pwc-archive/papers-with-abstracts huggingface dataset:
Inequality Measures: The Kolkata index in comparison with other measures
Date: 2020-10-26 00:00:00

We provide a survey of the Kolkata index of social inequality, focusing in
particular on income inequality. Based on the observation that inequality
functions (such as the Lorenz function), giving the measures of income or
wealth against that of the population, to be generally nonlinear, we show that
the fixed point (like Kolkata index k) of such a nonlinear function (or
related, like the complementary Lorenz function) offer better measure of
inequality than the average quantities (like Gini index). Indeed the Kolkata
index can be viewed as a generalized Hirsch index for a normalized inequality
function and gives the fraction k of the total wealth possessed by the rich
(1-k) fraction of the population. We analyze the structures of the inequality
indices for both continuous and discrete income distributions. We also compare
the Kolkata index to some other measures like the Gini coefficient and the
Pietra index. Lastly, we provide some empirical studies which illustrate the
differences between the Kolkata index and the Gini coefficient.
Optimal Charging Method for Effective Li-ion Battery Life Extension Based on Reinforcement Learning
Authors: Minho Kim, Jongchan Baek, Soohee Han
Date: 2020-05-18 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

A reinforcement learning-based optimal charging strategy is proposed for Li-ion batteries to extend the battery life and to ensure the end-user convenience. Unlike most previous studies that do not reflect real-world scenario well, in this work, end users can set the charge time flexibly according to their own situation rather than reducing the charge time as much as possible; this is possible by using soft actor-critic (SAC), which is one of the state-of-the-art reinforcement learning algorithms. In this way, the battery is more likely to extend its life without disturbing the end-users. The amount of aging is calculated quantitatively based on an accurate electrochemical battery model, which is directly minimized in the optimization procedure with SAC. SAC can deal with not only the flexible charge time but also varying parameters of the battery model caused by aging once the offline learning is completed, which is not the case for the previous studies; in the previous studies, time-consuming optimization has to be implemented for each battery model with a certain set of parameter values. The validation results show that the proposed method can both extend the battery life effectively and ensure the end-user convenience.
Metric Learning for Keyword Spotting
Date: 2020-05-18 00:00:00
Tasks: Keyword Spotting, Metric Learning

The goal of this work is to train effective representations for keyword
spotting via metric learning. Most existing works address keyword spotting as a
closed-set classification problem, where both target and non-target keywords
are predefined. Therefore, prevailing classifier-based keyword spotting systems
perform poorly on non-target sounds which are unseen during the training stage,
causing high false alarm rates in real-world scenarios. In reality, keyword
spotting is a detection problem where predefined target keywords are detected
from a variety of unknown sounds. This shares many similarities to metric
learning problems in that the unseen and unknown non-target sounds must be
clearly differentiated from the target keywords. However, a key difference is
that the target keywords are known and predefined. To this end, we propose a
new method based on metric learning that maximises the distance between target
and non-target keywords, but also learns per-class weights for target keywords
\`a la classification objectives. Experiments on the Google Speech Commands
dataset show that our method significantly reduces false alarms to unseen
non-target keywords, while maintaining the overall classification accuracy.
Spotr: GPS Spoofing Detection via Device Fingerprinting
Authors: Mahsa Foruhandeh, Abdullah Z. Mohammed, Gregor Kildow, Paul Berges, Ryan Gerdes
Date: 2020-05-18 00:00:00
Tasks: Management, Marketing

As the worlds predominant navigation system GPS is critical to modern life, finding applications in diverse areas like information security, healthcare, marketing, and power and water grid management. Unfortunately this diversification has only served to underscore the insecurity of GPS and the critical need to harden this system against manipulation and exploitation. A wide variety of attacks against GPS have already been documented, both in academia and industry. Several defenses have been proposed to combat these attacks, but they are ultimately insufficient due to scope, expense, complexity, or robustness. With this in mind, we present our own solution: fingerprinting of GPS satellites. We assert that it is possible to create signatures, or fingerprints, of the satellites (more specifically their transmissions) that allow one to determine nearly instantly whether a received GPS transmission is authentic or not. Furthermore, in this paper we demonstrate that this solution detects all known spoofing attacks, that it does so while being fast, cheap, and simpler than previous solutions, and that it is highly robust with respect to environmental factors.
Successive Training of a Generative Adversarial Network for the Design of an Optical Cloak
Date: 2020-05-12 00:00:00
Tasks: Generative Adversarial Network

