pwc-archive/papers-with-abstracts huggingface dataset:
Further results on the estimation of dynamic panel logit models with fixed effects
Authors: Hugo Kruiniger
Date: 2020-10-07 00:00:00
Tasks: valid

Kitazawa (2013, 2016) showed that the common parameters in the panel logit AR(1) model with strictly exogenous covariates and fixed effects are estimable at the root-n rate using the Generalized Method of Moments. Honor\'e and Weidner (2020) extended his results in various directions: they found additional moment conditions for the logit AR(1) model and also considered estimation of logit AR(p) models with p>1. In this note we prove a conjecture in their paper and show that for given values of the initial condition, the covariates and the common parameters 2^{T}-2T of their moment functions for the logit AR(1) model are linearly independent and span the set of valid moment functions, which is a 2^{T}-2T-dimensional linear subspace of the 2^{T}-dimensional vector space of real valued functions over the outcomes y element of {0,1}^{T}. We also prove that when p=2 and T element of {3,4,5}, there are, respectively, 2^{T}-4(T-1) and 2^{T}-(3T-2) linearly independent moment functions for the panel logit AR(2) models with and without covariates.
All for One and One for All: Improving Music Separation by Bridging Networks
Authors: Ryosuke Sawata, Stefan Uhlich, Shusuke Takahashi, Yuki Mitsufuji
Date: 2020-10-08 00:00:00
Tasks: All, Music Source Separation

This paper proposes several improvements for music separation with deep neural networks (DNNs), namely a multi-domain loss (MDL) and two combination schemes. First, by using MDL we take advantage of the frequency and time domain representation of audio signals. Next, we utilize the relationship among instruments by jointly considering them. We do this on the one hand by modifying the network architecture and introducing a CrossNet structure. On the other hand, we consider combinations of instrument estimates by using a new combination loss (CL). MDL and CL can easily be applied to many existing DNN-based separation methods as they are merely loss functions which are only used during training and which do not affect the inference step. Experimental results show that the performance of Open-Unmix (UMX), a well-known and state-of-the-art open source library for music separation, can be improved by utilizing our above schemes. Our modifications of UMX are open-sourced together with this paper.
Jump Models with delay -- option pricing and logarithmic Euler-Maruyama scheme
Authors: Nishant Agrawal, Yaozhong Hu
Date: 2020-10-08 00:00:00

In this paper, we obtain the existence, uniqueness and positivity of the solution to delayed stochastic differential equations with jumps. This equation is then applied to model the price movement of the risky asset in a financial market and the Black-Scholes formula for the price of European options is obtained together with the hedging portfolios. The option price is evaluated analytically at the last delayed period by using the Fourier transformation technique. But in general, there is no analytical expression for the option price. To evaluate the price numerically we then use the Monte-Carlo method. To this end, we need to simulate the delayed stochastic differential equations with jumps. We propose a logarithmic Euler-Maruyama scheme to approximate the equation and prove that all the approximations remain positive and the rate of convergence of the scheme is proved to be $0.5$.
MIMO ILC for Precision SEA robots using Input-weighted Complex-Kernel Regression
Date: 2020-10-23 00:00:00
Tasks: GPR, regression

This work improves the positioning precision of lightweight robots with
series elastic actuators (SEAs). Lightweight SEA robots, along with
low-impedance control, can maneuver without causing damage in uncertain,
confined spaces such as inside an aircraft wing during aircraft assembly.
Nevertheless, substantial modeling uncertainties in SEA robots reduce the
precision achieved by model-based approaches such as inversion-based
feedforward. Therefore, this article improves the precision of SEA robots
around specified operating points, through a multi-input multi-output (MIMO),
iterative learning control (ILC) approach. The main contributions of this
article are to (i) introduce an input-weighted complex kernel to estimate local
MIMO models using complex Gaussian process regression (c-GPR) (ii) develop
Ger\v{s}gorin-theorem-based conditions on the iteration gains for ensuring ILC
convergence to precision within noise-related limits, even with errors in the
estimated model; and (iii) demonstrate precision positioning with an
experimental SEA robot. Comparative experimental results, with and without ILC,
show around 90% improvement in the positioning precision (close to the
repeatability limit of the robot) and a 10-times increase in the SEA robot's
operating speed with the use of the MIMO ILC.
Secrecy Rate Maximization for Reconfigurable Intelligent Surface Aided Millimeter Wave System with Low-resolution DAC
Authors: Yue Xiu, Jun Zhao, Zhongpei Zhang
Date: 2020-10-09 00:00:00

