Tootfinder

Crosslisted article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[2/3]:
- Sharp Structure-Agnostic Lower Bounds for General Functional Estimation
Jikai Jin, Vasilis Syrgkanis
https://arxiv.org/abs/2512.17341 https://mastoxiv.page/@arXiv_statML_bot/115762312049963700
- Timely Information Updating for Mobile Devices Without and With ML Advice
Yu-Pin Hsu, Yi-Hsuan Tseng
https://arxiv.org/abs/2512.17381 https://mastoxiv.page/@arXiv_csNI_bot/115762180316858485
- SWE-Bench : A Framework for the Scalable Generation of Software Engineering Benchmarks from Open...
Wang, Ramalho, Celestino, Pham, Liu, Sinha, Portillo, Osunwa, Maduekwe
https://arxiv.org/abs/2512.17419 https://mastoxiv.page/@arXiv_csSE_bot/115762487015279852
- Perfect reconstruction of sparse signals using nonconvexity control and one-step RSB message passing
Xiaosi Gu, Ayaka Sakata, Tomoyuki Obuchi
https://arxiv.org/abs/2512.17426 https://mastoxiv.page/@arXiv_statML_bot/115762346108219997
- MULTIAQUA: A multimodal maritime dataset and robust training strategies for multimodal semantic s...
Jon Muhovi\v{c}, Janez Per\v{s}
https://arxiv.org/abs/2512.17450 https://mastoxiv.page/@arXiv_csCV_bot/115762717053353674
- When Data Quality Issues Collide: A Large-Scale Empirical Study of Co-Occurring Data Quality Issu...
Emmanuel Charleson Dapaah, Jens Grabowski
https://arxiv.org/abs/2512.17460 https://mastoxiv.page/@arXiv_csSE_bot/115762500123147574
- Behavioural Effects of Agentic Messaging: A Case Study on a Financial Service Application
Olivier Jeunen, Schaun Wheeler
https://arxiv.org/abs/2512.17462 https://mastoxiv.page/@arXiv_csIR_bot/115762430673347625
- Linear Attention for Joint Power Optimization and User-Centric Clustering in Cell-Free Networks
Irched Chafaa, Giacomo Bacci, Luca Sanguinetti
https://arxiv.org/abs/2512.17466 https://mastoxiv.page/@arXiv_eessSY_bot/115762336277179643
- Translating the Rashomon Effect to Sequential Decision-Making Tasks
Dennis Gross, J{\o}rn Eirik Betten, Helge Spieker
https://arxiv.org/abs/2512.17470 https://mastoxiv.page/@arXiv_csAI_bot/115762556506696539
- Alternating Direction Method of Multipliers for Nonlinear Matrix Decompositions
Atharva Awari, Nicolas Gillis, Arnaud Vandaele
https://arxiv.org/abs/2512.17473 https://mastoxiv.page/@arXiv_eessSP_bot/115762580078964235
- TwinSegNet: A Digital Twin-Enabled Federated Learning Framework for Brain Tumor Analysis
Almustapha A. Wakili, Adamu Hussaini, Abubakar A. Musa, Woosub Jung, Wei Yu
https://arxiv.org/abs/2512.17488 https://mastoxiv.page/@arXiv_csCV_bot/115762726884307901
- Resource-efficient medical image classification for edge devices
Mahsa Lavaei, Zahra Abadi, Salar Beigzad, Alireza Maleki
https://arxiv.org/abs/2512.17515 https://mastoxiv.page/@arXiv_eessIV_bot/115762459510336799
- PathBench-MIL: A Comprehensive AutoML and Benchmarking Framework for Multiple Instance Learning i...
Brussee, Valkema, Weijer, Doeleman, Schrader, Kers
https://arxiv.org/abs/2512.17517 https://mastoxiv.page/@arXiv_csCV_bot/115762741957639051
- HydroGym: A Reinforcement Learning Platform for Fluid Dynamics
Christian Lagemann, et al.
https://arxiv.org/abs/2512.17534 https://mastoxiv.page/@arXiv_physicsfludyn_bot/115762391350754768
- When De-noising Hurts: A Systematic Study of Speech Enhancement Effects on Modern Medical ASR Sys...
Chondhekar, Murukuri, Vasani, Goyal, Badami, Rana, SN, Pandia, Katiyar, Jagadeesh, Gulati
https://arxiv.org/abs/2512.17562 https://mastoxiv.page/@arXiv_csSD_bot/115762423443170715
- Enabling Disaggregated Multi-Stage MLLM Inference via GPU-Internal Scheduling and Resource Sharing
Lingxiao Zhao, Haoran Zhou, Yuezhi Che, Dazhao Cheng
https://arxiv.org/abs/2512.17574 https://mastoxiv.page/@arXiv_csDC_bot/115762425409322293
- SkinGenBench: Generative Model and Preprocessing Effects for Synthetic Dermoscopic Augmentation i...
N. A. Adarsh Pritam, Jeba Shiney O, Sanyam Jain
https://arxiv.org/abs/2512.17585 https://mastoxiv.page/@arXiv_eessIV_bot/115762479150695610
- MAD-OOD: A Deep Learning Cluster-Driven Framework for an Out-of-Distribution Malware Detection an...
Tosin Ige, Christopher Kiekintveld, Aritran Piplai, Asif Rahman, Olukunle Kolade, Sasidhar Kunapuli
https://arxiv.org/abs/2512.17594 https://mastoxiv.page/@arXiv_csCR_bot/115762509298207765
- Confidence-Credibility Aware Weighted Ensembles of Small LLMs Outperform Large LLMs in Emotion De...
Menna Elgabry, Ali Hamdi
https://arxiv.org/abs/2512.17630 https://mastoxiv.page/@arXiv_csCL_bot/115762575512981257
- Generative Multi-Objective Bayesian Optimization with Scalable Batch Evaluations for Sample-Effic...
Madhav R. Muthyala, Farshud Sorourifar, Tianhong Tan, You Peng, Joel A. Paulson
https://arxiv.org/abs/2512.17659 https://mastoxiv.page/@arXiv_statML_bot/115762554519447500
toXiv_bot_toot

