pwc-archive/papers-with-abstracts huggingface dataset:
Autonomous Emergency Collision Avoidance and Stabilisation in Structured Environments
Date: 2020-04-16 00:00:00
Tasks: Collision Avoidance, Friction, Trajectory Planning

In this paper, a novel closed-loop control framework for autonomous obstacle
avoidance on a curve road is presented. The proposed framework provides two
main functionalities; (i) collision free trajectory planning using MPC and (ii)
a torque vectoring controller for lateral/yaw stability designed using optimal
control concepts. This paper analyzes trajectory planning algorithm using
nominal MPC, offset-free MPC and robust MPC, along with separate implementation
of torque-vectoring control. Simulation results confirm the strengths of this
hierarchical control algorithm which are: (i) free from non-convex collision
avoidance constraints, (ii) to guarantee the convexity while driving on a curve
road (iii) to guarantee feasibility of the trajectory when the vehicle
accelerate or decelerate while performing lateral maneuver, and (iv) robust
against low friction surface. Moreover, to assess the performance of the
control structure under emergency and dynamic environment, the framework is
tested under low friction surface and different curvature value. The simulation
results show that the proposed collision avoidance system can significantly
improve the safety of the vehicle during emergency driving scenarios. In order
to stipulate the effectiveness of the proposed collision avoidance system, a
high-fidelity IPG carmaker and Simulink co-simulation environment is used to
validate the results.
Searching inhibitors for three important proteins of COVID-19 through molecular docking studies
Authors: Seshu Vardhan, Suban K Sahoo
Date: 2020-04-17 00:00:00
Tasks: Molecular Docking

The lack of recommended drugs or vaccines to deal with the COVID-19 is the main concern of this pandemic. The approved drugs for similar health problems, drugs under clinical trials, and molecules from medicinal plants extracts are investigated randomly to deal with the COVID-19 infection. Molecular docking, one of the best approach to search therapeutically potent drugs/molecules in real time with possible hope to apply on COVID-19. In this communication, molecular docking studies of 18 ligands were carried out with the three therapeutic target proteins of SARS-CoV-2, i.e., RNA-dependent RNA polymerase (RdRp), angiotensin-converting enzyme 2 (ACE2) and spike glycoprotein (SGp). The obtained results revealed that the phytochemicals showed better dock score in compared to the drugs paracetmol and hydroxychloroquine. Combining the dock score and medicinal properties, we believe the terpenoids based phytochemicals limonin and scopadulcic acid B can be further explored for potential use against COVID-19.
The direct and spillover effects of a nationwide socio-emotional learning program for disruptive students
Date: 2020-04-17 00:00:00

Social and emotional learning (SEL) programs teach disruptive students to
improve their classroom behavior. Small-scale programs in high-income countries
have been shown to improve treated students' behavior and academic outcomes.
Using a randomized experiment, we show that a nationwide SEL program in Chile
has no effect on eligible students. We find evidence that very disruptive
students may hamper the program's effectiveness. ADHD, a disorder correlated
with disruptiveness, is much more prevalent in Chile than in high-income
countries, so very disruptive students may be more present in Chile than in the
contexts where SEL programs have been shown to work.
Mean Field Game Approach to Bitcoin Mining
Authors: Charles Bertucci, Louis Bertucci, Jean-Michel Lasry, Pierre-Louis Lions
Date: 2020-04-17 00:00:00

We present an analysis of the Proof-of-Work consensus algorithm, used on the Bitcoin blockchain, using a Mean Field Game framework. Using a master equation, we provide an equilibrium characterization of the total computational power devoted to mining the blockchain (hashrate). From a simple setting we show how the master equation approach allows us to enrich the model by relaxing most of the simplifying assumptions. The essential structure of the game is preserved across all the enrichments. In deterministic settings, the hashrate ultimately reaches a steady state in which it increases at the rate of technological progress. In stochastic settings, there exists a target for the hashrate for every possible random state. As a consequence, we show that in equilibrium the security of the underlying blockchain is either $i)$ constant, or $ii)$ increases with the demand for the underlying cryptocurrency.
UWB-Based Localization for Multi-UAV Systems and Collaborative Heterogeneous Multi-Robot Systems: a Survey
Date: 2020-04-28 00:00:00
Tasks: Survey