We present an optimization algorithm based on a deep convolution generative
adversarial network (DCGAN) to design a 2-Dimensional optical cloak. The
optical cloak consists in a shell of uniform and isotropical dielectric
material, and the cloaking is achieved via the geometry of the shell. We use a
feedback loop from the solutions of the DCGAN to successively retrain it and
improve its ability to predict and find optimal geometries.
Classification of Visual Perception and Imagery based EEG Signals Using Convolutional Neural Networks
Authors: Ji-Seon Bang, Ji-Hoon Jeong, Dong-Ok Won
Date: 2020-05-15 00:00:00
Tasks: Binary Classification, Brain Computer Interface, EEG, Electroencephalogram (EEG), General Classification

Recently, visual perception (VP) and visual imagery (VI) paradigms are investigated in several brain-computer interface (BCI) studies. VP and VI are defined as a changing of brain signals when perceiving and memorizing visual information, respectively. These paradigms could be alternatives to the previous visual-based paradigms which have limitations such as fatigue and low information transfer rates (ITR). In this study, we analyzed VP and VI to investigate the possibility to control BCI. First, we conducted a time-frequency analysis with event-related spectral perturbation. In addition, two types of decoding accuracies were obtained with convolutional neural network to verify whether the brain signals can be distinguished from each class in the VP and whether they can be differentiated with VP and VI paradigms. As a result, the 6-class classification performance in VP was 32.56% and the binary classification performance which classifies two paradigms was 90.16%.
COVID-19 pandemic: a mobility-dependent SEIR model with undetected cases in Italy, Europe and US
Authors: Nicola Picchiotti, Monica Salvioli, Elena Zanardini, Francesco Missale
Date: 2020-05-18 00:00:00

OBJECTIVES: to describe the first wave of the COVID-19 pandemic with a focus on undetected cases and to evaluate different post-lockdown scenarios. DESIGN: the study introduces a SEIR compartmental model, taking into account the region-specific fraction of undetected cases, the effects of mobility restrictions, and the personal protective measures adopted, such as wearing a mask and washing hands frequently. SETTING AND PARTICIPANTS: the model is experimentally validated with data of all the Italian regions, some European countries, and the US. MAIN OUTCOME MEASURES: the accuracy of the model results is measured through the mean absolute percentage error (MAPE) and Lewis criteria; fitting parameters are in good agreement with previous literature. RESULTS: the epidemic curves for different countries and the amount of undetected and asymptomatic cases are estimated, which are likely to represent the main source of infections in the near future. The model is applied to the Hubei case study, which is the first place to relax mobility restrictions. Results show different possible scenarios. Mobility and the adoption of personal protective measures greatly influence the dynamics of the infection, determining either a huge and rapid secondary epidemic peak or a more delayed and manageable one. CONCLUSIONS: mathematical models can provide useful insights for healthcare decision makers to determine the best strategy in case of future outbreaks.
Learning Deep Models from Synthetic Data for Extracting Dolphin Whistle Contours
Date: 2020-05-18 00:00:00
Tasks: Density Estimation

We present a learning-based method for extracting whistles of toothed whales
(Odontoceti) in hydrophone recordings. Our method represents audio signals as
time-frequency spectrograms and decomposes each spectrogram into a set of
time-frequency patches. A deep neural network learns archetypical patterns
(e.g., crossings, frequency modulated sweeps) from the spectrogram patches and
predicts time-frequency peaks that are associated with whistles. We also
developed a comprehensive method to synthesize training samples from background
environments and train the network with minimal human annotation effort. We
applied the proposed learn-from-synthesis method to a subset of the public
Detection, Classification, Localization, and Density Estimation (DCLDE) 2011
workshop data to extract whistle confidence maps, which we then processed with
an existing contour extractor to produce whistle annotations. The F1-score of
our best synthesis method was 0.158 greater than our baseline whistle
extraction algorithm (~25% improvement) when applied to common dolphin
(Delphinus spp.) and bottlenose dolphin (Tursiops truncatus) whistles.
Disaster Resilience and Asset Prices
Authors: Marco Pagano, Christian Wagner, Josef Zechner
Date: 2020-05-18 00:00:00

This paper investigates whether security markets price the effect of social distancing on firms' operations. We document that firms that are more resilient to social distancing significantly outperformed those with lower resilience during the COVID-19 outbreak, even after controlling for the standard risk factors. Similar cross-sectional return differentials already emerged before the COVID-19 crisis: the 2014-19 cumulative return differential between more and less resilient firms is of similar size as during the outbreak, suggesting growing awareness of pandemic risk well in advance of its materialization. Finally, we use stock option prices to infer the market's return expectations after the onset of the pandemic: even at a two-year horizon, stocks of more pandemic-resilient firms are expected to yield significantly lower returns than less resilient ones, reflecting their lower exposure to disaster risk. Hence, going forward, markets appear to price exposure to a new risk factor, namely, pandemic risk.
Non-Extensive Value-at-Risk Estimation During Times of Crisis
Authors: Ahmad Hajihasani, Ali Namaki, Nazanin Asadi, Reza Tehrani
Date: 2020-05-18 00:00:00

