pwc-archive/papers-with-abstracts huggingface dataset:
Axiomatic Formulation of the Optimal Transaction Cost Theory in the Legal Process through Cobb-Douglas Optimization
Authors: Kwadwo Osei Bonsu
Date: 2021-07-15 00:00:00

In a legal dispute, parties engage in a series of negotiations so as to arrive at a reasonable settlement. The parties need to present a fair and reasonable bargain in order to induce the WTA, willingness to accept of the plaintiff and the WTP, willingness to pay of the defendant. Cooperation can only be attained when the WTA of the plaintiff is less than or equal to the WTP of the defendant. From an economic perspective, the legal process can considered as market place of buying and selling claims. High transaction costs decrease the reasonable bargain, thereby making cooperation more appealing to the defendant. On the other hand, low or zero transaction costs means the reasonable bargain is only dependent on the expected gain from winning at trial and the settlement benefit thereby making cooperation more appealing to the plaintiff. Hence, we need to find a way of adjusting the number of settlements with the number of trials in order to maximize the social utility value. This paper uses Cobb-Douglas optimization to formulate an optimal transaction cost algorithm considering the confinement of a generalized legal framework.
Panicle Counting in UAV Images For Estimating Flowering Time in Sorghum
Authors: Enyu Cai, Sriram Baireddy, Changye Yang, Melba Crawford, Edward J. Delp
Date: 2021-07-15 00:00:00

Flowering time (time to flower after planting) is important for estimating plant development and grain yield for many crops including sorghum. Flowering time of sorghum can be approximated by counting the number of panicles (clusters of grains on a branch) across multiple dates. Traditional manual methods for panicle counting are time-consuming and tedious. In this paper, we propose a method for estimating flowering time and rapidly counting panicles using RGB images acquired by an Unmanned Aerial Vehicle (UAV). We evaluate three different deep neural network structures for panicle counting and location. Experimental results demonstrate that our method is able to accurately detect panicles and estimate sorghum flowering time.
Region-of-Interest Prioritised Sampling for Constrained Autonomous Exploration Systems
Authors: Protim Bhattacharjee, Martin Burger, Anko Boerner, Veniamin I. Morgenshtern
Date: 2021-07-15 00:00:00
Tasks: compressed sensing

Goal oriented autonomous operation of space rovers has been known to increase scientific output of a mission. In this work we present an algorithm, called the RoI Prioritised Sampling (RPS), that prioritises Region-of-Interests (RoIs) in an exploration scenario in order to utilise the limited resources of the imaging instrument on the rover effectively. This prioritisation is based on an estimator that evaluates the change in information content at consecutive spatial scales of the RoIs without calculating the finer scale reconstruction. The estimator, called the Refinement Indicator (RI), is motivated and derived. Multi-scale acquisition approaches, based on classical and multilevel compressed sensing, with respect to the single pixel camera architecture are discussed. The performance of the algorithm is verified on remote sensing images and compared with the state-of-the-art multi-resolution reconstruction algorithms. At the considered sub-sampling rates the RPS is shown to better utilise the system resources for reconstructing the RoIs.
Blind Image Quality Assessment for MRI with A Deep Three-dimensional content-adaptive Hyper-Network
Authors: Kehan Qi, Haoran Li, Chuyu Rong, Yu Gong, Cheng Li, Hairong Zheng, Shanshan Wang
Date: 2021-07-13 00:00:00
Tasks: Blind Image Quality Assessment, Image Quality Assessment