Ultra-wideband technology has emerged in recent years as a robust solution
for localization in GNSS denied environments. In particular, its high accuracy
when compared to other wireless localization solutions is enabling a wider
range of collaborative and multi-robot application scenarios, being able to
replace more complex and expensive motion-capture areas for use cases where
accuracy in the order of tens of centimeters is sufficient. We present the
first survey of UWB-based localization focused on multi-UAV systems and
heterogeneous multi-robot systems. We have found that previous literature
reviews do not consider in-depth the challenges in both aerial navigation and
navigation with multiple robots, but also in terms of heterogeneous multi-robot
systems. In particular, this is, to the best of our knowledge, the first survey
to review recent advances in UWB-based (i) methods that enable ad-hoc and
dynamic deployments; (ii) collaborative localization techniques; and (iii)
cooperative sensing and cooperative maneuvers such as UAV docking on mobile
platforms. Finally, we also review existing datasets and discuss the potential
of this technology for both localization in GNSS-denied environments and
collaboration in multi-robot systems.
Calibrated Vehicle Paint Signatures for Simulating Hyperspectral Imagery
Date: 2020-04-16 00:00:00
Tasks: Diversity, Image Generation

We investigate a procedure for rapidly adding calibrated vehicle visible-near
infrared (VNIR) paint signatures to an existing hyperspectral simulator - The
Digital Imaging and Remote Sensing Image Generation (DIRSIG) model - to create
more diversity in simulated urban scenes. The DIRSIG model can produce
synthetic hyperspectral imagery with user-specified geometry, atmospheric
conditions, and ground target spectra. To render an object pixel's spectral
signature, DIRSIG uses a large database of reflectance curves for the
corresponding object material and a bidirectional reflectance model to
introduce s due to orientation and surface structure. However, this database
contains only a few spectral curves for vehicle paints and generates new paint
signatures by combining these curves internally. In this paper we demonstrate a
method to rapidly generate multiple paint spectra, flying a drone carrying a
pushbroom hyperspectral camera to image a university parking lot. We then
process the images to convert them from the digital count space to spectral
reflectance without the need of calibration panels in the scene, and port the
paint signatures into DIRSIG for successful integration into the newly rendered
sets of synthetic VNIR hyperspectral scenes.
Characterizing the memory capacity of transmon qubit reservoirs
Authors: Samudra Dasgupta, Kathleen E. Hamilton, Arnab Banerjee
Date: 2020-04-15 00:00:00
Tasks: Open-Ended Question Answering, Time Series Analysis, Time Series Forecasting

Quantum Reservoir Computing (QRC) exploits the dynamics of quantum ensemble systems for machine learning. Numerical experiments show that quantum systems consisting of 5-7 qubits possess computational capabilities comparable to conventional recurrent neural networks of 100 to 500 nodes. Unlike traditional neural networks, we do not understand the guiding principles of reservoir design for high-performance information processing. Understanding the memory capacity of quantum reservoirs continues to be an open question. In this study, we focus on the task of characterizing the memory capacity of quantum reservoirs built using transmon devices provided by IBM. Our hybrid reservoir achieved a Normalized Mean Square Error (NMSE) of 6x10^{-4} which is comparable to recent benchmarks. The Memory Capacity characterization of a n-qubit reservoir showed a systematic variation with the complexity of the topology and exhibited a peak for the configuration with n-1 self-loops. Such a peak provides a basis for selecting the optimal design for forecasting tasks.
3-D Context Entropy Model for Improved Practical Image Compression
Date: 2020-04-17 00:00:00
Tasks: Image Compression, MS-SSIM, SSIM

