests a new candidate gene, DNM1, for a hotspot on Chr XII, where IV methods could not distinguish between multiple genes located within the hotspot.
Modeling Microglia Activation and Inflammation-Based Neuroprotectant Strategies During Ischemic Stroke
Authors: Sara Amato, Andrea Arnold
Date: 2020-10-15 00:00:00

Neural inflammation immediately follows the onset of ischemic stroke. During this process, microglial cells can be activated into two different phenotypes: the M1 phenotype, which can worsen brain injury by producing pro-inflammatory cytokines; or the M2 phenotype, which can aid in long term recovery by producing anti-inflammatory cytokines. In this study, we formulate a nonlinear system of differential equations to model the activation of microglia post-ischemic stroke, which includes bidirectional switching between the microglia phenotypes, as well as the interactions between these cells and the cytokines that they produce. Further, we explore neuroprotectant-based modeling strategies to suppress the activation of the detrimental M1 phenotype, while promoting activation of the beneficial M2 phenotype. Through use of global sensitivity techniques, we analyze the effects of the model parameters on the ratio of M1 to M2 microglia and the total number of activated microglial cells in the system over time. Results demonstrate the significance of bidirectional microglia phenotype switching on the ratio of M1 to M2 microglia, in both the absence and presence of neuroprotectant terms. Simulations further suggest that early inhibition of M1 activation and support of M2 activation leads to a decreased minimum ratio of M1 to M2 microglia and allows for a larger number of M2 than M1 cells for a longer time period.
Minimizing Pumping Energy Cost in Real-time Operations of Water Distribution Systems using Economic Model Predictive Control
Authors: Ye Wang, Kevin Too Yok, Wenyan Wu, Angus R. Simpson, Erik Weyer, Chris Manzie
Date: 2020-10-15 00:00:00
Tasks: Management, Model Predictive Control, Scheduling

Optimizing pump operations is a challenging task for real-time management of water distribution systems (WDSs). With suitable pump scheduling, pumping costs can be significantly reduced. In this research, a novel economic model predictive control (EMPC) framework for real-time management of WDSs is proposed. Optimal pump operations are selected based on predicted system behavior over a receding time horizon with the aim to minimize the total pumping energy cost. Time-varying electricity tariffs are considered while all the required water demands are satisfied. The novelty of this framework is to choose the number of pumps to operate in each pump station as decision variables in order to optimize the total pumping energy costs. By using integer programming, the proposed EMPC is applied to a benchmark case study, the Richmond Pruned network. The simulation with an EPANET hydraulic simulator is implemented. Moreover, a comparison of the results obtained using the proposed EMPC with those obtained using trigger-level control demonstrates significant economic benefits of the proposed EMPC.
Lightweight End-to-End Speech Recognition from Raw Audio Data Using Sinc-Convolutions
Authors: Ludwig Kürzinger, Nicolas Lindae, Palle Klewitz, Gerhard Rigoll
Date: 2020-10-15 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), speech-recognition, Speech Recognition

Many end-to-end Automatic Speech Recognition (ASR) systems still rely on pre-processed frequency-domain features that are handcrafted to emulate the human hearing. Our work is motivated by recent advances in integrated learnable feature extraction. For this, we propose Lightweight Sinc-Convolutions (LSC) that integrate Sinc-convolutions with depthwise convolutions as a low-parameter machine-learnable feature extraction for end-to-end ASR systems. We integrated LSC into the hybrid CTC/attention architecture for evaluation. The resulting end-to-end model shows smooth convergence behaviour that is further improved by applying SpecAugment in time-domain. We also discuss filter-level improvements, such as using log-compression as activation function. Our model achieves a word error rate of 10.7% on the TEDlium v2 test dataset, surpassing the corresponding architecture with log-mel filterbank features by an absolute 1.9%, but only has 21% of its model size.
Vehicular Networks for Combating a Worldwide Pandemic: Preventing the Spread of COVID-19
Authors: Ahmet M. Elbir, Gokhan Gurbilek, Burak Soner, Anastasios K. Papazafeiropoulos, Pandelis Kourtessis, Sinem Coleri
Date: 2020-10-15 00:00:00
Tasks: Humanitarian

As a worldwide pandemic, the coronavirus disease-19 (COVID-19) has caused serious restrictions in people's social life, along with the loss of lives, the collapse of economies and the disruption of humanitarian aids. Despite the advance of technological developments, we, as researchers, have witnessed that several issues need further investigation for a better response to a pandemic outbreak. Therefore, researchers recently started developing ideas to stop or at least reduce the spread of the pandemic. While there have been some prior works on wireless networks for combating a pandemic scenario, vehicular networks and their potential bottlenecks have not yet been fully examined. Furthermore, the vehicular scenarios can be identified as the locations, where the social distancing is mostly violated. With this motivation, this article provides an extensive discussion on vehicular networking for combating a pandemic. We provide the major applications of vehicular networking for combating COVID-19 in public transportation, in-vehicle diagnosis, border patrol and social distance monitoring. Next, we identify the unique characteristics of the collected data in terms of privacy, flexibility and coverage, then highlight corresponding future directions in privacy preservation, resource allocation, data caching and data routing. We believe that this work paves the way for the development of new products and algorithms that can facilitate the social life and help controlling the spread of the pandemic.
Comment: Individualized Treatment Rules Under Endogeneity
Date: 2020-10-15 00:00:00

This note discusses two recent studies on identification of individualized
treatment rules using instrumental variables---Cui and Tchetgen Tchetgen (2020)
and Qiu et al. (2020). It also proposes identifying assumptions that are
alternative to what is used in both studies.
Short-term Wind Speed Forecasting based on LSSVM Optimized by Elitist QPSO
Authors: Ephrem Admasu Yekun, Alem Haddush Fitwi, S. Karpaga Selvi, Anubhav Kumar
Date: 2020-10-15 00:00:00
Tasks: Time Series, Time Series Analysis

