pwc-archive/papers-with-abstracts huggingface dataset:
Convergence rate of estimators of clustered panel models with misclassification
Date: 2020-08-11 00:00:00
Tasks: Clustering

We study kmeans clustering estimation of panel data models with a latent
group structure and $N$ units and $T$ time periods under long panel
asymptotics. We show that the group-specific coefficients can be estimated at
the parametric root $NT$ rate even if error variances diverge as $T \to \infty$
and some units are asymptotically misclassified. This limit case approximates
empirically relevant settings and is not covered by existing asymptotic
results.
Channel Estimation for Intelligent Reflecting Surface Assisted MIMO Systems: A Tensor Modeling Approach
Authors: Gilderlan T. de Araújo, André L. F. de Almeida, Rémy Boyer
Date: 2020-08-10 00:00:00

Intelligent reflecting surface (IRS) is an emerging technology for future wireless communications including 5G and especially 6G. It consists of a large 2D array of (semi-)passive scattering elements that control the electromagnetic properties of radio-frequency waves so that the reflected signals add coherently at the intended receiver or destructively to reduce co-channel interference. The promised gains of IRS-assisted communications depend on the accuracy of the channel state information. In this paper, we address the receiver design for an IRS-assisted multiple-input multiple-output (MIMO) communication system via a tensor modeling approach aiming at the channel estimation problem using supervised (pilot-assisted) methods. Considering a structured time-domain pattern of pilots and IRS phase shifts, we present two channel estimation methods that rely on a parallel factor (PARAFAC) tensor modeling of the received signals. The first one has a closed-form solution based on a Khatri-Rao factorization of the cascaded MIMO channel, by solving rank-1 matrix approximation problems, while the second one is an iterative alternating estimation scheme. The common feature of both methods is the decoupling of the estimates of the involved MIMO channel matrices (base station-IRS and IRS-user terminal), which provides performance enhancements in comparison to competing methods that are based on unstructured LS estimates of the cascaded channel. Design recommendations for both methods that guide the choice of the system parameters are discussed. Numerical results show the effectiveness of the proposed receivers, highlight the involved trade-offs, and corroborate their superior performance compared to competing LS-based solutions.
Constrained Active Classification Using Partially Observable Markov Decision Processes
Authors: Bo Wu, Niklas Lauffer, Mohamadreza Ahmadi, Suda Bharadwaj, Zhe Xu, Ufuk Topcu
Date: 2020-08-10 00:00:00
Tasks: Attribute, Classification, Medical Diagnosis

In this work, we study the problem of actively classifying the attributes of dynamical systems characterized as a finite set of Markov decision process (MDP) models. We are interested in finding strategies that actively interact with the dynamical system and observe its reactions so that the attribute of interest is classified efficiently with high confidence. We present a decision-theoretic framework based on partially observable Markov decision processes (POMDPs). The proposed framework relies on assigning a classification belief (a probability distribution) to the attributes of interest. Given an initial belief, a confidence level over which a classification decision can be made, a cost bound, safe belief sets, and a finite time horizon, we compute POMDP strategies leading to classification decisions. We present three different algorithms to compute such strategies. The first algorithm computes the optimal strategy exactly by value iteration. To overcome the computational complexity of computing the exact solutions, we propose a second algorithm based on adaptive sampling and a third based on a Monte Carlo tree search to approximate the optimal probability of reaching a classification decision. We illustrate the proposed methodology using examples from medical diagnosis, security surveillance, and wildlife classification.
Bracing for sustainable agriculture: the development and function of brace roots in members of Poaceae
Authors: Ashley N. Hostetler, Rajdeep S. Khangura, Brian P. Dilkes, Erin E. Sparks
Date: 2020-08-08 00:00:00

