pwc-archive/papers-with-abstracts huggingface dataset:
ECGtizer: a fully automated digitizing and signal recovery pipeline for electrocardiograms
Authors: Alex Lence, Ahmad Fall, Samuel David Cohen, Federica Granese, Jean-Daniel Zucker, Joe-Elie Salem, Edi Prifti
Date: 2024-12-09 00:00:00
Tasks: Diagnostic

Electrocardiograms (ECGs) are essential for diagnosing cardiac pathologies, yet traditional paper-based ECG storage poses significant challenges for automated analysis. This study introduces ECGtizer, an open-source, fully automated tool designed to digitize paper ECGs and recover signals lost during storage. ECGtizer facilitates automated analyses using modern AI methods. It employs automated lead detection, three pixel-based signal extraction algorithms, and a deep learning-based signal reconstruction module. We evaluated ECGtizer on two datasets: a real-life cohort from the COVID-19 pandemic (JOCOVID) and a publicly available dataset (PTB-XL). Performance was compared with two existing methods: the fully automated ECGminer and the semi-automated PaperECG, which requires human intervention. ECGtizer's performance was assessed in terms of signal recovery and the fidelity of clinically relevant feature measurement. Additionally, we tested these tools on a third dataset (GENEREPOL) for downstream AI tasks. Results show that ECGtizer outperforms existing tools, with its ECGtizerFrag algorithm delivering superior signal recovery. While PaperECG demonstrated better outcomes than ECGminer, it required human input. ECGtizer enhances the usability of historical ECG data and supports advanced AI-based diagnostic methods, making it a valuable addition to the field of AI in ECG analysis.
6D Rigid Body Localization and Velocity Estimation via Gaussian Belief Propagation
Authors: Niclas Führling, Volodymyr Vizitiv, Kuranage Roche Rayan Ranasinghe, Hyeon Seok Rou, Giuseppe Thadeu Freitas de Abreu, David González G., Osvaldo Gonsa
Date: 2024-12-06 00:00:00
Tasks: Motion Estimation, Translation

We propose a novel message-passing solution to the sixth-dimensional (6D) moving rigid body localization (RBL) problem, in which the three-dimensional (3D) translation vector and rotation angles, as well as their corresponding translational and angular velocities, are all estimated by only utilizing the relative range and Doppler measurements between the "anchor" sensors located at an 3D (rigid body) observer and the "target" sensors of another rigid body. The proposed method is based on a bilinear Gaussian belief propagation (GaBP) framework, employed to estimate the absolute sensor positions and velocities using a range- and Doppler-based received signal model, which is then utilized in the reconstruction of the RBL transformation model, linearized under a small-angle approximation. The method further incorporates a second bivariate GaBP designed to directly estimate the 3D rotation angles and translation vectors, including an interference cancellation (IC) refinement stage to improve the angle estimation performance, followed by the estimation of the angular and the translational velocities. The effectiveness of the proposed method is verified via simulations, which confirms its improved performance compared to equivalent state-of-the-art (SotA) techniques.
AI-driven Inverse Design of Band-Tunable Mechanical Metastructures for Tailored Vibration Mitigation
Authors: Tanuj Gupta, Arun Kumar Sharma, Ankur Dwivedi, Vivek Gupta, Subhadeep Sahana, Suryansh Pathak, Ashish Awasthi, Bishakh Bhattacharya
Date: 2024-12-03 00:00:00
Tasks: Band Gap

