pwc-archive/papers-with-abstracts huggingface dataset:
Downlink Transmit Design for Massive MIMO LEO Satellite Communications
Authors: Ke-Xin Li, Li You, Jiaheng Wang, Xiqi Gao, Christos G. Tsinos, Symeon Chatzinotas, Björn Ottersten
Date: 2020-08-12 00:00:00

This paper investigates the downlink (DL) transmit design for massive multiple-input multiple-output (MIMO) low-earth-orbit (LEO) satellite communication systems, where only the slow-varying statistical channel state information is exploited at the transmitter. The channel model for the DL massive MIMO LEO satellite system is established, in which both the satellite and the user terminals (UTs) are equipped with uniform planar arrays. Observing the rank-one property of the channel matrices, we show that the single-stream precoding for each UT is the optimal choice that maximizes the ergodic sum rate. This favorable result simplifies the complicated design of transmit covariance matrices into that of precoding vectors without any loss of optimality. Then, an efficient algorithm is devised to compute the precoding vectors. Furthermore, we formulate an approximate transmit design based on the upper bound on the ergodic sum rate, for which the optimality of single-stream precoding still holds. We show that, in this case, the design of precoding vectors can be simplified into that of scalar variables, for which an effective algorithm is developed. In addition, a low-complexity learning framework is proposed for optimizing the scalar variables. Simulation results demonstrate that the proposed approaches can achieve significant performance gains over the existing schemes.
Network reinforcement driven drug repurposing for COVID-19 by exploiting disease-gene-drug associations
Authors: Yonghyun Nam, Jae-Seung Yun, Seung Mi Lee, Ji Won Park, Ziqi Chen, Brian Lee, Anurag Verma, Xia Ning, Li Shen, Dokyoon Kim
Date: 2020-08-12 00:00:00

Currently, the number of patients with COVID-19 has significantly increased. Thus, there is an urgent need for developing treatments for COVID-19. Drug repurposing, which is the process of reusing already-approved drugs for new medical conditions, can be a good way to solve this problem quickly and broadly. Many clinical trials for COVID-19 patients using treatments for other diseases have already been in place or will be performed at clinical sites in the near future. Additionally, patients with comorbidities such as diabetes mellitus, obesity, liver cirrhosis, kidney diseases, hypertension, and asthma are at higher risk for severe illness from COVID-19. Thus, the relationship of comorbidity disease with COVID-19 may help to find repurposable drugs. To reduce trial and error in finding treatments for COVID-19, we propose building a network-based drug repurposing framework to prioritize repurposable drugs. First, we utilized knowledge of COVID-19 to construct a disease-gene-drug network (DGDr-Net) representing a COVID-19-centric interactome with components for diseases, genes, and drugs. DGDr-Net consisted of 592 diseases, 26,681 human genes and 2,173 drugs, and medical information for 18 common comorbidities. The DGDr-Net recommended candidate repurposable drugs for COVID-19 through network reinforcement driven scoring algorithms. The scoring algorithms determined the priority of recommendations by utilizing graph-based semi-supervised learning. From the predicted scores, we recommended 30 drugs, including dexamethasone, resveratrol, methotrexate, indomethacin, quercetin, etc., as repurposable drugs for COVID-19, and the results were verified with drugs that have been under clinical trials. The list of drugs via a data-driven computational approach could help reduce trial-and-error in finding treatment for COVID-19.
Chernoff Bounds and Saddlepoint Approximations for the Outage Probability in Intelligent Reflecting Surface Assisted Communication Systems
Authors: Tianxiong Wang, Gaojie Chen, Justin P. Coon, Mihai-Alin Badiu
Date: 2020-08-12 00:00:00
Tasks: Diversity

We analyze the outage probability of an intelligent reflecting surface (IRS)-assisted communication network. A tight upper bound on the outage probability is formulated based on the Chernoff inequality. Furthermore, through an exact asymptotic (a large number of reflecting elements) analysis based on a saddlepoint approximation, we derive closed-form expressions of the outage probability for systems with and without a direct link and obtain the corresponding diversity orders. Simulation results corroborate our theoretical analysis and show the inaccuracies inherent in using the central limit theorem (CLT) to analyze system performance. Our analysis is accurate even for a small number of IRS elements in the high signal-to-noise ratio (SNR) regime.
Identification of Time-Varying Transformation Models with Fixed Effects, with an Application to Unobserved Heterogeneity in Resource Shares
Authors: Irene Botosaru, Chris Muris, Krishna Pendakur
Date: 2020-08-12 00:00:00

