pwc-archive/papers-with-abstracts huggingface dataset:
Radar pulse correlation from uniformly translating targets
Authors: Timothy J. Garner, Akhlesh Lakhtakia
Date: 2020-07-29 00:00:00

Radars often use correlation of received signals with transmitted signals to identify targets. However, when a target translates at a high uniform speed, the correlation of the transmitted and received signals depends strongly on the target's velocity, even after the two-way Doppler shift has been removed. This is true whether the target is made of highly dispersive material or not.
Introduction to UAN Power Equipment Condition Datasets
Authors: M. Martin, F. Liu, J. Sun, K. Fiske, M. Sagin, J. Shackleford, N. Hellerstein, V. Chong
Date: 2020-07-28 00:00:00
Tasks: Management

Power systems are equipment and data intensive. In todays big data era, a large amount of data is being generated from power system equipment via various inspection and testing activities. The industry is encouraged to make optimal decisions for power equipment maintenance, replacement, configuration and planning. Utility companies are trying to leverage the use of big data and advanced analytics for equipment management in order to balance system reliability, performance and cost. To advance such applications and promote collaborations with external researchers, the Utility Analytics Network has made the effort to gather 15 datasets from multiple utility companies in North America on various types of power equipment. These datasets are now shared in public and this paper serves the purpose of providing a detailed introduction to the datasets.
Performance Analysis of Metaheuristic Optimization Algorithms in Estimating the Interfacial Heat Transfer Coefficient on Directional Solidification
Authors: Gianfranco de M. Stieven, Edilma P. Oliveira, Erb F. Lins
Date: 2020-07-18 00:00:00
Tasks: Metaheuristic Optimization

In this paper is proposed an evaluation of ten metaheuristic optimization algorithms applied on the inverse optimization of the Interfacial Heat Transfer Coefficient (IHTC) coupled on the solidification phenomenon. It was considered an upward directional solidification system for Al-7wt.% Si alloy and, for IHTC model, a exponential time function. All thermophysical properties of the alloy were considered constant. Scheil Rule was used as segregation model ahead phase-transformation interface. Optimization results from Markov Chain Monte Carlo method (MCMC) were considered as reference. Based on average, quantiles 95% and 5%, kurtosis, average iterations and absolute errors of the metaheuristic methods, in relation to MCMC results, the Flower Pollination Algorithm (FPA) and Moth-Flame Optimization (MFO) presented the most appropriate results, outperforming the other methods in this particular phenomenon, based on these metrics. The regions with the most probable values for parameters in IHTC time function were also determined.
Graphical Conditions for Rate Independence in Chemical Reaction Networks
Authors: Elisabeth Degrand, François Fages, Sylvain Soliman
Date: 2020-07-17 00:00:00

Chemical Reaction Networks (CRNs) provide a useful abstraction of molecular interaction networks in which molecular structures as well as mass conservation principles are abstracted away to focus on the main dynamical properties of the network structure. In their interpretation by ordinary differential equations, we say that a CRN with distinguished input and output species computes a positive real function $f : R+ $\rightarrow$ R+$, if for any initial concentration x of the input species, the concentration of the output molecular species stabilizes at concentration f (x). The Turing-completeness of that notion of chemical analog computation has been established by proving that any computable real function can be computed by a CRN over a finite set of molecular species. Rate-independent CRNs form a restricted class of CRNs of high practical value since they enjoy a form of absolute robustness in the sense that the result is completely independent of the reaction rates and depends solely on the input concentrations. The functions computed by rate-independent CRNs have been characterized mathematically as the set of piecewise linear functions from input species. However, this does not provide a mean to decide whether a given CRN is rate-independent. In this paper, we provide graphical conditions on the Petri Net structure of a CRN which entail the rate-independence property either for all species or for some output species. We show that in the curated part of the Biomodels repository, among the 590 reaction models tested, 2 reaction graphs were found to satisfy our rate-independence conditions for all species, 94 for some output species, among which 29 for some non-trivial output species. Our graphical conditions are based on a non-standard use of the Petri net notions of place-invariants and siphons which are computed by constraint programming techniques for efficiency reasons.
Superspreading of SARS-CoV-2 in the USA
Authors: Calvin Pozderac, Brian Skinner
Date: 2020-07-30 00:00:00

