pwc-archive/papers-with-abstracts huggingface dataset:
Deep learning based workflow for accelerated industrial X-ray Computed Tomography
Authors: Obaidullah Rahman, Singanallur V. Venkatakrishnan, Luke Scime, Paul Brackman, Curtis Frederick, Ryan Dehoff, Vincent Paquit, Amirkoushyar Ziabari
Date: 2023-09-24 00:00:00
Tasks: 3D Reconstruction, Deep Learning

X-ray computed tomography (XCT) is an important tool for high-resolution non-destructive characterization of additively-manufactured metal components. XCT reconstructions of metal components may have beam hardening artifacts such as cupping and streaking which makes reliable detection of flaws and defects challenging. Furthermore, traditional workflows based on using analytic reconstruction algorithms require a large number of projections for accurate characterization - leading to longer measurement times and hindering the adoption of XCT for in-line inspections. In this paper, we introduce a new workflow based on the use of two neural networks to obtain high-quality accelerated reconstructions from sparse-view XCT scans of single material metal parts. The first network, implemented using fully-connected layers, helps reduce the impact of BH in the projection data without the need of any calibration or knowledge of the component material. The second network, a convolutional neural network, maps a low-quality analytic 3D reconstruction to a high-quality reconstruction. Using experimental data, we demonstrate that our method robustly generalizes across several alloys, and for a range of sparsity levels without any need for retraining the networks thereby enabling accurate and fast industrial XCT inspections.
Design of Novel Loss Functions for Deep Learning in X-ray CT
Authors: Obaidullah Rahman, Ken D. Sauer, Madhuri Nagare, Charles A. Bouman, Roman Melnyk, Jie Tang, Brian Nett
Date: 2023-09-23 00:00:00
Tasks: Computed Tomography (CT)

Deep learning (DL) shows promise of advantages over conventional signal processing techniques in a variety of imaging applications. The networks' being trained from examples of data rather than explicitly designed allows them to learn signal and noise characteristics to most effectively construct a mapping from corrupted data to higher quality representations. In inverse problems, one has options of applying DL in the domain of the originally captured data, in the transformed domain of the desired final representation, or both. X-ray computed tomography (CT), one of the most valuable tools in medical diagnostics, is already being improved by DL methods. Whether for removal of common quantum noise resulting from the Poisson-distributed photon counts, or for reduction of the ill effects of metal implants on image quality, researchers have begun employing DL widely in CT. The selection of training data is driven quite directly by the corruption on which the focus lies. However, the way in which differences between the target signal and measured data is penalized in training generally follows conventional, pointwise loss functions. This work introduces a creative technique for favoring reconstruction characteristics that are not well described by norms such as mean-squared or mean-absolute error. Particularly in a field such as X-ray CT, where radiologists' subjective preferences in image characteristics are key to acceptance, it may be desirable to penalize differences in DL more creatively. This penalty may be applied in the data domain, here the CT sinogram, or in the reconstructed image. We design loss functions for both shaping and selectively preserving frequency content of the signal.
Energy-Saving Cell-Free Massive MIMO Precoders with a Per-AP Wideband Kronecker Channel Model
Authors: Emanuele Peschiera, Xavier Mestre, François Rottenberg
Date: 2023-09-27 00:00:00

We study cell-free massive multiple-input multiple-output precoders that minimize the power consumed by the power amplifiers subject to per-user per-subcarrier rate constraints. The power at each antenna is generally retrieved by solving a fixed-point equation that depends on the instantaneous channel coefficients. Using random matrix theory, we retrieve each antenna power as the solution to a fixed-point equation that depends only on the second-order statistics of the channel. Numerical simulations prove the accuracy of our asymptotic approximation and show how a subset of access points should be turned off to save power consumption, while all the antennas of the active access points are utilized with uniform power across them. This mechanism allows to save consumed power up to a factor of 9$\times$ in low-load scenarios.
Formation Wing-Beat Modulation (FWM): A Tool for Quantifying Bird Flocks Using Radar Micro-Doppler Signals
Authors: Jiangkun Gong, Jun Yan, Deyong Kong, Ruizhi Chen, Deren Li
Date: 2023-09-27 00:00:00

