Tootfinder

🇺🇦 #NowPlaying on #BBC6Music's #NickGrimshaw
Basement Jaxx:
🎵 Romeo
#BasementJaxx
https://jjuk.bandcamp.com/track/basement-jaxx-romeo-jj-edit
https://open.spotify.com/track/4tI7eIbEUfInHS3sxfqkkr

TypyBench: Evaluating LLM Type Inference for Untyped Python Repositories
Honghua Dong, Jiacheng Yang, Xun Deng, Yuhe Jiang, Gennady Pekhimenko, Fan Long, Xujie Si
https://arxiv.org/abs/2507.22086

TypyBench: Evaluating LLM Type Inference for Untyped Python Repositories
Type inference for dynamic languages like Python is a persistent challenge in software engineering. While large language models (LLMs) have shown promise in code understanding, their type inference capabilities remain underexplored. We introduce TypyBench, a benchmark designed to evaluate LLMs' type inference across entire Python repositories. TypyBench features two novel metrics: TypeSim, which captures nuanced semantic relationships between predicted and ground truth types, and TypeCheck, whi…

Empirical Comparison of Membership Inference Attacks in Deep Transfer Learning
Yuxuan Bai, Gauri Pradhan, Marlon Tobaben, Antti Honkela
https://arxiv.org/abs/2510.05753 https://…

Empirical Comparison of Membership Inference Attacks in Deep Transfer Learning
With the emergence of powerful large-scale foundation models, the training paradigm is increasingly shifting from from-scratch training to transfer learning. This enables high utility training with small, domain-specific datasets typical in sensitive applications.Membership inference attacks (MIAs) provide an empirical estimate of the privacy leakage by machine learning models. Yet, prior assessments of MIAs against models fine-tuned with transfer learning rely on a small subset of possible att…

Typofundstück #köln

There's a word for it. Who knew? #TipOfMyTongue

Analyzing Dialectical Biases in LLMs for Knowledge and Reasoning Benchmarks
Eileen Pan, Anna Seo Gyeong Choi, Maartje ter Hoeve, Skyler Seto, Allison Koenecke
https://arxiv.org/abs/2510.00962

Analyzing Dialectical Biases in LLMs for Knowledge and Reasoning Benchmarks
Large language models (LLMs) are ubiquitous in modern day natural language processing. However, previous work has shown degraded LLM performance for under-represented English dialects. We analyze the effects of typifying "standard" American English language questions as non-"standard" dialectal variants on multiple choice question answering tasks and find up to a 20% reduction in accuracy. Additionally, we investigate the grammatical basis of under-performance in non-"standard" English question…

TubeBEND: A Real-World Dataset for Geometry Prediction in Rotary Draw Bending
Zeyneddin Oz, Jonas Knoche, Alireza Yazdani, Bernd Engel, Kristof Van Laerhoven
https://arxiv.org/abs/2509.10272

TubeBEND: A Real-World Dataset for Geometry Prediction in Rotary Draw Bending
This paper presents TubeBEND, a real-world dataset comprising 318 rotary tube bending processes, which were collected and sorted by experts from various fields to evaluate machine learning and signal analysis methods. The dataset addresses the industrial challenge of predicting the geometry of a first-stage bend, which can be beneficial for designing machine clamping molds for the second-stage bend in two-stage rotary draw bending. Some geometry criteria, such as the tube's final bent angle (or…

TopoBind: Multi-Modal Prediction of Antibody-Antigen Binding Free Energy via Sequence Embeddings and Structural Topology
Ciyuan Yu, Hongzong Li, Jiahao Ma, Shiqin Tang, Ye-Fan Hu, Jian-Dong Huang
https://arxiv.org/abs/2508.19632

TopoBind: Multi-Modal Prediction of Antibody-Antigen Binding Free Energy via Sequence Embeddings and Structural Topology
Predicting the binding free energy between antibodies and antigens is a key challenge in structure-aware biomolecular modeling, with direct implications for antibody design. Most existing methods either rely solely on sequence embeddings or struggle to capture complex structural relationships, thus limiting predictive performance. In this work, we present a novel framework that integrates sequence-based representations from pre-trained protein language models (ESM-2) with a set of topological f…

Information-Theoretic Fairness with A Bounded Statistical Parity Constraint
Amirreza Zamani, Abolfazl Changizi, Ragnar Thobaben, Mikael Skoglund
https://arxiv.org/abs/2508.12847

Information-Theoretic Fairness with A Bounded Statistical Parity Constraint
In this paper, we study an information-theoretic problem of designing a fair representation that attains bounded statistical (demographic) parity. More specifically, an agent uses some useful data $X$ to solve a task $T$. Since both $X$ and $T$ are correlated with some sensitive attribute or secret $S$, the agent designs a representation $Y$ that satisfies a bounded statistical parity and/or privacy leakage constraint, that is, such that $I(Y;S) \leq ε$. Here, we relax the perfect demographic …

🇺🇦 #NowPlaying on #BBC6Music's #CraigCharles
Basement Jaxx:
🎵 Romeo
#BasementJaxx
https://jjuk.bandcamp.com/track/basement-jaxx-romeo-jj-edit
https://open.spotify.com/track/4tI7eIbEUfInHS3sxfqkkr