A number of epidemics, including the SARS-CoV-1 epidemic of 2002-2004, have been known to exhibit superspreading, in which a small fraction of infected individuals is responsible for the majority of new infections. The existence of superspreading implies a fat-tailed distribution of infectiousness (new secondary infections caused per day) among different individuals. Here, we present a simple method to estimate the variation in infectiousness by examining the variation in early-time growth rates of new cases among different subpopulations. We use this method to estimate the mean and variance in the infectiousness, $\beta$, for SARS-CoV-2 transmission during the early stages of the pandemic within the United States. We find that $\sigma_\beta/\mu_\beta \gtrsim 3.2$, where $\mu_\beta$ is the mean infectiousness and $\sigma_\beta$ its standard deviation, which implies pervasive superspreading. This result allows us to estimate that in the early stages of the pandemic in the USA, over 81% of new cases were a result of the top 10% of most infectious individuals.
Optimization with Zeroth-Order Oracles in Formation
Date: 2020-07-30 00:00:00
Tasks: Position

In this paper, we consider the optimisation of time varying functions by a
network of agents with no gradient information. The proposed a novel method to
estimate the gradient at each agent's position using only neighbour
information. The gradient estimation is coupled with a formation controller, to
minimise gradient estimation error and prevent agent collisions. Convergence
results for the algorithm are provided for functions which satisfy the
Polyak-Lojasiewicz inequality. Simulations and numerical results are provided
to support the theoretical results.
Sparse Based Super Resolution Multilayer Ultrasonic Array Imaging
Authors: Shahrokh Hamidi
Date: 2020-07-30 00:00:00
Tasks: Super-Resolution

In this paper, we model the signal propagation effect in ultrasonic imaging using Huygens principle and use this model to develop sparse signal representation based imaging techniques for a two-layer object immersed in water. Relying on the fact that the image of interest is sparse, we cast such an array based imaging problem as a sparse signal recovery problem and develop two types of imaging methods, one method uses only one transducer to illuminate the region of interest {and for this case the system is modeled as a single input multiple output (SIMO) system. The second method relies on all transducers to transmit ultrasonic waves into the material under test and in this case the system is modeled as a multiple input multiple output (MIMO) system}. We further extend our work to a scenario where the propagation velocity of the wave in the object under test is not known precisely. {We discuss different techniques such as greedy based algorithms as well as $\ell_1$-norm minimization based approach to solve the proposed sparse signal representation based method. We give an assessment of the computational complexity of the $\ell_1$-norm minimization based approach for the SIMO and the MIMO cases. We further point out the superiority of the $\ell_1$-norm minimization based approach over the greedy based algorithms. Then we give a comprehensive analysis of error for both the greedy based approaches as well as the $\ell_1$-norm minimization based technique for both the SIMO and the MIMO cases. The analysis utilizes tools from two powerful branches of modern analysis, \emph{local analysis in Banach spaces} and \emph{concentration of measure}}. We finally apply our methods to experimental data gathered from a solid test sample immersed in water and show that sparse signal recovery based techniques outperform the conventional methods available in the literature.
Joint DOD and DOA Estimation in Slow-Time MIMO Radar via PARAFAC Decomposition
Authors: Feng Xu, Sergiy A. Vorobyov, Xiaopeng Yang
Date: 2020-07-30 00:00:00

