pwc-archive/papers-with-abstracts huggingface dataset:
BCI learning induces core-periphery reorganization in M/EEG multiplex brain networks
Authors: Marie-Constance Corsi, Mario Chavez, Denis Schwartz, Nathalie George, Laurent Hugueville, Ari E. Kahn, Sophie Dupont, Danielle S. Bassett, Fabrizio De Vico Fallani
Date: 2020-10-26 00:00:00
Tasks: Decision Making, EEG, Electroencephalogram (EEG)

Brain-computer interfaces (BCIs) constitute a promising tool for communication and control. However, mastering non-invasive closed-loop systems remains a learned skill that is difficult to develop for a non-negligible proportion of users. The involved learning process induces neural changes associated with a brain network reorganization that remains poorly understood. To address this inter-subject variability, we adopted a multilayer approach to integrate brain network properties from electroencephalographic (EEG) and magnetoencephalographic (MEG) data resulting from a four-session BCI training program followed by a group of healthy subjects. Our method gives access to the contribution of each layer to multilayer network that tends to be equal with time. We show that regardless the chosen modality, a progressive increase in the integration of somatosensory areas in the alpha band was paralleled by a decrease of the integration of visual processing and working memory areas in the beta band. Notably, only brain network properties in multilayer network correlated with future BCI scores in the alpha2 band: positively in somatosensory and decision-making related areas and negatively in associative areas. Our findings cast new light on neural processes underlying BCI training. Integrating multimodal brain network properties provides new information that correlates with behavioral performance and could be considered as a potential marker of BCI learning.
Pairwise heuristic sequence alignment algorithm based on deep reinforcement learning
Authors: Yong Joon Song, Dong Jin Ji, Hye In Seo, Gyu Bum Han, Dong Ho Cho
Date: 2020-10-26 00:00:00
Tasks: Deep Reinforcement Learning, Multiple Sequence Alignment, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Various methods have been developed to analyze the association between organisms and their genomic sequences. Among them, sequence alignment is the most frequently used for comparative analysis of biological genomes. However, the traditional sequence alignment method is considerably complicated in proportion to the sequences' length, and it is significantly challenging to align long sequences such as a human genome. Currently, several multiple sequence alignment algorithms are available that can reduce the complexity and improve the alignment performance of various genomes. However, there have been relatively fewer attempts to improve the alignment performance of the pairwise alignment algorithm. After grasping these problems, we intend to propose a new sequence alignment method using deep reinforcement learning. This research shows the application method of the deep reinforcement learning to the sequence alignment system and the way how the deep reinforcement learning can improve the conventional sequence alignment method.
Unleashing the Potential of Networked Tethered Flying Platforms for B5G/6G: Prospects, Challenges, and Applications
Authors: Baha Eddine Youcef Belmekki, Mohamed-Slim Alouini
Date: 2020-10-26 00:00:00

Researchers are currently speculating about what the role of beyond fifth-generation (B5G) or sixth generation (6G) wireless systems will be. Several use cases and applications are proposed, ranging from enhanced mobile broadband communications and massive ultra-reliable low-latency Communication to holographic telepresence and tactile communications. One crucial and pivotal prospect of 6G is worldwide connectivity. Today, nearly 4 billion people do not have access to an internet connection. 6G not only intends to hyper-connect the already connected, but to bridge the digital divide by connecting the unconnected. For that purpose, 6G architectures will integrate airborne communications as they are considered key enablers for the next generation of wireless systems. Airborne communications can be done via free-flying platforms and tethered flying platforms. Free-flying platforms, such as unmanned aerial vehicles (UAVs) and high-altitude platforms (HAPs) have received great interest during the last decade. However, they lack endurance and backhaul capacity. Networked Tethered Flying Platforms (NTFPs), on the other hand, are enjoying the benefits of free-flying platforms while overcoming their limitations by providing a continuous supply of power and data via the tether. They are also cost-efficient and have good green credentials. Tether platforms have a wide range of applications beyond communications, such as energy harvesting, entertainment, science, research, public safety, disaster relief, government, and defense. In this survey paper, we intend to provide an extensive and comprehensive overview of NTFPs.
Power Scaling Law Analysis and Phase Shift Optimization of RIS-aided Massive MIMO Systems with Statistical CSI
Authors: Kangda Zhi, Cunhua Pan, Hong Ren, Kezhi Wang
Date: 2020-10-26 00:00:00

