pwc-archive/papers-with-abstracts huggingface dataset:
ECG-TEM: Time-based sub-Nyquist sampling for ECG signal reconstruction and Hardware Prototype
Authors: Hila Naaman, Daniel Bilik, Shlomi Savariego, Moshe Namer, Yonina C. Eldar
Date: 2024-05-22 00:00:00

Portable heart rate monitoring (HRM) systems based on electrocardiograms (ECGs) have become increasingly crucial for preventing lifestyle diseases. For such portable systems, minimizing power consumption and sampling rate is critical due to the substantial data generated during long-term ECG monitoring. The variable pulse-width finite rate of innovation (VPW-FRI) framework provides an effective solution for low-rate sampling and compression of ECG signals. We develop a time-based sub-Nyquist sampling and reconstruction method for ECG signals specifically designed for HRM applications. Our approach harnesses the integrate-and-fire time-encoding machine (IF-TEM) as a power-efficient, time-based, asynchronous sampler, generating a sequence of time instants without the need for a global clock. The ECG signal is represented as a linear combination of VPW-FRI pulses, which is then subjected to pre-filtering before being sampled by the IF-TEM sampler. A compactly supported robust filter with a frequency-domain alias cancellation condition is used to combat the effects of noise. Our reconstruction process involves consecutive partial summations of discrete representations of the input signal derived from the series of time encodings, further enhancing the accuracy of the reconstructed ECG signals. Additionally, we introduce an IF-TEM sampling hardware system for ECG signals, implemented using an analog filter device. The firing rate is 42-80Hz, equivalent to approximately 0.025-0.05 of the Nyquist rate. Our hardware validation bridges the gap between theory and practice and demonstrates the robust performance and practical applicability of our approach in accurately monitoring heart rates and reconstructing ECG signals.
Downlink Power Control based UE-Sided Initial Access for Tactical 5G NR
Authors: Akshay Jain, Karthik Upadhya, Mikko A. Uusitalo, Harish Viswanathan
Date: 2024-05-22 00:00:00

Communication technologies play a crucial role in battlefields. They are an inalienable part of any tactical response, whether at the battlefront or inland. Such scenarios require that the communication technologies be versatile, scalable, cost-effective, and stealthy. While multiple studies and past products have tried to address these requirements, none of them have been able to solve all the four challenges simultaneously. Hence, in this paper, we propose a tactical solution that is based on the versatile, scalable, and cost effective 5G NR system. Our focus is on the initial-access phase which is subject to a high probability of detection by an eavesdropper. To address this issue, we propose a novel approach that involves some modifications to the initial access procedure that lowers the probability of detection while not affecting standards compliance and not requiring any modifications to the user equipment chipset implementation. Further, we demonstrate that with a simple downlink power control algorithm, we reduce the probability of detection at an eavesdropper. The result is a 5G NR based initial-access method that improves stealthiness when compared with a vanilla 5G NR implementation.
Secure Communications in Near-Filed ISCAP Systems with Extremely Large-Scale Antenna Arrays
Authors: Zixiang Ren, Siyao Zhang, Xinmin Li, Ling Qiu, Jie Xu, Derrick Wing Kwan Ng
Date: 2024-05-22 00:00:00

This paper investigates secure communications in a near-field multi-functional integrated sensing, communication, and powering (ISCAP) system with an extremely large-scale antenna arrays (ELAA) equipped at the base station (BS). In this system, the BS sends confidential messages to a single communication user (CU), and at the same time wirelessly senses a point target and charges multiple energy receivers (ERs). It is assumed that the ERs and the sensing target are potential eavesdroppers that may attempt to intercept the confidential messages intended for the CU. We consider the joint transmit beamforming design to support secure communications while ensuring the sensing and powering requirements. In particular, the BS transmits dedicated sensing/energy beams in addition to the information beam, which also play the role of artificial noise (AN) for effectively jamming potential eavesdroppers. Building upon this, we maximize the secrecy rate at the CU, subject to the maximum \ac{crb} constraints for target sensing and the minimum harvested energy constraints for the ERs. Although the formulated joint beamforming problem is non-convex and challenging to solve, we acquire the optimal solution via the semi-definite relaxation (SDR) and fractional programming techniques together with a one-dimensional (1D) search. Subsequently, we present two alternative designs based on zero-forcing (ZF) beamforming and maximum ratio transmission (MRT), respectively. Finally, our numerical results show that our proposed approaches exploit both the distance-domain resolution of near-field ELAA and the joint beamforming design for enhancing secure communication performance while ensuring the sensing and powering requirements in ISCAP, especially when the CU and the target and ER eavesdroppers are located at the same angle (but different distances) with respect to the BS.
End-to-End Learning of Pulse-Shaper and Receiver Filter in the Presence of Strong Intersymbol Interference
Authors: Søren Føns Nielsen, Francesco Da Ros, Mikkel N. Schmidt, Darko Zibar
Date: 2024-05-22 00:00:00

