pwc-archive/papers-with-abstracts huggingface dataset:
Ambiguous Persuasion: An Ex-Ante Formulation
Authors: Xiaoyu Cheng
Date: 2020-10-12 00:00:00

Consider a persuasion game where both the sender and receiver are ambiguity averse with maxmin expected utility (MEU) preferences and the sender can choose an ambiguous information structure. This paper analyzes the game in an ex-ante formulation: the sender first commits to an information structure, and then the receiver best responds by choosing an ex-ante message-contingent action plan. Under this formulation, I show it is never strictly beneficial for the sender to use an ambiguous information structure as opposed to a standard unambiguous one. This result is robust to (i) both players having (possibly heterogeneous) ambiguous beliefs over the states, and/or (ii) the receiver having non-MEU, uncertainty-averse preferences. However, it is \emph{not} robust to the sender having non-MEU preferences.
A Discussion on Stabilization of Frequency Control for Power Systems
Date: 2020-10-12 00:00:00

How to practically maintain the frequency stability of large-scale power
systems by a decentralized way is a simple but non-trivial question. In other
words, is it possible to design any local controller without understanding the
other controlled areas and with less understanding of network structure? With
respect to the special properties of physical interaction between the local
areas, this paper suggests two existing theories for tackling this issue.
Firstly, passivity theory is shown to be a candidate for frequency control
problem using swing equation. Based on the passivity of swing dynamics, it is
possible to guarantee the system stability by designing for each local area a
passive controller. We further extend the passivity approach to the
hierarchically decentralized control system with unknown communication delay.
Secondly, we discuss the application of generalized frequency variable (GFV) to
the frequency control problem using area-control-error. Each local controller
is designed such that each local subsystem follows a nominal model set.
Utilizing GFV theory, we present a triad of conditions that sufficiently
guarantee the system stability. The conditions can be tested conveniently by a
limited set of inequalities established from the GFV and the eigenvalues of the
physical interaction matrix. The effectiveness, limitation, and challenge of
two theories are discussed by design examples with numerical simulations.
Deep Learning Prediction of Adverse Drug Reactions Using Open TG-GATEs and FAERS Databases
Authors: Attayeb Mohsen, Lokesh P. Tripathi, Kenji Mizuguchi
Date: 2020-10-12 00:00:00
Tasks: Drug Discovery

With the advancements in Artificial intelligence (AI) and the accumulation of healthrelated big data, it has become increasingly feasible and commonplace to leverage machine learning technologies to analyze clinical and omics metadata to assess the possibility of adverse drug reactions or events (ADRs) in the course of drug discovery. Here, we have described a novel approach that combined drug-induced gene expression profile from Open TG-GATEs (Toxicogenomics Project-Genomics Assisted Toxicity Evaluation Systems) and ADR occurrence information from FAERS (FDA [Food and Drug Administration] Adverse Events Reporting System) database to predict the likelihood of ADRs. We generated a total of 14 models using Deep Neural Networks (DNN) to predict different ADRs; in the validation tests, our models achieved a mean accuracy of 85.71%, indicating that our approach successfully and consistently predicted ADRs for a wide range of drugs. As an example, we have described the ADR model in the context of Duodenal ulcer. We believe that our models will help predict the likelihood of ADRs while testing novel pharmaceutical compounds, and will be useful for researchers in drug discovery.
A Cooperative Control Framework for CAV Lane Change in a Mixed Traffic Environment
Date: 2020-10-12 00:00:00
Tasks: Autonomous Vehicles, Collision Avoidance

