Tootfinder

SHORE: A Long-term User Lifetime Value Prediction Model in Digital Games
Shuaiqi Sun, Congde Yuan, Haoqiang Yang, Mengzhuo Guo, Guiying Wei, Jiangbo Tian
https://arxiv.org/abs/2506.10487

SHORE: A Long-term User Lifetime Value Prediction Model in Digital Games
In digital gaming, long-term user lifetime value (LTV) prediction is essential for monetization strategy, yet presents major challenges due to delayed payment behavior, sparse early user data, and the presence of high-value outliers. While existing models typically rely on either short-cycle observations or strong distributional assumptions, such approaches often underestimate long-term value or suffer from poor robustness. To address these issues, we propose SHort-cycle auxiliary with Order-pr…

FAPS: A Fast Platform for Protein Structureomics Analysis
Lucas Wilken, Nihjum Paul, Troy Timmerman, Sara A. Tolba, Amara Arshad, Di Wu, Wenjie Xia, Bakhtiyor Rasulev, Rick Jansen, Dali Sun
https://arxiv.org/abs/2506.10134

FAPS: A Fast Platform for Protein Structureomics Analysis
Protein quantification and analysis are well-accepted approaches for biomarker discovery but are limited to identification without structural information. High-throughput omics data (i.e., genomics, transcriptomics, and proteomics) have become pervasive in cancer biology studies and reach well beyond more specialized areas such as metabolomics, epigenomics, pharmacogenomics, and interact-omics. However, large-scale analysis based on the structure of the biomolecules, namely structure-omics, is …

Detecting State Manipulation Vulnerabilities in Smart Contracts Using LLM and Static Analysis
Hao Wu, Haijun Wang, Shangwang Li, Yin Wu, Ming Fan, Yitao Zhao, Ting Liu
https://arxiv.org/abs/2506.08561

Detecting State Manipulation Vulnerabilities in Smart Contracts Using LLM and Static Analysis
An increasing number of DeFi protocols are gaining popularity, facilitating transactions among multiple anonymous users. State Manipulation is one of the notorious attacks in DeFi smart contracts, with price variable being the most commonly exploited state variable-attackers manipulate token prices to gain illicit profits. In this paper, we propose PriceSleuth, a novel method that leverages the Large Language Model (LLM) and static analysis to detect Price Manipulation (PM) attacks proactively.…

Fun post: Dave Farquhar, History of #overclocking
#Celeron:
> In 1998, Intel released the Celeron to compete with cheap CPUs from AMD and Cyrix. To make it, they took all of the Level 2 cache off a Pentium II, clocked it at 266 or 300 MHz, and sold it at an AMD-like price. But due to the lack of L2 cache, it was slower than a previous-generation Pentium running at 233 MHz, let alone an AMD or Cyrix chip running at 266 or 300 MHz.
>
> But the L2 cache was the Pentium II’s limiting factor in overclocking. So a 266 MHz Celeron ran happily at 400 MHz, or potentially even 450 MHz, the same speed as the fastest Pentium II at the time. It was slower than a Pentium II at the same speed, but it worked well for 3D gaming.
>
> But the only people who bought those Celerons were overclockers and people who didn’t know anything about computers. […]

This https://arxiv.org/abs/2506.02965 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csLG_…

Memory-Efficient and Privacy-Preserving Collaborative Training for Mixture-of-Experts LLMs
Mixture-of-Experts (MoE) has been gaining popularity due to its successful adaptation to large language models (LLMs). In this work, we introduce Privacy-preserving Collaborative Mixture-of-Experts (PC-MoE), which leverages the sparsity of the MoE architecture for memory-efficient decentralized collaborative LLM training, enabling multiple parties with limited GPU-memory and data resources to collectively train more capable LLMs than they could achieve individually. At the same time, this appro…

Heavy Lasso: sparse penalized regression under heavy-tailed noise via data-augmented soft-thresholding
The Tien Mai
https://arxiv.org/abs/2506.07790 https:…

Heavy Lasso: sparse penalized regression under heavy-tailed noise via data-augmented soft-thresholding
High-dimensional linear regression is a fundamental tool in modern statistics, particularly when the number of predictors exceeds the sample size. The classical Lasso, which relies on the squared loss, performs well under Gaussian noise assumptions but often deteriorates in the presence of heavy-tailed errors or outliers commonly encountered in real data applications such as genomics, finance, and signal processing. To address these challenges, we propose a novel robust regression method, terme…

Lets be honest, we spend too much time cleaning data. {janitor} can help with that: #rstats

Automatic detection of abnormal clinical EEG: comparison of a finetuned foundation model with two deep learning models
Aurore Bussalb, Fran\c{c}ois Le Gac, Guillaume Jubien, Mohamed Rahmouni, Ruggero G. Bettinardi, Pedro Marinho R. de Oliveira, Phillipe Derambure, Nicolas Gaspard, Jacques Jonas, Louis Maillard, Laurent Vercueil, Herv\'e Vespignani, Philippe Laval, Laurent Koessler, Ulysse Gimenez

Automatic detection of abnormal clinical EEG: comparison of a finetuned foundation model with two deep learning models
Electroencephalography (EEG) is commonly used by physicians for the diagnosis of numerous neurological disorders. Due to the large volume of EEGs requiring interpretation and the specific expertise involved, artificial intelligence-based tools are being developed to assist in their visual analysis. In this paper, we compare two deep learning models (CNN-LSTM and Transformer-based) with BioSerenity-E1, a recently proposed foundation model, in the task of classifying entire EEG recordings as norm…

Science for Peace and the need for Civil Clauses at universities and civilian research institutions
J. Altmann, U. Amaldi, M. Barone, A. Bassalat, M. Bona, J. Beullens, H. Brand, S. Brentjes, D. Britzger, J. Ellis, S. Franchoo, A. Giammanco, A. Glazov, C. Heck, H. Jung, S. Kraml, L. L\"onnblad, M. Mangano, M. Renneberg, Th. Riebe, A. Sabio-Vera, R. Sanders, J. Scheffran, M. Schmelling, T. Schucker, T. Suzuki, A. Tanasijczuk, I. Tsakov, D. Valls-Gabaud, M. Walker

Science for Peace and the need for Civil Clauses at universities and civilian research institutions
After the end of World War II, the commitment to confine scientific activities in universities and research institutions to peaceful and civilian purposes has entered, in the form of {\it Civil Clauses}, the charters of many research institutions and universities. In the wake of recent world events, the relevance and scope of such Civil Clauses has been questioned in reports issued by some governments and by the EU Commission, a development that opens the door to a possible blurring of the dist…

A large scaling property of level sets for degenerate $p$-Laplacian equations with logarithmic BMO matrix weights
Thanh-Nhan Nguyen, Minh-Phuong Tran
https://arxiv.org/abs/2506.00390

A large scaling property of level sets for degenerate $p$-Laplacian equations with logarithmic BMO matrix weights
In this study, we deal with generalized regularity properties for solutions to $p$-Laplace equations with degenerate matrix weights. It has already been observed in previous interesting works [A. Kh. Balci, L. Diening, R. Giova, A. Passarelli di Napoli, SIAM J. Math. Anal. 54(2022), 2373-2412] and [A. Kh. Balci, S.-S. Byun, L. Diening, H.-S. Lee, J. Math. Pures Appl. (9) 177(2023), 484-530] that gaining Calderón-Zygmund estimates for nonlinear equations with degenerate weights under the so-cal…