pwc-archive/papers-with-abstracts huggingface dataset:
Vehicle occupancy estimation in Automated Guideway Transit via deep learning with Wi-Fi probe requests
Authors: Ziyue Li, Qianwen Guo
Date: 2025-01-28 00:00:00

This study contributes to the advancement of vehicle occupancy estimation in Automated Guideway Transit (AGT) systems using Wi-Fi probe requests and deep learning models. We propose a comprehensive framework for evaluating various approaches to occupancy estimation, particularly in the context of MAC address randomization. While many methods proposed in the literature claim effectiveness in simpler experimental settings, our research reveals that those methods are unreliable in the complex environment of AGT systems. Specifically, techniques for handling randomized MAC addresses and distinguishing between passenger and non-passenger data do not perform well in AGT systems. Despite challenges in tracking individual devices, our study demonstrates that accurate occupancy estimation using Wi-Fi probe requests remains feasible. A pilot study conducted on the Miami-Dade Metromover, an AGT system characterized by frequent stops, significant occupancy fluctuations, and absence of fare collection devices, provides a robust testing ground for the framework. Additionally, our findings show that deep learning models significantly outperform machine learning models in this context. The insights from this study can significantly enhance decision-making for transit agencies to optimize operations and elevate service quality.
Optimizing Smart Helper Placement for Enhanced Cache Efficiency in F-RANs
Authors: Hesameddin Mokhtarzadeh, Mohammed Saif, Md. Jahangir Hossain, Julian Cheng
Date: 2025-01-28 00:00:00

Smart helpers (SHs) have been proposed to improve content delivery delays and alleviate high fronthaul loads in fog radio access networks (F-RANs). They offer an alternative to deploying additional enhanced remote radio heads (RRHs), which are often infeasible due to site constraints.} The optimal placement of SHs can significantly increase the number of users they serve which leads to enhanced cache efficiency and improved content delivery delay. In this letter, we optimize SH placement within an F-RAN to maximize the cache hit rate and further reduce the content delivery latency. We model the SH cache hit rate as a function of outage probability and user density distribution. We develop a function to estimate user density distribution leveraging the radial basis functions (RBFs) method and optimize SH placement utilizing the particle swarm optimization (PSO) algorithm. \an{Our} numerical results confirm the effectiveness of the proposed approach in maximizing the \an{SH cache hit rate}, thereby improving delivery delays and fronthaul loads of the network.
MambaTron: Efficient Cross-Modal Point Cloud Enhancement using Aggregate Selective State Space Modeling
Authors: Sai Tarun Inaganti, Gennady Petrenko
Date: 2025-01-25 00:00:00
Tasks: Mamba, Point Cloud Completion, Point cloud reconstruction, State Space Models

Point cloud enhancement is the process of generating a high-quality point cloud from an incomplete input. This is done by filling in the missing details from a reference like the ground truth via regression, for example. In addition to unimodal image and point cloud reconstruction, we focus on the task of view-guided point cloud completion, where we gather the missing information from an image, which represents a view of the point cloud and use it to generate the output point cloud. With the recent research efforts surrounding state-space models, originally in natural language processing and now in 2D and 3D vision, Mamba has shown promising results as an efficient alternative to the self-attention mechanism. However, there is limited research towards employing Mamba for cross-attention between the image and the input point cloud, which is crucial in multi-modal problems. In this paper, we introduce MambaTron, a Mamba-Transformer cell that serves as a building block for our network which is capable of unimodal and cross-modal reconstruction which includes view-guided point cloud completion.We explore the benefits of Mamba's long-sequence efficiency coupled with the Transformer's excellent analytical capabilities through MambaTron. This approach is one of the first attempts to implement a Mamba-based analogue of cross-attention, especially in computer vision. Our model demonstrates a degree of performance comparable to the current state-of-the-art techniques while using a fraction of the computation resources.
Analysis of Neural Activation in Time-dependent Membrane Capacitance Models
Authors: Matías Courdurier, Leonel E. Medina, Esteban Paduro
Date: 2025-01-29 00:00:00

