pwc-archive/papers-with-abstracts huggingface dataset:
Multi-Target Acquisition in Multistatic MIMO-OFDM Joint Sensing and Communication
Authors: Elisabetta Matricardi, Lorenzo Pucci, Elia Favarelli, Enrico Paolini, Andrea Giorgetti
Date: 2025-03-28 00:00:00
Tasks: Diversity

In this work, we investigate a multistatic MIMO-OFDM joint sensing and communication (JSC) system that leverages cooperation among spatially distributed base stations (BSs) to detect and localize multiple targets through soft fusion of range-angle maps. We propose an innovative selective data fusion strategy that combines only the most reliable regions of range-angle maps from each bistatic pair, mitigating the adverse effects of residual clutter and target smearing inherent to bistatic configurations. To further enhance multi-view perception, we introduce a round-robin transmitter role strategy, enabling BSs to cooperate and exploit target spatial diversity. Finally, we assess the system performance in a cluttered environment using the generalized optimal subpattern assignment (GOSPA) and root mean squared error (RMSE) metrics, demonstrating the effectiveness of our approaches in improving detection and localization.
Caract{é}risation d'une Source Diffuse {à} partir des Moments de sa Densit{é} de Puissance en Tomographie SAR
Authors: Colin Cros, Laurent Ferro-Famil
Date: 2025-03-28 00:00:00

This paper presents a method for estimating the characteristics of a diffuse source from interferometric measurements in the context of SAR tomography. The proposed method is based on the use of central moments of the reflectivity density and does not use any a priori model. The method's performance is discussed as a function of antenna array parameters (resolution and ambiguity).
mmHRR: Monitoring Heart Rate Recovery with Millimeter Wave Radar
Authors: Ziheng Mao, Yuan He, Jia Zhang, Yimiao Sun, Yadong Xie, Xiuzhen Guo
Date: 2025-03-28 00:00:00

Heart rate recovery (HRR) within the initial minute following exercise is a widely utilized metric for assessing cardiac autonomic function in individuals and predicting mortality risk in patients with cardiovascular disease. However, prevailing solutions for HRR monitoring typically involve the use of specialized medical equipment or contact wearable sensors, resulting in high costs and poor user experience. In this paper, we propose a contactless HRR monitoring technique, mmHRR, which achieves accurate heart rate (HR) estimation with a commercial mmWave radar. Unlike HR estimation at rest, the HR varies quickly after exercise and the heartbeat signal entangles with the respiration harmonics. To overcome these hurdles and effectively estimate the HR from the weak and non-stationary heartbeat signal, we propose a novel signal processing pipeline, including dynamic target tracking, adaptive heartbeat signal extraction, and accurate HR estimation with composite sliding windows. Real-world experiments demonstrate that mmHRR exhibits exceptional robustness across diverse environmental conditions, and achieves an average HR estimation error of 3.31 bpm (beats per minute), 71% lower than that of the state-of-the-art method.
Beyond the Signal: Medication State Effect on EEG-Based AI models for Parkinson's Disease
Authors: Anna Kurbatskaya, Fredrik Nilsen Låder, Andreas Solvang Nese, Kolbjørn Brønnick, Alvaro Fernandez-Quilez
Date: 2025-03-27 00:00:00
Tasks: EEG

