pwc-archive/papers-with-abstracts huggingface dataset:
CentiTrack: Towards Centimeter-Level Passive Gesture Tracking with Commodity WiFi
Authors: Zijun Han, Zhaoming Lu, Xiangming Wen, Wei Zheng, Jingbo Zhao, Lingchao Guo
Date: 2020-11-13 00:00:00

Gesture awareness plays a crucial role in promoting human-computer interface. Previous works either depend on customized hardware or need a priori learning of wireless signal patterns, facing downsides in terms of the privacy concern, availability and reliability. In this paper, we propose CentiTrack, the first centimeter-level passive gesture tracking system that works with only three commodityWiFi devices, without any extra hardware modifications or wearable sensors. To this end, we first identify the Channel State Information (CSI) measurement error sources in the physical layer process, and then denoise CSI by the complex ratio between adjacent antennas. Principal Component Analysis (PCA) is further adopted to separate the reflected signals from noises. Benchmark experiments are conducted to verify that the phase changes of denoised CSI are proportional to the length changes of dynamic path reflected off the hand. In addition, we adopt the Multiple Signal Classification (MUSIC) algorithm to estimate the Angle-of-Arrivals (AoAs) of dynamic paths, and then locate the initial position of hands with triangulation. We also propose a novel static componnets elimination algorithm for tracking correction by eliminating the components unrelated to motion. A prototype of CentiTrack is fully realized and evaluated in various real scenarios. Extensive experiments show that CentiTrack is superior in terms of tracking accuracy, sensing range and device cost, compared with the state-of-the-arts.
Dynamic factor, leverage and realized covariances in multivariate stochastic volatility
Authors: Yuta Yamauchi, Yasuhiro Omori
Date: 2020-11-13 00:00:00

In the stochastic volatility models for multivariate daily stock returns, it has been found that the estimates of parameters become unstable as the dimension of returns increases. To solve this problem, we focus on the factor structure of multiple returns and consider two additional sources of information: first, the realized stock index associated with the market factor, and second, the realized covariance matrix calculated from high frequency data. The proposed dynamic factor model with the leverage effect and realized measures is applied to ten of the top stocks composing the exchange traded fund linked with the investment return of the SP500 index and the model is shown to have a stable advantage in portfolio performance.
An energy-based natural selection model
Authors: N. Abadi, G. Abramson
Date: 2020-11-13 00:00:00
Tasks: model

Genetic information and environmental factors determine the path of an individuals life and therefore, the evolution of its entire species. We have succeeded in proposing and studying a model that captures this idea. In our model, a renewable resource extended throughout the environment provides the energy necessary to sustain life, including movement and reproduction. Since the resource doesn't regrow immediately, it generates competition between individuals and therefore provides a natural selection pressure from which evolution of the genetic traits is observed. As a result of this, several phenomena characteristic of living systems emerge from this model without having to introduce them explicitly. These include speciation and punctuated equilibrium, competitive exclusion, and altruistic behaviour from selfish rules.
Bridging the Gap Between Diabetes and Stroke in Search of High Clinical Relevance Therapeutic Targets
Authors: Thierry Coppola, Sophie Béraud-Dufour, Patricia Lebrun, Nicolas Blondeau
Date: 2020-11-13 00:00:00

Diabetes affects more than 425 million people worldwide, a scale approaching pandemic proportion. Diabetes represents a major risk factor for stroke, and therefore is actively addressed for stroke prevention. However, how diabetes affects stroke severity has not yet been extensively considered, which is surprising given the evident but understudied common mechanistic features of both pathologies. The increase in number of diabetic people, in the incidence of stroke in presence of this specific risk factor, and the exacerbation of ischemic brain damage in diabetic conditions (at least in animal models) warrant the need to integrate this comorbidity in pre-clinical studies of brain ischemia to develop novel therapeutic approaches. Therefore, a better understanding of the commonalties involved in the course of both diseases would offer the promise of discovering novel neuroprotective pathways that would be more appropriated to clinical situations. In this article, we will review the relevant mechanisms that have been identified as common traits of both pathologies and that could be to our knowledge, potential targets for both pathologies.
A Secure Distributed Ledger for Transactive Energy: The Electron Volt Exchange (EVE) Blockchain
Date: 2020-11-13 00:00:00
Tasks: Scheduling