Most models of neurons incorporate a capacitor to account for the marked capacitive behavior exhibited by the cell membrane. However, such capacitance is widely considered constant, thereby neglecting the possible effects of time-dependent membrane capacitance on neural excitability. This study presents a modified formulation of a neuron model with time-dependent membrane capacitance and shows that action potentials can be elicited for certain capacitance dynamics. Our main results can be summarized as: (a) it is necessary to have significant and abrupt variations in the capacitance to generate action potentials; (b) certain simple and explicitly constructed capacitance profiles with strong variations do generate action potentials; (c) forcing abrupt changes in the capacitance too frequently may result in no action potentials. These findings can have great implications for the design of ultrasound-based or other neuromodulation strategies acting through transiently altering the membrane capacitance of neurons.
An SIRS-model considering waning efficiency and periodic re-vaccination
Authors: Joseph Páez Chávez, Aytül Gökçe, Thomas Götz, Burcu Gürbüz
Date: 2025-01-28 00:00:00
Tasks: Epidemiology

In this paper, we extend the classical SIRS (Susceptible-Infectious-Recovered-Susceptible) model from mathematical epidemiology by incorporating a vaccinated compartment, V, accounting for an imperfect vaccine with waning efficacy over time. The SIRSV-model divides the population into four compartments and introduces periodic re-vaccination for waning immunity. The efficiency of the vaccine is assumed to decay with the time passed since the vaccination. Periodic re-vaccinations are applied to the population. We develop a partial differential equation (PDE) model for the continuous vaccination time and a coupled ordinary differential equation (ODE) system when discretizing the vaccination period. We analyze the equilibria of the ODE model and investigate the linear stability of the disease-free equilibrium (DFE). Furthermore, we explore an optimization framework where vaccination rate, re-vaccination time, and non-pharmaceutical interventions (NPIs) are control variables to minimize infection levels. The optimization objective is defined using different norm-based measures of infected individuals. A numerical analysis of the model's dynamic behavior under varying control parameters is conducted using path-following methods. The analysis focuses on the impacts of vaccination strategies and contact limitation measures. Bifurcation analysis reveals complex behaviors, including bistability, fold bifurcations, forward and backward bifurcations, highlighting the need for combined vaccination and contact control strategies to manage disease spread effectively.
Modeling Cost-Associated Cooperation: A Dilemma of Species Interaction Unveiling New Aspects of Fear Effect
Authors: Suvranil Chowdhury, Susmita Sarkar, Joydev Chattopadhyay
Date: 2025-01-27 00:00:00

With limited resources, competition is widespread, yet cooperation persists across taxa, from microorganisms to large mammals. Recent observations reveal contingent factors often drive cooperative interactions, with the intensity heterogeneously distributed within species. While cooperation has beneficial outcomes, it may also incur significant costs, largely depending on species density. This creates a dilemma that is pivotal in shaping sustainable cooperation strategies. Understanding how cooperation intensity governs the cost-benefit balance, and whether an optimal strategy exists for species survival, is a fundamental question in ecological research, and the focus of this study. We develop a novel mathematical model within the Lotka-Volterra framework to explore the dynamics of cost-associated partial cooperation, which remains relatively unexplored in ODE model-based studies. Our findings demonstrate that partial cooperation benefits ecosystems up to a certain intensity, beyond which costs become dominant, leading to system collapse via heteroclinic bifurcation. This outcome captures the cost-cooperation dilemma, providing insights for adopting sustainable strategies and resource management for species survival. We propose a novel mathematical approach to detect and track heteroclinic orbits in predator-prey systems. Moreover, we show that introducing fear of predation can protect the regime shift, even with a type-I functional response, challenging traditional ecological views. Although fear is known to resolve the "paradox of enrichment," our results suggest that certain levels of partial cooperation can reestablish this dynamic even at higher fear intensity. Finally, we validate the system's dynamical robustness across functional responses through structural sensitivity analysis.
Sensitivity Analysis of SEIR SEI Model of Dengue Disease
Authors: Ganga Ram Phaijoo, Dil Bahadur Gurung
Date: 2025-01-27 00:00:00
Tasks: Sensitivity

