pwc-archive/papers-with-abstracts huggingface dataset:
Enhanced Self-Organizing Map Solution for the Traveling Salesman Problem
Authors: Joao P. A. Dantas, Andre N. Costa, Marcos R. O. A. Maximo, Takashi Yoneyama
Date: 2021-12-03 00:00:00
Tasks: Traveling Salesman Problem

Using an enhanced Self-Organizing Map method, we provided suboptimal solutions to the Traveling Salesman Problem. Besides, we employed hyperparameter tuning to identify the most critical features in the algorithm. All improvements in the benchmark work brought consistent results and may inspire future efforts to improve this algorithm and apply it to different problems.
RAMANMETRIX: a delightful way to analyze Raman spectra
Authors: Darina Storozhuk, Oleg Ryabchykov, Juergen Popp, Thomas Bocklitz
Date: 2022-01-19 00:00:00

Although Raman spectroscopy is widely used for the investigation of biomedical samples and has a high potential for use in clinical applications, it is not common in clinical routines. One of the factors that obstruct the integration of Raman spectroscopic tools into clinical routines is the complexity of the data processing workflow. Software tools that simplify spectroscopic data handling may facilitate such integration by familiarizing clinical experts with the advantages of Raman spectroscopy. Here, RAMANMETRIX is introduced as a user-friendly software with an intuitive web-based graphical user interface (GUI) that incorporates a complete workflow for chemometric analysis of Raman spectra, from raw data pretreatment to a robust validation of machine learning models. The software can be used both for model training and for the application of the pretrained models onto new data sets. Users have full control of the parameters during model training, but the testing data flow is frozen and does not require additional user input. RAMANMETRIX is available in two versions: as standalone software and web application. Due to the modern software architecture, the computational backend part can be executed separately from the GUI and accessed through an application programming interface (API) for applying a preconstructed model to the measured data. This opens up possibilities for using the software as a data processing backend for the measurement devices in real-time. The models preconstructed by more experienced users can be exported and reused for easy one-click data preprocessing and prediction, which requires minimal interaction between the user and the software. The results of such prediction and graphical outputs of the different data processing steps can be exported and saved.
Ordinal Causal Discovery
Authors: Yang Ni, Bani Mallick
Date: 2022-01-19 00:00:00
Tasks: Causal Discovery

Causal discovery for purely observational, categorical data is a long-standing challenging problem. Unlike continuous data, the vast majority of existing methods for categorical data focus on inferring the Markov equivalence class only, which leaves the direction of some causal relationships undetermined. This paper proposes an identifiable ordinal causal discovery method that exploits the ordinal information contained in many real-world applications to uniquely identify the causal structure. The proposed method is applicable beyond ordinal data via data discretization. Through real-world and synthetic experiments, we demonstrate that the proposed ordinal causal discovery method combined with simple score-and-search algorithms has favorable and robust performance compared to state-of-the-art alternative methods in both ordinal categorical and non-categorical data. An accompanied R package OrdCD is freely available on CRAN and at https://web.stat.tamu.edu/~yni/files/OrdCD_1.0.0.tar.gz.
Grep-BiasIR: A Dataset for Investigating Gender Representation-Bias in Information Retrieval Results
Authors: Klara Krieg, Emilia Parada-Cabaleiro, Gertraud Medicus, Oleg Lesota, Markus Schedl, Navid Rekabsaz
Date: 2022-01-19 00:00:00
Tasks: Information Retrieval, Retrieval

The provided contents by information retrieval (IR) systems can reflect the existing societal biases and stereotypes. Such biases in retrieval results can lead to further establishing and strengthening stereotypes in society and also in the systems. To facilitate the studies of gender bias in the retrieval results of IR systems, we introduce Gender Representation-Bias for Information Retrieval (Grep-BiasIR), a novel thoroughly-audited dataset consisting of 118 bias-sensitive neutral search queries. The set of queries covers a wide range of gender-related topics, for which a biased representation of genders in the search result can be considered as socially problematic. Each query is accompanied with one relevant and one non-relevant document, where the document is also provided in three variations of female, male, and neutral. The dataset is available at https://github.com/KlaraKrieg/GrepBiasIR.
Expert Finding in Legal Community Question Answering
Authors: Arian Askari, Suzan Verberne, Gabriella Pasi
Date: 2022-01-19 00:00:00
Tasks: Community Question Answering, Question Answering