In this paper, we present our image compression framework designed for CLIC
2020 competition. Our method is based on Variational AutoEncoder (VAE)
architecture which is strengthened with residual structures. In short, we make
three noteworthy improvements here. First, we propose a 3-D context entropy
model which can take advantage of known latent representation in current
spatial locations for better entropy estimation. Second, a light-weighted
residual structure is adopted for feature learning during entropy estimation.
Finally, an effective training strategy is introduced for practical adaptation
with different resolutions. Experiment results indicate our image compression
method achieves 0.9775 MS-SSIM on CLIC validation set and 0.9809 MS-SSIM on
test set.
Learning Strategies for Radar Clutter Classification
Authors: Pia Addabbo, Sudan Han, Danilo Orlando, Giuseppe Ricci
Date: 2020-04-17 00:00:00
Tasks: Classification, General Classification

In this paper, we address the problem of classifying clutter returns in order to partition them into statistically homogeneous subsets. The classification procedure relies on a model for the observables including latent variables that is solved by the expectation-maximization algorithm. The derivations are carried out by accounting for three different cases for the structure of the clutter covariance matrix. A preliminary performance analysis highlights that the proposed technique is a viable means to cluster clutter returns over the range.
Identification of deregulated transcription factors involved in subtypes of cancers
Authors: Magali Champion, Julien Chiquet, Pierre Neuvial, Mohamed Elati, François Radvanyi, Etienne Birmelé
Date: 2020-04-17 00:00:00

We propose a methodology for the identification of transcription factors involved in the deregulation of genes in tumoral cells. This strategy is based on the inference of a reference gene regulatory network that connects transcription factors to their downstream targets using gene expression data. The behavior of genes in tumor samples is then carefully compared to this network of reference to detect deregulated target genes. A linear model is finally used to measure the ability of each transcription factor to explain those deregulations. We assess the performance of our method by numerical experiments on a breast cancer data set. We show that the information about deregulation is complementary to the expression data as the combination of the two improves the supervised classification performance of samples into cancer subtypes.
Index Modulation-Based Flexible Non-Orthogonal Multiple Access
Date: 2020-04-17 00:00:00

Non-orthogonal multiple access (NOMA) is envisioned as an efficient candidate
for future communication systems. This letter proposes a novel orthogonal
frequency division multiplexing (OFDM) with index modulation (IM)-based NOMA
scheme, called OFDM-IM NOMA, for future multi-user communication systems.
Inspired by IM and classical OFDM-NOMA, users utilize flexibility by adjusting
power allocation factors and subcarrier activation ratios. Our new scheme
allows different service users to share available resources as in classical
NOMA, more efficiently. It is shown that OFDM-IM NOMA reliably supports a high
and low data rate user at the same resources by adjusting their subcarrier
activation ratios.
Loss aversion and the welfare ranking of policy interventions
Authors: Sergio Firpo, Antonio F. Galvao, Martyna Kobus, Thomas Parker, Pedro Rosa-Dias
Date: 2020-04-17 00:00:00

This paper develops theoretical criteria and econometric methods to rank policy interventions in terms of welfare when individuals are loss-averse. Our new criterion for "loss aversion-sensitive dominance" defines a weak partial ordering of the distributions of policy-induced gains and losses. It applies to the class of welfare functions which model individual preferences with non-decreasing and loss-averse attitudes towards changes in outcomes. We also develop new statistical methods to test loss aversion-sensitive dominance in practice, using nonparametric plug-in estimates; these allow inference to be conducted through a special resampling procedure. Since point-identification of the distribution of policy-induced gains and losses may require strong assumptions, we extend our comparison criteria, test statistics, and resampling procedures to the partially-identified case. We illustrate our methods with a simple empirical application to the welfare comparison of alternative income support programs in the US.
Is the anti-filarial drug diethylcarbamazine useful to treat COVID-19?
Authors: Anuruddha Abeygunasekera, Saroj Jayasinghe
Date: 2020-04-18 00:00:00

