pwc-archive/papers-with-abstracts huggingface dataset:
Bayesian Model Parameter Learning in Linear Inverse Problems with Application in EEG Focal Source Imaging
Authors: Alexandra Koulouri, Ville Rimpilainen
Date: 2025-01-07 00:00:00
Tasks: EEG, parameter estimation

Inverse problems can be described as limited-data problems in which the signal of interest cannot be observed directly. A physics-based forward model that relates the signal with the observations is typically needed. Unfortunately, unknown model parameters and imperfect forward models can undermine the signal recovery. Even though supervised machine learning offers promising avenues to improve the robustness of the solutions, we have to rely on model-based learning when there is no access to ground truth for the training. Here, we studied a linear inverse problem that included an unknown non-linear model parameter and utilized a Bayesian model-based learning approach that allowed signal recovery and subsequently estimation of the model parameter. This approach, called Bayesian Approximation Error approach, employed a simplified model of the physics of the problem augmented with an approximation error term that compensated for the simplification. An error subspace was spanned with the help of the eigenvectors of the approximation error covariance matrix which allowed, alongside the primary signal, simultaneous estimation of the induced error. The estimated error and signal were then used to determine the unknown model parameter. For the model parameter estimation, we tested different approaches: a conditional Gaussian regression, an iterative (model-based) optimization, and a Gaussian process that was modeled with the help of physics-informed learning. In addition, alternating optimization was used as a reference method. As an example application, we focused on the problem of reconstructing brain activity from EEG recordings under the condition that the electrical conductivity of the patient's skull was unknown in the model. Our results demonstrated clear improvements in EEG source localization accuracy and provided feasible estimates for the unknown model parameter, skull conductivity.
RadDet: A Wideband Dataset for Real-Time Radar Spectrum Detection
Authors: Zi Huang, Simon Denman, Akila Pemasiri, Terrence Martin, Clinton Fookes
Date: 2025-01-06 00:00:00

Real-time detection of radar signals in a wideband radio frequency spectrum is a critical situational assessment function in electronic warfare. Compute-efficient detection models have shown great promise in recent years, providing an opportunity to tackle the spectrum detection problem. However, progress in radar spectrum detection is limited by the scarcity of publicly available wideband radar signal datasets accompanied by corresponding annotations. To address this challenge, we introduce a novel and challenging dataset for radar detection (RadDet), comprising a large corpus of radar signals occupying a wideband spectrum across diverse radar density environments and signal-to-noise ratios (SNR). RadDet contains 40,000 frames, each generated from 1 million in-phase and quadrature (I/Q) samples across a 500 MHz frequency band. RadDet includes 11 classes of radar samples across 6 different SNR settings, 2 radar density environments, and 3 different time-frequency resolutions, with corresponding time-frequency and class annotations. We evaluate the performance of various state-of-the-art real-time detection models on RadDet and a modified radar classification dataset from NIST (NIST-CBRS) to establish a novel benchmark for wideband radar spectrum detection.
Automated Detection of Epileptic Spikes and Seizures Incorporating a Novel Spatial Clustering Prior
Authors: Hanyang Dong, Shurong Sheng, Xiongfei Wang, Jiahong Gao, Yi Sun, Wanli Yang, Kuntao Xiao, Pengfei Teng, Guoming Luan, Zhao Lv
Date: 2025-01-05 00:00:00
Tasks: EEG, Seizure Detection