Expert finding has been well-studied in community question answering (QA) systems in various domains. However, none of these studies addresses expert finding in the legal domain, where the goal is for citizens to find lawyers based on their expertise. In the legal domain, there is a large knowledge gap between the experts and the searchers, and the content on the legal QA websites consist of a combination formal and informal communication. In this paper, we propose methods for generating query-dependent textual profiles for lawyers covering several aspects including sentiment, comments, and recency. We combine query-dependent profiles with existing expert finding methods. Our experiments are conducted on a novel dataset gathered from an online legal QA service. We discovered that taking into account different lawyer profile aspects improves the best baseline model. We make our dataset publicly available for future work.
Validating Simulations of User Query Variants
Authors: Timo Breuer, Norbert Fuhr, Philipp Schaer
Date: 2022-01-19 00:00:00
Tasks: Retrieval

System-oriented IR evaluations are limited to rather abstract understandings of real user behavior. As a solution, simulating user interactions provides a cost-efficient way to support system-oriented experiments with more realistic directives when no interaction logs are available. While there are several user models for simulated clicks or result list interactions, very few attempts have been made towards query simulations, and it has not been investigated if these can reproduce properties of real queries. In this work, we validate simulated user query variants with the help of TREC test collections in reference to real user queries that were made for the corresponding topics. Besides, we introduce a simple yet effective method that gives better reproductions of real queries than the established methods. Our evaluation framework validates the simulations regarding the retrieval performance, reproducibility of topic score distributions, shared task utility, effort and effect, and query term similarity when compared with real user query variants. While the retrieval effectiveness and statistical properties of the topic score distributions as well as economic aspects are close to that of real queries, it is still challenging to simulate exact term matches and later query reformulations.
repro_eval: A Python Interface to Reproducibility Measures of System-oriented IR Experiments
Authors: Timo Breuer, Nicola Ferro, Maria Maistro, Philipp Schaer
Date: 2022-01-19 00:00:00
Tasks: Information Retrieval, Retrieval

In this work we introduce repro_eval - a tool for reactive reproducibility studies of system-oriented information retrieval (IR) experiments. The corresponding Python package provides IR researchers with measures for different levels of reproduction when evaluating their systems' outputs. By offering an easily extensible interface, we hope to stimulate common practices when conducting a reproducibility study of system-oriented IR experiments.
Automation of Citation Screening for Systematic Literature Reviews using Neural Networks: A Replicability Study
Authors: Wojciech Kusa, Allan Hanbury, Petr Knoth
Date: 2022-01-19 00:00:00
Tasks: Document Classification, Systematic Literature Review, Word Embeddings

In the process of Systematic Literature Review, citation screening is estimated to be one of the most time-consuming steps. Multiple approaches to automate it using various machine learning techniques have been proposed. The first research papers that apply deep neural networks to this problem were published in the last two years. In this work, we conduct a replicability study of the first two deep learning papers for citation screening and evaluate their performance on 23 publicly available datasets. While we succeeded in replicating the results of one of the papers, we were unable to replicate the results of the other. We summarise the challenges involved in the replication, including difficulties in obtaining the datasets to match the experimental setup of the original papers and problems with executing the original source code. Motivated by this experience, we subsequently present a simpler model based on averaging word embeddings that outperforms one of the models on 18 out of 23 datasets and is, on average, 72 times faster than the second replicated approach. Finally, we measure the training time and the invariance of the models when exposed to a variety of input features and random initialisations, demonstrating differences in the robustness of these approaches.
Small Cohort of Epilepsy Patients Showed Increased Activity on Facebook before Sudden Unexpected Death
Authors: Ian B. Wood, Rion Brattig Correia, Wendy R. Miller, Luis M. Rocha
Date: 2022-01-19 00:00:00
Tasks: Sentiment Analysis

