pwc-archive/papers-with-abstracts huggingface dataset:
Blind Mask to Improve Intelligibility of Non-Stationary Noisy Speech
Authors: F. Farias, R. Coelho
Date: 2020-08-20 00:00:00
Tasks: Noise Estimation, Speech Enhancement

This letter proposes a novel blind acoustic mask (BAM) designed to adaptively detect noise components and preserve target speech segments in time-domain. A robust standard deviation estimator is applied to the non-stationary noisy speech to identify noise masking elements. The main contribution of the proposed solution is the use of this noise statistics to derive an adaptive information to define and select samples with lower noise proportion. Thus, preserving speech intelligibility. Additionally, no information of the target speech and noise signals statistics is previously required to this non-ideal mask. The BAM and three competitive methods, Ideal Binary Mask (IBM), Target Binary Mask (TBM), and Non-stationary Noise Estimation for Speech Enhancement (NNESE), are evaluated considering speech signals corrupted by three non-stationary acoustic noises and six values of signal-to-noise ratio (SNR). Results demonstrate that the BAM technique achieves intelligibility gains comparable to ideal masks while maintaining good speech quality.
Fifteen Years of Progress at Zero Velocity: A Review
Authors: Johan Wahlström, Isaac Skog
Date: 2020-08-20 00:00:00

Fifteen years have passed since the publication of Foxlin's seminal paper "Pedestrian tracking with shoe-mounted inertial sensors". In addition to popularizing the zero-velocity update, Foxlin also hinted that the optimal parameter tuning of the zero-velocity detector is dependent on, for example, the user's gait speed. As demonstrated by the recent influx of related studies, the question of how to properly design a robust zero-velocity detector is still an open research question. In this review, we first recount the history of foot-mounted inertial navigation and characterize the main sources of error, thereby motivating the need for a robust solution. Following this, we systematically analyze current approaches to robust zero-velocity detection, while categorizing public code and data. The article concludes with a discussion on commercialization along with guidance for future research.
Adaptive multi-channel event segmentation and feature extraction for monitoring health outcomes
Authors: Xichen She, Yaya Zhai, Ricardo Henao, Christopher W. Woods, Christopher Chiu, Geoffrey S. Ginsburg, Peter X. K. Song, Alfred O. Hero
Date: 2020-08-20 00:00:00
Tasks: Event Segmentation, Transfer Learning

$\textbf{Objective}$: To develop a multi-channel device event segmentation and feature extraction algorithm that is robust to changes in data distribution. $\textbf{Methods}$: We introduce an adaptive transfer learning algorithm to classify and segment events from non-stationary multi-channel temporal data. Using a multivariate hidden Markov model (HMM) and Fisher's linear discriminant analysis (FLDA) the algorithm adaptively adjusts to shifts in distribution over time. The proposed algorithm is unsupervised and learns to label events without requiring $\textit{a priori}$ information about true event states. The procedure is illustrated on experimental data collected from a cohort in a human viral challenge (HVC) study, where certain subjects have disrupted wake and sleep patterns after exposure to a H1N1 influenza pathogen. $\textbf{Results}$: Simulations establish that the proposed adaptive algorithm significantly outperforms other event classification methods. When applied to early time points in the HVC data the algorithm extracts sleep/wake features that are predictive of both infection and infection onset time. $\textbf{Conclusion}$: The proposed transfer learning event segmentation method is robust to temporal shifts in data distribution and can be used to produce highly discriminative event-labeled features for health monitoring. $\textbf{Significance}$: Our integrated multisensor signal processing and transfer learning method is applicable to many ambulatory monitoring applications.
A matter of time: Using dynamics and theory to uncover mechanisms of transcriptional bursting
Authors: Nicholas C. Lammers, Yang Joon Kim, Jiaxi Zhao, Hernan G. Garcia
Date: 2020-08-20 00:00:00
Tasks: Decision Making

