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Search for Gravitational-wave Memory in PPTA and EPTA Data: A Complete Signal Model
No full textWe perform searches for gravitational-wave memory in the data of two major Pulsar Timing Array (PTA) experiments located in Europe and Australia. Supermassive black hole binaries (SMBHBs) are the primary sources of gravitational waves in PTA experiments. We develop and carry out the first search for late inspirals and mergers of these sources based on full numerical relativity waveforms with null (nonlinear) gravitational-wave memory. Additionally, we search for generic bursts of null gravitational-wave memory, exploring the possibilities of reducing the computational costs of these searches through kernel density estimation and normalizing flow approximations of the posteriors. We rule out mergers of SMBHBs with a chirp mass of 10(10)M(circle dot) up to 700 Mpc over 18 yr of observation at 95% credibility. We rule out the observation of generic displacement memory bursts with strain amplitudes >10(-14) in brief periods of observation time but across the sky, or over the whole observation time but for certain preferred sky positions, at 95% credibility
Perceptual Decision Making of Nonequilibrium Fluctuations
No full textHow does the brain recover a weak signal that is submerged in intense stochastic fluctuations to make fast yet accurate choices? We recast perceptual decision-making as the inference of a nonzero drift (v) in the presence of large diffusivity (D): the observer must determine the direction of motion when trajectories are dominated by diffusion. A concrete analogy is reading wind while hunting: turbulent gusts scramble moment-to-moment cues, yet a subtle, consistent drift in air motion carries actionable information about wind direction. In this perspective, the core problem is signal discovery under diffusion—extracting the sign and magnitude of a weak drift from time-resolved fluctuations.
This framing contrasts with the traditional random-dot motion (RDM) paradigm, where “coherence” (the percentage of dots moving consistently) is an effective but unitless control of difficulty. Although 50% coherence is intuitively “more signal” than 10%, its relationship to the quantity of evidence is ambiguous: is it five times more, or something else? Because coherence does not specify the physical statistics generating samples, an ideal-observer analysis (e.g., a Sequential Probability Ratio Test, SPRT) cannot be uniquely grounded in first principles. By instead specifying a physics-based stimulus with known parameters, this thesis makes the evidence quantified and the ideal-observer well-posed.
We generated visual motion stimuli from a drifted Brownian process. To index the distance from equilibrium, we employed an interpretable nonequilibrium measure (entropy production Σ) proportional to drift–diffusion contrast, which increases as directional drive overwhelms diffusivity. In this generative setting, the momentary log-likelihood ratio (LLR) for direction decisions and the optimal stopping boundaries of the SPRT are derived analytically from (v, D), providing a physics-grounded benchmark for ideal performance.
Across three behavioral experiments, we asked: i) whether human observers (N = 67) detect and exploit graded nonequilibrium dynamics; ii) how closely their choices approach an ideal-observer benchmark; iii) how evidence integration adapts as Σ varies; and iv) whether such adaptation depends on task structure and the spatiotemporal layout of the stimuli. Results showed that stimulus dynamics (Σ, v, D) robustly shaped decision metrics, demonstrating that observers are indeed sensitive to graded changes in Σ.
An analytical SPRT captured these effects and quantified deviations from ideal performance. Complementarily, an Evidence Integration Model (EIM) fitted to the data revealed a systematic adjustment of the temporal integration window with Σ: in each trial, observers assigned greater weight to the most recent portion of the trajectory (a recency effect) whose strength scaled with Σ. Observers were also sensitive to salient changes in trajectory directionality, consistent with adaptive weighting under nonstationary drift.
