pwc-archive/papers-with-abstracts huggingface dataset:
NRC-Net: Automated noise robust cardio net for detecting valvular cardiac diseases using optimum transformation method with heart sound signals
Authors: Samiul Based Shuvo, Syed Samiul Alam, Syeda Umme Ayman, Arbil Chakma, Prabal Datta Barua, U Rajendra Acharya
Date: 2023-04-29 00:00:00

Cardiovascular diseases (CVDs) can be effectively treated when detected early, reducing mortality rates significantly. Traditionally, phonocardiogram (PCG) signals have been utilized for detecting cardiovascular disease due to their cost-effectiveness and simplicity. Nevertheless, various environmental and physiological noises frequently affect the PCG signals, compromising their essential distinctive characteristics. The prevalence of this issue in overcrowded and resource-constrained hospitals can compromise the accuracy of medical diagnoses. Therefore, this study aims to discover the optimal transformation method for detecting CVDs using noisy heart sound signals and propose a noise robust network to improve the CVDs classification performance.For the identification of the optimal transformation method for noisy heart sound data mel-frequency cepstral coefficients (MFCCs), short-time Fourier transform (STFT), constant-Q nonstationary Gabor transform (CQT) and continuous wavelet transform (CWT) has been used with VGG16. Furthermore, we propose a novel convolutional recurrent neural network (CRNN) architecture called noise robust cardio net (NRC-Net), which is a lightweight model to classify mitral regurgitation, aortic stenosis, mitral stenosis, mitral valve prolapse, and normal heart sounds using PCG signals contaminated with respiratory and random noises. An attention block is included to extract important temporal and spatial features from the noisy corrupted heart sound.The results of this study indicate that,CWT is the optimal transformation method for noisy heart sound signals. When evaluated on the GitHub heart sound dataset, CWT demonstrates an accuracy of 95.69% for VGG16, which is 1.95% better than the second-best CQT transformation technique. Moreover, our proposed NRC-Net with CWT obtained an accuracy of 97.4%, which is 1.71% higher than the VGG16.
Secret Key Generation for IRS-Assisted Multi-Antenna Systems: A Machine Learning-Based Approach
Authors: Chen Chen, Junqing Zhang, Tianyu Lu, Magnus Sandell, Liquan Chen
Date: 2023-04-28 00:00:00

Physical-layer key generation (PKG) based on wireless channels is a lightweight technique to establish secure keys between legitimate communication nodes. Recently, intelligent reflecting surfaces (IRSs) have been leveraged to enhance the performance of PKG in terms of secret key rate (SKR), as it can reconfigure the wireless propagation environment and introduce more channel randomness. In this paper, we investigate an IRS-assisted PKG system, taking into account the channel spatial correlation at both the base station (BS) and the IRS. Based on the considered system model, the closed-form expression of SKR is derived analytically considering correlated eavesdropping channels. Aiming to maximise the SKR, a joint design problem of the BS precoding matrix and the IRS phase shift vector is formulated. To address this high-dimensional non-convex optimisation problem, we propose a novel unsupervised deep neural network (DNN)-based algorithm with a simple structure. Different from most previous works that adopt iterative optimisation to solve the problem, the proposed DNN-based algorithm directly obtains the BS precoding and IRS phase shifts as the output of the DNN. Simulation results reveal that the proposed DNN-based algorithm outperforms the benchmark methods with regard to SKR.
An Asymptotically Equivalent GLRT Test for Distributed Detection in Wireless Sensor Networks
Authors: Juan Augusto Maya, Leonardo Rey Vega, Andrea M. Tonello
Date: 2023-04-28 00:00:00