On-demand vibration mitigation in a mechanical system needs the suitable design of multiscale metastructures, involving complex unit cells. In this study, immersing in the world of patterns and examining the structural details of some interesting motifs are extracted from the mechanical metastructure perspective. Nine interlaced metastructures are fabricated using additive manufacturing, and corresponding vibration characteristics are studied experimentally and numerically. Further, the band-gap modulation with metallic inserts in the honeycomb interlaced metastructures is also studied. AI-driven inverse design of such complex metastructures with a desired vibration mitigation profile can pave the way for addressing engineering challenges in high-precision manufacturing. The current inverse design methodologies are limited to designing simple periodic structures based on limited variants of unit cells. Therefore, a novel forward analysis model with multi-head FEM-inspired spatial attention (FSA) is proposed to learn the complex geometry of the metastructures and predict corresponding transmissibility. Subsequently, a multiscale Gaussian self-attention (MGSA) based inverse design model with Gaussian function for 1D spectrum position encoding is developed to produce a suitable metastructure for the desired vibration transmittance. The proposed AI framework demonstrated outstanding performance corresponding to the expected locally resonant bandgaps in a targeted frequency range.
High-Throughput Detection of Risk Factors to Sudden Cardiac Arrest in Youth Athletes: A Smartwatch-Based Screening Platform
Authors: Evan Xiang, Thomas Wang, Vivan Poddar
Date: 2024-12-02 00:00:00
Tasks: Specificity

Sudden Cardiac Arrest (SCA) is the leading cause of death among athletes of all age levels worldwide. Current prescreening methods for cardiac risk factors are largely ineffective, and implementing the International Olympic Committee recommendation for 12-lead ECG screening remains prohibitively expensive. To address these challenges, a preliminary comprehensive screening system (CSS) was developed to efficiently and economically screen large populations for risk factors to SCA. A protocol was established to measure a 4-lead ECG using an Apple Watch. Additionally, two key advances were introduced and validated: 1) A decomposition regression model to upscale 4-lead data to 12 leads, reducing ECG cost and usage complexity. 2) A deep learning model, the Transformer Auto-Encoder System (TAES), was designed to extract spatial and temporal features from the data for beat-based classification. TAES demonstrated an average sensitivity of 95.3% and specificity of 99.1% respectively in the testing dataset, outperforming human physicians in the same dataset (Se: 94%, Sp: 93%). Human subject trials (n = 30) validated the smartwatch protocol, with Bland-Altman analysis showing no statistical difference between the smartwatch vs. ECG protocol. Further validation of the complete CSS on a 20-subject cohort (10 affected, 10 controls) did not result in any misidentifications. This paper presents a mass screening system with the potential to achieve superior accuracy in high-throughput cardiac pre-participation evaluation compared to the clinical gold standard.
Unlocking new capabilities in the analysis of GC$\times$GC-TOFMS data with shift-invariant multi-linearity
Authors: Paul-Albert Schneide, Michael Sorochan Armstrong, Neal Gallagher, Rasmus Bro
Date: 2024-12-02 00:00:00

This paper introduces a novel deconvolution algorithm, shift-invariant multi-linearity (SIML), which significantly enhances the analysis of data from a comprehensive two-dimensional gas chromatograph coupled to a mass spectrometric detector (GC$\times$GC-TOFMS). Designed to address the challenges posed by retention time shifts and high noise levels, SIML incorporates wavelet-based smoothing and Fourier-Transform based shift-correction within the multivariate curve resolution-alternating least squares (MCR-ALS) framework. We benchmarked the SIML algorithm against traditional methods such as MCR-ALS and Parallel Factor Analysis 2 with flexible coupling (PARAFAC2$\times$N) using both simulated and real GC$\times$GC-TOFMS datasets. Our results demonstrate that SIML provides unique solutions with significantly improved robustness, particularly in low signal-to-noise ratio scenarios, where it maintains high accuracy in estimating mass spectra and concentrations. The enhanced reliability of quantitative analyses afforded by SIML underscores its potential for broad application in complex matrix analyses across environmental science, food chemistry, and biological research.
AsyMov: Integrated Sensing and Communications with Asynchronous Moving Devices
Authors: Gianmaria Ventura, Zaman Bhalli, Michele Rossi, Jacopo Pegoraro
Date: 2024-11-29 00:00:00
Tasks: Activity Recognition, Human Activity Recognition, Integrated sensing and communication, ISAC

