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Related Articles from SNS
MedVision: Benchmarking Quantitative Medical Image Analysis
Announce Type: replace Abstract: Current vision-language models (VLMs) in medicine are primarily designed for categorical question answering (e.g., "Is this normal or abnormal?") or qualitative descriptive tasks. However, clinical decision-making often relies on quantitative assessments, such as measuring the size of a tumor or the angle of a joint, from which physicians draw their own diagnostic conclusions. This quantitative reasoning capability remains underexplored and poorly supported...
Surrogate distributed radiological sources III: quantitative distributed source reconstructions
arXiv:2412.02926v3 Announce Type: replace Abstract: In this third part of a multi-paper series, we present quantitative image reconstruction results from aerial measurements of eight different surrogate distributed gamma-ray sources on flat terrain. We show that our quantitative imaging methods can accurately reconstruct the expected shapes, and, after appropriate calibration, the absolute activity of the distributed sources. We conduct several studies of imaging performance versus various...
A physics-informed foundation model for quantitative diffusion MRI
Announce Type: cross Abstract: Understanding the human brain requires access to its microscopic tissue architecture. Diffusion magnetic resonance imaging (MRI) provides the only noninvasive window into whole-brain microstructure in vivo, yet reliable quantitative mapping remains confined to specialized research settings requiring dense sampling and optimized acquisition protocols. To address this gap, we present a physics-informed generative microstructure network (PIGMENT) that learns a...
MoE-dqINR: A Unified Mixture-of-Experts Implicit Neural Representation Framework for Scan-Specific Dynamic and Quantitative MRI Reconstruction
arXiv:2605.31302v1 Announce Type: cross Abstract: Undersampled magnetic resonance imaging (MRI) reconstruction seeks to recover temporally or contrast-varying image series from incomplete multicoil k-space data while preserving state-dependent fidelity for dynamic and quantitative MRI (qMRI). Existing scan-specific implicit neural representations (INRs) often use monolithic spatiotemporal coordinate fields, explicit subspaces, motion or deformation models, calibration variables, or...
LLM Agent-Assisted Reverse Engineering with Quantitative Readability Metrics
Announce Type: new Abstract: Automatic decompilers produce functionally correct but often unreadable C code. This paper addresses one stage of the reverse engineering workflow: improving the readability of decompiled code using LLM agents guided by quantitative metrics. We present a three-phase research evolution.
Coupled intracellular redox and extracellular respiration sensing for quantitative oxidative stress profiling
Abstract Quantitative assessment of cellular oxidative stress requires simultaneous measurement of intracellular redox state and extracellular respiratory activity, yet integrated sensing approaches remain limited. Here, we present a dual fluorescent sensing platform combining a genetically encoded redox biosensor (roGFP2-Tsa2{Delta}CR) with an optical oxygen sensor embedded in microwell plates for parallel, noninvasive quantification of intracellular reactive oxygen species (ROS) and oxygen...
Quantitative Movement Testing: Measuring Patient Movements from a Single Smartphone Video
arXiv:2606.02301v1 Announce Type: new Abstract: Chronic pain diminishes quality of life by decreasing functional ability, yet objectively measuring this functional impact remains challenging in real-world settings. While optical motion capture provides high precision for assessing altered movement quality, it is costly and restricted to laboratory environments. We aimed to develop and validate Quantitative Movement Testing (QMT), a computer vision pipeline extracting 3D kinematic biomarkers...
Quantitative Promise Theory: Intentionality and Inference in Autonomous Agents
Announce Type: new Abstract: I discuss some quantitative representations of Promise Theory for processes involving autonomous agents. Agent models are common in software systems, machine learning, and biology, for example, but may also apply to physics and other forms of engineering. I describe how Bayesian probability and information theoretic optimization, including Active Inference, may be incorporated with promise semantics -- as well as how Promise Theory supplements solutions, helping...
Quantitative Nonequilibrium Pathway from Fundamental Physics to the Emergence and Persistence of Exoplanetary Biospheres
arXiv:2606.02648v1 Announce Type: new Abstract: We present a physics-based framework that runs from fundamental interactions and constants to biospheres, using a sequence of quantitative nonequilibrium thresholds ("gates"). Each gate is an inequality in measurable variables-free-energy flux, reaction-transport rates, replication fidelity, coding capacity, ecological closure, and climate feedback gains. Crucially, the gate vector is anchored in fundamental physics: dimensionless constants,...
Quantitative Promise Theory: Intentionality and Inference in Autonomous Agents
Announce Type: cross Abstract: I discuss some quantitative representations of Promise Theory for processes involving autonomous agents. Agent models are common in software systems, machine learning, and biology, for example, but may also apply to physics and other forms of engineering. I describe how Bayesian probability and information theoretic optimization, including Active Inference, may be incorporated with promise semantics -- as well as how Promise Theory supplements solutions,...