pwc-archive/papers-with-abstracts huggingface dataset:
Robust Dynamic State Estimator of Integrated Energy Systems based on Natural Gas Partial Differential Equations
Authors: Liang Chen, Yang Li, Manyun Huang, Xinxin Hui, Songlin Gu
Date: 2022-02-04 00:00:00
Tasks: State Estimation

The reliability and precision of dynamic database are vital for the optimal operating and global control of integrated energy systems. One of the effective ways to obtain the accurate states is state estimations. A novel robust dynamic state estimation methodology for integrated natural gas and electric power systems is proposed based on Kalman filter. To take full advantage of measurement redundancies and predictions for enhancing the estimating accuracy, the dynamic state estimation model coupling gas and power systems by gas turbine units is established. The exponential smoothing technique and gas physical model are integrated in Kalman filter. Additionally, the time-varying scalar matrix is proposed to conquer bad data in Kalman filter algorithm. The proposed method is applied to an integrated gas and power systems formed by GasLib-40 and IEEE 39-bus system with five gas turbine units. The simulating results show that the method can obtain the accurate dynamic states under three different measurement error conditions, and the filtering performance are better than separate estimation methods. Additionally, the proposed method is robust when the measurements experience bad data.
Distributed State Estimation with Deep Neural Networks for Uncertain Nonlinear Systems under Event-Triggered Communication
Authors: Federico M. Zegers, Runhan Sun, Girish Chowdhary, Warren E. Dixon
Date: 2022-02-03 00:00:00
Tasks: State Estimation

Distributed state estimation is examined for a sensor network tasked with reconstructing a system's state through the use of a distributed and event-triggered observer. Each agent in the sensor network employs a deep neural network (DNN) to approximate the uncertain nonlinear dynamics of the system, which is trained using a multiple timescale approach. Specifically, the outer weights of each DNN are updated online using a Lyapunov-based gradient descent update law, while the inner weights and biases are trained offline using a supervised learning method and collected input-output data. The observer utilizes event-triggered communication to promote the efficient use of network resources. A nonsmooth Lyapunov analysis shows the distributed event-triggered observer has a uniformly ultimately bounded state reconstruction error. A simulation study is provided to validate the result and demonstrate the performance improvements afforded by the DNNs.
Effects of heterogeneity on cancer: a game theory perspective
Authors: Annick Laruelle, André Rocha, Claudia Manini, José I López, Elena Inarra
Date: 2022-02-04 00:00:00
Tasks: Vocal Bursts Type Prediction

In this study, we explore interactions between cancer cells by using the hawk-dove game. We analyze the heterogeneity of tumors by considering games with populations composed of 2 or 3 types of cells. We determine what strategies are evolutionarily stable in the 2-type and 3-type population games and the corresponding expected payoffs. Our results show that the payoff of the best-off cell in the 2-type population game is higher than that of the best-off cell in the 3-type population game. Translating these mathematical findings to the field of oncology suggests that a branching-type tumor pursues a less aggressive course than a punctuated-type one. Some histological and genomic data of clear cell renal cell carcinomas are consistent with these results.
The welfare effects of unemployment insurance in Argentina. New estimates using changes in the schedule of transfers
Authors: Martin Gonzalez-Rozada, Hernan Ruffo
Date: 2022-02-03 00:00:00

Unemployment insurance transfers should balance the provision of consumption to the unemployed with the disincentive effects on the search behavior. Developing countries face the additional challenge of informality. Workers can choose to hide their employment state and labor income in informal jobs, an additional form of moral hazard. To provide evidence about the effects of this policy in a country affected by informality we exploit kinks in the schedule of transfers in Argentina. Our results suggest that higher benefits induce moderate behavioral responses in job-finding rates and increase re-employment wages. We use a sufficient statistics formula from a model with random wage offers and we calibrate it with our estimates. We show that welfare could rise substantially if benefits were increased in Argentina. Importantly, our conclusion is relevant for the median eligible worker that is strongly affected by informality.
On Sustainability and Survivability in the Matchbox Two-Sector Model: A Complete Characterization of Optimal Extinction
Authors: Liuchun Deng, Minako Fujio, M. Ali Khan
Date: 2022-02-04 00:00:00

We provide a complete characterization of optimal extinction in a two-sector model of economic growth through three results, surprising in both their simplicity and intricacy. (i) When the discount factor is below a threshold identified by the well-known $\delta$-normality condition for the existence of a stationary optimal stock, the economy's capital becomes extinct in the long run. (ii) This extinction may be staggered if and only if the investment-good sector is capital intensive. (iii) We uncover a sequence of thresholds of the discount factor, identified by a family of rational functions, that represent bifurcations for optimal postponements on the path to extinction. We also report various special cases of the model having to do with unsustainable technologies and equal capital intensities that showcase long-term optimal growth, all of topical interest and all neglected in the antecedent literature.
Signal Shaping for Semantic Communication Systems with A Few Message Candidates
Authors: Shuaishuai Guo, Yanhu Wang, Peng Zhang
Date: 2022-02-04 00:00:00
Tasks: Semantic Communication

