pwc-archive/papers-with-abstracts huggingface dataset:
When RIS Meets GEO Satellite Communications: A New Sustainable Optimization Framework in 6G
Authors: Wali Ullah Khan, Eva Lagunas, Asad Mahmood, Symeon Chatzinotas, Bjorn Ottersten
Date: 2022-02-01 00:00:00

Reflecting intelligent surfaces (RIS) is a low-cost and energy-efficient solution to achieve high spectral efficiency in sixth-generation (6G) networks. The basic idea of RIS is to smartly reconfigure the signal propagation by using passive reflecting elements. On the other side, the demand of high throughput geostationary (GEO) satellite communications (SatCom) is rapidly growing to deliver broadband services in inaccessible/insufficient covered areas of terrestrial networks. This paper proposes a GEO SatCom network, where a satellite transmits the signal to a ground mobile terminal using multicarrier communications. To enhance the effective gain, the signal delivery from satellite to the ground mobile terminal is also assisted by RIS which smartly shift the phase of the signal towards ground terminal. We consider that RIS is mounted on a high building and equipped with multiple re-configurable passive elements along with smart controller. We jointly optimize the power allocation and phase shift design to maximize the channel capacity of the system. The joint optimization problem is formulated as nonconvex due to coupled variables which is hard to solve through traditional convex optimization methods. Thus, we propose a new $\epsilon-$optimal algorithm which is based on Mesh Adaptive Direct Search to obtain an efficient solution. Simulation results unveil the benefits of RIS-assisted SatCom in terms of system channel capacity.
Cardiac and respiratory motion extraction for MRI using Pilot Tone-a patient study
Authors: Chong Chen, Yingmin Liu, Orlando P. Simonetti, Matthew Tong, Ning Jin, Mario Bacher, Peter Speier, Rizwan Ahmad
Date: 2022-01-31 00:00:00

Background: Several studies have shown that both respiratory and cardiac motion can be extracted from the Pilot Tone (PT) signal successfully. However, most of these studies were performed in healthy volunteers. In addition, validating PT using ECG as a reference can be problematic because both PT and ECG tend to be unreliable in patients with arrhythmias. Purpose: We seek to evaluate the accuracy and reliability of the cardiac and respiratory signals extracted from PT in patients clinically referred for cardiovascular MRI with the image-derived signals as the reference. Methods: Twenty-three patients were scanned on a 1.5 T scanner using balanced steady-state free-precession real-time (RT) cine sequence. The PT signal was generated by a built-in PT transmitter integrated within the body array coil. For comparison, commercial ECG and BioMatrix (BM) respiratory sensor signals were synchronously recorded. Results: The respiratory motion extracted from PT correlated positively with the image-derived respiratory signal in all cases and showed a stronger correlation (absolute coefficient: 0.95+-0.09) than BM (0.72+-0.24). For the cardiac signal, PT trigger jitter (standard deviation of PT trigger locations relative to ECG triggers) ranged from 6.6 to 83.3 ms, with a median of 21.8 ms. The mean absolute difference between the PT and corresponding ECG cardiac cycle duration was less than 5% of the averaged ECG RR interval for 21 out of 23 patients. Overall, the performance of PT-based trigger extraction was comparable to that of ECG. We did not observe significant linear dependence (p>0.28) of PT delay and PT jitter on the patients' BMI or cardiac cycle duration. Conclusions: This study demonstrates the potential of PT to monitor both respiratory and cardiac motion in patients clinically referred for cardiovascular MRI.
A novel ECG signal denoising filter selection algorithm based on conventional neural networks
Authors: Chandresh Pravin, Varun Ojha
Date: 2022-01-28 00:00:00
Tasks: Denoising

