pwc-archive/papers-with-abstracts huggingface dataset:
Reweighting of Binaural Localization Cues Induced by Lateralization Training
Date: 2020-07-31 00:00:00

Normal-hearing listeners adapt to alterations in sound localization cues.
This adaptation can result from the establishment of a new spatial map of the
altered cues or from a stronger relative weighting of unaltered compared to
altered cues. Such reweighting has been shown for monaural vs. binaural cues.
However, studies attempting to reweight the two binaural cues, interaural
differences in time and level, yielded inconclusive results. In this study we
investigated whether binaural cue reweighting can be induced by a
lateralization training in a virtual audio-visual environment. 20
normal-hearing participants, divided into two groups, completed the experiment
consisting of a seven-day lateralization training in a virtual audio-visual
environment, preceded and followed by a test measuring the binaural cue
weights. During testing, the participants task was to lateralize 500-ms
bandpass-filtered (2-4 kHz) noise bursts containing various combinations of
spatially consistent and inconsistent ITDs and ILDs. During training, the task
was extended by visual cues reinforcing ITDs in one group and ILDs in the other
group as well as manipulating the azimuthal ranges of the two cues. In both
groups, the weight given to the reinforced cue increased significantly from
pre- to posttest, suggesting that participants reweighted the binaural cues in
the expected direction. This reweighting occurred predominantly within the
first training session. The present results are relevant as binaural cue
reweighting is, for example, likely to occur when normal-hearing listeners
adapt to new acoustic environments. Similarly, binaural cue reweighting might
be a factor underlying the low contribution of ITDs to sound localization of
cochlear-implant listeners as they typically do not experience reliable ITD
cues with their clinical devices.
Automatic Gain Control Design for Dynamic Visible Light Communication Systems
Authors: Yingwen Zhang, Xianqing Jin, Weibin Jiang, Xinmin Chen, Zhengyuan Xu
Date: 2020-07-31 00:00:00
Tasks: Quantization

For dynamic visible light communication (VLC) systems, received optical power fluctuates largely due to the fast movement of VLC terminals. Such fluctuations will cause possible signal clipping and quantization noise at the analog-to-digital converters (ADCs) and thus aggravate signal-to-noise ratio (SNR) and reliability of the communication link. To mitigate this effect, the automatic gain control (AGC) technique is often introduced in the receiver front-end to adaptively adjust the electrical signal strength. In this paper, we provided a VLC front-end analysis considering AGC model. Based on the model, some design principles of AGC are theoretically derivated: the effects of AGC index $m$, AGC maximum gain $g_{max}$ and AGC equilibrium range $DR$. Next, an analog AGC amplifier was carefully implemented and the effectiveness of our AGC modeling was proved through BER experiments. Finally, realtime 25Mb/s on-off-keying (OOK) experiments with AGC function were demonstrated in a dynamic link with different speeds. Experimental results show that the AGC can stabilize the BER performance and improve the system performance in the speed of 1m/s.
Optimal periodic closure for minimizing risk in emerging disease outbreaks
Date: 2020-07-31 00:00:00

Without vaccines and treatments, societies must rely on non-pharmaceutical intervention strategies to control the spread of emerging diseases such as COVID-19. Though complete lockdown is epidemiologically effective, because it eliminates infectious contacts, it comes with significant costs. Several recent studies have suggested that a plausible compromise strategy for minimizing epidemic risk is periodic closure, in which populations oscillate between wide-spread social restrictions and relaxation. However, no underlying theory has been proposed to predict and explain optimal closure periods as a function of epidemiological and social parameters. In this work we develop such an analytical theory for SEIR-like model diseases, showing how characteristic closure periods emerge that minimize the total outbreak, and increase predictably with the reproductive number and incubation periods of a disease, as long as both are within predictable limits. Using our approach we demonstrate a sweet-spot effect in which optimal periodic closure is maximally effective for diseases with similar incubation and recovery periods. Our results compare well to numerical simulations, including in COVID-19 models where infectivity and recovery show significant variability.
Extrapolation of Bandlimited Multidimensional Signals from Continuous Measurements
Authors: Cornelius Frankenbach, Pablo Martínez-Nuevo, Martin Møller, Walter Kellermann
Date: 2020-07-31 00:00:00