In this article, we consider the problem of distributed detection of a localized radio source emitting a signal. We consider that geographically distributed sensor nodes obtain energy measurements and compute cooperatively a statistic to decide if the source is present or absent. We model the radio source as a stochastic signal and deal with spatially statistically dependent measurements, whose probability density function (PDF) has unknown positive parameters when the radio source is active. Under the framework of the Generalized Likelihood Ratio Test (GLRT) theory, the positive constraint on the unknown multidimensional parameter makes the computation of the GLRT asymptotic performance (when the amount of sensor measurements tends to infinity) more involved. Nevertheless, we analytically characterize the asymptotic distribution of the statistic. Moreover, as the GLRT is not amenable for distributed settings because of the spatial statistical dependence of the measurements, we study a GLRT-like test where the joint PDF of the measurements is substituted by the product of its marginal PDFs, and therefore, the statistical dependence is completely discarded for building this test. Nevertheless, its asymptotic performance is proved to be identical to the original GLRT, showing that the statistically dependence of the measurements has no impact on the detection performance in the asymptotic scenario. Furthermore, the GLRT-like algorithm has a low computational complexity and demands low communication resources, as compared to the GLRT.
Distributed Quantized Detection of Sparse Signals Under Byzantine Attacks
Authors: Chen Quan, Yunghsiang S. Han, Baocheng Geng, Pramod K. Varshney
Date: 2023-04-27 00:00:00

This paper investigates distributed detection of sparse stochastic signals with quantized measurements under Byzantine attacks. Under this type of attack, sensors in the networks might send falsified data to degrade system performance. The Bernoulli-Gaussian (BG) distribution in terms of the sparsity degree of the stochastic signal is utilized for modeling the sparsity of signals. Several detectors with improved detection performance are proposed by incorporating the estimated attack parameters into the detection process. First, we propose the generalized likelihood ratio test with reference sensors (GLRTRS) and the locally most powerful test with reference sensors (LMPTRS) detectors with adaptive thresholds, given that the sparsity degree and the attack parameters are unknown. Our simulation results show that the LMPTRS and GLRTRS detectors outperform the LMPT and GLRT detectors proposed for an attack-free environment and are more robust against attacks. The proposed detectors can achieve the detection performance close to the benchmark likelihood ratio test (LRT) detector, which has perfect knowledge of the attack parameters and sparsity degree. When the fraction of Byzantine nodes are assumed to be known, we can further improve the system's detection performance. We propose the enhanced LMPTRS (E-LMPTRS) and enhanced GLRTRS (E-GLRTRS) detectors by filtering out potential malicious sensors with the knowledge of the fraction of Byzantine nodes in the network. Simulation results show the superiority of proposed enhanced detectors over LMPTRS and GLRTRS detectors.
Supervised and Unsupervised Deep Learning Approaches for EEG Seizure Prediction
Authors: Zakary Georgis-Yap, Milos R. Popovic, Shehroz S. Khan
Date: 2023-04-24 00:00:00
Tasks: Deep Learning, EEG, Seizure Detection, Seizure prediction

Epilepsy affects more than 50 million people worldwide, making it one of the world's most prevalent neurological diseases. The main symptom of epilepsy is seizures, which occur abruptly and can cause serious injury or death. The ability to predict the occurrence of an epileptic seizure could alleviate many risks and stresses people with epilepsy face. We formulate the problem of detecting preictal (or pre-seizure) with reference to normal EEG as a precursor to incoming seizure. To this end, we developed several supervised deep learning approaches to identify preictal EEG from normal EEG. We further develop novel unsupervised deep learning approaches to train the models on only normal EEG, and detecting pre-seizure EEG as an anomalous event. These deep learning models were trained and evaluated on two large EEG seizure datasets in a person-specific manner. We found that both supervised and unsupervised approaches are feasible; however, their performance varies depending on the patient, approach and architecture. This new line of research has the potential to develop therapeutic interventions and save human lives.
Calibration of Local Volatility Models with Stochastic Interest Rates using Optimal Transport
Authors: Benjamin Joseph, Gregoire Loeper, Jan Obloj
Date: 2023-04-29 00:00:00