Value-at-risk is one of the important subjects that extensively used by researchers and practitioners for measuring and managing uncertainty in financial markets. Although value-at-risk is a common risk control instrument, but there are criticisms about its performance. One of these cases, which has been studied in this research, is the value-at-risk underestimation during times of crisis. In these periods, the non-Gaussian behavior of markets intensifies and the estimated value-at-risks by normal models are lower than the real values. In fact, during times of crisis, the probability density of extreme values in financial return series increases and this heavy-tailed behavior of return series reduces the accuracy of the normal value-at-risk estimation models. A potential approach that can be used to describe non-Gaussian behavior of return series, is Tsallis entropy framework and non-extensive statistical methods. In this paper, we have used non-extensive value at risk model for analyzing the behavior of financial markets during times of crisis. By applying q-Gaussian probability density function, we can see a better value-at-risk estimation in comparison with the normal models, especially during times of crisis. We showed that q-Gaussian model estimates value-at-risk better than normal model. Also we saw in the mature markets, it is obvious that the difference of value-at-risk between normal condition and non-extensive approach increase more than one standard deviation during times of crisis, but in the emerging markets we cannot see a specific pattern.
Multi-Period Liability Clearing via Convex Optimal Control
Authors: Shane Barratt, Stephen Boyd
Date: 2020-05-18 00:00:00

We consider the problem of determining a sequence of payments among a set of entities that clear (if possible) the liabilities among them. We formulate this as an optimal control problem, which is convex when the objective function is, and therefore readily solved. For this optimal control problem, we give a number of useful and interesting convex costs and constraints that can be combined in any way for different applications. We describe a number of extensions, for example to handle unknown changes in cash and liabilities, to allow bailouts, to find the minimum time to clear the liabilities, or to minimize the number of non-cleared liabilities, when fully clearing the liabilities is impossible.
Synaptic Channel Modeling for DMC: Neurotransmitter Uptake and Spillover in the Tripartite Synapse
Authors: Sebastian Lotter, Arman Ahmadzadeh, Robert Schober
Date: 2020-05-18 00:00:00

In Diffusive Molecular Communication (DMC), information is transmitted by diffusing molecules. Synaptic signaling, as a natural implementation of this paradigm, encompasses functional components that, once understood, can facilitate the development of synthetic DMC systems. To unleash this potential, however, a thorough understanding of the synaptic communication channel based on biophysical principles is needed. Since synaptic transmission critically depends also on non-neural cells, such understanding requires the consideration of the so-called tripartite synapse. In this paper, we develop a comprehensive channel model of the tripartite synapse encompassing a three-dimensional, finite-size spatial model of the synaptic cleft, molecule uptake at the presynaptic neuron and at glial cells, reversible binding to individual receptors at the postsynaptic neuron, and spillover to the extrasynaptic space. Based on this model, we derive analytical time domain expressions for the channel impulse response (CIR) of the synaptic DMC system and for the number of molecules taken up at the presynaptic neuron and at glial cells, respectively. These expressions provide insight into the impact of macroscopic physical channel parameters on the decay rate of the CIR and the reuptake rate, and reveal fundamental limits for synaptic signal transmission induced by chemical reaction kinetics and the channel geometry. Adapted to realistic parameters, our model produces plausible results when compared to experimental and simulation studies and we provide results from particle-based computer simulations to further validate the analytical model. The proposed comprehensive channel model admits a wide range of synaptic configurations making it suitable for the investigation of many practically relevant questions, such as the impact of glial cell uptake and spillover on signal transmission in the tripartite synapse.
Satellite Navigation for the Age of Autonomy
Authors: Tyler G. R. Reid, Bryan Chan, Ashish Goel, Kazuma Gunning, Brian Manning, Jerami Martin, Andrew Neish, Adrien Perkins, Paul Tarantino
Date: 2020-05-19 00:00:00
Tasks: Self-Driving Cars

Global Navigation Satellite Systems (GNSS) brought navigation to the masses. Coupled with smartphones, the blue dot in the palm of our hands has forever changed the way we interact with the world. Looking forward, cyber-physical systems such as self-driving cars and aerial mobility are pushing the limits of what localization technologies including GNSS can provide. This autonomous revolution requires a solution that supports safety-critical operation, centimeter positioning, and cyber-security for millions of users. To meet these demands, we propose a navigation service from Low Earth Orbiting (LEO) satellites which deliver precision in-part through faster motion, higher power signals for added robustness to interference, constellation autonomous integrity monitoring for integrity, and encryption / authentication for resistance to spoofing attacks. This paradigm is enabled by the 'New Space' movement, where highly capable satellites and components are now built on assembly lines and launch costs have decreased by more than tenfold. Such a ubiquitous positioning service enables a consistent and secure standard where trustworthy information can be validated and shared, extending the electronic horizon from sensor line of sight to an entire city. This enables the situational awareness needed for true safe operation to support autonomy at scale.
Model predicts fundamental role of biomechanical control of cell cycle progression during liver regeneration after partial hepatectomy
Authors: Stefan Hoehme, Rolf Gebhardt, Jan G. Hengstler, Dirk Drasdo
Date: 2020-05-19 00:00:00