We propose a novel deep learning based denoising filter selection algorithm for noisy Electrocardiograph (ECG) signal preprocessing. ECG signals measured under clinical conditions, such as those acquired using skin contact devices in hospitals, often contain baseline signal disturbances and unwanted artefacts; indeed for signals obtained outside of a clinical environment, such as heart rate signatures recorded using non-contact radar systems, the measurements contain greater levels of noise than those acquired under clinical conditions. In this paper, we focus on heart rate signals acquired using non-contact radar systems for use in assisted living environments. Such signals contain more noise than those measured under clinical conditions and thus require a novel signal noise removal method capable of adaptive determining filters. Currently, the most common method of removing noise from such a waveform is through the use of filters; the most popular filtering method amongst which is the wavelet filter. There are, however, circumstances in which using a different filtering method may result in higher signal-to-noise-ratios (SNR) for a waveform; in this paper, we investigate the wavelet and elliptical filtering methods for the task of reducing noise in ECG signals acquired using assistive technologies. Our proposed convolutional neural network architecture classifies (with 92.8% accuracy) the optimum filtering method for noisy signal based on its expected SNR value.
Arrhythmia Classification using CGAN-augmented ECG Signals
Authors: Edmond Adib, Fatemeh Afghah, John J. Prevost
Date: 2022-01-26 00:00:00
Tasks: Arrhythmia Detection, Classification, Data Augmentation, Dynamic Time Warping

ECG databases are usually highly imbalanced due to the abundance of Normal ECG and scarcity of abnormal cases. As such, deep learning classifiers trained on imbalanced datasets usually perform poorly, especially on minor classes. One solution is to generate realistic synthetic ECG signals using Generative Adversarial Networks (GAN) to augment imbalanced datasets. In this study, we combined conditional GAN with WGAN-GP and developed AC-WGAN-GP in 1D form for the first time to be applied on MIT-BIH Arrhythmia dataset. We investigated the impact of data augmentation on arrhythmia classification. We employed two models for ECG generation: (i) unconditional GAN; Wasserstein GAN with gradient penalty (WGAN-GP) is trained on each class individually; (ii) conditional GAN; one Auxiliary Classifier WGAN-GP (AC-WGAN-GP) model is trained on all classes and then used to generate synthetic beats in all classes. Two scenarios are defined for each case: (a) unscreened; all the generated synthetic beats were used, and (b) screened; only a portion of generated beats are selected and used, based on their Dynamic Time Warping (DTW) distance to a designated template. A state-of-the-art ResNet classifier (EcgResNet34) is trained on each of the augmented datasets and the performance metrics (precision/recall/F1-Score micro- and macro-averaged, confusion matrices, multiclass precision-recall curves) were compared with those of the unaugmented imbalanced case. We also used a simple metric Net Improvement. All the three metrics show consistently that net improvement (total and minor-class), unconditional GAN with raw generated data (not screened) creates the best improvements.
Stochastic 2D Signal Generative Model with Wavelet Packets Basis Regarded as a Random Variable and Bayes Optimal Processing
Authors: Ryohei Oka, Yuta Nakahara, Toshiyasu Matsushima
Date: 2022-01-26 00:00:00

This study deals with two-dimensional (2D) signal processing using the wavelet packet transform. When the basis is unknown the candidate of basis increases in exponential order with respect to the signal size. Previous studies do not consider the basis as a random vaiables. Therefore, the cost function needs to be used to select a basis. However, this method is often a heuristic and a greedy search because it is impossible to search all the candidates for a huge number of bases. Therefore, it is difficult to evaluate the entire signal processing under a criterion and also it does not always gurantee the optimality of the entire signal processing. In this study, we propose a stochastic generative model in which the basis is regarded as a random variable. This makes it possible to evaluate entire signal processing under a unified criterion i.e. Bayes criterion. Moreover we can derive an optimal signal processing scheme that achieves the theoretical limit. This derived scheme shows that all the bases should be combined according to the posterior in stead of selecting a single basis. Although exponential order calculations is required for this scheme, we have derived a recursive algorithm for this scheme, which successfully reduces the computational complexity from the exponential order to the polynomial order.
Full-Duplex Wideband mmWave Integrated Access and Backhaul with Low Resolution ADCs
Authors: Elyes Balti, Brian L. Evans
Date: 2022-01-24 00:00:00

