pwc-archive/papers-with-abstracts huggingface dataset:
Enhancing Resilience of Distribution Networks by Coordinating Microgrids and Demand Response Programs in Service Restoration
Authors: Ali Shakeri Kahnamouei, Saeed Lotfifard
Date: 2021-07-13 00:00:00
Tasks: All

In case of high impact low probability events, in order to restore the critical loads of the distribution network as much as possible, it is necessary to employ all available resources such as microgrids and distributed generations. This paper presents a two-stage method for critical load restoration after an extreme event utilizing the coordination of all available distributed generations, microgrids, and demand response programs. In the first stage, the post-disaster reconfiguration of the network is determined, and in the second stage, the demand response outputs of the responsive loads and the restoration status of all critical loads are ascertained. In the first stage, an algorithm is proposed to determine electrical islands, and in the second stage, a new model based on the generalized Benders decomposition algorithm is proposed, which considers demand response programs and all operational constraints and aims to maximize the restored critical loads. The effectiveness of the proposed method is verified by simulating the 33-bus and 69-bus test systems.
Design of Adaptive Backstepping Control for Direct Power Control of Three-Phase PWM Rectifier)
Authors: Basit Olakunle Alawode, Sami El-Ferik
Date: 2021-07-13 00:00:00

In this paper, we focused on the design of an adaptive backstepping controller (adaptive-BSC) for direct power control (DPC) of a three-phase PWM rectifier. In the proposed system, it is desired to control both the output DC voltage of the rectifier and the reactive power simultaneously by making them track desired respective values. This was done by having independent virtual control signals for both the output voltage and the reactive power. The adaptive control signals were gotten from the dynamic equations of the three-phase system. For comparison, both the BSC and adaptive-BSC equations were developed. Numerical simulations were performed on both of them on a 5kW system. The proposed adaptive-BSC was designed to work under more challenging system variations as compared with the BSC as it has to estimate the value of the unknown system load. Despite this, it still performed better than its BSC counterpart.
Inverse Reinforcement Learning Based Stochastic Driver Behavior Learning
Authors: Mehmet Fatih Ozkan, Abishek Joseph Rocque, Yao Ma
Date: 2021-07-01 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Drivers have unique and rich driving behaviors when operating vehicles in traffic. This paper presents a novel driver behavior learning approach that captures the uniqueness and richness of human driver behavior in realistic driving scenarios. A stochastic inverse reinforcement learning (SIRL) approach is proposed to learn a distribution of cost function, which represents the richness of the human driver behavior with a given set of driver-specific demonstrations. Evaluations are conducted on the realistic driving data collected from the 3D driver-in-the-loop driving simulation. The results show that the learned stochastic driver model is capable of expressing the richness of the human driving strategies under different realistic driving scenarios. Compared to the deterministic baseline driver behavior model, the results reveal that the proposed stochastic driver behavior model can better replicate the driver's unique and rich driving strategies in a variety of traffic conditions.
Quantum propensities in the brain cortex and free will
Authors: Danko D. Georgiev
Date: 2021-07-14 00:00:00
Tasks: Ethics

Capacity of conscious agents to perform genuine choices among future alternatives is a prerequisite for moral responsibility. Determinism that pervades classical physics, however, forbids free will, undermines the foundations of ethics, and precludes meaningful quantification of personal biases. To resolve that impasse, we utilize the characteristic indeterminism of quantum physics and derive a quantitative measure for the amount of free will manifested by the brain cortical network. The interaction between the central nervous system and the surrounding environment is shown to perform a quantum measurement upon the neural constituents, which actualize a single measurement outcome selected from the resulting quantum probability distribution. Inherent biases in the quantum propensities for alternative physical outcomes provide varying amounts of free will, which can be quantified with the expected information gain from learning the actual course of action chosen by the nervous system. For example, neuronal electric spikes evoke deterministic synaptic vesicle release in the synapses of sensory or somatomotor pathways, with no free will manifested. In cortical synapses, however, vesicle release is triggered indeterministically with probability of 0.35 per spike. This grants the brain cortex, with its over 100 trillion synapses, an amount of free will exceeding 96 terabytes per second. Although reliable deterministic transmission of sensory or somatomotor information ensures robust adaptation of animals to their physical environment, unpredictability of behavioral responses initiated by decisions made by the brain cortex is evolutionary advantageous for avoiding predators. Thus, free will may have a survival value and could be optimized through natural selection.
Financial Return Distributions: Past, Present, and COVID-19
Authors: Marcin Wątorek, Jarosław Kwapień, Stanisław Drożdż
Date: 2021-07-14 00:00:00