Partial hepatectomy (PHx) is a surgical intervention where a part of the liver is removed. Due to its extraordinary capacity to regenerate, the liver is able to regenerate about two-thirds of its mass within a few weeks. Nevertheless, in some patients regeneration fails. Understanding the principles and limitations underlying regeneration may permit to control this process and prospectively improve the regeneration. Here, we established a simulation model to mimic the process of regeneration in the liver lobe of a mouse. This model represents each hepatocyte individually and builds upon a previous computational model of regeneration of drug induced damage in a single liver lobule. The present study simulates entire liver lobes that consist of hundreds to thousands of lobules. It accounts for biomechanical control of cell cycle progression (Biomechanical Growth Control), which has not been considered in that previous work. The model reproduced the available experimental observations only if BGC was taken into account. Interestingly, the model predicted that BGC minimizes the number of proliferating neighbor cells of a proliferating cell resulting in a checkerboard-like proliferation pattern. Moreover, the model predicted different cell proliferation patterns in pigs and mice that corresponded to data obtained from regenerating tissue of the two species. In conclusion, the here established model suggest that biomechanical control mechanisms may play a significant role in liver regeneration after PHx.
A Flexible Stochastic Conditional Duration Model
Date: 2020-05-19 00:00:00
Tasks: model

We introduce a new stochastic duration model for transaction times in asset
markets. We argue that widely accepted rules for aggregating seemingly related
trades mislead inference pertaining to durations between unrelated trades:
while any two trades executed in the same second are probably related, it is
extremely unlikely that all such pairs of trades are, in a typical sample. By
placing uncertainty about which trades are related within our model, we improve
inference for the distribution of durations between unrelated trades,
especially near zero. We introduce a normalized conditional distribution for
durations between unrelated trades that is both flexible and amenable to
shrinkage towards an exponential distribution, which we argue is an appropriate
first-order model. Thanks to highly efficient draws of state variables,
numerical efficiency of posterior simulation is much higher than in previous
studies. In an empirical application, we find that the conditional hazard
function for durations between unrelated trades varies much less than what most
studies find. We claim that this is because we avoid statistical artifacts that
arise from deterministic trade-aggregation rules and unsuitable parametric
distributions.
Transient behaviour towards the stable limit cycle in the Selkov model of Glycolysis: A physiological disorder
Authors: Tanmay Das, Muktish Acharyya
Date: 2020-05-19 00:00:00

A simplified model for the complex glycolytic process was historically proposed by Selkov. It showed the existence of stable limit cycle as an example of Poincare-Bendixson theorem. This limit cycle is nothing but the time eliminated Lissajous plot of the concentrations of Adenosine-diphosphate (ADP) and Fructose-6-phosphate (F6P) of a normal/healthy human. Deviation from this limit cycle is equivalent to the deviation of normal physiological behaviour. It is very important to know how long a human body will take to reach the glycolytic stable limit cycle, if deviated from it. However, till now the convergence time, depending upon different initial parameter values, was not studied in detail. This may have great importance in understanding the recovery time for a diseased individual deviated from normal cycle. Here the convergence time for different initial conditions has been calculated in original Selkov model. It is observed that convergence time, as a function of the distance from the limit cycle, gets saturated away from the cycle. This result seems to be a physiological disorder. A possible mathematical way to incorporate this in the Selkov model, has been proposed.
Achievable Rate Region of MISO Interference Channel Aided by Intelligent Reflecting Surface
Authors: Wei Huang, Yong Zeng, Yongming Huang
Date: 2020-05-19 00:00:00

This paper investigates the achievable rate region of the multiple-input single-output (MISO) interference channel aided by intelligent reflecting surfaces (IRSs). We exploit the the additional design degree of freedom provided by the coordinated IRSs to enhance the desired signal and suppress interference so as to enlarge the achievable rate region of the interference channel. To this end, we jointly optimize the active transmit beamforming at the transmitters and passive reflective beamforming at the IRSs, subject to the constant modulus constraints of reflective beamforming vectors. To address the non-convex optimization problem, we propose an iterative algorithm to optimize the transmit beamforming via second-order cone program (SOCP) and the reflective beamforming via the semi-definite relaxation (SDR). Numerical results demonstrate that the performance of the IRS-aided interference channel with the proposed algorithm can significantly outperform the conventional interference channel without IRS.