pwc-archive/papers-with-abstracts huggingface dataset:
Myopic equilibria, the spanning property, and sublime bundles
Date: 2020-07-25 00:00:00

For a set-valued function $F$ on a compact subset $W$ of a manifold, spanning
is a topological property that implies that $F(x) \ne 0$ for interior points
$x$ of $W$. A myopic equilibrium applies when for each action there is a payoff
whose functional value is not necessarily affine in the strategy space. We show
that if the payoffs satisfy the spanning property, then there exist a myopic
equilibrium (though not necessarily a Nash equilibrium). Furthermore, given a
parametrized collection of games and the spanning property to the structure of
payoffs in that collection, the resulting myopic equilibria and their payoffs
have the spanning property with respect to that parametrization. This is a far
reaching extension of the Kohberg-Mertens Structure Theorem. There are at least
four useful applications, when payoffs are exogenous to a finite game tree (for
example a finitely repeated game followed by an infinitely repeated game), when
one wants to understand a game strategically entirely with behaviour
strategies, when one wants to extends the subgame concept to subsets of a game
tree that are known in common, and for evolutionary game theory. The proofs
involve new topological results asserting that spanning is preserved by
relevant operations on set-valued functions.
Visibility graph analysis of economy policy uncertainty indices
Authors: Peng-Fei Dai, Xiong Xiong, Wei-Xing Zhou
Date: 2020-07-25 00:00:00
Tasks: Clustering

Uncertainty plays an important role in the global economy. In this paper, the economic policy uncertainty (EPU) indices of the United States and China are selected as the proxy variable corresponding to the uncertainty of national economic policy. By adopting the visibility graph algorithm, the four economic policy uncertainty indices of the United States and China are mapped into complex networks, and the topological properties of the corresponding networks are studied. The Hurst exponents of all the four indices are within $\left[0.5,1\right]$, which implies that the economic policy uncertainty is persistent. The degree distributions of the EPU networks have power-law tails and are thus scale-free. The average clustering coefficients of the four EPU networks are high and close to each other, while these networks exhibit weak assortative mixing. We also find that the EPU network in United States based on daily data shows the small-world feature since the average shortest path length increases logarithmically with the network size such that $L\left(N\right)=0.626\ln N+0.405$. Our research highlights the possibility to study the EPU from the view angle of complex networks.
Joint Design of Transmit Beamforming, IRS Platform, and Power Splitting SWIPT Receivers for Downlink Cellular Multiuser MISO
Authors: Shayan Zargari, Shahrokh Farahmand, Bahman Abolhassani
Date: 2020-07-25 00:00:00

A multiple antenna base station (BS) with an intelligent reflecting surface (IRS) platform, and several single antenna users are considered in the downlink mode. Simultaneous wireless information and power transfer (SWIPT) is utilized by the BS via transmit beamforming to convey information and power to all devices. Each device applies power splitting (PS) to dedicate separate parts of received power to information decoding and energy harvesting. We formulate a total transmit power minimization problem to jointly design the BS beamforming vectors, IRS phase shifts, and PS ratios at the receivers subject to minimum rate and harvested energy quality of service (QoS) constraints at all the receivers. First, we develop a block coordinate descent algorithm, also known as alternating optimization that can decrease the objective function with every iteration with guaranteed convergence. Afterwards, two low-complexity sub-optimal algorithms that rely on well-known maximum ratio transmission and zero-forcing beamforming techniques are introduced. These algorithms are beneficial when the number of BS antennas and/or number of users are large, or coherence times of channels are small. Simulations corroborate the expectation that by deploying a passive IRS, BS power can be reduced by $10-20$ dBw while maintaining similar guaranteed QoS. Furthermore, even the proposed sub-optimal algorithms outperform the globally optimal SWIPT solution without IRS for a modest number of IRS elements.
A Drone-Aided Blockchain-Based Smart Vehicular Network
Authors: Muhammad Asaad Cheema, Muhammad Karam Shehzad, Hassaan Khaliq Qureshi, Syed Ali Hassan, Haejoon Jung
Date: 2020-07-25 00:00:00

