pwc-archive/papers-with-abstracts huggingface dataset:
Fast implementation of synchrosqueezing transform based on downsampling for large-scale vibration signal analysis
Date: 2020-03-16 00:00:00

Synchrosqueezing transform (SST) is a useful tool for vibration signal
analysis due to its high time-frequency (TF) concentration and reconstruction
properties. However, existing SST requires much processing time for large-scale
data. In this paper, some fast implementation methods of SST based on
downsampled short-time Fourier transform (STFT) are proposed. By controlling
the downsampling factor both in time and frequency, combined with the proposed
selective reassignment and frequency subdivision scheme, one can keep a balance
between efficiency and accuracy according to practical needs. Moreover, the
reconstruction property is available, accomplished by an approximate but direct
inverse formula under downsampling. The effects of parameters on the
concentration, computing efficiency, and reconstruction accuracy are also
investigated quantitatively, followed by a mathematic model of reassignment
behavior with decimate factors. Experimental results on an aero-engine and a
spindle show that the fast implementation of SST can effectively characterize
the non-stationary characteristics of the large-scale vibration signal to
reveal the mechanism of mechanical systems.
Analysis of genetic differences between psychiatric disorders: Exploring pathways and cell-types/tissues involved and ability to differentiate the disorders by polygenic scores
Authors: Shitao Rao, Liangying Yin, Yong Xiang, Hon-Cheong So
Date: 2020-03-16 00:00:00

Although displaying genetic correlations, psychiatric disorders are clinically defined as categorical entities as they each have distinguishing clinical features and may involve different treatments. Identifying differential genetic variations between these disorders may reveal how the disorders differ biologically and help to guide more personalized treatment. Here we presented a comprehensive analysis to identify genetic markers differentially associated with various psychiatric disorders/traits based on GWAS summary statistics, covering 18 psychiatric traits/disorders and 26 comparisons. We also conducted comprehensive analysis to unravel the genes, pathways and SNP functional categories involved, and the cell types and tissues implicated. We also assessed how well one could distinguish between psychiatric disorders by polygenic risk scores (PRS). SNP-based heritabilities (h2SNP) were significantly larger than zero for most comparisons. Based on current GWAS data, PRS have mostly modest power to distinguish between psychiatric disorders. For example, we estimated that AUC for distinguishing schizophrenia from major depressive disorder (MDD), bipolar disorder (BPD) from MDD and schizophrenia from BPD were 0.694, 0.602 and 0.618 respectively, while the maximum AUC (based on h2SNP) were 0.763, 0.749 and 0.726 respectively. We also uncovered differences in each pair of studied traits in terms of their differences in genetic correlation with comorbid traits. For example, clinically-defined MDD appeared to more strongly genetically correlated with other psychiatric disorders and heart disease, when compared to non-clinically-defined depression in UK Biobank. Our findings highlight genetic differences between psychiatric disorders and the mechanisms involved. PRS may aid differential diagnosis of selected psychiatric disorders in the future with larger GWAS samples.
A Job-Assignment Heuristic for Lifelong Multi-Agent Path Finding Problem with Multiple Delivery Locations
Authors: Semiz Fatih, Polat Faruk
Date: 2020-04-25 00:00:00
Tasks: Multi-Agent Path Finding

In this paper we proposed multiple job-assignment heuristics to generate
low-total-cost solutions and determine the best performing method amongst them.
Consensusability of linear interconnected multi-agent systems
Date: 2020-03-17 00:00:00

Consensusability is an important property for many multi-agent systems (MASs)
as it implies the existence of networked controllers driving the states of MAS
subsystems to the same value. Consensusability is of interest even when the MAS
subsystems are physically coupled, which is the case for real-world systems
such as power networks. In this paper, we study necessary and sufficient
conditions for the consensusability of linear interconnected MASs. These
conditions are given in terms of the parameters of the subsystem matrices, as
well as the eigenvalues of the physical and communication graph Laplacians. Our
results show that weak coupling between subsystems and fast information
diffusion rates in the physical and communication graphs favor
consensusability. Technical results are verified through computer simulations.
Unbounded solutions of models for glycolysis
Authors: Alan D. Rendall, Pia Brechmann
Date: 2020-03-16 00:00:00