Reform have written to me. Wow, what a push poll. “Rising crime”, “mass immigration” (not devolved), but no mention of transport, let alone the environment (unless that’s what they mean by high energy prices).
Also, why only one set of choices for who I’m voting for? Have Reform forgotten that in a Scottish election you have two votes (constituency and list)?

"In Phrack Magazine, this author learned at the end of the 1990s the subtle art of smashing the stack, an exploit that would become the starting point of many a computer security book afterward.
There is one magazine that has been around for a decade: the “International Journal of Proof-of-Concept or Get The Fuck Out”, or “PoC||GTFO”.
(I should have probably warned readers about the profanity in the title, but nah, I assume them to be adults at this point.)"

Pastor Manul Laphroaig, T.G. S.B.
Paraphrasing a well-known software mogul who shall remain nameless in the pages of this magazine, insecure software is eating the world. The reasons of such sad state of things are varied and range from social to economic; the technological aspect is usually the one that concerns me the least. In this sea of unusable things and insecure networks, there is a “subculture” (a horrible word, but bear with me) of highly skilled individuals who teach each other how broken those things are. And ye…

Finished "Lobizona" by Romina Garber. I have extremely mixed feelings about this book. It's a powerful depiction of the fear of living as an undocumented child/teen and it has interesting things to say about rejection, belonging, and the choice between seeking to be recognized for who you are and wanting you blend in enough to be accepted as normal. However, it's also an explicit homage to Harry Potter, and while it doesn't include antisemitic tropes or glorify slavery or even have any anti-trans sentiments I can detect, to me the magical school setup felt forced and I thought it would have been a better book had it not tried to fit that mould. Also, it would have been a super interesting situation to explore trans issues, and while it's definitely fine for it not to do that, the author's praise of Rowling's work has me wondering...
There's a sequel that I think could in theory be amazing, but given the execution of the first book, I think I'll wait a bit before checking it out. By putting her main character in opposition to both ICE in the human world and the magical authorities in the other world, Garber explicitly sets the stage for a revolution standing between her protagonist and any kind of lasting peace. But I'm not confident she's capable of writing that story without relying on some kind of supernatural deus ex machina, which would be disappointing to me, since "a better world if only possible through divine intervention" is an inherently regressive message.
Overall, #OwnVoices fantasy centering an undocumented immigrant is an excellent thing, and I've certainly got a lot of privilege that surely influences my criticism. However, #OwnVoices stuff has a range of levels of craft and political stances, and it can be excellent for some reasons and mediocre for others.
On that point, if anyone reading this has suggestions for fiction books grappling with borders and the carceral state, Is be happy to hear them.
#AmReading

Two-Stage Gaussian Splatting Optimization for Outdoor Scene Reconstruction
Deborah Pintani, Ariel Caputo, Noah Lewis, Marc Stamminger, Fabio Pellacini, Andrea Giachetti
https://arxiv.org/abs/2510.09489

Two-Stage Gaussian Splatting Optimization for Outdoor Scene Reconstruction
Outdoor scene reconstruction remains challenging due to the stark contrast between well-textured, nearby regions and distant backgrounds dominated by low detail, uneven illumination, and sky effects. We introduce a two-stage Gaussian Splatting framework that explicitly separates and optimizes these regions, yielding higher-fidelity novel view synthesis. In stage one, background primitives are initialized within a spherical shell and optimized using a loss that combines a background-only photome…