A Magnetoencephalography (MEG) time-series recording consists of multi-channel signals collected by superconducting sensors, with each signal's intensity reflecting magnetic field changes over time at the sensor location. Automating epileptic MEG spike detection significantly reduces manual assessment time and effort, yielding substantial clinical benefits. Existing research addresses MEG spike detection by encoding neural network inputs with signals from all channel within a time segment, followed by classification. However, these methods overlook simultaneous spiking occurred from nearby sensors. We introduce a simple yet effective paradigm that first clusters MEG channels based on their sensor's spatial position. Next, a novel convolutional input module is designed to integrate the spatial clustering and temporal changes of the signals. This module is fed into a custom MEEG-ResNet3D developed by the authors, which learns to extract relevant features and classify the input as a spike clip or not. Our method achieves an F1 score of 94.73% on a large real-world MEG dataset Sanbo-CMR collected from two centers, outperforming state-of-the-art approaches by 1.85%. Moreover, it demonstrates efficacy and stability in the Electroencephalographic (EEG) seizure detection task, yielding an improved weighted F1 score of 1.4% compared to current state-of-the-art techniques evaluated on TUSZ, whch is the largest EEG seizure dataset.
SSM2Mel: State Space Model to Reconstruct Mel Spectrogram from the EEG
Authors: Cunhang Fan, Sheng Zhang, Jingjing Zhang, Zexu Pan, Zhao Lv
Date: 2025-01-03 00:00:00
Tasks: EEG, Mamba

Decoding speech from brain signals is a challenging research problem that holds significant importance for studying speech processing in the brain. Although breakthroughs have been made in reconstructing the mel spectrograms of audio stimuli perceived by subjects at the word or letter level using noninvasive electroencephalography (EEG), there is still a critical gap in precisely reconstructing continuous speech features, especially at the minute level. To address this issue, this paper proposes a State Space Model (SSM) to reconstruct the mel spectrogram of continuous speech from EEG, named SSM2Mel. This model introduces a novel Mamba module to effectively model the long sequence of EEG signals for imagined speech. In the SSM2Mel model, the S4-UNet structure is used to enhance the extraction of local features of EEG signals, and the Embedding Strength Modulator (ESM) module is used to incorporate subject-specific information. Experimental results show that our model achieves a Pearson correlation of 0.069 on the SparrKULee dataset, which is a 38% improvement over the previous baseline.
Higher-Order Ambiguity Attitudes
Authors: Mücahit Aygün, Roger J. A. Laeven, Mitja Stadje
Date: 2025-01-22 00:00:00

We introduce a model-free preference under ambiguity, as a primitive trait of behavior, which we apply once as well as repeatedly. Its single and double application yield simple, easily interpretable definitions of ambiguity aversion and ambiguity prudence. We derive their implications within canonical models for decision under risk and ambiguity. We establish in particular that our new definition of ambiguity prudence is equivalent to a positive third derivative of: (i) the capacity in the Choquet expected utility model, (ii) the dual conjugate of the divergence function under variational divergence preferences and (iii) the ambiguity attitude function in the smooth ambiguity model. We show that our definition of ambiguity prudent behavior may be naturally linked to an optimal insurance problem under ambiguity.
Optimal Rebate Design: Incentives, Competition and Efficiency in Auction Markets
Authors: Thibaut Mastrolia, Tianrui Xu
Date: 2025-01-22 00:00:00

This study explores the design of an efficient rebate policy in auction markets, focusing on a continuous-time setting with competition among market participants. In this model, a stock exchange collects transaction fees from auction investors executing block trades to buy or sell a risky asset, then redistributes these fees as rebates to competing market makers submitting limit orders. Market makers influence both the price at which the asset trades and their arrival intensity in the auction. We frame this problem as a principal-multi-agent problem and provide necessary and sufficient conditions to characterize the Nash equilibrium among market makers. The exchange's optimization problem is formulated as a high-dimensional Hamilton-Jacobi-Bellman equation with Poisson jump processes, which is solved using a verification result. To numerically compute the optimal rebate and transaction fee policies, we apply the Deep BSDE method. Our results show that optimal transaction fees and rebate structures improve market efficiency by narrowing the spread between the auction clearing price and the asset's fundamental value, while ensuring a minimal gain for both market makers indexed on the price of the asset on a coexisting limit order book.
The Mathematics of Artificial Intelligence
Authors: Gabriel Peyré
Date: 2025-01-15 00:00:00