Eukaryotic transcription generally occurs in bursts of activity lasting minutes to hours; however, state-of-the-art measurements have revealed that many of the molecular processes that underlie bursting, such as transcription factor binding to DNA, unfold on timescales of seconds. This temporal disconnect lies at the heart of a broader challenge in physical biology of predicting transcriptional outcomes and cellular decision-making from the dynamics of underlying molecular processes. Here, we review how new dynamical information about the processes underlying transcriptional control can be combined with theoretical models that predict not only averaged transcriptional dynamics, but also their variability, to formulate testable hypotheses about the molecular mechanisms underlying transcriptional bursting and control.
On the Optimal 3D Placement of a UAV Base Station for Maximal Coverage of UAV Users
Authors: Nesrine Cherif, Wael Jaafar, Halim Yanikomeroglu, Abbas Yongacoglu
Date: 2020-08-21 00:00:00

Unmanned aerial vehicles (UAVs) can be users that support new applications, or be communication access points that serve terrestrial and/or aerial users. In this paper, we focus on the connectivity problem of aerial users when they are exclusively served by aerial base stations (BS), i.e., UAVBSs. Specifically, the 3D placement problem of a directional antenna equipped UAV-BS, aiming to maximize the number of covered aerial users under a spectrum sharing policy with terrestrial networks, is investigated. Given a known spectrum sharing policy between the aerial and terrestrial networks, we propose a 3D placement algorithm that achieves optimality. Simulation results show the performance of our approach, in terms of number of covered aerial users, for different configurations and parameters, such as the spectrum sharing policy, antenna beamwidth, transmit power, and aerial users density. These results represent novel guidelines for exclusive aerial networks deployment and applications, distinctively for orthogonal and non-orthogonal spectrum sharing policies with terrestrial networks.
Empirical Likelihood Covariate Adjustment for Regression Discontinuity Designs
Authors: Jun Ma, Zhengfei Yu
Date: 2020-08-21 00:00:00
Tasks: regression

This paper proposes a versatile covariate adjustment method that directly incorporates covariate balance in regression discontinuity (RD) designs. The new empirical entropy balancing method reweights the standard local polynomial RD estimator by using the entropy balancing weights that minimize the Kullback--Leibler divergence from the uniform weights while satisfying the covariate balance constraints. Our estimator can be formulated as an empirical likelihood estimator that efficiently incorporates the information from the covariate balance condition as correctly specified over-identifying moment restrictions, and thus has an asymptotic variance no larger than that of the standard estimator without covariates. We demystify the asymptotic efficiency gain of Calonico, Cattaneo, Farrell, and Titiunik (2019)'s regression-based covariate-adjusted estimator, as their estimator has the same asymptotic variance as ours. Further efficiency improvement from balancing over sieve spaces is possible if our entropy balancing weights are computed using stronger covariate balance constraints that are imposed on functions of covariates. We then show that our method enjoys favorable second-order properties from empirical likelihood estimation and inference: the estimator has a small (bounded) nonlinearity bias, and the likelihood ratio based confidence set admits a simple analytical correction that can be used to improve coverage accuracy. The coverage accuracy of our confidence set is robust against slight perturbation to the covariate balance condition, which may happen in cases such as data contamination and misspecified "unaffected" outcomes used as covariates. The proposed entropy balancing approach for covariate adjustment is applicable to other RD-related settings.
Effects of drug resistance in the tumour-immune system with chemotherapy treatment
Authors: José Trobia, Enrique C Gabrick, Evandro G Seifert, Fernando S Borges, Paulo R Protachevicz, José D Szezech Jr, Kelly C Iarosz, Moises S Santos, Iberê L Caldas, Kun Tian, Hai-Peng Ren, Celso Grebogi, Antonio M Batista
Date: 2020-08-21 00:00:00

Cancer is a term used to refer to a large set of diseases. The cancerous cells grow and divide and, as a result, they form tumours that grow in size. The immune system recognise the cancerous cells and attack them, though, it can be weakened by the cancer. One type of cancer treatment is chemotherapy, which uses drugs to kill cancer cells. Clinical, experimental, and theoretical research has been developed to understand the dynamics of cancerous cells with chemotherapy treatment, as well as the interaction between tumour growth and immune system. We study a mathematical model that describes the cancer growth, immune system response, and chemotherapeutic agents. The immune system is composed of resting cells that are converted to hunting cells to combat the cancer. In this work, we consider drug sensitive and resistant cancer cells. We show that the tumour growth can be controlled not only by means of different chemotherapy protocols, but also by the immune system that attacks both sensitive and resistant cancer cells. Furthermore, for all considered protocols, we demonstrate that the time delay from resting to hunting cells plays a crucial role in the combat against cancer cells.
FairFly: A Fair Motion Planner for Fleets of Autonomous UAVs in Urban Airspace
Date: 2020-08-21 00:00:00
Tasks: Fairness

