Tootfinder

Task-Based Programming for Adaptive Mesh Refinement in Compressible Flow Simulations
Anjiang Wei, Hang Song, Mert Hidayetoglu, Elliott Slaughter, Sanjiva K. Lele, Alex Aiken
https://arxiv.org/abs/2508.05020

Task-Based Programming for Adaptive Mesh Refinement in Compressible Flow Simulations
High-order solvers for compressible flows are vital in scientific applications. Adaptive mesh refinement (AMR) is a key technique for reducing computational cost by concentrating resolution in regions of interest. In this work, we develop an AMR-based numerical solver using Regent, a high-level programming language for the Legion programming model. We address several challenges associated with implementing AMR in Regent. These include dynamic data structures for patch refinement/coarsening, mes…

#FridayDisco #AusGründen
https://youtu.be/Uz-htW9DTus?si=iwXIlFCtjCsDAYoz

From Formal Language Theory to Statistical Learning: Finite Observability of Subregular Languages
Katsuhiko Hayashi, Hidetaka Kamigaito
https://arxiv.org/abs/2509.22598 https://…

From Formal Language Theory to Statistical Learning: Finite Observability of Subregular Languages
We prove that all standard subregular language classes are linearly separable when represented by their deciding predicates. This establishes finite observability and guarantees learnability with simple linear models. Synthetic experiments confirm perfect separability under noise-free conditions, while real-data experiments on English morphology show that learned features align with well-known linguistic constraints. These results demonstrate that the subregular hierarchy provides a rigorous an…

🇺🇦 #NowPlaying on KEXP's #DriveTime
NoSo:
🎵 Sugar
#NoSo
https://noso.bandcamp.com/track/sugar
https://open.spotify.com/track/60HY8TH6tjKd64nwK6H73j

Turbulent Magnetic Fields and Molecular Cloud Interactions in the Supernova Remnant G1.9 0.3
Moeri Tao, Takaaki Tanaka, Hidetoshi Sano, Rami Z. E. Alsaberi, Jun Kataoka
https://arxiv.org/abs/2509.25833

Turbulent Magnetic Fields and Molecular Cloud Interactions in the Supernova Remnant G1.9+0.3
We present on results of a spatially resolved spectral analysis of G1.9+0.3, the youngest known supernova remnant in the Galaxy. The X-ray spectra are well described by synchrotron emission from a power-law electron distribution with an exponential cutoff. We found a cutoff energy $ε_0 \sim 1 ~ \rm{keV}$ in both the radio bright rim and the X-ray bright rims. In the loss-limited case, the cutoff energy depends on the shock velocity $v_{\rm{sh}}$ and the Bohm factor $η$, following the relation…

Crosslisted article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[3/7]:
- Decoding Uncertainty: The Impact of Decoding Strategies for Uncertainty Estimation in Large Langu...
Wataru Hashimoto, Hidetaka Kamigaito, Taro Watanabe

Clustering-based aggregate value regression
Kei Hirose, Hidetoshi Matsui, Hiroki Masuda
https://arxiv.org/abs/2508.15567 https://arxiv.org/pdf/2508.15567…

Clustering-based aggregate value regression
In various practical situations, forecasting of aggregate values rather than individual ones is often our main focus. For instance, electricity companies are interested in forecasting the total electricity demand in a specific region to ensure reliable grid operation and resource allocation. However, to our knowledge, statistical learning specifically for forecasting aggregate values has not yet been well-established. In particular, the relationship between forecast error and the number of clus…

🇺🇦 #NowPlaying on KEXP's #Expansions
Rhyw:
🎵 It Was All Happening
#Rhyw
https://rhyw.bandcamp.com/track/it-was-all-happening
https://open.spotify.com/track/2HDuD93jDVidvXsNzNNBKJ

Shift Parallelism: Low-Latency, High-Throughput LLM Inference for Dynamic Workloads
Mert Hidayetoglu, Aurick Qiao, Michael Wyatt, Jeff Rasley, Yuxiong He, Samyam Rajbhandari
https://arxiv.org/abs/2509.16495

Shift Parallelism: Low-Latency, High-Throughput LLM Inference for Dynamic Workloads
Efficient parallelism is necessary for achieving low-latency, high-throughput inference with large language models (LLMs). Tensor parallelism (TP) is the state-of-the-art method for reducing LLM response latency, however GPU communications reduces combined token throughput. On the other hand, data parallelism (DP) obtains a higher throughput yet is slow in response latency. Best of both worlds does not exist, and it is not possible to combine TP and DP because of the KV cache variance across th…

🇺🇦 #NowPlaying on KEXP's #AfternoonShow
NoSo:
🎵 Sugar
#NoSo
https://noso.bandcamp.com/track/sugar
https://open.spotify.com/track/60HY8TH6tjKd64nwK6H73j