In preparing for connected and autonomous vehicles (CAVs), a worrisome aspect
is the transition era which will be characterized by mixed traffic (where CAVs
and human-driven vehicles (HDVs) share the roadway). Consistent with
expectations that CAVs will improve road safety, on-road CAVs may adopt rather
conservative control policies, and this will likely cause HDVs to unduly
exploit CAV conservativeness by driving in ways that imperil safety. A context
of this situation is lane-changing by the CAV. Without cooperation from other
vehicles in the traffic stream, it can be extremely unsafe for the CAV to
change lanes under dense, high-speed traffic conditions. The cooperation of
neighboring vehicles is indispensable. To address this issue, this paper
develops a control framework where connected HDVs and CAV can cooperate to
facilitate safe and efficient lane changing by the CAV. Throughout the
lane-change process, the safety of not only the CAV but also of all neighboring
vehicles, is ensured through a collision avoidance mechanism in the control
framework. The overall traffic flow efficiency is analyzed in terms of the
ambient level of CHDV-CAV cooperation. The analysis outcomes are including the
CAVs lane-change feasibility, the overall duration of the lane change. Lane
change is a major source of traffic disturbance at multi-lane highways that
impair their traffic flow efficiency. In providing a control framework for lane
change in mixed traffic, this study shows how CHDV-CAV cooperation could help
enhancing system efficiency.
Using Empirical Trajectory Data to Design Connected Autonomous Vehicle Controllers for Traffic Stabilization
Date: 2020-10-12 00:00:00
Tasks: Autonomous Vehicles

Emerging transportation technologies offer unprecedented opportunities to
improve the efficiency of the transportation system from the perspectives of
energy consumption, congestion, and emissions. One of these technologies is
connected and autonomous vehicles (CAVs). With the prospective duality of
operations of CAVs and human driven vehicles in the same roadway space (also
referred to as a mixed stream), CAVs are expected to address a variety of
traffic problems particularly those that are either caused or exacerbated by
the heterogeneous nature of human driving. In efforts to realize such specific
benefits of CAVs in mixed-stream traffic, it is essential to understand and
simulate the behavior of human drivers in such environments, and microscopic
traffic flow (MTF) models can be used to carry out this task. By helping to
comprehend the fundamental dynamics of traffic flow, MTF models serve as a
powerful approach to assess the impacts of such flow in terms of safety,
stability, and efficiency. In this paper, we seek to calibrate MTF models based
on empirical trajectory data as basis of not only understanding traffic
dynamics such as traffic instabilities, but ultimately using CAVs to mitigate
stop-and-go wave propagation. The paper therefore duly considers the
heterogeneity and uncertainty associated with human driving behavior in order
to calibrate the dynamics of each HDV. Also, the paper designs the CAV
controllers based on the microscopic HDV models that are calibrated in real
time. The data for the calibration is from the Next Generation SIMulation
(NGSIM) trajectory datasets. The results are encouraging, as they indicate the
efficacy of the designed controller to significantly improve not only the
stability of the mixed traffic stream but also the safety of both CAVs and HDVs
in the traffic stream.
The loss optimisation of loan recovery decision times using forecast cash flows
Authors: Arno Botha, Conrad Beyers, Pieter de Villiers
Date: 2020-10-12 00:00:00

A theoretical method is empirically illustrated in finding the best time to forsake a loan such that the overall credit loss is minimised. This is predicated by forecasting the future cash flows of a loan portfolio up to the contractual term, as a remedy to the inherent right-censoring of real-world `incomplete' portfolios. Two techniques, a simple probabilistic model as well as an eight-state Markov chain, are used to forecast these cash flows independently. We train both techniques from different segments within residential mortgage data, provided by a large South African bank, as part of a comparative experimental framework. As a result, the recovery decision's implied timing is empirically illustrated as a multi-period optimisation problem across uncertain cash flows and competing costs. Using a delinquency measure as a central criterion, our procedure helps to find a loss-optimal threshold at which loan recovery should ideally occur for a given portfolio. Furthermore, both the portfolio's historical risk profile and forecasting thereof are shown to influence the timing of the recovery decision. This work can therefore facilitate the revision of relevant bank policies or strategies towards optimising the loan collections process, especially that of secured lending.
Reconfigurable intelligent surface (RIS): Eigenvalue Decomposition-Based Separate Channel Estimation
Authors: Salah Eddine Zegrar, Liza Afeef, Huseyin Arslan
Date: 2020-10-12 00:00:00

Reconfigurable intelligent surface (RIS) has recently drawn significant attention in wireless communication technologies. However, identifying, modeling, and estimating the RIS channel in multiple-input multiple-output (MIMO) systems are considered challenging in recent studies. In this paper, a disassembled channel estimation framework for the RIS-MIMO system is proposed based on the eigenvalue decomposition (EVD) concept to separate the cascaded channel links and estimate each link separately. This estimation is based on modeling the RIS-MIMO channel as a keyhole MIMO system model. Numerical results show that the proposed estimation method has a low estimation time overhead while providing less estimation error.
Modeling Mito-nuclear Compatibility and its Role in Species Identification
Authors: Debora Princepe, Marcus A. M. de Aguiar
Date: 2020-10-12 00:00:00