The staggering growth of the number of vehicles worldwide has become a critical challenge resulting in tragic incidents, environment pollution, congestion, etc. Therefore, one of the promising approaches is to design a smart vehicular system as it is beneficial to drive safely. Present vehicular system lacks data reliability, security, and easy deployment. Motivated by these issues, this paper addresses a drone-enabled intelligent vehicular system, which is secure, easy to deploy and reliable in quality. Nevertheless, an increase in the number of operating drones in the communication networks makes them more vulnerable towards the cyber-attacks, which can completely sabotage the communication infrastructure. To tackle these problems, we propose a blockchain-based registration and authentication system for the entities such as drones, smart vehicles (SVs) and roadside units (RSUs). This paper is mainly focused on the blockchain-based secure system design and the optimal placement of drones to improve the spectral efficiency of the overall network. In particular, we investigate the association of RSUs with the drones by considering multiple communication-related factors such as available bandwidth, maximum number of links a drone can support, and backhaul limitations. We show that the proposed model can easily be overlaid on the current vehicular network reaping benefits of secure and reliable communications.
Comparison of Machine Learning Methods for Predicting Karst Spring Discharge in North China
Authors: Shu Cheng, Xiaojuan Qiao, Yaolin Shi, Dawei Wang
Date: 2020-07-25 00:00:00
Tasks: BIG-bench Machine Learning, Time Series Analysis

The quantitative analyses of karst spring discharge typically rely on physical-based models, which are inherently uncertain. To improve the understanding of the mechanism of spring discharge fluctuation and the relationship between precipitation and spring discharge, three machine learning methods were developed to reduce the predictive errors of physical-based groundwater models, simulate the discharge of Longzici Spring's karst area, and predict changes in the spring on the basis of long time series precipitation monitoring and spring water flow data from 1987 to 2018. The three machine learning methods included two artificial neural networks (ANNs), namely, multilayer perceptron (MLP) and long short-term memory-recurrent neural network (LSTM-RNN), and support vector regression (SVR). A normalization method was introduced for data preprocessing to make the three methods robust and computationally efficient. To compare and evaluate the capability of the three machine learning methods, the mean squared error (MSE), mean absolute error (MAE), and root-mean-square error (RMSE) were selected as the performance metrics for these methods. Simulations showed that MLP reduced MSE, MAE, and RMSE to 0.0010, 0.0254, and 0.0318, respectively. Meanwhile, LSTM-RNN reduced MSE to 0.0010, MAE to 0.0272, and RMSE to 0.0329. Moreover, the decrease in MSE, MAE, and RMSE were 0.0910, 0.1852, and 0.3017, respectively, for SVR. Results indicated that MLP performed slightly better than LSTM-RNN, and MLP and LSTM-RNN performed considerably better than SVR. Furthermore, ANNs were demonstrated to be prior machine learning methods for simulating and predicting karst spring discharge.
Minimum Overhead Beamforming and Resource Allocation in D2D Edge Networks
Authors: JungHoon Kim, Taejoon Kim, Morteza Hashemi, Christopher G. Brinton, David J. Love
Date: 2020-07-25 00:00:00
Tasks: CPU, Distributed Computing, Management