We consider a wideband integrated access and backhaul system operating in full-duplex mode between the New Radio gNB donor and single user equipment. Due to high power consumption in millimeter wave systems, we use low-resolution analog-to-digital converters (ADCs) in the receivers. Our contributions include (1) hybrid beamformer to maximize sum spectral efficiency of the access and backhaul links by canceling self-interference and maximizing received power; (2) all-digital beamformer and upper bound on sum spectral efficiency; and (3) simulations to compare full vs. half duplex, finite vs. infinite ADC resolution, hybrid vs. all-digital beamforming, and the upper bound in spectral efficiency.
Study of filtered-x logarithmic recursive least $p$-power algorithm
Authors: Z. Zheng, L. Lu, Y. Yu, R. C. de Lamare, Z. Liu
Date: 2022-01-20 00:00:00

For active impulsive noise control, a filtered-x recursive least $p$-power (FxRLP) algorithm is proposed by minimizing the weighted summation of the $p$-power of the \emph{a posteriori} errors. Since the characteristic of the target noise is investigated, the FxRLP algorithm achieves good performance and robustness. To obtain a better performance, we develop a filtered-x logarithmic recursive least $p$-power (FxlogRLP) algorithm which integrates the $p$-order moment with the logarithmic-order moment. Simulation results demonstrate that the FxlogRLP algorithm is superior to the existing algorithms in terms of convergence rate and noise reduction.
Evaluation of Ultra Wide Band Technology as an Enhancement for BLE Based Location Estimation
Authors: Miro Bilge
Date: 2022-01-18 00:00:00
Tasks: Management

In the scope of this paper, the precise positioning of objects with the help of Ultra Wide Band technology is evaluated. To achieve this, a prototype module for the use with the Decawave transceiver DW1000 was implemented and added to FruityMesh, an Internet of Things (IoT) mesh network of the company M-Way Solutions. The focus of the network is low-power communication based on Bluetooth Low Energy (BLE). This includes, for example, Building Automation, Lighting Management and Asset Tracking. Especially the last stated point benefits from a position tracking as precise as possible and a long battery life. Therefore, FruityMesh is used to perform a signal-strength based localization with the existing BLE messages. Due to absorption, interference, and diffraction, these measurements tend to fluctuate and allow a positioning accuracy within 1.5m. With the integration of Ultra Wide Band and the Time of Arrival (ToA) method, a centimeter-precise localization was made possible in the course of this work, which at the same time causes only small additional costs in general. Since the connection of extra hardware is associated with decreased energy efficiency, an algorithm for optimizing the control was first developed and then tested against the created scenarios. In addition to the motion-based control of the hardware, various configurations and adjustments were analyzed to reduce the power consumption by Ultra-Wideband (UWB) transmission. Finally, the developed prototype was compared with a realistic reproduction of the existing Asset Tracking to evaluate the benefits for the use in the productive application.
Analysis of an SIR--model with global and local infections
Authors: Thomas Götz
Date: 2022-02-01 00:00:00

An epidemic model where disease transmission can occur either through global contacts or through local, nearest neighbor interactions is considered. The classical SIR--model describing the global interactions is extended by adding additional equations for the density of local pairs in different epidemic states. A locality parameter $p\in [0,1]$ characterizes the probability of global or local infections. The equilibria of the resulting model are analyzed in dependence of the locality parameter and the transmission rate of the pathogen. An explicit expression for the reproduction number in terms of the locality parameter and the disease parameters is obtained. Transient simulations confirm these findings. Neighboring pairs of one infected and one susceptible can be considered as active pairs, since local transmission of the disease can only occur in that situation. Our analysis shows, that the fraction of active pairs is minimal for intermediate values of the locality parameter.
Gene set proximity analysis: expanding gene set enrichment analysis through learned geometric embeddings
Authors: Henry Cousins, Taryn Hall, Yinglong Guo, Luke Tso, Kathy Tzy-Hwa Tzeng, Le Cong, Russ Altman
Date: 2022-01-31 00:00:00

