pwc-archive/papers-with-abstracts huggingface dataset:
Generation of Synthetic Multi-Resolution Time Series Load Data
Authors: Andrea Pinceti, Lalitha Sankar, Oliver Kosut
Date: 2021-07-08 00:00:00
Tasks: Generative Adversarial Network, Time Series, Time Series Analysis

The availability of large datasets is crucial for the development of new power system applications and tools; unfortunately, very few are publicly and freely available. We designed an end-to-end generative framework for the creation of synthetic bus-level time-series load data for transmission networks. The model is trained on a real dataset of over 70 Terabytes of synchrophasor measurements spanning multiple years. Leveraging a combination of principal component analysis and conditional generative adversarial network models, the scheme we developed allows for the generation of data at varying sampling rates (up to a maximum of 30 samples per second) and ranging in length from seconds to years. The generative models are tested extensively to verify that they correctly capture the diverse characteristics of real loads. Finally, we develop an open-source tool called LoadGAN which gives researchers access to the fully trained generative models via a graphical interface.
Synthetic Time-Series Load Data via Conditional Generative Adversarial Networks
Authors: Andrea Pinceti, Lalitha Sankar, Oliver Kosut
Date: 2021-07-08 00:00:00
Tasks: Time Series, Time Series Analysis

A framework for the generation of synthetic time-series transmission-level load data is presented. Conditional generative adversarial networks are used to learn the patterns of a real dataset of hourly-sampled week-long load profiles and generate unique synthetic profiles on demand, based on the season and type of load required. Extensive testing of the generative model is performed to verify that the synthetic data fully captures the characteristics of real loads and that it can be used for downstream power system and/or machine learning applications.
New Hybrid Maximum Power Point Tracking Methods for Fuel Cell using Artificial Intelligent
Authors: Mohammad Sarvi, Masoud Safarishaal
Date: 2021-07-07 00:00:00
Tasks: Point Tracking

In this paper, two maximum power point tracking (MPPT) methods for Fuel Cell (FC) systems based on Adaptive Neuro-Fuzzy Inference Systems (ANFIS) and Imperialist Competitive Algorithm trained Neural Network (ICANN) are presented. The first operation voltage of the fuel cell corresponding to maximum power point is determined based on the data, and then the duty cycle of a DC/DC converter is adjusted using fuzzy logic controller to force the system that operates in conditions which match up with its maximum power point, in order to minimize the fuel consumption. The proposed systems and conventional fuzzy controller system are simulated in the MATLAB environment and results show acceptable operation under fast variation of conditions as well as normal conditions in minimum time.
The Influence of Frequency Containment Reserve Flexibilization on the Economics of Electric Vehicle Fleet Operation
Authors: Jan Figgener, Benedikt Tepe, Fabian Rücker, Ilka Schoeneberger, Christopher Hecht, Andreas Jossen, Dirk Uwe Sauer
Date: 2021-07-07 00:00:00

In recent years, the market for frequency containment reserve (FCR) has become a relevant source of revenue for stationary battery storage systems in Germany. During this period, prices for FCR have decreased, while the market has become increasingly flexible with shorter service periods and lower minimum power requirements. This flexibility makes the market attractive for pools of electric vehicles (EVs). Their idle times are now often longer than FCR service periods, providing the opportunity to earn additional revenue. In this paper, multi-year measurement data from 22 commercial EVs are used to develop a simulation model for FCR commercialization. In addition, the driving logbooks of more than 460 vehicles from different commercial fleets are analyzed. Based on our simulations, the impact of FCR flexibilization on the economics of an EV pool is analyzed using the German FCR market design from 2011 to 2020. It is shown that depending on the fleet, especially the recent change in service periods from one week to four hours generates the largest increase in available pool power. Further reductions in FCR service periods will like produce minor benefits, as idle times are often longer than service periods. Overall, the increase in flexibility greatly offsets the decreasing FCR prices and leads to higher revenues for most fleets analyzed. According to our model, revenues of about 250 EUR/a to 400 EUR/a could have been achieved per EV in the German FCR market in 2020.
A Highway Toll Lane Framework that Unites Autonomous Vehicles and High-occupancy Vehicles
Authors: Ruolin Li, Philip N. Brown, Roberto Horowitz
Date: 2021-07-07 00:00:00
Tasks: Autonomous Vehicles