Dengue is a vector borne infectious disease. The disease is transmitted byAedes mosquitoes. In the present work, SEIR - SEI compartmental epidemiological model is used to describe dengue disease transmission dynamics. The human population is classified into four epidemiological states: Susceptible, Exposed, Infectious and Recovered humans. The mosquito population is divided into three epidemiological states: Susceptible, Exposed and Infectious mosquitoes. Associated basic reproduction number which determines whether the disease will persist or die out, is calculated by Next Generation method. Stability of the equilibrium points of the model is analyzed in terms of basic reproduction number. Sensitivity analysis is carried out to study importance of modelparameters.
Which Optimizer Works Best for Physics-Informed Neural Networks and Kolmogorov-Arnold Networks?
Authors: Elham Kiyani, Khemraj Shukla, Jorge F. Urbán, Jérôme Darbon, George Em Karniadakis
Date: 2025-01-22 00:00:00
Tasks: Kolmogorov-Arnold Networks

Physics-Informed Neural Networks (PINNs) have revolutionized the computation of PDE solutions by integrating partial differential equations (PDEs) into the neural network's training process as soft constraints, becoming an important component of the scientific machine learning (SciML) ecosystem. More recently, physics-informed Kolmogorv-Arnold networks (PIKANs) have also shown to be effective and comparable in accuracy with PINNs. In their current implementation, both PINNs and PIKANs are mainly optimized using first-order methods like Adam, as well as quasi-Newton methods such as BFGS and its low-memory variant, L-BFGS. However, these optimizers often struggle with highly non-linear and non-convex loss landscapes, leading to challenges such as slow convergence, local minima entrapment, and (non)degenerate saddle points. In this study, we investigate the performance of Self-Scaled BFGS (SSBFGS), Self-Scaled Broyden (SSBroyden) methods and other advanced quasi-Newton schemes, including BFGS and L-BFGS with different line search strategies approaches. These methods dynamically rescale updates based on historical gradient information, thus enhancing training efficiency and accuracy. We systematically compare these optimizers -- using both PINNs and PIKANs -- on key challenging linear, stiff, multi-scale and non-linear PDEs, including the Burgers, Allen-Cahn, Kuramoto-Sivashinsky, and Ginzburg-Landau equations. Our findings provide state-of-the-art results with orders-of-magnitude accuracy improvements without the use of adaptive weights or any other enhancements typically employed in PINNs. More broadly, our results reveal insights into the effectiveness of second-order optimization strategies in significantly improving the convergence and accurate generalization of PINNs and PIKANs.
Solvability of the Gaussian Kyle model with imperfect information and risk aversion
Authors: Reda Chhaibi, Ibrahim Ekren, Eunjung Noh
Date: 2025-01-27 00:00:00

We investigate a Kyle model under Gaussian assumptions where a risk-averse informed trader has imperfect information on the fundamental price of an asset. We show that an equilibrium can be constructed by considering an optimal transport problem that is solved under a measure that renders the utility of the informed trader martingale and a filtering problem under the historical measure.
High Throughput Screening of Expression Constructs using Microcapillary Arrays
Authors: Khushank Singhal, Harry Adamson, Thomas Baer, Howard Salis, Melik Demirel
Date: 2025-01-29 00:00:00

Gene expression is a complex phenomenon involving numerous interlinked variables, and studying these variables to control expression is essential in bioengineering and biomanufacturing. While cloning techniques for achieving plasmid libraries that cover large design spaces exist, multiplex techniques offering cell culture screening at similar scales are still lacking. We introduced a microcapillary array-based platform aimed at high-throughput, multiplex screening of miniature cell cultures through fluorescent reporters.
Human Genome Book: Words, Sentences and Paragraphs
Authors: Wang Liang
Date: 2025-01-23 00:00:00
Tasks: Protein Structure Prediction, Sentence segmentation, Transfer Learning