Optimization of crop production requires root systems to function in water uptake, nutrient use, and anchorage. In maize, two types of nodal roots-subterranean crown and aerial brace roots function in anchorage and water uptake and preferentially express multiple water and nutrient transporters. Brace root development shares genetic control with juvenile-to-adult phase change and flowering time. We present a comprehensive list of the genes known to alter brace roots and explore these as candidates for QTL studies in maize and sorghum. Brace root development and function may be conserved in other members of Poaceae, however research is limited. This work highlights the critical knowledge gap of aerial nodal root development and function and suggests new focus areas for breeding resilient crops.
Anytime Guarantees for Reachability in Uncountable Markov Decision Processes
Authors: Kush Grover, Jan Křetínský, Tobias Meggendorfer, Maximilian Weininger
Date: 2020-08-10 00:00:00

We consider the problem of approximating the reachability probabilities in Markov decision processes (MDP) with uncountable (continuous) state and action spaces. While there are algorithms that, for special classes of such MDP, provide a sequence of approximations converging to the true value in the limit, our aim is to obtain an algorithm with guarantees on the precision of the approximation. As this problem is undecidable in general, assumptions on the MDP are necessary. Our main contribution is to identify sufficient assumptions that are as weak as possible, thus approaching the "boundary" of which systems can be correctly and reliably analyzed. To this end, we also argue why each of our assumptions is necessary for algorithms based on processing finitely many observations. We present two solution variants. The first one provides converging lower bounds under weaker assumptions than typical ones from previous works concerned with guarantees. The second one then utilizes stronger assumptions to additionally provide converging upper bounds. Altogether, we obtain an anytime algorithm, i.e. yielding a sequence of approximants with known and iteratively improving precision, converging to the true value in the limit. Besides, due to the generality of our assumptions, our algorithms are very general templates, readily allowing for various heuristics from literature in contrast to, e.g., a specific discretization algorithm. Our theoretical contribution thus paves the way for future practical improvements without sacrificing correctness guarantees.
Classification of Radio Signals Using Truncated Gaussian Discriminant Analysis of Convolutional Neural Network-Derived Features
Authors: J. B. Persons, Lauren J. Wong, W. Chris Headley, Michael C. Fowler
Date: 2020-08-11 00:00:00
Tasks: Classification, General Classification

To improve the utility and scalability of distributed radio frequency (RF) sensor and communication networks, reduce the need for convolutional neural network (CNN) retraining, and efficiently share learned information about signals, we examined a supervised bootstrapping approach for RF modulation classification. We show that CNN-bootstrapped features of new and existing modulation classes can be considered as mixtures of truncated Gaussian distributions, allowing for maximumlikelihood-based classification of new classes without retraining the network. In this work, the authors observed classification performance using maximum likelihood estimation of CNNbootstrapped features to be comparable to that of a CNN trained on all classes, even for those classes on which the bootstrapping CNN was not trained. This performance was achieved while reducing the number of parameters needed for new class definition from over 8 million to only 200. Furthermore, some physical features of interest, not directly labeled during training, e.g. signal-to-noise ratio (SNR), can be learned or estimated from these same CNN-derived features. Finally, we show that SNR estimation accuracy is highest when classification accuracy is lowest and therefore can be used to calibrate a confidence in the classification.
Demand side management impacts on electricity network vulnerability
Date: 2020-08-11 00:00:00
Tasks: Decision Making, Management

The demand for electricity is undergoing considerable spatial and temporal
change. With the uptake of efficient technologies and increased
electrification, a better understanding of how potential changes in demand
patterns can affect network reliability is necessary. We quantify the
macro-economic impacts of potential future changes in demand profiles for an
electricity network undergoing generation shortages. Applied to Great Britain,
potential savings or losses are assessed for changes to peak demands under four
different load profiles: (i) current day, (ii) widespread uptake of efficient
appliances, (iii) deployment of heat pumps, and (iv) moving to an idealised
fully balanced load profile. Considerable variation in economic disruption is
observed both between different demand profiles and across Great Britain. When
the networks generation capacity is severely disrupted, we estimate hourly
macro-economic impacts to increase by up to {\pounds}1.23 million per
additional GW of national electricity demand. A similar reduction in impacts
can be achieved if peak demands are reduced by demand side management. We
conclude that risk-related economic impacts are directly linked to the temporal
pattern of energy demands and need to be included in the decision-making around
demand side measures. We find that decarbonisation strategies without
accompanying demand side management may lead to increased economic losses
without supply side interventions.
Robust Output Feedback MPC with Reduced Conservatism under Ellipsoidal Uncertainty
Authors: Tianchen Ji, Junyi Geng, Katherine Driggs-Campbell
Date: 2020-08-11 00:00:00
Tasks: Robust Design

