pwc-archive/papers-with-abstracts huggingface dataset:
Reliability-Aware Probabilistic Reserve Procurement
Authors: Lars Herre, Pierre Pinson, Spyros Chatzivasileiadis
Date: 2021-10-21 00:00:00

Current reserve procurement approaches ignore the stochastic nature of reserve asset availability itself and thus limit the type and volume of reserve offers. This paper develops a reliability-aware probabilistic approach that allows renewable generators to offer reserve capacity with reliability attributes. Offers with low reliability are priced at lower levels. The original non-convex market clearing problem is approximated by a MILP reformulation. The proposed probabilistic reserve procurement allows restricted reserve providers to enter the market, thereby increases liquidity and has the potential to lower procurement costs in power systems with high shares of variable renewable energy sources.
A Physics-Based Data-Driven Approach for Finite Time Estimation of Pandemic Growth
Authors: Harshvardhan Uppaluru, Hamid Emadi, Hossein Rastgoftar
Date: 2021-10-21 00:00:00

COVID-19 is a global health crisis that has had unprecedented, widespread impact on households across the United States and has been declared a global pandemic on March 11, 2020 by World Health Organization (WHO) [1]. According to Centers for Disease Control and Prevention (CDC) [2], the spread of COVID-19 occurs through person-to-person transmission i.e. close contact with infected people through contaminated surfaces and respiratory fluids carrying infectious virus. This paper presents a data-driven physics-based approach to analyze and predict the rapid growth and spread dynamics of the pandemic. Temporal and Spatial conservation laws are used to model the evolution of the COVID-19 pandemic. We integrate quadratic programming and neural networks to learn the parameters and estimate the pandemic growth. The proposed prediction model is validated through finite time estimation of the pandemic growth using the total number of cases, deaths and recoveries in the United States recorded from March 12, 2020 until October 1, 2021 [3].
Introduction to data assimilation for parameter estimation
Authors: Loc Luong
Date: 2021-10-18 00:00:00
Tasks: parameter estimation

In this study, two classes of methods including statistical and variational data assimilation algorithms will be described. In statistical methods, the model state is updated sequentially based on the previous estimate. Variational methods, on the other hand, seek an estimation in space and time by minimizing a cost function. Both of these methods require estimates of background state which is the prior information of the system and its error covariances. In terms of linear and Gaussian problems, they have the same solution. In the family of Kalman Filter algorithms, the conventional Kalman Filter (KF) and Ensemble Kalman Filter (EnKF) will be implemented. A three-dimension variational method (3D-Var) will be employed to illustrate the variational approaches. A simple case of an ordinary differential equation (ODE) is coupled to highlight the difference between these algorithms. Namely, a mass-spring system governed by a second order differential equation will be examined. We also look at the situation where a periodic external force is applied to the system. Then different data assimilation algorithms will be applied to this system. Results from the experiments will be analyzed to showcase the advantages and disadvantages of each method.
Video-Data Pipelines for Machine Learning Applications
Authors: Sohini Roychowdhury, James Y. Sato
Date: 2021-10-15 00:00:00
Tasks: Autonomous Driving, BIG-bench Machine Learning, object-detection, Object Detection, Retrieval

Data pipelines are an essential component for end-to-end solutions that take machine learning algorithms to production. Engineering data pipelines for video-sequences poses several challenges including isolation of key-frames from video sequences that are high quality and represent significant variations in the scene. Manual isolation of such quality key-frames can take hours of sifting through hours worth of video data. In this work, we present a data pipeline framework that can automate this process of manual frame sifting in video sequences by controlling the fraction of frames that can be removed based on image quality and content type. Additionally, the frames that are retained can be automatically tagged per sequence, thereby simplifying the process of automated data retrieval for future ML model deployments. We analyze the performance of the proposed video-data pipeline for versioned deployment and monitoring for object detection algorithms that are trained on outdoor autonomous driving video sequences. The proposed video-data pipeline can retain anywhere between 0.1-20% of the all input frames that are representative of high image quality and high variations in content. This frame selection, automated scene tagging followed by model verification can be completed in under 30 seconds for 22 video-sequences under analysis in this work. Thus, the proposed framework can be scaled to additional video-sequence data sets for automating ML versioned deployments.
Effect of delay on the emergent stability patterns in Generalized Lotka-Volterra ecological dynamics
Authors: Meghdad Saeedian, Emanuele Pigani, Amos Maritan, Samir Suweis, Sandro Azaele
Date: 2021-10-22 00:00:00
Tasks: Diversity, Relation