Image Quality Assessment (IQA) is of great value in the workflow of Magnetic Resonance Imaging (MRI)-based analysis. Blind IQA (BIQA) methods are especially required since high-quality reference MRI images are usually not available. Recently, many efforts have been devoted to developing deep learning-based BIQA approaches. However, the performance of these methods is limited due to the utilization of simple content-non-adaptive network parameters and the waste of the important 3D spatial information of the medical images. To address these issues, we design a 3D content-adaptive hyper-network for MRI BIQA. The overall 3D configuration enables the exploration of comprehensive 3D spatial information from MRI images, while the developed content-adaptive hyper-network contributes to the self-adaptive capacity of network parameters and thus, facilitates better BIQA performance. The effectiveness of the proposed method is extensively evaluated on the open dataset, MRIQC. Promising performance is achieved compared with the corresponding baseline and 4 state-of-the-art BIQA methods. We make our code available at \url{https://git.openi.org.cn/SIAT_Wangshanshan/HyS-Net}.
Graph-Embedded Multi-Agent Learning for Smart Reconfigurable THz MIMO-NOMA Networks
Authors: Xiaoxia Xu, Qimei Chen, Xidong Mu, Yuanwei Liu, Hao Jiang
Date: 2021-07-15 00:00:00
Tasks: Deep Reinforcement Learning

With the accelerated development of immersive applications and the explosive increment of internet-of-things (IoT) terminals, 6G would introduce terahertz (THz) massive multiple-input multiple-output non-orthogonal multiple access (MIMO-NOMA) technologies to meet the ultra-high-speed transmission and massive connectivity requirements. Nevertheless, the unreliability of THz transmissions and the extreme heterogeneity of device requirements pose critical challenges for practical applications. To address these challenges, we propose a novel smart reconfigurable THz MIMO-NOMA framework, which can realize customizable and intelligent communications by flexibly and coordinately reconfiguring hybrid beams through the cooperation between access points (APs) and reconfigurable intelligent surfaces (RISs). The optimization problem is formulated as a decentralized partially-observable Markov decision process (Dec-POMDP) to maximize the network energy efficiency, while guaranteeing the diversified users' performance, via a joint RIS element selection, coordinated discrete phase-shift control, and power allocation strategy. To solve the above non-convex, strongly coupled, and highly complex mixed integer nonlinear programming (MINLP) problem, we propose a novel multi-agent deep reinforcement learning (MADRL) algorithm, namely graph-embedded value-decomposition actor-critic (GE-VDAC), that embeds the interaction information of agents, and learns a locally optimal solution through a distributed policy. Numerical results demonstrate that the proposed algorithm achieves highly customized communications and outperforms traditional MADRL algorithms.
Two-Stage Channel Estimation for Hybrid RIS Assisted MIMO Systems
Authors: Rafaela Schroeder, Jiguang He, Glauber Brante, Markku Juntti
Date: 2021-07-15 00:00:00
Tasks: Vocal Bursts Valence Prediction

Reconfigurable intelligent surfaces (RISs) have been proposed as a key enabler to improve the coverage of the signals and mitigate the frequent blockages in millimeter wave (mmWave) multiple-input multiple-output (MIMO) communications. However, the channel state information (CSI) acquisition is one of the major challenges for the practical deployment of the RIS. The passive RIS without any baseband processing capabilities brings difficulty on the channel estimation (CE), since the individual channels or the cascaded one can be estimated only at base station (BS) via uplink training or mobile station (MS) via downlink training. In order to facilitate the CSI acquisition, we focus on the hybrid RIS architecture, where a small number of elements are active and able to receive and process the pilot signals at the RIS. The CE is performed in two stages by following the atomic norm minimization to recover the channel parameters, i.e., angles of departure (AoDs), angles of arrival (AoAs), and propagation path gains. Simulation results show that the proposed scheme can outperform the passive RIS CE under the same training overhead. Furthermore, we also study the theoretical performance limits in terms of mean square error (MSE) via Cram\'er-Rao lower bound (CRLB) analyses.
Untrained DNN for Channel Estimation of RIS-Assisted Multi-User OFDM System with Hardware Impairments
Authors: Nipuni Ginige, K. B. Shashika Manosha, Nandana Rajatheva, Matti Latva-aho
Date: 2021-07-13 00:00:00