Device-to-device (D2D) communications is expected to be a critical enabler of distributed computing in edge networks at scale. A key challenge in providing this capability is the requirement for judicious management of the heterogeneous communication and computation resources that exist at the edge to meet processing needs. In this paper, we develop an optimization methodology that considers the network topology jointly with device and network resource allocation to minimize total D2D overhead, which we quantify in terms of time and energy required for task processing. Variables in our model include task assignment, CPU allocation, subchannel selection, and beamforming design for multiple-input multiple-output (MIMO) wireless devices. We propose two methods to solve the resulting non-convex mixed integer program: semi-exhaustive search optimization, which represents a "best-effort" at obtaining the optimal solution, and efficient alternate optimization, which is more computationally efficient. As a component of these two methods, we develop a novel coordinated beamforming algorithm which we show obtains the optimal beamformer for a common receiver characteristic. Through numerical experiments, we find that our methodology yields substantial improvements in network overhead compared with local computation and partially optimized methods, which validates our joint optimization approach. Further, we find that the efficient alternate optimization scales well with the number of nodes, and thus can be a practical solution for D2D computing in large networks.
Cyclic Imaging for All-Sky Interference Forecasting with Array Radio Telescopes
Authors: Gregory Hellbourg, Ian Morrison
Date: 2020-07-26 00:00:00
Tasks: All, Astronomy

Radio Frequency Interference (RFI) is threatening modern radio astronomy. A classic approach to mitigate its impact on astronomical data involves discarding the corrupted time and frequency data samples through a process called flagging and blanking. We propose the exploitation of the cyclostationary properties of the RFI signals to reliably detect and predict their locations within an array radio telescope field-of-view, and dynamically schedule the astronomical observations such as to minimize the probability of RFI data corruption.
Scalable Bayesian estimation in the multinomial probit model
Authors: Ruben Loaiza-Maya, Didier Nibbering
Date: 2020-07-26 00:00:00
Tasks: model, parameter estimation

The multinomial probit model is a popular tool for analyzing choice behaviour as it allows for correlation between choice alternatives. Because current model specifications employ a full covariance matrix of the latent utilities for the choice alternatives, they are not scalable to a large number of choice alternatives. This paper proposes a factor structure on the covariance matrix, which makes the model scalable to large choice sets. The main challenge in estimating this structure is that the model parameters require identifying restrictions. We identify the parameters by a trace-restriction on the covariance matrix, which is imposed through a reparametrization of the factor structure. We specify interpretable prior distributions on the model parameters and develop an MCMC sampler for parameter estimation. The proposed approach significantly improves performance in large choice sets relative to existing multinomial probit specifications. Applications to purchase data show the economic importance of including a large number of choice alternatives in consumer choice analysis.
Aerial Intelligent Reflecting Surface: Joint Placement and Passive Beamforming Design with 3D Beam Flattening
Authors: Haiquan Lu, Yong Zeng, Shi Jin, Rui Zhang
Date: 2020-07-27 00:00:00

This paper proposes a new three-dimensional (3D) wireless passive relaying system enabled by aerial IRS (AIRS). Compared to the conventional terrestrial IRS, AIRS enjoys more deployment flexibility as well as wider-range signal reflection, thanks to its high altitude and thus more likelihood of establishing line-of-sight (LoS) links with ground source/destination nodes. Specifically, we aim to maximize the worst-case signal-to-noise ratio (SNR) over all locations in a target area by jointly optimizing the transmit beamforming for the source node and the placement as well as 3D passive beamforming for the AIRS. The formulated problem is non-convex and thus difficult to solve. To gain useful insights, we first consider the special case of maximizing the SNR at a given target location, for which the optimal solution is obtained in closed-form. The result shows that the optimal horizontal AIRS placement only depends on the ratio between the source-destination distance and the AIRS altitude. Then for the general case of AIRS-enabled area coverage, we propose an efficient solution by decoupling the AIRS passive beamforming design to maximize the worst-case array gain, from its placement optimization by balancing the resulting angular span and the cascaded channel path loss. Our proposed solution is based on a novel 3D beam broadening and flattening technique, where the passive array of the AIRS is divided into sub-arrays of appropriate size, and their phase shifts are designed to form a flattened beam pattern with adjustable beamwidth catering to the size of the coverage area. Both the uniform linear array (ULA)-based and uniform planar array (UPA)-based AIRSs are considered in our design, which enable two-dimensional (2D) and 3D passive beamforming, respectively. Numerical results show that the proposed designs achieve significant performance gains over the benchmark schemes.
Recent Advances and Prospects in the Research of Nascent Adhesions
Authors: Henning Stumpf, Andreja Ambriović-Ristov, Aleksandra Radenovic, Ana-Sunčana Smith
Date: 2020-07-27 00:00:00