We present a solution to the problem of fairly planning a fleet of Unmanned
Aerial Vehicles (UAVs) that have different missions and operators, such that no
one operator unfairly gets to finish its missions early at the expense of
others - unless this was explicitly negotiated. When hundreds of UAVs share an
urban airspace, the relevant authorities should allocate corridors to them such
that they complete their missions, but no one vehicle is accidentally given an
exceptionally fast path at the expense of another, which is thus forced to wait
and waste energy. Our solution, FairFly, addresses the fair planning question
for general autonomous systems, including UAV fleets, subject to complex
missions typical of urban applications. FairFly formalizes each mission in
temporal logic. An offline search finds the fairest paths that satisfy the
missions and can be flown by the UAVs, leading to lighter online control load.
It allows explicit negotiation between UAVs to enable imbalanced path durations
if desired. We present three fairness notions, including one that reduces
energy consumption. We validate our results in simulation, and demonstrate a
lighter computational load and less UAV energy consumption as a result of
flying fair trajectories.
Dispersal-induced resilience to stochastic environmental fluctuations in populations with Allee effect
Authors: Rodrigo Crespo, Javier Jarillo, Francisco J. Cao-García
Date: 2020-08-21 00:00:00
Tasks: valid

Many species are unsustainable at small population densities (Allee Effect), i.e., below a threshold named Allee threshold, the population decreases instead of growing. In a closed local population, environmental fluctuations always lead to extinction. Here, we show how, in spatially extended habitats, dispersal can lead to a sustainable population in a region, provided the amplitude of environmental fluctuations is below an extinction threshold. We have identified two types of sustainable populations: high-density and low-density populations (through a mean-field approximation, valid in the limit of large dispersal length). Our results show that patches where population is high, low or extinct, coexist when the population is close to global extinction (even for homogeneous habitats). The extinction threshold is maximum for characteristic dispersal distances much larger than the spatial scale of synchrony of environmental fluctuations. The extinction threshold increases proportionally to the square root of the dispersal rate and decreases with the Allee threshold. The low-density population solution can allow understanding difficulties in recovery after harvesting. This theoretical framework provides a novel approach to address other factors, such as habitat fragmentation or harvesting, impacting population resilience to environmental fluctuations.
Deep Learning for Wireless Coded Caching with Unknown and Time-Variant Content Popularity
Authors: Zhe Zhang, Meixia Tao
Date: 2020-08-21 00:00:00
Tasks: Clustering

Coded caching is effective in leveraging the accumulated storage size in wireless networks by distributing different coded segments of each file in multiple cache nodes. This paper aims to find a wireless coded caching policy to minimize the total discounted network cost, which involves both transmission delay and cache replacement cost, using tools from deep learning. The problem is known to be challenging due to the unknown, time-variant content popularity as well as the continuous, high-dimensional action space. We first propose a clustering based long short-term memory (C-LTSM) approach to predict the number of content requests using historical request information. This approach exploits the correlation of the historical request information between different files through clustering. Based on the predicted results, we then propose a supervised deep deterministic policy gradient (SDDPG) approach. This approach, on one hand, can learn the caching policy in continuous action space by using the actor-critic architecture. On the other hand, it accelerates the learning process by pre-training the actor network based on the solution of an approximate problem that minimizes the per-slot cost. Real-world trace-based numerical results show that the proposed prediction and caching policy using deep learning outperform the considered existing methods.
Low-Complexity Detection of Small Frequency Changes by the Generalized LMPU Test
Authors: Eyal Levy, Tirza Routtenberg
Date: 2020-08-21 00:00:00