We numerically demonstrate that joint optimization of FIR based pulse-shaper and receiver filter results in an improved system performance, and shorter filter lengths (lower complexity), for 4-PAM 100 GBd IM/DD systems.
Floor-Plan-aided Indoor Localization: Zero-Shot Learning Framework, Data Sets, and Prototype
Authors: Haiyao Yu, Changyang She, Yunkai Hu, Geng Wang, Rui Wang, Branka Vucetic, Yonghui Li
Date: 2024-05-22 00:00:00
Tasks: Graph Neural Network, Indoor Localization, Zero-Shot Learning

Machine learning has been considered a promising approach for indoor localization. Nevertheless, the sample efficiency, scalability, and generalization ability remain open issues of implementing learning-based algorithms in practical systems. In this paper, we establish a zero-shot learning framework that does not need real-world measurements in a new communication environment. Specifically, a graph neural network that is scalable to the number of access points (APs) and mobile devices (MDs) is used for obtaining coarse locations of MDs. Based on the coarse locations, the floor-plan image between an MD and an AP is exploited to improve localization accuracy in a floor-plan-aided deep neural network. To further improve the generalization ability, we develop a synthetic data generator that provides synthetic data samples in different scenarios, where real-world samples are not available. We implement the framework in a prototype that estimates the locations of MDs. Experimental results show that our zero-shot learning method can reduce localization errors by around $30$\% to $55$\% compared with three baselines from the existing literature.
A hybrid framework for compartmental models enabling simulation-based inference
Authors: Domenic P. J. Germano, Alexander E. Zarebski, Sophie Hautphenne, Robert Moss, Jennifer A. Flegg, Mark B. Flegg
Date: 2024-05-21 00:00:00

Multi-scale systems often exhibit stochastic and deterministic dynamics. Capturing these aspects in a compartmental model is challenging. Notably, low occupancy compartments exhibit stochastic dynamics and high occupancy compartments exhibit deterministic dynamics. Failing to account for stochasticity in small populations can produce 'atto-foxes', e.g. in the Lotka-Volterra ordinary differential equation (ODE) model. This limitation becomes problematic when studying extinction of species or the clearance of infection, but it can be resolved by using discrete stochastic models e.g. continuous time Markov chains (CTMCs). Unfortunately, simulating CTMCs is infeasible for most realistic populations. We develop a novel mathematical framework, to couple continuous ODEs and discrete CTMCs: 'Jump-Switch-Flow' (JSF). In this framework populations can reach extinct states ("absorbing states"), thereby resolving atto-fox-type problems. JSF has the desired behaviours of exact CTMC simulation, but is substantially faster than existing alternatives. JSF's utility for simulation-based inference, particularly multi-scale problems, is demonstrated by several case-studies. In a simulation study, we demonstrate how JSF can enable a more nuanced analysis of the efficacy of public health interventions. We also carry out a novel analysis of longitudinal within-host data from SARS-CoV-2 infections to quantify the timing of viral clearance. JSF offers a novel approach to compartmental model development and simulation.
Almost sure convergence rates of stochastic gradient methods under gradient domination
Authors: Simon Weissmann, Sara Klein, Waïss Azizian, Leif Döring
Date: 2024-05-22 00:00:00
Tasks: Policy Gradient Methods, reinforcement-learning, Reinforcement Learning