The Selkov oscillator, a simple description of glycolysis, is a system of two ordinary differential equations with mass action kinetics. In previous work the authors established several properties of the solutions of this system. In the present paper we extend this to prove that this system has solutions which diverge to infinity in an oscillatory manner at late times. This system was originally derived from another system with Michaelis-Menten kinetics. It is shown that the Michaelis-Menten system, like that with mass action, has solutions which diverge to infinity in a monotone manner. It is also shown to admit subcritical Hopf bifurcations and thus unstable periodic solutions. We discuss to what extent the unbounded solutions cast doubt on the biological relevance of the Selkov oscillator and compare it with other models in the literature.
Stochastic Frontier Analysis with Generalized Errors: inference, model comparison and averaging
Date: 2020-10-11 00:00:00
Tasks: Bayesian Inference

Contribution of this paper lies in the formulation and estimation of a
generalized model for stochastic frontier analysis (SFA) that nests virtually
all forms used and includes some that have not been considered so far. The
model is based on the generalized t distribution for the observation error and
the generalized beta distribution of the second kind for the
inefficiency-related term. We use this general error structure framework for
formal testing, to compare alternative specifications and to conduct model
averaging. This allows us to deal with model specification uncertainty, which
is one of the main unresolved issues in SFA, and to relax a number of
potentially restrictive assumptions embedded within existing SF models. We also
develop Bayesian inference methods that are less restrictive compared to the
ones used so far and demonstrate feasible approximate alternatives based on
maximum likelihood.
Game Theoretic Consequences of Resident Matching
Authors: Yue Wu
Date: 2020-03-12 00:00:00

The resident matching algorithm, Gale-Shapley, currently used by SF Match and the National Residency Match Program (NRMP), has been in use for over 50 years without fundamental alteration. The algorithm is a 'stable-marriage' method that favors applicant outcomes. However, in these 50 years, there has been a big shift in the supply and demand of applicants and programs. These changes along with the way the Match is implemented have induced a costly race among applicants to apply and interview at as many programs as possible. Meanwhile programs also incur high costs as they maximize their probability of matching by interviewing as many candidates as possible.
Phase-randomised Fourier transform model for the generation of synthetic wind speeds
Authors: D. D'Ambrosio, J. Schoukens, T. De Troyer, M. Zivanovic, M. C. Runacres
Date: 2020-03-13 00:00:00
Tasks: Time Series, Time Series Analysis

The increasing sophistication of wind turbine design and control generates a need for high-quality data. Therefore, the relatively limited set of measured wind data may be extended with computer-generated surrogate data, e.g. to make reliable statistical studies of energy production and mechanical loads. This paper presents a data-driven, statistical model for the generation of realistic surrogate time series that is based on the phase-randomised Fourier transform. The proposed model simulates an ergodic, pseudo-random process that makes use of an iterative rank-reordering procedure to yield synthetic time series that possess the power spectral density of the target data and concurrently converges to the probability distribution of the target data with an arbitrary, user-defined precision. A comparison with two established data-driven modelling techniques for generating surrogate wind speeds is presented. The proposed model is tested under the same input conditions given in the test cases of the selected models, and its performance is investigated in terms of the agreement with the target statistical descriptors. Simulation results show that the proposed model can reproduce with high fidelity the statistical descriptors of the input datasets and is able to capture the nonstationary diurnal and seasonal variations of the wind speed.
Genome Variant Calling with a Deep Averaging Network
Authors: Nikolai Yakovenko, Avantika Lal, Johnny Israeli, Bryan Catanzaro
Date: 2020-03-13 00:00:00
Tasks: Position