Parkinson's disease (PD) poses a growing challenge due to its increasing prevalence, complex pathology, and functional ramifications. Electroencephalography (EEG), when integrated with artificial intelligence (AI), holds promise for monitoring disease progression, identifying sub-phenotypes, and personalizing treatment strategies. However, the effect of medication state on AI model learning and generalization remains poorly understood, potentially limiting EEG-based AI models clinical applicability. This study evaluates how medication state influences the training and generalization of EEG-based AI models. Paired EEG recordings were utilized from individuals with PD in both ON- and OFF-medication states. AI models were trained on recordings from each state separately and evaluated on independent test sets representing both ON- and OFF-medication conditions. Model performance was assessed using multiple metrics, with accuracy (ACC) as the primary outcome. Statistical significance was assessed via permutation testing (p-values<0.05). Our results reveal that models trained on OFF-medication data exhibited consistent but suboptimal performance across both medication states (ACC_OFF-ON=55.3\pm8.8 and ACC_OFF-OFF=56.2\pm8.7). In contrast, models trained on ON-medication data demonstrated significantly higher performance on ON-medication recordings (ACC_ON-ON=80.7\pm7.1) but significantly reduced generalization to OFF-medication data (ACC_ON-OFF=76.0\pm7.2). Notably, models trained on ON-medication data consistently outperformed those trained on OFF-medication data within their respective states (ACC_ON-ON=80.7\pm7.1 and ACC_OFF-OFF=56.2\pm8.7). Our findings suggest that medication state significantly influences the patterns learned by AI models. Addressing this challenge is essential to enhance the robustness and clinical utility of AI models for PD characterization and management.
Low-Complexity Near-Field Beam Training with DFT Codebook based on Beam Pattern Analysis
Authors: Zijun Wang, Rama Kiran, Shawn Tsai, Rui Zhang
Date: 2025-03-27 00:00:00

Extremely large antenna arrays (ELAAs) operating in high-frequency bands have spurred the development of near-field communication, driving advancements in beam training design. This paper introduces an efficient near-field beam training method that utilizes the discrete Fourier transform (DFT) codebook traditionally employed for far-field users (FUs). We begin by analyzing the received beam pattern and deriving closed-form expressions for its width and central beam gain as a function of user location. Building on these derived expressions, we propose a beam training method that estimates user distance from the received signal powers using DFT beams, with a computational complexity of O(1). Simulation results confirm the efficacy of our approach, demonstrating its ability to perform low-complexity near-field beam training with high estimation accuracy. Notably, our proposed scheme achieves up to a 1.96 dB SNR gain over exhaustive search methods in multi-user beamforming scenarios.
The sexy and formidable male body: men's height and weight are condition-dependent, sexually selected traits
Authors: David Giofrè, David C. Geary, Lewis G. Halsey
Date: 2025-03-30 00:00:00

On average men are taller and more muscular than women, which confers on them advantages related to female choice and during physical competition with other men. Sexual size dimorphisms such as these come with vulnerabilities due to higher maintenance and developmental costs for the sex with the larger trait. These costs are in keeping with evolutionary theory that posits large, elaborate, sexually selected traits are signals of health and vitality because stressor exposure (e.g.\ early disease) will compromise them (e.g.\ shorter stature) more than other traits. We provide a large-scale test of this hypothesis for the human male and show that with cross-national and cross-generational improvements in living conditions, where environmental stressors recede, men's gains in height and weight are more than double those of women's, increasing sexual size dimorphism. Our study combines evolutionary biology with measures of human wellbeing, providing novel insights into how socio-ecological factors and sexual selection shape key physical traits.
Equilibrium Reward for Liquidity Providers in Automated Market Makers
Authors: Alif Aqsha, Philippe Bergault, Leandro Sánchez-Betancourt
Date: 2025-03-28 00:00:00

We find the equilibrium contract that an automated market maker (AMM) offers to their strategic liquidity providers (LPs) in order to maximize the order flow that gets processed by the venue. Our model is formulated as a leader-follower stochastic game, where the venue is the leader and a representative LP is the follower. We derive approximate closed-form equilibrium solutions to the stochastic game and analyze the reward structure. Our findings suggest that under the equilibrium contract, LPs have incentives to add liquidity to the pool only when higher liquidity on average attracts more noise trading. The equilibrium contract depends on the external price, the pool reference price, and the pool reserves. Our framework offers insights into AMM design for maximizing order flow while ensuring LP profitability.
Geometry and stability of species complexes: larger species speciate less often
Authors: Amaury Lambert, Emmanuel Schertzer, Yannic Wenzel
Date: 2025-03-28 00:00:00

