Tootfinder

In practice, seems like this amounts to a (very large) tax on businesses that hire employees on H1B visas,
One that goes far beyond any possible fee for administrative/processing costs into major revenue raising reserved for Congress (based on objections to executive taxation going back to James I)
https://

Aaron Reichlin-Melnick (@reichlinmelnick.bsky.social)
WHOA. I am genuinely *shocked.* [contains quote post or other embedded content]

HTTP has a new method: QUERY. Tl;dr: GET with a body.
https://www.ietf.org/archive/id/draft-ietf-httpbis-safe-method-w-body-14.html
(Doesn’t have an RFC number yet but has been approved, will get one in a few weeks.)

How do people record images for Mapillary while on a bicycle? GoPro stuck to a helmet? Is that stable enough? Does something in the pipeline do post-processing to correct for camera tilt?
#biketooter #mapillary #camera

Wow, une petite révolution en train de se discuter entre experts très investis.
https://www.ietf.org/archive/id/draft-ietf-httpbis-safe-method-w-body-14.html
On n'a plus qu'Š attendre 3-5 ans pour pouvoir utiliser des QUERY au lieu de PO…

Higher-Order Feature Attribution: Bridging Statistics, Explainable AI, and Topological Signal Processing
Kurt Butler, Guanchao Feng, Petar Djuric
https://arxiv.org/abs/2510.06165

Higher-Order Feature Attribution: Bridging Statistics, Explainable AI, and Topological Signal Processing
Feature attributions are post-training analysis methods that assess how various input features of a machine learning model contribute to an output prediction. Their interpretation is straightforward when features act independently, but becomes less direct when the predictive model involves interactions such as multiplicative relationships or joint feature contributions. In this work, we propose a general theory of higher-order feature attribution, which we develop on the foundation of Integrate…

Low-Discrepancy Set Post-Processing via Gradient Descent
Fran\c{c}ois Cl\'ement, Linhang Huang, Woorim Lee, Cole Smidt, Braeden Sodt, Xuan Zhang
https://arxiv.org/abs/2511.10496 https://arxiv.org/pdf/2511.10496 https://arxiv.org/html/2511.10496
arXiv:2511.10496v1 Announce Type: new
Abstract: The construction of low-discrepancy sets, used for uniform sampling and numerical integration, has recently seen great improvements based on optimization and machine learning techniques. However, these methods are computationally expensive, often requiring days of computation or access to GPU clusters. We show that simple gradient descent-based techniques allow for comparable results when starting with a reasonably uniform point set. Not only is this method much more efficient and accessible, but it can be applied as post-processing to any low-discrepancy set generation method for a variety of standard discrepancy measures.
toXiv_bot_toot

A large-scale, unsupervised pipeline for automatic corpus annotation using LLMs: variation and change in the English consider construction
Cameron Morin, Matti Marttinen Larsson
https://arxiv.org/abs/2510.12306

A large-scale, unsupervised pipeline for automatic corpus annotation using LLMs: variation and change in the English consider construction
As natural language corpora expand at an unprecedented rate, manual annotation remains a significant methodological bottleneck in corpus linguistic work. We address this challenge by presenting a scalable, unsupervised pipeline for automating grammatical annotation in voluminous corpora using large language models (LLMs). Unlike previous supervised and iterative approaches, our method employs a four-phase workflow: prompt engineering, pre-hoc evaluation, automated batch processing, and post-hoc…

PRNet: Original Information Is All You Have
PeiHuang Zheng, Yunlong Zhao, Zheng Cui, Yang Li
https://arxiv.org/abs/2510.09531 https://arxiv.org/pdf/2510.09…

PRNet: Original Information Is All You Have
Small object detection in aerial images suffers from severe information degradation during feature extraction due to limited pixel representations, where shallow spatial details fail to align effectively with semantic information, leading to frequent misses and false positives. Existing FPN-based methods attempt to mitigate these losses through post-processing enhancements, but the reconstructed details often deviate from the original image information, impeding their fusion with semantic conte…

Habitable World Discovery and Characterization: Coronagraph Concept of Operations and Data Post-Processing
Michael W. McElwain, Dimitri Mawet, Jean-Baptiste Ruffio, Roser Juanola Parramon, Kellen Lawson, Herv\'e Le Coroller, Christian Marois, Max Millar-Blanchaer, Bijan Nemati, Susan Redmond, Bin Ren, Laurent Pueyo, Christopher Stark, Scott Will
https://

Habitable World Discovery and Characterization: Coronagraph Concept of Operations and Data Post-Processing
The discovery and characterization of habitable worlds was the top scientific recommendation of the Astro2020 decadal survey and is a key objective of the Habitable Worlds Observatory. Biosignature identification drives exceedingly challenging observations, which require raw contrasts of roughly 10$^{-10}$ contrast and ultimately, 1$σ$ photometric precision of roughly 3$\times 10^{-12}$ contrast. Despite significant advances for the Nancy Grace Roman Space Telescope's Coronagraph Instrument, t…