Stochastic gradient methods are among the most important algorithms in training machine learning problems. While classical assumptions such as strong convexity allow a simple analysis they are rarely satisfied in applications. In recent years, global and local gradient domination properties have shown to be a more realistic replacement of strong convexity. They were proved to hold in diverse settings such as (simple) policy gradient methods in reinforcement learning and training of deep neural networks with analytic activation functions. We prove almost sure convergence rates $f(X_n)-f^*\in o\big( n^{-\frac{1}{4\beta-1}+\epsilon}\big)$ of the last iterate for stochastic gradient descent (with and without momentum) under global and local $\beta$-gradient domination assumptions. The almost sure rates get arbitrarily close to recent rates in expectation. Finally, we demonstrate how to apply our results to the training task in both supervised and reinforcement learning.
On Convergence of the Alternating Directions SGHMC Algorithm
Authors: Soumyadip Ghosh, Yingdong Lu, Tomasz Nowicki
Date: 2024-05-21 00:00:00

We study convergence rates of Hamiltonian Monte Carlo (HMC) algorithms with leapfrog integration under mild conditions on stochastic gradient oracle for the target distribution (SGHMC). Our method extends standard HMC by allowing the use of general auxiliary distributions, which is achieved by a novel procedure of Alternating Directions. The convergence analysis is based on the investigations of the Dirichlet forms associated with the underlying Markov chain driving the algorithms. For this purpose, we provide a detailed analysis on the error of the leapfrog integrator for Hamiltonian motions with both the kinetic and potential energy functions in general form. We characterize the explicit dependence of the convergence rates on key parameters such as the problem dimension, functional properties of both the target and auxiliary distributions, and the quality of the oracle.
Range-Limited Heaps' Law for Functional DNA Words in the Human Genome
Authors: Wentian Li, Yannis Almirantis, Astero Provata
Date: 2024-05-22 00:00:00

Heaps' or Herdan's law is a linguistic law describing the relationship between the vocabulary/dictionary size (type) and word counts (token) to be a power-law function. Its existence in genomes with certain definition of DNA words is unclear partly because the dictionary size in genome could be much smaller than that in a human language. We define a DNA word as a coding region in a genome that codes for a protein domain. Using human chromosomes and chromosome arms as individual samples, we establish the existence of Heaps' law in the human genome within limited range. Our definition of words in a genomic or proteomic context is different from other definitions such as over-represented k-mers which are much shorter in length. Although an approximate power-law distribution of protein domain sizes due to gene duplication and the related Zipf's law is well known, their translation to the Heaps' law in DNA words is not automatic. Several other animal genomes are shown herein also to exhibit range-limited Heaps' law with our definition of DNA words, though with various exponents. When tokens were randomly sampled and sample sizes reach to the maximum level, a deviation from the Heaps' law was observed, but a quadratic regression in log-log type-token plot fits the data perfectly. Investigation of type-token plot and its regression coefficients could provide an alternative narrative of reusage and redundancy of protein domains as well as creation of new protein domains from a linguistic perspective.
Prospective and retrospective coding in cortical neurons
Authors: Simon Brandt, Mihai Alexandru Petrovici, Walter Senn, Katharina Anna Wilmes, Federico Benitez
Date: 2024-05-23 00:00:00

Brains can process sensory information from different modalities at astonishing speed; this is surprising as the integration of inputs through the membrane of each individual neuron already causes a delayed response. Neuronal recordings {\em in vitro} reveal a possible explanation for this fast processing, in terms of individual neurons advancing their output firing rates with respect to the input, a concept which we refer to as prospective coding. The underlying mechanisms of prospective coding, however, are not completely understood. We propose a mechanistic explanation for individual neurons advancing their output on the level of single action potentials and instantaneous firing rates. We show that the spike generation mechanism can be the source for prospective (advanced) or retrospective (delayed) responses. A simplified Hodgkin-Huxley model identifies sodium inactivation as a source for prospective firing, controlling the timing of the neuron's output as a function of the voltage and its temporal derivative. We further show that slow adaptation processes, such as spike-frequency adaptation or deactivating dendritic currents, represent mechanisms generating prospective firing for inputs that undergo slow temporal modulations. In general, we show that adaptation processes at different time scales can cause advanced neuronal responses to time-varying inputs that are modulated on the corresponding time scales.
DendroPy 5: a mature Python library for phylogenetic computing
Authors: Matthew Andres Moreno, Mark T. Holder, Jeet Sukumaran
Date: 2024-05-23 00:00:00
Tasks: Evolutionary Algorithms