We develop a non-parametric, semimartingale optimal transport, calibration methodology for local volatility models with stochastic interest rate. The method finds a fully calibrated model which is the closest, in a way that can be defined by a general cost function, to a given reference model. We establish a general duality result which allows to solve the problem by optimising over solutions to a second order fully non-linear Hamilton-Jacobi-Bellman equation. Our methodology is analogous to Guo, Loeper, and Wang, 2022 and Guo, Loeper, Obloj, et al., 2022a but features a novel element of solving for discounted densities, or sub-probability measures. As an example, we apply the method to a sequential calibration problem, where a Vasicek model is already given for the interest rates and we seek to calibrate a stock price's local volatility model with volatility coefficient depending on time, the underlying and the short rate process, and the two processes driven by possibly correlated Brownian motions. The equity model is calibrated to any number of European options prices.
On Irreducibility of Tensor Products of Yangian Modules
Authors: Maxim Nazarov, Vitaly Tarasov
Date: 1997-11-30 00:00:00

We study the tensor product $V$ of any number of "elementary" irreducible modules over the Yangian of the general linear Lie algebra. An elementary module is determined by a skew Young diagram and by a complex parameter, and contains a vector called singular. We give sufficient conditions for cyclicity in $V$ of the tensor product of these singular vectors. By using this result, we give an irreducibility criterion for $V$ when each of the skew Young diagrams determining the tensor factors has rectangular shape.
Polynomial-time parametric optimisation
Authors: Doan Dai Nguyen
Date: 2023-04-05 00:00:00

In biology, predicting RNA secondary structures plays a vital role in determining its physical and chemical properties. Although we have powerful energy models to predict them as well as parametric analysis to understand the models themselves, the large number of parameters involved makes exploring the parameter space and effective fine-tuning complicated at best. The literature describes an approach via so-called RNA polytopes and several attempts to compute them entirely, but computing explicitly the polytopes is both practically and theoretically intractable. In this thesis, we demonstrate how to further modify the dynamic programming algorithms used in RNA secondary structure prediction, and more generally how to use only supporting functions to gather some information about the polytopes without explicit construction. We provide the mathematical frameworks with proofs or sketch thereof whenever necessary, and carry out some numerical experiments to show that our proposed methods are practical even when the number of parameters is large. As it turns out, one of our methods provides a solution to another problem in computational geometry previously unsolved to our knowledge, and we hope this thesis will accommodate future studies in RNA, as well as inspire further researches on the potential uses of polytopes' supporting functions in computational geometry.
Metabolic Regulatory Network Kinetic Modeling with Multiple Isotopic Tracers for iPSCs
Authors: Keqi Wang, Wei Xie, Sarah W. Harcum
Date: 2023-04-29 00:00:00
Tasks: Cultural Vocal Bursts Intensity Prediction

The rapidly expanding market for regenerative medicines and cell therapies highlights the need to advance the understanding of cellular metabolisms and improve the prediction of cultivation production process for human induced pluripotent stem cells (iPSCs). In this paper, a metabolic kinetic model was developed to characterize underlying mechanisms of iPSC culture process, which can predict cell response to environmental perturbation and support process control. This model focuses on the central carbon metabolic network, including glycolysis, pentose phosphate pathway (PPP), tricarboxylic acid (TCA) cycle, and amino acid metabolism, which plays a crucial role to support iPSC proliferation. Heterogeneous measures of extracellular metabolites and multiple isotopic tracers collected under multiple conditions were used to learn metabolic regulatory mechanisms. Systematic cross-validation confirmed the model's performance in terms of providing reliable predictions on cellular metabolism and culture process dynamics under various culture conditions. Thus, the developed mechanistic kinetic model can support process control strategies to strategically select optimal cell culture conditions at different times, ensure cell product functionality, and facilitate large-scale manufacturing of regenerative medicines and cell therapies.
SinglePointRNA, an user-friendly application implementing single cell RNA-seq analysis software
Authors: Laura Puente-Santamaría, Luis del Peso
Date: 2023-04-28 00:00:00

