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Operators can replace or upgrade their instruments and distribute longitude coverage across a network
from the top result below
https://arxiv.org/html/2609.03371v1
Operators can replace or upgrade their instruments and distribute longitude coverage across a network
https://arxiv.org/list/cs.CV/new
CV)
Fine-grained wildlife classification in aerial imagery is limited not only by model performance, but also by unreliable labels: animals occupy few pixels, key visual cues vary seasonally, and modality-specific evidence can be ambiguous
https://arxiv.org/list/cs.AI/new
We demonstrate that it can self-assemble functional circuits from scratch and rapidly re-route logic around permanent, previously unseen hardware faults
https://arxiv.org/html/2607.22511v2
A statement can be free of sorry and still be mathematically uninformative: a definition may reduce to True; or an unproven step may be introduced as an axiom that the kernel accepts
https://arxiv.org/html/2608.02851v1
org perpetual non-exclusive license
arXiv:2608
https://arxiv.org/abs/2608.12700
A kernel can pass that test and still be silently wrong
https://arxiv.org/list/cs.NA/new
all
New submissions (showing 18 of 18 entries)
[1] arXiv:2608
https://arxiv.org/abs/2204.04218
In many situations, computed tomography (CT) or magnetic resonance imaging (MRI) techniques capture several scans (modes) during a single investigation, which can jointly be used (in a multimodal fashion) to further boost the quality of super-resolution results
https://arxiv.org/html/2607.27099v1
org perpetual non-exclusive license
arXiv:2607
https://arxiv.org/html/2609.02852v1
However, various strands of evidence indicate that an LLM’s externalized linguistic outputs and mechanistically-extracted linguistic features can be an unreliable lens for understanding internal model computation
https://arxiv.org/html/2511.06451v1
Spectral safety and discrete Green kernel
https://arxiv.org/list/math/new
Their finite iterates can develop rapidly increasing numbers of linear pieces, making them a natural test case for the expressive power of deep neural networks
https://arxiv.org/list/cs.DC/new
SE)
Automating GPU kernel optimization remains difficult in practice: generated variants can violate correctness constraints, runtime measurements are noisy, and search often stalls early
https://arxiv.org/html/2508.20101v1
In an empirical application, we demonstrate how the model can be applied to financial stock networks
https://arxiv.org/list/cs.SE/new
Yet it remains unclear when further evolution helps, how successful and failed trajectories shape revision, and whether extra test-time computation can recover the same gains
https://arxiv.org/list/math.ST/new
If \(T_\star\) is the selected physical trajectory length and \(a_\star=\sqrt m\,T_\star\), a terminal-depth transfer argument yields restricted conductance and warm-start mixing without lazifying either kernel
https://arxiv.org/html/2608.02817v1
expressed here are those of the authors and not necessarily those of the Federal Reserve Bank of Atlanta or the Federal Reserve System. The authors are grateful for the invaluable feedback they received on an earlier draft from Jón
https://arxiv.org/html/2607.29442v2
In an asymptotic framework inspired by Rosenbaum2018, we show that the rescaled price process converges to a Bachelier-type semimartingale plus a Gaussian moving average whose kernel is determined explicitly by the trawl function
https://arxiv.org/list/eess/new
The design relies on two affine Volterra transformations involving both Fredholm- and Volterra-type integral terms, which results in a four-PIDE kernel equations
https://arxiv.org/pdf/2508.11152
document was published isbn Text external ISBN for the publication in which the document was published doi Text external Digital Object Identifier for the document url URL external URL at which the document can be found byteCount Integer internal Approximate file size in octets pageCount Integer internal Number of
https://arxiv.org/pdf/2006.02318
org/pdfua/ns/id/ pdfuaid PDF/UA ID Schema internal Part of PDF/UA standard part Integer PRISM metadata http://prismstandard
https://arxiv.org/pdf/2607.26327v3
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https://arxiv.org/pdf/2609.18842
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https://arxiv.org/pdf/2608.02694
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https://arxiv.org/pdf/2506.01844
SmolVLA: A Vision-Language-Action Model for Affordable and Efficient RoboticsMustafa ShukorDana AubakirovaFrancesco CapuanoPepijn KooijmansSteven PalmaAdil ZouitineMichel AractingiCaroline PascalMartino RussiAndres MarafiotiSimon
https://arxiv.org/pdf/2608.02609
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https://arxiv.org/src/2405.20232
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https://arxiv.org/src/1910.03771
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https://arxiv.org/pdf/2608.02872
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https://arxiv.org/src/2608.04007
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