Radar echoes from bird flocks contain modulation signals, which we find are produced by the flapping gaits of birds in the flock, resulting in a group of spectral peaks with similar amplitudes spaced at a specific interval. We call this the formation wing-beat modulation (FWM) effect. FWM signals are micro-Doppler modulated by flapping wings and are related to the bird number, wing-beat frequency, and flight phasing strategy. Our X-band radar data show that FWM signals exist in radar signals of a seagull flock, providing tools for quantifying the bird number and estimating the mean wingbeat rate of birds. This new finding could aid in research on the quantification of bird migration numbers and estimation of bird flight behavior in radar ornithology and aero-ecology.
An Exploration of Optimal Parameters for Efficient Blind Source Separation of EEG Recordings Using AMICA
Authors: Gwenevere Frank, Seyed Yahya Shirazi, Jason Palmer, Gert Cauwenberghs, Scott Makeig, Arnaud Delorme
Date: 2023-09-27 00:00:00
Tasks: blind source separation, EEG

EEG continues to find a multitude of uses in both neuroscience research and medical practice, and independent component analysis (ICA) continues to be an important tool for analyzing EEG. A multitude of ICA algorithms for EEG decomposition exist, and in the past, their relative effectiveness has been studied. AMICA is considered the benchmark against which to compare the performance of other ICA algorithms for EEG decomposition. AMICA exposes many parameters to the user to allow for precise control of the decomposition. However, several of the parameters currently tend to be set according to "rules of thumb" shared in the EEG community. Here, AMICA decompositions are run on data from a collection of subjects while varying certain key parameters. The running time and quality of decompositions are analyzed based on two metrics: Pairwise Mutual Information (PMI) and Mutual Information Reduction (MIR). Recommendations for selecting starting values for parameters are presented.
Scaling Representation Learning from Ubiquitous ECG with State-Space Models
Authors: Kleanthis Avramidis, Dominika Kunc, Bartosz Perz, Kranti Adsul, Tiantian Feng, Przemysław Kazienko, Stanisław Saganowski, Shrikanth Narayanan
Date: 2023-09-26 00:00:00
Tasks: Representation Learning, State Space Models

Ubiquitous sensing from wearable devices in the wild holds promise for enhancing human well-being, from diagnosing clinical conditions and measuring stress to building adaptive health promoting scaffolds. But the large volumes of data therein across heterogeneous contexts pose challenges for conventional supervised learning approaches. Representation Learning from biological signals is an emerging realm catalyzed by the recent advances in computational modeling and the abundance of publicly shared databases. The electrocardiogram (ECG) is the primary researched modality in this context, with applications in health monitoring, stress and affect estimation. Yet, most studies are limited by small-scale controlled data collection and over-parameterized architecture choices. We introduce \textbf{WildECG}, a pre-trained state-space model for representation learning from ECG signals. We train this model in a self-supervised manner with 275,000 10s ECG recordings collected in the wild and evaluate it on a range of downstream tasks. The proposed model is a robust backbone for ECG analysis, providing competitive performance on most of the tasks considered, while demonstrating efficacy in low-resource regimes. The code and pre-trained weights are shared publicly at https://github.com/klean2050/tiles_ecg_model.
Fully Adaptive Time-Varying Wave-Shape Model: Applications in Biomedical Signal Processing
Authors: Joaquin Ruiz, Gastón Schlotthauer, Leandro Vignolo, Marcelo A. Colominas
Date: 2023-09-26 00:00:00
Tasks: Denoising