We consider the scenario where human-driven/autonomous vehicles with low/high occupancy are sharing a segment of highway and autonomous vehicles are capable of increasing the traffic throughput by preserving a shorter headway than human-driven vehicles. We propose a toll lane framework where a lane on the highway is reserved freely for autonomous vehicles with high occupancy, which have the greatest capability to increase social mobility, and the other three classes of vehicles can choose to use the toll lane with a toll or use the other regular lanes freely. All vehicles are assumed to be only interested in minimizing their own travel costs. We explore the resulting lane choice equilibria under the framework and establish desirable properties of the equilibria, which implicitly compare high-occupancy vehicles with autonomous vehicles in terms of their capabilities to increase social mobility. We further use numerical examples in the optimal toll design, the occupancy threshold design, and the policy design problems to clarify the various potential applications of this toll lane framework that unites high-occupancy vehicles and autonomous vehicles. To our best knowledge, this is the first work that systematically studies a toll lane framework that unites autonomous vehicles and high-occupancy vehicles on the roads.
The Risk of Hidden Failures to the United States Electrical Grid and Potential for Mitigation
Authors: Arthur K. Barnes, Adam Mate, Jose E. Tabarez
Date: 2021-07-07 00:00:00

Hidden failures present a noticeable impact to the reliability of the United States electrical grid. These hazards are responsible for protective device misoperations and can cause multiple-element contingencies across nearby components, greatly increasing the likelihood of cascading events. This paper provides an in-depth overview of the causes and risks of hidden failures and discusses methods for identifying critical locations where hidden failures could pose a risk of cascading failure, with the ultimate goal being to identify efficient mitigation methods that can prevent their occurrence in protective relays.
Public preferences for marine plastic litter reductions across Europe
Authors: Salma Khedr, Katrin Rehdanz, Roy Brouwer, Hanna Dijkstra, Sem Duijndam, Pieter van Beukering, Ikechukwu C. Okoli
Date: 2021-07-08 00:00:00
Tasks: Management

Plastic pollution is one of the most challenging problems affecting the marine environment of our time. Based on a unique dataset covering four European seas and eight European countries, this paper adds to the limited empirical evidence base related to the societal welfare effects of marine litter management. We use a discrete choice experiment to elicit public willingness-to-pay (WTP) for macro and micro plastic removal to achieve Good Environmental Status across European seas as required by the European Marine Strategy Framework Directive. Using a common valuation design and following best-practice guidelines, we draw meaningful comparisons between countries, seas and policy contexts. European citizens have strong preferences to improve the environmental status of the marine environment by removing both micro and macro plastic litter favouring a pan-European approach. However, public WTP estimates differ significantly across European countries and seas. We explain why and discuss implications for policymaking.
Limited intelligence and performance-based compensation: An agent-based model of the hidden action problem
Authors: Patrick Reinwald, Stephan Leitner, Friederike Wall
Date: 2021-07-08 00:00:00

