pwc-archive/papers-with-abstracts huggingface dataset:
Model-based learning for joint channel estimationand hybrid MIMO precoding
Authors: Nay Klaimi, Amira Bedoui, Clément Elvira, Philippe Mary, Luc Le Magoarou
Date: 2025-05-07 00:00:00

Hybrid precoding is a key ingredient of cost-effective massive multiple-input multiple-output transceivers. However, setting jointly digital and analog precoders to optimally serve multiple users is a difficult optimization problem. Moreover, it relies heavily on precise knowledge of the channels, which is difficult to obtain, especially when considering realistic systems comprising hardware impairments. In this paper, a joint channel estimation and hybrid precoding method is proposed, which consists in an end-to-end architecture taking received pilots as inputs and outputting pre-coders. The resulting neural network is fully model-based, making it lightweight and interpretable with very few learnable parameters. The channel estimation step is performed using the unfolded matching pursuit algorithm, accounting for imperfect knowledge of the antenna system, while the precoding step is done via unfolded projected gradient ascent. The great potential of the proposed method is empirically demonstrated on realistic synthetic channels.
The Evolution of Rough Sets 1970s-1981
Authors: Viktor Marek, Ewa Orłowska, Ivo Düntsch
Date: 2025-04-08 00:00:00

In this note research and publications by Zdzis{\l}aw Pawlak and his collaborators from 1970s and 1981 are recalled. Focus is placed on the sources of inspiration which one can identify on the basis of those publications. Finally, developments from 1981 related to rough sets and information systems are outlined.
Loss-Versus-Rebalancing under Deterministic and Generalized block-times
Authors: Alex Nezlobin, Martin Tassy
Date: 2025-05-08 00:00:00

Although modern blockchains almost universally produce blocks at fixed intervals, existing models still lack an analytical formula for the loss-versus-rebalancing (LVR) incurred by Automated Market Makers (AMMs) liquidity providers in this setting. Leveraging tools from random walk theory, we derive the following closed-form approximation for the per block per unit of liquidity expected LVR under constant block time: \[ \overline{\mathrm{ARB}}= \frac{\,\sigma_b^{2}} {\,2+\sqrt{2\pi}\,\gamma/(|\zeta(1/2)|\,\sigma_b)\,}+O\!\bigl(e^{-\mathrm{const}\tfrac{\gamma}{\sigma_b}}\bigr)\;\approx\; \frac{\sigma_b^{2}}{\,2 + 1.7164\,\gamma/\sigma_b}, \] where $\sigma_b$ is the intra-block asset volatility, $\gamma$ the AMM spread and $\zeta$ the Riemann Zeta function. Our large Monte Carlo simulations show that this formula is in fact quasi-exact across practical parameter ranges. Extending our analysis to arbitrary block-time distributions as well, we demonstrate both that--under every admissible inter-block law--the probability that a block carries an arbitrage trade converges to a universal limit, and that only constant block spacing attains the asymptotically minimal LVR. This shows that constant block intervals provide the best possible protection against arbitrage for liquidity providers.
Modelling the effects of biological intervention in a dynamical gene network
Authors: Nicolas Champagnat, Rodolphe Loubaton, Laurent Vallat, Pierre Vallois
Date: 2025-05-07 00:00:00
Tasks: parameter estimation

Cellular response to environmental and internal signals can be modeled by dynamical gene regulatory networks (GRN). In the literature, three main classes of gene network models can be distinguished: (i) non-quantitative (or data-based) models which do not describe the probability distribution of gene expressions; (ii) quantitative models which fully describe the probability distribution of all genes coexpression; and (iii) mechanistic models which allow for a causal interpretation of gene interactions. We propose two rigorous frameworks to model gene alteration in a dynamical GRN, depending on whether the network model is quantitative or mechanistic. We explain how these models can be used for design of experiment, or, if additional alteration data are available, for validation purposes or to improve the parameter estimation of the original model. We apply these methods to the Gaussian graphical model, which is quantitative but non-mechanistic, and to mechanistic models of Bayesian networks and penalized linear regression.
A new architecture of high-order deep neural networks that learn martingales
Authors: Syoiti Ninomiya, Yuming Ma
Date: 2025-05-01 00:00:00
Tasks: Deep Learning