In this letter, we investigate the secrecy rate of an reconfigurable intelligent surface (RIS)-aided millimeter-wave (mmWave) system with hardware limitations. Compared to the RIS-aided systems in most existing works, we consider the case of the RIS-aided mmWave system with low-resolution digital-to-analog converters (LDACs). We formulate a secrecy rate maximization problem hardware constraints. Then by optimizing the RIS phase shift and the transmit beamforming to maximize the secrecy rate. Due to the nonconvexity of the problem, the formulated problem is intractable. To handle the problem, an alternating optimization (AO)-based algorithm is proposed. Specifically, we first use the successive convex approximation (SCA) method to obtain the transmit beamforming. Then the element-wise block coordinate descent (BCD) method is used to obtain the RIS phase shift. Numerical results demonstrate that the RIS can mitigate the hardware loss, and the proposed AO-based algorithm with low complexity outperformances the baselines.
Robust Adaptive Control of Linear Parameter-Varying Systems with Unmatched Uncertainties
Authors: Pan Zhao, Steven Snyder, Naira Hovakimyana, Chengyu Cao
Date: 2020-10-09 00:00:00

In controlling systems with large operating envelopes, it is often necessary to adjust the desired dynamics according to operating conditions. This paper presents a robust adaptive control architecture for linear parameter-varying (LPV) systems that allows for the desired dynamics to be systematically scheduled, while being able to handle a broad class of uncertainties, both matched and unmatched, which can depend on both time and states. The proposed controller adopts an L1 adaptive control architecture for designing the adaptive control law and peak-to-peak gain (PPG) minimization for designing the robust control law to mitigate the effect of unmatched uncertainties. Leveraging the PPG bound of an LPV system, we derive transient and steady-state performance bounds in terms of the input and output signals of the actual closed-loop system as compared to the same signals of a nominal system. The efficacy of the proposed method is validated by extensive simulations using the short-period dynamics of an F-16 aircraft operating in a large envelope.
A clustering-based biased Monte Carlo approach to protein titration curve prediction
Authors: Arun V. Sathanur, Nathan A. Baker
Date: 2020-10-10 00:00:00
Tasks: Clustering, valid

In this work, we developed an efficient approach to compute ensemble averages in systems with pairwise-additive energetic interactions between the entities. Methods involving full enumeration of the configuration space result in exponential complexity. Sampling methods such as Markov Chain Monte Carlo (MCMC) algorithms have been proposed to tackle the exponential complexity of these problems; however, in certain scenarios where significant energetic coupling exists between the entities, the efficiency of the such algorithms can be diminished. We used a strategy to improve the efficiency of MCMC by taking advantage of the cluster structure in the interaction energy matrix to bias the sampling. We pursued two different schemes for the biased MCMC runs and show that they are valid MCMC schemes. We used both synthesized and real-world systems to show the improved performance of our biased MCMC methods when compared to the regular MCMC method. In particular, we applied these algorithms to the problem of estimating protonation ensemble averages and titration curves of residues in a protein.
Asymptotic Properties of the Maximum Likelihood Estimator in Regime-Switching Models with Time-Varying Transition Probabilities
Authors: Chaojun Li, Yan Liu
Date: 2020-10-10 00:00:00