Estimating the Doppler frequency shift caused by moving targets is one of the key objectives of Integrated Sensing And Communication (ISAC) systems. In the case of Wi-Fi sensing, a reliable estimation of the Doppler enables applications such as target classification, human activity recognition, and gait analysis. However, in practical scenarios, Doppler estimation is hindered by the movement of transmitter and receiver devices, and by the phase offsets caused by their clock asynchrony. Existing approaches have separately addressed these two aspects, either assuming clock-synchronous moving devices or asynchronous static ones. Jointly tackling device motion and clock asynchrony is extremely challenging, as the Doppler shift from device movement differs for each propagation path and the phase offsets are time-varying. In this paper, we present AsyMov, a method to estimate the bistatic Doppler frequency of a target and the device velocity in ISAC setups featuring mobile and asynchronous devices. Our method leverages the channel impulse response at the receiver, by originally exploiting the invariance of phase offsets across propagation paths and the bistatic geometry. Moreover, AsyMov handles irregular channel sampling in the time domain and can be seamlessly integrated with device velocity measurements obtained from onboard sensors (if available), to enhance its reliability. AsyMov is thoroughly characterized from a theoretical perspective, via numerical simulation, and experimentally, implementing it on an IEEE 802.11ay testbed. Numerical and experimental results show superior performance against traditional methods based on the discrete Fourier transform and are on par with scenarios featuring static ISAC devices.
A Novel Wavelet-base Algorithm for Reconstruction of the Time-Domain Impulse Response from Band-limited Scattering Parameters with Applications
Authors: Shantia Yarahmadian, Maryam Rahmani, Michael Mazzola
Date: 2024-11-28 00:00:00

In this paper, we introduce a novel waveletbased algorithm for reconstructing time-domain impulse responses from band-limited scattering parameters (frequencydomain data) with a particular focus on ship hull applications. We establish the algorithm and demonstrate its convergence, as well as its efficiency for a class of functions that can be expanded as exponential functions. We provide simulation results to validate our numerical results.
Diffusion and Discrete Temporal Models of the Growth of Free-Ranging Cats in Urban Areas
Authors: Rodrigo Perusquía Cortés, Pablo Padilla Longoria
Date: 2024-12-19 00:00:00

The survival of the domestic cat (Felis catus) in various ecosystems has become increasingly relevant due to its impact on wildlife, public health, and society. In countries like Mexico, social factors such as abandonment have led to the feralization of the species and an unexpected increase in its population in urban areas. To design and implement effective population control methods, a thorough analysis of the species' population dynamics, along with the social factors influencing it, is necessary. We propose a reaction-diffusion model to simulate the natural dispersal of the population within a bounded domain. After exploring the species' spreading ability, we construct a complex dynamical system based on the biological characteristics of cats and their intraspecific and interspecific interactions, which we explain and study in detail. Both deterministic and stochastic parameters are considered to enhance the realism of the simulations. Our results indicate that the population reaches equilibrium, highlighting the need for control methods combined with social regulations to achieve sustainability in the system.
A model for the dynamics of COVID-19 infection transmission in human with latent delay
Authors: Amar N. Chatterjee, Teklebirhan Abraha, Fahad Al Basir, Delfim F. M. Torres
Date: 2024-12-16 00:00:00
Tasks: Human Dynamics, Unity

In this research, we have derived a mathematical model for within human dynamics of COVID-19 infection using delay differential equations. The new model considers a 'latent period' and 'the time for immune response' as delay parameters, allowing us to study the effects of time delays in human COVID-19 infection. We have determined the equilibrium points and analyzed their stability. The disease-free equilibrium is stable when the basic reproduction number, $R_0$, is below unity. Stability switch of the endemic equilibrium occurs through Hopf-bifurcation. This study shows that the effect of latent delay is stabilizing whereas immune response delay has a destabilizing nature.
On stochastic control problems with higher-order moments
Authors: Yike Wang, Jingzhen Liu, Alain Bensoussan, Ka-Fai Cedric Yiu, Jiaqin Wei
Date: 2024-12-18 00:00:00