Species complexes are groups of closely related populations exchanging genes through dispersal. We study the dynamics of the structure of species complexes in a class of metapopulation models where demes can exchange genetic material through migration and diverge through the accumulation of new mutations. Importantly, we model the ecological feedback of differentiation on gene flow by assuming that the success of migrations decreases with genetic distance, through a specific function $h$. We investigate the effects of metapopulation size on the coherence of species structures, depending on some mathematical characteristics of the feedback function $h$. Our results suggest that with larger metapopulation sizes, species form increasingly coherent, transitive, and uniform entities. We conclude that the initiation of speciation events in large species requires the existence of idiosyncratic geographic or selective restrictions on gene flow.
Short-time behavior of the At-The-Money implied volatility for the jump-diffusion stochastic volatility Bachelier model
Authors: Elisa Alòs, Òscar Burés, Josep Vives
Date: 2025-03-28 00:00:00

In this paper we use Malliavin Calculus techniques in order to obtain expressions for the short-time behavior of the at-the-money implied volatility (ATM-IV) level and skew for a jump-diffusion stock price. The diffusion part is assumed to be the stochastic volatility Bachelier model and the jumps are modeled by a pure-jump L\'evy process with drift so that the stock price is a martingale. Regarding the level, we show that the short-time behavior of the ATM-IV level is the same for all pure-jump L\'evy processes and, regarding the skew, we give conditions on the law of the jumps for the skew to exist. We also give several numerical examples of stochastic volatilities and L\'evy processes that confirm the theoretical results found in the paper.
Selection of the fittest or selection of the luckiest: the emergence of Goodhart's law in evolution
Authors: Bastien Mallein, Francesco Paparella, Emmanuel Schertzer, Zsófia Talyigás
Date: 2025-03-27 00:00:00

Biological evolution depends on the passing down to subsequent generations of genetic information encoding beneficial traits, and on the removal of unfit individuals by a selection mechanism. However, selection acts on phenotypes, and is affected by random contingencies. Thus, a combination of fitness and luck determines which individuals will successfully reproduce and give rise to the next generation. To understand how randomness in the selection mechanism affects the long-term patterns of evolution, we studied an idealized evolution model. We show through simulations and mathematical analysis, that the speed of adaptation increases with increasing selection pressure only up to a threshold. Beyond the threshold, any increase of the selection pressure results in more weight given to random effects rather than on genetic fitness in determining which individuals will successfully reproduce. This severely reduces the speed of adaptation and the diversity in the gene pool. Our findings may be considered as a biological instance of Goodhart's law: "When a measure becomes a target, it ceases to be a good measure". Finally, we show that this intricate response of evolution to natural selection can be mathematically explained by a novel phase transition for pulled traveling waves.
Modeling Maximum drawdown Records with Piecewise Deterministic Markov Processe in Capital Markets
Authors: Rolando Rubilar-Torrealba, Lisandro Fermin, Soledad Torres
Date: 2025-03-29 00:00:00

We propose to model the records of the maximum Drawdown in capital markets by means a Piecewise Deterministic Markov Process (PDMP). We derive statistical results such as the mean and variance that describes the sequence of maximum Drawdown records. In addition, we developed a simulation study and techniques for estimating the parameters governing the stochastic process, using a practical example in the capital market to illustrate the procedure.
Why does tinnitus vary with naps? A polysomnographic prospective study exploring the somatosensory hypothesis
Authors: Robin Guillard, Vincent Philippe, Adam Hessas, Brice Faraut, Sarah Michiels, Minchul Park, Marco Congedo, Alain Londero, Damien Léger
Date: 2025-03-31 00:00:00

Background: Tinnitus, defined as the conscious awareness of a noise without any identifiable corresponding external acoustic source, can be modulated by various factors. Among these factors, tinnitus patients commonly report drastic increases of tinnitus loudness following nap sleep. Previous studies have suggested that this clinical pattern could be attributed to a somatosensory modulation of tinnitus. To our knowledge, no polysomnographic study has been carried out to assess this hypothesis. Methods: For this observational prospective study, 37 participants reporting frequent increases of tinnitus following naps were recruited. They participated to six full-polysomnography nap attempts over two days. Audiological and kinesiologic tests were conducted before and after each nap attempt. Results: 197 naps were collected. Each nap at each time of day elicited an overall significant increase in tinnitus minimum masking level (MML). Each inter nap period elicited an overall significant decrease. Tinnitus modulations were found significantly correlated with nap sleep duration (Visual numeric scale on tinnitus loudness, VNS-L, p < 0.05), with snoring duration (MML, p < 0.001), with snoring average sound level (VNS on tinnitus intrusiveness, VNS-I, p < 0.05) and with sleep apnea count (VNS-I, p < 0.001). Conclusions: This study confirms objectively that tinnitus may increase following naps. No association was found between these modulations and somatosensory modulations involving the temporomandibular joint and cervical areas. However, it may be possible that nap-induced tinnitus modulations are a hidden form of somatosensory modulation as snoring and sleep apnea events are often related to tensor veli palatini muscle dysfunction.
Time, control, and the nervous system
Authors: Caroline Haimerl, Filipe S. Rodrigues, Joseph J. Paton
Date: 2025-03-29 00:00:00