Crucially, these effects were stronger in a blocked design—where Σ was held constant within blocks—than in an intermixed design, where Σ varied from trial to trial, indicating that stable nonequilibrium statistics facilitate calibration of integration timescales. Finally, sensitivity to the nonequilibrium structure was modulated not only by the physical parameters (v, D) but also by the spatial and temporal layout of the stimuli. Overall, by embedding perceptual evidence in a physics-based process that specifies its quantity, this work refines the characterization of variables that govern perceptual decisions and clarifies the temporal dynamics underlying efficient sensory evidence integration. It shows that when evidence is measured—rather than merely manipulated—ideal-observer analyses become principled, enabling precise tests of how the brain detects weak directional signals under high diffusion
Blind Prediction of Complex Water and Ion Ensembles Around RNA in CASP16
No full text: Biomolecules rely on water and ions for stable folding, but these interactions are often transient, dynamic, or disordered and thus hidden from experiments and evaluation challenges that represent biomolecules as single, ordered structures. Here, we compare blindly predicted ensembles of water and ion structure to the cryo-EM densities observed around the Tetrahymena ribozyme at 2.2-2.3 Å resolution, collected through target R1260 in the CASP16 competition. Twenty-six groups participated in this solvation "cryo-ensemble" prediction challenge, submitting over 350 million atoms in total, offering the first opportunity to compare blind predictions of dynamic solvent shell ensembles to cryo-EM density. Predicted atomic ensembles were converted to density through local alignment and these densities were compared to the cryo-EM densities using Pearson correlation, Spearman correlation, mutual information, and precision-recall curves. These predictions show that an ensemble representation is able to capture information of transient or dynamic water and ions better than traditional atomic models, but there remains a large accuracy gap to the performance ceiling set by experimental uncertainty. Overall, molecular dynamics approaches best matched the cryo-EM density, with blind predictions from bussilab_plain_md, SoutheRNA, bussilab_replex, coogs2, and coogs3 outperforming the baseline molecular dynamics prediction. This study indicates that simulations of water and ions can be quantitatively evaluated with cryo-EM maps. We propose that further community-wide blind challenges can drive and evaluate progress in modeling water, ions, and other previously hidden components of biomolecular systems
RNA Dynamics and Interactions Revealed Through Atomistic Simulations
No full text: RNA function is deeply intertwined with its conformational dynamics. In this review, we survey recent advances in the use of atomistic molecular dynamics simulations to characterize RNA dynamics in diverse contexts, including isolated molecules and complexes with ions, small molecules, or proteins. We highlight how enhanced sampling techniques and integrative approaches can improve both the precision and accuracy of the resulting structural ensembles. Finally, we examine the emerging role of artificial intelligence in accelerating progress in RNA modeling and simulation
A Bayesian Need for Simplicity: A study of how the brain selects and implements internal models, using a normative approach and Bayesian data analysis methods
No full textA prominent hypothesis in cognitive neuroscience is that the brain operates as an inference engine, relying on internal models to interpret sensory input and guide decision-making. While a great variety of behaviors and biases are explained through this approach, it is less clear how the brain builds and, most importantly, chooses among different models to interpret its environment. A general bias toward simpler interpretations is well-established, but critical gaps remain regarding the computational mechanisms driving this preference. Specifically, it is unclear whether the brain assesses the simplicity (or complexity) of an interpretation through optimal, theory-aligned computations or if it relies on heuristic approximation that can be skewed by perceptual biases. Furthermore, we don't know if the measure of complexity is fixed or adapted to the detailed features of the models at hand.
The first part of this work examines the criteria humans use to select between alternative interpretations of noisy data. First, investigating the effect of a variable amount of data, we show that human interpretations, while qualitatively following the principle of parsimony, diverge from optimal behavior. Our results suggest that intuitive model selection is based on a perceptual evaluation of the sample size, rather than a principled computation of the model fit.
Second, we address which properties of the model affect the perception of its complexity. We show that the brain does not rely on a generic complexity metric, as some model selection criteria do. Instead, characteristics unique to the models considered have a direct effect on the choice between interpretations.
Broadening the scope from intuitive model selection to biological implementation, the latter half of this work investigates how internal models shape sensory perception and how they are instantiated in neural populations, leveraging the same probabilistic frameworks used in the first half. In the domain of sensory processing, we apply Bayesian inference to explain perceptual biases in rats. We show that the influence of task-irrelevant sounds on visual discrimination is best explained by an internal model affected by a compressive warping of visual representations caused by auditory inputs, providing behavioral evidence for direct sensory interaction. Finally, we develop a novel Bayesian method for analyzing neural population activity. Applied to data from mice performing a value-based decision-making task, this technique allows for the decoding of latent variables required to construct and update an internal model of reward contingency.