Nascent adhesions are submicron transient structures promoting the early adhesion of cells to the extracellular matrix. Nascent adhesions typically consist of several tens of integrins, and serve as platforms for the recruitment and activation of proteins to build mature focal adhesions. They are also associated with early stage signalling and the mechanoresponse. Despite their crucial role in sampling the local extracellular matrix, very little is known about the mechanism of their formation. Consequently, there is a strong scientific activity focused on elucidating the physical and biochemical foundation of their development and function. Precisely the results of this effort will be summarized in this article.
Intelligent Trajectory Planning in UAV-mounted Wireless Networks: A Quantum-Inspired Reinforcement Learning Perspective
Authors: Yuanjian Li, A. Hamid Aghvami, Daoyi Dong
Date: 2020-07-27 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Trajectory Planning

In this paper, we consider a wireless uplink transmission scenario in which an unmanned aerial vehicle (UAV) serves as an aerial base station collecting data from ground users. To optimize the expected sum uplink transmit rate without any prior knowledge of ground users (e.g., locations, channel state information and transmit power), the trajectory planning problem is optimized via the quantum-inspired reinforcement learning (QiRL) approach. Specifically, the QiRL method adopts novel probabilistic action selection policy and new reinforcement strategy, which are inspired by the collapse phenomenon and amplitude amplification in quantum computation theory, respectively. Numerical results demonstrate that the proposed QiRL solution can offer natural balancing between exploration and exploitation via ranking collapse probabilities of possible actions, compared to the traditional reinforcement learning approaches which are highly dependent on tuned exploration parameters.
A Preliminary Investigation in the Molecular Basis of Host Shutoff Mechanism in SARS-CoV
Authors: Niharika Pandala, Casey A. Cole, Devaun McFarland, Anita Nag, Homayoun Valafar
Date: 2020-07-23 00:00:00

Recent events leading to the worldwide pandemic of COVID-19 have demonstrated the effective use of genomic sequencing technologies to establish the genetic sequence of this virus. In contrast, the COVID-19 pandemic has demonstrated the absence of computational approaches to understand the molecular basis of this infection rapidly. Here we present an integrated approach to the study of the nsp1 protein in SARS-CoV-1, which plays an essential role in maintaining the expression of viral proteins and further disabling the host protein expression, also known as the host shutoff mechanism. We present three independent methods of evaluating two potential binding sites speculated to participate in host shutoff by nsp1. We have combined results from computed models of nsp1, with deep mining of all existing protein structures (using PDBMine), and binding site recognition (using msTALI) to examine the two sites consisting of residues 55-59 and 73-80. Based on our preliminary results, we conclude that the residues 73-80 appear as the regions that facilitate the critical initial steps in the function of nsp1. Given the 90% sequence identity between nsp1 from SARS-CoV-1 and SARS-CoV-2, we conjecture the same critical initiation step in the function of COVID-19 nsp1.
A Gauss-Newton-Like Hessian Approximation for Economic NMPC
Date: 2020-10-28 00:00:00
Tasks: Model Predictive Control

Economic Model Predictive Control (EMPC) has recently become popular because
of its ability to control constrained nonlinear systems while explicitly
optimizing a prescribed performance criterion. Large performance gains have
been reported for many applications and closed-loop stability has been recently
investigated. However, computational performance still remains an open issue
and only few contributions have proposed real-time algorithms tailored to EMPC.
We perform a step towards computationally cheap algorithms for EMPC by
proposing a new positive-definite Hessian approximation which does not hinder
fast convergence and is suitable for being used within the real-time iteration
(RTI) scheme. We provide two simulation examples to demonstrate the
effectiveness of RTI-based EMPC relying on the proposed Hessian approximation.
Rate-Splitting Multiple Access: Unifying NOMA and SDMA in MISO VLC Channels
Authors: Shimaa Naser, Lina Bariah, Wael Jaafar, Sami Muhaidat, Paschalis C. Sofotasios, Mahmoud Al-Qutayri, Octavia A. Dobre
Date: 2020-07-24 00:00:00

