Tootfinder

Data reification in a concurrent rely-guarantee algebra
Larissa A. Meinicke, Ian J. Hayes, Cliff B. Jones
https://arxiv.org/abs/2405.05546 https://<…

Data reification in a concurrent rely-guarantee algebra
Specifications of significant systems can be made short and perspicuous by using abstract data types; data reification can provide a clear, stepwise, development history of programs that use more efficient concrete representations. Data reification (or "refinement") techniques for sequential programs are well established. This paper applies these ideas to concurrency, in particular, an algebraic theory supporting rely-guarantee reasoning about concurrency. A concurrent version of the Galler-Fis…

Revisiting Battle of Kyiv with Illia Ponomarenko: https://benborges.xyz/2024/05/08/revisiting-battle-of.html

Outlier-robust Kalman Filtering through Generalised Bayes
Gerardo Duran-Martin, Matias Altamirano, Alexander Y. Shestopaloff, Leandro S\'anchez-Betancourt, Jeremias Knoblauch, Matt Jones, Fran\c{c}ois-Xavier Briol, Kevin Murphy
https://arxiv.org/abs/2405.05646

Outlier-robust Kalman Filtering through Generalised Bayes
We derive a novel, provably robust, and closed-form Bayesian update rule for online filtering in state-space models in the presence of outliers and misspecified measurement models. Our method combines generalised Bayesian inference with filtering methods such as the extended and ensemble Kalman filter. We use the former to show robustness and the latter to ensure computational efficiency in the case of nonlinear models. Our method matches or outperforms other robust filtering methods (such as t…

Insights into Deep Learning Refactoring: Bridging the Gap Between Practices and Expectations
SiQi Wang, Xing Hu, Bei Wang, WenXin Yao, Xin Xia, XingYu Wang
https://arxiv.org/abs/2405.04861

Insights into Deep Learning Refactoring: Bridging the Gap Between Practices and Expectations
With the rapid development of deep learning, the implementation of intricate algorithms and substantial data processing have become standard elements of deep learning projects. As a result, the code has become progressively complex as the software evolves, which is difficult to maintain and understand. Existing studies have investigated the impact of refactoring on software quality within traditional software. However, the insight of code refactoring in the context of deep learning is still unc…

Towards Robust Domain Generation Algorithm Classification
Arthur Drichel, Marc Meyer, Ulrike Meyer
https://arxiv.org/abs/2404.06236 https://

Towards Robust Domain Generation Algorithm Classification
In this work, we conduct a comprehensive study on the robustness of domain generation algorithm (DGA) classifiers. We implement 32 white-box attacks, 19 of which are very effective and induce a false-negative rate (FNR) of $\approx$ 100\% on unhardened classifiers. To defend the classifiers, we evaluate different hardening approaches and propose a novel training scheme that leverages adversarial latent space vectors and discretized adversarial domains to significantly improve robustness. In our…

On Website Technicals (2019-01) - Tech updates: Happy New Ear, cssgip, work storage, AMP srcset, LEDs, details, 400kpx image warning, bad AutoAd, indigestion, multi-hero, OGP revisited. - https://www.earth.org.uk/note-on-site-technicals-20.html

On Website Technicals (2019-01)
Tech updates: Happy New Ear, cssgip, work storage, AMP srcset, LEDs, details, 400kpx image warning, bad AutoAd, indigestion, multi-hero, OGP revisited.

Revisiting Willy Adames’ Quest for Big Bucks
#MLB #Baseball #Fangraphs

Contextual API Completion for Unseen Repositories Using LLMs
Noor Nashid, Taha Shabani, Parsa Alian, Ali Mesbah
https://arxiv.org/abs/2405.04600 https://…

Contextual API Completion for Unseen Repositories Using LLMs
Large language models have made substantial progress in addressing diverse code-related tasks. However, their adoption is hindered by inconsistencies in generating output due to the lack of real-world, domain-specific information, such as for intra-repository API calls for unseen software projects. We introduce a novel technique to mitigate hallucinations by leveraging global and local contextual information within a code repository for API completion tasks. Our approach is tailored to refine c…