Nowadays, wind power is considered as one of the most widely used renewable energy applications due to its efficient energy use and low pollution. In order to maintain high integration of wind power into the electricity market, efficient models for wind speed forecasting are in high demand. The non-stationary and nonlinear characteristics of wind speed, however, makes the task of wind speed forecasting challenging. LSSVM has proven to be a good forecasting algorithm mainly for time-series applications such as wind data. To boost the learning performance and generalization capability of the algorithm, LSSVM has two hyperparameters, known as the regularization and kernel parameters, that require careful tuning. In this paper, a modified QPSO algorithm is proposed that uses the principle of transposon operators to breed the personal best and global best particles of QPSO and improve global searching capabilities. The optimization algorithm is then used to generate optimum values for the LSSVM hyperparameters. Finally, the performance of the proposed model is compared with previously known PSO and QPSO optimized LSSVM models. Empirical results show that the proposed model displayed improved performance compared to the competitive methods.
High-resolution single-shot phase-shifting interference microscopy using deep neural network for quantitative phase imaging of biological samples
Authors: Sunil Bhatt, Ankit Butola, Sheetal Raosaheb Kanade, Anand Kumar, Dalip Singh Mehta
Date: 2020-10-14 00:00:00

White light phase-shifting interference microscopy (WL-PSIM) is a prominent technique for high-resolution quantitative phase imaging (QPI) of industrial and biological specimens. However, multiple interferograms with accurate phase-shifts are essentially required in WL-PSIM for measuring the accurate phase of the object. Here, we present single-shot phase-shifting interferometric techniques for accurate phase measurement using filtered white light phase-shifting interference microscopy (F-WL-PSIM) and deep neural network (DNN). The methods are incorporated by training the DNN to generate 1) four phase-shifted frames and 2) direct phase from a single interferogram. The training of network is performed on two different samples i.e., optical waveguide and MG63 osteosarcoma cells. Further, performance of F-WL-PSIM+DNN framework is validated by comparing the phase map extracted from network generated and experimentally recorded interferograms. The current approach can further strengthen QPI techniques for high-resolution phase recovery using a single frame for different biomedical applications.
Muse: Multi-modal target speaker extraction with visual cues
Authors: Zexu Pan, Ruijie Tao, Chenglin Xu, Haizhou Li
Date: 2020-10-15 00:00:00
Tasks: Target Speaker Extraction

Speaker extraction algorithm relies on the speech sample from the target speaker as the reference point to focus its attention. Such a reference speech is typically pre-recorded. On the other hand, the temporal synchronization between speech and lip movement also serves as an informative cue. Motivated by this idea, we study a novel technique to use speech-lip visual cues to extract reference target speech directly from mixture speech during inference time, without the need of pre-recorded reference speech. We propose a multi-modal speaker extraction network, named MuSE, that is conditioned only on a lip image sequence. MuSE not only outperforms other competitive baselines in terms of SI-SDR and PESQ, but also shows consistent improvement in cross-dataset evaluations.
Multiscale Optimal Filtering on the Sphere
Authors: Adeem Aslam, Zubair Khalid, Jason D. McEwen
Date: 2020-10-15 00:00:00
Tasks: Denoising

We present a framework for the optimal filtering of spherical signals contaminated by realizations of an additive, zero-mean, uncorrelated and anisotropic noise process on the sphere. Filtering is performed in the wavelet domain given by the scale-discretized wavelet transform on the sphere. The proposed filter is optimal in the sense that it minimizes the mean square error between the filtered wavelet representation and wavelet representation of the noise-free signal. We also present a simplified formulation of the filter for the case when azimuthally symmetric wavelet functions are used. We demonstrate the use of the proposed optimal filter for denoising of an Earth topography map in the presence of additive, zero-mean, uncorrelated and white Gaussian noise, and show that the proposed filter performs better than the hard thresholding method and weighted spherical harmonic~(weighted-SPHARM) signal estimation framework.
Mass Flow Analysis of SARS-CoV-2 for quantified COVID-19 Risk Analysis
Date: 2020-10-29 00:00:00

How may exposure risks to SARS-CoV-2 be assessed quantitatively? The material
metabolism approach of Industrial Ecology can be applied to the mass flows of
these virions by their numbers, as a key step in the analysis of the current
pandemic. Several transmission routes of SARS-2 from emission by a person to
exposure of another person have been modelled and quantified. Start is a
COVID-19 illness progression model specifying rising emissions by an infected
person: the human virion factory. The first route covers closed spaces, with an
emission, concentration, and decay model quantifying exposure. A next set of
routes covers person-to-person contacts mostly in open spaces, modelling the
spatial distribution of exhales towards inhalation. These models also cover
incidental exposures, like coughs and sneezes, and exposure through objects.
Routes through animal contacts, excrements, and food, have not been quantified.
Potential exposures differ by six orders of magnitude. Closed rooms, even with
reasonably (VR 2) to good (VR 5) ventilation, constitute the major exposure
risks. Close person-to-person contacts of longer duration create two orders of
magnitude lower exposure risks. Open spaces may create risks an order of
magnitude lower again. Burst of larger droplets may cause a common cold but not
viral pneumonia as the virions in such droplets cannot reach the alveoli.
Fomites have not shown viable viruses in hospitals, let alone infections.
Infection by animals might be possible, as by cats and ferrets kept as pets.
These results indicate priority domains for individual and collective measures.
The wide divergence in outcomes indicates robustness to most modelling and data
improvements, hardly leading to major changes in relative exposure potentials.
However, models and data can substantially be improved.