Conventional sampling and interpolation commonly rely on discrete measurements. In this paper, we develop a theoretical framework for extrapolation of signals in higher dimensions from knowledge of the continuous waveform on bounded high-dimensional regions. In particular, we propose an iterative method to reconstruct bandlimited multidimensional signals based on truncated versions of the original signal to bounded regions---herein referred to as continuous measurements. In the proposed method, the reconstruction is performed by iterating on a convex combination of region-limiting and bandlimiting operations. We show that this iteration consists of a firmly nonexpansive operator and prove strong convergence for multidimensional bandlimited signals. In order to improve numerical stability, we introduce a regularized iteration and show its connection to Tikhonov regularization. The method is illustrated numerically for two-dimensional signals.
Investigating the association between meteorological factors and the transmission and fatality of COVID-19 in the US
Date: 2020-07-30 00:00:00

A novel coronavirus disease (COVID-19) is sweeping the world and has taken
away thousands of lives. As the current epicenter, the United States has the
largest number of confirmed and death cases of COVID-19. Meteorological factors
have been found associated with many respiratory diseases in the past studies.
In order to understand that how and during which period of time do the
meteorological factors have the strongest association with the transmission and
fatality of COVID-19, we analyze the correlation between each meteorological
factor during different time periods within the incubation window and the
confirmation and fatality rate, and develop statistic models to quantify the
effects at county level. Results show that meteorological variables except
maximum wind speed during the day 13 - 0 before current day shows the most
significant correlation (P < 0.05) with the daily confirmed rate, while
temperature during the day 13 - 8 before are most significantly correlated (P <
0.05) with the daily fatality rate. Temperature is the only meteorological
factor showing dramatic positive association nationally, particularly in the
southeast US where the current outbreak most intensive. The influence of
temperature is remarkable on the confirmed rate with an increase of over 5 pmp
in many counties, but not as much on the fatality rate (mostly within 0.01%).
Findings in this study will help understanding the role of meteorological
factors in the spreading of COVID-19 and provide insights for public and
individual in fighting against this global epidemic.
No-Reference Video Quality Assessment Using Space-Time Chips
Date: 2020-08-23 00:00:00
Tasks: Video Quality Assessment, Visual Question Answering (VQA)

We propose a new prototype model for no-reference video quality assessment
(VQA) based on the natural statistics of space-time chips of videos. Space-time
chips (ST-chips) are a new, quality-aware feature space which we define as
space-time localized cuts of video data in directions that are determined by
the local motion flow. We use parametrized distribution fits to the bandpass
histograms of space-time chips to characterize quality, and show that the
parameters from these models are affected by distortion and can hence be used
to objectively predict the quality of videos. Our prototype method, which we
call ChipQA-0, is agnostic to the types of distortion affecting the video, and
is based on identifying and quantifying deviations from the expected statistics
of natural, undistorted ST-chips in order to predict video quality. We train
and test our resulting model on several large VQA databases and show that our
model achieves high correlation against human judgments of video quality and is
competitive with state-of-the-art models.
SARS-CoV-2 and miRNA-like inhibition power
Authors: Jacques Demongeot, Hervé Seligmann
Date: 2020-07-31 00:00:00

(1) Background: RNA viruses and especially coronaviruses could act inside host cells not only by building their own proteins, but also by perturbing the cell metabolism. We show the possibility of miRNA-like inhibitions by the SARS-CoV-2 concerning for example the hemoglobin and type I interferons syntheses, hence highly perturbing oxygen distribution in vital organs and immune response as described by clinicians; (2) Methods: We compare RNA subsequences of SARS-CoV-2 protein S and RNA-dependent RNA polymerase genes to mRNA sequences of beta-globin and type I interferons; (3) Results: RNA subsequences longer than eight nucleotides from SARS-CoV-2 genome could hybridize subsequences of the mRNA of beta-globin and of type I interferons; (4) Conclusions: Beyond viral protein production, Covid-19 might affect vital processes like host oxygen transport and immune response.
Gene family amplification facilitates adaptation in freshwater Unionid bivalve Megalonaias nervosa
Authors: Rebekah L. Rogers, Stephanie L. Grizzard, James E. Titus-McQuillan, Katherine Bockrath, Sagar Patel, John P. Wares, Jeffrey T. Garner, Cathy C. Moore
Date: 2020-08-01 00:00:00