Semantic communications target to reliably convey the semantic meaning of messages. It is different from existing communication systems focusing on reliable bit transmission. To achieve the goal of semantic communications, we propose a signal shaping method by minimizing the semantic loss, which is measured by the pretrained bidirectional encoder representation from transformers (BERT) model. The signal set optimization problem for semantic communication systems with a few message candidates is investigated. We propose an efficient projected gradient descent method to solve the problem and prove its convergence. Simulation results show that the proposed method outperforms existing signal shaping methods in minimizing the semantic loss.
Non-Coherent MIMO-OFDM Uplink empowered by the Spatial Diversity in Reflecting Surfaces
Authors: Kun Chen-Hu, George C. Alexandropoulos, Ana García Armada
Date: 2022-02-04 00:00:00
Tasks: Diversity

Reflecting Surfaces (RSs) are being lately envisioned as an energy efficient solution capable of enhancing the signal coverage in cases where obstacles block the direct communication from Base Stations (BSs), especially at high frequency bands due to attenuation loss increase. In the current literature, wireless communications via RSs are exclusively based on traditional coherent demodulation, which necessitates the estimation of accurate Channel State Information (CSI). However, this requirement results in an increased overhead, especially in time-varying channels, which reduces the resources that can be used for data communication. In this paper, we consider the uplink between a single-antenna user and a multi-antenna BS and present a novel RS-empowered Orthogonal Frequency Division Multiplexing (OFDM) communication system based on the differential phase shift keying, which is suitable for high noise and/or mobility scenarios. As a benchmark, analytical expressions for the Signal-to-Interference and Noise Ratio (SINR) of the proposed system are presented. Our extensive simulation results verify the accuracy of the presented analysis and showcase the performance and superiority of the proposed system over coherent demodulation.
Global Stability of a Diffusive SEIR Epidemic Model with Distributed Delay
Authors: Abdesslem Lamrani Alaoui, Moulay Rchid Sidi Ammi, Mouhcine Tilioua, Delfim F. M. Torres
Date: 2022-02-01 00:00:00

We study the global dynamics of a reaction-diffusion SEIR infection model with distributed delay and nonlinear incidence rate. The well-posedness of the proposed model is proved. By means of Lyapunov functionals, we show that the disease free equilibrium state is globally asymptotically stable when the basic reproduction number is less or equal than one, and that the disease endemic equilibrium is globally asymptotically stable when the basic reproduction number is greater than one. Numerical simulations are provided to illustrate the obtained theoretical results.
Relating cognition to both brain structure and function: A systematic review of methods
Authors: Marta Czime Litwińczuk, Nelson Trujillo-Barreto, Nils Muhlert, Lauren Cloutman, Anna Woollams
Date: 2022-02-04 00:00:00

Cognitive neuroscience explores the mechanisms of cognition by studying its structural and functional brain correlates. Here, we report the first systematic review that assesses how information from structural and functional neuroimaging methods can be integrated to investigate the brain substrates of cognition. Web of Science and Scopus databases were searched for studies of healthy young adult populations that collected cognitive data, and structural and functional neuroimaging data. Five percent of screened studies met all inclusion criteria. Next, 54% of included studies related cognitive performance to brain structure and function without quantitative analysis of the relationship. Finally, 32% of studies formally integrated structural and functional brain data. Overall, many studies consider either structural or functional neural correlates of cognition, and of those that consider both, they have rarely been integrated. We identified four emergent approaches to the characterisation of the relationship between brain structure, function and cognition; comparative, predictive, fusion and complementary. We discuss the insights provided each approach and how authors can select approaches to suit their research questions.
A Comparison of Representation Learning Methods for Dimensionality Reduction of fMRI Scans for Classification of ADHD
Authors: Bhaskar Sen
Date: 2022-02-04 00:00:00
Tasks: Classification, Dimensionality Reduction, Representation Learning, Specificity

This paper compares three feature representation techniques used to represent resting state functional magnetic resonance (fMRI) scans. The proposed models of feature representation consider the time averaged fMRI scans as raw representation of image data. The effectiveness of the representation is evaluated by using these features for classification of Attention Deficit Hyperactivity Disorder (ADHD) patients from healthy controls. The dimensionality reduction methods used for feature representation are maximum-variance unfolding, locally linear embedding and auto-encoders. The classifiers tested for classification purpose were neural net and support vector machine. Using auto-encoders with four hidden layers along with a support vector machine classifier yielded a classification accuracy of 61.25% along with 65.69% sensitivity and 52.20% specificity.
Rearrangement Events on Circular Genomes
Authors: Joshua Stevenson, Venta Terauds, Jeremy Sumner
Date: 2022-02-04 00:00:00

