pwc-archive/papers-with-abstracts huggingface dataset:
Extinction and quasi-stationarity for discrete-time, endemic SIS and SIR models
Date: 2020-05-17 00:00:00

Stochastic discrete-time SIS and SIR models of endemic diseases are
introduced and analyzed. For the deterministic, mean-field model, the basic
reproductive number $R_0$ determines their global dynamics. If $R_0\le 1$, then
the frequency of infected individuals asymptotically converges to zero. If
$R_0>1$, then the infectious class uniformly persists for all time; conditions
for a globally stable, endemic equilibrium are given. In contrast, the
infection goes extinct in finite time with probability one in the stochastic
models for all $R_0$ values. To understand the length of the transient prior to
extinction as well as the behavior of the transients, the quasi-stationary
distributions and the associated mean time to extinction are analyzed using
large deviation methods. When $R_0>1$, these mean times to extinction are shown
to increase exponentially with the population size $N$. Moreover, as $N$
approaches $\infty$, the quasi-stationary distributions are supported by a
compact set bounded away from extinction; sufficient conditions for convergence
to a Dirac measure at the endemic equilibrium of the deterministic model are
also given. In contrast, when $R_0<1$, the mean times to extinction are bounded
above $1/(1-\alpha)$ where $\alpha<1$ is the geometric rate of decrease of the
infection when rare; as $N$ approaches $\infty$, the quasi-stationary
distributions converge to a Dirac measure at the disease-free equilibrium for
the deterministic model. For several special cases, explicit formulas for
approximating the quasi-stationary distribution and the associated mean
extinction are given. These formulas illustrate how for arbitrarily small $R_0$
values, the mean time to extinction can be arbitrarily large, and how for
arbitrarily large $R_0$ values, the mean time to extinction can be arbitrarily
large.
Blind Direction-of-Arrival Estimation in Acoustic Vector-Sensor Arrays via Tensor Decomposition and Kullback-Leibler Divergence Covariance Fitting
Authors: Amir Weiss
Date: 2020-05-17 00:00:00
Tasks: Direction of Arrival Estimation, Tensor Decomposition

A blind Direction-of-Arrivals (DOAs) estimate of narrowband signals for Acoustic Vector-Sensor (AVS) arrays is proposed. Building upon the special structure of the signal measured by an AVS, we show that the covariance matrix of all the received signals from the array admits a natural low-rank 4-way tensor representation. Thus, rather than estimating the DOAs directly from the raw data, our estimate arises from the unique parametric Canonical Polyadic Decomposition (CPD) of the observations' Second-Order Statistics (SOSs) tensor. By exploiting results from fundamental statistics and the recently re-emerging tensor theory, we derive a consistent blind CPD-based DOAs estimate without prior assumptions on the array configuration. We show that this estimate is a solution to an equivalent approximate joint diagonalization problem, and propose an ad-hoc iterative solution. Additionally, we derive the Cram\'er-Rao lower bound for Gaussian signals, and use it to derive the iterative Fisher scoring algorithm for the computation of the Maximum Likelihood Estimate (MLE) in this particular signal model. We then show that the MLE for the Gaussian model can in fact be used to obtain improved DOAs estimates for non-Gaussian signals as well (under mild conditions), which are optimal under the Kullback-Leibler divergence covariance fitting criterion, harnessing additional information encapsulated in the SOSs. Our analytical results are corroborated by simulation experiments in various scenarios, which also demonstrate the considerable improved accuracy w.r.t. a competing state-of-the-art blind estimate for AVS arrays, reducing the resulting root mean squared error by up to more than an order of magnitude.
Learning-based Prediction, Rendering and Association Optimization for MEC-enabled Wireless Virtual Reality (VR) Network
Authors: Xiaonan Liu, Yansha Deng
Date: 2020-05-17 00:00:00
Tasks: Deep Reinforcement Learning