We provide new results showing identification of a large class of fixed-T panel models, where the response variable is an unknown, weakly monotone, time-varying transformation of a latent linear index of fixed effects, regressors, and an error term drawn from an unknown stationary distribution. Our results identify the transformation, the coefficient on regressors, and features of the distribution of the fixed effects. We then develop a full-commitment intertemporal collective household model, where the implied quantity demand equations are time-varying functions of a linear index. The fixed effects in this index equal logged resource shares, defined as the fractions of household expenditure enjoyed by each household member. Using Bangladeshi data, we show that women's resource shares decline with household budgets and that half of the variation in women's resource shares is due to unobserved household-level heterogeneity.
A dynamic ordered logit model with fixed effects
Date: 2020-08-12 00:00:00
Tasks: model, regression

We study a fixed-$T$ panel data logit model for ordered outcomes that
accommodates fixed effects and state dependence. We provide identification
results for the autoregressive parameter, regression coefficients, and the
threshold parameters in this model. Our results require only four observations
on the outcome variable. We provide conditions under which a composite
conditional maximum likelihood estimator is consistent and asymptotically
normal. We use our estimator to explore the determinants of self-reported
health in a panel of European countries over the period 2003-2016. We find
that: (i) the autoregressive parameter is positive and analogous to a linear
AR(1) coefficient of about 0.25, indicating persistence in health status; (ii)
the association between income and health becomes insignificant once we control
for unobserved heterogeneity and persistence.
Transmission of market orders through communication line with relativistic delay
Authors: Peter B. Lerner
Date: 2020-08-12 00:00:00

The notion of "relativistic finance" became ingrained in public imagination and has been asserted in many mass-media reports. Yet, despite an observed drive of the most reputable Wall Street firms to establish their servers ever closer to the trading hubs, there is surprisingly little "hard" information related to relativistic delay of the trading orders. In this paper, the author uses modified M/M/G queue theory to describe propagation of the trading signal with finite velocity.
Impact of Disturbances on Mixed Traffic Control with Autonomous Vehicles
Date: 2020-08-12 00:00:00
Tasks: Autonomous Vehicles

This paper investigates the impact of disturbances on controlling an
autonomous vehicle to smooth mixed traffic flow in a ring road setup. By
exploiting the ring structure of this system, it is shown that velocity
perturbations impacting any vehicle on the ring enter an uncontrollable and
marginally stable mode defined by the sum of relative vehicle spacings. These
disturbances are then integrated up by the system and cannot be unwound via
controlling the autonomous vehicle. In particular, if the velocity disturbances
are zero-mean Gaussians, then the traffic flow on the ring will undergo a
random walk with the variance growing indefinitely and independently of the
control policy applied. In contrast, the impact of acceleration disturbances is
benign as these disturbances do no enter the uncontrollable mode, meaning that
they can be easily regulated using the autonomous vehicle. Our results support
and complement the existing theoretic analysis and field experiments.
Neurodegenerative damage reduces firing coherence in a continuous attractor model of grid cells
Authors: Yuduo Zhi, Daniel L. Cox
Date: 2020-08-12 00:00:00