In this work, we propose a time-varying wave-shape extraction algorithm based on a modified version of the adaptive non-harmonic model for non-stationary signals. The model codifies the time-varying wave-shape information in the relative amplitude and phase of the harmonic components of the wave-shape. The algorithm was validated on both real and synthetic signals for the tasks of denoising, decomposition and adaptive segmentation. For the denoising task, both monocomponent and multicomponent synthetic signals were considered. In both cases, the proposed algorithm can accurately recover the time-varying wave-shape of non-stationary signals, even in the presence of high levels of noise, outperforming existing wave-shape estimation algorithms and denoising methods based on short-time Fourier transform thresholding. The denoising of an electroencephalograph signal was also performed, giving similar results. For decomposition, our proposal was able to recover the composing waveforms more accurately by considering the time variations from the harmonic amplitude functions when compared to existing methods. Finally, the algorithm was used for the adaptive segmentation of synthetic signals and an electrocardiograph of a patient undergoing ventricular fibrillation.
Wave-shape Function Model Order Estimation by Trigonometric Regression
Authors: Joaquin Ruiz, Marcelo A. Colominas
Date: 2023-09-26 00:00:00
Tasks: Denoising, Model Selection, regression

The adaptive non-harmonic (ANH) model is a powerful tool to compactly represent oscillating signals with time-varying amplitude and phase, and non-sinusoidal oscillating morphology. Given good estimators of instantaneous amplitude and phase we can construct an adaptive model, where the morphology of the oscillation is described by the wave-shape function (WSF), a 2{\pi}-periodic more general periodic function. In this paper, we address the problem of estimating the number of harmonic components of the WSF, a problem that remains underresearched, by adapting trigonometric regression model selection criteria into this context. We study the application of these criteria, originally developed in the context of stationary signals, to the case of signals with time-varying amplitudes and phases. We then incorporate the order estimation to the ANH model reconstruction procedure and analyze its performance for noisy AM-FM signals. Experimental results on synthethic signals indicate that these criteria enable the adaptive estimation of the waveform of non-stationary signals with non-sinusoidal oscillatory patterns, even in the presence of considerable amount of noise. We also apply our reconstruction procedure to the task of denoising simulated pulse wave signals and determine that the proposed technique performs competitively to other denoising schemes. We conclude this work by showing that our adaptive order estimation algorithm takes into account the interpatient waveform variability of the electrocardiogram (ECG) and respiratory signals by analyzing recordings from the Fantasia Database.
ML-based PBCH symbol detection and equalization for 5G Non-Terrestrial Networks
Authors: Inés Larráyoz-Arrigote, Marcele O. K. Mendonca, Alejandro Gonzalez-Garrido, Jevgenij Krivochiza, Sumit Kumar, Jorge Querol, Joel Grotz, Stefano Andrenacci, Symeon Chatzinotas
Date: 2023-09-26 00:00:00

This paper delves into the application of Machine Learning (ML) techniques in the realm of 5G Non-Terrestrial Networks (5G-NTN), particularly focusing on symbol detection and equalization for the Physical Broadcast Channel (PBCH). As 5G-NTN gains prominence within the 3GPP ecosystem, ML offers significant potential to enhance wireless communication performance. To investigate these possibilities, we present ML-based models trained with both synthetic and real data from a real 5G over-the-satellite testbed. Our analysis includes examining the performance of these models under various Signal-to-Noise Ratio (SNR) scenarios and evaluating their effectiveness in symbol enhancement and channel equalization tasks. The results highlight the ML performance in controlled settings and their adaptability to real-world challenges, shedding light on the potential benefits of the application of ML in 5G-NTN.
Toward Energy Efficient Multiuser IRS-Assisted URLLC Systems: A Novel Rank Relaxation Method
Authors: Jalal Jalali, Filip Lemic, Hina Tabassum, Rafael Berkvens, Jeroen Famaey
Date: 2023-09-26 00:00:00