Wireless-connected Virtual Reality (VR) provides immersive experience for VR users from any-where at anytime. However, providing wireless VR users with seamless connectivity and real-time VR video with high quality is challenging due to its requirements in high Quality of Experience (QoE) and low VR interaction latency under limited computation capability of VR device. To address these issues,we propose a MEC-enabled wireless VR network, where the field of view (FoV) of each VR user can be real-time predicted using Recurrent Neural Network (RNN), and the rendering of VR content is moved from VR device to MEC server with rendering model migration capability. Taking into account the geographical and FoV request correlation, we propose centralized and distributed decoupled Deep Reinforcement Learning (DRL) strategies to maximize the long-term QoE of VR users under the VR interaction latency constraint. Simulation results show that our proposed MEC rendering schemes and DRL algorithms substantially improve the long-term QoE of VR users and reduce the VR interaction latency compared to rendering at VR devices
Energy and Information Management of Electric Vehicular Network: A Survey
Authors: Nan Chen, Miao Wang, Ning Zhang, Xuemin, Shen
Date: 2020-05-17 00:00:00
Tasks: Management, Scheduling, Survey

The connected vehicle paradigm empowers vehicles with the capability to communicate with neighboring vehicles and infrastructure, shifting the role of vehicles from a transportation tool to an intelligent service platform. Meanwhile, the transportation electrification pushes forward the electric vehicle (EV) commercialization to reduce the greenhouse gas emission by petroleum combustion. The unstoppable trends of connected vehicle and EVs transform the traditional vehicular system to an electric vehicular network (EVN), a clean, mobile, and safe system. However, due to the mobility and heterogeneity of the EVN, improper management of the network could result in charging overload and data congestion. Thus, energy and information management of the EVN should be carefully studied. In this paper, we provide a comprehensive survey on the deployment and management of EVN considering all three aspects of energy flow, data communication, and computation. We first introduce the management framework of EVN. Then, research works on the EV aggregator (AG) deployment are reviewed to provide energy and information infrastructure for the EVN. Based on the deployed AGs, we present the research work review on EV scheduling that includes both charging and vehicle-to-grid (V2G) scheduling. Moreover, related works on information communication and computing are surveyed under each scenario. Finally, we discuss open research issues in the EVN.
Deuteros 2.0: Peptide-level significance testing of data from hydrogen deuterium exchange mass spectrometry
Date: 2020-05-17 00:00:00
Tasks: Attribute, Data Summarization

Summary: Hydrogen deuterium exchange mass spectrometry (HDX-MS) is becoming
increasing routine for monitoring changes in the structural dynamics of
proteins. Differential HDX-MS allows comparison of individual protein states,
such as in the absence or presence of a ligand. This can be used to attribute
changes in conformation to binding events, allowing the mapping of entire
con-formational networks. As such, the number of necessary cross-state
comparisons quickly increas-es as additional states are introduced to the
system of study. There are currently very few software packages available that
offer quick and informative comparison of HDX-MS datasets and even few-er which
offer statistical analysis and advanced visualization. Following the feedback
from our origi-nal software Deuteros, we present Deuteros 2.0 which has been
redesigned from the ground up to fulfil a greater role in the HDX-MS analysis
pipeline. Deuteros 2.0 features a repertoire of facilities for back exchange
correction, data summarization, peptide-level statistical analysis and advanced
data plotting features. Availability: Deuteros 2.0 can be downloaded from
https://github.com/andymlau/Deuteros_2.0 under the Apache 2.0 license.
Installation of Deuteros 2.0 requires the MATLAB Runtime Library available free
of charge from MathWorks
(https://www.mathworks.com/products/compiler/matlab-runtime.html) and is
available for both Windows and Mac operating systems.
Low-Complexity Vector Quantized Compressed Sensing via Deep Neural Networks
Authors: Markus Leinonen, Marian Codreanu
Date: 2020-05-17 00:00:00
Tasks: compressed sensing, Decoder, Quantization