Sudden Unexpected Death in Epilepsy (SUDEP) remains a leading cause of death in people with epilepsy. Despite the constant risk for patients and bereavement to family members, to date the physiological mechanisms of SUDEP remain unknown. Here we explore the potential to identify putative predictive signals of SUDEP from online digital behavioral data using text and sentiment analysis. Specifically, we analyze Facebook timelines of six epilepsy patients deceased due to SUDEP, donated by surviving family members. We find preliminary evidence for behavioral changes detectable by text and sentiment analysis tools. Namely, in the months preceding their SUDEP event patient social media timelines show: i) increase in verbosity; ii) increased use of functional words; and iii) sentiment shifts as measured by different sentiment analysis tools. Combined, these results suggest that social media engagement, as well as its sentiment, may serve as possible early-warning signals for SUDEP in people with epilepsy. While the small sample of patient timelines analyzed in this study prevents generalization, our preliminary investigation demonstrates the potential of social media data as complementary data in larger studies of SUDEP and epilepsy.
COVID-19 impact on the international trade
Authors: Célestin Coquidé, José Lages, Leonardo Ermann, Dima L. Shepelyansky
Date: 2022-01-19 00:00:00

Using the United Nations Comtrade database, we perform the Google matrix analysis of the multiproduct World Trade Network (WTN) for the years 2018-2020 comprising the emergence of the COVID-19 as a global pandemic. The applied algorithms -- the PageRank, the CheiRank and the reduced Google matrix -- take into account the multiplicity of the WTN links providing new insights on the international trade comparing to the usual import-export analysis. These algorithms establish new rankings and trade balances of countries and products considering every countries on equal grounds, independently of their wealth, and every products on the basis of their relative exchanged volumes. In comparison with the pre-COVID-19 period, significant changes in these metrics occur for the year 2020 highlighting a major rewiring of the international trade flows induced by the COVID-19 pandemic crisis. We define a new PageRank-CheiRank product trade balance, either export or import oriented, which is significantly perturbed by the pandemic.
Optimal Lockdown to Manage an Epidemic
Authors: Jagtap Kalyani Devendra, Kundan Kandhway
Date: 2022-01-18 00:00:00

We formulate an optimal control problem to determine the lockdown policy to curb an epidemic where other control measures are not available yet. We present a unified framework to model the epidemic and economy that allows us to study the effect of lockdown on both of them together. The objective function considers cost of deaths and infections during the epidemic, as well as economic losses due to reduced interactions due to lockdown. We tune the parameters of our model for Covid-19 epidemic and the economies of Burundi, India, and the United States (the low, medium and high income countries). We study the optimal lockdown policies and effect of system parameters for all of these countries. Our framework and results are useful for policymakers to design optimal lockdown strategies that account for both epidemic related infections and deaths, and economic losses due to lockdown.
Consensus of Homogeneous Agents with General Linear Dynamics under Switching Communication Networks
Authors: Chong Jin Ong, Ilayda Canyakmaz
Date: 2022-01-19 00:00:00

This work addresses the synchronization/consensus problem of identical multi-agent system (MAS) where the agents' dynamics are linear and the communication network is arbitrarily switching among connected topologies. The approach uses a gain matrix of a special structure in a dynamic compensator for each agent. Under reasonable conditions, the approach ensures that consensus is reached when the gain is sufficiently large. This result holds for general linear systems including the case where agents have repeated unstable eigenvalues. The proposed controller structure can be seen as a special case of the existing MAS controller structures but offers consensus conditions that are simpler than existing results, especially for the case when the network is switching among connected graphs. The works shows its application to three communication settings: fixed graph, switching among undirected and connected graphs and switching among directed and connected graphs. An example is provided to illustrate the results.
Damping Identification of an Operational Offshore Wind Turbine using Kalman filter-based Subspace Identification
Authors: Aemilius A W van Vondelen, Alexandros Iliopoulos, Sachin T Navalkar, Daan C van der Hoek, Jan-Willem van Wingerden
Date: 2022-01-19 00:00:00

