pwc-archive/papers-with-abstracts huggingface dataset:
Effect of population migration and punctuated lockdown on the spread of infectious diseases
Authors: Ravi Kiran, Madhumita Roy, Syed Abbas, A. Taraphder
Date: 2020-06-26 00:00:00

One of the critical measures to control infectious diseases is a lockdown. Once past the lockdown stage in many parts of the world, the crucial question now concerns the effects of relaxing the lockdown and finding the best ways to implement further lockdown(s), if required, to control the spread. With the relaxation of lockdown, people migrate to different cities and enhance the spread of the disease. This work presents the population migration model for n-cities and applies the model for migration between two and three cities. The reproduction number is calculated, and the effect of the migration rate is analyzed. A punctuated lockdown is implemented to simulate a protocol of repeated lockdowns that limits the resurgence of infections. A damped oscillatory behavior is observed with multiple peaks over a period.
An unsupervised deep learning approach in solving partial integro-differential equations
Date: 2020-06-26 00:00:00
Tasks: Deep Learning

We investigate solving partial integro-differential equations (PIDEs) using unsupervised deep learning in this paper. To price options, assuming underlying processes follow Levy processes, we require to solve PIDEs. In supervised deep learning, pre-calculated labels are used to train neural networks to fit the solution of the PIDE. In an unsupervised deep learning, neural networks are employed as the solution, and the derivatives and the integrals in the PIDE are calculated based on the neural network. By matching the PIDE and its boundary conditions, the neural network gives an accurate solution of the PIDE. Once trained, it would be fast for calculating options values as well as option Greeks.
Option Pricing Under a Discrete-Time Markov Switching Stochastic Volatility with Co-Jump Model
Authors: Michael C. Fu, Bingqing Li, Rongwen Wu, Tianqi Zhang
Date: 2020-06-26 00:00:00
Tasks: Clustering

We consider option pricing using a discrete-time Markov switching stochastic volatility with co-jump model, which can model volatility clustering and varying mean-reversion speeds of volatility. For pricing European options, we develop a computationally efficient method for obtaining the probability distribution of average integrated variance (AIV), which is key to option pricing under stochastic-volatility-type models. Building upon the efficiency of the European option pricing approach, we are able to price an American-style option, by converting its pricing into the pricing of a portfolio of European options. Our work also provides constructive guidance for analyzing derivatives based on variance, e.g., the variance swap. Numerical results indicate our methods can be implemented very efficiently and accurately.
Conductance-based dendrites perform reliability-weighted opinion pooling
Date: 2020-06-26 00:00:00

Cue integration, the combination of different sources of information to
reduce uncertainty, is a fundamental computational principle of brain function.
Starting from a normative model we show that the dynamics of multi-compartment
neurons with conductance-based dendrites naturally implement the required
probabilistic computations. The associated error-driven plasticity rule allows
neurons to learn the relative reliability of different pathways from data
samples, approximating Bayes-optimal observers in multisensory integration
tasks. Additionally, the model provides a functional interpretation of neural
recordings from multisensory integration experiments and makes specific
predictions for membrane potential and conductance dynamics of individual
neurons.
All Recognition is Accomplished By Interacting Bottom-Up Sensory and Top-Down Context Bias in Occipital to Frontal Cortex Neural Networks
Date: 2020-06-26 00:00:00
Tasks: All

Recognition of every word is accomplished by close collaboration of bottom-up
sub-word and word recognition neural networks with top-down cognitive word
context expectations. The utility of this context appropriate collaboration is
substantial savings in recognition time, accuracy and cortical neural
processing resources. Repetition priming, the simplest form of context
facilitation, has been studied extensively, but behavioral and cognitive
neuroscience research has failed to produce a common shared model. Facilitation
is attributed to temporary lowered word recognition thresholds. Recent fMRI
evidence identifies frontal, prefrontal, left temporal cortex interactions as
the source of this priming bias. Five experiments presented here clearly
demonstrate that word recognition facilitation is a bias effect. Context-Biased
Fast Accurate Recognition, a recurrent neural network model, shows how this
anticipatory bias is accomplished by interactions among top-down conceptual
cognitive networks and bottom-up lexical word recognition networks. Signal
detection theory says that this facilitation bias is offset by the cost of
miss-recognizing similar, but different words. However, the prime typically
creates a temporary time-space recognition window within which probability of
prime recurrence is substantially raised paradoxically transforming bias into
sensitivity.
Relative Arbitrage Opportunities with Interactions among $N$ Investors
Authors: Tomoyuki Ichiba, Nicole Tianjiao Yang
Date: 2020-06-26 00:00:00

