Tootfinder

Ich habe mich für eine Recherche zu KI in #Taiwan beworben. Wird leider nichts, dank Elon Musk und Donald Trump:

“Excursion (same-day) travel to the United States by automobile dropped abruptly at the start of 2025, after holding consistently positive throughout 2024. Same-day returns fell by 40.3% year over year in May 2025”
That is an extremely high amount of change. After September 11, it was in the 30% range.
I miss my cross-border trips, but it’s just a bad risk reward calculation right now. #canada

This https://arxiv.org/abs/2412.03238 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csDS_…

Dynamic Consistent $k$-Center Clustering with Optimal Recourse
Given points from an arbitrary metric space and a sequence of point updates sent by an adversary, what is the minimum recourse per update (i.e., the minimum number of changes needed to the set of centers after an update), in order to maintain a constant-factor approximation to a $k$-clustering problem? This question has received attention in recent years under the name consistent clustering. Previous works by Lattanzi and Vassilvitskii [ICLM '17] and Fichtenberger, Lattanzi, Norouzi-Fard, and…

The birth of young radio jets in changing-look AGN: a population study
Sufia Birmingham, Charlotte Ward, Kristina Nyland, Dougal Dobie, Matthew J. Graham, David L. Kaplan, Tara Murphy
https://arxiv.org/abs/2507.01355

The birth of young radio jets in changing-look AGN: a population study
Changing-Look Active Galactic Nuclei (CLAGN) are a rare subset of AGN that show significant changes to the flux of broad Balmer emission lines. Recent studies of CLAGN, such as 1ES 1927+654 and Mrk 590, have revealed that changes in the optically observed accretion rate are accompanied by changes in radio activity. We present a time-domain population study of 474 spectroscopically confirmed CLAGN at radio wavelengths using the Australia SKA Pathfinder Variable and Slow Transients Survey and the…

This https://arxiv.org/abs/2503.19753 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csGR_…

A Survey on Event-driven 3D Reconstruction: Development under Different Categories
Event cameras have gained increasing attention for 3D reconstruction due to their high temporal resolution, low latency, and high dynamic range. They capture per-pixel brightness changes asynchronously, allowing accurate reconstruction under fast motion and challenging lighting conditions. In this survey, we provide a comprehensive review of event-driven 3D reconstruction methods, including stereo, monocular, and multimodal systems. We further categorize recent developments based on geometric, …

This https://arxiv.org/abs/2412.03238 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csDS_…

Dynamic Consistent $k$-Center Clustering with Optimal Recourse
Given points from an arbitrary metric space and a sequence of point updates sent by an adversary, what is the minimum recourse per update (i.e., the minimum number of changes needed to the set of centers after an update), in order to maintain a constant-factor approximation to a $k$-clustering problem? This question has received attention in recent years under the name consistent clustering. Previous works by Lattanzi and Vassilvitskii [ICLM '17] and Fichtenberger, Lattanzi, Norouzi-Fard, and…

The Genetic Code Paradox: Extreme Conservation Despite Demonstrated Flexibility
Marc Bara Iniesta
https://arxiv.org/abs/2507.01139 https://

The Genetic Code Paradox: Extreme Conservation Despite Demonstrated Flexibility
The universal genetic code presents a fundamental paradox in molecular biology. Recent advances in synthetic biology have demonstrated that the code is remarkably flexible--organisms can survive with 61 codons instead of 64, natural variants have reassigned codons 38+ times, and fitness costs of recoding stem primarily from secondary mutations rather than code changes themselves. Yet despite billions of years of evolution and this proven flexibility, approximately 99% of life maintains an ident…

Resolving structural dynamics in situ through cryogenic electron tomography
Jackson Carrion, Joseph H. Davis
https://arxiv.org/abs/2506.22719 https://

Resolving structural dynamics in situ through cryogenic electron tomography
Cryo-electron tomography (cryo-ET) has emerged as a powerful tool for studying the structural heterogeneity of proteins and their complexes, offering insights into macromolecular dynamics directly within cells. Driven by recent computational advances, including powerful machine learning frameworks, researchers can now resolve both discrete structural states and continuous conformational changes from 3D subtomograms and stacks of 2D particle-images acquired across tilt-series. In this review, we…

Ranking dynamics in movies and music
Hyun-Woo Lee, Gerardo I\~niguez, Hang-Hyun Jo, Hye Jin Park
https://arxiv.org/abs/2506.21944 https://