The adoption of blockchain for Transactive Energy has gained significant
momentum as it allows mutually non-trusting agents to trade energy services in
a trustless energy market. Research to date has assumed that the built-in
Byzantine Fault Tolerance in recording transactions in a ledger is sufficient
to ensure integrity. Such work must be extended to address security gaps
including random bilateral transactions that do not guarantee reliable and
efficient market operation, and market participants having incentives to cheat
when reporting actual production/consumption figures. Work herein introduces
the Electron Volt Exchange framework with the following characteristics: 1) a
distributed protocol for pricing and scheduling prosumers'
production/consumption while keeping constraints and bids private, and 2) a
distributed algorithm to prevent theft that verifies prosumers' compliance to
scheduled transactions using information from grid sensors (such as smart
meters) and mitigates the impact of false data injection attacks. Flexibility
and robustness of the approach are demonstrated through simulation and
implementation using Hyperledger Fabric.
Ensuring Data Freshness for Blockchain-enabled Monitoring Networks
Authors: Minsu Kim, Sungho Lee, Chanwon Park, Jemin Lee, Walid Saad
Date: 2020-11-12 00:00:00

The age of information (AoI) is a recently proposed metric for quantifying data freshness in real-time status monitoring systems where timeliness is of importance. In this paper, the problem of characterizing and controlling the AoI is studied in the context of blockchain-enabled monitoring networks (BeMN). In BeMN, status updates from sources are transmitted and recorded in a blockchain. To investigate the statistical characteristics of the AoI in BeMN, the transmission latency and the consensus latency are first rigorously modeled. Then, the average AoI, the AoI violation probability, and the peak AoI violation probability are derived in a closed form so as to quantify the performance of BeMN. Furthermore, a simplified form is derived for the AoI violation probability, and it is shown that this quantity can capture the upper or lower bound of the actual AoI violation probability. Simulation results show that each BeMN parameters (i.e., target successful transmission probability, block size, and timeout) can have conflicting effects on the AoI-related performance. Subsequently, design insights are provided to maintain the freshness of the status data in BeMN. Then, experimental results with a real Hyperledger Fabric platform further validate the accuracy of our modeling and analysis.
Identifying Causal Effects in Experiments with Spillovers and Non-compliance
Authors: Francis J. DiTraglia, Camilo Garcia-Jimeno, Rossa O'Keeffe-O'Donovan, Alejandro Sanchez-Becerra
Date: 2020-11-13 00:00:00
Tasks: Experimental Design

This paper shows how to use a randomized saturation experimental design to identify and estimate causal effects in the presence of spillovers--one person's treatment may affect another's outcome--and one-sided non-compliance--subjects can only be offered treatment, not compelled to take it up. Two distinct causal effects are of interest in this setting: direct effects quantify how a person's own treatment changes her outcome, while indirect effects quantify how her peers' treatments change her outcome. We consider the case in which spillovers occur within known groups, and take-up decisions are invariant to peers' realized offers. In this setting we point identify the effects of treatment-on-the-treated, both direct and indirect, in a flexible random coefficients model that allows for heterogeneous treatment effects and endogenous selection into treatment. We go on to propose a feasible estimator that is consistent and asymptotically normal as the number and size of groups increases. We apply our estimator to data from a large-scale job placement services experiment, and find negative indirect treatment effects on the likelihood of employment for those willing to take up the program. These negative spillovers are offset by positive direct treatment effects from own take-up.
Satisfaction of linear temporal logic specifications through recurrence tools for hybrid systems
Authors: Andrea Bisoffi, Dimos V. Dimarogonas
Date: 2020-11-13 00:00:00

In this work we formulate the problem of satisfying a linear temporal logic formula on a linear plant with output feedback, through a recent hybrid systems formalism. We relate this problem to the notion of recurrence introduced for the considered formalism, and we then extend Lyapunov-like conditions for recurrence of an open, unbounded set. One of the proposed relaxed conditions allows certifying recurrence of a suitable set, and this guarantees that the high-level evolution of the plant satisfies the formula, without relying on discretizations of the plant. Simulations illustrate the proposed approach.
A Reputation for Honesty
Authors: Drew Fudenberg, Ying Gao, Harry Pei
Date: 2020-11-13 00:00:00

We analyze situations in which players build reputations for honesty rather than for playing particular actions. A patient player facing a sequence of short-run opponents makes an announcement about their intended action after observing an idiosyncratic shock, and before players act. The patient player is either an honest type whose action coincides with their announcement, or an opportunistic type who can freely choose their actions. We show that the patient player can secure a high payoff by building a reputation for being honest when the short-run players face uncertainty about which of the patient player's actions are currently feasible, but may receive a low payoff when there is no such uncertainty.
On the balanced truncation error bound and sign parameters from arrowhead realizations
Authors: Sean Reiter, Tobias Damm, Mark Embree, Serkan Gugercin
Date: 2020-11-13 00:00:00