Because organisms are able to sense its passage, it is perhaps tempting to treat time as a sensory modality, akin to vision or audition. Indeed, certain features of sensory estimation, such as Weber's law, apply to timing and sensation alike (Gibbon, 1977; Pardo-Vazquez et al., 2019). However, from an organismal perspective, time is a derived feature of other signals, not a stimulus that can be readily transduced by sensory receptors. Its importance for biology lies in the fact that the physical world comprises a complex dynamical system. The multiscale spatiotemporal structure of sensory and internally generated signals within an organism is the informational fabric underlying its ability to control behavior. Viewed this way, temporal computations assume a more fundamental role than is implied by treating time as just another element of the experienced world (Paton & Buonomano, 2018). Thus, in this review we focus on temporal processing as a means of approaching the more general problem of how the nervous system produces adaptive behavior.
How to set up a psychedelic study: Unique considerations for research involving human participants
Authors: Marcus J. Glennon, Catherine I. V. Bird, Prateek Yadav, Patrick Kleine, Shayam Suseelan, Christina Boman-Markaki, Vasileia Kotoula, Matt Butler, Robert Leech, Leor Roseman, David Erritzoe, Deepak P. Srivastava, Celia Morgan, Christopher Timmermann, Greg Cooper, Jeremy I. Skipper, James Rucker, Sunjeev K. Kamboj, Mitul A. Mehta, Ravi K. Das, Anjali Bhat
Date: 2025-03-28 00:00:00

Setting up a psychedelic study can be a long, arduous, and kafkaesque process. This rapidly-developing field poses several unique challenges for researchers, necessitating a range of considerations that have not yet been standardised. Many of the complexities inherent to psychedelic research also challenge existing assumptions around, for example, approaches to psychiatric prescribing, the conceptual framing of the placebo effect, and definitions of selfhood. This review paper brings together several of the major psychedelic research teams across the United Kingdom to formalise these unique considerations, identify continuing areas of debate, and provide a practical, experience-based guide, with recommendations for policymakers and future researchers intending to set up a psychedelic research study or clinical trial. We approach this such that the paper can either be read end to end, or treated as a manual: readers can dip into relevant sections as needed.
From Eye to Mind: brain2text Decoding Reveals the Neural Mechanisms of Visual Semantic Processing
Authors: Feihan Feng, Jingxin Nie
Date: 2025-03-15 00:00:00
Tasks: Brain Decoding

Deciphering the neural mechanisms that transform sensory experiences into meaningful semantic representations is a fundamental challenge in cognitive neuroscience. While neuroimaging has mapped a distributed semantic network, the format and neural code of semantic content remain elusive, particularly for complex, naturalistic stimuli. Traditional brain decoding, focused on visual reconstruction, primarily captures low-level perceptual features, missing the deeper semantic essence guiding human cognition. Here, we introduce a paradigm shift by directly decoding fMRI signals into textual descriptions of viewed natural images. Our novel deep learning model, trained without visual input, achieves state-of-the-art semantic decoding performance, generating meaningful captions that capture the core semantic content of complex scenes. Neuroanatomical analysis reveals the critical role of higher-level visual regions, including MT+, ventral stream visual cortex, and inferior parietal cortex, in this semantic transformation. Category-specific decoding further demonstrates nuanced neural representations for semantic dimensions like animacy and motion. This text-based decoding approach provides a more direct and interpretable window into the brain's semantic encoding than visual reconstruction, offering a powerful new methodology for probing the neural basis of complex semantic processing, refining our understanding of the distributed semantic network, and potentially inspiring brain-inspired language models.
DNA and Human Language: Epigenetic Memory and Redundancy in Linear Sequence
Authors: Li Yang, Dongbo Wang
Date: 2025-03-30 00:00:00

