pwc-archive/papers-with-abstracts huggingface dataset:
Learning From Alarms: A Robust Learning Approach for Accurate Photoplethysmography-Based Atrial Fibrillation Detection using Eight Million Samples Labeled with Imprecise Arrhythmia Alarms
Authors: Cheng Ding, Zhicheng Guo, Cynthia Rudin, Ran Xiao, Amit Shah, Duc H. Do, Randall J Lee, Gari Clifford, Fadi B Nahab, Xiao Hu
Date: 2022-11-07 00:00:00
Tasks: Atrial Fibrillation Detection, Computational Efficiency, Photoplethysmography (PPG), Pseudo Label

Atrial fibrillation (AF) is a common cardiac arrhythmia with serious health consequences if not detected and treated early. Detecting AF using wearable devices with photoplethysmography (PPG) sensors and deep neural networks has demonstrated some success using proprietary algorithms in commercial solutions. However, further advancement of this paradigm of continuous AF detection in ambulatory settings, towards a population-wide screening use case, still faces several challenges, one of which is the lack of large-scale labeled training data. To address this challenge, in this study, we propose to leverage AF alarms from bedside patient monitors to label concurrent PPG signals, resulting in the largest PPG-AF dataset so far (8.5M 30-second records from 24100 patients) and demonstrating a practical approach to build large labeled PPG datasets. Furthermore, we recognize that the AF labels thus obtained contain errors because of false AF alarms generated from imperfect built-in algorithms from bedside monitors. Dealing with label noise with unknown distribution characteristics in this case requires advanced algorithms. We, therefore, introduce and open source a novel loss design, the cluster membership consistency (CMC) loss, to mitigate label errors. By comparing CMC with state-of-the-art methods selected from a noisy label competition, we demonstrate its superiority in multiple aspects including handling label noise in PPG data, resilience to poor-quality signals, and computational efficiency.
Pan-Tompkins++: A Robust Approach to Detect R-peaks in ECG Signals
Authors: Naimul Khan, Md Niaz Imtiaz
Date: 2022-11-06 00:00:00
Tasks: Arrhythmia Detection, Heart Rate Variability

R-peak detection is crucial in electrocardiogram (ECG) signal processing as it is the basis of heart rate variability analysis. The Pan-Tompkins algorithm is the most widely used QRS complex detector for the monitoring of many cardiac diseases including arrhythmia detection. However, the performance of the Pan-Tompkins algorithm in detecting the QRS complexes degrades in low-quality and noisy signals. This article introduces Pan-Tompkins++, an improved Pan-Tompkins algorithm. A bandpass filter with a passband of 5--18 Hz followed by an N-point moving average filter has been applied to remove the noise without discarding the significant signal components. Pan-Tompkins++ uses three thresholds to distinguish between R-peaks and noise peaks. Rather than using a generalized equation, different rules are applied to adjust the thresholds based on the pattern of the signal for the accurate detection of R-peaks under significant changes in signal pattern. The proposed algorithm reduces the False Positive and False Negative detections, and hence improves the robustness and performance of Pan-Tompkins algorithm. Pan-Tompkins++ has been tested on four open source datasets. The experimental results show noticeable improvement for both R-peak detection and execution time. We achieve 2.8% and 1.8% reduction in FP and FN, respectively, and 2.2% increase in F-score on average across four datasets, with 33% reduction in execution time. We show specific examples to demonstrate that in situations where the Pan-Tompkins algorithm fails to identify R-peaks, the proposed algorithm is found to be effective. The results have also been contrasted with other well-known R-peak detection algorithms. Code available at: https://github.com/Niaz-Imtiaz/Pan-Tompkins-Plus-Plus
Intelligent Omni-Surfaces Aided Wireless Communications: Does the Reciprocity Hold?
Authors: Shaohua Yue, Shuhao Zeng, Hongliang Zhang, Fenghan Lin, Liang Liu, Boya Di
Date: 2022-11-06 00:00:00
Tasks: Open-Ended Question Answering