A new deep-learning neural network architecture based on high-order weak approximation algorithms for stochastic differential equations (SDEs) is proposed. The architecture enables the efficient learning of martingales by deep learning models. The behaviour of deep neural networks based on this architecture, when applied to the problem of pricing financial derivatives, is also examined. The core of this new architecture lies in the high-order weak approximation algorithms of the explicit Runge--Kutta type, wherein the approximation is realised solely through iterative compositions and linear combinations of vector fields of the target SDEs.
Using random perturbations to infer the structure of feedback control in gene expression
Authors: Seshu Iyengar, Andreas Hilfinger
Date: 2025-05-08 00:00:00

Feedback in cellular processes is typically inferred through cellular responses to experimental perturbations. Modular response analysis provides a theoretical framework for translating specific perturbations into feedback sensitivities between cellular modules. However, in large-scale drug perturbation studies the effect of any given drug may not be known and may not only affect one module at a time. Here, we analyze the response of gene expression models to random perturbations that affect multiple modules simultaneously. In the deterministic regime we analytically show how cellular responses to infinitesimal random perturbations can be used to infer the nature of feedback regulation in gene expression, as long as the effects of perturbations are statistically independent between modules. We numerically extend this deterministic analysis to the response of average abundances of stochastic gene expression models to finite perturbations. Across a large sample of stochastic models, the response of average abundances generally obeyed predicted bounds from the deterministic analysis, but dramatic deviations occurred in systems with bimodal or fat-tailed stationary state distributions. These discrepancies demonstrate how deterministic analyses can fail to capture the effect of perturbations on averages of stochastic cellular feedback systems--even in the linear response regime.
Towards a Vision-Language Episodic Memory Framework: Large-scale Pretrained Model-Augmented Hippocampal Attractor Dynamics
Authors: Chong Li, Taiping Zeng, xiangyang xue, Jianfeng Feng
Date: 2025-05-07 00:00:00
Tasks: Hippocampus

Modeling episodic memory (EM) remains a significant challenge in both neuroscience and AI, with existing models either lacking interpretability or struggling with practical applications. This paper proposes the Vision-Language Episodic Memory (VLEM) framework to address these challenges by integrating large-scale pretrained models with hippocampal attractor dynamics. VLEM leverages the strong semantic understanding of pretrained models to transform sensory input into semantic embeddings as the neocortex, while the hippocampus supports stable memory storage and retrieval through attractor dynamics. In addition, VLEM incorporates prefrontal working memory and the entorhinal gateway, allowing interaction between the neocortex and the hippocampus. To facilitate real-world applications, we introduce EpiGibson, a 3D simulation platform for generating episodic memory data. Experimental results demonstrate the VLEM framework's ability to efficiently learn high-level temporal representations from sensory input, showcasing its robustness, interpretability, and applicability in real-world scenarios.
Neural Representational Consistency Emerges from Probabilistic Neural-Behavioral Representation Alignment
Authors: Yu Zhu, Chunfeng Song, Wanli Ouyang, Shan Yu, Tiejun Huang
Date: 2025-05-07 00:00:00