Grid cells in the dorsolateral band of the medial entorhinal cortex(dMEC) display strikingly regular periodic firing patterns on a lattice of positions in 2-D space. This helps animals to encode relative spatial location without reference to external cues. The dMEC is damaged in the early stages of Alzheimer's Disease, which affects navigation ability of a disease victim, reducing the synaptic density of neurons in the network. Within an established 2-dimensional continuous attractor neural network model of grid cell activity, we introduce damage parameterized by radius and by the strength of the synaptic output for neurons in the damaged region. The proportionality of the grid field flow on the dMEX to the velocity of the model organism is maintained, but when we examine the coherence of the grid cell firing field in the form of the Fourier transform (Bragg peaks) of the grid lattice, we find that a wide range of damage radius and strength induces an incoherent structure with only a single central peak, adjacent to narrow bands of striped (two additional peaks), which abut an orthorhombic pattern (four additional peaks), that abuts the undamaged hexagonal region (six additional peaks). Within the damaged region, grid cells show no Bragg peaks, and outside the damaged region the central Bragg peak strength is largely unaffected. There is a re-entrant region of normal grid firing for very large damage area. We anticipate that the modified grid cell behavior can be observed in non-invasive fMRI imaging of the dMEC.
Dynamic Active Average Consensus and its Application in Containment Control
Date: 2020-08-13 00:00:00

This paper proposes a continuous-time dynamic active weighted average
consensus algorithm in which the agents can alternate between active and
passive modes depending on their ability to access to their reference input.
The objective is to enable all the agents, both active and passive, to track
the weighted average of the reference inputs of the active agents. The
algorithm is modeled as a switched linear system whose convergence properties
are carefully studied considering the agents' piece-wise constant access to the
reference signals and possible piece-wise constant weights of the agents. We
also study the discrete-time implementation of this algorithm. Next, we show
how a containment control problem, in which a group of followers should track
the convex hull of a set of observed leaders, can be cast as an active average
consensus problem, and solved efficiently by our proposed dynamic active
average consensus algorithm. Numerical examples demonstrate our results.
Variability in seeds\' physicochemical characteristics, germination and seedling growth within and between two French Populus nigra populations
Authors: Marlène Lefebvre, Marc Villar, Nathalie Boizot, Armelle Delile, Benjamin Dimouro, Anne-Marie Lomelech, Caroline Teyssier
Date: 2020-08-13 00:00:00

To improve understanding of the physiology, ecology and regeneration requirements of black poplar (Populus nigra), a severely endangered tree species in Europe, we analysed the biochemical composition of seeds from 20 families derived from open pollination of 20 trees in two contrasting environments in France, along the Dr\^ome and Loire rivers. Significant between-family differences were detected. Total lipid contents differed qualitatively between seed of the two populations and were higher in Dr\^ome population (214 more or less 29.8 versus 172.72 more or less 19.77 mg.g\-1 dw). Soluble sugars were less abundant in seeds of the Dr\^ome population (78.78 more or less 10.01 versus 104.10 more or less 25.32 mg.g\-1 dw). The protein content (322 more or less 74.3 mg.g\-1 dw) and quantity of reserve proteins did not differ between seeds of the two populations. Measurements in three consecutive years showed that seeds of the Loire population had significantly higher dry weight (0.69 more or less 0.10 versus 0.45 more or less 0.10 mg.g\-1 dw), but this did not significantly affect the germination rate or root growth of the seedlings measured 5 days after sowing. However, one group of Loire seedlings had substantially longer juvenile roots than another group. Overall, the results suggest that intrinsic qualities of the seeds are not critical factors in early stages and in selection pressure of the species life cycle and recruitment.
Chance-Constrained Model Predictive Control A reformulated approach suitable for sewer networks
Date: 2020-08-13 00:00:00
Tasks: Model Predictive Control

In this work, a revised formulation of Chance-Constrained (CC) Model
Predictive Control (MPC) is presented. The focus of this work is on the
mathematical formulation of the revised CC-MPC, and the reason behind the need
for its revision. The revised formulation is given in the context of sewer
systems, and their weir overflow structures. A linear sewer model of the
Astlingen Benchmark sewer model is utilized to illustrate the application of
the formulation, both mathematically and performance-wise through simulations.
Based on the simulations, a comparison of performance is done between the
revised CC-MPC and a comparable deterministic MPC, with a focus on overflow
avoidance, computation time, and operational behavior. The simulations show
similar performance for overflow avoidance for both types of MPC, while the
computation time increases slightly for the CC-MPC, together with operational
behaviors getting limited
Exact solutions for a Solow-Swan model with non-constant returns to scale
Date: 2020-08-13 00:00:00