Robust design of autonomous systems under uncertainty is an important yet challenging problem. This work proposes a robust controller that consists of a state estimator and a tube based predictive control law. The class of linear systems under ellipsoidal uncertainty is considered. In contrast to existing approaches based on polytopic sets, the constraint tightening is directly computed from the ellipsoidal sets of disturbances without over-approximation, thus leading to less conservative bounds. Conditions to guarantee robust constraint satisfaction and robust stability are presented. Further, by avoiding the usage of Minkowski sum in set computation, the proposed approach can also scale up to high-dimensional systems. The results are illustrated by examples.
MOVESTAR: An Open-Source Vehicle Fuel and Emission Model based on USEPA MOVES
Authors: Ziran Wang, Guoyuan Wu, George Scora
Date: 2020-08-11 00:00:00

In this paper, we introduce an open-source model "MOVESTAR" to calculate the fuel consumption and pollutant emissions of motor vehicles. This model is developed based on U.S. Environmental Protection Agency's (EPA) Motor Vehicle Emission Simulator (MOVES), which provides an accurate estimate of vehicle emissions under a wide range of user-defined conditions. Originally, MOVES requires users to specify many parameters through its software, including vehicle types, time periods, geographical areas, pollutants, vehicle operating characteristics, and road types. In this paper, MOVESTAR is developed as a simplified version, which only takes the second-by-second vehicle speed data and vehicle type as inputs. To enable easy integration of this model, its source code is provided in various languages, including Python, MATLAB and C++. A case study is introduced in this paper to illustrate the effectiveness of the model in the development of advanced vehicle technology.
Single-channel EEG features during n-back task correlate with working memory load
Date: 2020-08-11 00:00:00
Tasks: EEG, Electroencephalogram (EEG)

Working Memory (WM) load is an important cognitive feature that is highly
correlated with mental effort. Several neurological biomarkers such as theta
power and mid-frontal activity show increased activity with increasing WM load.
Such correlations often break down in cognitively impaired individuals, making
WM load biomarkers a valuable tool for the detection of cognitive impairment.
However, most studies have used a multi-channel EEG or an fMRI, which are not
massively accessible. In the present study, we evaluate the ability of novel
features extracted from a single-channel EEG located on the forehead, to serve
as markers of WM load. We employed the widely used n-back task to manipulate WM
load. Fourteen participants performed the n-back task while their brain
activity was recorded with the Neurosteer inc Aurora EEG device. The results
showed that the activity of the newly introduced features increased with WM
load, similar to the theta band, but exhibited higher sensitivity to finer WM
load changes. These more sensitive biomarkers of WM load are a promising tool
for mass screening of mild cognitive impairment.
A Monte Carlo approach to model COVID-19 deaths and infections using Gompertz functions
Date: 2020-08-11 00:00:00