Variant calling, the problem of estimating whether a position in a DNA sequence differs from a reference sequence, given noisy, redundant, overlapping short sequences that cover that position, is fundamental to genomics. We propose a deep averaging network designed specifically for variant calling. Our model takes into account the independence of each short input read sequence by transforming individual reads through a series of convolutional layers, limiting the communication between individual reads to averaging and concatenating operations. Training and testing on the precisionFDA Truth Challenge (pFDA), we match state of the art overall 99.89 F1 score. Genome datasets exhibit extreme skew between easy examples and those on the decision boundary. We take advantage of this property to converge models at 5x the speed of standard epoch-based training by skipping easy examples during training. To facilitate future work, we release our code, trained models and pre-processed public domain datasets.
A probabilistic validation approach for penalty function design in Stochastic Model Predictive Control
Date: 2020-03-16 00:00:00
Tasks: Model Predictive Control

In this paper, we consider a stochastic Model Predictive Control able to
account for effects of additive stochastic disturbance with unbounded support,
and requiring no restrictive assumption on either independence nor Gaussianity.
We revisit the rather classical approach based on penalty functions, with the
aim of designing a control scheme that meets some given probabilistic
specifications. The main difference with previous approaches is that we do not
recur to the notion of probabilistic recursive feasibility, and hence we do not
consider separately the unfeasible case. In particular, two probabilistic
design problems are envisioned. The first randomization problem aims to design
\textit{offline} the constraint set tightening, following an approach inherited
from tube-based MPC. For the second probabilistic scheme, a specific
probabilistic validation approach is exploited for tuning the penalty
parameter, to be selected \textit{offline} among a finite-family of possible
values. The simple algorithm here proposed allows designing a \textit{single}
controller, always guaranteeing feasibility of the online optimization problem.
The proposed method is shown to be more computationally tractable than previous
schemes. This is due to the fact that the sample complexity for both
probabilistic design problems depends on the prediction horizon in a
logarithmic way, unlike scenario-based approaches which exhibit linear
dependence. The efficacy of the proposed approach is demonstrated with a
numerical example.
Learning Driver Behaviors Using A Gaussian Process Augmented State-Space Model
Date: 2020-03-16 00:00:00
Tasks: Position, State Space Models

An inference method for Gaussian process augmented state-space models are
presented. This class of grey-box models enables domain knowledge to be
incorporated in the inference process to guarantee a minimum of performance,
still they are flexible enough to permit learning of partially unknown model
dynamics and inputs. To facilitate online (recursive) inference of the model a
sparse approximation of the Gaussian process based upon inducing points is
presented. To illustrate the application of the model and the inference method,
an example where it is used to track the position and learn the behavior of a
set of cars passing through an intersection, is presented. Compared to the case
when only the state-space model is used, the use of the augmented state-space
model gives both a reduced estimation error and bias.
Keeping the Listener Engaged: a Dynamic Model of Bayesian Persuasion
Authors: Yeon-Koo Che, KyungMin Kim, Konrad Mierendorff
Date: 2020-03-16 00:00:00

We consider a dynamic model of Bayesian persuasion in which information takes time and is costly for the sender to generate and for the receiver to process, and neither player can commit to their future actions. Persuasion may totally collapse in a Markov perfect equilibrium (MPE) of this game. However, for persuasion costs sufficiently small, a version of a folk theorem holds: outcomes that approximate Kamenica and Gentzkow (2011)'s sender-optimal persuasion as well as full revelation and everything in between are obtained in MPE, as the cost vanishes.
Calibrated Intervention and Containment of the COVID-19 Pandemic
Date: 2020-11-17 00:00:00

