Measurement Artifacts
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Related Articles from SNS
Learning What's Real: Disentangling Signal and Measurement Artifacts in Multi-Sensor Data, with Applications to Astrophysics
arXiv:2604.09787v2 Announce Type: replace-cross Abstract: Data collected from the physical world is always a combination of multiple sources: an underlying signal from the physical process of interest and a signal from measurement-dependent artifacts from the sensor or instrument. This secondary signal acts as a confounding factor, limiting our ability to extract information about the physics underlying the phenomena we observe.
The Unsampled Truth: Psychometrics in SLMs Measure Prompt Artifacts, Not Psychological Constructs
arXiv:2606.03357v1 Announce Type: new Abstract: When prompting SLMs for psychometric assessments, researchers assume the outputs reflect semantic reasoning. We evaluate this premise across 13 open-weights models (0.6B to 14B parameters) using a prompt variation framework that separates semantic signals from prompt artifacts.
Heart Artifact Removal in Electrohysterography Measurements Using Algebraic Differentiators
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Three-dimensional density and air-rock interface reconstruction with muography: Application to the TianQin tunnel
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AutoLab: Can Frontier Models Solve Long-Horizon Auto Research and Engineering Tasks?
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Safety Under Scaffolding: How Evaluation Conditions Shape Measured Safety
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Uncovering Turbulent Dynamics in Stenotic Flows from 4D-flow MRI Measurements via Resolvent Analysis and Data Assimilation
arXiv:2606.03838v1 Announce Type: new Abstract: This study presents a hybrid experimental and computational framework that couples in vitro 4D phase-contrast magnetic resonance imaging (4D-flow MRI) measurements with data assimilation and linear modeling to characterize the flow linear amplification mechanisms. We manufacture an idealized stenosis phantom with a cosine-shaped contraction and acquire three-dimensional (3D) mean velocity measurements at Reynolds number 3960 using 4D-flow MRI....
Early reaction time variability predicts implicit statistical learning: a comparison of four variability indices
High intra-individual reaction time variability (RTV) is traditionally viewed through a deficit perspective and interpreted as a maladaptive signature of attentional lapses, cognitive inefficiency, and systemic noise. However, theories from motor learning and the competitive neurocognitive networks framework suggest that behavioral variability and reduced top-down control might actually facilitate certain forms of implicit skill acquisition. The present study addresses the apparent conflict...
Cellpin enables reference-based imputation and denoising of spatial transcriptomes
Spatially resolved transcriptomics enables gene expression profiling within tissue architecture, but targeted panels leave much of the transcriptome unmeasured and spatial artifacts such as RNA diffusion and segmentation errors introduce technical noise. These limitations necessitate computational imputation and denoising, yet existing methods typically incorporate spatial measurements during training, limiting scalability and risking the embedding of technology-specific artifacts into...
The solution might be cancelling my AI subscription
I am trying to think of a list of all the wonderful things I've built with AI: Except for the SaaS, almost none of this is useful and I don't want to maintain any of it. I accidentally run a news outlet which is surely a liability. Sure, it has helped me "learn AI tooling" and I use many of these tools, but I didn't need them.