We prove the asymptotic properties of the maximum likelihood estimator (MLE) in time-varying transition probability (TVTP) regime-switching models. This class of models extends the constant regime transition probability in Markov-switching models to a time-varying probability by including information from observations. An important feature in this proof is the mixing rate of the regime process conditional on the observations, which is time varying owing to the time-varying transition probabilities. Consistency and asymptotic normality follow from the almost deterministic geometrically decaying bound of the mixing rate. The assumptions are verified in regime-switching autoregressive models with widely-applied TVTP specifications. A simulation study examines the finite-sample distributions of the MLE and compares the estimates of the asymptotic variance constructed from the Hessian matrix and the outer product of the score. The simulation results favour the latter. As an empirical example, we compare three leading economic indicators in terms of describing U.S. industrial production.
Contact Tracing & Super-Spreaders in the Branching-Process Model
Authors: Johannes Müller, Volker Hösel
Date: 2020-10-10 00:00:00

In recent years, it became clear that super-spreader events play an important role, particularly in the spread of airborne infections. We investigate a novel model for super-spreader events, not based on a heterogeneous contact graph but on a random contact rate: Many individuals become infected synchronously in single contact events. We use the branching-process approach for contact tracing to analyze the impact of super-spreader events on the effect of contact tracing. Here we neglect a tracing delay. Roughly speaking, we find that contact tracing is more efficient in the presence of super-spreaders if the fraction of symptomatics is small, the tracing probability is high, or the latency period is distinctively larger than the incubation period. In other cases, the effect of contact tracing can be decreased by super-spreaders. Numerical analysis with parameters suited for SARS-CoV-2 indicates that super-spreaders do not decrease the effect of contact tracing crucially in case of that infection.
Pinched Hysteresis Loops In Nonlinear Resonators
Authors: A. S. Elwakil, M. E. Fouda, S. Majzoub, A. G. Radwan
Date: 2020-10-10 00:00:00

This paper shows that pinched hysteresis can be observed in simple nonlinear resonance circuits containing a single diode that behaves as a voltage-controlled switch. Mathematical models are derived and numerically validated for both series and parallel resonator circuits. The lobe area of the pinched loop is found to increase with increased frequency and multiple pinch-points are possible with an odd symmetrical nonlinearity such as a cubic nonlinearity. Experiments have been performed to prove the existence of pinched hysteresis with a single diode and with two anti-parallel diodes. The formation of a pinched loop in these circuits confirms that: 1) pinched hysteresis is not a finger-print of memristors and that 2) the existence of a nonlinearity is essential for generating this behavior. Finally, an application in a digital logic circuit is validated.
Nowcasting of COVID-19 confirmed cases: Foundations, trends, and challenges
Date: 2020-10-10 00:00:00
Tasks: Time Series, Time Series Analysis, Time Series Forecasting, Univariate Time Series Forecasting

The coronavirus disease 2019 (COVID-19) has become a public health emergency
of international concern affecting more than 200 countries and territories
worldwide. As of September 30, 2020, it has caused a pandemic outbreak with
more than 33 million confirmed infections and more than 1 million reported
deaths worldwide. Several statistical, machine learning, and hybrid models have
previously tried to forecast COVID-19 confirmed cases for profoundly affected
countries. Due to extreme uncertainty and nonstationarity in the time series
data, forecasting of COVID-19 confirmed cases has become a very challenging
job. For univariate time series forecasting, there are various statistical and
machine learning models available in the literature. But, epidemic forecasting
has a dubious track record. Its failures became more prominent due to
insufficient data input, flaws in modeling assumptions, high sensitivity of
estimates, lack of incorporation of epidemiological features, inadequate past
evidence on effects of available interventions, lack of transparency, errors,
lack of determinacy, and lack of expertise in crucial disciplines. This chapter
focuses on assessing different short-term forecasting models that can forecast
the daily COVID-19 cases for various countries. In the form of an empirical
study on forecasting accuracy, this chapter provides evidence to show that
there is no universal method available that can accurately forecast pandemic
data. Still, forecasters' predictions are useful for the effective allocation
of healthcare resources and will act as an early-warning system for government
policymakers.
Proportional resource allocation in dynamic n-player Blotto games
Authors: Nejat Anbarcı, Kutay Cingiz, Mehmet S. Ismail
Date: 2020-10-10 00:00:00