Within a short period of time, COVID-19 grew into a world-wide pandemic.
Transmission by pre-symptomatic and asymptomatic viral carriers rendered
intervention and containment of the disease extremely challenging. Based on
reported infection case studies, we construct an epidemiological model that
focuses on transmission around the symptom onset. The model is calibrated
against incubation period and pairwise transmission statistics during the
initial outbreaks of the pandemic outside Wuhan with minimal non-pharmaceutical
interventions. Mathematical treatment of the model yields explicit expressions
for the size of latent and pre-symptomatic subpopulations during the
exponential growth phase, with the local epidemic growth rate as input. We then
explore reduction of the basic reproduction number R_0 through specific disease
control measures such as contact tracing, testing, social distancing, wearing
masks and sheltering in place. When these measures are implemented in
combination, their effects on R_0 multiply. We also compare our model behaviour
to the first wave of the COVID-19 spreading in various affected regions and
highlight generic and less generic features of the pandemic development.
A Bayesian brain model of adaptive behavior: An application to the Wisconsin Card Sorting Task
Date: 2020-03-16 00:00:00
Tasks: Bayesian Inference

Adaptive behavior emerges through a dynamic interaction between cognitive
agents and changing environmental demands. The investigation of information
processing underlying adaptive behavior relies on controlled experimental
settings in which individuals are asked to accomplish demanding tasks whereby a
hidden state or an abstract rule has to be learned dynamically. Although
performance in such tasks is regularly considered as a proxy for measuring
high-level cognitive processes, the standard approach consists in summarizing
response patterns by simple heuristic scoring measures. With this work, we
propose and validate a new computational Bayesian model accounting for
individual performance in the established Wisconsin Card Sorting Test. We embed
the new model within the mathematical framework of Bayesian Brain Theory,
according to which beliefs about the hidden environmental states are
dynamically updated following the logic of Bayesian inference. Our
computational model maps distinct cognitive processes into separable,
neurobiologically plausible, information-theoretic constructs underlying
observed response patterns. We assess model identification and expressiveness
in accounting for meaningful human performance through extensive simulation
studies. We further apply the model to real behavioral data in order to
highlight the utility of the proposed model in recovering cognitive dynamics at
an individual level. Practical and theoretical implications of our
computational modeling approach for clinical and cognitive neuroscience
research are finally discussed, as well as potential future improvements.
Nonlinear Stochastic Estimators on the Special Euclidean Group SE(3) using Uncertain IMU and Vision Measurements
Authors: Hashim A Hashim, Frank L Lewis
Date: 2020-03-16 00:00:00
Tasks: Position

Two novel robust nonlinear stochastic full pose (i.e, attitude and position) estimators on the Special Euclidean Group SE(3) are proposed using the available uncertain measurements. The resulting estimators utilize the basic structure of the deterministic pose estimators adopting it to the stochastic sense. The proposed estimators for six degrees of freedom (DOF) pose estimations consider the group velocity vectors to be contaminated with constant bias and Gaussian random noise, unlike nonlinear deterministic pose estimators which disregard the noise component in the estimator derivations. The proposed estimators ensure that the closed loop error signals are semi-globally uniformly ultimately bounded in mean square. The efficiency and robustness of the proposed estimators are demonstrated by the numerical results which test the estimators against high levels of noise and bias associated with the group velocity and body-frame measurements and large initialization error. Keywords: Nonlinear stochastic pose filter, pose observer, position, attitude, Ito, stochastic differential equations, Brownian motion process, adaptive estimate, feature, inertial measurement unit, inertial vision system, 6 DOF, IMU, SE(3), SO(3), orientation, landmark, Gaussian, noise.
Implications of Stahl's Theorems to Holomorphic Embedding Pt. 2: Numerical Convergence
Date: 2020-03-18 00:00:00

What has become known as Stahl's Theorem in power-engineering circles has
been used to justify a convergence guarantee of the Holomorphic Embedding
Method (HEM) as it applies to the power-flow problem. In this, the second part
of a two-part paper, we examine implications to numerical convergence of HEM
and the numerical properties of a Pad\'e approximant algorithm. We show that
even if the convergence domain is identical to the function's domain, numerical
convergence of the sequence of Pad\'e approximants computed with finite
precision is not guaranteed. We also show that the study of convergence
properties of the Pad\'e approximant is the study of the location of
branch-points of the function, which dictate branch-cut topology and capacity
and, therefore, convergence rate. We show how poorly chosen embeddings can
prevent numerical convergence.
A Framework for Distributed and Compositional Stability Analysis of Power Grids
Date: 2020-03-16 00:00:00
Tasks: Privacy Preserving