Operational Modal Analysis (OMA) provides essential insights into the structural dynamics of an Offshore Wind Turbine (OWT). In these dynamics, damping is considered an especially important parameter as it governs the magnitude of the response at the natural frequencies. Violation of the stationary white noise excitation requirement of classical OMA algorithms has troubled the identification of operational OWTs due to harmonic excitation caused by rotor rotation. Recently, a novel algorithm was presented that mitigates harmonics by estimating a harmonic subsignal using a Kalman filter and orthogonally removing this signal from the response signal, after which the Stochastic Subspace Identification algorithm is used to identify the system. In this paper, the algorithm is tested on field data obtained from a multi-megawatt operational OWT using an economical sensor setup with two accelerometer levels. The first three tower bending modes could be distinguished, and, through the LQ-decomposition used in the algorithm, the identification results could be improved further by concatenating multiple datasets. A comparison against established harmonics-mitigating algorithms, Modified Least-squared Complex Exponential and PolyMAX, was done to validate the results.
Gaussian Process Position-Dependent Feedforward: With Application to a Wire Bonder
Authors: Max van Haren, Maurice Poot, Dragan Kostić, Robin van Es, Jim Portegies, Tom Oomen
Date: 2022-01-19 00:00:00
Tasks: Gaussian Processes, Position

Mechatronic systems have increasingly stringent performance requirements for motion control, leading to a situation where many factors, such as position-dependency, cannot be neglected in feedforward control. The aim of this paper is to compensate for position-dependent effects by modeling feedforward parameters as a function of position. A framework to model and identify feedforward parameters as a continuous function of position is developed by combining Gaussian processes and feedforward parameter learning techniques. The framework results in a fully data-driven approach, which can be readily implemented for industrial control applications. The framework is experimentally validated and shows a significant performance increase on a commercial wire bonder.
ISO and DSO Coordination: A Parametric Programming Approach
Authors: Mohammad Mousavi, Meng Wu
Date: 2022-01-19 00:00:00

In this paper, a framework is proposed to coordinate the operation of the independent system operator (ISO) and distribution system operator (DSO) to leverage the wholesale market participation of distributed energy resources (DERs) aggregators while ensuring secure operation of distribution grids. The proposed coordination framework is based on parametric programming. The DSO builds the bid-in cost function based on the distribution system market considering its market player constraints and distribution system physical constraints including the power balance equations and voltage limitation constraints. The DSO submits the resulting bid-in cost function to the wholesale market operated by the ISO. After the clearance of the wholesale market, the DSO determines the share of its retail market participants (i.e., DER aggregators). Case studies are performed to verify the effectiveness of the proposed method.
Functional observability and target state estimation in large-scale networks
Authors: Arthur N. Montanari, Chao Duan, Luis A. Aguirre, Adilson E. Motter
Date: 2022-01-18 00:00:00
Tasks: State Estimation

The quantitative understanding and precise control of complex dynamical systems can only be achieved by observing their internal states via measurement and/or estimation. In large-scale dynamical networks, it is often difficult or physically impossible to have enough sensor nodes to make the system fully observable. Even if the system is in principle observable, high-dimensionality poses fundamental limits on the computational tractability and performance of a full-state observer. To overcome the curse of dimensionality, we instead require the system to be functionally observable, meaning that a targeted subset of state variables can be reconstructed from the available measurements. Here, we develop a graph-based theory of functional observability, which leads to highly scalable algorithms to i) determine the minimal set of required sensors and ii) design the corresponding state observer of minimum order. Compared to the full-state observer, the proposed functional observer achieves the same estimation quality with substantially less sensing and computational resources, making it suitable for large-scale networks. We apply the proposed methods to the detection of cyber-attacks in power grids from limited phase measurement data and the inference of the prevalence rate of infection during an epidemic under limited testing conditions. The applications demonstrate that the functional observer can significantly scale up our ability to explore otherwise inaccessible dynamical processes on complex networks.
Opinion Dynamics in Financial Markets via Random Networks
Authors: Mateus F. B. Granha, André L. M. Vilela, Chao Wang, Kenric P. Nelson, H. Eugene Stanley
Date: 2022-01-14 00:00:00