Reconfigurable intelligent surface (RIS) is an emerging technology for improving performance in fifth-generation (5G) and beyond networks. Practically channel estimation of RIS-assisted systems is challenging due to the passive nature of the RIS. The purpose of this paper is to introduce a deep learning-based, low complexity channel estimator for the RIS-assisted multi-user single-input-multiple-output (SIMO) orthogonal frequency division multiplexing (OFDM) system with hardware impairments. We propose an untrained deep neural network (DNN) based on the deep image prior (DIP) network to denoise the effective channel of the system obtained from the conventional pilot-based least-square (LS) estimation and acquire a more accurate estimation. We have shown that our proposed method has high performance in terms of accuracy and low complexity compared to conventional methods. Further, we have shown that the proposed estimator is robust to interference caused by the hardware impairments at the transceiver and RIS.
FMNet: Latent Feature-wise Mapping Network for Cleaning up Noisy Micro-Doppler Spectrogram
Authors: Chong Tang, Wenda Li, Shelly Vishwakarma, Fangzhan Shi, Simon Julier, Kevin Chetty
Date: 2021-07-09 00:00:00
Tasks: Decoder

Micro-Doppler signatures contain considerable information about target dynamics. However, the radar sensing systems are easily affected by noisy surroundings, resulting in uninterpretable motion patterns on the micro-Doppler spectrogram. Meanwhile, radar returns often suffer from multipath, clutter and interference. These issues lead to difficulty in, for example motion feature extraction, activity classification using micro Doppler signatures ($\mu$-DS), etc. In this paper, we propose a latent feature-wise mapping strategy, called Feature Mapping Network (FMNet), to transform measured spectrograms so that they more closely resemble the output from a simulation under the same conditions. Based on measured spectrogram and the matched simulated data, our framework contains three parts: an Encoder which is used to extract latent representations/features, a Decoder outputs reconstructed spectrogram according to the latent features, and a Discriminator minimizes the distance of latent features of measured and simulated data. We demonstrate the FMNet with six activities data and two experimental scenarios, and final results show strong enhanced patterns and can keep actual motion information to the greatest extent. On the other hand, we also propose a novel idea which trains a classifier with only simulated data and predicts new measured samples after cleaning them up with the FMNet. From final classification results, we can see significant improvements.
Short-term Hourly Streamflow Prediction with Graph Convolutional GRU Networks
Authors: Muhammed Sit, Bekir Demiray, Ibrahim Demir
Date: 2021-07-07 00:00:00

The frequency and impact of floods are expected to increase due to climate change. It is crucial to predict streamflow, consequently flooding, in order to prepare and mitigate its consequences in terms of property damage and fatalities. This paper presents a Graph Convolutional GRUs based model to predict the next 36 hours of streamflow for a sensor location using the upstream river network. As shown in experiment results, the model presented in this study provides better performance than the persistence baseline and a Convolutional Bidirectional GRU network for the selected study area in short-term streamflow prediction.
Assign Hysteresis Parameter For Ericsson BTS Power Saving Algorithm Using Unsupervised Learning
Authors: Thaer Sahmoud, Wesam Ashor
Date: 2021-07-05 00:00:00
Tasks: Clustering

Gaza Strip suffers from a chronic electricity deficit that affects all industries including the telecommunication field, so there is a need to optimize and reduce power consumption of the telecommunication equipment. In this paper we propose a new model that helps GSM radio frequency engineers to choose the optimal value of hysteresis parameter for Ericsson BTS power saving algorithm which aims to switch OFF unused frequency channels, our model is based on unsupervised machine learning clustering K-means algorithm. By using our model with BTS power saving algorithm we reduce number of active TRX by 20.9%.
Requisitos para um rádio digital interativo no Brasil e América Latina
Authors: Rafael Diniz
Date: 2021-06-29 00:00:00

This article discusses the feasibility and requirements for using Ginga as the middleware of a Digital Radio System. Ginga was adopted by Brazil and several other countries in Latin America as the standard for interactivity in Digital TV (ISDB-T International). In this article the two digital radio standards being considered for adoption by Brazil are briefly presented, a discussion about the requirements for broadcast radio interactivity is presented, and finally a prototype of a Ginga interactive application for digital radio that was transmitted and received over the air using a digital radio standard is detailed.
A Method for Detecting Abnormal Data of Network Nodes Based on Convolutional Neural Network
Authors: Yihao Zang, Xianhao Shen, Shaohua Niu
Date: 2021-06-28 00:00:00
Tasks: Anomaly Detection, Self-Learning