Understanding the conditions of feasibility and stability in ecological systems is a major challenge in theoretical ecology. The seminal work of May in 1972 and recent developments based on the theory of random matrices have shown the existence of emergent universal patterns of both stability and feasibility in ecological dynamics. However, only a few studies have investigated the role of delay coupled with population dynamics in the emergence of feasible and stable states. In this work, we study the effects of delay on Generalized Loka-Volterra population dynamics of several interacting species in closed ecological environments. First, we investigate the relation between feasibility and stability of the modeled ecological community in the absence of delay and find a simple analytical relation when intra-species interactions are dominant. We then show how, by increasing the time delay, there is a transition in the stability phases of the population dynamics: from an equilibrium state to a stable non-point attractor phase. We calculate analytically the critical delay of that transition and show that it is in excellent agreement with numerical simulations. Finally, we introduce a measure of stability that holds for out of equilibrium dynamics and we show that in the oscillatory regime induced by the delay stability increases for increasing ecosystem diversity.
A Two-stage Pricing Strategy Considering Learning Effects and Word-of-Mouth
Authors: YanRong Li, Lai Wei, Wei Jiang
Date: 2021-10-22 00:00:00

This paper proposes a two-stage pricing strategy for nondurable (such as typical electronics) products, where retail price is cut down at certain time points of the product lifecycle. We consider learning effect of electronic products that, with the accumulation of production, average production cost decreases over time as manufacturers get familiar with the production process. Moreover, word-of-mouth (WOM) of existing customers is used to analyze future demand, which is sensitive to the difference between the actual reliability and the perceived reliability of products. We theoretically prove the existence and uniqueness of the optimal switch time between the two stages and the optimal price in each stage. In addition, warranty as another important factor of electronic products is also considered, whose interaction with word-of-mouth as well as the corresponding influences on total profit are analyzed. Interestingly, our findings indicate that (1) the main reason for manufacturers to cut down prices for electronic products pertains to the learning effects; (2) even through both internal factors (e.g., the learning effects of manufacturers) and external factors (e.g., the price elasticity of customers) have impacts on product price, their influence on manufacturer's profit is widely divergent; (3) generally warranty weakens the influence of external advertising on the reliability estimate, because warranty price only partially reflects the actual reliability information of products; (4) and the optimal warranty price can increase the profits for the manufacturer by approximately 10%.
Cycles to compute the full set of many-to-many stable matchings
Authors: Agustin G. Bonifacio, Noelia Juarez, Pablo Neme, Jorge Oviedo
Date: 2021-10-22 00:00:00

In a many-to-many matching model in which agents' preferences satisfy substitutability and the law of aggregate demand, we present an algorithm to compute the full set of stable matchings. This algorithm relies on the idea of "cycles in preferences" and generalizes the algorithm presented in Roth and Sotomayor (1990) for the one-to-one model.
Free Riding in Networks
Authors: Markus Kinateder, Luca Paolo Merlino
Date: 2021-10-22 00:00:00

Players allocate their budget to links, a local public good and a private good. A player links to free ride on others' public good provision. We derive sufficient conditions for the existence of a Nash equilibrium. In equilibrium, large contributors link to each other, while others link to them. Poorer players can be larger contributors if linking costs are sufficiently high. In large societies, free riding reduces inequality only in networks in which it is initially low; otherwise, richer players benefit more, as they can afford more links. Finally, we study the policy implications, deriving income redistribution that increases welfare and personalized prices that implement the efficient solution.
Algebraic Properties of Blackwell's Order and A Cardinal Measure of Informativeness
Authors: Andrew Kosenko
Date: 2021-10-21 00:00:00
Tasks: Informativeness, Translation