Models of economic decision makers often include idealized assumptions, such as rationality, perfect foresight, and access to all relevant pieces of information. These assumptions often assure the models' internal validity, but, at the same time, might limit the models' power to explain empirical phenomena. This paper is particularly concerned with the model of the hidden action problem, which proposes an optimal performance-based sharing rule for situations in which a principal assigns a task to an agent, and the action taken to carry out this task is not observable by the principal. We follow the agentization approach and introduce an agent-based version of the hidden action problem, in which some of the idealized assumptions about the principal and the agent are relaxed so that they only have limited information access, are endowed with the ability to gain information, and store it in and retrieve it from their (limited) memory. We follow an evolutionary approach and analyze how the principal's and the agent's decisions affect the sharing rule, task performance, and their utility over time. The results indicate that the optimal sharing rule does not emerge. The principal's utility is relatively robust to variations in intelligence, while the agent's utility is highly sensitive to limitations in intelligence. The principal's behavior appears to be driven by opportunism, as she withholds a premium from the agent to assure the optimal utility for herself.
a theoretical look at ordinal classification methods based on comparing actions with limiting boundaries between adjacent classes
Authors: Eduardo Fernandez, Jose Rui Figueira, Jorge Navarro
Date: 2021-07-07 00:00:00
Tasks: Ordinal Classification

This paper addresses the general problem of designing ordinal classification methods based on comparing actions with limiting boundaries of ordered classes (categories). The fundamental requirement of the method consists of setting a relational system (D,S), where S and D are reflexive and transitive relations, respectively, S should be compatible with the order of the set of classes, and D is a subset of S. An asymmetric preference relation P is defined from S. Other requirements are imposed on the actions which compose the limiting boundaries between adjacent classes, in such a way that each class is closed below and above. The paper proposes S-based and P-based assignment procedures. Each of them is composed of two complementary assignment procedures, which correspond through the transposition operation and should be used conjointly. The methods work under several basic conditions on the set of limiting boundaries. Under other more demanding separability requirements, each procedure fulfills the set of structural properties established for other outranking-based ordinal classification methods. Our proposal avoids the conflict between the required correspondence through the transposition operation and the assignment of limiting actions to the classes to which they belong. We thus propose very diverse S and P-based ordinal classification approaches with desirable properties, which can be designed by using decision models with the capacity to build preference relations fulfilling the basic requirements to S and D.
Diffraction Tomography with Helmholtz Equation: Efficient and Robust Multigrid-Based Solver
Authors: Tao Hong, Thanh-an Pham, Eran Treister, Michael Unser
Date: 2021-07-08 00:00:00

Diffraction tomography is a noninvasive technique that estimates the refractive indices of unknown objects and involves an inverse-scattering problem governed by the wave equation. Recent works have shown the benefit of nonlinear models of wave propagation that account for multiple scattering and reflections. In particular, the Lippmann-Schwinger~(LiS) model defines an inverse problem to simulate the wave propagation. Although accurate, this model is hard to solve when the samples are highly contrasted or have a large physical size. In this work, we introduce instead a Helmholtz-based nonlinear model for inverse scattering. To solve the corresponding inverse problem, we propose a robust and efficient multigrid-based solver. Moreover, we show that our method is a suitable alternative to the LiS model, especially for strongly scattering objects. Numerical experiments on simulated and real data demonstrate the effectiveness of the Helmholtz model, as well as the efficiency of the proposed multigrid method.
Automated Gain Control Through Deep Reinforcement Learning for Downstream Radar Object Detection
Authors: Tristan S. W. Stevens, R. Firat Tigrek, Eric S. Tammam, Ruud J. G. van Sloun
Date: 2021-07-08 00:00:00
Tasks: Active Learning, Deep Reinforcement Learning, Object, object-detection, Object Detection, Radar Object Detection, reinforcement-learning, Reinforcement Learning (RL)

Cognitive radars are systems that rely on learning through interactions of the radar with the surrounding environment. To realize this, radar transmit parameters can be adapted such that they facilitate some downstream task. This paper proposes the use of deep reinforcement learning (RL) to learn policies for gain control under the object detection task. The YOLOv3 single-shot object detector is used for the downstream task and will be concurrently used alongside the RL agent. Furthermore, a synthetic dataset is introduced which models the radar environment with use of the Grand Theft Auto V game engine. This approach allows for simulation of vast amounts of data with flexible assignment of the radar parameters to aid in the active learning process.
Single Pole-To-Earth Fault Detection and Location on the Tehran Railway System Using ICA and PSO Trained Neural Network
Authors: Masoud Safarishaal
Date: 2021-07-08 00:00:00
Tasks: Fault Detection