Balanced truncation and singular perturbation approximation for linear dynamical systems yield reduced-order models that satisfy a well-known error bound involving the Hankel singular values. We show that this bound holds with equality for single-input, single-output systems, if the sign parameters corresponding to the truncated Hankel singular values are all equal. These signs are determined by a generalized state-space symmetry property of the corresponding linear model. For a special class of systems having arrowhead realizations, the signs can be determined directly from the off-diagonal entries of the corresponding arrowhead matrix. We describe how such arrowhead systems arise naturally in certain applications of network modeling, and illustrate these results with a power system model that motivated this study.
Synchronization in dynamical systems coupled via multiple directed networks
Authors: Chai Wah Wu
Date: 2020-11-14 00:00:00

We study synchronization and consensus in a group of dynamical systems coupled via multiple directed networks. We show that even though the coupling in a single network may not be sufficient to synchronize the systems, combination of multiple networks can contribute to synchronization. We illustrate how the effectiveness of a collection of networks to synchronize the coupled systems depends on the graph topology. In particular, we show that if the graph sum is a directed graph whose reversal contains a spanning directed tree, then the network synchronizes if the coupling is strong enough. This is intuitive as there is a root node that influence every other node via a set of edges where each edge in the set is in one of the networks.
Visual Tool for Assessing Stability of DER Configurations on Three-Phase Radial Networks
Authors: Jaimie Swartz, Brittany Wais, Elizabeth Ratnam, Alexandra von Meier
Date: 2020-11-14 00:00:00

We present a method and tool for evaluating the placement of Distributed Energy Resources (DER) on distribution circuits in order to control voltages and power flows. Our previous work described Phasor-Based Control (PBC), a novel control framework where DERs inject real and reactive power to track voltage magnitude and phase angle targets. Here, we employ linearized power flow equations and integral controllers to develop a linear state space model for PBC acting on a three-phase unbalanced network. We use this model to evaluate whether a given inverter-based DER configuration admits a stable set of controller gains, which cannot be done by analyzing controllability nor by using the Lyapunov equation. Instead, we sample over a parameter space to identify a stable set of controller gains. Our stability analysis requires only a line impedance model and does not entail simulating the system or solving an optimization problem. We incorporate this assessment into a publicly available visualization tool and demonstrate three processes for evaluating many control configurations on the IEEE 123-node test feeder (123NF).
Channel Estimation for Large Intelligent Surface Aided MISO Communications: From LMMSE to Deep Learning Solutions
Authors: Neel Kanth Kundu, Matthew R. McKay
Date: 2020-11-14 00:00:00
Tasks: Denoising, Image Denoising

We consider multi-antenna wireless systems aided by large intelligent surfaces (LIS). LIS presents a new physical layer technology for improving coverage and energy efficiency by intelligently controlling the propagation environment. In practice however, achieving the anticipated gains of LIS requires accurate channel estimation. Recent attempts to solve this problem have considered the least-squares (LS) approach, which is simple but also sub-optimal. The optimal channel estimator, based on the minimum mean-squared-error (MMSE) criterion, is challenging to obtain and is non-linear due to the non-Gaussianity of the effective channel seen at the receiver. Here we present approaches to approximate the optimal MMSE channel estimator. As a first approach, we analytically develop the best linear estimator, the LMMSE, together with a corresponding majorization-minimization based algorithm designed to optimize the LIS phase shift matrix during the training phase. This estimator is shown to yield improved accuracy over the LS approach by exploiting second-order statistical properties of the wireless channel and the noise. To further improve performance and better approximate the globally-optimal MMSE channel estimator, we propose data-driven non-linear solutions based on deep learning. Specifically, by posing the MMSE channel estimation problem as an image denoising problem, we propose two convolutional neural network (CNN) based methods to perform the denoising and approximate the optimal MMSE channel estimation solution. Our numerical results show that these CNN-based estimators give superior performance compared with linear estimation approaches. They also have low computational complexity requirements, thereby motivating their potential use in future LIS-aided wireless communication systems.
FedRec: Federated Learning of Universal Receivers over Fading Channels
Authors: Mahdi Boloursaz Mashhadi, Nir Shlezinger, Yonina C. Eldar, Deniz Gunduz
Date: 2020-11-14 00:00:00
Tasks: Federated Learning

Wireless communications is often subject to channel fading. Various statistical models have been proposed to capture the inherent randomness in fading, and conventional model-based receiver designs rely on accurate knowledge of this underlying distribution, which, in practice, may be complex and intractable. In this work, we propose a neural network-based symbol detection technique for downlink fading channels, which is based on the maximum a-posteriori probability (MAP) detector. To enable training on a diverse ensemble of fading realizations, we propose a federated training scheme, in which multiple users collaborate to jointly learn a universal data-driven detector, hence the name FedRec. The performance of the resulting receiver is shown to approach the MAP performance in diverse channel conditions without requiring knowledge of the fading statistics, while inducing a substantially reduced communication overhead in its training procedure compared to centralized training.
Application of deep quantum neural networks to finance
Authors: Takayuki Sakuma
Date: 2020-11-14 00:00:00
Tasks: Management