The SARS-CoV-2 virus has resulted in a devastating pandemic of COVID-19. Exploring compounds that could offer a breakthrough in treatment is the need of the hour. Re-positioning cheap, freely available and safe drugs is a priority. The paper proposes evidence for the potential use of diethylcarbamazine (DEC) in the treatment of COVID-19. DEC has inhibitory effects on arachidonic acid metabolism to prostaglandins, little known anti-viral effects on animal retroviruses and demonstrated anti-inflammatory actions in animal models of lung inflammation indicating the need to explore this hypothesis further. We believe this is the first time DEC is being proposed to treat COVID-19.
Optimal UAV Deployment for Rate Maximization in IoT Networks
Date: 2020-04-18 00:00:00

We consider multiple unmanned aerial vehicles (UAVs) at a common altitude
serving as data collectors to a network of IoT devices. First, using a
probabilistic line of sight channel model, the optimal assignment of IoT
devices to the UAVs is determined. Next, for the asymptotic regimes of a large
number of UAVs and/or large UAV altitudes, we propose closed-form analytical
expressions for the optimal data rate and characterize the corresponding
optimal UAV deployments. We also propose a simple iterative algorithm to find
the optimal deployments with a small number of UAVs at high altitudes. Globally
optimal numerical solutions to the general rate maximization problem are found
using particle swarm optimization.
MatchboxNet: 1D Time-Channel Separable Convolutional Neural Network Architecture for Speech Commands Recognition
Date: 2020-04-21 00:00:00
Tasks: Data Augmentation, Time Series Analysis

We present an MatchboxNet - an end-to-end neural network for speech command
recognition. MatchboxNet is a deep residual network composed from blocks of 1D
time-channel separable convolution, batch-normalization, ReLU and dropout
layers. MatchboxNet reaches state-of-the-art accuracy on the Google Speech
Commands dataset while having significantly fewer parameters than similar
models. The small footprint of MatchboxNet makes it an attractive candidate for
devices with limited computational resources. The model is highly scalable, so
model accuracy can be improved with modest additional memory and compute.
Finally, we show how intensive data augmentation using an auxiliary noise
dataset improves robustness in the presence of background noise.
A Survey of 6G Wireless Communications: Emerging Technologies
Date: 2020-04-18 00:00:00
Tasks: Survey

While fifth-generation (5G) communications are being rolled out around the world, sixth-generation (6G) communications have attracted much attention from both the industry and academia. Compared with 5G, 6G will have a wider frequency band, higher transmission rate, spectrum efficiency, greater connection capacity, shorter delay, wider coverage and stronger anti-interference capability, so as to meet the various network requirements for industries. In this paper, we present a survey of potential essential technologies in 6G. In particular, we will introduce artificial intelligence, intelligent surfaces, terahertz communications technologies in detail, while giving a brief introduction to other potential technologies, including visible light communications, Blockchain-enabled wireless network, advanced duplex and holographic radio.
A new method for life table and life expectancy calculation
Authors: Weidong Huang
Date: 2020-04-18 00:00:00

The existing life table method needs to calculate the age-specific mortality first, not only has too many and complicated calculation steps, but also introduces the multiple approximation to bring error. This paper redefines the probability of death for the life table as the average probability of death of a group of people born in a certain period at a later time. Based on this definition, a new method for the life table is proposed to obtain the life expectancy, which has the same meaning to that from the traditional life table. Using the Japanese population data to verify the method, the results show that it is consistent with the life expectancy of the birth of the baby, the maximum relative difference is no more than 0.1%, and average relative difference is less than 0.03%. The theory and method of life table described in this paper are simple and easy to understand. The needed data are easy to obtained from statistics, and the calculation is easy, the results obtained are accurate and reliable. It should be a very valuable demographic method for application.
Frequency Diverse Arrays (FDAs) vs. Phased Arrays: On the Application of FDAs for Secure Wireless Communications
Date: 2020-04-18 00:00:00