Intelligent omni-surfaces (IOS) have attracted great attention recently due to its potential to achieve full-dimensional communications by simultaneously reflecting and refracting signals toward both sides of the surface. However, it still remains an open question whether the reciprocity holds between the uplink and downlink channels in the IOS-aided wireless communications. In this work, we first present a physics-compliant IOS related channel model, based on which the channel reciprocity is investigated. We then demonstrate the angle-dependent electromagnetic response of the IOS element in terms of both incident and departure angles. This serves as the key feature of IOS that drives our analytical results on beam non-reciprocity. Finally, simulation and experimental results are provided to verify our theoretical analyses.
Design of Reconfigurable Intelligent Surface-Aided Cross-Media Communications
Authors: Mingming Wu, Yue Xiao, Yulan Gao, Ming Xiao
Date: 2022-11-05 00:00:00
Tasks: Management

A novel reconfigurable intelligent surface (RIS)-aided hybrid reflection/transmitter design is proposed for achieving information exchange in cross-media communications. In pursuit of the balance between energy efficiency and low-cost implementations, the cloud-management transmission protocol is adopted in the integrated multi-media system. Specifically, the messages of devices using heterogeneous propagation media, are firstly transmitted to the medium-matched AP, with the aid of the RIS-based dual-hop transmission. After the operation of intermediate frequency conversion, the access point (AP) uploads the received signals to the cloud for further demodulating and decoding process. Based on time division multiple access (TDMA), the cloud is able to distinguish the downlink data transmitted to different devices and transforms them into the input of the RIS controller via the dedicated control channel. Thereby, the RIS can passively reflect the incident carrier back into the original receiver with the exchanged information during the preallocated slots, following the idea of an index modulation-based transmitter. Moreover, the iterative optimization algorithm is utilized for optimizing the RIS phase, transmit rate and time allocation jointly in the delay-constrained cross-media communication model. Our simulation results demonstrate that the proposed RIS-based scheme can improve the end-to-end throughput than that of the AP-based transmission, the equal time allocation, the random and the discrete phase adjustment benchmarks.
Detection of High Impedance Faults in Microgrids using Machine Learning
Authors: Pallav Kumar Bera, Vajendra Kumar, Samita Rani Pani, Vivek Bargate
Date: 2022-11-04 00:00:00
Tasks: feature selection, Vocal Bursts Intensity Prediction

This article presents differential protection of the distribution line connecting a wind farm in a microgrid. Machine Learning (ML) based models are built using differential features extracted from currents at both ends of the line to assist in relaying decisions. Wavelet coefficients obtained after feature selection from an extensive list of features are used to train the classifiers. Internal faults are distinguished from external faults with CT saturation. The internal faults include the high impedance faults (HIFs) which have very low currents and test the dependability of the conventional relays. The faults are simulated in a 5-bus system in PSCAD/EMTDC. The results show that ML-based models can effectively distinguish faults and other transients and help maintain security and dependability of the microgrid operation.
Relaxing the Differentiability Assumption in Taylor Theorem and Optimization: Applications to the HJB PDE and Finance
Authors: Moawia Alghalith
Date: 2012-11-25 00:00:00

We overcome an obstacle in mathematical optimization. In so doing, we explicitly derive the optimal values without assuming the smoothness of the value function. We apply our method to the portfolio model.
On the enumeration of leaf-labelled increasing trees with arbitrary node-degree
Authors: Johannes Wirtz
Date: 2022-11-07 00:00:00
Tasks: All

We consider the counting problem of the number of \textit{leaf-labeled increasing trees}, where internal nodes may have an arbitrary number of descendants. The set of all such trees is a discrete representation of the genealogies obtained under certain population-genetical models such as multiple-merger coalescents. While the combinatorics of the binary trees among those are well understood, for the number of all trees only an approximate asymptotic formula is known. In this work, we validate this formula up to constant terms and compare the asymptotic behavior of the number of all leaf-labelled increasing trees to that of binary, ternary and quaternary trees.
Modeling knotted proteins with tangles
Authors: Isabel K. Darcy, Garrett Jones, Puttipong Pongtanapaisan
Date: 2022-11-07 00:00:00