Single-cell transcriptomics techniques, such as scRNA-seq, attempt to characterize gene expression profiles in each cell of a heterogeneous sample individually. Due to growing amounts of data generated and the increasing complexity of the computational protocols needed to process the resulting datasets, the demand for dedicated training in mathematical and programming skills may preclude the use of these powerful techniques by many teams. In order to help close that gap between wet-lab and dry-lab capabilities we have developed SinglePointRNA, a shiny-based R application that provides a graphic interface for different publicly available tools to analyze single cell RNA-seq data. The aim of SinglePointRNA is to provide an accessible and transparent tool set to researchers that allows them to perform detailed and custom analysis of their data autonomously. SinglePointRNA is structured in a context-driven framework that prioritizes providing the user with solid qualitative guidance at each step of the analysis process and interpretation of the results. Additionally, the rich user guides accompanying the software are intended to serve as a point of entry for users to learn more about computational techniques applied to single cell data analysis. The SinglePointRNA app, as well as case datasets for the different tutorials are available at www.github.com/ScienceParkMadrid/SinglePointRNA
Hierarchical Wilson-Cowan Models and Connection Matrices
Authors: W. A. Zúñiga-Galindo, B. A. Zambrano-Luna
Date: 2023-04-28 00:00:00

This work aims to study the interplay between the Wilson-Cowan model and the connection matrices. These matrices describe the cortical neural wiring, while the Wilson-Cowan equations provide a dynamical description of neural interaction. We formulate the Wilson-Cowan equations on locally compact Abelian groups. We show that the Cauchy problem is well-posed. We then select a type of group that allows us to incorporate the experimental information provided by the connection matrices. We argue that the classical Wilson-Cowan model is incompatible with the small-world property. A necessary condition to have this property is that the Wilson-Cowan equations be formulated on a compact group. We propose a p-adic version of the Wilson-Cowan model, a hierarchical version in which the neurons are organized into an infinite rooted tree. We present several numerical simulations showing that the p-adic version matches the predictions of the classical version in relevant experiments. The p-adic version allows the incorporation of the connection matrices into the Wilson-Cowan model. We present several numerical simulations using a neural network model that incorporates a p-adic approximation of the connection matrix of the cat cortex.
Ecologically mapped neuronal identity: Towards standardizing activity across heterogeneous experiments
Authors: Kevin Luxem, David Eriksson
Date: 2023-04-28 00:00:00
Tasks: Hippocampus

The brain's diversity of neurons enables a rich behavioral repertoire and flexible adaptation to new situations. Assuming that the ecological pressure has optimized this neuronal variety, we propose exploiting na\"ive behavior to map the neuronal identity. Here we investigate the feasibility of identifying neurons "ecologically" using their activation for natural behavioral and environmental parameters. Such a neuronal ECO-marker might give a finer granularity than possible with genetic or molecular markers, thereby facilitating the comparison of the functional characteristics of individual neurons across animals. In contrast to a potential mapping using artificial stimuli and trained behavior which have an unlimited parameter space, an ecological mapping is experimentally feasible since it is bounded by the ecology. Home-cage environment is an excellent basis for this ECO-mapping covering an extensive behavioral repertoire and since home-cage behavior is similar across laboratories. We review the possibility of adding area-specific environmental enrichment and automatized behavioral tasks to identify neurons in specific brain areas. In this work, we focus on the visual cortex, motor cortex, prefrontal cortex, and hippocampus. Fundamental to achieving this identification is to take advantage of state-of-the-art behavioral tracking, sensory stimulation protocols, and the plethora of creative behavioral solutions for rodents. We find that motor areas might be easiest to address, followed by prefrontal, hippocampal, and visual areas. The possibility of acquiring a near-complete ecological identification with minimal animal handling, minimal constraints on the main experiment, and data compatibility across laboratories might outweigh the necessity of implanting electrodes or imaging devices.
Applications of Computer Vision in Analysis of the Clock-Drawing Test as a Metric of Cognitive Impairment
Authors: Luzhou Zhang
Date: 2023-04-26 00:00:00