The Solow-Swan model is shortly reviewed from a mathematical point of view.
By considering non-constant returns to scale, we obtain a general solution
strategy. We then compute the exact solution for the Cobb-Douglas production
function, for both the classical model and the von Bertalanffy model. Numerical
simulations are provided.
On some predictions of the poly-tRNA model for the origin and evolution of genetic coding
Date: 2020-08-14 00:00:00

The poly-tRNA model was recently presented for the origin and evolution of
genetic coding. This model has led to a rather precise description of what
might have occurred at the beginning of protein synthesis in the first life
form. Here, we further discuss some interesting implications of this model.
First, the system of encoded peptide/protein synthesis appears to have started
and developed on the breeding ground of a rich RNA world, responsible for the
infancy of life existence and complexity. Furthermore, once protein synthesis
was fully established and apparently superseding the RNA world, and at a very
early stage of life beginnings, we already see what has been a recurrent theme
in the likely interpretation of modern comparative molecular studies on
species: the full ability of this nascent life entity to develop itself by
tinkering, using all kinds of available pieces to improve itself. Lastly, and
very instructively, it is deduced from this model that the first peptides to be
produced probably had unique properties, not shared by the arsenal of molecules
present hitherto, which allowed a functional connection between the then
omnipotent RNA world and the lipid membrane vesicles containing it. These
specific functions might have initially been at the origin of the Darwinian
selection of the full-blown protein-synthesis machinery.
Population stratification enables modeling effects of reopening policies on mortality and hospitalization rates
Date: 2020-08-10 00:00:00
Tasks: counterfactual, Decision Making

Objective: We study the influence of local reopening policies on the
composition of the infectious population and their impact on future
hospitalization and mortality rates. Materials and Methods: We collected
datasets of daily reported hospitalization and cumulative morality of COVID 19
in Houston, Texas, from May 1, 2020 until June 29, 2020. These datasets are
from multiple sources (USA FACTS, Southeast Texas Regional Advisory Council
COVID 19 report, TMC daily news, and New York Times county level mortality
reporting). Our model, risk stratified SIR HCD uses separate variables to model
the dynamics of local contact (e.g., work from home) and high contact (e.g.,
work on site) subpopulations while sharing parameters to control their
respective $R_0(t)$ over time. Results: We evaluated our models forecasting
performance in Harris County, TX (the most populated county in the Greater
Houston area) during the Phase I and Phase II reopening. Not only did our model
outperform other competing models, it also supports counterfactual analysis to
simulate the impact of future policies in a local setting, which is unique
among existing approaches. Discussion: Local mortality and hospitalization are
significantly impacted by quarantine and reopening policies. No existing model
has directly accounted for the effect of these policies on local trends in
infections, hospitalizations, and deaths in an explicit and explainable manner.
Our work is an attempt to close this important technical gap to support
decision making. Conclusion: Despite several limitations, we think it is a
timely effort to rethink about how to best model the dynamics of pandemics
under the influence of reopening policies.
NOMA for Multiple Access Channel and Broadcast Channel in Indoor VLC
Authors: T. Uday, Abhinav Kumar, L. Natarajan
Date: 2020-08-13 00:00:00

Orthogonal frequency division multiplexing (OFDM) based non-orthogonal multiple access (NOMA) has increased complexity and reduced spectral efficiency in visible light communications (VLC) NOMA compared to radiofrequency NOMA due to non-negative real value constraints on transmit symbols. To address this issue, we propose a generalized non-OFDM based scheme for two scenarios of indoor VLC; i) Multiple access channel (MAC), ii) Broadcast channel (BC). We evaluate the performance of the proposed scheme for MAC using successive interference cancellation (SIC) based decoding, joint maximum likelihood (JML) decoding, and a combination of SIC and JML decoding. For BC, we evaluate the performance using SIC based decoding. It is observed that, for MAC, the proposed scheme with JML decoding performs better than the state-of-the-art orthogonal multiple access both in terms of bit error rate (BER) and computations. For BC, the proposed scheme is computationally efficient with trade-off on BER.
Multi-Agent Double Deep Q-Learning for Beamforming in mmWave MIMO Networks
Authors: Xueyuan Wang, M. Cenk Gursoy
Date: 2020-08-13 00:00:00
Tasks: Q-Learning