This paper considers an uplink reconfigurable intelligent surface (RIS)-aided massive multiple-input multiple-output (MIMO) system with statistical channel state information (CSI). The RIS is deployed to help conventional massive MIMO networks serve the users in the dead zone. We consider the Rician channel model and exploit the long-time statistical CSI to design the phase shifts of the RIS, while the maximum ratio combination (MRC) technique is applied for the active beamforming at the base station (BS) relying on the instantaneous CSI. Firstly, we reveal the power scaling laws and derive the closed-form expressions for the uplink achievable rate which holds for arbitrary numbers of base station (BS) antennas. Based on the theoretical expressions, we discuss the rate performance under some special cases and provide the average asymptotic rate when using random phase shifts. Then, we consider the sum-rate maximization and the minimum user rate maximization problems by optimizing the phase shifts at the RIS. However, these two optimization problems are challenging to solve due to the complicated data rate expression. To solve these problems, we propose a novel genetic algorithm (GA) with low complexity but can achieve considerable performance. Finally, extensive simulations are provided to validate the benefits by integrating RIS into conventional massive MIMO systems. Besides, our simulations demonstrate the feasibility of deploying large-size but low-resolution RIS in massive MIMO systems.
A Finite Element Approach to the Numerical Solutions of Leland's Mode
Authors: Dongming Wei, Yogi Ahmad Erlangga, Gulzat Zhumakhanova
Date: 2020-10-26 00:00:00

In this paper, finite element method is applied to Leland's model for numerical simulation of option pricing with transaction costs. Spatial finite element models based on P1 and/or P2 elements are formulated in combination with a Crank-Nicolson-type temporal scheme. The temporal scheme is implemented using the Rannacher approach. Examples with several sets of parameter values are presented and compared with finite difference results in the literature. Spatial-temporal mesh-size ratios are observed for controlling the stability of our method. Our results compare favorably with the finite difference results in the literature for the model.
Exploiting Cell-Free Massive MIMO for Enabling Simultaneous Wireless Information and Power Transfer
Authors: Diluka Loku Galappaththige, Rajan Shrestha, Gayan Amarasuriya Aruma Baduge
Date: 2020-10-26 00:00:00
Tasks: Fairness

The performance of simultaneous wireless information and power transfer (SWIPT) in downlink (DL) cell-free massive multiple-input multiple-output (MIMO) is investigated. Tight approximations to the DL harvested energy and the DL/uplink (UL) achievable rates are derived for two practical channel state information (CSI) cases by using a non-linear energy harvesting model for time-switching and power-splitting protocols. Max-min fairness-based transmit power control policies are employed to mitigate the deleterious near-far effects caused by distributed transmissions/receptions in cell-free massive MIMO. The achievable common DL energy-rate trade-off is derived, and thereby, it is shown that the proposed max-min power control guarantees user-fairness regardless of near-far effects in terms of both harvested energy and achievable rate. The benefits of user estimated DL CSI to boost the SWIPT performance are explored. These performance metrics are compared against those of the conventional co-located massive MIMO, and thereby, it is revealed that the reduction of path-losses and lower average transmit powers offered by cell-free massive MIMO can be exploited to boost the energy-rate trade-off of SWIPT at the expense of increased backhaul requirements.
The finite size effects and the two-state paradigm of protein folding
Authors: Artem Badasyan, Matjaz Valant, Joze Grdadolnik, Vladimir N. Uversky
Date: 2020-10-26 00:00:00
Tasks: Protein Folding

