The paper presents a Bayesian inference framework for magnetic resonance spectroscopy (MRS) that employs Sylvester normalizing flows (SNFs) to approximate posterior distributions over metabolite concentrations. A physics-based decoder incorporates prior knowledge of MRS signal formation, ensuring realistic distribution representations. Validation on simulated 7T proton MRS data shows accurate metabolite quantification, well-calibrated uncertainties, and insights into parameter correlations and multi‑modal distributions.
arXiv:2608. 07610v1 Announce Type: cross Abstract: Overlapping peaks and sample-dependent chemical shift variability prevent reliable metabolite recovery from complex biological spectra.
Magnetic resonance imaging (MRI) super-resolution is vital for improving diagnostic accessibility, yet most methods treat it as a deterministic mapping from a fixed low-resolution input to a high-resolution target. This overlooks a key property of MRI acquisition physics: spatial resolution and signal-to-noise ratio (SNR) are inherently coupled, making any given low-resolution scan merely one of many possible realizations under varying acquisition trade-offs.
arXiv:2609.14569v1 Announce Type: cross Abstract: Constraining reaction rate coefficients is a central challenge in the development of explicit atmospheric chemical mechanisms, particularly for autox...
Monroe is a new molecular foundation model that improves upon existing models by pre‑training on over 81 million molecules from the PM6 quantum chemistry dataset, enhancing stereochemistry representation, and introducing novel training losses such as conformer denoising and embedding decorrelation. It also incorporates a prior‑data‑fitted model (TabPFN) for downstream in‑context prediction and demonstrates superior performance on Polaris benchmarks and activity cliff tests. Ablation studies show that the PFN‑based downstream approach can upgrade other models, producing state‑of‑the‑art variants MiniMol_PFN and CheMeleon_PFN.
arXiv:2607. 06132v1 Announce Type: cross Abstract: Multi-Pool Chemical Exchange Saturation Transfer (CEST) MRI provides valuable metabolic information but is clinically limited by long acquisition times.
The paper introduces a Bayesian framework that models BOLD dynamics as coupled Ornstein‑Uhlenbeck processes and uses Sequential Neural Posterior Estimation to produce connectivity posteriors while accounting for measurement noise. Applied to 28 healthy controls scanned at 7T, the method quantifies uncertainty from scanner noise, subject variability, and scan length, revealing that about 46 voxels per ROI and 7 minutes of 7T data suffice for 90% of asymptotic precision. It also shows that 7T achieves within‑session precision 40% faster than 3T and requires roughly 37 times less per‑subject scan time to reach population‑level convergence, offering concrete, scanner‑specific guidance for protocol optimization.
The paper introduces MSAlign, a lightweight model that aligns frozen foundation models for mass spectra (DreaMS) and molecules (MolDeBERTa) to improve metabolite identification from MS/MS spectra. It presents a unified framework for representation alignment and contrastive learning, demonstrates that a score fusion strategy further boosts performance at minimal cost, and addresses evaluation challenges by quantifying distribution shift in data splitting strategies. All resources, including datasets, splits, and code, are publicly released to promote reproducible research.
arXiv:2607. 19816v1 Announce Type: cross Abstract: Determining molecular structures from spectroscopic data remains fundamentally challenging because the inverse problem is intrinsically underdetermined: individual spectra are sparse, low-dimensional, and encode only partial structural evidence relative to the vast space of possible molecules.
arXiv:2608. 00090v1 Announce Type: cross Abstract: The extraction of $\beta$-feeding distributions in Total Absorption $\gamma$-ray Spectroscopy constitutes a challenging inverse problem, particularly in nuclei with complex decay schemes involving a large number of excited states.
arXiv:2608. 13234v1 Announce Type: new Abstract: In order to understand complex systems such as the human metabolome or human brain, different sensing technologies are used, generating complex data.
arXiv:2508. 11657v2 Announce Type: replace-cross Abstract: Objective: Sparse Bayesian learning provides an effective framework to solve high-dimensional problems in brain signal decoding.
arXiv:2608. 19377v1 Announce Type: cross Abstract: Bayesian inference via Markov Chain Monte Carlo (MCMC) provides effective parameter estimation, but its real-time application in complex physical systems is hindered by heavy computational bottlenecks and extreme sensitivity to statistical noise.