In a railroad feeding system, detecting a location of pole to earth faults is important for safe operation of the system. The goal of this paper is to use a combination of the evolutionary algorithm and neural networks to increase the accuracy of single pole-to-earth fault detection and location on Tehran railroad power supply system. Accordingly, Imperialist Competitive Algorithm (ICA) and Particle Swarm Optimization (PSO) are used to train the neural network for enhancing learning process accuracy and the convergence. Owing to the nonlinearity of system, the fault detection is an ideal application for the proposed method where 600 Hz harmonic ripple method is used in this paper for fault detection. The substations were simulated by considering various situations in feeding the circuit, the transformer and the silicon rectifier has been developed by typical Tehran metro parameters. Required data for the network learning the process have been gathered from simulation results. 600Hz components value will change with the change of the location of single pole to earth fault. Therefore, 600Hz components are used as inputs of the neural network when fault location is the output of the network system. The simulation results show that the fault location can be accurately predicted in proposed methods.
Derivative Based Proportionate Approach for Sparse Impulse Response Identification
Authors: Murat Babek Salman, Tolga Ciloglu
Date: 2021-07-08 00:00:00
Tasks: Vocal Bursts Type Prediction

Proportionate type algorithms were developed and excessively used in the echo cancellation problems due to sparse characteristics of the echo channels. In the past, most of the attention was paid to a particular type of proportionate approach, which assigns step-sizes to filter coefficients proportional to the magnitude of the corresponding coefficient. In this letter, we propose a new proportionate type algorithm, which takes dynamic behavior of the estimated filter coefficient into account while assigning individual step-sizes to each coefficient. Proposed algorithm introduces an effective way to assign individual step-sizes using the time derivatives of the filter coefficients. Computational complexity of the proposed algorithm is similar to those of previously proposed algorithms. Simulation results have shown the improvements in the convergence rate achieved by the proposed algorithm.
Stool Studies Don't Pass the Sniff Test: A Systematic Review of Human Gut Microbiome Research Suggests Widespread Misuse of Machine Learning
Authors: Thomas P. Quinn
Date: 2021-07-08 00:00:00
Tasks: Articles, Cultural Vocal Bursts Intensity Prediction, Diagnostic

In the machine learning culture, an independent test set is required for proper model verification. Failures in model verification, including test set omission and test set leakage, make it impossible to know whether or not a trained model is fit for purpose. In this article, we present a systematic review and quantitative analysis of human gut microbiome classification studies, conducted to measure the frequency and impact of test set omission and test set leakage on area under the receiver operating curve (AUC) reporting. Among 102 articles included for analysis, we find that only 12% of studies report a bona fide test set AUC, meaning that the published AUCs for 88% of studies cannot be trusted at face value. Our findings cast serious doubt on the general validity of research claiming that the gut microbiome has high diagnostic or prognostic potential in human disease.
Cell phenotypic transition proceeds through concerted reorganization of gene regulatory network
Authors: Weikang Wang, Dante Poe, Ke Ni, Jianhua Xing
Date: 2021-07-08 00:00:00

Phenotype transition takes place in many biological processes such as differentiation, and understanding how a cell reprograms its global gene expression profile is a problem of rate theories. A cell phenotype transition accompanies with switching of expression rates of clusters of genes, analogous to domain flipping in an Ising system. Here through analyzing single cell RNA sequencing data in the framework of transition path theory, we set to study how such a genome-wide expression program switching proceeds in three different cell transition processes. For each process after reconstructing a Markov transition model in the cell state space, we formed an ensemble of shortest paths connecting the initial and final cell states, reconstructed a reaction coordinate describing the transition progression, and inferred the gene regulation network (GRN) along the reaction coordinate. In all three processes we observed common pattern that the frustration of gene regulatory network (GRN), defined as overall confliction between the regulation received by genes and their expression states, first increases then decreases when approaching a new phenotype. The results support a mechanism of concerted silencing of genes that are active in the initial phenotype and activation of genes that are active in the final phenotype.
The classical mean negative asynchrony in sensorimotor synchronization is not universal in humans. A cross-cultural study
Authors: Julien Lagarde
Date: 2021-07-08 00:00:00