The relative arbitrage portfolio outperforms a benchmark portfolio over a given time-horizon with probability one. With market price of risk processes depending on the market portfolio and investors, this paper analyzes the multi-agent optimization of relative arbitrage opportunities in the coupled system of market and wealth dynamics. We construct a well-posed market dynamical system of McKean-Vlasov type under an empirical measure of investors, where each investor seeks for relative arbitrage with respect to a benchmark dependent on market and all the agents. We show the conditions to guarantee relative arbitrage opportunities among competitive investors through the Fichera drift. Under mild conditions, we derive the optimal strategies for investors and the unique Nash equilibrium that depends on the smallest nonnegative solution of a Cauchy problem.
Forecasting Precipitable Water Vapor Using LSTMs
Authors: Mayank Jain, Shilpa Manandhar, Yee Hui Lee, Stefan Winkler, Soumyabrata Dev
Date: 2020-06-26 00:00:00
Tasks: Time Series, Time Series Analysis, Time Series Forecasting

Long-Short-Term-Memory (LSTM) networks have been used extensively for time series forecasting in recent years due to their ability of learning patterns over different periods of time. In this paper, this ability is applied to learning the pattern of Global Positioning System (GPS)-based Precipitable Water Vapor (PWV) measurements over a period of 4 hours. The trained model was evaluated on more than 1500 hours of recorded data. It achieves a root mean square error (RMSE) of 0.098 mm for a forecasting interval of 5 minutes in the future, and outperforms the naive approach for a lead-time of up to 40 minutes.
Improving MF-DFA model with applications in precious metals market
Authors: Zhongjun Wang, Mengye Sun, A. M. Elsawah
Date: 2020-06-26 00:00:00
Tasks: Time Series, Time Series Analysis

With the aggravation of the global economic crisis and inflation, the precious metals with safe-haven function have become more popular. An improved MF-DFA method is proposed to analyze price fluctuations of the precious metals market. Based on the widely used multifractal detrended fluctuation analysis method (MF-DFA), we compare these two methods and find that the Bi-OSW-MF-DFA method possesses better efficiency. This article analyzes the degree of multifractality between spot gold market and spot silver market as well as their risks. From the numerical results and figures, it is found that two elements constitute the contributions in the formation of multifractality in time series and the risk of the spot silver market is higher than that of the spot gold market. This attempt could lead to a better understanding of complicated precious metals market.
Evaluation of Embedded Platforms for Lower Limb Prosthesis with Visual Sensing Capabilities
Authors: Rafael L. da Silva, Nathan Starliper, Boxuan Zhong, He Helen Huang, Edgar Lobaton
Date: 2020-06-26 00:00:00
Tasks: CPU, Decision Making, Raspberry Pi 3

Lower limb prosthesis can benefit from embedded systems capable of applying computer vision techniques to enhance autonomous control and context awareness for intelligent decision making. In order to fill in the gap of current literature of autonomous systems for prosthetic legs employing computer vision methods, we evaluate the performance capabilities of two off-the-shelf platforms, the Jetson TX2 and Raspberry Pi 3, by assessing their CPU load, memory usage, run time and classification accuracy for different image sizes and widespread computer vision algorithms. We make use of a dataset that we collected for terrain recognition using images from a camera mounted on a leg, which would enable context-awareness for lower limb prosthesis. We show that, given reasonably large images and an appropriate frame selection method, it is possible to identify the terrain that a subject is going to step on, with the possibility of reconstructing the surface and obtaining its inclination. This is part of a proposed system equipped with an embedded camera and inertial measurement unit to recognize different types of terrain and estimate the slope of the surface in front of the subject.
HypervolGAN: An efficient approach for GAN with multi-objective training function
Date: 2020-06-26 00:00:00
Tasks: Image Enhancement, Image Super-Resolution, Super-Resolution