Individual brains exhibit striking structural and physiological heterogeneity, yet neural circuits can generate remarkably consistent functional properties across individuals, an apparent paradox in neuroscience. While recent studies have observed preserved neural representations in motor cortex through manual alignment across subjects, the zero-shot validation of such preservation and its generalization to more cortices remain unexplored. Here we present PNBA (Probabilistic Neural-Behavioral Representation Alignment), a new framework that leverages probabilistic modeling to address hierarchical variability across trials, sessions, and subjects, with generative constraints preventing representation degeneration. By establishing reliable cross-modal representational alignment, PNBA reveals robust preserved neural representations in monkey primary motor cortex (M1) and dorsal premotor cortex (PMd) through zero-shot validation. We further establish similar representational preservation in mouse primary visual cortex (V1), reflecting a general neural basis. These findings resolve the paradox of neural heterogeneity by establishing zero-shot preserved neural representations across cortices and species, enriching neural coding insights and enabling zero-shot behavior decoding.
Depolarization block induction via slow NaV1.1 inactivation in Dravet syndrome
Authors: Louisiane Lemaire, Mathieu Desroches, Serafim Rodrigues, Fabien Campillo
Date: 2025-05-06 00:00:00

Dravet syndrome is a developmental and epileptic encephalopathy, characterized by the early onset of drug-resistant seizures and various comorbidities. Most cases of this severe and complex pathology are due to mutations of NaV1.1, a sodium channel mainly expressed in fast-spiking inhibitory neurons. Layer et al. (Front. Cell. Neurosci. 15, 2021) showed that one of these mutations alters the voltage dependence of channel activation, as well as the voltage dependence and kinetics of slow inactivation. Implementing the three effects into a computational model, they predict that altered activation has the largest impact on channel function, as it causes the most severe firing rate reduction. Using a conductance-based model tailored to the dynamics of fast-spiking inhibitory neurons, we look deeper into slow inactivation. We exploit the timescale difference between this very slow process and the rest of the system to conduct a multiple-timescale analysis. We find that, upon prolonged stimulation, the onset of slow inactivation at lower voltage in mutant channels promotes depolarization block, another possible firing deficit aside from frequency reduction. The accelerated kinetics of slow inactivation in mutant channels hastens this transition. This suggests that slow inactivation alterations might for some Dravet variant contribute to the pathological mechanism.
PersiST: Robust Identification of Spatially Variable Features in Spatial Omics Datasets via Topological Data Analysis
Authors: James Boyle, Gregory Hamm, Eleanor Williams, Robin JG Hartman, Magnus Soderburg, Ian Henry, Michael Casey
Date: 2025-05-07 00:00:00
Tasks: Topological Data Analysis

Spatial transcriptomics studies are becoming increasingly large and commonplace, necessitating the analysis of a large number of spatially resolved variables. With spatial transcriptomics data sets typically containing data on thousands of different genes, on increasingly large numbers of samples, there is a need for bioinformatics tools that enable the comparison of spatial structure across large numbers of variables. Here we present PersiST, an exploratory tool that uses topology to automatically compute a continuous measure of spatial structure for each gene in a spatial transcriptomics sample. This quantification can be used for analytical tasks such as spatially variable gene identification, or searching for spatial differences in the expression of a gene between samples. We use PersiST to derive biologically meaningful insights into two public spatial transcriptomics data sets, and we experiment with applying PersiST to a spatial metabolomics data set, making use of PersiST's non-parametric approach to enable application across different measurement types. Our work showcases the advantages of using a continuous quantification of spatial structure over p-value based approaches to SVG identification, the potential for developing unified methods for the analysis of different spatial `omics modalities, and the utility of persistent homology in big data applications.
Deep Reinforcement Learning for Investor-Specific Portfolio Optimization: A Volatility-Guided Asset Selection Approach
Authors: Arishi Orra, Aryan Bhambu, Himanshu Choudhary, Manoj Thakur, Selvaraju Natarajan
Date: 2025-04-20 00:00:00
Tasks: Deep Reinforcement Learning, Portfolio Optimization