Ranking dynamics in movies and music
Ranking systems are widely used to simplify and interpret complex data across diverse domains, from economic indicators and sports scores to online content popularity. While previous studies including the Zipf's law have focused on the static, aggregated properties of ranks, in recent years researchers have begun to uncover generic features in their temporal dynamics. In this work, we introduce and study a series of system-level indices that quantify the compositional changes in ranking lists o…

#Windows11's new #StickyNotes app (a thinly disguised #OneNote) is fucking annoying. Since it is so-called "smart", and it attempts to provide context around the "source" of your note, its window is…

Reconsidering Explicit Longitudinal Mammography Alignment for Enhanced Breast Cancer Risk Prediction
Solveig Thrun, Stine Hansen, Zijun Sun, Nele Blum, Suaiba A. Salahuddin, Kristoffer Wickstr{\o}m, Elisabeth Wetzer, Robert Jenssen, Maik Stille, Michael Kampffmeyer
https://arxiv.org/abs/2506.19363

Reconsidering Explicit Longitudinal Mammography Alignment for Enhanced Breast Cancer Risk Prediction
Regular mammography screening is essential for early breast cancer detection. Deep learning-based risk prediction methods have sparked interest to adjust screening intervals for high-risk groups. While early methods focused only on current mammograms, recent approaches leverage the temporal aspect of screenings to track breast tissue changes over time, requiring spatial alignment across different time points. Two main strategies for this have emerged: explicit feature alignment through deformab…

Code Researcher: Deep Research Agent for Large Systems Code and Commit History
Ramneet Singh, Sathvik Joel, Abhav Mehrotra, Nalin Wadhwa, Ramakrishna B Bairi, Aditya Kanade, Nagarajan Natarajan
https://arxiv.org/abs/2506.11060

Code Researcher: Deep Research Agent for Large Systems Code and Commit History
Large Language Model (LLM)-based coding agents have shown promising results on coding benchmarks, but their effectiveness on systems code remains underexplored. Due to the size and complexities of systems code, making changes to a systems codebase is a daunting task, even for humans. It requires researching about many pieces of context, derived from the large codebase and its massive commit history, before making changes. Inspired by the recent progress on deep research agents, we design the fi…

Nonequilibrium Theory for Adaptive Systems in Varying Environments
Ying-Jen Yang, Charles D. Kocher, Ken A. Dill
https://arxiv.org/abs/2506.19018 https://

Nonequilibrium Theory for Adaptive Systems in Varying Environments
Biological organisms thrive by adapting to their environments, which are often unpredictably changeable. We apply recent results from nonequilibrium physics to show that organisms' fitness parses into a static generalist component and a nonequilibrium tracking component. Our findings: (1) Environmental changes that are too fast or too small are not worth tracking. (2) Well-timed anticipatory tracking enhances fitness in coherent environments. (3) We compute and explain the optimal adaptive stra…

Projecting U.S. coastal storm surge risks and impacts with deep learning
Julian R. Rice, Karthik Balaguru, Fadia Ticona Rollano, John Wilson, Brent Daniel, David Judi, Ning Sun, L. Ruby Leung
https://arxiv.org/abs/2506.13963

Projecting U.S. coastal storm surge risks and impacts with deep learning
Storm surge is one of the deadliest hazards posed by tropical cyclones (TCs), yet assessing its current and future risk is difficult due to the phenomenon's rarity and physical complexity. Recent advances in artificial intelligence applications to natural hazard modeling suggest a new avenue for addressing this problem. We utilize a deep learning storm surge model to efficiently estimate coastal surge risk in the United States from 900,000 synthetic TC events, accounting for projected changes i…

Shake-down spectroscopy as state- and site-specific probe of ultrafast chemical dynamics
Henry Thompson, Matteo Bonanomi, Jacob Pedersen, Oksana Plekan, Nitish Pal, Cesare Grazioli, Kevin C. Prince, Bruno N. C. Tenorio, Michele Devetta, Davide Faccial\`a, Caterina Vozzi, Paolo Piseri, Miltcho B. Danailov, Alexander Demidovich, Alexander D. Brynes, Alberto Simoncig, Marco Zangrando, Marcello Coreno, Raimund Feifel, Richard J. Squibb, David M. P. Holland, Felix Allum, Daniel Rolles, Pier…

Shake-down spectroscopy as state- and site-specific probe of ultrafast chemical dynamics
Tracking the multifarious ultrafast electronic and structural changes occurring in a molecule during a photochemical transformation is a challenging endeavor that benefits from recent experimental and computational progress in time-resolved techniques. Measurements of valence electronic states, which provide a global picture of the bonding structure of the molecule, and core electronic states, which provide insight into the local environment, traditionally require different approaches and are o…