Since the advent of generative adversarial networks (GANs), various loss
functions have been developed and combined to constitute the overall training
objective function, in order to improve model performance or for specific
learning tasks. For instance, in image enhancement or restoration, there are
often several criteria to consider such as signal-noise ratio, smoothness,
structures and details. However, when the optimization goal has more than one
adversarial loss, balancing multiple losses in the overall function becomes a
challenging, critical and time-consuming problem. In this paper, we propose to
tackle the problem by means of efficient multi-objective optimization. The
proposed HypervolGAN adopts an adapted version of hypervolume maximization
method to effectively define the multi-objective training function for GAN. We
tested our proposed method on solving single image super-resolution problem.
Experiments show that the proposed HypervolGAN is efficient in saving
computational time and efforts for fine-tuning weights of various losses, and
can generate enhanced samples that have better quality than results given by
baseline GANs. The work explores the integration of adversarial learning and
optimization techniques, which can benefit not only image processing but also a
wide range of applications.
Measurement-Based Estimation of System State Matrix for AC Power Systems with Integrated VSCs
Authors: Jinpeng Guo, Xiaozhe Wang, Boon-Teck Ooi
Date: 2020-06-27 00:00:00

In this paper, a wide-area measurement system (WAMS)-based method is proposed to estimate the system state matrix for AC system with integrated voltage source converters (VSCs) and identify the electromechanical modes. The proposed method is purely model-free, requiring no knowledge of accurate network topology and system parameters. Numerical studies in the IEEE 68-bus system with integrated VSCs show that the proposed measurementbased method can accurately identify the electromechanical modes and estimate the damping ratios, the mode shapes, and the participation factors. The work may serve as a basis for developing WAMS-based damping control using VSCs in the future.
Model-Driven Deep Learning for Massive Multiuser MIMO Constant Envelope Precoding
Authors: Yunfeng He, Hengtao, He, Chao-Kai Wen, Shi Jin
Date: 2020-06-27 00:00:00

Constant envelope (CE) precoding design is of great interest for massive multiuser multi-input multi-output systems because it can significantly reduce hardware cost and power consumption. However, existing CE precoding algorithms are hindered by excessive computational overhead. In this letter, a novel model-driven deep learning (DL)-based network that combines DL with conjugate gradient algorithm is proposed for CE precoding. Specifically, the original iterative algorithm is unfolded and parameterized by trainable variables. With the proposed architecture, the variables can be learned efficiently from training data through unsupervised learning approach. Thus, the proposed network learns to obtain the search step size and adjust the search direction. Simulation results demonstrate the superiority of the proposed network in terms of multiuser interference suppression capability and computational overhead.
3D Mobility Models and Analysis for UAVs
Authors: Peter J. Smith, Pawel A. Dmochowski, Ikram Singh, Richard Green, Carl P. Dettmann, Justin P. Coon
Date: 2020-06-27 00:00:00
Tasks: Position

We present a flexible family of 3D mobility models suitable for unmanned aerial vehicles (UAV). Based on stochastic differential equations, the models offer a unique property of explicitly incorporating the mobility control mechanism and environmental perturbation, while enabling tractable steady state solutions for properties such as position and connectivity. Specifically, motivated by UAV flight data, for a symmetric mobility model with an arbitrary control mechanism, we derive the steady state distribution of the distance from the target position. We provide closed form expressions for the special cases of the Ornstein-Uhlenbeck (OU) process and on-off control (OC). We extend the model to incorporate imperfect positioning and asymmetric control. For a practically relevant scenario of partial symmetry (such as in the x-y plane), we present steady state position results for the OU control. Building on these results, we derive UAV connectivity probability results based on a SNR criterion in a Rayleigh fading environment.
Biased-Belief Equilibrium
Date: 2020-06-27 00:00:00