The present study examines to what extent cultural background determines sensorimotor synchronization in humans
A Review on MR Based Human Brain Parcellation Methods
Authors: Pantea Moghimi, Anh The Dang, Theoden I. Netoff, Kelvin O. Lim, Gowtham Atluri
Date: 2021-07-07 00:00:00

Brain parcellations play a ubiquitous role in the analysis of magnetic resonance imaging (MRI) datasets. Over 100 years of research has been conducted in pursuit of an ideal brain parcellation. Different methods have been developed and studied for constructing brain parcellations using different imaging modalities. More recently, several data-driven parcellation methods have been adopted from data mining, machine learning, and statistics communities. With contributions from different scientific fields, there is a rich body of literature that needs to be examined to appreciate the breadth of existing research and the gaps that need to be investigated. In this work, we review the large body of in vivo brain parcellation research spanning different neuroimaging modalities and methods. A key contribution of this work is a semantic organization of this large body of work into different taxonomies, making it easy to understand the breadth and depth of the brain parcellation literature. Specifically, we categorized the existing parcellations into three groups: Anatomical parcellations, functional parcellations, and structural parcellations which are constructed using T1-weighted MRI, functional MRI (fMRI), and diffusion-weighted imaging (DWI) datasets, respectively. We provide a multi-level taxonomy of different methods studied in each of these categories, compare their relative strengths and weaknesses, and highlight the challenges currently faced for the development of brain parcellations.
On the Selection of Loss Severity Distributions to Model Operational Risk
Authors: Daniel Hadley, Harry Joe, Natalia Nolde
Date: 2021-07-08 00:00:00

Accurate modeling of operational risk is important for a bank and the finance industry as a whole to prepare for potentially catastrophic losses. One approach to modeling operational is the loss distribution approach, which requires a bank to group operational losses into risk categories and select a loss frequency and severity distribution for each category. This approach estimates the annual operational loss distribution, and a bank must set aside capital, called regulatory capital, equal to the 0.999 quantile of this estimated distribution. In practice, this approach may produce unstable regulatory capital calculations from year-to-year as selected loss severity distribution families change. This paper presents truncation probability estimates for loss severity data and a consistent quantile scoring function on annual loss data as useful severity distribution selection criteria that may lead to more stable regulatory capital. Additionally, the Sinh-arcSinh distribution is another flexible candidate family for modeling loss severities that can be easily estimated using the maximum likelihood approach. Finally, we recommend that loss frequencies below the minimum reporting threshold be collected so that loss severity data can be treated as censored data.
Numerical approximation of hybrid Poisson-jump Ait-Sahalia-type interest rate model with delay
Authors: Emmanuel Coffie
Date: 2021-07-08 00:00:00
Tasks: Time Series, Time Series Analysis

While the original Ait-Sahalia interest rate model has been found considerable use as a model for describing time series evolution of interest rates, it may not possess adequate specifications to explain responses of interest rates to empirical phenomena such as volatility 'skews' and 'smiles', jump behaviour, market regulatory lapses, economic crisis, financial clashes, political instability, among others collectively. The aim of this paper is to propose a modified version of this model by incorporating additional features to collectively describe these empirical phenomena adequately. Moreover, due to lack of a closed-form solution to the proposed model, we employ several new truncated EM techniques to examine this model and justify the scheme within Monte Carlo framework to compute expected payoffs of some financial quantities such as a bond and a barrier option.