Application of range-angle focusing frequency diverse array (FDA) for secure
wireless communication has been proposed in recent years. However, it can be
proven that the FDAs are not suitable for secure wireless communication and the
same properties of the focusing FDA can be obtained by using traditional phased
arrays, more effectively. Besides, in this paper, it is indicated that FDAs are
not good candidates for constructing time-variant focusing beampatterns, while
constructing time-invariant focusing beampatterns is impossible at all.
Furthermore, some incorrect considerations about the FDAs are highlighted.
Resonant Beam Communications: Principles and Designs
Authors: Mingliang Xiong, Qingwen Liu, Gang Wang, Georgios B. Giannakis, Chuan Huang
Date: 2020-04-18 00:00:00

Wireless optical communications (WOC) has carriers up to several hundred terahertz, which offers several advantages, such as ultrawide bandwidth and no electromagnetic interference. Conventional WOC that uses light transmitters such as light emitting diodes (LEDs), directed LEDs, and lasers are facing major challenges in attenuation or tracking. Resonant beam communications (RBCom) meets the requirements of low attenuation, non-mechanical mobility, and multiple access. Yet, RBCom channel undergoes echo interference in contrast to common non-echo channels. An interference elimination method based on optical filtering is presented along with an exemplary interference-free RBCom system.
An arbitrage-free interpolation of class $C^2$ for option prices
Authors: Fabien Le Floc'h
Date: 2020-04-18 00:00:00

This paper presents simple formulae for the local variance gamma model of Carr and Nadtochiy, extended with a piecewise-linear local variance function. The new formulae allow to calibrate the model efficiently to market option quotes. On a small set of quotes, exact calibration is achieved under one millisecond. This effectively results in an arbitrage-free interpolation of class $C^2$. The paper proposes a good regularization when the quotes are noisy. Finally, it puts in evidence an issue of the model at-the-money, which is also present in the related one-step finite difference technique of Andreasen and Huge, and gives two solutions for it.
Information flow networks of Chinese stock market sectors
Date: 2020-04-19 00:00:00
Tasks: Specificity, Time Series Analysis

Transfer entropy measures the strength and direction of information flow
between different time series. We study the information flow networks of the
Chinese stock market and identify important sectors and information flow paths.
This paper uses the daily closing price data of the 28 level-1 sectors from
Shenyin \& Wanguo Securities ranging from 2000 to 2017 to study the information
transmission between different sectors. We construct information flow networks
with the sectors as the nodes and the transfer entropy between them as the
corresponding edges. Then we adopt the maximum spanning arborescence (MSA) to
extracting important information flows and the hierarchical structure of the
networks. We find that, during the whole sample period, the \textit{composite}
sector is an information source of the whole stock market, while the
\textit{non-bank financial} sector is the information sink. We also find that
the \textit{non-bank finance}, \textit{bank}, \textit{computer},
\textit{media}, \textit{real estate}, \textit{medical biology} and
\textit{non-ferrous metals} sectors appear as high-degree root nodes in the
outgoing and incoming information flow MSAs. Especially, the \textit{non-bank
finance} and \textit{bank} sectors have significantly high degrees after 2008
in the outgoing information flow networks. We uncover how stock market turmoils
affect the structure of the MSAs. Finally, we reveal the specificity of
information source and sink sectors and make a conclusion that the root node
sector as the information sink of the incoming information flow networks.
Overall, our analyses show that the structure of information flow networks
changes with time and the market exhibits a sector rotation phenomenon. Our
work has important implications for market participants and policy makers in
managing market risks and controlling the contagion of risks.
Diffusion LMS with Communication Delays: Stability and Performance Analysis
Date: 2020-04-19 00:00:00

We study the problem of distributed estimation over adaptive networks where
communication delays exist between nodes. In particular, we investigate the
diffusion Least-Mean- Square (LMS) strategy where delayed intermediate
estimates (due to the communication channels) are employed during the
combination step. One important question is: Do the delays affect the stability
condition and performance? To answer this question, we conduct a detailed
performance analysis in the mean and in the mean-square-error sense of the
diffusion LMS with delayed estimates. Stability conditions, transient and
steady-state mean-square-deviation (MSD) expressions are provided. One of the
main findings is that diffusion LMS with delays can still converge under the
same step-sizes condition of the diffusion LMS without delays. Finally,
simulation results illustrate the theoretical findings.