This overview article highlights the critical role of mathematics in artificial intelligence (AI), emphasizing that mathematics provides tools to better understand and enhance AI systems. Conversely, AI raises new problems and drives the development of new mathematics at the intersection of various fields. This article focuses on the application of analytical and probabilistic tools to model neural network architectures and better understand their optimization. Statistical questions (particularly the generalization capacity of these networks) are intentionally set aside, though they are of crucial importance. We also shed light on the evolution of ideas that have enabled significant advances in AI through architectures tailored to specific tasks, each echoing distinct mathematical techniques. The goal is to encourage more mathematicians to take an interest in and contribute to this exciting field.
Spikes can transmit neurons' subthreshold membrane potentials
Authors: Valentin Schmutz
Date: 2025-01-23 00:00:00

Neurons primarily communicate through the emission of action potentials, or spikes. To generate a spike, a neuron's membrane potential must cross a defined threshold. Does this spiking mechanism inherently prevent neurons from transmitting their subthreshold membrane potential fluctuations to other neurons? We prove that, in theory, it does not. The subthreshold membrane potential fluctuations of a presynaptic population of spiking neurons can be perfectly transmitted to a downstream population of neurons. Mathematically, this surprising result is an example of concentration phenomenon in high dimensions.
Binary Galton-Watson trees with mutations
Authors: Qiao Huang, Nicolas Privault
Date: 2025-01-19 00:00:00

We consider a family of marked binary Galton-Watson trees that can model individual types in population genetics, by allowing for mutation and reversion in discrete and continuous time. We derive a recursive formula for the computation of the joint distribution of types conditional to the value of the total progeny. This allows us to compute the evolution of various expected quantities, such as the mean proportions of different types as the tree size or time increases. Some generating functions are determined in explicit forms using generalized Catalan numbers, and integrability criteria are obtained as a consequence.
When algebra twinks system biology: a conjecture on the structure of Gröbner bases in complex chemical reaction networks
Authors: Paola Ferrari, Sara Sommariva, Michele Piana, Federico Benvenuto, Matteo Varbaro
Date: 2025-01-21 00:00:00

We address the challenge of identifying all real positive steady states in chemical reaction networks (CRNs) governed by mass-action kinetics. Traditional numerical methods often require specific initial guesses and may fail to find all the solutions in systems exhibiting multistability. Gr\"obner bases offer an algebraic framework that systematically transforms polynomial equations into simpler forms, facilitating comprehensive solution enumeration. In this work, we propose a conjecture that CRNs with at most pairwise interactions yield Gr\"obner bases possessing a near-"triangular" structure, under appropriate assumptions. We illustrate this phenomenon using examples from a gene regulatory network and the Wnt signaling pathway, where the Gr\"obner basis approach reliably captures all real positive solutions. Our computational experiments reveal the potential of Gr\"obner bases to overcome limitations of local numerical methods for finding the steady states of complex biological systems, making them a powerful tool for understanding dynamical processes across diverse biochemical models.
Incorporating stochastic gene expression, signaling-mediated intercellular interactions, and regulated cell proliferation in models of coordinated tissue development
Authors: Casey O. Barkan, Tom Chou
Date: 2025-01-20 00:00:00

Formulating quantitative and predictive models for tissue development requires consideration of the complex, stochastic gene expression dynamics, its regulation via cell-to-cell interactions, and cell proliferation. Including all of these processes into a practical mathematical framework requires complex expressions that are difficult to interpret and apply. We construct a simple theory that incorporates intracellular stochastic gene expression dynamics, signaling chemicals that influence these dynamics and mediate cell-cell interactions, and cell proliferation and its accompanying differentiation. Cellular states (genetic and epigenetic) are described by a Waddington vector field that allows for non-gradient dynamics (cycles, entropy production, loss of detailed balance) which is precluded in Waddington potential landscape representations of gene expression dynamics. We define an epigenetic fitness landscape that describes the proliferation of different cell types, and elucidate how this fitness landscape is related to Waddington's vector field. We illustrate the applicability of our framework by analyzing two model systems: an interacting two-gene differentiation process and a spatiotemporal organism model inspired by planaria.
Genomic Analysis of Date Palm Fruit Size Traits and Identification of Candidate Genes through GWAS
Authors: Shameem Younuskunju, Yasmin A. Mohamoud, Lisa Sara Mathew, Klaus F. X. Mayer, Karsten Suhre, Joel A. Malek
Date: 2025-01-21 00:00:00