We investigate the financial market dynamics by introducing a heterogeneous agent-based opinion formation model. In this work, we organize the individuals in a financial market by their trading strategy, namely noise traders and fundamentalists. The opinion of a local majority compels the market exchanging behavior of noise traders, whereas the global behavior of the market influences the fundamentalist agents' decisions. We introduce a noise parameter $q$ to represent a level of anxiety and perceived uncertainty regarding the market behavior, enabling the possibility for an adrift financial action. We place the individuals as nodes in an Erd\"os-R\'enyi random graph, where the links represent their social interaction. At a given time, they assume one of two possible opinion states $\pm 1$ regarding buying or selling an asset. The model exhibits such fundamental qualitative and quantitative real-world market features as the distribution of logarithmic returns with fat-tails, clustered volatility, and long-term correlation of returns. We use Student's t distributions to fit the histograms of logarithmic returns, showing the gradual shift from a leptokurtic to a mesokurtic regime, depending on the fraction of fundamentalist agents. We also compare our results with the distribution of logarithmic returns of several real-world financial indices.
Biophysical and Biochemical mechanisms underlying Collective Cell Migration in Cancer Metastasis
Authors: Ushasi Roy, Tyler Collins, Mohit K. Jolly, Parag Katira
Date: 2022-01-18 00:00:00

Multicellular collective migration is a ubiquitous strategy of cells to translocate spatially in diverse tissue environments to accomplish a wide variety of biological phenomena, viz. embryonic development, wound healing, and tumor progression. Diverse cellular functions and behaviors, for instance, cell protrusions, active contractions, cell-cell adhesion, biochemical signaling, remodeling of tissue micro-environment, etc., play their own role concomitantly to have a single concerted consequence of multicellular migration. Thus unveiling the driving principles, both biochemical and biophysical, of the inherently complex process of collective cell migration is an insurmountable task. Mathematical and computational models, in tandem with experimental data, help in shedding some light on it. Here we review different factors influencing Collective Cell Migration and then focus on different mathematical and computational models - discrete, hybrid, and continuum - which helps in revealing different aspects of multicellular migration. Finally, we discuss the applications of these modeling frameworks specific to cancer
Asymptotic properties of Bayesian inference in linear regression with a structural break
Authors: Kenichi Shimizu
Date: 2022-01-18 00:00:00
Tasks: Bayesian Inference, regression

This paper studies large sample properties of a Bayesian approach to inference about slope parameters $\gamma$ in linear regression models with a structural break. In contrast to the conventional approach to inference about $\gamma$ that does not take into account the uncertainty of the unknown break location $\tau$, the Bayesian approach that we consider incorporates such uncertainty. Our main theoretical contribution is a Bernstein-von Mises type theorem (Bayesian asymptotic normality) for $\gamma$ under a wide class of priors, which essentially indicates an asymptotic equivalence between the conventional frequentist and Bayesian inference. Consequently, a frequentist researcher could look at credible intervals of $\gamma$ to check robustness with respect to the uncertainty of $\tau$. Simulation studies show that the conventional confidence intervals of $\gamma$ tend to undercover in finite samples whereas the credible intervals offer more reasonable coverages in general. As the sample size increases, the two methods coincide, as predicted from our theoretical conclusion. Using data from Paye and Timmermann (2006) on stock return prediction, we illustrate that the traditional confidence intervals on $\gamma$ might underrepresent the true sampling uncertainty.