Abnormal data detection is an important step to ensure the accuracy and reliability of node data in wireless sensor networks. In this paper, a data classification method based on convolutional neural network is proposed to solve the problem of data anomaly detection in wireless sensor networks. First, Normal data and abnormal data generated after injection fault are normalized and mapped to gray image as input data of the convolutional neural network. Then, based on the classical convolution neural network, three new convolutional neural network models are designed by designing the parameters of the convolutional layer and the fully connected layer. This model solves the problem that the performance of traditional detection algorithm is easily affected by relevant threshold through self-learning data characteristics of convolution layer. The experimental results show that this method has better detection performance and higher reliability.
An Overview of Machine Learning-aided Optical Performance Monitoring Techniques
Authors: Dativa K. Tizikara, Jonathan Serugunda, Andrew Katumba
Date: 2021-06-20 00:00:00
Tasks: BIG-bench Machine Learning

Future communication systems are faced with increased demand for high capacity, dynamic bandwidth, reliability and heterogeneous traffic. To meet these requirements, networks have become more complex and thus require new design methods and monitoring techniques, as they evolve towards becoming autonomous. Machine learning has come to the forefront in recent years as a promising technology to aid in this evolution. Optical fiber communications can already provide the high capacity required for most applications, however, there is a need for increased scalability and adaptability to changing user demands and link conditions. Accurate performance monitoring is an integral part of this transformation. In this paper we review optical performance monitoring techniques where machine learning algorithms have been applied. Moreover, since alot of OPM depends on knowledge of the signal type, we also review work for modulation format recognition and bitrate identification. We additionally briefly introduce a neuromorphic approach to OPM as an emerging technique that has only recently been applied to this domain.
Shape Complementarity Optimization of Antibody-Antigen Interfaces: the Application to SARS-CoV-2 Spike Protein
Authors: Alfredo De Lauro, Lorenzo Di Rienzo, Mattia Miotto, Pier Paolo Olimpieri, Edoardo Milanetti, Giancarlo Ruocco
Date: 2021-07-15 00:00:00
Tasks: Molecular Docking

Many factors influence biomolecules binding, and its assessment constitutes an elusive challenge in computational structural biology. In this respect, the evaluation of shape complementarity at molecular interfaces is one of the main factors to be considered. We focus on the particular case of antibody-antigen complexes to quantify the complementarities occurring at molecular interfaces. We relied on a method we recently developed, which employs the 2D Zernike descriptors, to characterize investigated regions with an ordered set of numbers summarizing the local shape properties. Collected a structural dataset of antibody-antigen complexes, we applied this method and we statistically distinguished, in terms of shape complementarity, pairs of interacting regions from non-interacting ones. Thus, we set up a novel computational strategy based on \textit{in-silico} mutagenesis of antibody binding site residues. We developed a Monte Carlo procedure to increase the shape complementarity between the antibody paratope and a given epitope on a target protein surface. We applied our protocol against several molecular targets in SARS-CoV-2 spike protein, known to be indispensable for viral cell invasion. We, therefore, optimized the shape of template antibodies for the interaction with such regions. As the last step of our procedure, we performed an independent molecular docking validation of the results of our Monte Carlo simulations.
Why and How Coronavirus Has Evolved to Be Uniquely Contagious, with Uniquely Successful Stable Vaccines
Authors: Marcelo A. Moret, Gilney F. Zebende, J. C. Phillips
Date: 2021-07-01 00:00:00