Beamforming is one of the key techniques in millimeter wave (mmWave) multi-input multi-output (MIMO) communications. Designing appropriate beamforming not only improves the quality and strength of the received signal, but also can help reduce the interference, consequently enhancing the data rate. In this paper, we propose a distributed multi-agent double deep Q-learning algorithm for beamforming in mmWave MIMO networks, where multiple base stations (BSs) can automatically and dynamically adjust their beams to serve multiple highly-mobile user equipments (UEs). In the analysis, largest received power association criterion is considered for UEs, and a realistic channel model is taken into account. Simulation results demonstrate that the proposed learning-based algorithm can achieve comparable performance with respect to exhaustive search while operating at much lower complexity.
Cross attentive pooling for speaker verification
Date: 2020-08-13 00:00:00
Tasks: Speaker Verification, Text-Independent Speaker Verification

The goal of this paper is text-independent speaker verification where utterances come from 'in the wild' videos and may contain irrelevant signal. While speaker verification is naturally a pair-wise problem, existing methods to produce the speaker embeddings are instance-wise. In this paper, we propose Cross Attentive Pooling (CAP) that utilizes the context information across the reference-query pair to generate utterance-level embeddings that contain the most discriminative information for the pair-wise matching problem. Experiments are performed on the VoxCeleb dataset in which our method outperforms comparable pooling strategies.
Tetracycline as an inhibitor to the coronavirus SARS-CoV-2
Authors: Tom Y. Zhao, Neelesh A. Patankar
Date: 2020-08-13 00:00:00

The coronavirus SARS-CoV-2 remains an extant threat against public health on a global scale. Cell infection begins when the spike protein of SARS-CoV-2 binds with the cell receptor, angiotensin-converting enzyme 2 (ACE2). Here, we address the role of Tetracycline as an inhibitor for the receptor-binding domain (RBD) of the spike protein. Targeted molecular investigation show that Tetracycline binds more favorably to the RBD (-9.40 kcal/mol) compared to Chloroquine (-6.31 kcal/mol) or Doxycycline (-8.08 kcal/mol) and inhibits attachment to ACE2 to a greater degree (binding efficiency of 2.98 $\frac{\text{kcal}}{\text{mol}\cdot \text{nm}^2}$ for Tetracycline-RBD, 5.59 $\frac{\text{kcal}}{\text{mol}\cdot \text{nm}^2}$ for Chloroquine-RBD, 5.16 $\frac{\text{kcal}}{\text{mol}\cdot \text{nm}^2}$ for Doxycycline-RBD). Stronger Tetracycline inhibition is verified with nonequilibrium PMF calculations, for which the Tetracycline-RBD complex exhibits the lowest free energy profile along the dissociation pathway from ACE2. Tetracycline appears to target viral residues that are usually involved in significant hydrogen bonding with ACE2; this inhibition of cellular infection complements the anti-inflammatory and cytokine suppressing capability of Tetracycline, and may further reduce the duration of ICU stays and mechanical ventilation induced by the coronavirus SARS-CoV-2.
Image Processing Tools for Financial Time Series Classification
Authors: Bairui Du, Delmiro Fernandez-Reyes, Paolo Barucca
Date: 2020-08-13 00:00:00
Tasks: BIG-bench Machine Learning, Classification, Denoising, General Classification, Time Series, Time Series Analysis, Time Series Classification, Time Series Forecasting

The application of deep learning to time series forecasting is one of the major challenges in present machine learning. We propose a novel methodology that combines machine learning and image processing methods to define and predict market states with intraday financial data. A wavelet transform is applied to the log-return of stock prices for both image extraction and denoising. A convolutional neural network then extracts patterns from denoised wavelet images to classify daily time series, i.e. a market state is associated with the binary prediction of the daily close price movement based on the wavelet image constructed from the price changes in the first hours of the day. This method overcomes the low signal-to-noise ratio problem in financial time series and gets a competitive prediction accuracy of the market states 'Up' and 'Down' of financial data as tested on the S&P 500.