I establish a translation invariance property of the Blackwell order over experiments, show that garbling experiments bring them closer together, and use these facts to define a cardinal measure of informativeness. Experiment $A$ is inf-norm more informative (INMI) than experiment $B$ if the infinity norm of the difference between a perfectly informative structure and $A$ is less than the corresponding difference for $B$. The better experiment is "closer" to the fully revealing experiment; distance from the identity matrix is interpreted as a measure of informativeness. This measure coincides with Blackwell's order whenever possible, is complete, order invariant, and prior-independent, making it an attractive and computationally simple extension of the Blackwell order to economic contexts.
Multi-Layered Recursive Least Squares for Time-Varying System Identification
Authors: Mohammad Towliat, Zheng Guo, Leonard J. Cimini, Xiang-Gen Xia, Aijun Song
Date: 2021-10-22 00:00:00

Traditional recursive least square (RLS) adaptive filtering is widely used to estimate the impulse responses (IR) of an unknown system. Nevertheless, the RLS estimator shows poor performance when tracking rapidly time-varying systems. In this paper, we propose a multi-layered RLS (m-RLS) estimator to address this concern. The m-RLS estimator is composed of multiple RLS estimators, each of which is employed to estimate and eliminate the misadjustment of the previous layer. It is shown that the mean square error (MSE) of the m-RLS estimate can be minimized by selecting the optimum number of layers. We provide a method to determine the optimum number of layers. A low-complexity implementation of m-RLS is discussed and it is indicated that the complexity order of the proposed estimator can be reduced to O(M), where M is the IR length. In addition, by performing simulations, we show that m-RLS outperforms the classic RLS and the RLS methods with a variable forgetting factor.
Flexible Physical Layer Security for Joint Data and Pilots in Future Wireless Networks
Authors: Salah Eddine Zegrar, Haji M. Furqan, Huseyin Arslan
Date: 2021-10-22 00:00:00

In this work, novel physical layer security (PLS) schemes are proposed for orthogonal frequency-division multiplexing (OFDM) to secure both data and pilots. The majority of previous studies focus on only securing the data without considering the security of the pilots used for channel estimation. However, the leakage of channel state information (CSI) from a legitimate node to an eavesdropper allows the latter to acquire knowledge about the channel of the legitimate nodes. To this end, we propose adaptive and flexible PLS algorithms which can 1) secure data, 2) secure pilots, and 3) jointly secure both data and pilots. Particularly, minimum-phase all-pass channel decomposition is exploited, where the proposed algorithms use the all-pass component to provide security without harming the performance of the legitimate user. In the analysis for data security, we evaluated the secrecy under correlated and uncorrelated eavesdropping channels via closed-form bit error rate (BER) formulas. For pilot security, we analyzed the normalized mean squared error (NMSE) performance of the estimated channel. The simulation results along with theoretical analysis demonstrate that the proposed algorithms can effectively enhance the communication secrecy of the overall system.
Identifiability of local and global features of phylogenetic networks from average distances
Authors: Jingcheng Xu, Cécile Ané
Date: 2021-10-22 00:00:00

Phylogenetic networks extend phylogenetic trees to model non-vertical inheritance, by which a lineage inherits material from multiple parents. The computational complexity of estimating phylogenetic networks from genome-wide data with likelihood-based methods limits the size of networks that can be handled. Methods based on pairwise distances could offer faster alternatives. We study here the information that average pairwise distances contain on the underlying phylogenetic network, by characterizing local and global features that can or cannot be identified. For general networks, we clarify that the root and edge lengths adjacent to reticulations are not identifiable, and then focus on the class of zipped-up semidirected networks. We provide a criterion to swap subgraphs locally, such as 3-cycles, resulting in indistinguishable networks. We propose the "distance split tree", which can be constructed from pairwise distances, and prove that it is a refinement of the network's tree of blobs, capturing the tree-like features of the network. For level-1 networks, this distance split tree is equal to the tree of blobs refined to separate polytomies from blobs, and we prove that the mixed representation of the network is identifiable. The information loss is localized around 4-cycles, for which the placement of the reticulation is unidentifiable. The mixed representation combines split edges for 4-cycles, regular tree and hybrid edges from the semidirected network, and edge parameters that encode all information identifiable from average pairwise distances.
Probability of Insect Capture in a Trap Network: Low Prevalence and Detection Trapping with TrapGrid
Authors: Nicholas C Manoukis, Matthew P. Hill
Date: 2021-10-21 00:00:00