Sparse signals, encountered in many wireless and signal acquisition applications, can be acquired via compressed sensing (CS) to reduce computations and transmissions, crucial for resource-limited devices, e.g., wireless sensors. Since the information signals are often continuous-valued, digital communication of compressive measurements requires quantization. In such a quantized compressed sensing (QCS) context, we address remote acquisition of a sparse source through vector quantized noisy compressive measurements. We propose a deep encoder-decoder architecture, consisting of an encoder deep neural network (DNN), a quantizer, and a decoder DNN, that realizes low-complexity vector quantization aiming at minimizing the mean-square error of the signal reconstruction for a given quantization rate. We devise a supervised learning method using stochastic gradient descent and backpropagation to train the system blocks. Strategies to overcome the vanishing gradient problem are proposed. Simulation results show that the proposed non-iterative DNN-based QCS method achieves higher rate-distortion performance with lower algorithm complexity as compared to standard QCS methods, conducive to delay-sensitive applications with large-scale signals.
Symbolic Models for a Class of Impulsive Systems
Date: 2020-05-21 00:00:00

Symbolic models have been used as the basis of a systematic framework to
address control design of several classes of hybrid systems with sophisticated
control objectives. However, results available in the literature are not
concerned with impulsive systems which are an important modeling framework of
many applications. In this paper, we provide an approach for constructing
symbolic models for a class of impulsive systems possessing some stability
properties. We formally relate impulsive systems and their symbolic models
using a notion of so-called alternating simulation function. We show that
behaviors of the constructed symbolic models are approximately equivalent to
those of the impulsive systems. Finally, we illustrate the effectiveness of our
results through a model of storage-delivery process by constructing its
symbolic model and designing controllers enforcing some safety specifications.
Attentron: Few-Shot Text-to-Speech Utilizing Attention-Based Variable-Length Embedding
Date: 2020-08-12 00:00:00
Tasks: Speech Synthesis, text-to-speech, Text to Speech

On account of growing demands for personalization, the need for a so-called
few-shot TTS system that clones speakers with only a few data is emerging. To
address this issue, we propose Attentron, a few-shot TTS model that clones
voices of speakers unseen during training. It introduces two special encoders,
each serving different purposes. A fine-grained encoder extracts
variable-length style information via an attention mechanism, and a
coarse-grained encoder greatly stabilizes the speech synthesis, circumventing
unintelligible gibberish even for synthesizing speech of unseen speakers. In
addition, the model can scale out to an arbitrary number of reference audios to
improve the quality of the synthesized speech. According to our experiments,
including a human evaluation, the proposed model significantly outperforms
state-of-the-art models when generating speech for unseen speakers in terms of
speaker similarity and quality.
Epidemic parameters for COVID-19 in several regions of India
Date: 2020-05-18 00:00:00
Tasks: Time Series, Time Series Analysis

Bayesian analysis of publicly available time series of cases and fatalities
in different geographical regions of India during April 2020 is reported. It is
found that the initial apparent rapid growthin infections could be partly due
to confounding factors such as initial rapid ramp-up of disease surveillance. A
brief discussion is given of the fallacies which arise if this possibility is
neglected. The growth after April 10 is consistent with a time independent but
region dependent exponential. From this, R0 is extracted using both known cases
and fatalities. The two estimates are seen to agree in many cases; for these
CFR is reported. It is seen that CFR and R0 increase together. Some public
health implications of this observation are discussed, including a target
doubling interval if medical facilities are to remain adequate.
Aligning biological sequences by exploiting residue conservation and coevolution
Authors: Anna Paola Muntoni, Andrea Pagnani, Martin Weigt, Francesco Zamponi
Date: 2020-05-18 00:00:00

Sequences of nucleotides (for DNA and RNA) or amino acids (for proteins) are central objects in biology. Among the most important computational problems is that of sequence alignment, i.e. arranging sequences from different organisms in such a way to identify similar regions, to detect evolutionary relationships between sequences, and to predict biomolecular structure and function. This is typically addressed through profile models, which capture position-specificities like conservation in sequences, but assume an independent evolution of different positions. Over the last years, it has been well established that coevolution of different amino-acid positions is essential for maintaining three-dimensional structure and function. Modeling approaches based on inverse statistical physics can catch the coevolution signal in sequence ensembles; and they are now widely used in predicting protein structure, protein-protein interactions, and mutational landscapes. Here, we present DCAlign, an efficient alignment algorithm based on an approximate message-passing strategy, which is able to overcome the limitations of profile models, to include coevolution among positions in a general way, and to be therefore universally applicable to protein- and RNA-sequence alignment without the need of using complementary structural information. The potential of DCAlign is carefully explored using well-controlled simulated data, as well as real protein and RNA sequences.
The Natural Capital Indicator Framework (NCIF): A framework of indicators for national natural capital reporting
Date: 2020-05-18 00:00:00
Tasks: Decision Making