In this paper, we consider the detection of a small change in the frequency of sinusoidal signals, which arises in various signal processing applications. The generalized likelihood ratio test (GLRT) for this problem uses the maximum likelihood (ML) estimator of the frequency, and therefore suffers from high computational complexity. In addition, the GLRT is not necessarily optimal and its performance may degrade for non-asymptotic scenarios that are characterized by close hypotheses and small sample sizes. In this paper we propose a new detection method, named the generalized locally most powerful unbiased (GLMPU) test, which is a general method for local detection in the presence of nuisance parameters. A closed-form expression of the GLMPU test is developed for the detection of frequency deviation in the case where the complex amplitudes of the measured signals are unknown. Numerical simulations show improved performance over the GLRT in terms of probability of detection performance and computational complexity.
Coherent Multi-antenna Receiver for BPSK-modulated Ambient Backscatter Tags
Authors: Xiyu Wang, Hüseyin Yiğitler, Ruifeng Duan, Estifanos Yohannes Menta, Riku Jäntti
Date: 2020-08-21 00:00:00
Tasks: TAG

Ambient Backscatter Communication (AmBC) is an emerging communication technology that can enable green Internet-of-Things deployments. The widespread acceptance of this paradigm is limited by low Signal-to-Interference-Plus-Noise Ratio (SINR) of the signal impinging on the receiver antenna due to the strong direct path interference and unknown ambient signal. The adverse impact of these two factors can be mitigated by using non-coherent multi-antenna receivers, which is known to require higher SINR to reach Bit-Error-Rate (BER) performance of coherent receivers. However, in literature, coherent receivers for AmBC systems are little-studied because of unknown ambient signal, unknown location of AmBC tags, and varying channel conditions. In this paper, a coherent multi-antenna receiver, which does not require a prior information of the ambient signal, for decoding Binary-Phase-shift-Keying (BPSK) modulated signal is presented. The performance of the proposed receiver is compared with the ideal coherent receiver that has a perfect phase information, and also with the performance of non-coherent receiver, which assumes distributions for ambient signal and phase offset caused by excess length of the backscatter path. Comparative simulation results show the designed receiver can achieve the same BER-performance of the ideal coherent receiver with 1-dB more SINR, which corresponds to 5-dB or more gain with respect to non-coherent reception of On-Off-Keying modulated signals. Variation of the detection performance with the tag location shows that the coverage area is in the close vicinity of the transmitter and a larger region around the receiver, which is consistent with the theoretical results.
A theoretical look at ELECTRE TRI-nB and related sorting models
Authors: Denis Bouyssou, Thierry Marchant, Marc Pirlot
Date: 2020-08-20 00:00:00

Electre Tri is a set of methods designed to sort alternatives evaluated on several criteria into ordered categories. In these methods, alternatives are assigned to categories by comparing them with reference profiles that represent either the boundary or central elements of the category. The original Electre Tri-B method uses one limiting profile for separating a category from the category below. A more recent method, Electre Tri-nB, allows one to use several limiting profiles for the same purpose. We investigate the properties of Electre Tri-nB using a conjoint measurement framework. When the number of limiting profiles used to define each category is not restricted, Electre Tri-nB is easy to characterize axiomatically and is found to be equivalent to several other methods proposed in the literature. We extend this result in various directions.
Dynamic mmWave Channel Emulation in a Cost-Effective MPAC with Dominant-Cluster Concept
Authors: Xuesong Cai, Yang Miao, Jinxing Li, Fredrik Tufvesson, Gert Frølund Pedersen, Wei Fan
Date: 2020-08-21 00:00:00

Millimeter-Wave (mmWave) massive multiple-input multiple-output (MIMO) has been considered as a key enabler for the fifth-generation (5G) communications. It is essential to design and test mmWave 5G devices under various realistic scenarios, since the radio propagation channels pose intrinsic limitations on the performance. This requires emulating realistic dynamic mmWave channels in a reproducible manner in laboratories, which is the goal of this paper. In this contribution, we firstly illustrate the dominant-cluster(s) concept, where the non-dominant clusters in the mmWave channels are pruned, for mmWave 5G devices applying massive MIMO beamforming. This demonstrates the importance and necessity to accurately emulate the mmWave channels at a cluster level rather than the composite-channel level. Thus, an over-the-air (OTA) emulation strategy for dynamic mmWave channels is proposed based on the concept of dominant-cluster(s) in a sectored multiprobe anechoic chamber (SMPAC). The key design parameters including the probe number and the angular spacing of probes are investigated through comprehensive simulations. A cost-effective switchcircuit is also designed for this purpose and validated in the simulation. Furthermore, a dynamic mmWave channel measured in an indoor scenario at 28-30 GHz is presented, where the proposed emulation strategy is also validated by reproducing the measured reality.
TRICE: A Channel Estimation Framework for RIS-Aided Millimeter-Wave MIMO Systems
Date: 2020-08-21 00:00:00