Spike proteins, 1200 amino acids, are divided into two nearly equal parts, S1 and S2. We review here phase transition theory, implemented quantitatively by thermodynamic scaling. The theory explains the evolution of Coronavirus extremely high contagiousness caused by a few mutations from CoV2003 to CoV2019 identified among hundreds in S1. The theory previously predicted the unprecedented success of spike-based vaccines. Here we analyze impressive successes by McClellan et al., 2020, in stabilizing their original S2P vaccine to Hexapro. Hexapro has expanded the two proline mutations of S2P, 2017, to six combined proline mutations in S2. Their four new mutations are the result of surveying 100 possibilities in their detailed structure-based context Our analysis, based on only sparse publicly available data, suggests new proline mutations could improve the Hexapro combination to Octapro or beyond.
Standardisation needs for COVID-19. Scoping exercise on potential standards gaps carried out among JRC scientists. Putting Science into Standards (PSIS)
Authors: Andreas Jenet, Samira Nik, Livia Mian, Stella-Zoe Schmidtler, Alessandro Annunziato, Montserrat Marin-Ferrer, Josephine McCourt, Anne Sophie Lequarre, Ashok Ganesh, Fabio Taucer
Date: 2021-07-15 00:00:00

The Joint Research Centre in collaboration with the European standardization bodies CEN and CENELEC launched a scoping exercise on standardization needs in response to COVID-19 and future pandemics. The purpose of the exercise was to identify ongoing harmonization initiatives, as was well as further standardization needs in relevant sectors such as artisanal reusable face masks, medical face masks, and social distancing in closed public or commercial spaces. An overview of already ongoing standardization activities relevant to COVID-19 in Spain and Italy illustrate, although fragmented and partially complete, the importance of standardization in key sectors for combatting pandemics, such as in health, social, safety and security. This report informs colleagues in European institutions and Member States about the crucial role standardization plays in the common efforts to overcome the COVID-19 pandemic. Examples include potential inputs to the drafting of guidelines, methods and or interoperability standards. Finally, the report also provides practical examples of agile standardization activities and deliverables that have the potential to enable the EU to respond more effectively and multilaterally to future crises. With this report we aim to raise awareness about the opportunities that standardization and harmonization can bring in the context of the COVID-19 pandemic.
Commentaries on the publication entitled Evidence for continuity of interstitial spaces across tissue and organ boundaries in humans
Authors: Hongyi Li
Date: 2021-07-15 00:00:00

Interstitial connective tissues are one of the four basic types of animal tissue and continuously distributed throughout the body. Illustrated by the movements of the micron-sized particles across the defined tissue boundaries and the continuously distributions of the fluorescently stained hyaluronic acid within the fibrous tissues around blood vessels and nerves, the authors of this paper suggested that interstitial fluid (ISF) is not fixed locally but would flow through fibrous matrices for a long-distance, even between more distant parts of the body. However,the fluid-filled interstitial spaces are similar with the interstitial tissue channel, lacking a distinct spatial structure for "free" interstitial fluid flow, which might be the key to comprehend the whereabouts and directions of a systemic fluid flow and circulation.
Computing near-optimal Value-at-Risk portfolios using Integer Programming techniques
Authors: Onur Babat, Juan C. Vera, Luis F. Zuluaga
Date: 2021-06-22 00:00:00
Tasks: Management

Value-at-Risk (VaR) is one of the main regulatory tools used for risk management purposes. However, it is difficult to compute optimal VaR portfolios; that is, an optimal risk-reward portfolio allocation using VaR as the risk measure. This is due to VaR being non-convex and of combinatorial nature. In particular, it is well known that the VaR portfolio problem can be formulated as a mixed integer linear program (MILP) that is difficult to solve with current MILP solvers for medium to large-scale instances of the problem. Here, we present an algorithm to compute near-optimal VaR portfolios that takes advantage of this MILP formulation and provides a guarantee of the solution's near-optimality. As a byproduct, we obtain an algorithm to compute tight lower bounds on the VaR portfolio problem that outperform related algorithms proposed in the literature for this purpose. The near-optimality guarantee provided by the proposed algorithm is obtained thanks to the relation between minimum risk portfolios satisfying a reward benchmark and the corresponding maximum reward portfolios satisfying a risk benchmark. These alternate formulations of the portfolio allocation problem have been frequently studied in the case of convex risk measures and concave reward functions. Here, this relationship is considered for general risk measures and reward functions. To illustrate the efficiency of the presented algorithm, numerical results are presented using historical asset returns from the US financial market.