Mitochondrial genetic material is widely used for phylogenetic reconstruction and as a barcode for species identification. Here we study how mito-nuclear interactions affect the accuracy of species identification by mtDNA, as well as the speciation process itself. We simulate the evolution of a population of individuals who carry a recombining nuclear genome and a mitochondrial genome inherited maternally. We compare a null model fitness landscape that lacks any mito-nuclear interaction against a scenario in which interactions influence fitness. Fitness is assigned to individuals according to their mito-nuclear compatibility, which drives the coevolution of the nuclear and mitochondrial genomes. When the population breaks into distinct species we analyze the accuracy of mtDNA barcode for species identification. Remarkably, we find that species identification by mtDNA is equally accurate in the presence or absence of mito-nuclear coupling and that the success of the DNA barcode derives mainly from population geographical isolation during speciation. Nevertheless, selection imposed by mito-nuclear compatibility influences the diversification process and leaves signatures in the genetic content and spatial distribution of the populations, in three ways: phylogenetic trees are more balanced; clades correlate strongly with the spatial distribution and; there is a substantial increase in the intraspecies mtDNA similarity. We compare the evolutionary patterns observed in our model to empirical data from copepods (\textit{T. californicus}). We find good qualitative agreement in the geographic patterns and the topology of the phylogenetic tree, provided the model includes selection based on mito-nuclear interactions. These results highlight the role of mito-nuclear compatibility in the speciation process and its reconstruction from genetic data.
Quenching to fix metastable states in models of prebiotic chemistry
Authors: Qianyi Sheng, Ben Intoy, J. W. Halley
Date: 2020-10-12 00:00:00

For prebiotic chemistry to succeed in producing a starting metastable, autocatalytic and reproducing system subject to evolutionary selection it must satisfy at least two apparently contradictory requirements: Because such systems are rare, a search among vast numbers of molecular combinations must take place naturally, requiring rapid rearrangement and breaking of covalent bonds. But once a relevant system is found, such rapid disruption and rearrangement would be very likely to destroy the system before much evolution could take place. In this paper we explore the possibility, using a model developed previously, that the search process could occur under different environmental conditions than the subsequent fixation and growth of a lifelike chemical system. We use the example of a rapid change in temperature to illustrate the effect and refer to the rapid change as a `quench' borrowing terminology from study of the physics and chemistry of glass formation. The model study shows that interrupting a high temperature nonequilibrium state with a rapid quench to lower temperatures can substantially increase the probability of producing a chemical state with lifelike characteristics of nonequilibrium metastability, internal dynamics and exponential population growth in time. Previously published data on the length distributions of proteomes of prokaryotes may be consistent with such an idea and suggest a prebiotic high temperature `search' phase near the boiling point of water. A rapid change in pH could have a similar effect. We discuss possible scenarios on early earth which might have allowed frequent quenches of the sort considered here to have occurred. The models show a strong dependence of the effect on the number of chemical monomers available for bond formation.
Multi-Level Group Testing with Application to One-Shot Pooled COVID-19 Tests
Authors: Amit Solomon, Alejandro Cohen, Nir Shlezinger, Yonina C. Eldar, Muriel Médard
Date: 2020-10-12 00:00:00