Contemporary bioinformatics has seen in profound new visibility into the composition, structure, and history of the natural world around us. Arguably, the central pillar of bioinformatics is phylogenetics -- the study of hereditary relatedness among organisms. Insight from phylogenetic analysis has touched nearly every corner of biology. Examples range across natural history, population genetics and phylogeography, conservation biology, public health, medicine, in vivo and in silico experimental evolution, application-oriented evolutionary algorithms, and beyond. High-throughput genetic and phenotypic data has realized groundbreaking results, in large part, through conjunction with open-source software used to process and analyze it. Indeed, the preceding decades have ushered in a flourishing ecosystem of bioinformatics software applications and libraries. Over the course of its nearly fifteen-year history, the DendroPy library for phylogenetic computation in Python has established a generalist niche in serving the bioinformatics community. Here, we report on the recent major release of the library, DendroPy version 5. The software release represents a major milestone in transitioning the library to a sustainable long-term development and maintenance trajectory. As such, this work positions DendroPy to continue fulfilling a key supporting role in phyloinformatics infrastructure.
Lessons to learn for better safeguarding of genetic resources during tree pandemics: the case of ash dieback in Europe
Authors: Jan-Peter George, Mari Rusanen, Egbert Beuker, Leena Yrjänä, Volkmar Timmermann, Nenad Potocic, Sakari Välimäki, Heino Konrad
Date: 2024-05-22 00:00:00
Tasks: Diversity, Philosophy

Ash dieback (ADB) is threatening populations of European ash (Fraxinus excelsior & F. angustifolia) for more than three decades. Although much knowledge has been gathered in the recent past, practical conservation measures have been mostly implemented at local scale. Since range contraction in both ash species will be exacerbated in the near future by westward expansion of the emerald ash borer and climate change, systematic conservation frameworks need to be developed to avoid long-term population-genetic consequences and depletion of genomic diversity. In this article, we address the advantages and obstacles of conservation approaches aiming to conserve genetic diversity in-situ or ex-situ during tree pandemics. We are reviewing 47 studies which were published on ash dieback to unravel three important dimensions of ongoing conservation approaches or perceived conservation problems: i) conservation philosophy (i.e. natural selection, resistance breeding or genetic conservation), ii) the spatial scale (ecosystem, country, continent), and iii) the integration of genetic safety margins in conservation planning. Although nearly equal proportions of the reviewed studies mention breeding or active conservation as possible long-term solutions, only 17% consider that additional threats exist which may further reduce genetic diversity in both ash species. We also identify and discuss several knowledge gaps and limitations which may have limited the initiation of conservation projects at national and international level so far. Finally, we demonstrate that there is not much time left for filling these gaps, because European-wide forest health monitoring data indicates a significant decline of ash populations in the last 5 years.
Multilevel functional data analysis modeling of human glucose response to meal intake
Authors: Marcos Matabuena, Joe Sartini, Francisco Gude
Date: 2024-05-23 00:00:00
Tasks: Nutrition

Glucose meal response information collected via Continuous Glucose Monitoring (CGM) is relevant to the assessment of individual metabolic status and the support of personalized diet prescriptions. However, the complexity of the data produced by CGM monitors pushes the limits of existing analytic methods. CGM data often exhibits substantial within-person variability and has a natural multilevel structure. This research is motivated by the analysis of CGM data from individuals without diabetes in the AEGIS study. The dataset includes detailed information on meal timing and nutrition for each individual over different days. The primary focus of this study is to examine CGM glucose responses following patients' meals and explore the time-dependent associations with dietary and patient characteristics. Motivated by this problem, we propose a new analytical framework based on multilevel functional models, including a new functional mixed R-square coefficient. The use of these models illustrates 3 key points: (i) The importance of analyzing glucose responses across the entire functional domain when making diet recommendations; (ii) The differential metabolic responses between normoglycemic and prediabetic patients, particularly with regards to lipid intake; (iii) The importance of including random, person-level effects when modelling this scientific problem.
HoverFast: an accurate, high-throughput, clinically deployable nuclear segmentation tool for brightfield digital pathology images
Authors: Petros Liakopoulos, Julien Massonnet, Jonatan Bonjour, Medya Tekes Mizrakli, Simon Graham, Michel A. Cuendet, Amanda H. Seipel, Olivier Michielin, Doron Merkler, Andrew Janowczyk
Date: 2024-05-22 00:00:00
Tasks: GPU, Knowledge Distillation, Nuclear Segmentation, Segmentation, whole slide images