Since the completion of the human genome sequencing project in 2001, significant progress has been made in areas such as gene regulation editing and protein structure prediction. However, given the vast amount of genomic data, the segments that can be fully annotated and understood remain relatively limited. If we consider the genome as a book, constructing its equivalents of words, sentences, and paragraphs has been a long-standing and popular research direction. Recently, studies on transfer learning in large language models have provided a novel approach to this challenge.Multilingual transfer ability, which assesses how well models fine-tuned on a source language can be applied to other languages, has been extensively studied in multilingual pre-trained models. Similarly, the transfer of natural language capabilities to "DNA language" has also been validated. Building upon these findings, we first trained a foundational model capable of transferring linguistic capabilities from English to DNA sequences. Using this model, we constructed a vocabulary of DNA words and mapped DNA words to their English equivalents.Subsequently, we fine-tuned this model using English datasets for paragraphing and sentence segmentation to develop models capable of segmenting DNA sequences into sentences and paragraphs. Leveraging these models, we processed the GRCh38.p14 human genome by segmenting, tokenizing, and organizing it into a "book" comprised of genomic "words," "sentences," and "paragraphs." Additionally, based on the DNA-to-English vocabulary mapping, we created an "English version" of the genomic book. This study offers a novel perspective for understanding the genome and provides exciting possibilities for developing innovative tools for DNA search, generation, and analysis.
The time-dependent reproduction number for epidemics in heterogeneous populations
Authors: Ioana Bouros, Robin Thompson, David Gavaghan, Ben Lambert
Date: 2025-01-29 00:00:00

The time-dependent reproduction number Rt can be used to track pathogen transmission and to assess the efficacy of interventions. This quantity can be estimated by fitting renewal equation models to time series of infectious disease case counts. These models almost invariably assume a homogeneous population. Individuals are assumed not to differ systematically in the rates at which they come into contact with others. It is also assumed that the typical time that elapses between one case and those it causes (known as the generation time distribution) does not differ across groups. But contact patterns are known to widely differ by age and according to other demographic groupings, and infection risk and transmission rates have been shown to vary across groups for a range of directly transmitted diseases. Here, we derive from first principles a renewal equation framework which accounts for these differences in transmission across groups. We use a generalisation of the classic McKendrick-von Foerster equation to handle populations structured into interacting groups. This system of partial differential equations allows us to derive a simple analytical expression for Rt which involves only group-level contact patterns and infection risks. We show that the same expression emerges from both deterministic and stochastic discrete-time versions of the model and demonstrate via simulations that our Rt expression governs the long-run fate of epidemics. Our renewal equation model provides a basis from which to account for more realistic, diverse populations in epidemiological models and opens the door to inferential approaches which use known group characteristics to estimate Rt.
IRONMAP: Iron Network Mapping and Analysis Protocol for Detecting Over-Time Brain Iron Abnormalities in Neurological Disease
Authors: Jack A. Reeves, Fahad Salman, Michael G. Dwyer, Niels Bergsland, Sarah Muldoon, Bianca Weinstock-Guttman, Robert Zivadinov, Ferdinand Schweser
Date: 2025-01-29 00:00:00
Tasks: Binary Classification

Pathologically altered iron levels, detected using iron-sensitive MRI techniques such as quantitative susceptibility mapping (QSM), are observed in neurological disorders such as multiple sclerosis (MS) and may play a crucial role in disease pathophysiology. However, brain iron changes occur slowly, even in neurological diseases, and can be influenced by physiological factors such as diet. Therefore, novel analysis methods are needed to improve sensitivity to disease-related iron changes as compared to conventional region-based analysis methods. This study introduces IRONMAP, Iron Network Mapping and Analysis Protocol, which is a novel network-based analysis method to evaluate over-time changes in magnetic susceptibility. With this novel methodology, we analyzed short-term (<1 year) longitudinal QSM data from a cohort of individuals with MS (pwMS) and healthy controls (HCs) and assessed disease-related network patterns, comparing the new approach to a conventional per-region rate-of-change method. IRONMAP analysis was able to detect over-time, MS-related brain iron abnormalities that were undetectable using the rate-of-change approach. IRONMAP was applicable on the per-subject level, improving binary classification of pwMS vs HCs compared to rate-of-change data alone (areas under the curve: 0.773 vs 0.636, p = 0.024). Further analysis revealed that the observed IRONMAP-derived HC network structure closely aligned with simulated networks based on healthy aging-related susceptibility data, suggesting that disruptions in normal aging-related iron changes may contribute to the network differences seen in pwMS. IRONMAP is generalizable to any neurological disease, including Alzheimer's disease and Parkinson's disease, and may allow for study of brain iron abnormalities over shorter timeframes than previously possible.
A technical review of multi-omics data integration methods: from classical statistical to deep generative approaches
Authors: Ana R. Baião, Zhaoxiang Cai, Rebecca C Poulos, Phillip J. Robinson, Roger R Reddel, Qing Zhong, Susana Vinga, Emanuel Gonçalves
Date: 2025-01-29 00:00:00
Tasks: Contrastive Learning, Data Integration, Disentanglement, Imputation, Missing Values