Early literature on genome rearrangement modelling views the problem of computing evolutionary distances as an inherently combinatorial one. In particular, attention was given to estimating distances using the minimum number of events required to transform one genome into another. In hindsight, this approach is analogous to early methods for inferring phylogenetic trees from DNA sequences such as maximum parsimony -- both are motivated by the principle that the true distance minimises evolutionary change, and both are effective if this principle is a true reflection of reality. Recent literature considers genome rearrangement under statistical models, continuing this parallel with DNA-based methods; the goal here is to use model-based methods (for example maximum likelihood techniques) to compute distance estimates that incorporate the large number of rearrangement paths that can transform one genome into another. Crucially, this approach requires one to decide upon a set of feasible rearrangement events and, in this paper, we focus on characterising well-motivated models for signed, uni-chromosomal circular genomes, where the number of regions remains fixed. Since rearrangements are often mathematically described using permutations, we isolate the sets of permutations representing rearrangements that are biologically reasonable in this context, for example inversions and translocations. We provide precise mathematical expressions for these rearrangements, and then describe them in terms of the set of cuts made in the genome when they are applied. We directly compare cuts to breakpoints, and use this concept to count the distinct rearrangement actions which apply a given number of cuts. Finally, we provide some examples of rearrangement models, and include a discussion of some questions that arise when defining plausible models.
Portfolio Choice with Indivisible and Illiquid Housing Assets: The Case of Spain
Authors: Sergio Mayordomo, María Rodriguez-Moreno, Juan Ignacio Peña
Date: 2022-02-04 00:00:00

This paper studies the investment decision of the Spanish households using a unique data set, the Spanish Survey of Household Finance (EFF). We propose a theoretical model in which households, given a fixed investment in housing, allocate their net wealth across bank time deposits, stocks, and mortgage. Besides considering housing as an indivisible and illiquid asset that restricts the portfolio choice decision, we take into account the financial constraints that households face when they apply for external funding. For every representative household in the EFF we solve this theoretical problem and obtain the theoretically optimal portfolio that is compared with households' actual choices. We find that households significantly underinvest in stocks and deposits while the optimal and actual mortgage investments are alike. Considering the three types of financial assets at once, we find that the households headed by highly financially sophisticated, older, retired, richer, and unconstrained persons are the ones investing more efficiently.
Evaluating conditional covariance estimates via a new targeting approach and a networks-based analysis
Authors: Carlo Drago, Andrea Scozzari
Date: 2022-02-04 00:00:00
Tasks: Time Series, Time Series Analysis

Modeling and forecasting of dynamically varying covariances have received much attention in the literature. The two most widely used conditional covariances and correlations models are BEKK and DCC. In this paper, we advance a new method to introduce targeting in both models to estimate matrices associated with financial time series. Our approach is based on specific groups of highly correlated assets in a financial market, and these relationships remain unaltered over time. Based on the estimated parameters, we evaluate our targeting method on simulated series by referring to two well-known loss functions introduced in the literature and Network analysis. We find all the maximal cliques in correlation graphs to evaluate the effectiveness of our method. Results from an empirical case study are encouraging, mainly when the number of assets is not large.
Measuring Systemic Risk: Common Factor Exposures and Tail Dependence Effects
Authors: Wan-Chien Chiu, Juan Ignacio Peña, Chih-Wei Wang
Date: 2022-02-04 00:00:00

We model systemic risk using a common factor that accounts for market-wide shocks and a tail dependence factor that accounts for linkages among extreme stock returns. Specifically, our theoretical model allows for firm-specific impacts of infrequent and extreme events. Using data on the four sectors of the U.S. financial industry from 1996 to 2011, we uncover two key empirical findings. First, disregarding the effect of the tail dependence factor leads to a downward bias in the measurement of systemic risk, especially during weak economic times. Second, when these measures serve as leading indicators of the St. Louis Fed Financial Stress Index, measures that include a tail dependence factor offer better forecasting ability than measures based on a common factor only.
Are all Credit Default Swap Databases equal?
Authors: Sergio Mayordomo, Juan Ignacio Peña, Eduardo S. Schwartz
Date: 2022-02-04 00:00:00
Tasks: All

We compare the five major sources of corporate Credit Default Swap prices: GFI, Fenics, Reuters, CMA, and Markit, using the most liquid single name 5-year CDS in the iTraxx and CDX indexes from 2004 to 2010. Deviations from the common trend among prices in the different databases are not random but are explained by idiosyncratic factors, financing costs, global risk, and other trading factors. The CMA quotes lead the price discovery process. Moreover, we find that there is not a full agreement among databases in the results of the price discovery analysis between stock and CDS returns.
Industry Characteristics and Financial Risk Spillovers
Authors: Wan-Chien Chiua, Juan Ignacio Peña, Chih-Wei Wang
Date: 2022-02-04 00:00:00

