pwc-archive/papers-with-abstracts huggingface dataset:
ARQ-based Average Consensus over Directed Network Topologies with Unreliable Communication Links
Authors: Evagoras Makridis, Themistoklis Charalambous, Christoforos N. Hadjicostis
Date: 2022-09-29 00:00:00

In this paper, we address the discrete-time average consensus problem in strongly connected directed graphs, where nodes exchange information over unreliable error-prone communication links. We enhance the Robustified Ratio Consensus algorithm by exploiting features of the (Hybrid) Automatic Repeat ReQuest - (H)ARQ protocol used for error control of data transmissions, in order to allow the nodes to reach asymptotic average consensus even when information is exchanged over error-prone directional networks. This strategy, apart from handling time-varying information delays induced by retransmissions of erroneous packets, can also handle packet drops that occur when exceeding a predefined packet retransmission limit. Invoking the (H)ARQ protocol allows nodes to: (a) exploit the incoming error-free acknowledgement feedback to initially acquire or later update their out-degree, (b) know whether a packet has arrived or not, and (c) determine a local upper-bound on the delays imposed by the retransmission limit. By augmenting the network's corresponding weight matrix, we show that nodes utilizing our proposed (H)ARQ Ratio Consensus algorithm can reach asymptotic average consensus over unreliable networks, while improving their convergence speed and maintaining low values in their local buffers compared to the current state-of-the-art.
Centralized and Decentralized Techniques for Analysis and Synthesis of Non-Linear Networked Systems
Authors: Shirantha Welikala, Hai Lin, Panos J. Antsaklis
Date: 2022-09-29 00:00:00

In this paper, we develop centralized and decentralized techniques for analyzing and synthesizing networked systems comprised of interconnected sets of non-linear subsystems - only using the subsystem dissipativity properties. In particular, this paper extends a recent work that proposed dissipativity based centralized analysis techniques for non-linear networked systems as linear matrix inequality (LMI) problems. First, we consider four networked system configurations (NSCs) of interest and provide centralized stability/dissipativity \emph{analysis} techniques for them as LMI problems. Second, we show that the centralized interconnection topology \emph{synthesis} techniques for these NSCs can also be developed as LMI problems. This enables synthesizing the interconnection topology among the subsystems so that specific stability/dissipativity measures of the networked system are optimized. Next, we show that the proposed analysis and synthesis techniques can be implemented in a decentralized and compositional manner (i.e., subsystems can be added/removed conveniently). Finally, we include several numerical results to demonstrate our contributions.
Optimal Retail Tariff Design with Prosumers: Pursuing Equity at the Expenses of Economic Efficiencies?
Authors: Yihsu Chen, Andrew L. Liu, Makoto Tanaka, Ryuta Takashima
Date: 2022-09-29 00:00:00

Distributed renewable resources owned by prosumers can be an effective way of fortifying grid resilience and enhancing sustainability. However, prosumers serve their own interests and their objectives are unlikely to align with that of society. This paper develops a bilevel model to study the optimal design of retail electricity tariffs considering the balance between economic efficiency and energy equity. The retail tariff entails a fixed charge and a volumetric charge tied to electricity usage to recover utilities' fixed costs. We analyze solution properties of the bilevel problem and prove an optimal rate design, which is to use fixed charges to recover fixed costs and to balance energy equity among different income groups. This suggests that programs similar to CARE (California Alternative Rate of Energy), which offer lower retail rates to low-income households, are unlikely to be efficient, even if they are politically appealing.
Robust Lattice-based Motion Planning
Authors: Abhishek Dhar, Carl Hynén, Johan Löfberg, Daniel Axehill
Date: 2022-09-28 00:00:00
Tasks: Motion Planning