A key requirement in containing contagious diseases, such as the Coronavirus disease 2019 (COVID-19) pandemic, is the ability to efficiently carry out mass diagnosis over large populations. Some of the leading testing procedures, such as those utilizing qualitative polymerase chain reaction, involve using dedicated machinery which can simultaneously process a limited amount of samples. A candidate method to increase the test throughput is to examine pooled samples comprised of a mixture of samples from different patients. In this work we study pooling based tests which operate in a one-shot fashion, while providing an indication not solely on the presence of infection, but also on its level, without additional pool tests, as often required in COVID-19 testing. As these requirements limit the application of traditional group-testing (GT) methods, we propose a multi-level GT scheme, which builds upon GT principles to enable accurate recovery using much fewer tests than patients, while operating in a one-shot manner and providing multi-level indications. We provide a theoretical analysis of the proposed scheme and characterize conditions under which the algorithm operates reliably and at affordable computational complexity. Our numerical results demonstrate that multi level GT accurately and efficiently detects infection levels, while achieving improved performance over previously proposed one-shot COVID-19 pooled-testing methods.
Interoception Underlies The Therapeutic Effects of Mindfulness Meditation for Post-Traumatic Stress Disorder: A Randomized Clinical Trial
Date: 2020-10-12 00:00:00
Tasks: EEG, Electroencephalogram (EEG)

Mindfulness-based interventions have proven its efficacy in treating
post-traumatic stress disorder (PTSD), but the underlying neurobiological
mechanism is unknown. To determine the neurobiological mechanism of action of
mindfulness-based stress reduction (MBSR) treating PTSD, we conducted a
randomized clinical trial (RCT) in which 98 veterans with PTSD were randomly
assigned to receive MBSR therapy (n = 47) or present-centered group therapy
(PCGT; n = 51; an active-control condition). Pre- and post-intervention
measures of PTSD symptom severity (PTSD Checklist) and brain activity measures
of electroencephalography (EEG) were assessed, including spectral power of
spontaneous neural oscillatory activities during resting and meditation
periods, time-frequency (TF) power of cognitive task-related brain responses,
and TF power of heartbeat-evoked brain responses (HEBR) that reflect cardiac
interoceptive brain responses during resting and meditation. Compared to
controls, the MBSR group had greater improvements in PTSD symptoms, spontaneous
EEG alpha (8-13 Hz) power in posterior sites, task-related frontal theta power
(4-7 Hz in 140-220 ms post-stimulus), and frontal theta HEBR (3-5 Hz and
265-328 ms post-R-peak). Latent difference score modeling found that only the
changes in the frontal theta HEBR mediated the MBSR treatment effect. Brain
source-level analysis found that the theta HEBR changes in the anterior
cingulate cortex, anterior insular cortex, and the lateral prefrontal cortex
predicted PTSD symptom improvements. These results indicated that mindfulness
meditation improves spontaneous brain activities reflecting internally oriented
relaxation and brain functions of attentional control. However, interoceptive
brain capacity enhanced by MBSR appears to be the primary cerebral mechanism
that regulates emotional disturbances and improves anxiety symptoms of PTSD.
Single-Antenna-Based GPS Antijamming Method Exploiting Polarization Diversity
Authors: Kwansik Park, Jiwon Seo
Date: 2020-10-13 00:00:00
Tasks: Diversity

The vulnerability of Global Positioning System (GPS) receivers to jammers is a major concern owing to the extremely weak received signal power of GPS. Researches have been conducted on a variety of antenna array techniques to be used as countermeasures to GPS jammers, and their antijamming performance is known to be greater than that of single antenna methods. However, the application of antenna arrays remains limited because of their size, cost, and computational complexity. This study proposes and experimentally validates a novel space-time-polarization domain adaptive processing for a single-element dual-polarized antenna (STPAPS) by focusing on the polarization diversity of a dual-polarized antenna. The mathematical models of arbitrarily polarized signals received by dual-polarized antenna are derived, and an appropriate constraint matrix for dual-polarized-antenna-based GPS antijam is suggested. To reduce the computational complexity of the constraint matrix approach, the eigenvector constraint design scheme is adopted. The performance of STPAPS is quantitively and qualitatively evaluated through experiments as follows. 1) The carrier-to-noise-density ratio (C/N0) of STPAPS under synthetic jamming is demonstrated to be higher than that of the previous minimum mean squared error (MMSE) or minimum variance distortionless response (MVDR) based dual-polarized antenna methods. 2) The strengths and weaknesses of STPAPS are qualitatively compared with those of the previous single-element dual-polarized antenna methods that are not based on the MMSE or MVDR algorithms. 3) The characteristics of STPAPS (in terms of the directions and polarizations of the GPS and jamming signals) are compared with those of the conventional two-element single-polarized antenna array method, which has the same degree of freedom as that of STPAPS.
Transcranial Bipolar Direct Current Stimulation of the Frontoparietal Cortex Reduces Ketamine-Induced Oscillopathies: A Pilot Study in the Sedated Rat
Date: 2020-10-13 00:00:00