This paper proposes a new measure of tail risk spillover. The empirical application provides evidence of significant volatility and tail risk spillovers from the financial sector to many real economy sectors in the U.S. economy in the period from 2001 to 2011. These spillovers increase in crisis periods. The conditional coexceedance in a given sector is positively related to its amount of debt financing, and negatively related to its relative valuation and investment. Real economy sectors which require substantial external financing, and whose value and investment activity are relatively lower, are prime candidates for depreciation in the wake of crisis in the financial sector.
Derivatives Holdings and Systemic Risk in the U.S. Banking Sector
Authors: Sergio Mayordomo, Maria Rodriguez-Moreno, Juan Ignacio Peña
Date: 2022-02-04 00:00:00

Foreign exchange and credit derivatives increase the bank's contributions to systemic risk. Interest rate derivatives decrease it. The proportion of non-performing loans over total loans and the leverage ratio have stronger impact on systemic risk than derivatives holdings.
A Distributed Artificial Intelligence Framework to Evolve Infrastructure Resilience in Telecommunications Sector
Authors: Ahmed A. Mawgoud, Mohamed M. Eltabey, Amr Abu-Talleb
Date: 2021-03-05 00:00:00
Proceeding: Advanced Machine Learning Technologies and Applications 2021 3
Tasks: Management

Severe natural threats have been increased by climate changes and this originates various high-level threats. This paper discusses the concept of resilience in the context of these problems for telecommunications sector. One of the recent examples that led to a wide power instability was the power cut off. Many telecommunications operators forced actions to limit the spread of natural risks. The paper’s main focus is on two main purposes, the first one is the to propose a mechanism for enhancing resilience in telecom infrastructure, while the second purpose is to quantify the financial value to achieve this resilience. From this point, early definitions of power systems resilience were provided. Then, we examine the main effects of natural disasters on the resilience of the infrastructure in telecommunication sectors, especially the ones related to energy. Eventually, a decentralized plan of action is proposed for a Resilient Management System (RMS) with the aid of demand response resources and distributed storage. The high-precision model which we pro-pose provides telecom operators, regulatory authorities and IT sectors with a clear image for taking defensive measures and making strategic decisions.
Autonomous Driving on Curvy Roads Without Reliance on Frenet Frame: A Cartesian-Based Trajectory Planning Method
Authors: Bai Li, Yakun Ouyang, Li Li, Youmin Zhang
Date: 2022-02-03 00:00:00
Proceeding: IEEE Transactions on Intelligent Transportation Systems 2022 2
Tasks: Autonomous Driving, Collision Avoidance, Trajectory Planning

Curvy roads are a particular type of urban road scenario, wherein the curvature of the road centerline changes drastically. This paper is focused on the trajectory planning task for autonomous driving on a curvy road. The prevalent on-road trajectory planners in the Frenet frame cannot impose accurate restrictions on the trajectory curvature, thus easily making the resultant trajectories beyond the ego vehicle's kinematic capability. Regarding planning in the Cartesian frame, selection-based methods suffer from the curse of dimensionality. By contrast, optimization-based methods in the Cartesian frame are more flexible to find optima in the continuous solution space, but the new challenges are how to tackle the intractable collision-avoidance constraints and nonconvex kinematic constraints. An iterative computation framework is proposed to accumulatively handle the complex constraints. Concretely, an intermediate problem is solved in each iteration, which contains linear and tractably scaled collision-avoidance constraints and softened kinematic constraints. Compared with the existing optimization-based planners, our proposal is less sensitive to the initial guess especially when it is not kinematically feasible. The efficiency of the proposed planner is validated by both simulations and real-world experiments. Source codes of this work are available at https://github.com/libai1943/CartesianPlanner.
Boundary-aware Information Maximization for Self-supervised Medical Image Segmentation
Authors: Jizong Peng, Ping Wang, Marco Pedersoli, Christian Desrosiers
Date: 2022-02-04 00:00:00
Tasks: Contrastive Learning, Image Segmentation, Medical Image Segmentation, Segmentation, Semantic Segmentation, Unsupervised Pre-training

Unsupervised pre-training has been proven as an effective approach to boost various downstream tasks given limited labeled data. Among various methods, contrastive learning learns a discriminative representation by constructing positive and negative pairs. However, it is not trivial to build reasonable pairs for a segmentation task in an unsupervised way. In this work, we propose a novel unsupervised pre-training framework that avoids the drawback of contrastive learning. Our framework consists of two principles: unsupervised over-segmentation as a pre-train task using mutual information maximization and boundary-aware preserving learning. Experimental results on two benchmark medical segmentation datasets reveal our method's effectiveness in improving segmentation performance when few annotated images are available.