This paper proposes a robust lattice-based motion-planning algorithm for nonlinear systems affected by a bounded disturbance. The proposed motion planner utilizes the nominal disturbance-free system model to generate motion primitives, which are associated with fixed-size tubes. These tubes are characterized through designing a feedback controller, that guarantees boundedness of the errors occurring due to mismatch between the disturbed nonlinear system and the nominal system. The motion planner then sequentially implements the tube-based motion primitives while solving an online graph-search problem. The objective of the graph-search problem is to connect the initial state to the final state, through sampled states in a suitably discretized state space, such that the tubes do not pass through any unsafe states (representing obstacles) appearing during runtime. The proposed strategy is implemented on an Euler-Lagrange based ship model which is affected by significant wind disturbance. It is shown that the uncertain system trajectories always stay within a suitably constructed tube around the nominal trajectory and terminate within a region around the final state, whose size is dictated by the size of the tube.
Using Multivariate Linear Regression for Biochemical Oxygen Demand Prediction in Waste Water
Authors: Isaiah K. Mutai, Kristof Van Laerhoven, Nancy W. Karuri, Robert K. Tewo
Date: 2022-09-08 00:00:00
Tasks: Prediction, regression

There exist opportunities for Multivariate Linear Regression (MLR) in the prediction of Biochemical Oxygen Demand (BOD) in waste water, using the diverse water quality parameters as the input variables. The goal of this work is to examine the capability of MLR in prediction of BOD in waste water through four input variables: Dissolved Oxygen (DO), Nitrogen, Fecal Coliform and Total Coliform. The four input variables have higher correlation strength to BOD out of the seven parameters examined for the strength of correlation. Machine Learning (ML) was done with both 80% and 90% of the data as the training set and 20% and 10% as the test set respectively. MLR performance was evaluated through the coefficient of correlation (r), Root Mean Square Error (RMSE) and the percentage accuracy in prediction of BOD. The performance indices for the input variables of Dissolved Oxygen, Nitrogen, Fecal Coliform and Total Coliform in prediction of BOD are: RMSE=6.77mg/L, r=0.60 and accuracy 70.3% for training dataset of 80% and RMSE=6.74mg/L, r=0.60 and accuracy of 87.5% for training set of 90% of the dataset. It was found that increasing the percentage of the training set above 80% of the dataset improved the accuracy of the model only but did not have a significant impact on the prediction capacity of the model. The results showed that MLR model could be successfully employed in the estimation of BOD in waste water using appropriately selected input parameters.
Motion clouds: model-based stimulus synthesis of natural-like random textures for the study of motion perception
Authors: Paula Sanz Leon, Ivo Vanzetta, Guillaume S Masson, Laurent U Perrinet
Date: 2012-08-31 00:00:00
Tasks: Translation

Choosing an appropriate set of stimuli is essential to characterize the response of a sensory system to a particular functional dimension, such as the eye movement following the motion of a visual scene. Here, we describe a framework to generate random texture movies with controlled information content, i.e., Motion Clouds. These stimuli are defined using a generative model that is based on controlled experimental parametrization. We show that Motion Clouds correspond to dense mixing of localized moving gratings with random positions. Their global envelope is similar to natural-like stimulation with an approximate full-field translation corresponding to a retinal slip. We describe the construction of these stimuli mathematically and propose an open-source Python-based implementation. Examples of the use of this framework are shown. We also propose extensions to other modalities such as color vision, touch, and audition.
Pixel Level Segmentation Based Drivable Road Region Detection and Steering Angle Estimation Method for Autonomous Driving on Unstructured Roads
Authors: Marya Rasib, Muhammad Atif Butt, Faisal Riaz, Adel Sulaiman, Muhammad Akram
Date: 2021-12-10 00:00:00
Proceeding: IEEE Access 2021 12
Tasks: Autonomous Driving, Self-Driving Cars, Steering Control