The Clock-Drawing test is a well known and widely used neuropsychological metric to assess basic cognitive function. My objective is to combine methods of machine learning in computer vision and image analysis to predict a subject's level of cognitive impairment.
Directionality Theory and the Origin of Life
Authors: Lloyd Demetrius
Date: 2023-04-28 00:00:00

The origin of cellular life can be described in terms of the transition from inorganic matter: solids, liquids and gases, to the emergence of cooperative assemblies of organic matter, DNA and proteins,capable of replication and metabolism. Directionality Theory is a mathematical model of the collective behavior of populations of organic matter: cells and higher organisms. Evolutionary entropy, the cornerstone of the theory, is a statistical measure of the cooperativity of the interacting components that comprise the population. The main tenet of Directionality Theory is the Entropic Principle of Collective Behavior: The collective behavior of aggregates of organic matter is contingent on the population size and the external energy source, and characterized by extremal states of evolutionary entropy. This article invokes Directionality Theory to provide an evolutionary rationale for the following sequence of transformations which define the emergence of cellular life: 1. The self-assembly of activated macromolecules from inorganic matter 2. The emergence of an RNA world, defined by RNA molecules with catalytic and replicative properties 3. The origin of cellular life, the integration of the three carbon-based polymers: DNA, proteins and lipids, to generate a metabolic and replicative unit.
A Wall-time Minimizing Parallelization Strategy for Approximate Bayesian Computation
Authors: Emad Alamoudi, Felipe Reck, Nils Bundgaard, Frederik Graw, Lutz Brusch, Jan Hasenauer, Yannik Schälte
Date: 2023-04-30 00:00:00
Tasks: Scheduling

Approximate Bayesian Computation (ABC) is a widely applicable and popular approach to estimating unknown parameters of mechanistic models. As ABC analyses are computationally expensive, parallelization on high-performance infrastructure is often necessary. However, the existing parallelization strategies leave resources unused at times and thus do not optimally leverage them yet. We present look-ahead scheduling, a wall-time minimizing parallelization strategy for ABC Sequential Monte Carlo algorithms, which utilizes all available resources at practically all times by proactive sampling for prospective tasks. Our strategy can be integrated in e.g. adaptive distance function and summary statistic selection schemes, which is essential in practice. Evaluation of the strategy on different problems and numbers of parallel cores reveals speed-ups of typically 10-20% and up to 50% compared to the best established approach. Thus, the proposed strategy allows to substantially improve the cost and run-time efficiency of ABC methods on high-performance infrastructure.
Using neural ordinary differential equations to predict complex ecological dynamics from population density data
Authors: Jorge Arroyo-Esquivel, Christopher A Klausmeier, Elena Litchman
Date: 2023-04-29 00:00:00
Tasks: Prediction Intervals, Time Series

Simple models have been used to describe ecological processes for over a century. However, the complexity of ecological systems makes simple models subject to modeling bias due to simplifying assumptions or unaccounted factors, limiting their predictive power. Neural Ordinary Differential Equations (NODEs) have surged as a machine-learning algorithm that preserves the dynamic nature of the data \cite{chen_neural_2018}. Although preserving the dynamics in the data is an advantage, the question of how NODEs perform as a forecasting tool of ecological communities is unanswered. Here we explore this question using simulated time series of competing species in a time-varying environment. We find that NODEs provide more precise forecasts than ARIMA models. {We also find that untuned NODEs have a similar forecasting accuracy as untuned Long-Short Term Memory neural networks (LSTMs) and both are outperformed in accuracy and precision by EDM models. However, we also find NODEs generally outperform all other methods when evaluating with the interval score, which evaluates precision and accuracy in terms of prediction intervals rather than pointwise accuracy.} We also discuss ways to improve the forecasting performance {of NODEs}. The power of a forecasting tool such as NODEs is that it can provide insights into population dynamics and should thus broaden the approaches to studying time series of ecological communities.