Tootfinder

EMBER2024 -- A Benchmark Dataset for Holistic Evaluation of Malware Classifiers
Robert J. Joyce, Gideon Miller, Phil Roth, Richard Zak, Elliott Zaresky-Williams, Hyrum Anderson, Edward Raff, James Holt
https://arxiv.org/abs/2506.05074

EMBER2024 -- A Benchmark Dataset for Holistic Evaluation of Malware Classifiers
A lack of accessible data has historically restricted malware analysis research, and practitioners have relied heavily on datasets provided by industry sources to advance. Existing public datasets are limited by narrow scope - most include files targeting a single platform, have labels supporting just one type of malware classification task, and make no effort to capture the evasive files that make malware detection difficult in practice. We present EMBER2024, a new dataset that enables holisti…

🇺🇦 #NowPlaying on #BBC6Music's #6MusicsIndieForever
Jamie xx & Honey Dijon:
🎵 Baddy On The Floor
#Jamiexx #HoneyDijon
https://lovewillsayno.bandcamp.com/track/jamie-xx-feat-honey-dijon-baddy-on-the-floor-love-will-say-no-edit
https://open.spotify.com/track/3CqaTHbiU2nBy3Ar9RnDHN

Quantifying Query Fairness Under Unawareness
Thomas Jaenich, Alejandro Moreo, Alessandro Fabris, Graham McDonald, Andrea Esuli, Iadh Ounis, Fabrizio Sebastiani
https://arxiv.org/abs/2506.04140

Quantifying Query Fairness Under Unawareness
Traditional ranking algorithms are designed to retrieve the most relevant items for a user's query, but they often inherit biases from data that can unfairly disadvantage vulnerable groups. Fairness in information access systems (IAS) is typically assessed by comparing the distribution of groups in a ranking to a target distribution, such as the overall group distribution in the dataset. These fairness metrics depend on knowing the true group labels for each item. However, when groups are defin…

Consecutive collision orbits in the restricted three-body problem above the first critical energy value
Jungsoo Kang, Kevin Ruck
https://arxiv.org/abs/2506.01735

Consecutive collision orbits in the restricted three-body problem above the first critical energy value
In this paper, we study the planar circular restricted three-body problem for energy levels slightly above the first critical value. We first observe that the energy hypersurfaces in the Birkhoff regularization corresponding to these energy levels are of contact type. Then, using a version of Rabinowitz Floer homology and an analytic continuation argument, we establish the existence of a symmetric consecutive collision orbit and, furthermore, prove the existence of infinitely many such orbits f…

A New Deep-learning-Based Approach For mRNA Optimization: High Fidelity, Computation Efficiency, and Multiple Optimization Factors
Zheng Gong, Ziyi Jiang, Weihao Gao, Deng Zhuo, Lan Ma
https://arxiv.org/abs/2505.23862

A New Deep-learning-Based Approach For mRNA Optimization: High Fidelity, Computation Efficiency, and Multiple Optimization Factors
The mRNA optimization is critical for therapeutic and biotechnological applications, since sequence features directly govern protein expression levels and efficacy. However, current methods face significant challenges in simultaneously achieving three key objectives: (1) fidelity (preventing unintended amino acid changes), (2) computational efficiency (speed and scalability), and (3) the scope of optimization variables considered (multi-objective capability). Furthermore, existing methods often…

Topological Machine Learning for Protein-Nucleic Acid Binding Affinity Changes Upon Mutation
Xiang Liu, Junjie Wee, Guo-Wei Wei
https://arxiv.org/abs/2505.22786

Topological Machine Learning for Protein-Nucleic Acid Binding Affinity Changes Upon Mutation
Understanding how protein mutations affect protein-nucleic acid binding is critical for unraveling disease mechanisms and advancing therapies. Current experimental approaches are laborious, and computational methods remain limited in accuracy. To address this challenge, we propose a novel topological machine learning model (TopoML) combining persistent Laplacian (from topological data analysis) with multi-perspective features: physicochemical properties, topological structures, and protein Tran…