Although rare, an increasing number of proteins have been observed to contain entanglements in their native structures. To gain more insight into the significance of protein knotting, researchers have been investigating protein knot formation using both experimental and theoretical methods. Motivated by the hypothesized folding pathway of $\alpha$-haloacid dehalogenase (DehI) protein, Flapan, He, and Wong proposed a theory of how protein knots form, which includes existing folding pathways described by Taylor and B\"olinger et al. as special cases. In their topological descriptions, two loops in an unknotted open protein chain containing at most two twists each come close together, and one end of the protein eventually passes through the two loops. In this paper, we build on Flapan, He, and Wong's theory where we pay attention to the crossing signs of the threading process and assume that the unknotted protein chain may arrange itself into a more complicated configuration before threading occurs. We then apply tangle calculus, originally developed by Ernst and Sumners to analyze the action of specific proteins on DNA, to give all possible knots or knotoids that may be discovered in the future according to our model and give recipes for engineering specific knots in proteins from simpler pieces. We show why twists knots are the most likely knots to occur in proteins. We use chirality to show that the most likely knots to occur in proteins via Taylor's twisted hairpin model are the knots $+3_1$, $4_1$, and $-5_2$.
The infinitesimal model with dominance
Authors: N. H. Barton, A. M. Etheridge, A. Véber
Date: 2022-10-31 00:00:00
Tasks: model

The classical infinitesimal model is a simple and robust model for the inheritance of quantitative traits. In this model, a quantitative trait is expressed as the sum of a genetic and a non-genetic (environmental) component and the genetic component of offspring traits within a family follows a normal distribution around the average of the parents' trait values, and has a variance that is independent of the trait values of the parents. In previous work, Barton et al.(2017), we showed that when trait values are determined by the sum of a large number of Mendelian factors, each of small effect, one can justify the infinitesimal model as limit of Mendelian inheritance. In this paper, we show that the robustness of the infinitesimal model extends to include dominance. We define the model in terms of classical quantities of quantitative genetics, before justifying it as a limit of Mendelian inheritance as the number, M, of underlying loci tends to infinity. As in the additive case, the multivariate normal distribution of trait values across the pedigree can be expressed in terms of variance components in an ancestral population and probabilities of identity by descent determined by the pedigree. In this setting, it is natural to decompose trait values, not just into the additive and dominance components, but into a component that is shared by all individuals within the family and an independent `residual' for each offspring, which captures the randomness of Mendelian inheritance. We show that, even if we condition on parental trait values, both the shared component and the residuals within each family will be asymptotically normally distributed as the number of loci tends to infinity, with an error of order 1/\sqrt{M}. We illustrate our results with some numerical examples.
An Efficient MCMC Approach to Energy Function Optimization in Protein Structure Prediction
Authors: Lakshmi A. Ghantasala, Risi Jaiswal, Supriyo Datta
Date: 2022-11-06 00:00:00
Tasks: CPU, Drug Design, GPU, Protein Structure Prediction

Protein structure prediction is a critical problem linked to drug design, mutation detection, and protein synthesis, among other applications. To this end, evolutionary data has been used to build contact maps which are traditionally minimized as energy functions via gradient descent based schemes like the L-BFGS algorithm. In this paper we present what we call the Alternating Metropolis-Hastings (AMH) algorithm, which (a) significantly improves the performance of traditional MCMC methods, (b) is inherently parallelizable allowing significant hardware acceleration using GPU, and (c) can be integrated with the L-BFGS algorithm to improve its performance. The algorithm shows an improvement in energy of found structures of 8.17% to 61.04% (average 38.9%) over traditional MH and 0.53% to 17.75% (average 8.9%) over traditional MH with intermittent noisy restarts, tested across 9 proteins from recent CASP competitions. We go on to map the Alternating MH algorithm to a GPGPU which improves sampling rate by 277x and improves simulation time to a low energy protein prediction by 7.5x to 26.5x over CPU. We show that our approach can be incorporated into state-of-the-art protein prediction pipelines by applying it to both trRosetta2's energy function and the distogram component of Alphafold1's energy function. Finally, we note that specially designed probabilistic computers (or p-computers) can provide even better performance than GPUs for MCMC algorithms like the one discussed here.
Predicting biomolecular binding kinetics: A review
Authors: Jinan Wang, Hung N. Do, Kushal Koirala, Yinglong Miao
Date: 2022-11-05 00:00:00