The coil to globule transition of the polypeptide chain is the physical phenomenon behind the folding of globular proteins. Globular proteins with a single domain usually consist of about 30 to 100 amino acid residues, and this finite size extends the transition interval of the coil-globule phase transition. Based on the pedantic derivation of the two-state model, we introduce the number of amino acid residues of a polypeptide chain as a parameter in the expressions for two cooperativity measures and reveal their physical significance. We conclude that the $k_2$ measure, related to the degeneracy of the denatured state, describes the number of cooperative units involved in the transition; additionally is found that the famous condition $k_2=1$ is just the necessary condition to classify the protein as the two-state folder. We also find that $\Omega_c$ is simply proportional to the square of the transition interval. This fact allows us to perform the classical size scaling analysis of the coil-globule phase transition. Moreover, these two measures are shown to describe different characteristics of protein folding.
Frequency Response Characteristic (FRC) Curve and Fast Frequency Response Assessment in High Renewable Power Systems
Authors: Shutang You
Date: 2020-10-21 00:00:00

This paper introduces a frequency response characteristic (FRC) curve and its application in high renewable power systems. In addition, the paper presents a method for fast frequency response assessment and frequency nadir prediction without performing dynamic simulations using detailed models. The proposed FRC curve and fast frequency response assessment method are useful for operators to understand frequency response performance of high renewable systems in real time.
Wind Power Transmission System Integration -- a Case Study of China Wind Power Base
Authors: Jianxue Wang, Shutang You, Xingzhong Bai, Mingqiao Peng
Date: 2020-10-22 00:00:00

Due to a series of supporting policies in recent years, China wind power has developed rapidly through a large-scale and centralized mode. This paper analyzes the two major concerns faced by wind power development in China: wind generation reliability and wind energy balancing. More specifically, wind farm tripping-off-grid incidents and wind power curtailment issues, which caused huge economical loss, are investigated in details. Based on operation experience of large wind power bases, technical recommendations and economic incentives are proposed to improve wind power integration and power grid reliability. As a summary and outlook of wind power development in China, this paper provides a reference on future wind power development for other countries.
Derivatives Pricing in Non-Arbitrage Market
Authors: N. S. Gonchar
Date: 2020-10-26 00:00:00

The general method is proposed for constructing a family of martingale measures for a wide class of evolution of risky assets. The sufficient conditions are formulated for the evolution of risky assets under which the family of equivalent martingale measures to the original measure is a non-empty set. The set of martingale measures is constructed from a set of strictly nonneg ative random variables, satisfying certain conditions. The inequalities are obtained for the non-negative random variables satisfying certain conditions. Using these inequalities, a new simple proof of optional decomposition theorem for the nonnegative super-martingale is proposed. The family of spot measures is introduced and the representation is found for them. The conditions are found under which each martingale measure is an integral over the set of spot measures. On the basis of nonlinear processes such as ARCH and GARCH, the parametric family of random processes is introduced for which the interval of non-arbitrage prices are found. The formula is obtained for the fair price of the contract with option of European type for the considered parametric processes. The parameters of the introduced random processes are estimated and the estimate is found at which the fair price of contract with option is the least.
A Systematic Evaluation of Coding Strategies for Sparse Binary Images
Authors: Rahul Mohideen Kaja Mohideen, Pascal Peter, Joachim Weickert
Date: 2020-10-26 00:00:00
Tasks: Image Compression

Inpainting-based compression represents images in terms of a sparse subset of its pixel data. Storing the carefully optimised positions of known data creates a lossless compression problem on sparse and often scattered binary images. This central issue is crucial for the performance of such codecs. Since it has only received little attention in the literature, we conduct the first systematic investigation of this problem so far. To this end, we first review and compare a wide range of existing methods from image compression and general purpose coding in terms of their coding efficiency and runtime. Afterwards, an ablation study enables us to identify and isolate the most useful components of existing methods. With context mixing, we combine those ingredients into new codecs that offer either better compression ratios or a more favourable trade-off between speed and performance.
Social learning under inferential attacks
Authors: Konstantinos Ntemos, Virginia Bordignon, Stefan Vlaski, Ali H. Sayed
Date: 2020-10-26 00:00:00