Operating modern power grids with stability guarantees is admittedly
imperative. Classic stability methods are not well-suited for these dynamic
systems as they involve centralized gathering of information and computation of
the system's eigenvalues, processes which are oftentimes not privacy-preserving
and computationally burdensome. System operators (SOs) would nowadays have to
be able to quickly and efficiently assess small-signal stability as the power
grid operating conditions change more dynamically while also respect the
privacy of the distributed energy resources (DERs). Motivated by all these, in
this paper we introduce a framework that comprises a computationally efficient,
privacy-preserving, distributed and compositional stability assessment method.
Our proposed method first calls for representative agents at various buses to
exchange information with their neighbors and design their local controls in
order to meet some local stability conditions. Following that, the agents are
required to notify the SO whether their local conditions are satisfied or not.
In case the agents cannot verify their local conditions they can augment their
local controls using a global control input. The SO can then warrant stability
of the interconnected power grid by assembling the local stability guarantees,
established by the agents, in a compositional manner. We analytically derive
the local stability conditions and prove that when they are collectively
satisfied stability of the interconnected system ensues. We illustrate the
effectiveness of our proposed DSA method via a numerical example centered
around a three-bus power grid.
Implications of Stahl's Theorems to Holomorphic Embedding Pt. 1: Theoretical Convergence
Date: 2020-03-18 00:00:00

What has become known as Stahl's Theorem in power engineering circles has
been used to justify a convergence guarantee of the Holormorphic Embedding
Method (HEM) as it applies to the power flow (PF) problem. In this two-part
paper, we examine in more detail the implications of Stahl's theorems to both
theoretcial and numerical convergence for a wider range of problems to which
these theorems are now being applied. In Pt. 1, we introduce the theorem using
the necessary mathematical parlance and then translate the language to show its
implications to convergence of nonlinear problems in general and the PF problem
specifically. We show that among other possibilities the existence of the
Chebotarev points, which are embedding specific, are a possible theoretical
impediment to convergence. Numerical impediments to convergences are discussed
in the companion paper.
Do COVID-19 and crude oil prices drive the US economic policy uncertainty?
Authors: Claudiu Albulescu
Date: 2020-03-17 00:00:00

This paper investigates the effect of the novel coronavirus and crude oil prices on the United States (US) economic policy uncertainty (EPU). Using daily data for the period January 21-March 13, 2020, our Autoregressive Distributed Lag (ARDL) model shows that the new infection cases reported at global level, and the death ratio, have no significant effect on the US EPU, whereas the oil price negative dynamics leads to increased uncertainty. However, analyzing the situation outside China, we discover that both new case announcements and the COVID-19 associated death ratio have a positive influence on the US EPU.
End-to-end Recurrent Denoising Autoencoder Embeddings for Speaker Identification
Authors: Esther Rituerto-González, Carmen Peláez-Moreno
Date: 2020-03-13 00:00:00
Tasks: Data Augmentation, Denoising, Representation Learning, Speaker Identification, Speaker Recognition

Speech 'in-the-wild' is a handicap for speaker recognition systems due to the variability induced by real-life conditions, such as environmental noise and the emotional state of the speaker. Taking advantage of the principles of representation learning, we aim to design a recurrent denoising autoencoder that extracts robust speaker embeddings from noisy spectrograms to perform speaker identification. The end-to-end proposed architecture uses a feedback loop to encode information regarding the speaker into low-dimensional representations extracted by a spectrogram denoising autoencoder. We employ data augmentation techniques by additively corrupting clean speech with real-life environmental noise in a database containing real stressed speech. Our study presents that the joint optimization of both the denoiser and speaker identification modules outperforms independent optimization of both components under stress and noise distortions as well as hand-crafted features.