Attractant-based trap networks targeting insects are ubiquitous worldwide. These networks have diverse targets, goals, and efficiencies, but all are constrained by practical considerations like cost and available lures. An important way to balance goals and constrains is through quantitative mathematical modeling. Here we describe an extension of a computer model of trapping networks known as "TrapGrid" to include an alternative mode of calculating the probability of capture over time in a trapping network: Strict detection ("capture of one or more") compared with the average probability of capture as implemented in the original version. We suggest that this new calculation may be useful in situations of low prevalence where trap network operators wish to interpret the meaning of zero captures at a small scale. The original remains preferred for comparing the sensitivity and suitability of alternate trap networks (i.e. density of traps, their placement, lure attractiveness, etc.).
Kruskal-Wallis Power Studies Utilizing Bernstein Distributions; preliminary empirical studies using simulations/medical studies
Authors: Jeremy S. C. Clark, Piotr Kulig, Konrad Podsiadlo, Kamila Rydzewska, Krzysztof Arabski, Monika Bialecka, Krzysztof Safranow, Andrzej Ciechanowicz
Date: 2021-10-22 00:00:00

Bernstein fits implemented into R allow another route for Kruskal-Wallis power-study tool development. Monte-Carlo Kruskal-Wallis power studies were compared with measured power, with Monte-Carlo ANOVA equivalent and with an analytical method, with or without normalization, using four simulated runs each with 60-100 populations (each population with N=30000 from a set of Pearson-type ranges): random selection gave 6300 samples analysed for predictive power. Three medical-study datasets (Dialysis/systolic blood pressure; Diabetes/sleep-hours; Marital-status/high-density-lipoprotein cholesterol) were also analysed. In three from four simulated runs (run_one, run_one_relaxed, and run_three) with Pearson types pooled, Monte-Carlo Kruskal-Wallis gave predicted sample sizes significantly slightly lower than measured but more accurate than with ANOVA methods; the latter gave high sample-size predictions. Populations (run_one_relaxed) with ANOVA assumptions invalid gave Kruskal-Wallis predictions similar to those measured. In two from three medical studies, Kruskal-Wallis predictions (Dialysis: similar predictions; Marital: higher than measured) were more accurate than ANOVA (both higher than measured) but in one (Diabetes) the reverse was found (Kruskal-Wallis: lower; Monte-Carlo ANOVA: similar to measured). These preliminary studies appear to show that Monte-Carlo Kruskal-Wallis power studies based on Bernstein fits might perform better than ANOVA equivalents in many settings (and provide reasonable results when ANOVA cannot be used); and both Monte-Carlo methods appeared considerably more accurate than the analysed analytical version.
Mathematical Model of Volume Kinetics and Renal Function after Burn Injury and Resuscitation
Authors: Ghazal ArabiDarrehDor, Ali Tivay, Ramin Bighamian, Chris Meador, George C. Kramer, Jin-OH Hahn, Jose Salinas
Date: 2021-10-22 00:00:00

This paper presents a mathematical model of blood volume kinetics and renal function in response to burn injury and resuscitation, which is applicable to the development and non-clinical testing of burn resuscitation protocols and algorithms. Prior mathematical models of burn injury and resuscitation are not ideally suited to such applications due to their limited credibility in predicting blood volume and urinary output observed in wide-ranging burn patients as well as in incorporating contemporary knowledge of burn pathophysiology. Our mathematical model consists of an established multi-compartmental model of blood volume kinetics, a hybrid mechanistic-phenomenological model of renal function, and novel lumped-parameter models of burn-induced perturbations in volume kinetics and renal function equipped with contemporary knowledge on burn-related physiology and pathophysiology. Using the dataset collected from 16 sheep, we showed that our mathematical model can be characterized with physiologically plausible parameter values to accurately predict blood volume kinetic and renal function responses to burn injury and resuscitation on an individual basis against a wide range of pathophysiological variability. Pending validation in humans, our mathematical model may serve as an effective basis for an in-depth understanding of complex burn-induced volume kinetic and renal function responses as well as development and non-clinical testing of burn resuscitation protocols and algorithms. Keywords: Mathematical Model, Burn Injury, Burn Resuscitation, Non-Clinical Testing, Burn Resuscitation Protocols, and Algorithms
Liquidity-free implied volatilities: an approach using conic finance
Authors: Matteo Michielon, Asma Khedher, Peter Spreij
Date: 2021-10-22 00:00:00