We analyze the price return distributions of currency exchange rates, cryptocurrencies, and contracts for differences (CFDs) representing stock indices, stock shares, and commodities. Based on recent data from the years 2017--2020, we model tails of the return distributions at different time scales by using power-law, stretched exponential, and $q$-Gaussian functions. We focus on the fitted function parameters and how they change over the years by comparing our results with those from earlier studies and find that, on the time horizons of up to a few minutes, the so-called "inverse-cubic power-law" still constitutes an appropriate global reference. However, we no longer observe the hypothesized universal constant acceleration of the market time flow that was manifested before in an ever faster convergence of empirical return distributions towards the normal distribution. Our results do not exclude such a scenario but, rather, suggest that some other short-term processes related to a current market situation alter market dynamics and may mask this scenario. Real market dynamics is associated with a continuous alternation of different regimes with different statistical properties. An example is the COVID-19 pandemic outburst, which had an enormous yet short-time impact on financial markets. We also point out that two factors -- speed of the market time flow and the asset cross-correlation magnitude -- while related (the larger the speed, the larger the cross-correlations on a given time scale), act in opposite directions with regard to the return distribution tails, which can affect the expected distribution convergence to the normal distribution.
Comparing Intellectual property policy in the Global North and South -- A one-size-fits-all policy for economic prosperity?
Authors: S Sidhartha Narayan, Malavika Ranjan, Madhumitha Raghuraman
Date: 2021-07-14 00:00:00
Tasks: All

This paper attempts to analyse policymaking in the field of Intellectual Property (IP) as an instrument of economic growth across the Global North and South. It begins by studying the links between economic growth and IP, followed by an understanding of Intellectual Property Rights (IPR) development in the US, a leading proponent of robust IPR protection internationally. The next section compares the IPR in the Global North and South and undertakes an analysis of the diverse factors that result in these differences. The paper uses the case study of the Indian Pharmaceutical Industry to understand how IPR may differentially affect economies and conclude that there may not yet be a one size fits all policy for the adoption of Intellectual Property Rights.
Winners and losers of immigration
Authors: Davide Fiaschi, Cristina Tealdi
Date: 2021-07-14 00:00:00

We study the impact of low-skilled immigration in a general equilibrium search and matching model, with heterogeneous workers producing intermediate goods, which are used in the production of two final goods. In addition to complementarity/substitution between native and non-native workers, we explore how immigration affects the relative prices of final goods and wages. An application to Italy reveals a positive contribution of immigrants to GDP, public revenues, and the per capita provision of public goods. Employers and employees in the high-skilled-intensive market are winners, while losers are employers in the low-skilled-intensive market. The effects on low-skilled employees are instead inconclusive.
Low complexity online convolutional beamforming
Authors: Sebastian Braun, Ivan Tashev
Date: 2021-07-14 00:00:00

Convolutional beamformers integrate the multichannel linear prediction model into beamformers, which provide good performance and optimality for joint dereverberation and noise reduction tasks. While longer filters are required to model long reverberation times, the computational burden of current online solutions grows fast with the filter length and number of microphones. In this work, we propose a low complexity convolutional beamformer using a Kalman filter derived affine projection algorithm to solve the adaptive filtering problem. The proposed solution is several orders of magnitude less complex than comparable existing solutions while slightly outperforming them on the REVERB challenge dataset.
A Low Complexity Learning-based Channel Estimation for OFDM Systems with Online Training
Authors: Kai Mei, Jun Liu, Xiaoying Zhang, Kuo Cao, Nandana Rajatheva, Jibo Wei
Date: 2021-07-14 00:00:00
Tasks: BIG-bench Machine Learning