A common assumption in the social learning literature is that agents exchange information in an unselfish manner. In this work, we consider the scenario where a subset of agents aims at driving the network beliefs to the wrong hypothesis. The adversaries are unaware of the true hypothesis. However, they will "blend in" by behaving similarly to the other agents and will manipulate the likelihood functions used in the belief update process to launch inferential attacks. We will characterize the conditions under which the network is misled. Then, we will explain that it is possible for such attacks to succeed by showing that strategies exist that can be adopted by the malicious agents for this purpose. We examine both situations in which the agents have minimal or no information about the network model.
Optimizing Coverage and Capacity in Cellular Networks using Machine Learning
Authors: Ryan M. Dreifuerst, Samuel Daulton, Yuchen Qian, Paul Varkey, Maximilian Balandat, Sanjay Kasturia, Anoop Tomar, Ali Yazdan, Vish Ponnampalam, Robert W. Heath
Date: 2020-10-22 00:00:00
Tasks: Bayesian Optimization, BIG-bench Machine Learning, Reinforcement Learning (RL)

Wireless cellular networks have many parameters that are normally tuned upon deployment and re-tuned as the network changes. Many operational parameters affect reference signal received power (RSRP), reference signal received quality (RSRQ), signal-to-interference-plus-noise-ratio (SINR), and, ultimately, throughput. In this paper, we develop and compare two approaches for maximizing coverage and minimizing interference by jointly optimizing the transmit power and downtilt (elevation tilt) settings across sectors. To evaluate different parameter configurations offline, we construct a realistic simulation model that captures geographic correlations. Using this model, we evaluate two optimization methods: deep deterministic policy gradient (DDPG), a reinforcement learning (RL) algorithm, and multi-objective Bayesian optimization (BO). Our simulations show that both approaches significantly outperform random search and converge to comparable Pareto frontiers, but that BO converges with two orders of magnitude fewer evaluations than DDPG. Our results suggest that data-driven techniques can effectively self-optimize coverage and capacity in cellular networks.
On The Joint Effects of HPA Nonlinearities and IQ Imbalance On Mixed RF/FSO Cooperative Systems
Authors: Elyes Balti, Brian K. Johnson
Date: 2020-10-26 00:00:00

In this work, we provide a framework analysis of dual-hop hybrid Millimeter Wave Radio Frequency (RF)/Free Space Optical (FSO) MIMO relaying system. The source is equipped with multiple antennas and employs conjugate beamforming while the destination consists of multiple apertures with selection combining. The system also consists of a relay operating at amplify-and-forward mode. The RF channels are subject to Nakagami-m fading while the optical links experience the M\'alaga distribution. In addition, we introduce the impairments to the relay and receiver. In fact, the relay is impaired by the High Power Amplifier (HPA) nonlinearities while the receiver suffers from the In phase and Quadrature Imbalance. Moreover, we assume two types of HPA nonlinearities impairments called Soft Envelope Limiter (SEL) and Traveling Wave Tube Amplifier (TWTA). Closed-forms of the outage probability, the bit error probability, and the ergodic capacity are derived. Capitalizing on these performances, we derive the high SNR asymptotes to unpack insightful metrics such as the diversity gain. We also address the impacts of some key factors on the system performance such as the impairments, the interferers, the number of antennas and apertures and the pointing errors, etc. Finally, the analytical expressions are confirmed by Monte Carlo simulation.
Linear Predictive Coding for Acute Stress Prediction from Computer Mouse Movements
Authors: Lawrence H. Kim, Rahul Goel, Jia Liang, Mert Pilanci, Pablo E. Paredes
Date: 2020-10-26 00:00:00
Tasks: Binary Classification

Prior work demonstrated the potential of using the Linear Predictive Coding (LPC) filter to approximate muscle stiffness and damping from computer mouse movements to predict acute stress levels of users. Theoretically, muscle stiffness and damping in the arm can be estimated using a mass-spring-damper (MSD) biomechanical model. However, the damping frequency (i.e., stiffness) and damping ratio values derived using LPC were not yet compared with those from a theoretical MSD model. This work demonstrates that the damping frequency and damping ratio from LPC are significantly correlated with those from an MSD model, thus confirming the validity of using LPC to infer muscle stiffness and damping. We also compare the stress level binary classification performance using the values from LPC and MSD with each other and with neural network-based baselines. We found comparable performance across all conditions demonstrating LPC and MSD model-based stress prediction efficacy, especially for longer mouse trajectories. Clinical relevance: This work demonstrates the validity of the LPC filter to approximate muscle stiffness and damping and predict acute stress from computer mouse movements.
Deep learning for CVA computations of large portfolios of financial derivatives
Authors: Kristoffer Andersson, Cornelis W. Oosterlee
Date: 2020-10-26 00:00:00