As organisms are faced with intense rapidly changing selective pressures, new genetic material is required to facilitate adaptation. Among sources of genetic novelty, gene duplications and transposable elements (TEs) offer new genes or new regulatory patterns that can facilitate evolutionary change. With advances in genome sequencing it is possible to gain a broader view of how gene family proliferation and TE content evolve in non-model species when populations become threatened. Freshwater bivalves (Unionidae) currently face severe anthropogenic challenges. Over 70% of species in the United States are threatened, endangered or extinct due to pollution, damming of waterways, and overfishing. We have created a reference genome for M. nervosa to determine how genome content has evolved in the face of these widespread environmental challenges. We observe a burst of recent transposable element proliferation causing a 382 Mb expansion in genome content. Gene family expansion is common, with a duplication rate of 1.16 x 10^-8 per gene per generation. Cytochrome P450, ABC transporters, Hsp70 genes, von Willebrand proteins, chitin metabolism genes, mitochondria eating proteins, and opsin gene families have experienced significantly greater amplification and show signatures of selection. We use evolutionary theory to assess the relative contribution of SNPs and duplications in evolutionary change. Estimates suggest that gene family evolution may offer an exceptional substrate of genetic variation in M. nervosa, with Psgv=0.185 compared with Psgv=0.067 for single nucleotide changes. Hence, we suggest that gene family evolution is a source of "hopeful monsters" within the genome that facilitate adaptation.
Male Earnings Volatility in LEHD before, during, and after the Great Recession
Authors: Kevin L. McKinney, John M. Abowd
Date: 2020-08-01 00:00:00
Tasks: Sensitivity

This paper is part of a coordinated collection of papers on prime-age male earnings volatility. Each paper produces a similar set of statistics for the same reference population using a different primary data source. Our primary data source is the Census Bureau's Longitudinal Employer-Household Dynamics (LEHD) infrastructure files. Using LEHD data from 1998 to 2016, we create a well-defined population frame to facilitate accurate estimation of temporal changes comparable to designed longitudinal samples of people. We show that earnings volatility, excluding increases during recessions, has declined over the analysis period, a finding robust to various sensitivity analyses.
Contact Classification in COVID-19 Tracing
Authors: Christoph Günther, Daniel Günther
Date: 2020-08-02 00:00:00
Tasks: Classification, General Classification

The present paper addresses the task of reliably identifying critical contacts by using COVID-19 tracing apps. A reliable classification is crucial to ensure a high level of protection, and at the same time to prevent many people from being sent to quarantine by the app. Tracing apps are based on the capabilities of current smartphones to enable a broadest possible availability. Existing capabilities of smartphones include the exchange of Bluetooth Low Energy (BLE) signals and of audio signals, as well as the use of gyroscopes and magnetic sensors. The Bluetooth power measurements, which are often used today, may be complemented by audio ranging and attitude estimation in the future. Smartphones are worn in different ways, often in pockets and bags, which makes the propagation of signals and thus the classification rather unpredictable. Relying on the cooperation of users to wear their phones hanging from their neck would change the situation considerably. In this case the performance, achievable with BLE and audio measurements, becomes predictable. Our analysis identifies parameters that result in accurate warnings, at least within the scope of validity of the models. A significant reduction of the spreading of the disease can then be achieved by the apps, without causing many people to unduly go to quarantine. The present paper is the first of three papers which analyze the situation in some detail.
Fate of Duplicated Neural Structures
Authors: Luís F Seoane
Date: 2020-08-02 00:00:00