Portfolio optimization requires dynamic allocation of funds by balancing the risk and return tradeoff under dynamic market conditions. With the recent advancements in AI, Deep Reinforcement Learning (DRL) has gained prominence in providing adaptive and scalable strategies for portfolio optimization. However, the success of these strategies depends not only on their ability to adapt to market dynamics but also on the careful pre-selection of assets that influence overall portfolio performance. Incorporating the investor's preference in pre-selecting assets for a portfolio is essential in refining their investment strategies. This study proposes a volatility-guided DRL-based portfolio optimization framework that dynamically constructs portfolios based on investors' risk profiles. The Generalized Autoregressive Conditional Heteroscedasticity (GARCH) model is utilized for volatility forecasting of stocks and categorizes them based on their volatility as aggressive, moderate, and conservative. The DRL agent is then employed to learn an optimal investment policy by interacting with the historical market data. The efficacy of the proposed methodology is established using stocks from the Dow $30$ index. The proposed investor-specific DRL-based portfolios outperformed the baseline strategies by generating consistent risk-adjusted returns.
Cryptogenic stroke and migraine: using probabilistic independence and machine learning to uncover latent sources of disease from the electronic health record
Authors: Joshua W. Betts, John M. Still, Thomas A. Lasko
Date: 2025-04-22 00:00:00

Migraine is a common but complex neurological disorder that doubles the lifetime risk of cryptogenic stroke (CS). However, this relationship remains poorly characterized, and few clinical guidelines exist to reduce this associated risk. We therefore propose a data-driven approach to extract probabilistically-independent sources from electronic health record (EHR) data and create a 10-year risk-predictive model for CS in migraine patients. These sources represent external latent variables acting on the causal graph constructed from the EHR data and approximate root causes of CS in our population. A random forest model trained on patient expressions of these sources demonstrated good accuracy (ROC 0.771) and identified the top 10 most predictive sources of CS in migraine patients. These sources revealed that pharmacologic interventions were the most important factor in minimizing CS risk in our population and identified a factor related to allergic rhinitis as a potential causative source of CS in migraine patients.
How Social is It? A Benchmark for LLMs' Capabilities in Multi-user Multi-turn Social Agent Tasks
Authors: Yusen Wu, Junwu Xiong, Xiaotie Deng
Date: 2025-04-04 00:00:00

Expanding the application of large language models (LLMs) to societal life, instead of primary function only as auxiliary assistants to communicate with only one person at a time, necessitates LLMs' capabilities to independently play roles in multi-user, multi-turn social agent tasks within complex social settings. However, currently the capability has not been systematically measured with available benchmarks. To address this gap, we first introduce an agent task leveling framework grounded in sociological principles. Concurrently, we propose a novel benchmark, How Social Is It (we call it HSII below), designed to assess LLM's social capabilities in comprehensive social agents tasks and benchmark representative models. HSII comprises four stages: format parsing, target selection, target switching conversation, and stable conversation, which collectively evaluate the communication and task completion capabilities of LLMs within realistic social interaction scenarios dataset, HSII-Dataset. The dataset is derived step by step from news dataset. We perform an ablation study by doing clustering to the dataset. Additionally, we investigate the impact of chain of thought (COT) method on enhancing LLMs' social performance. Since COT cost more computation, we further introduce a new statistical metric, COT-complexity, to quantify the efficiency of certain LLMs with COTs for specific social tasks and strike a better trade-off between measurement of correctness and efficiency. Various results of our experiments demonstrate that our benchmark is well-suited for evaluating social skills in LLMs.
Image Fusion Using Quaternion Wavelet Transform and Multiple Features
Authors: Pengfei Chai, Xiaoqing Luo, Zhancheng Zhang All Authors
Date: 2017-03-22 00:00:00
Proceeding: journal 2017 3