Active Rosensweig Patterns
Carlo Rigoni, Max Philipp Holl, Alberto Scacchi, Emil Str{\aa}ka, Fereshteh Sohrabi, Mikko P. Haataja, Maria Sammalkorpi, Jaakko V. I. Timonen
https://arxiv.org/abs/2510.09099

Active Rosensweig Patterns
Ferrofluids, colloidal dispersions of magnetic nanoparticles, are renowned for pattern formation like few other materials. The Rosensweig instability of a horizontal ferrofluid-air interface in perpendicular magnetic field is especially well known classically, this instability sets the air-ferrofluid interface into an array of spikes that correspond to a new free energy minimum of the system. However, once the pattern is formed, it does not exhibit any notable thermal or non-equilibrium fluctua…

Migration and spreading of a droplet driven by a chemical step
Zhuo Long, Peng Gao
https://arxiv.org/abs/2510.12514 https://arxiv.org/pdf/2510.12514…

Migration and spreading of a droplet driven by a chemical step
The chemical step is an elementary pattern in chemically heterogeneous substrates, featuring two regions of different wettability separated by a sharp border. Within the framework of lubrication theory, we investigate droplet motion and the contact-line dynamics driven by a chemical step, with the contact-line singularity addressed by the Navier slip condition. For both two-dimensional (2D) and three-dimensional (3D) droplets, two successive stages are identified: the migration stage, when the …

A multiscale evolutionary study of molecular gas in STARFORGE. I. Synthetic observations of SEDIGISM-like molecular clouds
K. R. Neralwar, D. Colombo, S. Offner, A. Karska, M. Figueira, F. Wyrowski, S. Neupane, J. S. Urquhart, A. Duarte-Cabral
https://arxiv.org/abs/2510.07393

A multiscale evolutionary study of molecular gas in STARFORGE. I. Synthetic observations of SEDIGISM-like molecular clouds
Molecular clouds are active sites of star formation in galaxies, and their formation and evolution are largely affected by stellar feedback. This includes outflows and winds from newly formed stars, radiation from young clusters, and supernova explosions. High-resolution molecular line observations allow for the identification of individual star-forming regions and the study of their integrated properties. Moreover, simulations are now capable of accurately replicating the evolution of MCs incl…

Regularized Random Fourier Features and Finite Element Reconstruction for Operator Learning in Sobolev Space
Xinyue Yu, Hayden Schaeffer
https://arxiv.org/abs/2512.17884 https://arxiv.org/pdf/2512.17884 https://arxiv.org/html/2512.17884
arXiv:2512.17884v1 Announce Type: new
Abstract: Operator learning is a data-driven approximation of mappings between infinite-dimensional function spaces, such as the solution operators of partial differential equations. Kernel-based operator learning can offer accurate, theoretically justified approximations that require less training than standard methods. However, they can become computationally prohibitive for large training sets and can be sensitive to noise. We propose a regularized random Fourier feature (RRFF) approach, coupled with a finite element reconstruction map (RRFF-FEM), for learning operators from noisy data. The method uses random features drawn from multivariate Student's $t$ distributions, together with frequency-weighted Tikhonov regularization that suppresses high-frequency noise. We establish high-probability bounds on the extreme singular values of the associated random feature matrix and show that when the number of features $N$ scales like $m \log m$ with the number of training samples $m$, the system is well-conditioned, which yields estimation and generalization guarantees. Detailed numerical experiments on benchmark PDE problems, including advection, Burgers', Darcy flow, Helmholtz, Navier-Stokes, and structural mechanics, demonstrate that RRFF and RRFF-FEM are robust to noise and achieve improved performance with reduced training time compared to the unregularized random feature model, while maintaining competitive accuracy relative to kernel and neural operator tests.
toXiv_bot_toot

Real-Time Rendering of Dynamic Line Sets using Voxel Ray Tracing
Bram Kraaijeveld, Andrei C. Jalba, Anna Vilanova, Maxime Chamberland
https://arxiv.org/abs/2510.09081 https://…

Real-Time Rendering of Dynamic Line Sets using Voxel Ray Tracing
Real-time rendering of dynamic line sets is relevant in many visualization tasks, including unsteady flow visualization and interactive white matter reconstruction from Magnetic Resonance Imaging. High-quality global illumination and transparency are important for conveying the spatial structure of dense line sets, yet remain difficult to achieve at interactive rates. We propose an efficient voxel-based ray-tracing framework for rendering large dynamic line sets with ambient occlusion and groun…