DNA is often described as the 'language of life', but whether it possesses formal linguistic properties remains unresolved. Here, we present the first empirical evidence that DNA sequences exhibit core linguistic features, specifically, functional and information redundancy, through comprehensive analysis of genomic and epigenetic datasets. By mapping DNA sequences into a linguistic feature space, we demonstrate that fixed-length (41 bp) DNA segments encode information analogously to human language, with redundancy contributing to signal stability in aqueous intracellular environments. Moreover, we provide the first evidence of one-dimensional epigenetic memory, showing that linear DNA sequences can maintain epigenetic marks such as 6mA methylation, contrasting with models focusing on epigenetic memory transmission via 3D chromatin organization[1]. Our tailored linguistic mapping strategy also addresses persistent challenges in genomic data processing, significantly improving data cleaning and feature extraction. Together, these findings establish a conceptual paradigm that bridges molecular information encoding and linguistic theory, laying the foundation for next-generation large language models (LLMs) specifically tailored to DNA, marking a shift at the interface of molecular biology, information theory, and artificial intelligence (AI).
Modelling the impact of phenotypic heterogeneity on cell migration: a continuum framework derived from individual-based principles
Authors: Rebecca M. Crossley, Philip K. Maini, Ruth E. Baker
Date: 2025-03-30 00:00:00

Collective cell migration plays a crucial role in numerous biological processes, including tumour growth, wound healing, and the immune response. Often, the migrating population consists of cells with various different phenotypes. This study derives a general mathematical framework for modelling cell migration in the local environment, which is coarse-grained from an underlying individual-based model that captures the dynamics of cell migration that are influenced by the phenotype of the cell, such as random movement, proliferation, phenotypic transitions, and interactions with the local environment. The resulting, flexible, and general model provides a continuum, macroscopic description of cell invasion, which represents the phenotype of the cell as a continuous variable and is much more amenable to simulation and analysis than its individual-based counterpart when considering a large number of phenotypes. We showcase the utility of the generalised framework in three biological scenarios: range expansion; cell invasion into the extracellular matrix; and T cell exhaustion. The results highlight how phenotypic structuring impacts the spatial and temporal dynamics of cell populations, demonstrating that different environmental pressures and phenotypic transition mechanisms significantly influence migration patterns, a phenomenon that would be computationally very expensive to explore using an individual-based model alone. This framework provides a versatile and robust tool for understanding the role of phenotypic heterogeneity in collective cell migration, with potential applications in optimising therapeutic strategies for diseases involving cell migration.
Evaluation of respiratory disease hospitalisation forecasts using synthetic outbreak data
Authors: Grégoire Béchade, Torbjörn Lundh, Philip Gerlee
Date: 2025-03-28 00:00:00

Forecasts of hospitalisations of infectious diseases play an important role for allocating healthcare resources during epidemics and pandemics. Large-scale analysis of model forecasts during the COVID-19 pandemic has shown that the model rank distribution with respect to accuracy is heterogeneous and that ensemble forecasts have the highest average accuracy. Building on that work we generated a maximally diverse synthetic dataset of 324 different hospitalisation time-series that correspond to different disease characteristics and public health responses. We evaluated forecasts from 14 component models and 6 different ensembles. Our results show that component model accuracy was heterogeneous and varied depending on the current rate of disease transmission. Going from 7 day to 14 day forecasts mechanistic models improved in relative accuracy compared to statistical models. A novel adaptive ensemble method outperforms all other ensembles, but is closely followed by a median ensemble. We also investigated the relationship between ensemble error and variability of component forecasts and show that the coefficient of variation is predictive of future error. Lastly, we validated the results on data from the COVID-19 pandemic in Sweden. Our findings have the potential to improve epidemic forecasting, in particular the ability to assign confidence to ensemble forecasts at the time of prediction based on component forecast variability.