Running title: Frontoparietal anodal tDCS reduces ketamine-induced
oscillopathies.Abstract: During the prodromal phase of schizophrenia with its
complex and insidious clinical picture, electroencephalographic recordings
detect widespread oscillation disturbances (or oscillopathies). Neural
oscillations are electro-biomarkers of the connectivity state within systems. A
single systemic administration of ketamine, a non-competitive NMDA glutamate
receptor antagonist, transiently reproduces the oscillopathies with a clinical
picture reminiscent of the psychosis prodrome. This acute pharmacological model
may help the research and development of innovative treatments against the
psychotic transition. Transcranial electrical stimulation is recognized as an
appropriate non-invasive therapeutic modality since it can increase cognitive
performance and modulate neural oscillations with little or no side effects.
Therefore, our objective was to set up, in the sedated adult rat, a stimulation
method able to normalize the ketamine-induced oscillopathies. Unilateral
transcranial frontoparietal anodal stimulation by direct current (<+1 mA) was
applied in ketamine-treated rats. A concomitant electroencephalographic
recording of the parietal cortex measured the stimulation effects on its
spontaneously-occurring oscillations. A 5-min bipolar anodal tDCS immediately
and quickly reduced, significantly with an intensity-effect relationship, the
ketamine-induced oscillopathies at least in the bilateral parietal cortex. A
duration effect was also recorded. These preliminary neurophysiological
findings are promising for developing a therapeutic proof-of-concept against
neuropsychiatric disorders.
Exploring Universal Speech Attributes for Speaker Verification with an Improved Cross-stitch Network
Authors: Jiajun Qi, Wu Guo, Jingjing Shi, Yafeng Chen, Tan Liu
Date: 2020-10-13 00:00:00
Tasks: Attribute, Speaker Verification

The universal speech attributes for x-vector based speaker verification (SV) are addressed in this paper. The manner and place of articulation form the fundamental speech attribute unit (SAU), and then new speech attribute (NSA) units for acoustic modeling are generated by tied tri-SAU states. An improved cross-stitch network is adopted as a multitask learning (MTL) framework for integrating these universal speech attributes into the x-vector network training process. Experiments are conducted on common condition 5 (CC5) of the core-core and the 10 s-10 s tests of the NIST SRE10 evaluation set, and the proposed algorithm can achieve consistent improvements over the baseline x-vector on both these tasks.
Novel Architectures for Unsupervised Information Bottleneck based Speaker Diarization of Meetings
Date: 2020-10-13 00:00:00
Tasks: Clustering, speaker-diarization, Speaker Diarization

Speaker diarization is an important problem that is topical, and is
especially useful as a preprocessor for conversational speech related
applications. The objective of this paper is two-fold: (i) segment
initialization by uniformly distributing speaker information across the initial
segments, and (ii) incorporating speaker discriminative features within the
unsupervised diarization framework. In the first part of the work, a varying
length segment initialization technique for Information Bottleneck (IB) based
speaker diarization system using phoneme rate as the side information is
proposed. This initialization distributes speaker information uniformly across
the segments and provides a better starting point for IB based clustering. In
the second part of the work, we present a Two-Pass Information Bottleneck
(TPIB) based speaker diarization system that incorporates speaker
discriminative features during the process of diarization. The TPIB based
speaker diarization system has shown improvement over the baseline IB based
system. During the first pass of the TPIB system, a coarse segmentation is
performed using IB based clustering. The alignments obtained are used to
generate speaker discriminative features using a shallow feed-forward neural
network and linear discriminant analysis. The discriminative features obtained
are used in the second pass to obtain the final speaker boundaries. In the
final part of the paper, variable segment initialization is combined with the
TPIB framework. This leverages the advantages of better segment initialization
and speaker discriminative features that results in an additional improvement
in performance. An evaluation on standard meeting datasets shows that a
significant absolute improvement of 3.9% and 4.7% is obtained on the NIST and
AMI datasets, respectively.