This study describes the dynamics of COVID-19 deaths and infections via a
Monte Carlo approach. The analyses include death's data from USA, Brazil,
Mexico, UK, India and Russia, which comprise the four countries with the
highest number of deaths/confirmed cases, as of Aug 07, 2020, according to the
WHO. The Gompertz functions were fitted to the data of weekly averaged
confirmed deaths per day by mapping the $\chi^2$ values. The uncertainties,
variances and covariances of the model parameters were calculated by
propagation. The fitted functions for the average deaths per day for USA and
India have an upward trend, with the former having a higher growth rate and
quite huge uncertainties. For Mexico, UK and Russia, the fits are consistent
with a slope down pattern. For Brazil we found a subtle trend down, but with
significant uncertainties. The USA, UK and India data shown a first peak with a
higher growth rate when compared to the second one, demonstrating the benefits
of non-pharmaceutical interventions of sanitary measures and social distance
flattening the curve. For USA, a third peak seems quite plausible, most likely
related with the recent relaxation policies. Brazil's data are satisfactorily
described by two highly overlapped Gompertz functions with similar growth
rates, suggesting a two-steps process for the pandemic spreading. The 95% CI
for the total number of deaths ($\times 10^3$) predicted by the model for Aug
31, 2020 are 160 to 220, 110 to 130, 59 to 62, 46.6 to 47.3, 54 to 63 and 16.0
to 16.7 for USA, Brazil, Mexico, UK, India and Russia, respectively. Our
estimates for the prevalences of infections are in reasonable agreement with
some preliminary reports from serological studies carried out in USA and
Brazil. The method represents an effective framework to estimate the line-shape
of the infection curves and the uncertainties of the relevant parameters based
on the actual data.
Inferring phenomenological models of first passage processes
Date: 2020-08-11 00:00:00

Biochemical processes in cells are governed by complex networks of many
chemical species interacting stochastically in diverse ways and on different
time scales. Constructing microscopically accurate models of such networks is
often infeasible. Instead, here we propose a systematic framework for building
phenomenological models of such networks from experimental data, focusing on
accurately approximating the time it takes to complete the process, the First
Passage (FP) time. Our phenomenological models are mixtures of Gamma
distributions, which have a natural biophysical interpretation. The complexity
of the models is adapted automatically to account for the amount of available
data and its temporal resolution. The framework can be used for predicting the
behavior of various FP systems under varying external conditions. To
demonstrate the utility of the approach, we build models for the distribution
of inter-spike intervals of a morphologically complex neuron, a Purkinje cell,
from experimental and simulated data. We demonstrate that the developed models
can not only fit the data but also make nontrivial predictions. We demonstrate
that our coarse-grained models provide constraints on more mechanistically
accurate models of the involved phenomena.
Research on the construction method of vehicle driving cycle based on Mean Shift clustering
Authors: Yongjiang He
Date: 2020-08-12 00:00:00
Tasks: Clustering, energy management, Management

In this study, a novel method for the construction of a driving cycle based on Mean Shift clustering is proposed to solve the problems existing in the traditional micro-trips method. Firstly, 1701 kinematic segments are obtained by processing and dividing the driving data in real road conditions. Secondly, 12 kinematic parameters are calculated for each segment, and the dimensionality of parameters is reduced through principal component analysis (PCA). Three principal components are chosen to classify all cycles into three types by the Mean Shift algorithm. Finally, according to the principle of minimum deviation, representative micro-trips are selected from each type of cycle to complete the construction of the final driving cycle. Further, the construction method in this paper is compared with the micro-trips construction method by the K-Means clustering. The results show that the construction method by Mean Shift clustering can more effectively reflect the real driving data. This study realizes the innovation in the construction method of micro-trips and provides a preliminary theoretical basis for the formulation of automobile working condition standards, energy management of new-energy vehicles, and optimal control of vehicle dynamics in driverless vehicles.
Comments on Mathematical Modeling of Current Source Matrix Converter with Venturini and SVM
Authors: Irfan Ahmad Khan, Anshul Agarwal
Date: 2020-08-12 00:00:00

In this paper, authors want to comment on a recently published article describing the Mathematical Modeling of Current Source Matrix Converter (CSMC) with two modulation strategies, namely: Venturini and Space Vector Modulation (SVM). Reported flaws are broadly classified into two (2) categories; namely: (1) Technical Flaws and (2) Rubric and Grammar Flaws. This paper not only reports these flaws but also provides suggestive rectifications for correction and improvement of the published article.
Tail risk forecasting using Bayesian realized EGARCH models
Authors: Vica Tendenan, Richard Gerlach, Chao Wang
Date: 2020-08-12 00:00:00