A variety of social, economic, and political interactions have long been modelled after Blotto games. In this paper, we introduce a general model of dynamic $n$-player Blotto contests. The players have asymmetric resources, and the battlefield prizes are not necessarily homogeneous. Each player's probability of winning the prize in a battlefield is governed by a contest success function and players' resource allocation on that battlefield. We show that there exists a subgame perfect equilibrium in which players allocate their resources proportional to the battlefield prizes for every history. This result is robust to exogenous resource shocks throughout the game.
Combining Observational and Experimental Data to Improve Efficiency Using Imperfect Instruments
Authors: George Z. Gui
Date: 2020-10-10 00:00:00

Randomized controlled trials generate experimental variation that can credibly identify causal effects, but often suffer from limited scale, while observational datasets are large, but often violate desired identification assumptions. To improve estimation efficiency, I propose a method that leverages imperfect instruments - pretreatment covariates that satisfy the relevance condition but may violate the exclusion restriction. I show that these imperfect instruments can be used to derive moment restrictions that, in combination with the experimental data, improve estimation efficiency. I outline estimators for implementing this strategy, and show that my methods can reduce variance by up to 50%; therefore, only half of the experimental sample is required to attain the same statistical precision. I apply my method to a search listing dataset from Expedia that studies the causal effect of search rankings on clicks, and show that the method can substantially improve the precision.
Efficient Spectrum Utilization via Pulse Shape Design for Fixed Transmission Networks
Authors: Elena-Iulia Dobre, Ayman Mostafa, Lutz Lampe, Hoda ShahMohammadian, Ming Jian
Date: 2020-10-11 00:00:00

Microwave backhaul links are characterized by high signal-to-noise ratios permitting spectrally-efficient transmission. The used signal constellation sizes and achievable data rates are typically limited by transceiver impairments, predominantly by phase noise from non-ideal carrier generation. In this paper, we propose a new method to improve the data rate over such microwave links. We make use of the fact that adjacent frequency channels are inactive in many deployment scenarios. We argue that additional data can be transmitted in the skirts of the spectral mask imposed on the transmission signal by regulation. To accomplish this task, we present a shaped wideband single-carrier transmission using non-Nyquist pulse shapes. In particular, we design spectrum-skirt filling (SSF) pulse shaping filters that follow the spectral mask response, and perform detection using an accordingly increased sampling frequency at the receiver. We evaluate the achievable information rates of the SSF-based transmission considering practical dispersive channels and non-ideal transmitter and receiver processing. To compensate for phase noise impairments, we derive carrier phase tracking and estimation techniques, and utilize them in tandem with nonlinear precoding which mitigates the intersymbol interference introduced by the non-Nyquist SSF shaping filter. Quantitative performance evaluations show that the proposed system design achieves higher data rates in a dispersive microwave propagation environment with respect to the conventional transmission with Nyquist pulse shaping.
End-to-End Radio Fingerprinting with Neural Networks
Authors: Ryan M. Dreifuerst, Andrew Graff, Sidharth Kumar, Clive Unger, Dylan Bray
Date: 2020-10-11 00:00:00
Tasks: General Classification

This paper presents a novel method for classifying radio frequency (RF) devices from their transmission signals. Given a collection of signals from identical devices, we accurately classify both the distance of the transmission and the specific device identity. We develop a multiple classifier system that accurately discriminates between channels and classifies devices using normalized in-phase and quadrature (IQ) samples. Our network uses residual connections for both distance and device classification, reaching 88.33% accuracy classifying 16 unique devices over 11 different distances and two different times, on a task that was previously unlearnable. Furthermore, we demonstrate the efficacy for pre-training neural networks for massive data domains and subtle classification differences.
Identifying causal channels of policy reforms with multiple treatments and different types of selection
Date: 2020-10-11 00:00:00