With the recent emergence of deep learning, computer vision-based applications have demonstrated better applicability in accomplishing driving tasks including drivable road region detection, lane keeping and steering control in self-driving cars. Till recently, numerous lane-marking detection based steering control and lane keeping methods have been proposed to perform autonomous driving on urban well-structured roads. But the matter of fact is that these methods are not feasible on roads where lane markings are not available or faded over time which makes drivable road region detection a crucial task. Moreover, it is highly arduous task to estimate steering angle on deteriorated roads using existing road detection and steering angle estimation methods. To the best of our knowledge, there is no standard benchmark available for drivable road region detection and steering angle estimation on unstructured roads. To this end, we present a large-scale dataset for drivable region road detection, comprising of 15,000-pixel level high quality fine annotations. Alongside dataset, we also present an end-to-end drivable road region detection and steering angle estimation method to ensure the autonomous driving on generalized urban, rural, and unstructured road conditions. The proposed method performs pixel-level segmentation to extract drivable road region and quantifies lane interception to estimate the steering angle of self-driving cars. A comprehensive qualitative and quantitative analysis has been carried out to demonstrate the effectiveness of our proposed dataset, road detection and steering angle estimation methods. Our benchmark is available at https://carl-dataset.github.io/index/ .
Sentiment Analysis on Inflation after Covid-19
Authors: Xinyu Li, Zihan Tang
Date: 2022-09-25 00:00:00
Tasks: Sentiment Analysis

We implement traditional machine learning and deep learning methods for global tweets from 2017-2022 to build a high-frequency measure of the public's sentiment index on inflation and analyze its correlation with other online data sources such as google trend and market-oriented inflation index. We use manually labeled trigrams to test the prediction performance of several machine learning models(logistic regression,random forest etc.) and choose Bert model for final demonstration. Later, we sum daily tweets' sentiment scores gained from Bert model to obtain the predicted inflation sentiment index, and we further analyze the regional and pre/post covid patterns of these inflation indexes. Lastly, we take other empirical inflation-related data as references and prove that twitter-based inflation sentiment analysis method has an outstanding capability to predict inflation. The results suggest that Twitter combined with deep learning methods can be a novel and timely method to utilize existing abundant data sources on inflation expectations and provide daily indicators of consumers' perception on inflation.
With big data come big problems: pitfalls in measuring basis risk for crop index insurance
Authors: Matthieu Stigler, Apratim Dey, Andrew Hobbs, David Lobell
Date: 2022-09-29 00:00:00

New satellite sensors will soon make it possible to estimate field-level crop yields, showing a great potential for agricultural index insurance. This paper identifies an important threat to better insurance from these new technologies: data with many fields and few years can yield downward biased estimates of basis risk, a fundamental metric in index insurance. To demonstrate this bias, we use state-of-the-art satellite-based data on agricultural yields in the US and in Kenya to estimate and simulate basis risk. We find a substantive downward bias leading to a systematic overestimation of insurance quality. In this paper, we argue that big data in crop insurance can lead to a new situation where the number of variables $N$ largely exceeds the number of observations $T$. In such a situation where $T\ll N$, conventional asymptotics break, as evidenced by the large bias we find in simulations. We show how the high-dimension, low-sample-size (HDLSS) asymptotics, together with the spiked covariance model, provide a more relevant framework for the $T\ll N$ case encountered in index insurance. More precisely, we derive the asymptotic distribution of the relative share of the first eigenvalue of the covariance matrix, a measure of systematic risk in index insurance. Our formula accurately approximates the empirical bias simulated from the satellite data, and provides a useful tool for practitioners to quantify bias in insurance quality.
Fast Inference for Quantile Regression with Tens of Millions of Observations
Authors: Sokbae Lee, Yuan Liao, Myung Hwan Seo, Youngki Shin
Date: 2022-09-29 00:00:00
Tasks: quantile regression, regression, Time Series, Time Series Analysis, Time Series Regression