Gate Teleportation vs Circuit Cutting in Distributed Quantum Computing
Shobhit Gupta, Nikolay Sheshko, Daniel J. Dilley, Alvin Gonzales, Manish K. Singh, Zain H. Saleem
https://arxiv.org/abs/2510.08894

Gate Teleportation vs Circuit Cutting in Distributed Quantum Computing
Distributing circuits across quantum processor modules will enable the execution of circuits larger than the qubit count limitations of monolithic processors. While distributed quantum computation has primarily utilized circuit cutting, it incurs an exponential growth of sub-circuit sampling and classical post-processing overhead with an increasing number of cuts. The entanglement-based gate teleportation approach does not inherently incur exponential sampling overhead, provided that quantum in…

SeFEF: A Seizure Forecasting Evaluation Framework
Ana Sofia Carmo, Louren\c{c}o Abrunhosa Rodrigues, Ana Rita Peralta, Ana Fred, Carla Bentes, Hugo Pl\'acido da Silva
https://arxiv.org/abs/2510.11275

SeFEF: A Seizure Forecasting Evaluation Framework
The lack of standardization in seizure forecasting slows progress in the field and limits the clinical translation of forecasting models. In this work, we introduce a Python-based framework aimed at streamlining the development, assessment, and documentation of individualized seizure forecasting algorithms. The framework automates data labeling, cross-validation splitting, forecast post-processing, performance evaluation, and reporting. It supports various forecasting horizons and includes a …

Audio Forensics Evaluation (SAFE) Challenge
Kirill Trapeznikov, Paul Cummer, Pranay Pherwani, Jai Aslam, Michael S. Davinroy, Peter Bautista, Laura Cassani, Matthew Stamm, Jill Crisman
https://arxiv.org/abs/2510.03387

Audio Forensics Evaluation (SAFE) Challenge
The increasing realism of synthetic speech generated by advanced text-to-speech (TTS) models, coupled with post-processing and laundering techniques, presents a significant challenge for audio forensic detection. In this paper, we introduce the SAFE (Synthetic Audio Forensics Evaluation) Challenge, a fully blind evaluation framework designed to benchmark detection models across progressively harder scenarios: raw synthetic speech, processed audio (e.g., compression, resampling), and laundered a…

Gaussian Ensemble Topology (GET): A New Explicit and Inherently Smooth Framework for Manufacture-Ready Topology Optimization
Xinyu Ma, Chengxin Wang, Meng Wang, Xu Guo, Liu Yang, Huajian Gao
https://arxiv.org/abs/2510.05572

Gaussian Ensemble Topology (GET): A New Explicit and Inherently Smooth Framework for Manufacture-Ready Topology Optimization
We introduce the Gaussian Ensemble Topology (GET) method, a new explicit and manufacture-ready framework for topology optimization in which design geometries are represented as superpositions of anisotropic Gaussian functions. By combining explicit Gaussian descriptions with a level-set-like Heaviside projection, GET inherently generates smooth, curvature-continuous designs without requiring post-processing steps such as mesh or corner smoothing and feature extraction. The method is validated o…

BGRem: A background noise remover for astronomical images based on a diffusion model
R. Nicolaas, S. Caron, F. Stoppa, S. Bhattacharyya, R. Ruiz de Austri, P. J. Groot, A. J. Levan
https://arxiv.org/abs/2510.04718

BGRem: A background noise remover for astronomical images based on a diffusion model
Context: Astronomical imaging aims to maximize signal capture while minimizing noise. Enhancing the signal-to-noise ratio directly on detectors is difficult and expensive, leading to extensive research in advanced post-processing techniques. Aims: Removing background noise from images is a valuable pre-processing step catalog-building tasks. We introduce BGRem, a machine learning (ML) based tool to remove background noise from astronomical images. Methods: BGRem uses a diffusion-based model…

Correcting quantum errors using a classical code and one additional qubit
Tenzan Araki, Joseph F. Goodwin, Zhenyu Cai
https://arxiv.org/abs/2510.05008 https://

Correcting quantum errors using a classical code and one additional qubit
Classical error-correcting codes are powerful but incompatible with quantum noise, which includes both bit-flips and phase-flips. We introduce Hadamard-based Virtual Error Correction (H-VEC), a protocol that empowers any classical bit-flip code to correct arbitrary Pauli noise with the addition of only a single ancilla qubit and two layers of controlled-Hadamard gates. Through classical post-processing, H-VEC virtually filters the error channel, projecting the noise into pure Y-type errors that…

Replaced article(s) found for q-bio.TO. https://arxiv.org/list/q-bio.TO/new
[1/1]:
- Periodontal Bone Loss Analysis via Keypoint Detection With Heuristic Post-Processing
Banks, Thengane, Guerrero, Garc\'ia-Madue\~no, Li, Tang, Chaurasia

Unlocking ceramic 3D printing for next-generation chemical reactors
#materials #chemistry #ceramic #manufacturing

Unlocking ceramic 3D printing for next-generation chemical reactors
Scientists at the U.S. Department of Energy's Oak Ridge National Laboratory have integrated binder jet additive manufacturing with an advanced post-processing method to fabricate leak‐tight ceramic components, overcoming a key challenge of ceramic additive manufacturing.