We study the identification of channels of policy reforms with multiple
treatments and different types of selection for each treatment. We disentangle
reform effects into policy effects, selection effects, and time effects under
the assumption of conditional independence, common trends, and an additional
exclusion restriction on the non-treated. Furthermore, we show the
identification of direct- and indirect policy effects after imposing additional
sequential conditional independence assumptions on mediating variables. We
illustrate the approach using the German reform of the allocation system of
vocational training for unemployed persons. The reform changed the allocation
of training from a mandatory system to a voluntary voucher system.
Simultaneously, the selection criteria for participants changed, and the reform
altered the composition of course types. We consider the course composition as
a mediator of the policy reform. We show that the empirical evidence from
previous studies reverses when considering the course composition. This has
important implications for policy conclusions.
Non-Cooperative MDS Coding Game In CSMA Wireless Networks
Authors: Mohamed Lamine Boucenna, Malek Benslama
Date: 2020-10-11 00:00:00

Maximum distance separable erasure coding has been introduced in wireless networks based on random medium access protocols in order to recover collided and erased packets. So, this help to avoid retransmission process which weaken the network throughput and prolong the overall propagation delay. However, erasure coding may degrade the network performances by causing overload traffic due to added redundancy (parity) and also when extending the propagation delay due to additional calculations and encoding/decoding process time which can lead quickly the network into saturation. Here, the redundancy should be applied properly. In addition, multiple access make conflicting situations due to simultaneous access and selfish behaviour of users which affect too the network performances. For this purpose, we present a game model based on the use of game theory to analyse and manage multiple users that assuming random access protocol and erasure coding algorithm. Our model is used to operate the network at equilibrium by selecting a suitable type of erasure coding. Making the network work at equilibrium, leads to decrease collisions, decrease also the overall transmission delay and as well as increasing the throughput.
Evolution of cooperation and consistent personalities in public goods games
Authors: Mohammad Salahshour
Date: 2020-10-11 00:00:00

The evolution of cooperation has remained an important problem in evolutionary theory and social sciences. In this regard, a curious question is why consistent cooperative and defective personalities exist and if they serve a role in the evolution of cooperation? To shed light on these questions, here, I consider a population of individuals who possibly play two consecutive rounds of public goods game, with different enhancement factors. Importantly, individuals have independent strategies in the two rounds. However, their strategy in the first round affects the game they play in the second round. I consider two different scenarios where either only first-round cooperators play a second public goods game, or both first-round cooperators and first-round defectors play a second public goods game, but in different groups. The first scenario can be considered a reward dilemma, and the second can be considered an assortative public goods game but with independent strategies of the individuals in the two rounds. Both models show cooperators can survive either in a fixed point or through different periodic orbits. Interestingly, due to the emergence of a correlation between the strategies of the individuals in the two rounds, individuals develop consistent personalities during the evolution. This, in turn, helps cooperation to flourish. These findings shed new light on the evolution of cooperation and show how consistent cooperative and defective personalities can evolve and play a positive role in solving social dilemmas.
Using Information to Amplify Competition
Authors: Wenhao Li
Date: 2020-10-11 00:00:00
Tasks: Segmentation

I characterize the consumer-optimal market segmentation in competitive markets where multiple firms selling differentiated products to consumers with unit demand. This segmentation is public---in that each firm observes the same market segments---and takes a simple form: in each market segment, there is a dominant firm favored by all consumers in that segment. By segmenting the market, all but the dominant firm maximally compete to poach the consumer's business, setting price to equal marginal cost. Information, thus, is being used to amplify competition. This segmentation simultaneously generates an efficient allocation and delivers to each firm its minimax profit.