Tootfinder

#MutualAid @… needs some help:
https://social.wildeboer.net/@jwild…

Uncertainty-Informed Scheduling of Decision Points for Intelligent Mobile Health Interventions
Asim H. Gazi, Bhanu T. Gullapalli, Daiqi Gao, Benjamin M. Marlin, Vivek Shetty, Susan A. Murphy
https://arxiv.org/abs/2507.10798

Uncertainty-Informed Scheduling of Decision Points for Intelligent Mobile Health Interventions
Timely decision making is critical to the effectiveness of mobile health (mHealth) interventions. At predefined timepoints called "decision points," intelligent mHealth systems such as just-in-time adaptive interventions (JITAIs) estimate an individual's biobehavioral context from sensor or survey data and determine whether and how to intervene. For interventions targeting habitual behavior (e.g., oral hygiene), effectiveness often hinges on delivering support shortly before the target behavior…

From Chaos to Automation: Enabling the Use of Unstructured Data for Robotic Process Automation
Kelly Kurowski, Xixi Lu, Hajo A. Reijers
https://arxiv.org/abs/2507.11364

From Chaos to Automation: Enabling the Use of Unstructured Data for Robotic Process Automation
The growing volume of unstructured data within organizations poses significant challenges for data analysis and process automation. Unstructured data, which lacks a predefined format, encompasses various forms such as emails, reports, and scans. It is estimated to constitute approximately 80% of enterprise data. Despite the valuable insights it can offer, extracting meaningful information from unstructured data is more complex compared to structured data. Robotic Process Automation (RPA) has ga…

Primordial planet spin driven by boundary layer effects in a decretion disc
Rebecca G. Martin, Stephen H. Lubow, David Vallet, Madeline Overton, Stephen Lepp, Zhaohuan Zhu, Shunquan Huang
https://arxiv.org/abs/2508.09273

Primordial planet spin driven by boundary layer effects in a decretion disc
Accretion of material from a protoplanetary disc on to a forming giant planet can spin the planet up to close to its breakup rate, $Ω_{\rm b}=(G M_{\rm p}/R_{\rm p}^3)$, where $M_{\rm p}$ is the mass and $R_{\rm p}$ is the radius of the planet. After the protoplanetary disc dissipates, the rapidly rotating planet may eject a decretion (outflowing) disc in a similar way to a Be star. Boundary layer effects in a hydrodynamic disc allow for decretion disc formation at spin rates below the breakup…

Recently a friend asked me why I don't publish my videos on Youtube. I'd get more views.
So I checked my views - and know what: RIGHT, I don't get a lot of views - but most of the #videos on #Peertube show *more* views than I got on Youtube.
Well, the YT algorithm never love…

FastTrack: a fast method to evaluate mass transport in solid leveraging universal machine learning interatomic potential
Hanwen Kang, Tenglong Lu, Zhanbin Qi, Jiandong Guo, Sheng Meng, Miao Liu
https://arxiv.org/abs/2508.10505

FastTrack: a fast method to evaluate mass transport in solid leveraging universal machine learning interatomic potential
We introduce a rapid, accurate framework for computing atomic migration barriers in crystals by combining universal machine learning force fields (MLFFs) with 3D potential energy surface sampling and interpolation. Our method suppresses periodic self interactions via supercell expansion, builds a continuous PES from MLFF energies on a spatial grid, and extracts minimum energy pathways without predefined NEB images. Across twelve benchmark electrode and electrolyte materials including LiCoO2, Li…

Causal Negative Sampling via Diffusion Model for Out-of-Distribution Recommendation
Chu Zhao, Eneng Yang, Yizhou Dang, Jianzhe Zhao, Guibing Guo, Xingwei Wang
https://arxiv.org/abs/2508.07243

Causal Negative Sampling via Diffusion Model for Out-of-Distribution Recommendation
Heuristic negative sampling enhances recommendation performance by selecting negative samples of varying hardness levels from predefined candidate pools to guide the model toward learning more accurate decision boundaries. However, our empirical and theoretical analyses reveal that unobserved environmental confounders (e.g., exposure or popularity biases) in candidate pools may cause heuristic sampling methods to introduce false hard negatives (FHNS). These misleading samples can encourage the …

Integrating Rules and Semantics for LLM-Based C-to-Rust Translation
Feng Luo, Kexing Ji, Cuiyun Gao, Shuzheng Gao, Jia Feng, Kui Liu, Xin Xia, Michael R. Lyu
https://arxiv.org/abs/2508.06926

Integrating Rules and Semantics for LLM-Based C-to-Rust Translation
Automated translation of legacy C code into Rust aims to ensure memory safety while reducing the burden of manual migration. Early approaches in code translation rely on static rule-based methods, but they suffer from limited coverage due to dependence on predefined rule patterns. Recent works regard the task as a sequence-to-sequence problem by leveraging large language models (LLMs). Although these LLM-based methods are capable of reducing unsafe code blocks, the translated code often exhibit…

The feeling when you find the root cause for a problem that has been bothering you for months. And it turns out to be fixable by adding a single letter. (To turn an "unsigned long" literal into an "unsigned long long" one.) (Actually I made it use the UINT64_C() macro from <cstdint>.)
Writing portable code is hard. And thanks, Windows, for keeping "long" as 32 bits even in 64-bit code.
Now, if only Clang or gcc on Linux would warn about such po…

E3-Rewrite: Learning to Rewrite SQL for Executability, Equivalence,and Efficiency
Dongjie Xu, Yue Cui, Weijie Shi, Qingzhi Ma, Hanghui Guo, Jiaming Li, Yao Zhao, Ruiyuan Zhang, Shimin Di, Jia Zhu, Kai Zheng, Jiajie Xu
https://arxiv.org/abs/2508.09023

E3-Rewrite: Learning to Rewrite SQL for Executability, Equivalence,and Efficiency
SQL query rewriting aims to reformulate a query into a more efficient form while preserving equivalence. Most existing methods rely on predefined rewrite rules. However, such rule-based approaches face fundamental limitations: (1) fixed rule sets generalize poorly to novel query patterns and struggle with complex queries; (2) a wide range of effective rewriting strategies cannot be fully captured by declarative rules. To overcome these issues, we propose using large language models (LLMs) to ge…