Big data analytics has opened new avenues in economic research, but the challenge of analyzing datasets with tens of millions of observations is substantial. Conventional econometric methods based on extreme estimators require large amounts of computing resources and memory, which are often not readily available. In this paper, we focus on linear quantile regression applied to "ultra-large" datasets, such as U.S. decennial censuses. A fast inference framework is presented, utilizing stochastic subgradient descent (S-subGD) updates. The inference procedure handles cross-sectional data sequentially: (i) updating the parameter estimate with each incoming "new observation", (ii) aggregating it as a $\textit{Polyak-Ruppert}$ average, and (iii) computing a pivotal statistic for inference using only a solution path. The methodology draws from time-series regression to create an asymptotically pivotal statistic through random scaling. Our proposed test statistic is calculated in a fully online fashion and critical values are calculated without resampling. We conduct extensive numerical studies to showcase the computational merits of our proposed inference. For inference problems as large as $(n, d) \sim (10^7, 10^3)$, where $n$ is the sample size and $d$ is the number of regressors, our method generates new insights, surpassing current inference methods in computation. Our method specifically reveals trends in the gender gap in the U.S. college wage premium using millions of observations, while controlling over $10^3$ covariates to mitigate confounding effects.
The Network Propensity Score: Spillovers, Homophily, and Selection into Treatment
Authors: Alejandro Sanchez-Becerra
Date: 2022-09-28 00:00:00

I establish primitive conditions for unconfoundedness in a coherent model that features heterogeneous treatment effects, spillovers, selection-on-observables, and network formation. I identify average partial effects under minimal exchangeability conditions. If social interactions are also anonymous, I derive a three-dimensional network propensity score, characterize its support conditions, relate it to recent work on network pseudo-metrics, and study extensions. I propose a two-step semiparametric estimator for a random coefficients model which is consistent and asymptotically normal as the number and size of the networks grows. I apply my estimator to a political participation intervention Uganda and a microfinance application in India.
Economic effects of Chile FTAs and an eventual CTPP accession
Authors: Vargas Sepulveda, Mauricio "Pacha"
Date: 2022-09-28 00:00:00

In this article, we show the benefits derived from the Chile-USA (in-force Jan, 2004) and Chile-China (in-force Oct, 2006) FTA on GDP consumer and producers to conclude that Chile improved its welfare improved after its subscription. From that point, we extrapolate to show the direct and indirect benefits of CTPP accession.
Government Spending and Money Supply Roles in Alleviating Poverty in Africa
Authors: Gbatsoron Anjande, Simeon T Asom, Ngutsav Ayila, Bridget Ngodoo Mile, Victor Ushahemba Ijirshar
Date: 2022-09-28 00:00:00
Tasks: valid

This study examines the roles of government spending and money supply on alleviating poverty in Africa. The study used 48 Sub-Saharan Africa countries from 2001 to 2017. The study employed one step and two-step system GMM and found that both the procedures have similar results. Different specifications were employed and the model selected was robust, with valid instruments and absence of autocorrelation at the second order. The study revealed that government spending and foreign direct investment have significant negative influence on reducing poverty while money supply has positive influence on the level of poverty in the region. The implication of the finding is that monetary policy tool of money supply has no strong influence in combating the menace of poverty. The study therefore recommends that emphasis should be placed on increasing more of government spending that would impact on the quality of life of the people in the region through multiplier effect, improving the financial system for effective monetary policy and attracting foreign direct inflows through enabling business environment in Africa.
Estimation of Electrical Characteristics of Inhomogeneous Walls Using Generative Adversarial Networks
Authors: Kainat Yasmeen, Shobha Sundar Ram
Date: 2022-09-29 00:00:00

Through-wall radars are researched and developed for the detection, localization, and tracking of human activities in indoor environments. Electromagnetic wave propagation through walls introduces refraction, attenuation, multipath, and ghost targets in the radar signatures. Estimation of wall characteristics (dielectric profile and thickness) can enable wall effects to be deconvolved from through-wall radar signatures. We propose to use generative adversarial networks (GAN) to estimate wall characteristics from narrowband scattered electric fields on the same side of the wall as the transmitter. We demonstrate that the GANs, consisting of two neural networks configured in an adversarial manner, are capable of solving the highly nonlinear regression problem with limited training data to estimate the dielectric profile and thickness of actual walls up to 95\% accuracy based on training with simulated data generated from full-wave solvers.
Enhanced Secure Wireless Transmission Using IRS-aided Directional Modulation
Authors: Yeqing Lin, Rongen Dong, Peng Zhang, Feng Shu, Jiangzhou Wang
Date: 2022-09-29 00:00:00