Taken together, these findings offer a multi-level perspective on internal models, from cognition to perception to neural implementation, shedding light on the algorithms the brain uses to manage uncertainty and providing methodological tools to investigate them further
Uniqueness of asymptotic solutions for linear systems of ODEs with isolated singularities of general type
No full textAbstract. This article is a review of our paper [18] which, for a wide class of ODEs, provides sufficient conditions of existence and uniqueness of a fundamental system of solutions, with specified asymptotic behaviour, in wide sectors centered at an isolated singularity of the coefficients. These singularity can be general, not just of pole type
Machine Learning for RNA Secondary Structure Prediction: a review of current methods and challenges
No full text: Predicting the secondary structure of RNA is a core challenge in computational biology, essential for understanding molecular function and designing novel therapeutics. The field has evolved from foundational but accuracy-limited thermodynamic approaches to a new data-driven paradigm dominated by machine learning and deep learning. These models learn folding patterns directly from data, leading to significant performance gains. This review surveys the modern landscape of these methods, covering single-sequence, evolutionary-based, and hybrid models that blend machine learning with biophysics. A central theme is the field's "generalization crisis," where powerful models were found to fail on new RNA families, prompting a community-wide shift to stricter, homology-aware benchmarking. In response to the underlying challenge of data scarcity, RNA foundation models have emerged, learning from massive, unlabeled sequence corpora to improve generalization. Finally, we look ahead to the next set of major hurdles-including the accurate prediction of complex motifs like pseudoknots, scaling to kilobase-length transcripts, incorporating the chemical diversity of modified nucleotides, and shifting the prediction target from static structures to the dynamic ensembles that better capture biological function. We also highlight the need for a standardized, prospective benchmarking system to ensure unbiased validation and accelerate progress
Extension of a spectral difference method for the diffused-interface five-equation model
No full textThe present work focuses on the extension of the Spectral Difference (SD) scheme to the five-equation Baer-Nunziato model for the simulation of immiscible compressible fluids. This five-equation model is augmented with the Allen-Cahn regularisation to avoid both over-diffusion and over-thinning of the phase field representing the interface. In order to preserve contact discontinuities, in the reconstruction step of the SD scheme, a change of variables from conservative to primitive is used. This approach is shown to be beneficial in avoiding pressure oscillations at material interfaces. An extensive series of numerical tests, considering both two- and three-dimensional problems, are performed to assess accuracy and robustness of the present method. Specifically, both laminar and turbulent flows, as well as low-Mach and highly compressible flows, are considered, including cases with and without surface tension. The proposed change of variables is shown to improve the stability of the scheme, significantly reducing pressure oscillations at the material interfaces. This improved robustness enables the method to achieve accurate and stable solutions across a broad range of flow conditions
Electro-mechanical wrinkling of soft dielectric films bonded to hyperelastic substrates
No full textActive control of wrinkling in soft film-substrate composites using electric fields is a critical challenge in tunable material systems. Here, we investigate the electro-mechanical instability of a soft dielectric film bonded to a hyperelastic substrate, revealing the fundamental mechanisms that enable on-demand surface patterning. For the linearized stability analysis, we use the Stroh formalism and the surface impedance method to obtain exact and sixth-order approximate bifurcation equations that signal the onset of wrinkles. We derive the explicit bifurcation equations giving the critical stretch and critical voltage for wrinkling, as well as the corresponding critical wavenumber. We look at scenarios where the voltage is kept constant and the stretch changes, and vice versa. We provide the thresholds of the shear modulus ratio rc0 or pre-stretch λc0 below which the film-substrate system wrinkles mechanically, prior to the application of a voltage. These predictions offer theoretical guidance for practical structural design, as the shear modulus ratio r and/or the pre-stretch λ can be chosen to be slightly greater than rc0 and/or λc0, so that the film-substrate system wrinkles with a small applied voltage. Finally, we simulate the full nonlinear behavior using the Finite Element method (FEniCS) to validate our formulas and conduct a post-buckling analysis. This work advances the fundamental understanding of electro-mechanical wrinkling instabilities in soft material systems. By enabling active control of surface morphologies via applied electric fields, our findings open new avenues for adaptive technologies in soft robotics, flexible electronics, smart surfaces, and bioinspired systems
Uniqueness of Gauge Covariant Renormalisation of Stochastic 3D Yang–Mills–Higgs
No full textLocal solutions to the 3D stochastic quantisation equations of Yang-Mills-Higgs were constructed in Chandra (Invent Math 237:541-696, 2024), and it was shown that, in the limit of smooth mollifications, there exists a mass renormalisation of the Yang-Mills field such that the solution is gauge covariant. In this paper we prove the uniqueness of the mass renormalisation that leads to gauge covariant solutions. This strengthens the main result of Chandra (Invent Math 237:541-696, 2024), and is potentially important for the identification of the limit of other approximations, such as lattice dynamics. Our proof relies on systematic short-time expansions of singular stochastic PDEs and of regularised Wilson loops. We also strengthen the recently introduced state spaces of Cao (Comm Part Diff Equ 48:209-251, 2023); Cao (Comm Math Phys 405:3, 2024); Chandra (Invent Math 237:541-696, 2024) to allow for finer control on line integrals appearing in expansions of Wilson loops