The recent development of quantum computing gives us an opportunity to explore its potential applications to many fields, with the field of finance being no exception. In this paper, we apply the deep quantum neural network proposed by Beer et al. (2020) and discuss such potential in the context of simple experiments such as learning implied volatilities and option prices. Furthermore, Greeks such as delta and gamma, which are important measures in risk management, can be computed analytically with the neural network, and our numerical experiments show that the deep quantum neural network is a promising technique for solving such numerical problems arising in finance efficiently.
Smart Close-out Netting
Authors: Akber Datoo, Christopher D. Clack
Date: 2020-11-14 00:00:00

Smart Close-out Netting aims to standardise and automate specific operational aspects of the legal and regulatory processes of close-out netting for prudentially regulated financial institutions. This article provides a review, analysis and perspective of these operational processes, their benefits for prudentially regulated trading institutions, their current inefficiencies, and the extent to which they are amenable to standardisation and automation. The main concepts of Smart Close-out Netting are introduced, including the use of a controlled natural language in legal opinions and the use of a data-driven framework during netting determination.
State-Dependent Temperature Control for Langevin Diffusions
Authors: Xuefeng Gao, Zuo Quan Xu, Xun Yu Zhou
Date: 2020-11-15 00:00:00

We study the temperature control problem for Langevin diffusions in the context of non-convex optimization. The classical optimal control of such a problem is of the bang-bang type, which is overly sensitive to errors. A remedy is to allow the diffusions to explore other temperature values and hence smooth out the bang-bang control. We accomplish this by a stochastic relaxed control formulation incorporating randomization of the temperature control and regularizing its entropy. We derive a state-dependent, truncated exponential distribution, which can be used to sample temperatures in a Langevin algorithm, in terms of the solution to an HJB partial differential equation. We carry out a numerical experiment on a one-dimensional baseline example, in which the HJB equation can be easily solved, to compare the performance of the algorithm with three other available algorithms in search of a global optimum.
Nonlinearity Characteristic of High Impedance Fault at Resonant Distribution Networks: Theoretical Basis to Identify the Faulty Feeder
Authors: Mingjie Wei, Hengxu Zhang, Fang Shi, Weijiang Chen, Vladmir Terzija
Date: 2020-11-15 00:00:00
Tasks: Diversity

Feeder identification is indispensable for distribution networks to locate faults at a specific feeder, especially when measuring de-vices are insufficient for precise locations. For the high imped-ance fault (HIF), the feeder identification is much more compli-cated and related approaches are still in the early stage. This paper thoroughly and theoretically reveals the features of dif-ferent feeders when a HIF happens at the resonant grounded neutral (RGN) network, which is the most challenging condition for feeder identification. Firstly, the diversity of nonlinearity existing in HIFs is explained from the aspect of energy. Then, the differences of nonlinearities of zero-sequence currents between healthy and faulty feeders are deduced theoretically. Variations of the detuning index and damping ratio that exist in industries are both considered. Afterward, these theoretical conclusions are verified by the HIF cases experimented in a real 10kV system. Finally, based on the theories, we discuss about why the existing approaches are not reliable enough, and suggest some improve-ments.
Assessing the Impact of Offshore Wind Siting Strategies on the Design of the European Power System
Authors: David Radu, Mathias Berger, Antoine Dubois, Raphael Fonteneau, Hrvoje Pandzic, Yury Dvorkin, Quentin Louveaux, Damien Ernst
Date: 2020-11-15 00:00:00

This paper provides a detailed account of the impact of different offshore wind siting strategies on the design of the European power system. To this end, a two-stage method is proposed. In the first stage, a highly-granular siting problem identifies a suitable set of sites where offshore wind plants could be deployed according to a pre-specified criterion. Two siting schemes are analysed and compared within a realistic case study. These schemes essentially select a pre-specified number of sites so as to maximise their aggregate power output and their spatiotemporal complementarity, respectively. In addition, two variants of these siting schemes are provided, wherein the number of sites to be selected is specified on a country-by-country basis rather than Europe-wide. In the second stage, the subset of previously identified sites is passed to a capacity expansion planning (CEP) framework that sizes the power generation, transmission and storage assets that should be deployed and operated in order to satisfy pre-specified electricity demand levels at minimum cost. Results show that the complementarity-based siting criterion leads to system designs which are up to 5% cheaper than the ones relying the power output-based criterion when offshore wind plants are deployed with no consideration for country-based deployment targets. On the contrary, the power output-based scheme leads to system designs which are consistently 2% cheaper than the ones leveraging the complementarity-based siting strategy when such constraints are enforced. The robustness of the results is supported by a sensitivity analysis on offshore wind capital expenditure and inter-annual weather variability, respectively.