In this paper, we focus on a class of time-inconsistent stochastic control problems, where the objective function includes the mean and several higher-order central moments of the terminal value of state. To tackle the time-inconsistency, we seek both the closed-loop and the open-loop Nash equilibrium controls as time-consistent solutions. We establish a partial differential equation (PDE) system for deriving a closed-loop Nash equilibrium control, which does not include the equilibrium value function and is different from the extended Hamilton-Jacobi-Bellman (HJB) equations as in Bj\"ork et al. (Finance Stoch. 21: 331-360, 2017). We show that our PDE system is equivalent to the extended HJB equations that seems difficult to be solved for our higher-order moment problems. In deriving an open-loop Nash equilibrium control, due to the non-separable higher-order moments in the objective function, we make some moment estimates in addition to the standard perturbation argument for developing a maximum principle. Then, the problem is reduced to solving a flow of forward-backward stochastic differential equations. In particular, we investigate linear controlled dynamics and some objective functions affine in the mean. The closed-loop and the open-loop Nash equilibrium controls are identical, which are independent of the state value, random path and the preference on the odd-order central moments. By sending the highest order of central moments to infinity, we obtain the time-consistent solutions to some control problems whose objective functions include some penalty functions for deviation.
Inferring protein folding mechanisms from natural sequence diversity
Authors: Ezequiel A. Galpern, Ernesto A. Roman, Diego U. Ferreiro
Date: 2024-12-18 00:00:00
Tasks: Diversity, Protein Folding

Protein sequences serve as a natural record of the evolutionary constraints that shape their functional structures. We show that it is possible to use only sequence information to go beyond predicting native structures and global stability to infer the folding mechanisms of globular proteins. The one- and two-body evolutionary energy fields at the amino-acid level are mapped to a coarse-grained description of folding, where proteins are divided into contiguous folding elements, commonly referred to as foldons. For 15 diverse protein families, we calculated the folding mechanisms of hundreds of proteins by simulating an Ising chain of foldons, with their energetics determined by the amino acid sequences. We show that protein topology imposes limits on the variability of folding cooperativity within a family. While most beta and alpha/beta structures exhibit only a few possible mechanisms despite high sequence diversity, alpha topologies allow for diverse folding scenarios among family members. We show that both the stability and cooperativity changes induced by mutations can be computed directly using sequence-based evolutionary models.
InterPLM: Discovering Interpretable Features in Protein Language Models via Sparse Autoencoders
Authors: Elana Simon, James Zou
Date: 2024-11-13 00:00:00

Protein language models (PLMs) have demonstrated remarkable success in protein modeling and design, yet their internal mechanisms for predicting structure and function remain poorly understood. Here we present a systematic approach to extract and analyze interpretable features from PLMs using sparse autoencoders (SAEs). By training SAEs on embeddings from the PLM ESM-2, we identify up to 2,548 human-interpretable latent features per layer that strongly correlate with up to 143 known biological concepts such as binding sites, structural motifs, and functional domains. In contrast, examining individual neurons in ESM-2 reveals up to 46 neurons per layer with clear conceptual alignment across 15 known concepts, suggesting that PLMs represent most concepts in superposition. Beyond capturing known annotations, we show that ESM-2 learns coherent concepts that do not map onto existing annotations and propose a pipeline using language models to automatically interpret novel latent features learned by the SAEs. As practical applications, we demonstrate how these latent features can fill in missing annotations in protein databases and enable targeted steering of protein sequence generation. Our results demonstrate that PLMs encode rich, interpretable representations of protein biology and we propose a systematic framework to extract and analyze these latent features. In the process, we recover both known biology and potentially new protein motifs. As community resources, we introduce InterPLM (interPLM.ai), an interactive visualization platform for exploring and analyzing learned PLM features, and release code for training and analysis at github.com/ElanaPearl/interPLM.
Artificial Intelligence for Central Dogma-Centric Multi-Omics: Challenges and Breakthroughs
Authors: Lei Xin, Caiyun Huang, Hao Li, Shihong Huang, Yuling Feng, Zhenglun Kong, Zicheng Liu, Siyuan Li, Chang Yu, Fei Shen, Hao Tang
Date: 2024-12-17 00:00:00
Tasks: Articles, Disease Prediction