The increased proliferation of connected devices requires a paradigm shift towards the development of innovative technologies for the next generation of wireless systems. One of the key challenges, however, is the spectrum scarcity, owing to the unprecedented broadband penetration rate in recent years. Visible light communications (VLC) has recently emerged as a possible solution to enable high-speed short-range communications. However, VLC systems suffer from several limitations, including the limited modulation bandwidth of light-emitting diodes. Consequently, several multiple access techniques (MA), e.g., space-division multiple access (SDMA) and non-orthogonal multiple access (NOMA), have been considered for VLC networks. Despite their promising multiplexing gains, their performance is somewhat limited. In this article, we first provide an overview of the key MA technologies used in VLC systems. Then, we introduce rate-splitting multiple access (RSMA), which was initially proposed for RF systems and discuss its potentials in VLC systems. Through system modeling and simulations of an RSMA-based two-use scenario, we illustrate the flexibility of RSMA in generalizing NOMA and SDMA, as well as its superiority in terms of weighted sum rate (WSR) in VLC. Finally, we discuss challenges, open issues, and research directions, which will enable the practical realization of RSMA in VLC.
Are low frequency macroeconomic variables important for high frequency electricity prices?
Authors: Claudia Foroni, Francesco Ravazzolo, Luca Rossini
Date: 2020-07-24 00:00:00
Tasks: Bayesian Inference, Vocal Bursts Intensity Prediction

Recent research finds that forecasting electricity prices is very relevant. In many applications, it might be interesting to predict daily electricity prices by using their own lags or renewable energy sources. However, the recent turmoil of energy prices and the Russian-Ukrainian war increased attention in evaluating the relevance of industrial production and the Purchasing Managers' Index output survey in forecasting the daily electricity prices. We develop a Bayesian reverse unrestricted MIDAS model which accounts for the mismatch in frequency between the daily prices and the monthly macro variables in Germany and Italy. We find that the inclusion of macroeconomic low frequency variables is more important for short than medium term horizons by means of point and density measures. In particular, accuracy increases by combining hard and soft information, while using only surveys gives less accurate forecasts than using only industrial production data.
Understanding controlled EV charging impacts using scenario-based forecasting models
Date: 2020-07-24 00:00:00

Electrification of transport is a key strategy in reducing carbon emissions. Many countries have adopted policies of complete but gradual transformation to electric vehicles (EVs). However, mass EV adoption also means a spike in load (kW), which in turn can disrupt existing electricity infrastructure. Smart or controlled charging is widely seen as a potential solution to alleviate this stress on existing networks. Learning from the recent EV trials in the UK and elsewhere we take into account two key aspects which are largely ignored in current research: EVs actually charging at any given time and wide range of EV types, especially battery capacity-wise. Taking a minimalistic scenario-based approach, we study forecasting models for mean number of active chargers and mean EV consumption for distinct scenarios. Focusing on residential charging the models we consider range from simple regression models to more advanced machine and deep learning models such as XGBoost and LSTMs. We then use these models to evaluate the impacts of different levels of future EV penetration on a specimen distribution transformer that captures typical real-world scenarios. In doing so, we also initiate the study of different types of controlled charging when fully controlled charging is not possible. This aligns with the outcomes from recent trials which show that a sizeable proportion of EV owners may not prefer fully controlled centralized charging. We study two possible control regimes and show that one is more beneficial from load-on-transformer point of view, while the other may be preferred for other objectives. We show that a minimum of 60% control is required to ensure that transformers are not overloaded during peak hours.