This paper develops a Bayesian framework for the realized exponential generalized autoregressive conditional heteroskedasticity (realized EGARCH) model, which can incorporate multiple realized volatility measures for the modelling of a return series. The realized EGARCH model is extended by adopting a standardized Student-t and a standardized skewed Student-t distribution for the return equation. Different types of realized measures, such as sub-sampled realized variance, sub-sampled realized range, and realized kernel, are considered in the paper. The Bayesian Markov chain Monte Carlo (MCMC) estimation employs the robust adaptive Metropolis algorithm (RAM) in the burn in period and the standard random walk Metropolis in the sample period. The Bayesian estimators show more favourable results than maximum likelihood estimators in a simulation study. We test the proposed models with several indices to forecast one-step-ahead Value at Risk (VaR) and Expected Shortfall (ES) over a period of 1000 days. Rigorous tail risk forecast evaluations show that the realized EGARCH models employing the standardized skewed Student-t distribution and incorporating sub-sampled realized range are favored, compared to a range of models.
Assessing Intervention Strategies for Non Homogeneous Populations Using a Closed Form Formula for R0
Date: 2020-08-12 00:00:00
Tasks: Form

A general stochastic model for susceptible -> infective -> recovered (SIR)
epidemics in non homogeneous populations is considered. The heterogeneity is a
very important aspect here since it allows more realistic but also more complex
models. The basic reproduction number $R_0$, an indication of the probability
of an outbreak for homogeneous populations does not indicate the probability of
an outbreak for non homogeneous models anymore, because it changes with the
initially infected case. Therefore, we use "individual $R_0$" that is the
expected number of secondary cases for a given initially infected individual.
Thus, the effectiveness of intervention strategies can be assessed by their
capability to reduce individual $R_0$ values. Also an intelligent vaccination
plan for fully heterogeneous populations is proposed. It is based on the
recursive calculation of individual R0 values.
Covid-19 and Flattening the Curve: a Feedback Control Perspective
Date: 2020-10-22 00:00:00

Many of the control policies that were put into place during the Covid-19
pandemic had a common goal: to flatten the curve of the number of infected
people so that its peak remains under a critical threshold. This letter
considers the challenge of engineering a strategy that enforces such a goal
using control theory. We introduce a simple formulation of the optimal
flattening problem, and provide a closed form solution.This is augmented
through nonlinear closed loop tracking of the nominal solution, with the aim of
ensuring close-to-optimal performance under uncertain conditions. A key
contribution ofthis paper is to provide validation of the method with extensive
and realistic simulations in a Covid-19 scenario, with particular focus on the
case of Codogno - a small city in Northern Italy that has been among the most
harshly hit by the pandemic.
An Ansatz for computational undecidability in RNA automata
Authors: Adam J. Svahn, Mikhail Prokopenko
Date: 2020-08-12 00:00:00

In this Ansatz we consider theoretical constructions of RNA polymers into automata, a form of computational structure. The basis for transitions in our automata are plausible RNA enzymes that may perform ligation or cleavage. Limited to these operations, we construct RNA automata of increasing complexity; from the Finite Automaton (RNA-FA) to the Turing Machine equivalent 2-stack PDA (RNA-2PDA) and the universal RNA-UPDA. For each automaton we show how the enzymatic reactions match the logical operations of the RNA automaton. A critical theme of the Ansatz is the self-reference in RNA automata configurations which exploits the program-data duality but results in computational undecidability. We describe how computational undecidability is exemplified in the self-referential Liar paradox that places a boundary on a logical system, and by construction, any RNA automata. We argue that an expansion of the evolutionary space for RNA-2PDA automata can be interpreted as a hierarchical resolution of computational undecidability by a meta-system (akin to Turing's oracle), in a continual process analogous to Turing's ordinal logics and Post's extensible recursively generated logics. On this basis, we put forward the hypothesis that the resolution of undecidable configurations in RNA automata represent a novelty generation mechanism and propose avenues for future investigation of biological automata.