Biomolecular binding kinetics including the association (kon) and dissociation (koff) rates are critical parameters for therapeutic design of small-molecule drugs, peptides and antibodies. Notably, drug molecule residence time or dissociation rate has been shown to correlate with their efficacies better than binding affinities. A wide range of modeling approaches including quantitative structure-kinetic relationship models, Molecular Dynamics simulations, enhanced sampling and Machine Learning have been developed to explore biomolecular binding and dissociation mechanisms and predict binding kinetic rates. Here, we review recent advances in computational modeling of biomolecular binding kinetics, with an outlook for future improvements.
Identification and Molecular Dynamic Simulation of Flavonoids from Mediterranean species of Oregano against the Zika NS2B-NS3 Protease
Authors: Anushikha Ghosh, Arka Sanyal, Sameer Sharma
Date: 2022-11-05 00:00:00
Tasks: Molecular Docking

The Zika virus, is an emerging infectious disease causing severe complications such as microcephaly in infants and Guillain Barre syndrome in adults. There is no licensed vaccination or approved medicine to treat ZIKV infection. Therefore, extensive research is being carried out to find compounds that can be used effectively as therapeutic molecules to treat ZIKV infection. Oregano, a commonly found herb in the Mediterranean region, has been used predominantly for culinary purposes. The fact that the members of the Origanum species are a storehouse of various bioactive compounds gives us a solid reason to study compounds extracted from it for therapeutic purposes. In this study, were retrieved 20 Flavonoids found in various Origanum species belonging to the Mediterranean region from the PubChem database and pharmacological analysis using SwissADME and Molecular docking using AutoDock Vina 4.0. were carried out against the NS2B NS3 protease since it serves as an effective drug target owing to its role in viral replication and immune evasion within the host. The best hit compounds were subjected to MD simulation at 100 ns using Desmond Schrodinger to analyze the molecule's stability. We observed Cirsiliol as the best hit compound against the NS2B NS3 complex with a binding affinity of -8.5 kcal per mol. It also showed good stability during MD simulation. We recommend the use of Cirsiliol for in vitro and in vivo studies for further investigation concerning the ZIKA virus.
Current Landscape of Mesenchymal Stem Cell Therapy in COVID Induced Acute Respiratory Distress Syndrome
Authors: Adrita Chanda, Adrija Aich, Arka Sanyal, Anantika Chandra, Saumyadeep Goswami
Date: 2022-11-05 00:00:00

The severe acute respiratory syndrome coronavirus 2 outbreak in Chinas Hubei area in late 2019 has now created a global pandemic that has spread to over 150 countries. In most people, COVID 19 is a respiratory infection that produces fever, cough, and shortness of breath. Patients with severe COVID 19 may develop ARDS. MSCs can come from a number of places, such as bone marrow, umbilical cord, and adipose tissue. Because of their easy accessibility and low immunogenicity, MSCs were often used in animal and clinical research. In recent studies, MSCs have been shown to decrease inflammation, enhance lung permeability, improve microbial and alveolar fluid clearance, and accelerate lung epithelial and endothelial repair. Furthermore, MSC-based therapy has shown promising outcomes in preclinical studies and phase 1 clinical trials in sepsis and ARDS. In this paper, we posit the therapeutic strategies using MSC and dissect how and why MSC therapy is a potential treatment option for COVID 19 induced ARDS. We cite numerous promising clinical trials, elucidate the potential advantages of MSC therapy for COVID 19 ARDS patients, examine the detriments of this therapeutic strategy and suggest possibilities of subsequent research.
Branched chain amino acids, an ''essential'' link between diet, clock and sleep?
Authors: Laurent Seugnet
Date: 2022-11-07 00:00:00