The rapid advancement of high-throughput sequencing and other assay technologies has resulted in the generation of large and complex multi-omics datasets, offering unprecedented opportunities for advancing precision medicine strategies. However, multi-omics data integration presents significant challenges due to the high dimensionality, heterogeneity, experimental gaps, and frequency of missing values across data types. Computational methods have been developed to address these issues, employing statistical and machine learning approaches to uncover complex biological patterns and provide deeper insights into our understanding of disease mechanisms. Here, we comprehensively review state-of-the-art multi-omics data integration methods with a focus on deep generative models, particularly variational autoencoders (VAEs) that have been widely used for data imputation and augmentation, joint embedding creation, and batch effect correction. We explore the technical aspects of loss functions and regularisation techniques including adversarial training, disentanglement and contrastive learning. Moreover, we discuss recent advancements in foundation models and the integration of emerging data modalities, while describing the current limitations and outlining future directions for enhancing multi-modal methodologies in biomedical research.
ILETIA: An AI-enhanced method for individualized trigger-oocyte pickup interval estimation of progestin-primed ovarian stimulation protocol
Authors: Binjian Wu, Qian Li, Zhe Kuang, Hongyuan Gao, Haiyan Guo, Qiuju Chen, Xinyi Liu, Yangruizhe Jiang, Yuqi Zhang, Jinyin Zha, Mingyu Li, Qiuhan Ren, Sishuo Feng, Haicang Zhang, Xuefeng Lu, Jian Zhang
Date: 2025-01-25 00:00:00

In vitro fertilization-embryo transfer (IVF-ET) stands as one of the most prevalent treatments for infertility. During an IVF-ET cycle, the time interval between trigger shot and oocyte pickup (OPU) is a pivotal period for follicular maturation, which determines mature oocytes yields and impacts the success of subsequent procedures. However, accurately predicting this interval is severely hindered by the variability of clinicians'experience that often leads to suboptimal oocyte retrieval rate. To address this challenge, we propose ILETIA, the first machine learning-based method that could predict the optimal trigger-OPU interval for patients receiving progestin-primed ovarian stimulation (PPOS) protocol. Specifically, ILETIA leverages a Transformer to learn representations from clinical tabular data, and then employs gradient-boosted trees for interval prediction. For model training and evaluating, we compiled a dataset PPOS-DS of nearly ten thousand patients receiving PPOS protocol, the largest such dataset to our knowledge. Experimental results demonstrate that our method achieves strong performance (AUROC = 0.889), outperforming both clinicians and other widely used computational models. Moreover, ILETIA also supports premature ovulation risk prediction in a specific OPU time (AUROC = 0.838). Collectively, by enabling more precise and individualized decisions, ILETIA has the potential to improve clinical outcomes and lay the foundation for future IVF-ET research.
An Analysis of the Interdependence Between Peanut and Other Agricultural Commodities in China's Futures Market
Authors: Suke Li
Date: 2025-01-28 00:00:00
Tasks: Financial Analysis

This study analyzes historical data from five agricultural commodities in the Chinese futures market to explore the correlation, cointegration, and Granger causality between Peanut futures and related futures. Multivariate linear regression models are constructed for prices and logarithmic returns, while dynamic relationships are examined using VAR and DCC-EGARCH models. The results reveal a significant dynamic linkage between Peanut and Soybean Oil futures through DCC-EGARCH, whereas the VAR model suggests limited influence from other futures. Additionally, the application of MLP, CNN, and LSTM neural networks for price prediction highlights the critical role of time step configurations in forecasting accuracy. These findings provide valuable insights into the interconnectedness of agricultural futures markets and the efficacy of advanced modeling techniques in financial analysis.