We investigate how distorted, yet structured, beliefs can persist in
strategic situations. Specifically, we study two-player games in which each
player is endowed with a biased-belief function that represents the discrepancy
between a player's beliefs about the opponent's strategy and the actual
strategy. Our equilibrium condition requires that (i) each player choose a
best-response strategy to his distorted belief about the opponent's strategy,
and (ii) the distortion functions form best responses to one another. We obtain
sharp predictions and novel insights into the set of stable outcomes and their
supporting stable biases in various classes of games.
A closed-form solution to the risk-taking motivation of subordinated debtholders
Date: 2020-06-27 00:00:00
Tasks: Form

Black and Cox (1976) claim that the value of junior debt is increasing in
asset risk when the firm's value is low. We show, using closed-form solution,
that the junior debt's value is hump-shaped. This has interesting implications
for the market-discipline role of banks' junior debt.
Observations on Cooperation
Date: 2020-06-27 00:00:00

We study environments in which agents are randomly matched to play a
Prisoner's Dilemma, and each player observes a few of the partner's past
actions against previous opponents. We depart from the existing related
literature by allowing a small fraction of the population to be commitment
types. The presence of committed agents destabilizes previously proposed
mechanisms for sustaining cooperation. We present a novel intuitive combination
of strategies that sustains cooperation in various environments. Moreover, we
show that under an additional assumption of stationarity, this combination of
strategies is essentially the unique mechanism to support full cooperation, and
it is robust to various perturbations. Finally, we extend the results to a
setup in which agents also observe actions played by past opponents against the
current partner, and we characterize which observation structure is optimal for
sustaining cooperation.
A Theory of Equivalent Expectation Measures for Contingent Claim Returns
Authors: Sanjay K. Nawalkha, Xiaoyang Zhuo
Date: 2020-06-27 00:00:00

This paper introduces a dynamic change of measure approach for computing the analytical solutions of expected future prices (and therefore, expected returns) of contingent claims over a finite horizon. The new approach constructs hybrid probability measures called the "equivalent expectation measures"(EEMs), which provide the physical expectation of the claim's future price until before the horizon date, and serve as pricing measures on or after the horizon date. The EEM theory can be used for empirical investigations of both the cross-section and the term structure of returns of contingent claims, such as Treasury bonds, corporate bonds, and financial derivatives.
Logarithmic regret for episodic continuous-time linear-quadratic reinforcement learning over a finite-time horizon
Authors: Matteo Basei, Xin Guo, Anran Hu, Yufei Zhang
Date: 2020-06-27 00:00:00
Tasks: parameter estimation, Reinforcement Learning (RL)

We study finite-time horizon continuous-time linear-quadratic reinforcement learning problems in an episodic setting, where both the state and control coefficients are unknown to the controller. We first propose a least-squares algorithm based on continuous-time observations and controls, and establish a logarithmic regret bound of order $O((\ln M)(\ln\ln M))$, with $M$ being the number of learning episodes. The analysis consists of two parts: perturbation analysis, which exploits the regularity and robustness of the associated Riccati differential equation; and parameter estimation error, which relies on sub-exponential properties of continuous-time least-squares estimators. We further propose a practically implementable least-squares algorithm based on discrete-time observations and piecewise constant controls, which achieves similar logarithmic regret with an additional term depending explicitly on the time stepsizes used in the algorithm.
Compress or Interfere?
Authors: Alaa Awad Abdellatif, Lutfi Samara, Amr Mohamed, Mohsen Guizani, Aiman Erbad, Abdulla Al-Ali
Date: 2020-06-27 00:00:00