It is now widely recognised that components of the environment play the role
of economic assets, termed natural capital, that are a foundation of social and
economic development. National governments monitor the state and trends of
natural capital through a range of activities including natural capital
accounting, national ecosystem assessments, ecosystem service valuation, and
economic and environmental analyses. Indicators play an integral role in these
activities as they facilitate the reporting of complex natural capital
information. One factor that hinders the success of these activities and their
comparability across countries is the absence of a coherent framework of
indicators concerning natural capital (and its benefits) that can aid
decision-making. Here we present an integrated Natural Capital Indicator
Framework (NCIF) alongside example indicators, which provides an illustrative
structure for countries to select and organise indicators to assess their use
of and dependence on natural capital. The NCIF sits within a wider context of
indicators related to natural, human, social and manufactured capital, and
associated flows of benefits. The framework provides decision-makers with a
structured approach to selecting natural capital indicators with which to make
decisions about economic development that take into account national natural
capital and associated flows of benefits.
Energy Efficiency Optimization for Millimeter Wave System with Resolution-Adaptive ADCs
Authors: Hualian Sheng, Xihan Chen, Xiongfei Zhai, An Liu, Min-Jian Zhao
Date: 2020-05-18 00:00:00
Tasks: Quantization

This letter investigates the uplink of a multi-user millimeter wave (mmWave) system, where the base station (BS) is equipped with a massive multiple-input multiple-output (MIMO) array and resolution-adaptive analog-to-digital converters (RADCs). Although employing massive MIMO at the BS can significantly improve the spectral efficiency, it also leads to high hardware complexity and huge power consumption. To overcome these challenges, we seek to jointly optimize the beamspace hybrid combiner and the ADC quantization bits allocation to maximize the system energy efficiency (EE) under some practical constraints. The formulated problem is non-convex due to the non-linear fractional objective function and the non-convex feasible set which is generally intractable. In order to handle these difficulties, we first apply some fractional programming (FP) techniques and introduce auxiliary variables to recast this problem into an equivalent form amenable to optimization. Then, we propose an efficient double-loop iterative algorithm based on the penalty dual decomposition (PDD) and the majorization-minimization (MM) methods to find local stationary solutions. Simulation results reveal significant gain over the baselines.
Benchmarking the Gerchberg-Saxton Algorithm
Date: 2020-05-18 00:00:00
Tasks: Benchmarking

Due to the proliferation of spatial light modulators, digital holography is
finding wide-spread use in fields from augmented reality to medical imaging to
additive manufacturing to lithography to optical tweezing to
telecommunications. There are numerous types of SLM available with a multitude
of algorithms for generating holograms. Each algorithm has limitations in terms
of convergence speed, power efficiency, accuracy and data storage requirement.
  Here, we consider probably the most common algorithm for computer generated
holography - Gerchberg-Saxton - and examine the trade-off in convergent
quality, performance and efficiency. In particular, we focus on measuring and
understanding the factors that control runtime and convergence.
Reactive Global Minimum Variance Portfolios with $k-$BAHC covariance cleaning
Authors: Christian Bongiorno, Damien Challet
Date: 2020-05-18 00:00:00
Tasks: Clustering