In computational digital pathology, accurate nuclear segmentation of Hematoxylin and Eosin (H&E) stained whole slide images (WSIs) is a critical step for many analyses and tissue characterizations. One popular deep learning-based nuclear segmentation approach, HoverNet, offers remarkably accurate results but lacks the high-throughput performance needed for clinical deployment in resource-constrained settings. Our approach, HoverFast, aims to provide fast and accurate nuclear segmentation in H&E images using knowledge distillation from HoverNet. By redesigning the tool with software engineering best practices, HoverFast introduces advanced parallel processing capabilities, efficient data handling, and optimized postprocessing. These improvements facilitate scalable high-throughput performance, making HoverFast more suitable for real-time analysis and application in resource-limited environments. Using a consumer grade Nvidia A5000 GPU, HoverFast showed a 21x speed improvement as compared to HoverNet; reducing mean analysis time for 40x WSIs from ~2 hours to 6 minutes while retaining a concordant mean Dice score of 0.91 against the original HoverNet output. Peak memory usage was also reduced 71% from 44.4GB, to 12.8GB, without requiring SSD-based caching. To ease adoption in research and clinical contexts, HoverFast aligns with best-practices in terms of (a) installation, and (b) containerization, while (c) providing outputs compatible with existing popular open-source image viewing tools such as QuPath. HoverFast has been made open-source and is available at andrewjanowczyk.com/open-source-tools/hoverfast.
An Asymptotic CVaR Measure of Risk for Markov Chains
Authors: Shivam Patel, Vivek Borkar
Date: 2024-05-22 00:00:00
Tasks: Decision Making, Density Estimation

Risk sensitive decision making finds important applications in current day use cases. Existing risk measures consider a single or finite collection of random variables, which do not account for the asymptotic behaviour of underlying systems. Conditional Value at Risk (CVaR) is the most commonly used risk measure, and has been extensively utilized for modelling rare events in finite horizon scenarios. Naive extension of existing risk criteria to asymptotic regimes faces fundamental challenges, where basic assumptions of existing risk measures fail. We present a complete simulation based approach for sequentially computing Asymptotic CVaR (ACVaR), a risk measure we define on limiting empirical averages of markovian rewards. Large deviations theory, density estimation, and two-time scale stochastic approximation are utilized to define a 'tilted' probability kernel on the underlying state space to facilitate ACVaR simulation. Our algorithm enjoys theoretical guarantees, and we numerically evaluate its performance over a variety of test cases.
Continuous-time Equilibrium Returns in Markets with Price Impact and Transaction Costs
Authors: Michail Anthropelos, Constantinos Stefanakis
Date: 2024-05-23 00:00:00

We consider an Ito-financial market at which the risky assets' returns are derived endogenously through a market-clearing condition amongst heterogeneous risk-averse investors with quadratic preferences and random endowments. Investors act strategically by taking into account the impact that their orders have on the assets' drift. A frictionless market and an one with quadratic transaction costs are analysed and compared. In the former, we derive the unique Nash equilibrium at which investors' demand processes reveal different hedging needs than their true ones, resulting in a deviation of the Nash equilibrium from its competitive counterpart. Under price impact and transaction costs, we characterize the Nash equilibrium as the (unique) solution of a system of FBSDEs and derive its closed-form expression. We furthermore show that under common risk aversion and absence of noise traders, transaction costs do not change the equilibrium returns. On the contrary, when noise traders are present, the effect of transaction costs on equilibrium returns is amplified due to price impact.