Gene set analysis methods rely on knowledge-based representations of genetic interactions in the form of both gene set collections and protein-protein interaction (PPI) networks. Explicit representations of genetic interactions often fail to capture complex interdependencies among genes, limiting the analytic power of such methods. Here we propose an extension of gene set enrichment analysis to a latent feature space reflecting PPI network topology, called gene set proximity analysis (GSPA). Compared with existing methods, GSPA provides improved ability to identify disease-associated pathways in disease-matched gene expression datasets, while improving reproducibility of enrichment statistics for similar gene sets. GSPA is statistically straightforward, reducing to classical gene set enrichment through a single user-defined parameter. We apply our method to identify novel drug associations with SARS-CoV-2 viral entry. Finally, we validate our drug association predictions through retrospective clinical analysis of claims data from 8 million patients, supporting a role for gabapentin as a risk factor and metformin as a protective factor for COVID-19 hospitalization.
Periodic Traveling Waves in an Integro-Difference Equation With Non-Monotonic Growth and Strong Allee Effect
Authors: Michael Nestor, Bingtuan Li
Date: 2022-02-01 00:00:00

We derive sufficient conditions for the existence of a periodic traveling wave solution to an integro-difference equation with a piecewise constant growth function exhibiting a stable period2 cycle and strong Allee effect. The mean traveling wave speed is shown to be the asymptotic spreading speed of solutions with compactly supported initial data under appropriate conditions. We then conduct case studies for the Laplace kernel and uniform kernel.
Insights into performance evaluation of com-pound-protein interaction prediction methods
Authors: Adiba Yaseen, Imran Amin, Naeem Akhter, Asa Ben-Hur, Fayyaz Minhas
Date: 2022-01-28 00:00:00
Tasks: Drug Design

Motivation: Machine learning based prediction of compound-protein interactions (CPIs) is important for drug design, screening and repurposing studies and can improve the efficiency and cost-effectiveness of wet lab assays. Despite the publication of many research papers reporting CPI predictors in the recent years, we have observed a number of fundamental issues in experiment design that lead to over optimistic estimates of model performance. Results: In this paper, we analyze the impact of several important factors affecting generalization perfor-mance of CPI predictors that are overlooked in existing work: 1. Similarity between training and test examples in cross-validation 2. The strategy for generating negative examples, in the absence of experimentally verified negative examples. 3. Choice of evaluation protocols and performance metrics and their alignment with real-world use of CPI predictors in screening large compound libraries. Using both an existing state-of-the-art method (CPI-NN) and a proposed kernel based approach, we have found that assessment of predictive performance of CPI predictors requires careful con-trol over similarity between training and test examples. We also show that random pairing for gen-erating synthetic negative examples for training and performance evaluation results in models with better generalization performance in comparison to more sophisticated strategies used in existing studies. Furthermore, we have found that our kernel based approach, despite its simple design, exceeds the prediction performance of CPI-NN. We have used the proposed model for compound screening of several proteins including SARS-CoV-2 Spike and Human ACE2 proteins and found strong evidence in support of its top hits. Availability: Code and raw experimental results available at https://github.com/adibayaseen/HKRCPI Contact: Fayyaz.minhas@warwick.ac.uk
Systemic Risk Models for Disjoint and Overlapping Groups with Equilibrium Strategies
Authors: Yichen Feng, Jean-Pierre Fouque, Ruimeng Hu, Tomoyuki Ichiba
Date: 2022-02-01 00:00:00

We analyze the systemic risk for disjoint and overlapping groups (e.g., central clearing counterparties (CCP)) by proposing new models with realistic game features. Specifically, we generalize the systemic risk measure proposed in [F. Biagini, J.-P. Fouque, M. Frittelli, and T. Meyer-Brandis, Finance and Stochastics, 24(2020), 513--564] by allowing individual banks to choose their preferred groups instead of being assigned to certain groups. We introduce the concept of Nash equilibrium for these new models, and analyze the optimal solution under Gaussian distribution of the risk factor. We also provide an explicit solution for the risk allocation of the individual banks, and study the existence and uniqueness of Nash equilibrium both theoretically and numerically. The developed numerical algorithm can simulate scenarios of equilibrium, and we apply it to study the bank-CCP structure with real data and show the validity of the proposed model.
Building a Dynamic System of Advanced Risk Management and Risk Assessment of the Company
Authors: Denis S. Gusev, Elena G. Demidova, Olga A. Novikova
Date: 2022-01-11 00:00:00
Tasks: Management