Statistical mechanics determines the abundance of different arrangements of matter depending on cost-benefit balances. Its formalism and phenomenology percolate throughout biological processes and set limits to effective computation. Under specific conditions, self-replicating and computationally complex patterns become favored, yielding life, cognition, and Darwinian evolution. Neurons and neural circuits sit at a crossroads between statistical mechanics, computation, and (through their role in cognition) natural selection. Can we establish a {\em statistical physics} of neural circuits? Such theory would tell what kinds of brains to expect under set energetic, evolutionary, and computational conditions. With this big picture in mind, we focus on the fate of duplicated neural circuits. We look at examples from central nervous systems, with a stress on computational thresholds that might prompt this redundancy. We also study a naive cost-benefit balance for duplicated circuits implementing complex phenotypes. From this we derive {\em phase diagrams} and (phase-like) transitions between single and duplicated circuits, which constrain evolutionary paths to complex cognition. Back to the big picture, similar phase diagrams and transitions might constrain I/O and internal connectivity patterns of neural circuits at large. The formalism of statistical mechanics seems a natural framework for thsi worthy line of research.
The potential for complex computational models of aging
Date: 2020-10-26 00:00:00

The gradual accumulation of damage and dysregulation during the aging of
living organisms can be quantified. Even so, the aging process is complex and
has multiple interacting physiological scales -- from the molecular to cellular
to whole tissues. In the face of this complexity, we can significantly advance
our understanding of aging with the use of computational models that simulate
realistic individual trajectories of health as well as mortality. To do so,
they must be systems-level models that incorporate interactions between
measurable aspects of age-associated changes. To incorporate individual
variability in the aging process, models must be stochastic. To be useful they
should also be predictive, and so must be fit or parameterized by data from
large populations of aging individuals. In this perspective, we outline where
we have been, where we are, and where we hope to go with such computational
models of aging. Our focus is on data-driven systems-level models, and on their
great potential in aging research.
Modular End-to-end Automatic Speech Recognition Framework for Acoustic-to-word Model
Date: 2020-07-31 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), speech-recognition, Speech Recognition

End-to-end (E2E) systems have played a more and more important role in
automatic speech recognition (ASR) and achieved great performance. However, E2E
systems recognize output word sequences directly with the input acoustic
feature, which can only be trained on limited acoustic data. The extra text
data is widely used to improve the results of traditional artificial neural
network-hidden Markov model (ANN-HMM) hybrid systems. The involving of extra
text data to standard E2E ASR systems may break the E2E property during
decoding. In this paper, a novel modular E2E ASR system is proposed. The
modular E2E ASR system consists of two parts: an acoustic-to-phoneme (A2P)
model and a phoneme-to-word (P2W) model. The A2P model is trained on acoustic
data, while extra data including large scale text data can be used to train the
P2W model. This additional data enables the modular E2E ASR system to model not
only the acoustic part but also the language part. During the decoding phase,
the two models will be integrated and act as a standard acoustic-to-word (A2W)
model. In other words, the proposed modular E2E ASR system can be easily
trained with extra text data and decoded in the same way as a standard E2E ASR
system. Experimental results on the Switchboard corpus show that the modular
E2E model achieves better word error rate (WER) than standard A2W models.
Existence and uniqueness of recursive utilities without boundedness
Authors: Timothy M. Christensen
Date: 2020-07-30 00:00:00

This paper derives primitive, easily verifiable sufficient conditions for existence and uniqueness of (stochastic) recursive utilities for several important classes of preferences. In order to accommodate models commonly used in practice, we allow both the state-space and per-period utilities to be unbounded. For many of the models we study, existence and uniqueness is established under a single, primitive "thin tail" condition on the distribution of growth in per-period utilities. We present several applications to robust preferences, models of ambiguity aversion and learning about hidden states, and Epstein-Zin preferences.
Making Decisions under Model Misspecification
Authors: Simone Cerreia-Vioglio, Lars Peter Hansen, Fabio Maccheroni, Massimo Marinacci
Date: 2020-08-01 00:00:00
Tasks: Decision Making, model