This paper proposes an energy efficient resource allocation design algorithm for an intelligent reflecting surface (IRS)-assisted downlink ultra-reliable low-latency communication (URLLC) network. This setup features a multi-antenna base station (BS) transmitting data traffic to a group of URLLC users with short packet lengths. We maximize the total network's energy efficiency (EE) through the optimization of active beamformers at the BS and passive beamformers (a.k.a. phase shifts) at the IRS. The main non-convex problem is divided into two sub-problems. An alternating optimization (AO) approach is then used to solve the problem. Through the use of the successive convex approximation (SCA) with a novel iterative rank relaxation method, we construct a concave-convex objective function for each sub-problem. The first sub-problem is a fractional program that is solved using the Dinkelbach method and a penalty-based approach. The second sub-problem is then solved based on semi-definite programming (SDP) and the penalty-based approach. The iterative solution gradually approaches the rank-one for both the active beamforming and unit modulus IRS phase-shift sub-problems. Our results demonstrate the efficacy of the proposed solution compared to existing benchmarks.
Beyond Log-Concavity: Theory and Algorithm for Sum-Log-Concave Optimization
Authors: Mastane Achab
Date: 2023-09-26 00:00:00
Tasks: regression

This paper extends the classic theory of convex optimization to the minimization of functions that are equal to the negated logarithm of what we term as a sum-log-concave function, i.e., a sum of log-concave functions. In particular, we show that such functions are in general not convex but still satisfy generalized convexity inequalities. These inequalities unveil the key importance of a certain vector that we call the cross-gradient and that is, in general, distinct from the usual gradient. Thus, we propose the Cross Gradient Descent (XGD) algorithm moving in the opposite direction of the cross-gradient and derive a convergence analysis. As an application of our sum-log-concave framework, we introduce the so-called checkered regression method relying on a sum-log-concave function. This classifier extends (multiclass) logistic regression to non-linearly separable problems since it is capable of tessellating the feature space by using any given number of hyperplanes, creating a checkerboard-like pattern of decision regions.
Homotopy Relaxation Training Algorithms for Infinite-Width Two-Layer ReLU Neural Networks
Authors: Yahong Yang, Qipin Chen, Wenrui Hao
Date: 2023-09-26 00:00:00

In this paper, we present a novel training approach called the Homotopy Relaxation Training Algorithm (HRTA), aimed at accelerating the training process in contrast to traditional methods. Our algorithm incorporates two key mechanisms: one involves building a homotopy activation function that seamlessly connects the linear activation function with the ReLU activation function; the other technique entails relaxing the homotopy parameter to enhance the training refinement process. We have conducted an in-depth analysis of this novel method within the context of the neural tangent kernel (NTK), revealing significantly improved convergence rates. Our experimental results, especially when considering networks with larger widths, validate the theoretical conclusions. This proposed HRTA exhibits the potential for other activation functions and deep neural networks.
Measuring risk contagion in financial networks with CoVaR
Authors: Bikramjit Das, Vicky Fasen-Hartmann
Date: 2023-09-27 00:00:00

The stability of a complex financial system may be assessed by measuring risk contagion between various financial institutions with relatively high exposure. We consider a financial network model using a bipartite graph of financial institutions (e.g., banks, investment companies, insurance firms) on one side and financial assets on the other. Following empirical evidence, returns from such risky assets are modeled by heavy-tailed distributions, whereas their joint dependence is characterized by copula models exhibiting a variety of tail dependence behavior. We consider CoVaR, a popular measure of risk contagion and study its asymptotic behavior under broad model assumptions. We further propose the Extreme CoVaR Index (ECI) for capturing the strength of risk contagion between risk entities in such networks, which is particularly useful for models exhibiting asymptotic independence. The results are illustrated by providing precise expressions of CoVaR and ECI when the dependence of the assets is modeled using two well-known multivariate dependence structures: the Gaussian copula and the Marshall-Olkin copula.
Network Pharmacology, Molecular Docking, and MR Analysis: Targets and Mechanisms of Gegen Qinlian Decoction for Helicobacter pylori
Authors: Ruotong. Lu, Xiaozhe. Huang, Sihuan. Deng, Haikun. Du
Date: 2023-09-26 00:00:00
Tasks: Molecular Docking