Multi-scale-based image fusion is one of main fusion methods, in which multi-scale decomposition tool and feature extraction play very important roles. The quaternion wavelet transform (QWT) is one of the effective multi-scale decomposition tools. Therefore, this paper proposes a novel multimodal image fusion method using QWT and multiple features. First, we perform QWT on each source image to obtain low-frequency coefficients and high-frequency coefficients. Second, a weighted average fusion rule based on the phase and magnitude of low-frequency subband and spatial variance is proposed to fuse the low-frequency subbands. Next, a choose-max fusion rule based on the contrast and energy of coefficient is proposed to integrate the high-frequency subbands. Finally, the final fused image is constructed by inverse QWT. The proposed method is conducted on multi-focus images, medical images, infrared-visible images, and remote sensing images, respectively. Experimental results demonstrate the effectiveness of the proposed method.
Perception-Informed Neural Networks: Beyond Physics-Informed Neural Networks
Authors: Mehran Mazandarani, Marzieh Najariyan
Date: 2025-05-02 00:00:00
Tasks: Mixture-of-Experts

This article introduces Perception-Informed Neural Networks (PrINNs), a framework designed to incorporate perception-based information into neural networks, addressing both systems with known and unknown physics laws or differential equations. Moreover, PrINNs extend the concept of Physics-Informed Neural Networks (PINNs) and their variants, offering a platform for the integration of diverse forms of perception precisiation, including singular, probability distribution, possibility distribution, interval, and fuzzy graph. In fact, PrINNs allow neural networks to model dynamical systems by integrating expert knowledge and perception-based information through loss functions, enabling the creation of modern data-driven models. Some of the key contributions include Mixture of Experts Informed Neural Networks (MOEINNs), which combine heterogeneous expert knowledge into the network, and Transformed-Knowledge Informed Neural Networks (TKINNs), which facilitate the incorporation of meta-information for enhanced model performance. Additionally, Fuzzy-Informed Neural Networks (FINNs) as a modern class of fuzzy deep neural networks leverage fuzzy logic constraints within a deep learning architecture, allowing online training without pre-training and eliminating the need for defuzzification. PrINNs represent a significant step forward in bridging the gap between traditional physics-based modeling and modern data-driven approaches, enabling neural networks to learn from both structured physics laws and flexible perception-based rules. This approach empowers neural networks to operate in uncertain environments, model complex systems, and discover new forms of differential equations, making PrINNs a powerful tool for advancing computational science and engineering.
Investigation of Machine Learning Methods for Early Prediction of Neurodevelopmental Disorders in Children
Authors: Sumbul Alam, Pravinth Raja, Yonis Gulzar
Date: 2022-07-08 00:00:00
Proceeding: https://onlinelibrary.wiley.com/doi/pdf/10.1155/2022/5766386 2022 7
Tasks: Diagnostic

Several variables, for instance, inheritance and surroundings, influence the growth of neurodevelopmental disorders, e.g., autism spectrum disorder (ASD) and attention deficit hyperactivity disorder (ADHD) during the first 36 months of life. ADHD and ASD diagnosis mainly rely heavily on traditional clinical assessments from the last few decades. These traditional methods are based on massive data collection from multiple respondents’ responses and the extent of various behavioral descriptors, which are then recognized by the researcher while forming a diagnostic criterion. However, opting for traditional diagnostic methods, there is a high possibility of being misdiagnosed, which may lead to the administration of unnecessary long-term pharmaceutical treatment. That may lead to reduction in functioning and an increase in the risk of developing additional social and clinical issues. Moreover, such diagnostic procedures are also time-consuming and costly. In this sense, rapid and advanced criteria are required to be accurate and cost-eﬀective. Consequently, this study emphasizes the application of machine learning (ML) tools and deep learning (DL) techniques such as convolutional neural network (CNN) and Deep Learning APIs (Application Programming Interface), for the early diagnosis and treatment of ADHD and ASD symptoms. From this investigation, it can be concluded that diagnostic techniques based on ML reduce the intervention time and increase the accuracy with simultaneous understanding of the techniques and algorithms applied to diﬀerent types of Image data. Numerous studies have been done on ASD and ADHD separately, but our investigation also focuses on cooccurrences of these disorders in one individual.