The purpose of the research presented in this article is to develop a dynamic system for forecasting and minimizing the risks of an industrial company based on their quantitative assessment. The article considers the conceptual apparatus of the essential content of risk management of an industrial enterprise, reviews the theoretical aspects of risk management systems and the most significant risk management methods from a practical point of view. The methodological apparatus of qualitative and quantitative analysis and risk assessment has been expanded on the basis of some conditionality of risk classification features identified and a systematic approach to the classification of risks of an industrial enterprise has been proposed, taking into account the dynamics of their impact on the object, the stages of building a dynamic risk management system are given. The article substantiates the need to supplement the dynamic risk management system of industrial enterprises with methods of qualitative and quantitative risk assessment in order to form effective risk management strategies
New Collectivity Measures for Financial Covariances and Correlations
Authors: Anton J. Heckens, Thomas Guhr
Date: 2022-02-01 00:00:00

Complex systems are usually non-stationary and their dynamics is often dominated by collective effects. Collectivity, defined as coherent motion of the whole system or of some of its parts, manifests itself in the time-dependent structures of covariance and correlation matrices. The largest eigenvalue corresponds to the collective motion of the system as a whole, while the other large, isolated, eigenvalues indicate collectivity in parts of the system. In the case of finance, these are industrial sectors. By removing the collective motion of the system as a whole, the latter effects are much better revealed. We measure a remaining collectivity to which we refer as average sector collectivity. We identify collective signals around the Lehman Brothers crash and after the dot-com bubble burst. For the Lehman Brothers crash, we find a potential precursor. We analyze 213 US stocks over a period of more than 30 years from 1990 to 2021. We plot the average sector collectivity versus the collectivity corresponding to the largest eigenvalue to study the whole market trajectory in a two dimensional space spanned by both collectivities. Therefore, we capture the average sector collectivity in a much more precise way. Additionally, we observe that larger values in the average sector collectivity are often accompanied by trend shifts in the mean covariances and mean correlations. As of 2015/2016 the collectivity in the US stock markets changed fundamentally.
Zero-Inflated Autoregressive Conditional Duration Model for Discrete Trade Durations with Excessive Zeros
Authors: Francisco Blasques, Vladimír Holý, Petra Tomanová
Date: 2018-12-18 00:00:00

In finance, durations between successive transactions are usually modeled by the autoregressive conditional duration model based on a continuous distribution omitting zero values. Zero or close-to-zero durations can be caused by either split transactions or independent transactions. We propose a discrete model allowing for excessive zero values based on the zero-inflated negative binomial distribution with score dynamics. This model allows to distinguish between the processes generating split and standard transactions. We use the existing theory on score models to establish the invertibility of the score filter and verify that sufficient conditions hold for the consistency and asymptotic normality of the maximum likelihood of the model parameters. In an empirical study, we find that split transactions cause between 92 and 98 percent of zero and close-to-zero values. Furthermore, the loss of decimal places in the proposed approach is less severe than the incorrect treatment of zero values in continuous models.
RabindraNet, Creating Literary Works in the Style of Rabindranath Tagore
Authors: Asadullah Al Galib
Date: 2022-01-05 00:00:00
Tasks: Text Generation

Bengali literature has a rich history of hundreds of years with luminary figures such as Rabindranath Tagore and Kazi Nazrul Islam. However, analytical works involving the most recent advancements in NLP have barely scratched the surface utilizing the enormous volume of the collected works from the writers of the language. In order to bring attention to the analytical study involving the works of Bengali writers and spearhead the text generation endeavours in the style of existing literature, we are introducing RabindraNet, a character level RNN model with stacked-LSTM layers trained on the works of Rabindranath Tagore to produce literary works in his style for multiple genres. We created an extensive dataset as well by compiling the digitized works of Rabindranath Tagore from authentic online sources and published as open source dataset on data science platform Kaggle.