In this paper, we devise a highly efficient machine learning-based channel estimation for orthogonal frequency division multiplexing (OFDM) systems, in which the training of the estimator is performed online. A simple learning module is employed for the proposed learning-based estimator. The training process is thus much faster and the required training data is reduced significantly. Besides, a training data construction approach utilizing least square (LS) estimation results is proposed so that the training data can be collected during the data transmission. The feasibility of this novel construction approach is verified by theoretical analysis and simulations. Based on this construction approach, two alternative training data generation schemes are proposed. One scheme transmits additional block pilot symbols to create training data, while the other scheme adopts a decision-directed method and does not require extra pilot overhead. Simulation results show the robustness of the proposed channel estimation method. Furthermore, the proposed method shows better adaptation to practical imperfections compared with the conventional minimum mean-square error (MMSE) channel estimation. It outperforms the existing machine learning-based channel estimation techniques under varying channel conditions.
Impact of Rotary-Wing UAV Wobbling on Millimeter-wave Air-to-Ground Wireless Channel
Authors: Songjiang Yang, Zitian Zhang, Jiliang Zhang, Jie Zhang
Date: 2021-07-14 00:00:00

Millimeter-wave rotary-wing (RW) unmanned aerial vehicle (UAV) air-to-ground (A2G) links face unpredictable Doppler effect arising from the inevitable wobbling of RW UAV. Moreover, the time-varying channel characteristics during transmission lead to inaccurate channel estimation, which in turn results in the deteriorated bit error probability performance of the UAV A2G link. This paper studies the impact of mechanical wobbling on the Doppler effect of the millimeter-wave wireless channel between a hovering RW UAV and a ground node. Our contributions of this paper lie in: i) modeling the wobbling process of a hovering RW UAV; ii) developing an analytical model to derive the channel temporal autocorrelation function (ACF) for the millimeter-wave RW UAV A2G link in a closed-form expression; and iii) investigating how RW UAV wobbling impacts the Doppler effect on the millimeter-wave RW UAV A2G link. Numerical results show that different RW UAV wobbling patterns impact the amplitude and the frequency of ACF oscillation in the millimeter-wave RW UAV A2G link. For UAV wobbling, the channel temporal ACF decreases quickly and the impact of the Doppler effect is significant on the millimeter-wave A2G link.
A Unified Formula of the Optimal Portfolio for Piecewise Hyperbolic Absolute Risk Aversion Utilities
Authors: Zongxia Liang, Yang Liu, Ming Ma, Rahul Pothi Vinoth
Date: 2021-07-14 00:00:00
Tasks: Financial Analysis, Management

We propose a general family of piecewise hyperbolic absolute risk aversion (PHARA) utilities, including many classic and non-standard utilities as examples. A typical application is the composition of a HARA preference and a piecewise linear payoff in asset allocation. We derive a unified closed-form formula of the optimal portfolio, which is a four-term division. The formula has clear economic meanings, reflecting the behavior of risk aversion, risk seeking, loss aversion and first-order risk aversion. We conduct a general asymptotic analysis to the optimal portfolio, which directly serves as an analytical tool for financial analysis. We compare this PHARA portfolio with those of other utility families both analytically and numerically. One main finding is that risk-taking behaviors are greatly increased by non-concavity and reduced by non-differentiability of the PHARA utility. Finally, we use financial data to test the performance of the PHARA portfolio in the market.
A General Approach for Parisian Stopping Times under Markov Processes
Authors: Gongqiu Zhang, Lingfei Li
Date: 2021-07-14 00:00:00