We develop a new tensor model for slow-time multiple-input multiple output (MIMO) radar and apply it for joint direction-of-departure (DOD) and direction-of-arrival (DOA) estimation. This tensor model aims to exploit the independence of phase modulation matrix and receive array in the received signal for slow-time MIMO radar. Such tensor can be decomposed into two tensors of different ranks, one of which has identical structure to that of the conventional tensor model for MIMO radar, and the other contains all phase modulation values used in the transmit array. We then develop a modification of the alternating least squares algorithm to enable parallel factor decomposition of tensors with extra constants. The Vandermonde structure of the transmit and receive steering matrices (if both arrays are uniform and linear) is then utilized to obtain angle estimates from factor matrices. The multi-linear structure of the received signal is maintained to take advantage of tensor-based angle estimation algorithms, while the shortage of samples in Doppler domain for slow-time MIMO radar is mitigated. As a result, the joint DOD and DOA estimation performance is improved as compared to existing angle estimation techniques for slow-time MIMO radar. Simulation results verify the effectiveness of the proposed method.
Secrecy Outage Probability Analysis for RIS-Assisted NOMA Systems
Authors: Liang Yang, Yongjie Yuan
Date: 2020-07-31 00:00:00

In this paper, the physical layer security (PLS) for a novel reconfigurable intelligent surface (RIS)-assisted non-orthogonal multiple access (NOMA) system in a multi-user scenario is investigated, where we consider the worst case that the eavesdropper also utilizes the advantage of the RISs. More specifically, we derive analytical results for the secrecy outage probability (SOP). From the numerical results, we observe that the use of RISs can improve the secrecy performance compared to traditional NOMA systems. However, for the worst case that the received signals at the eavesdropper comes from the RISs and source, increasing the number of intelligent elements on the RIS has a negative impact on the secrecy performance. At high SNRs, the system's SOP tends to a constant. Finally, the secrecy performance can be improved through the group selection.
Long-Term Noise Characterization of Narrowband Power Line Communications
Authors: Simone Raponi, Javier Hernandez, Aymen Omri, Gabriele Oligeri
Date: 2020-07-31 00:00:00

Noise modeling in power line communications has recently drawn the attention of researchers. However, when characterizing the noise process in narrowband communications, previous works have only focused on small-scale phenomena involving fine-grained details. Nevertheless, the communication link's reliability is also affected by long-term noise phenomena that might affect transfer rates at higher layers as well. This paper addresses the problem of long-term noise characterization for narrowband power line communications and provides a statistical analysis of the long-term trends affecting the noise levels. We present a statistical description of the noise process in the time and frequency domains based on real field measurements in the FCC band (10 kHz - 490 kHz). The collected data comprises more than 1.8 billion samples taken from three different locations over a time period of approximately 10 days. The noise samples have been statistically analyzed by considering stationarity, autocorrelation, and independence. Although our results -- being unprecedented -- are interesting per se, they improve the noise pattern knowledge, thus paving the way for the design and implementation of more robust PLC protocols.
Bond Graphs Unify Stoichiometric Analysis and Thermodynamics
Authors: Peter J. Gawthrop
Date: 2020-07-31 00:00:00

Whole-cell modelling is constrained by the laws of nature in general and the laws of thermodynamics in particular. This paper shows how one prolific source of information, stoichiometric models of biomolecular systems, can be integrated with thermodynamic principles using the bond graph approach to network thermodynamics.
Geometric Total Variation for Image Vectorization, Zooming and Pixel Art Depixelizing
Date: 2020-07-31 00:00:00
Tasks: Quantization, valid

We propose an original method for vectorizing an image or zooming it at an
arbitrary scale. The core of our method relies on the resolution of a geometric
variational model and therefore offers theoretic guarantees. More precisely, it
associates a total variation energy to every valid triangulation of the image
pixels. Its minimization induces a trian-gulation that reflects image
gradients. We then exploit this triangulation to precisely locate
discontinuities, which can then simply be vectorized or zoomed. This new
approach works on arbitrary images without any learning phase. It is
particularly appealing for processing images with low quantization like pixel
art and can be used for depixelizing such images. The method can be evaluated
with an online demonstrator, where users can reproduce results presented here
or upload their own images.
Towards personalized computer simulations of breast cancer treatment
Authors: Alvaro Köhn-Luque, Xiaoran Lai, Arnoldo Frigessi
Date: 2020-07-31 00:00:00
Tasks: Bayesian Optimization, Diagnostic