We consider the channel estimation problem in point-to-point reconfigurable intelligent surface (RIS)-aided millimeter-wave (mmWave) MIMO systems. By exploiting the low-rank nature of mmWave channels in the angular domains, we propose a non-iterative Two-stage RIS-aided Channel Estimation (TRICE) framework, where every stage is formulated as a multidimensional direction-of-arrival (DOA) estimation problem. As a result, our TRICE framework is very general in the sense that any efficient multidimensional DOA estimation solution can be readily used in every stage to estimate the associated channel parameters. Numerical results show that the TRICE framework has a lower training overhead and a lower computational complexity, as compared to benchmark solutions.
Optimum Multi-Antenna Ambient Backscatter Receiver for General Binary-Modulated Signal
Authors: Xiyu Wang, Hüseyin Yiğitler, Riku Jäntti
Date: 2020-08-21 00:00:00

Ambient backscatter communication (AmBC) is becoming increasingly popular for enabling green communication amidst the continual development of the Internet-of-things paradigm. Efforts have been put into backscatter signal detection as the detection performance is limited by the low signal-to-interference-plus-noise ratio (SINR) of the signal at the receiver. The low SINR can be improved by adopting a multi-antenna receiver. In this paper, the optimum multi-antenna receiver that does not impose any constraints on the types of binary modulation performed by the backscatter device and the waveform used by the ambient source system is studied. The proposed receiver owns a simple structure formed by two beamformers. Bit error rate (BER) performances of the optimum receiver are derived under constant-amplitude ambient signal and Gaussian-distributed ambient signal. Moreover, to facilitate the implementation of the optimum receiver, a simplified receiver is proposed and practical approximations to required beamformers are provided. The derived optimum receiver avoids the complex direct path interference cancellation and coherent reception, but exploits the fact that backscatter signal changes the composite channel impinging at the receiver and the directivity of receiver antenna array. Comparative simulation results show that the performance of the optimum receiver achieves the same performance as the coherent receiver even though it realizes non-coherent reception. The studied receivers provide high flexibility for implementing simple and low-cost receivers in different AmBC systems.
Achieving Disaster-Resilient Distribution Systems via Emergency Response Resources: A Practical Approach
Date: 2020-08-21 00:00:00
Tasks: Prediction

This paper presents a practical approach to utilizing emergency response
resources (ERRs) and post-disaster available distributed energy resources
(PDA-DERs) to improve the resilience of power distribution systems against
natural disasters. The proposed approach consists of two sequential steps:
first, the minimum amount of ERRs is determined in a pre-disaster planning
model; second, a post-disaster restoration model is proposed to co-optimize the
dispatch of pre-planned ERRs and PDA-DERs to minimize the impact of disasters
on customers, i.e., unserved energy for the entire restoration window. Compared
with existing restoration strategies using ERRs, the proposed approach is more
tractable since 1) in the pre-disaster stage, the needed EERs are determined
based on the prediction of energy shortage and disaster-induced damages using
machine learning-based algorithms (i.e., cost-sensitive-RFQRF for prediction of
outage customers, random forest for prediction of outage duration, and CART for
prediction of disaster-induced damages); 2) in the post-disaster stage, the
super-node approximation (SNA) and the convex hull relaxation (CHR) of
distribution networks are introduced to achieve the best trade-off between
computational burden and accuracy. Tests of the proposed approach on IEEE test
feeders demonstrated that a combination of SNA and CHR remarkably reduces the
solution time of the post-disaster restoration model.