We propose a method based on continuous time Markov chain approximation to compute the distribution of Parisian stopping times and price Parisian options under general one-dimensional Markov processes. We prove the convergence of the method under a general setting and obtain sharp estimate of the convergence rate for diffusion models. Our theoretical analysis reveals how to design the grid of the CTMC to achieve faster convergence. Numerical experiments are conducted to demonstrate the accuracy and efficiency of our method for both diffusion and jump models. To show the versality of our approach, we develop extensions for multi-sided Parisian stopping times, the joint distribution of Parisian stopping times and first passage times, Parisian bonds and for more sophisticated models like regime-switching and stochastic volatility models.
Statistical spatial analysis for cryo-electron tomography
Authors: Antonio Martinez-Sanchez, Wolfgang Baumeister, Vladan Lučić
Date: 2021-07-14 00:00:00
Tasks: Electron Tomography

Cryo-electron tomography (cryo-ET) is uniquely suited to precisely localize macromolecular complexes in situ, that is in a close-to-native state within their cellular compartments, in three-dimensions at high resolution. Point pattern analysis (PPA) allows quantitative characterization of the spatial organization of particles. However, current implementations of PPA functions are not suitable for applications to cryo-ET data because they do not consider the real, typically irregular 3D shape of cellular compartments and molecular complexes. Here, we designed and implemented first and the second-order, uni- and bivariate PPA functions in a Python package for statistical spatial analysis of particles located in three dimensional regions of arbitrary shape, such as those encountered in cellular cryo-ET imaging (PyOrg). To validate the implemented functions, we applied them to specially designed synthetic datasets. This allowed us to find the algorithmic solutions that provide the best accuracy and computational performance, and to evaluate the precision of the implemented functions. Applications to experimental data showed that despite the higher computational demand, the use of the second-order functions is advantageous to the first-order ones, because they allow characterization of the particle organization and statistical inference over a range of distance scales, as well as the comparative analysis between experimental groups comprising multiple tomograms. Altogether, PyOrg is a versatile, precise, and efficient open-source software for reliable quantitative characterization of macromolecular organization within cellular compartments imaged in situ by cryo-ET, as well as to other 3D imaging systems where real-size particles are located within regions possessing complex geometry.
Modelling Neuronal Behaviour with Time Series Regression: Recurrent Neural Networks on C. Elegans Data
Authors: Gonçalo Mestre, Ruxandra Barbulescu, Arlindo L. Oliveira, L. Miguel Silveira
Date: 2021-07-01 00:00:00
Tasks: Benchmarking, regression, Time Series, Time Series Analysis, Time Series Regression

Given the inner complexity of the human nervous system, insight into the dynamics of brain activity can be gained from understanding smaller and simpler organisms, such as the nematode C. Elegans. The behavioural and structural biology of these organisms is well-known, making them prime candidates for benchmarking modelling and simulation techniques. In these complex neuronal collections, classical, white-box modelling techniques based on intrinsic structural or behavioural information are either unable to capture the profound nonlinearities of the neuronal response to different stimuli or generate extremely complex models, which are computationally intractable. In this paper we show how the nervous system of C. Elegans can be modelled and simulated with data-driven models using different neural network architectures. Specifically, we target the use of state of the art recurrent neural networks architectures such as LSTMs and GRUs and compare these architectures in terms of their properties and their accuracy as well as the complexity of the resulting models. We show that GRU models with a hidden layer size of 4 units are able to accurately reproduce with high accuracy the system's response to very different stimuli.
Non-isomorphic Inter-modality Graph Alignment and Synthesis for Holistic Brain Mapping
Authors: Islem Mhiri, Ahmed Nebli, Mohamed Ali Mahjoub, Islem Rekik
Date: 2021-06-30 00:00:00
Tasks: Generative Adversarial Network, Graph Generation