Cancer pathology is unique to a given individual, and developing personalized diagnostic and treatment protocols are a primary concern. Mathematical modeling and simulation is a promising approach to personalized cancer medicine. Yet, the complexity, heterogeneity and multiscale nature of cancer present severe challenges. One of the major barriers to use mathematical models to predict the outcome of therapeutic regimens in a particular patient lies in their initialization and parameterization in order to reflect individual cancer characteristics accurately. Here we present a study where we used multitype measurements acquired routinely on a single breast tumor, including histopathology, magnetic resonance imaging (MRI), and molecular profiling, to personalize a multiscale hybrid cellular automaton model of breast cancer treated with chemotherapeutic and antiangiogenic agents. We model drug pharmacokinetics and pharmacodynamics at the tumor tissue level but with cellular and subcellular resolution. We simulate those spatio-temporal dynamics in 2D cross-sections of tumor portions over 12-week therapy regimes, resulting in complex and computationally intensive simulations. For such computationally demanding systems, formal statistical inference methods to estimate individual parameters from data have not been feasible in practice to until most recently, after the emergence of machine learning techniques applied to likelihood-free inference methods. Here we use the inference advances provided by Bayesian optimization to fit our model to simulated data of individual patients. In this way, we investigate if some key parameters can be estimated from a series of measurements of cell density in the tumor tissue, as well as how often the measurements need to be taken to allow reliable predictions.
Robustness of behaviourally-induced oscillations in epidemic models under a low rate of imported cases
Date: 2020-10-14 00:00:00

This paper is concerned with the robustness of the sustained oscillations
predicted by an epidemic ODE model defined on contact networks. The model
incorporates the spread of awareness among individuals and, moreover, a small
inflow of imported cases. These cases prevent stochastic extinctions when we
simulate the epidemics and, hence, they allow to check whether the average
dynamics for the fraction of infected individuals are accurately predicted by
the ODE model. Stochastic simulations confirm the existence of sustained
oscillations for different types of random networks, with a sharp transition
from a non-oscillatory asymptotic regime to a periodic one as the alerting rate
of susceptible individuals increases from very small values. This abrupt
transition to periodic epidemics of high amplitude is quite accurately
predicted by the Hopf-bifurcation curve computed from the ODE model using the
alerting rate and the infection transmission rate for aware individuals as
tuning parameters.
Truthful Equilibria in Generalized Common Agency Models
Date: 2020-07-31 00:00:00

In this paper I discuss truthful equilibria in common agency models.
Specifically, I provide general conditions under which truthful equilibria are
plausible, easy to calculate and efficient. These conditions generalize similar
results in the literature and allow the use of truthful equilibria in novel
economic applications. Moreover, I provide two such applications. The first
application is a market game in which multiple sellers sell a uniform good to a
single buyer. The second application is a lobbying model in which there are
externalities in contributions between lobbies. This last example indicates
that externalities between principals do not necessarily prevent efficient
equilibria. In this regard, this paper provides a set of conditions, under
which, truthful equilibria in common agency models with externalities are
efficient.
Beamformed Energy Detection in the Presence of an Interferer for Cognitive mmWave Network
Authors: Madhuri Latha Mannedu, Sai Krishna Charan Dara, Sachin Chaudhari, Neeraj Varshney
Date: 2020-07-31 00:00:00

In this paper, we propose beamformed energy detection (BFED) spectrum sensing schemes for a single secondary user (SU) or a cognitive radio to detect a primary user (PU) transmission in the presence of an interferer. In the millimeter wave (mmWave) band, due to high attenuation, there are fewer multipaths, and the channel is sparse, giving rise to fewer directions of arrivals (DoAs). Sensing in only these paths instead of blind energy detection can reap significant benefits. An analog beamforming weight vector is designed such that the beamforming gain in the true DoAs of the PU signal is maximized while minimizing interference from the interferer. To demonstrate the bound on the system performance, the proposed sensing scheme is designed under the knowledge of full channel state information (CSI) at the SU for the PU-SU and Interferer-SU channels. However, as the CSI may not be available at the SU, another BFED sensing scheme is proposed, which only utilizes the estimate the DoAs. To model the estimates of DoAs, perturbations are added to the true DoAs. The distribution of the test statistic for BFED with full CSI schemes is derived under the null hypothesis so that the threshold of the Neyman-Pearson detector can be found analytically. The performance of both schemes is also compared with the traditional energy detector for multi-antenna systems.
The Hansen ratio in mean--variance portfolio theory
Authors: Aleš Černý
Date: 2020-07-31 00:00:00