We consider the problem of calculating risk-neutral implied volatilities of European options without relying on option mid prices but solely on bid and ask prices. We provide an approach, based on the conic finance paradigm, that allows to uniquely strip risk-neutral implied volatilities from bid and ask quotes, and that does not require restrictive assumptions. Our methodology also allows to jointly calculate the implied liquidity of the market. The idea outlined in this paper can be applied to calculate other implied parameters from bid and ask security prices as soon as their theoretical risk-neutral counterparts are strictly increasing with respect to the former.
A Meta Path Based Evaluation Method for Enterprise Credit Risk
Authors: Marui Du, Yue Ma, Zuoquan Zhang
Date: 2021-10-22 00:00:00

Nowadays small and medium-sized enterprises have become an essential part of the national economy. With the increasing number of such enterprises, how to evaluate their credit risk becomes a hot issue. Unlike big enterprises with massive data to analyze, it is hard to find enough information of small enterprises to assess their financial status. Limited by the lack of primary data, how to inference small enterprises' credit risk from secondary data, like information of their upstream, downstream, parent, and subsidiary enterprises attracts big attention from industry and academy. Targeting on accurately evaluating the credit risk of the small and medium-sized enterprise (SME), in this paper, we exploit the representative power of Information Network on various kinds of SME entities and SME relationships to solve the problem. A novel feature named meta path feature proposed to measure the credit risk, which makes us able to evaluate the financial status of SMEs from various perspectives. Experiments show that our method is effective to identify SMEs with credit risks.
Deep learning-based NLP Data Pipeline for EHR Scanned Document Information Extraction
Authors: Enshuo Hsu, Ioannis Malagaris, Yong-Fang Kuo, Rizwana Sultana, Kirk Roberts
Date: 2021-09-14 00:00:00
Tasks: Optical Character Recognition, Optical Character Recognition (OCR), regression

Scanned documents in electronic health records (EHR) have been a challenge for decades, and are expected to stay in the foreseeable future. Current approaches for processing often include image preprocessing, optical character recognition (OCR), and text mining. However, there is limited work that evaluates the choice of image preprocessing methods, the selection of NLP models, and the role of document layout. The impact of each element remains unknown. We evaluated this method on a use case of two key indicators for sleep apnea, Apnea hypopnea index (AHI) and oxygen saturation (SaO2) values, from scanned sleep study reports. Our data that included 955 manually annotated reports was secondarily utilized from a previous study in the University of Texas Medical Branch. We performed image preprocessing: gray-scaling followed by 1 iteration of dilating and erode, and 20% contrast increasing. The OCR was implemented with the Tesseract OCR engine. A total of seven Bag-of-Words models (Logistic Regression, Ridge Regression, Lasso Regression, Support Vector Machine, k-Nearest Neighbor, Na\"ive Bayes, and Random Forest) and three deep learning-based models (BiLSTM, BERT, and Clinical BERT) were evaluated. We also evaluated the combinations of image preprocessing methods (gray-scaling, dilate & erode, increased contrast by 20%, increased contrast by 60%), and two deep learning architectures (with and without structured input that provides document layout information). Our proposed method using Clinical BERT reached an AUROC of 0.9743 and document accuracy of 94.76% for AHI, and an AUROC of 0.9523, and document accuracy of 91.61% for SaO2. We demonstrated the proper use of image preprocessing and document layout could be beneficial to scanned document processing.