In this paper, we propose a neural network-based method for CVA computations of a portfolio of derivatives. In particular, we focus on portfolios consisting of a combination of derivatives, with and without true optionality, \textit{e.g.,} a portfolio of a mix of European- and Bermudan-type derivatives. CVA is computed, with and without netting, for different levels of WWR and for different levels of credit quality of the counterparty. We show that the CVA is overestimated with up to 25\% by using the standard procedure of not adjusting the exercise strategy for the default-risk of the counterparty. For the Expected Shortfall of the CVA dynamics, the overestimation was found to be more than 100\% in some non-extreme cases.
Numerical analysis of the strain distribution in skin domes formed upon the application of hypobaric pressure
Authors: Daniel Sebastia-Saez, Faiza Benaouda, Charlie Lim, Guoping Lian, Stuart Jones, Tao Chen, Liang Cui
Date: 2020-10-26 00:00:00

Suction cups are widely used in applications such as in measurement of mechanical properties of skin in vivo, in drug delivery devices or in acupuncture treatment. Understanding the mechanical response of skin under hypobaric pressure are of great importance for users of suction cups. The aims of this work are to assess the capability of linear elasticity (Young's modulus) or hyperelasticity in predicting hypobaric pressure induced 3D stretching of the skin. Using experiments and computational Finite Element Method modelling, this work demonstrated that although it was possible to predict the suction dome apex height using both linear elasticity and hyperelasticity for the typical range of hypobaric pressure in medical applications (up to -10 psi), linear elasticity theory showed limitations when predicting the strain distribution across the suction dome. The reason is that the stretch ratio reaches values exceeding the initial linear elastic stage of the stress-strain characteristic curve for skin. As a result, the linear elasticity theory overpredicts the stretch along the rim of domes where there is stress concentration. In addition, the modelling showed that the skin was compressed consistently along the thickness direction, leading to reduced thickness. Using hyperelasticity modelling to predict the 3D strain distribution paves the way to accurately design safe commercial products that interface with skin.
Modeling Long Cycles
Authors: Natasha Kang, Vadim Marmer
Date: 2020-10-26 00:00:00
Tasks: valid

Recurrent boom-and-bust cycles are a salient feature of economic and financial history. Cycles found in the data are stochastic, often highly persistent, and span substantial fractions of the sample size. We refer to such cycles as "long". In this paper, we develop a novel approach to modeling cyclical behavior specifically designed to capture long cycles. We show that existing inferential procedures may produce misleading results in the presence of long cycles, and propose a new econometric procedure for the inference on the cycle length. Our procedure is asymptotically valid regardless of the cycle length. We apply our methodology to a set of macroeconomic and financial variables for the U.S. We find evidence of long stochastic cycles in the standard business cycle variables, as well as in credit and house prices. However, we rule out the presence of stochastic cycles in asset market data. Moreover, according to our result, financial cycles as characterized by credit and house prices tend to be twice as long as business cycles.
MarbleNet: Deep 1D Time-Channel Separable Convolutional Neural Network for Voice Activity Detection
Date: 2020-10-26 00:00:00
Tasks: Action Detection, Activity Detection

We present MarbleNet, an end-to-end neural network for Voice Activity
Detection (VAD). MarbleNet is a deep residual network composed from blocks of
1D time-channel separable convolution, batch-normalization, ReLU and dropout
layers. When compared to a state-of-the-art VAD model, MarbleNet is able to
achieve similar performance with roughly 1/10-th the parameter cost. We further
conduct extensive ablation studies on different training methods and choices of
parameters in order to study the robustness of MarbleNet in real-world VAD
tasks.