As an excellent aided communication tool, intelligent reflecting surface (IRS) can make a significant rate enhancement and coverage extension. In this paper, we present an investigation on beamforming in an IRS-aided directional modulation (DM) network. To fully explore the advantages of IRS, two beamforming methods with enhanced secrecy rate (SR) performance are proposed. The first method of maximizing secrecy rate (Max-SR) alternately optimizes confidential message (CM) beamforming vector, artificial noise (AN) beamforming vector and phase shift matrix. The first optimization vector is directly computed by the Rayleigh ratio and the last two are solved with generalized power iteration (GPI). This method is called Max-SR-GPI. To reduce the computational complexity, a new method of maximizing receive power with zero-forcing constraint (Max-RP-ZFC) of only reflecting CM and no AN is proposed. Simulation results show that the proposed two methods harvest about 30 percent rate gains over the cases of random-phase IRS and no IRS, and the proposed Max-SR-GPI performs slightly better than the Max-RP-ZFC in terms of SR, particularly in the small-large IRS.
A review of univariate and multivariate multifractal analysis illustrated by the analysis of marathon runners physiological data
Authors: Stéphane Jaffard, Guillaume Saës, Wejdene Ben Nasr, Florent Palacin, Véronique Billat
Date: 2022-09-29 00:00:00

We review the central results concerning wavelet methods in multifractal analysis, which consists in analysis of the pointwise singularities of a signal, and we describe its recent extension to multivariate multifractal analysis, which deals with the joint analysis of several signals; we focus on the mathematical questions that this new techniques motivate. We illustrate these methods by an application to data recorded on marathon runners.
Circularly Polarized Fabry-Perot Cavity Sensing Antenna Design using Generative Model
Authors: Kainat Yasmeen, Kumar Vijay Mishra, A. V. Subramanyam, Shobha Sundar Ram
Date: 2022-09-29 00:00:00
Tasks: Generative Adversarial Network

We consider the problem of designing a circularly polarized Fabry-P'erot cavity (FPC) antenna for S-band sensing applications such as satellite navigation and communication. The spatial distribution of peripheral roughness of the unit cell of FPC's partially reflecting surface (PRS) serves as an important design optimization criterion. However, the evaluation of each candidate design using a full-wave solver is computationally expensive. To this end, we propose a deep generative adversarial network (GAN) for realizing a surrogate model that is trained with input-output pairs of antenna designs and their corresponding patterns. Using the GAN framework, we quickly evaluate the characteristics of a large volume of candidate designs and choose the antenna design with an axial ratio of 0.4 dB, a gain of 7.5 dB, and a bandwidth of 269 MHz.
Low-dimensional models of single neurons: A review
Authors: Ulises Chialva, Vicente González Boscá, Horacio G. Rotstein
Date: 2022-09-29 00:00:00

The classical Hodgkin-Huxley (HH) point-neuron model of action potential generation is four-dimensional. It consists of four ordinary differential equations describing the dynamics of the membrane potential and three gating variables associated to a transient sodium and a delayed-rectifier potassium ionic currents. Conductance-based models of HH type are higher-dimensional extensions of the classical HH model. They include a number of supplementary state variables associated with other ionic current types, and are able to describe additional phenomena such as sub-threshold oscillations, mixed-mode oscillations (subthreshold oscillations interspersed with spikes), clustering and bursting. In this manuscript we discuss biophysically plausible and phenomenological reduced models that preserve the biophysical and/or dynamic description of models of HH type and the ability to produce complex phenomena, but the number of effective dimensions (state variables) is lower. We describe several representative models. We also describe systematic and heuristic methods of deriving reduced models from models of HH type.
W-shaped implied volatility curves in a variance-gamma mixture model
Authors: Martin Keller-Ressel
Date: 2022-09-29 00:00:00

In liquid option markets, W-shaped implied volatility curves have occasionally be observed. We show that such shapes can be reproduced in a mixture of two variance-gamma models. This is in contrast to lognormal models, where at least three different distributions have to be mixed in order to produce a W-shape, as recently shown by Glasserman and Pirjol.