With the rapid development of high-throughput sequencing platforms, an increasing number of omics technologies, such as genomics, metabolomics, and transcriptomics, are being applied to disease genetics research. However, biological data often exhibit high dimensionality and significant noise, making it challenging to effectively distinguish disease subtypes using a single-omics approach. To address these challenges and better capture the interactions among DNA, RNA, and proteins described by the central dogma, numerous studies have leveraged artificial intelligence to develop multi-omics models for disease research. These AI-driven models have improved the accuracy of disease prediction and facilitated the identification of genetic loci associated with diseases, thus advancing precision medicine. This paper reviews the mathematical definitions of multi-omics, strategies for integrating multi-omics data, applications of artificial intelligence and deep learning in multi-omics, the establishment of foundational models, and breakthroughs in multi-omics technologies, drawing insights from over 130 related articles. It aims to provide practical guidance for computational biologists to better understand and effectively utilize AI-based multi-omics machine learning algorithms in the context of central dogma.
Accessing the topological properties of human brain functional sub-circuits in Echo State Networks
Authors: Bach Nguyen, Tianlong Chen, Shu Yang, BoJian Hou, Li Shen, Duy Duong-Tran
Date: 2024-12-19 00:00:00
Tasks: Memorization

Recent years have witnessed an emerging trend in neuromorphic computing that centers around the use of brain connectomics as a blueprint for artificial neural networks. Connectomics-based neuromorphic computing has primarily focused on embedding human brain large-scale structural connectomes (SCs), as estimated from diffusion Magnetic Resonance Imaging (dMRI) modality, to echo-state networks (ESNs). A critical step in ESN embedding requires pre-determined read-in and read-out layers constructed by the induced subgraphs of the embedded reservoir. As \textit{a priori} set of functional sub-circuits are derived from functional MRI (fMRI) modality, it is unknown, till this point, whether the embedding of fMRI-induced sub-circuits/networks onto SCs is well justified from the neuro-physiological perspective and ESN performance across a variety of tasks. This paper proposes a pipeline to implement and evaluate ESNs with various embedded topologies and processing/memorization tasks. To this end, we showed that different performance optimums highly depend on the neuro-physiological characteristics of these pre-determined fMRI-induced sub-circuits. In general, fMRI-induced sub-circuit-embedded ESN outperforms simple bipartite and various null models with feed-forward properties commonly seen in MLP for different tasks and reservoir criticality conditions. We provided a thorough analysis of the topological properties of pre-determined fMRI-induced sub-circuits and highlighted their graph-theoretical properties that play significant roles in determining ESN performance.
A unified theory for the development of tinnitus and hyperacusis based on associative plasticity in the dorsal cochlear nucleus
Authors: Holger Schulze, Achim Schilling
Date: 2024-12-19 00:00:00

Tinnitus and hyperacusis can occur together or in isolation, with hyperacusis being associated with tinnitus much more frequently than vice versa. This striking correlation between tinnitus and hyperacusis prevalence implicates that there might be a common origin such as a (hidden) hearing loss and possibly interrelated neural mechanisms of pathological development of those two conditions. In this theoretical paper, we propose such interrelated pathological mechanisms, localized in the dorsal cochlear nucleus (DCN) of the brainstem, that are based on classical mechanisms of Hebbian and associative plasticity known from classical conditioning. Specifically, our model proposes that hyperacusis results from synaptic enhancement of cochlear input to the DCN, whereas chronic tinnitus results from synaptic enhancement of somatosensory input to the DCN. Specific conditions leading to one or the other condition are discussed. Our model predicts, that hearing loss leads to chronic tinnitus, while noise exposure (which may also cause hearing loss) leads to hyperacusis.