Brain graph synthesis marked a new era for predicting a target brain graph from a source one without incurring the high acquisition cost and processing time of neuroimaging data. However, existing multi-modal graph synthesis frameworks have several limitations. First, they mainly focus on generating graphs from the same domain (intra-modality), overlooking the rich multimodal representations of brain connectivity (inter-modality). Second, they can only handle isomorphic graph generation tasks, limiting their generalizability to synthesizing target graphs with a different node size and topological structure from those of the source one. More importantly, both target and source domains might have different distributions, which causes a domain fracture between them (i.e., distribution misalignment). To address such challenges, we propose an inter-modality aligner of non-isomorphic graphs (IMANGraphNet) framework to infer a target graph modality based on a given modality. Our three core contributions lie in (i) predicting a target graph (e.g., functional) from a source graph (e.g., morphological) based on a novel graph generative adversarial network (gGAN); (ii) using non-isomorphic graphs for both source and target domains with a different number of nodes, edges and structure; and (iii) enforcing the predicted target distribution to match that of the ground truth graphs using a graph autoencoder to relax the designed loss oprimization. To handle the unstable behavior of gGAN, we design a new Ground Truth-Preserving (GT-P) loss function to guide the generator in learning the topological structure of ground truth brain graphs. Our comprehensive experiments on predicting functional from morphological graphs demonstrate the outperformance of IMANGraphNet in comparison with its variants. This can be further leveraged for integrative and holistic brain mapping in health and disease.
A Comparative Genomic Analysis of Coronavirus Families Using Chaos Game Representation and Fisher-Shannon Complexity
Authors: S. K. Laha
Date: 2021-07-13 00:00:00
Tasks: Information Plane

From its first emergence in Wuhan, China in December, 2019 the COVID-19 pandemic has caused unprecedented health crisis throughout the world. The novel coronavirus disease is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) which belongs to the coronaviridae family. In this paper, a comparative genomic analysis of eight coronaviruses namely Human coronavirus OC43 (HCoV-OC43), Human coronavirus HKU1 (HCoV-HKU1), Human coronavirus 229E (HCoV-229E), Human coronavirus NL63 (HCoV-NL63), Severe acute respiratory syndrome coronavirus (SARS-CoV), Middle East respiratory syndrome-related coronavirus (MERS-CoV), Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and Bat coronavirus RaTG13 has been carried out using Chaos Game Representation and Fisher-Shannon Complexity (CGR-FSC) measure. Chaos Game Representation (CGR) is a unique alignment-free method to visualize one dimensional DNA sequence in a two-dimensional fractal-like pattern. The two-dimensional CGR pattern is then quantified by Fisher-Shannon Complexity (FSC) measure. The CGR-FSC can effectively identify the viruses uniquely and their similarity/dissimilarity can be revealed in the Fisher-Shannon Information Plane (FSIP).
The evolution of the GALactose utilization pathway in budding yeasts
Authors: Marie-Claire Harrison, Abigail L. LaBella, Chris Todd Hittinger, Antonis Rokas
Date: 2021-07-14 00:00:00
Tasks: Clustering

The Leloir galactose utilization or GAL pathway of budding yeasts, including that of the baker's yeast Saccharomyces cerevisiae and the opportunistic human pathogen Candida albicans, breaks down the sugar galactose for energy and biomass production. The GAL pathway has long served as a model system for understanding how eukaryotic metabolic pathways, including their modes of regulation, evolve. More recently, the physical linkage of the structural genes GAL1, GAL7, and GAL10 in diverse budding yeast genomes has been used as a model for understanding the evolution of gene clustering. In this opinion, we summarize exciting recent work on three different aspects of this iconic pathway's evolution: gene cluster organization, GAL gene regulation, and the population genetics of the GAL pathway.
Total Effect Analysis of Vaccination on Household Transmission in the Office for National Statistics COVID-19 Infection Survey
Authors: Thomas House, Lorenzo Pellis, Emma Pritchard, Angela R. McLean, A. Sarah Walker
Date: 2021-07-14 00:00:00
Tasks: Survey

We investigate the distribution of numbers of secondary cases in households in the Office for National Statistics COVID-19 Infection Survey (ONS CIS), stratified by timing of vaccination and infection in the households. This shows a total effect of a statistically significant approximate halving of the secondary attack rate in households following vaccination.