Rapid evolution of wireless medical devices and network technologies has fostered the growth of remote monitoring systems. Such new technologies enable monitoring patients' medical records anytime and anywhere without limiting patients' activities. However, critical challenges have emerged with remote monitoring systems due to the enormous amount of generated data that need to be efficiently processed and wirelessly transmitted to the service providers in time. Thus, in this paper, we leverage full-duplex capabilities for fast transmission, while tackling the trade-off between Quality of Service (QoS) requirements and consequent self-interference (SI) for efficient remote monitoring healthcare systems. The proposed framework jointly considers the residual SI resulting from simultaneous transmission and reception along with the compressibility feature of medical data in order to optimize the data transmission over wireless channels, while maintaining the application's QoS constraint. Our simulation results demonstrate the efficiency of the proposed solution in terms of minimizing the transmission power, residual self-interference, and encoding distortion.
Optimal Asset Allocation For Outperforming A Stochastic Benchmark Target
Authors: Chendi Ni, Yuying Li, Peter Forsyth, Ray Carroll
Date: 2020-06-27 00:00:00

We propose a data-driven Neural Network (NN) optimization framework to determine the optimal multi-period dynamic asset allocation strategy for outperforming a general stochastic target. We formulate the problem as an optimal stochastic control with an asymmetric, distribution shaping, objective function. The proposed framework is illustrated with the asset allocation problem in the accumulation phase of a defined contribution pension plan, with the goal of achieving a higher terminal wealth than a stochastic benchmark. We demonstrate that the data-driven approach is capable of learning an adaptive asset allocation strategy directly from historical market returns, without assuming any parametric model of the financial market dynamics. Following the optimal adaptive strategy, investors can make allocation decisions simply depending on the current state of the portfolio. The optimal adaptive strategy outperforms the benchmark constant proportion strategy, achieving a higher terminal wealth with a 90% probability, a 46% higher median terminal wealth, and a significantly more right-skewed terminal wealth distribution. We further demonstrate the robustness of the optimal adaptive strategy by testing the performance of the strategy on bootstrap resampled market data, which has different distributions compared to the training data.
Using micro- and macro-level network metrics unveils top communicative gene modules in psoriasis
Authors: Reyhaneh Naderi, Homa Saadati Mollaei, Arne Elofsson, Saman Hosseini Ashtiani
Date: 2020-06-27 00:00:00

Background: Psoriasis is a multifactorial chronic inflammatory disorder of the skin with significant morbidity, characterized by hyper proliferation of the epidermis. Even though psoriasis etiology is not fully understood, it is believed to be multifactorial with numerous key components. Methods: In order to cast light on the complex molecular interactions in psoriasis vulgaris at both protein-protein interactions and transcriptomics levels, we analyzed a set of microarray gene expression analysis consisting of 170 paired lesional and non-lesional samples. Afterwards, a network analysis was conducted on protein-protein interaction network of differentially expressed genes based on micro- and macro-level network metrics at a systemic level standpoint. Results: We found 17 top communicative genes, all of which experimentally proven to be pivotal in psoriasis were identified in two modules, namely, cell cycle and immune system. Intra- and inter-gene interaction subnetworks from the top communicative genes might provide further insight into the corresponding characteristic mechanisms. Conclusions: Potential gene combinations for therapeutic/diagnostics purposes were identified. Moreover, our proposed pipeline could be of interest to a broader range of biological network analysis studies.
Large deviation principles for stochastic volatility models with reflection and three faces of the Stein and Stein model
Date: 2020-06-27 00:00:00

We introduce stochastic volatility models, in which the volatility is
described by a time-dependent nonnegative function of a reflecting diffusion.
The idea to use reflecting diffusions as building blocks of the volatility came
into being because of a certain volatility misspecification in the classical
Stein and Stein model. A version of this model that uses the reflecting
Ornstein-Uhlenbeck process as the volatility process is a special example of a
stochastic volatility model with reflection. The main results obtained in the
present paper are sample path and small-noise large deviation principles for
the log-price process in a stochastic volatility model with reflection under
rather mild restrictions. We use these results to study the asymptotic behavior
of binary barrier options and call prices in the small-noise regime.