It is shown that the ratio between the mean and the $L^2$-norm leads to a particularly parsimonious description of the mean-variance efficient frontier and the dual pricing kernel restrictions known as the Hansen-Jagannathan (HJ) bounds. Because this ratio has not appeared in economic theory previously, it seems appropriate to name it the Hansen ratio. The initial treatment of the mean-variance theory via the Hansen ratio is extended in two directions, to monotone mean-variance preferences and to arbitrary Hilbert space setting. A multiperiod example with IID returns is also discussed.
Investment sizing with deep learning prediction uncertainties for high-frequency Eurodollar futures trading
Authors: Trent Spears, Stefan Zohren, Stephen Roberts
Date: 2020-07-31 00:00:00

In this work we show that prediction uncertainty estimates gleaned from deep learning models can be useful inputs for influencing the relative allocation of risk capital across trades. In this way, consideration of uncertainty is important because it permits the scaling of investment size across trade opportunities in a principled and data-driven way. We showcase this insight with a prediction model and find clear outperformance based on a Sharpe ratio metric, relative to trading strategies that either do not take uncertainty into account, or that utilize an alternative market-based statistic as a proxy for uncertainty. Of added novelty is our modelling of high-frequency data at the top level of the Eurodollar Futures limit order book for each trading day of 2018, whereby we predict interest rate curve changes on small time horizons. We are motivated to study the market for these popularly-traded interest rate derivatives since it is deep and liquid, and contributes to the efficient functioning of global finance -- though there is relatively little by way of its modelling contained in the academic literature. Hence, we verify the utility of prediction models and uncertainty estimates for trading applications in this complex and multi-dimensional asset price space.
Data-driven Inverter-based Volt/VAr Control for Partially Observable Distribution Networks
Authors: Tong Xu, Wenchuan Wu, Yiwen Hong, Junjie Yu, Fazhong Zhang
Date: 2020-07-31 00:00:00
Tasks: regression

For active distribution networks (ADNs) integrated with massive inverter-based energy resources, it is impractical to maintain the accurate model and deploy measurements at all nodes due to the large-scale of ADNs. Thus, current models of ADNs are usually involving significant errors or even unknown. Moreover, ADNs are usually partially observable since only a few measurements are available at pilot nodes or nodes with significant users. To provide a practical Volt/Var control (VVC) strategy for such networks, a data-driven VVC method is proposed in this paper. Firstly, the system response policy, approximating the relationship between the control variables and states of monitoring nodes, is estimated by a recursive regression closed-form solution. Then, based on real-time measurements and the newly updated system response policy, a VVC strategy with convergence guarantee is realized. Since the recursive regression solution is embedded in the control stage, a data-driven closed-loop VVC framework is established. The effectiveness of the proposed method is validated in an unbalanced distribution system considering nonlinear loads where not only the rapid and self-adaptive voltage regulation is realized but also system-wide optimization is achieved.
Exact closed-form solution of a modified SIR model
Date: 2020-11-10 00:00:00
Tasks: Form

The exact analytical solution in closed form of a modified SIR system where
recovered individuals are removed from the population is presented. In this
dynamical system the populations $S(t)$ and $R(t)$ of susceptible and recovered
individuals are found to be generalized logistic functions, while infective
ones $I(t)$ are given by a generalized logistic function times an exponential,
all of them with the same characteristic time. The dynamics of this modified
SIR system is analyzed and the exact computation of some epidemiologically
relevant quantities is performed. The main differences between this modified
SIR model and original SIR one are presented and explained in terms of the
zeroes of their respective conserved quantities. Moreover, it is shown that the
modified SIR model with time-dependent transmission rate can be also solved in
closed form for certain realistic transmission rate functions.