Recent trends in socio-epidemic modelling: behaviours and their determinants
Daniele Proverbio, Riccardo Tessarin, Giulia Giordano
https://arxiv.org/abs/2506.13837

Recent trends in socio-epidemic modelling: behaviours and their determinants
The spreading dynamics of infectious diseases is influenced by individual behaviours, which are in turn affected by the level of awareness about the epidemic. Modelling the co-evolution of disease transmission and behavioural changes within a population enables better understanding, prediction and control of epidemics. Here, our primary goal is to provide an overview of the most popular modelling approaches, ranging from compartmental mean-field to agent-based models, with a particular focus on…

Tunable Photodetectors Based on 2D Hybrid Structures from Transition Metal Dichalcogenides and Photochromic Molecules
Sewon Park, Jaehoon Ji, Joakim Andreasson, Jeong Ho You, Jong Hyun Choi
https://arxiv.org/abs/2506.11249

Tunable Photodetectors Based on 2D Hybrid Structures from Transition Metal Dichalcogenides and Photochromic Molecules
This article reviews recent progress in two-dimensional (2D) hybrid structures that integrate transition metal dichalcogenides (TMDs) with photochromic molecules for photodetector applications. Atomically thin TMD semiconductors offer strong light-matter interaction, tunable bandgaps, and efficient carrier transport, making them suitable for photodetectors. Photochromic molecules, capable of reversible structural changes in response to external light, can modulate their chemical, optical, and e…

Collaborative Texture Filtering
Tomas Akenine-M\"oller, Pontus Ebelin, Matt Pharr, Bartlomiej Wronski
https://arxiv.org/abs/2506.17770 https://…

Collaborative Texture Filtering
Recent advances in texture compression provide major improvements in compression ratios, but cannot use the GPU's texture units for decompression and filtering. This has led to the development of stochastic texture filtering (STF) techniques to avoid the high cost of multiple texel evaluations with such formats. Unfortunately, those methods can give undesirable visual appearance changes under magnification and may contain visible noise and flicker despite the use of spatiotemporal denoisers. Re…

Speech Neurophysiology in Realistic Contexts: Big Hype or Big Leap?
Giovanni M. Di Liberto, Emily Y. J. Ip
https://arxiv.org/abs/2506.05494 https://…

Speech Neurophysiology in Realistic Contexts: Big Hype or Big Leap?
Understanding the neural basis of speech communication is essential for uncovering how sounds are translated into meaning, how that changes with development, ageing, and speech-related deficits, as well as contributing to brain-computer interfaces research. While traditional neurophysiological studies have relied on simplified, controlled paradigms, recent advances have shifted the field toward more ecologically-valid approaches. Here, we examine the impact of continuous speech research and dis…

Asymmetric price adjustment over the business cycle
Daniel Levy (Allan), Haipeng (Allan), Chen, Sourav Ray, Elliot Charette, Xiao Ling, Weihong Zhao, Mark Bergen, Avichai Snir
https://arxiv.org/abs/2506.10640

Asymmetric price adjustment over the business cycle
Studies of micro-level price datasets find more frequent small price increases than decreases, which can be explained by consumer inattention because time-constrained shoppers might ignore small price changes. Recent empirical studies of the link between shopping behavior and price attention over the business cycle find that consumers are more attentive to prices during economic downturns, and less attentive during economic booms. These two sets of findings have a testable implication. The asym…

Can knowledge reclassification accelerate technological innovation?
Peter Persoon
https://arxiv.org/abs/2506.08656 https://arxiv.org/…

Can knowledge reclassification accelerate technological innovation?
Technological knowledge evolves not only through the generation of new ideas, but also through the reinterpretation of existing ones. Reinterpretations lead to changes in the classification of knowledge, that is, reclassification. This study investigates how reclassified inventions can serve as renewed sources of innovation, thereby accelerating technological progress. Drawing on patent data as a proxy for technological knowledge, I discuss two empirical patterns: (i) more recent patents are mo…

Learning-based density-equalizing map
Yanwen Huang, Lok Ming Lui, Gary P. T. Choi
https://arxiv.org/abs/2506.10027 https://arxiv.org/…

Learning-based density-equalizing map
Density-equalizing map (DEM) serves as a powerful technique for creating shape deformations with the area changes reflecting an underlying density function. In recent decades, DEM has found widespread applications in fields such as data visualization, geometry processing, and medical imaging. Traditional approaches to DEM primarily rely on iterative numerical solvers for diffusion equations or optimization-based methods that minimize handcrafted energy functionals. However, these conventional t…