The branched-chain amino acids: leucine, isoleucine and valine occupy a special place among the essential amino acids because of their importance not only in the structure of proteins but also in general and cerebral metabolism. Among the first amino acids absorbed after food intake, they play a major role in the regulation of protein synthesis and insulin secretion. They are involved in the modulation of brain uptake of monoamine precursors with which they may compete for occupancy of a common transporter. In the brain, branched-chain amino acids are involved not only in protein synthesis but also in the metabolic cycles of GABA and Glutamate, and in energy metabolism. In particular, they can affect GABAergic neurons and the excitation/inhibition balance. Branched-chain amino acids are known for the 24-hour rhythmicity of their plasma concentrations, which is remarkably conserved in rodent models. This rhythmicity is partly circadian, independent of sleep and food. Moreover, their concentration increases when sleep is disturbed and in obesity and diabetes. The mechanisms regulating these rhythms and their physiological impact remain poorly understood. In this context, the Drosophila model has not yet been widely used, but it is highly relevant and the first results indicate that it can generate new concepts. The elucidation of the metabolism and fluxes of branched-chain amino acids is beginning to shed light on the mysterious connections between clock, sleep, and metabolism, opening the possibility of new therapies.
Optimal intercellular competition in senescence and cancer
Authors: Thomas C. T. Michaels, L. Mahadevan
Date: 2022-11-07 00:00:00

Effective multicellularity requires both cooperation and competition between constituent cells. Cooperation involves sacrificing individual fitness in favor of that of the community, but excessive cooperation makes the community susceptible to senescence and aging. Competition eliminates unfit senescent cells via natural selection and thus slows down aging, but excessive competition makes the community susceptible to cheaters, as exemplified by cancer and cancer-like phenomena. These observations suggest that an optimal level of intercellular competition in a multicellular organism maximizes organismal vitality by delaying the inevitability of aging. We quantify this idea using a statistical mechanical framework that leads to a generalized replicator dynamical system for the population of cells that change their vitality and cooperation due to somatic mutations that make them susceptible to aging and/or cancer. By accounting for the cost of cooperation and strength of competition in a minimal setting, we show that our model predicts an optimal value of competition that maximizes vitality and delays the inevitability of senescence or cancer.
Computational anatomy atlas using multilayer perceptron with Lipschitz regularization
Authors: Konstantin Ushenin, Maksim Dzhigil, Vladislav Dordiuk
Date: 2022-11-06 00:00:00
Tasks: Anatomy

A computational anatomy atlas is a set of internal organ geometries. It is based on data of real patients and complemented with virtual cases by using a some numerical approach. Atlases are in demand in computational physiology, especially in cardiological and neurophysiological applications. Usually, atlas generation uses explicit object representation, such as voxel models or surface meshes. In this paper, we propose a method of atlas generation using an implicit representation of 3D objects. Our approach has two key stages. The first stage converts voxel models of segmented organs to implicit form using the usual multilayer perceptron. This stage smooths the model and reduces memory consumption. The second stage uses a multilayer perceptron with Lipschitz regularization. This neural network provides a smooth transition between implicitly defined 3D geometries. Our work shows examples of models of the left and right human ventricles. All code and data for this work are open.
On Pricing of Discrete Asian and Lookback Options under the Heston Model
Authors: Leonardo Perotti, Lech A. Grzelak
Date: 2022-11-07 00:00:00

We propose a new, data-driven approach for efficient pricing of - fixed- and float-strike - discrete arithmetic Asian and Lookback options when the underlying process is driven by the Heston model dynamics. The method proposed in this article constitutes an extension of our previous work, where the problem of sampling from time-integrated stochastic bridges was addressed. The model relies on the Seven-League scheme, where artificial neural networks are employed to "learn" the distribution of the random variable of interest utilizing stochastic collocation points. The method results in a robust procedure for Monte Carlo pricing. Furthermore, semi-analytic formulae for option pricing are provided in a simplified, yet general, framework. The model guarantees high accuracy and a reduction of the computational time up to thousands of times compared to classical Monte Carlo pricing schemes.