The commercial value of economically significant fruits, including date palm fruit (dates), is influenced by various factors, such as biochemical composition and morphological features like size, shape, and visual appearance, which are key determinants of their quality and market value. Dates are typically consumed at the dry stage (Tamar), during which they exhibit a wide range of physical characteristics, such as color, length, weight, and skin appearance. Understanding the genetic basis of these traits is crucial for improving crop quality and breeding new cultivars. In this study, we integrated a genome dataset from highly diverse date cultivars with phenotypes of dry fruit such as length, width, area, and weight, identifying multiple significant genetic loci (SNPs) associated with these traits. We also identified candidate genes located near the associated SNPs that are involved in biological processes such as cell differentiation, proliferation, growth, and the regulation of signalling pathways for growth regulators like auxin and abscisic acid, as observed in other plants. Gene expression analysis reveals that many of these genes are highly expressed in the early stage of fruit development when the fruit attains its maximum size and weight. These findings will enhance our understanding of genetic determinants of fruit size particularly at the commercially important Tamar stage.
CoverM: Read alignment statistics for metagenomics
Authors: Samuel T. N. Aroney, Rhys J. P. Newell, Jakob N. Nissen, Antonio Pedro Camargo, Gene W. Tyson, Ben J. Woodcroft
Date: 2025-01-20 00:00:00
Tasks: Computational Efficiency

Genome-centric analysis of metagenomic samples is a powerful method for understanding the function of microbial communities. Calculating read coverage is a central part of analysis, enabling differential coverage binning for recovery of genomes and estimation of microbial community composition. Coverage is determined by processing read alignments to reference sequences of either contigs or genomes. Per-reference coverage is typically calculated in an ad-hoc manner, with each software package providing its own implementation and specific definition of coverage. Here we present a unified software package CoverM which calculates several coverage statistics for contigs and genomes in an ergonomic and flexible manner. It uses 'Mosdepth arrays' for computational efficiency and avoids unnecessary I/O overhead by calculating coverage statistics from streamed read alignment results. CoverM is free software available at https://github.com/wwood/coverm. CoverM is implemented in Rust, with Python (https://github.com/apcamargo/pycoverm) and Julia (https://github.com/JuliaBinaryWrappers/CoverM_jll.jl) interfaces.
Simple Proofs of the Summation and Connectivity Theorems in Metabolic Control Analysis
Authors: Weijiu Liu
Date: 2025-01-21 00:00:00
Tasks: Mathematical Proofs

In the early 1970s, the Kacser/Burns and the Heinrich/Rapoport groups independently discovered the important summation and connectivity theorems in metabolic control analysis. These theorems were derived originally by using thought experiments and proved mathematically later. The mathematical proofs are not easy for me to read and follow. But the proofs actually can be very simple and need only a couple of lines as I give here.
Language modulates vision: Evidence from neural networks and human brain-lesion models
Authors: Haoyang Chen, Bo Liu, Shuyue Wang, Xiaosha Wang, Wenjuan Han, Yixin Zhu, Xiaochun Wang, Yanchao Bi
Date: 2025-01-23 00:00:00

Comparing information structures in between deep neural networks (DNNs) and the human brain has become a key method for exploring their similarities and differences. Recent research has shown better alignment of vision-language DNN models, such as CLIP, with the activity of the human ventral occipitotemporal cortex (VOTC) than earlier vision models, supporting the idea that language modulates human visual perception. However, interpreting the results from such comparisons is inherently limited due to the "black box" nature of DNNs. To address this, we combined model-brain fitness analyses with human brain lesion data to examine how disrupting the communication pathway between the visual and language systems causally affects the ability of vision-language DNNs to explain the activity of the VOTC. Across four diverse datasets, CLIP consistently outperformed both label-supervised (ResNet) and unsupervised (MoCo) models in predicting VOTC activity. This advantage was left-lateralized, aligning with the human language network. Analyses of the data of 33 stroke patients revealed that reduced white matter integrity between the VOTC and the language region in the left angular gyrus was correlated with decreased CLIP performance and increased MoCo performance, indicating a dynamic influence of language processing on the activity of the VOTC. These findings support the integration of language modulation in neurocognitive models of human vision, reinforcing concepts from vision-language DNN models. The sensitivity of model-brain similarity to specific brain lesions demonstrates that leveraging manipulation of the human brain is a promising framework for evaluating and developing brain-like computer models.
SPM 25: open source neuroimaging analysis software
Authors: Tim M. Tierney, Nicholas A. Alexander, Nicole Labra Avila, Yael Balbastre, Gareth Barnes, Yulia Bezsudnova, Mikael Brudfors, Korbinian Eckstein, Guillaume Flandin, Karl Friston, Amirhossein Jafarian, Olivia S. Kowalczyk, Vladimir Litvak, Johan Medrano, Stephanie Mellor, George O'Neill, Thomas Parr, Adeel Razi, Ryan Timms, Peter Zeidman
Date: 2025-01-21 00:00:00