Objective: The study explored therapeutic targets and mechanisms of Gegen Qinlian Decoction for Helicobacter pylori infection and related gastric cancer using network pharmacology, molecular docking, and Mendelian randomization. Methods: Medicinal components of Gegen Qinlian Decoction were extracted from TCMSP and HERB databases. Disease treatment targets were sourced from DisGeNET and PubChem. Interaction networks were constructed via the STRING database and visualized using Cytoscape 3.9.1. Enrichment analysis of intersected targets was performed using DAVID and Metascapes. Molecular docking employed Autodock Tools 1.5.6 and PyMOL 2.5.2. Mendelian randomization was based on the ukb-b-531 sample from UK Biobank. Results: 146 active components and 248 targets from Gegen Qinlian Decoction were identified. 66 targets overlapped with Helicobacter pylori infection genes. Molecular docking highlighted interactions between primary drug components like quercetin, wogonin, kaempferol, and target genes PTGS1, PTGS2, MAPK14. Mendelian randomization pinpointed genes like IGF2, PIK3CG, GJA1, and PLAU associated with Helicobacter pylori infection. Conclusion: Gegen Qinlian Decoction's active components target Helicobacter pylori infection through diverse targets and pathways, presenting potential research avenues.
Automated Detection of Persistent Inflammatory Biomarkers in Post-COVID-19 Patients Using Machine Learning Techniques
Authors: Ghizal fatima, Fadhil G. Al-Amran, Maitham G. Yousif
Date: 2023-09-26 00:00:00
Tasks: feature selection

The COVID-19 pandemic has left a lasting impact on individuals, with many experiencing persistent symptoms, including inflammation, in the post-acute phase of the disease. Detecting and monitoring these inflammatory biomarkers is critical for timely intervention and improved patient outcomes. This study employs machine learning techniques to automate the identification of persistent inflammatory biomarkers in 290 post-COVID-19 patients, based on medical data collected from hospitals in Iraq. The data encompassed a wide array of clinical parameters, such as C-reactive protein and interleukin-6 levels, patient demographics, comorbidities, and treatment histories. Rigorous data preprocessing and feature selection processes were implemented to optimize the dataset for machine learning analysis. Various machine learning algorithms, including logistic regression, random forests, support vector machines, and gradient boosting, were deployed to construct predictive models. These models exhibited promising results, showcasing high accuracy and precision in the identification of patients with persistent inflammation. The findings of this study underscore the potential of machine learning in automating the detection of persistent inflammatory biomarkers in post-COVID-19 patients. These models can serve as valuable tools for healthcare providers, facilitating early diagnosis and personalized treatment strategies for individuals at risk of persistent inflammation, ultimately contributing to improved post-acute COVID-19 care and patient well-being. Keywords: COVID-19, post-COVID-19, inflammation, biomarkers, machine learning, early detection.
Simultaneity of consciousness with physical reality: the key that unlocks the mind-matter problem
Authors: John Sanfey
Date: 2023-09-27 00:00:00

The problem of explaining the relationship between subjective experience and physical reality remains difficult and unresolved. In most explanations, consciousness is epiphenomenal, without causal power. The most notable exception is Integrated Information Theory (IIT), which provides a causal explanation for consciousness. However, IIT relies on an identity between subjectivity and a particular type of physical structure, namely with an information structure that has intrinsic causal power greater than the sum of its parts. Any theory that relies on a psycho-physical identity must eventually appeal to panpsychism, which undermines that theorys claim to be fundamental. IIT has recently pivoted towards a strong version of causal emergence, but macroscopic causal structures cannot be causally stronger than its microscopic parts without some new physical law or governing principle. The approach taken here is designed to uncover such a principle. The decisive argument is entirely deductive from initial premises that are phenomenologically certain. If correct, the arguments prove that conscious experience is sufficient to create additional degrees of causal freedom independently of the content of experience, and in a manner that is unpredictable and unobservable by any temporally sequential means. This provides a fundamental principle about consciousness, and a conceptual bridge between it and the physics describing what is experienced. The principle makes testable predictions about brain function, with notable differences from IIT, some of which are also empirically testable.