We introduce a $k$-fold boosted version of our Boostrapped Average Hierarchical Clustering cleaning procedure for correlation and covariance matrices. We then apply this method to global minimum variance portfolios for various values of $k$ and compare their performance with other state-of-the-art methods. Generally, we find that our method yields better Sharpe ratios after transaction costs than competing filtering methods, despite requiring a larger turnover.
Evaluation of Accounting and Market Performance: A Study on Listed Islamic Banks of Bangladesh
Authors: Nusrat Jahan, M. Ayub Islam
Date: 2020-05-15 00:00:00

This study compared accounting performance of Islamic banks with their market performance and also assessed the effect of firm-specific determinants and cross-sectional effect on accounting and market performance. This study selected all six listed Islamic banks of Chittagong Stock Exchange and the data were collected for the period of 2009 to 2013. This study reported that Social Islamic Bank Limited exhibits superior accounting performance whereas Islami Bank Bangladesh Limited holds better market performance. However, banks exhibiting superior accounting performance reported to have inferior market performance. Further, random-effect model for ROA reports that there exist significant entity or crosssectional effect on ROA; and operational efficiency and bank size are significantly negatively associated with ROA. However, random-effect model for Tobins Q failed to ascertain entity or cross-sectional effect on Tobins Q and also reveals that firm-specific determinants have no significant impact on Tobins Q.
Glycan processing in the Golgi -- optimal information coding and constraints on cisternal number and enzyme specificity
Authors: Alkesh Yadav, Quentin Vagne, Pierre Sens, Garud Iyengar, Madan Rao
Date: 2020-05-18 00:00:00
Tasks: Specificity

Many proteins that undergo sequential enzymatic modification in the Golgi cisternae are displayed at the plasma membrane as cell identity markers. The modified proteins, called glycans, represent a molecular code. The fidelity of this glycan code is measured by how accurately the glycan synthesis machinery realises the desired target glycan distribution for a particular cell type and niche. In this paper, we quantitatively analyse the tradeoffs between the number of cisternae and the number and specificity of enzymes, in order to synthesize a prescribed target glycan distribution of a certain complexity. We find that to synthesize complex distributions, such as those observed in real cells, one needs to have multiple cisternae and precise enzyme partitioning in the Golgi. Additionally, for fixed number of enzymes and cisternae, there is an optimal level of specificity of enzymes that achieves the target distribution with high fidelity. Our results show how the complexity of the target glycan distribution places functional constraints on the Golgi cisternal number and enzyme specificity.
Cyberattack on the Microgrids Through Price Modification
Date: 2020-05-15 00:00:00

Recent massive failures in the power grid acted as a wake up call for all
utilities and consumers. This leads to aggressive pursue a more intelligent
grid which addresses the concerns of reliability, efficiency, security, quality
and sustainability for the energy consumers and producers alike. One of the
many features of the smart grid is a discrete energy system consisting of
distributed energy sources capable of operating independently from the main
grid known as the microgrid. The main focus of the microgrid is to ensure a
reliable and affordable energy security. However, it also can be vulnerable to
cyber attack and we study the effect of price modification of electricity
attack on the microgrid, given that they are able to operate independently from
the main grid. This attack consists of two stages, 1) Separate the microgrids
from the main grid (islanding) and 2) Failing the nodes inside the microgrid.
Empirical results on IEEE Bus data help us evaluate our approach under various
settings of grid parameters.
Determinants of Profitability of Banks: Evidence from Islamic Banks of Bangladesh
Authors: Nusrat Jahan
Date: 2020-05-15 00:00:00

This empirical study is conducted on randomly selected six Islamic banks of Bangladesh. This study utilizes widely used Measures of banks profitability which are Return on Asset (ROA), Return on Equity (ROE) and Return on Deposit (ROD) and these are also commonly suggested tools by Bangladesh Bank to evaluate banks performance. In addition, this study examined the relationship of ROA with Asset Utilization (AU), Operational Efficiency (OE)and ROD. The result reveals that EXIM Bank Limited is performing very good in terms of all profitability measures ROA, ROE and ROD even though average asset size of Islami Bank Bangladesh Limited is found to be largest among all six Islamic Banks. The result of regression found the explanatory variable ROD is significantly associated with ROA but failed to establish any significant association with operational efficiency and asset utilization.