Statistical Parametric Mapping (SPM) is an integrated set of methods for testing hypotheses about the brain's structure and function, using data from imaging devices. These methods are implemented in an open source software package, SPM, which has been in continuous development for more than 30 years by an international community of developers. This paper reports the release of SPM 25.01, a major new version of the software that incorporates novel analysis methods, optimisations of existing methods, as well as improved practices for open science and software development.
Artificial Neural Networks for Magnetoencephalography: A review of an emerging field
Authors: Arthur Dehgan, Hamza Abdelhedi, Vanessa Hadid, Irina Rish, Karim Jerbi
Date: 2025-01-20 00:00:00
Tasks: Brain Decoding

Magnetoencephalography (MEG) is a cutting-edge neuroimaging technique that measures the intricate brain dynamics underlying cognitive processes with an unparalleled combination of high temporal and spatial precision. MEG data analytics has always relied on advanced signal processing and mathematical and statistical tools for various tasks ranging from data cleaning to probing the signals' rich dynamics and estimating the neural sources underlying the surface-level recordings. Like in most domains, the surge in Artificial Intelligence (AI) has led to the increased use of Machine Learning (ML) methods for MEG data classification. More recently, an emerging trend in this field is using Artificial Neural Networks (ANNs) to address many MEG-related tasks. This review provides a comprehensive overview of how ANNs are being used with MEG data from three vantage points: First, we review work that employs ANNs for MEG signal classification, i.e., for brain decoding. Second, we report on work that has used ANNs as putative models of information processing in the human brain. Finally, we examine studies that use ANNs as techniques to tackle methodological questions in MEG, including artifact correction and source estimation. Furthermore, we assess the current strengths and limitations of using ANNs with MEG and discuss future challenges and opportunities in this field. Finally, by establishing a detailed portrait of the field and providing practical recommendations for the future, this review seeks to provide a helpful reference for both seasoned MEG researchers and newcomers to the field who are interested in using ANNs to enhance the exploration of the complex dynamics of the human brain with MEG.
Evolution of diverse (and advanced) cognitive abilities through adaptive fine-tuning of learning and chunking mechanisms
Authors: Arnon Lotem, Joseph Y. Halpern
Date: 2025-01-20 00:00:00
Tasks: Chunking, Decision Making

The evolution of cognition is frequently discussed as the evolution of cognitive abilities or the evolution of some neuronal structures in the brain. However, since such traits or abilities are often highly complex, understanding their evolution requires explaining how they could have gradually evolved through selection acting on heritable variations in simpler cognitive mechanisms. With this in mind, making use of a previously proposed theory, here we show how the evolution of cognitive abilities can be captured by the fine-tuning of basic learning mechanisms and, in particular, chunking mechanisms. We use the term chunking broadly for all types of non-elemental learning, claiming that the process by which elements are combined into chunks and associated with other chunks, or elements, is critical for what the brain can do, and that it must be fine-tuned to ecological conditions. We discuss the relevance of this approach to studies in animal cognition, using examples from animal foraging and decision-making, problem solving, and cognitive flexibility. Finally, we explain how even the apparent human-animal gap in sequence learning ability can be explained in terms of different fine-tunings of a similar chunking process.