We use decision theory to confront uncertainty that is sufficiently broad to incorporate "models as approximations." We presume the existence of a featured collection of what we call "structured models" that have explicit substantive motivations. The decision maker confronts uncertainty through the lens of these models, but also views these models as simplifications, and hence, as misspecified. We extend the max-min analysis under model ambiguity to incorporate the uncertainty induced by acknowledging that the models used in decision-making are simplified approximations. Formally, we provide an axiomatic rationale for a decision criterion that incorporates model misspecification concerns.
A Passive STAR Microwave Circuit for 1-3 GHz Self-Interference Cancellation
Authors: Udara De Silva, Sravan Pulipati, Satheesh Bojja Venkatakrishnan, Shubhendu Bhardwaj, Arjuna Madanayake
Date: 2020-08-03 00:00:00

Simultaneous transmit and receive (STAR) allows full-duplex operation of a radio, which leads to doubled capacity for a given bandwidth. A circulator with high-isolation between transmit and receive ports, and low-loss from the antenna to receive port is typically required for achieving STAR. Conventional circulators do not offer wideband performance. Although wideband circulators have been proposed using parametric, switched delay-line/capacitor, and N-path filter techniques using custom integrated circuits, these magnet-free devices have non-linearity, noise, aliasing, and switching noise injection issues. In this paper, a STAR front-end based on passive linear microwave circuit is proposed. Here, a dummy antenna located inside a miniature RF-silent absorption chamber allows circulator-free STAR using simple COTS components. The proposed approach is highly-linear, free from noise, does not require switching or parametric modulation circuits, and has virtually unlimited bandwidth only set by the performance of COTS passive microwave components. The trade-off is relatively large size of the miniature RF-shielded chamber, making this suitable for base-station side applications. Preliminary results show the measured performance of Tx/Rx isolation between 25-60 dB in the 1.0-3.0 GHz range, and 50-60 dB for the 2.4-2.7 GHz range.
Fully Decentralized Reinforcement Learning-based Control of Photovoltaics in Distribution Grids for Joint Provision of Real and Reactive Power
Authors: Rayan El Helou, Dileep Kalathil, Le Xie
Date: 2020-08-03 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

In this paper, we introduce a new framework to address the problem of voltage regulation in unbalanced distribution grids with deep photovoltaic penetration. In this framework, both real and reactive power setpoints are explicitly controlled at each solar panel smart inverter, and the objective is to simultaneously minimize system-wide voltage deviation and maximize solar power output. We formulate the problem as a Markov decision process with continuous action spaces and use proximal policy optimization, a reinforcement learning-based approach, to solve it, without the need for any forecast or explicit knowledge of network topology or line parameters. By representing the system in a quasi-steady state manner, and by carefully formulating the Markov decision process, we reduce the complexity of the problem and allow for fully decentralized (communication-free) policies, all of which make the trained policies much more practical and interpretable. Numerical simulations on a 240-node unbalanced distribution grid, based on a real network in Midwest U.S., are used to validate the proposed framework and reinforcement learning approach.
Glucose-insulin mathematical model for the combined effect of medications and life style of Type 2 diabetic patients
Authors: Mohamad Al Ahdab, John Leth, Torben Knudsen, Peter Vestergaard, Henrik Clausen
Date: 2020-08-04 00:00:00

The goal of this paper is to propose a new mathematical model for the combined effect of different treatments and lifestyles on the glucose-insulin dynamics of Type 2 diabetes (T2D) patients. The model gives the possibility to take into consideration physical activity, stress, meals, and medications while evaluating or designing treatment plans for T2D patients. The model is proposed by combining and modifying some of the available models in the literature. Simulations were performed for the modifications to show how the model confirm with literature on T2D patients. Additionally, a discussion is provided to demonstrate the ability of the model to be used in the assessment of treatment plans and in the design for robust insulin dose guidance algorithms. An open source code for the model is additionally provided.