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Earlier sources: Science
Science investigates the natural world through observation, experiments, and mathematical models. Explore papers and developments in biology, medicine, materials, neuroscience, physics, climate, and mathematics; a reported finding is not necessarily established consensus.
SYMTRI organizes observations from public sources into a living map. Connections reflect shared subject matter; they do not establish causation or endorse a source.
Open in the map ↗1325 classified signals in the fourteen-day window. Recent momentum: steady. These counts include signals awaiting readable evidence.
Earlier sources
Up to ten sources with readable evidence, ordered by their feed timestamp. These archive sources can be older than the live fourteen-day window. This list is a sample, not a measure of total activity. Dates distinguish paper publication, repository creation, model/dataset activity, Hacker News posts, and KEV catalog additions. “First archived” records when SYMTRI first stored the signal.
- OpenAlex · Published · First archived
Physics-constrained liquid neural network for wind-induced vibrations prediction of hanger cables on long-span suspension bridges ↗
Wind-induced vibrations (WIVs) of hanger cables on long-span suspension bridges could accelerate fatigue in suspension bridge suspension cables, damage damping devices, and other serviceability issues. The existing high-fidelity fluid-structure simulations are costly for real-time capable monitoring and purely data-driven approaches often violate basic physical laws and struggle to generalize under changing wind conditions. Prediction of hanger cable WIVs under strong non-stationarity and cross-domain distribution shifts has long faced challenges such as insufficient generalization, extrapolat…
- OpenAlex · Published · First archived
Cross-modal attention fusion of ultrasound imaging and clinical tabular data for breast lesion classification: A multimodal deep learning framework ↗
Abstract Accurate classification of breast lesions in ultrasound images is an important yet challenging task, plagued by visual ambiguity between classes and morphological variability within classes. This paper presents a multimodal deep learning framework combining EfficientNet-B4 visual features with an 11-dimensional, leakage-free clinical tabular feature vector, organized into three feature-group tokens (intensity, texture, and geometry), through a multi-token cross-modal attention fusion mechanism. A systematic audit of candidate tabular features identified that eight commonly used lesion…
- OpenAlex · Published · First archived
The relationship between lean mass and protein intake in females of African and East Asian descent ↗
Low lean mass (LM) is associated with increased cardiometabolic disease risk and increased injury risk. Protein (PRO) intake is an important strategy to build and maintain LM. There is limited data quantifying PRO intake and evaluating the association between PRO intake and LM in non-athletic, young adult, female populations, and even less in females of color. The purpose of this study was to characterize PRO intake and evaluate the association between PRO intake and LM in females of color. Eighty-five females of African ( n = 42) and East Asian ( n = 43) descent (Age: 22.5 ± 3.8 yrs, BMI: 23.…
- OpenAlex · Published · First archived
Neural network-based multipartite entanglement classification with prior guidance from quantum uncertainty relations ↗
Quantum entanglement is a crucial resource in quantum information processing, yet its efficient, scalable and robust classification in multipartite systems remains theoretically challenging. Although supervised machine-learning has been applied to this task, most existing methods still suffer from high measurement costs, computational consumption, and weak noise robustness. In this work, by incorporating multipartite uncertainty relations as prior guidance, we propose a neural network approach to classify distinct Stochastic Local Operations and Classical Communication (SLOCC) multipartite ent…
- OpenAlex · Published · First archived
The Super Bigbite Spectrometer Physics Program ↗
The structure of the nucleon is a central problem in strong interaction physics in the non-perturbative regime. Indeed, the vast majority of the known matter in the Universe is made of protons and neutrons which are a remarkable emergent phenomenon of quantum chromodynamics. A critical aspect of investigating nucleon structure experimentally is the measurement of fundamental quantities such as the elastic nucleon form factors. Also important is the measurement of the transverse momentum dependent distribution functions. Accessing such quantities experimentally, however, is challenging because…
- OpenAlex · Published · First archived
States of 2D Yang-Mills and large-volume entanglement ↗
A bstract We explore the relation between geometry, topology and entanglement for quantum states in two-dimensional Yang-Mills theory. The most familiar class of states in this theory are states defined by Euclidean path integrals over Riemann surfaces. Bipartite states of this class have thermofield double structure, with entanglement consistently reducing with total area and the number of topological defects, becoming separable in the infinite-area limit. In contrast, Wilson lines and loops generate rich non-monotonic behavior of the entanglement entropy. Most notably, we find that for a cer…
- OpenAlex · Published · First archived
Process bigraphs and the architecture of compositional systems biology ↗
Building multiscale biological models requires the integration of independently developed submodels, which means moving shared variables between them and coordinating when each submodel runs. Existing tools typically address isolated biological mechanisms with specific numerical methods, rarely specify which variables each model reads and writes, how those variables are translated, and how model updates are synchronized. We present Process Bigraph, a framework for composing and simulating multiscale models. Process Bigraph generalizes the architectural ideas previously used in the Vivarium sof…
- OpenAlex · Published · First archived
A residual weighted physics informed neural network for forward and inverse problems of reaction diffusion equations ↗
Physics-Informed Neural Networks (PINNs) constitute an effective deep learning approach for solving partial differential equations (PDEs). In this work, we propose a Residual-Weighted Physics-Informed Neural Network (RW-PINN), a new method designed to enhance the accuracy of PINNs -based algorithms. We construct a deep learning framework with two residual-weighting schemes to solve reaction–diffusion equations and evaluate its performance on both forward and inverse problems. The approach computes weights proportional to the PDE residuals, rescales them, and incorporates these scaled residuals…
- Europe PMC · Published · First archived
Error correction of SARS-CoV-2 genomic sequences using a phylogenetic prior ↗
Genotype calling of sequencing data has improved over recent years thanks to technological and statistical breakthroughs. One such advance is the use of imputation-based genotyping methods in human genetics. For haploid and mostly non-recombining organisms, a natural equivalent is to leverage phylogenetic signal for imputation-based genotype calling. SARS-CoV-2 is one such case, and the use of improved genotyping methods in this species is particularly pertinent given the well-documented systematic problems with read alignment and assembly in rapidly evolving regions of its genome, motivating
- Europe PMC · Published · First archived
Multidimensional divergence supports parapatric speciation along an elevational gradient in ground-dwelling frogs on an oceanic island ↗
Geographic isolation is a major driver of lineage divergence. However, strong ecological selection can promote reproductive isolation despite opportunities for gene flow in geographically continuous landscapes such as mountains. Here, we test whether ecological divergence across elevational gradients can promote speciation within a single mountain range. Integrating phylogenomic (ddRAD), morphological, and bioacoustic data, we test predictions of elevation-mediated parapatric speciation in Platymantis ( Lahatnanguri ) ground-dwelling frogs on Mindanao Island, Philippines. Phylogenomic analyses
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Check each original source for its methods, release status, limitations, and supporting data. A repository announcement, a preprint, and a published study provide different kinds of evidence. SYMTRI’s subject classification helps discovery; it does not independently validate the source’s claims.
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