Tootfinder

PhreshPhish: A Real-World, High-Quality, Large-Scale Phishing Website Dataset and Benchmark
Thomas Dalton, Hemanth Gowda, Girish Rao, Sachin Pargi, Alireza Hadj Khodabakhshi, Joseph Rombs, Stephan Jou, Manish Marwah
https://arxiv.org/abs/2507.10854

PhreshPhish: A Real-World, High-Quality, Large-Scale Phishing Website Dataset and Benchmark
Phishing remains a pervasive and growing threat, inflicting heavy economic and reputational damage. While machine learning has been effective in real-time detection of phishing attacks, progress is hindered by lack of large, high-quality datasets and benchmarks. In addition to poor-quality due to challenges in data collection, existing datasets suffer from leakage and unrealistic base rates, leading to overly optimistic performance results. In this paper, we introduce PhreshPhish, a large-scale…

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📺 Aquí TeleMadrid decía que cerraba 1 año, que se cumplirš pronto:

Two Variations of Quantum Phase Estimation for Reducing Circuit Error Rates: Application to the Harrow--Hassidim--Lloyd Algorithm
Yonghae Lee, Minjin Choi, Youngho Min, Eunok Bae, Sunghyun Bae
https://arxiv.org/abs/2507.06711

Two Variations of Quantum Phase Estimation for Reducing Circuit Error Rates: Application to the Harrow--Hassidim--Lloyd Algorithm
We introduce two variations of the quantum phase estimation algorithm: quantum shifted phase estimation and quantum punctured phase estimation. The shifted method employs a bit-string left shift to discard the most significant bit and focus on lower-order phase components, and the punctured method removes qubits corresponding to known phase bits, thereby streamlining the circuit. To demonstrate the effectiveness of the two variations, we integrate them into a hybrid quantum-classical implementa…

Active Learning for Forecasting Severity among Patients with Post Acute Sequelae of SARS-CoV-2
Jing Wang, Amar Sra, Jeremy C. Weiss
https://arxiv.org/abs/2506.22444

Active Learning for Forecasting Severity among Patients with Post Acute Sequelae of SARS-CoV-2
The long-term effects of Postacute Sequelae of SARS-CoV-2, known as PASC, pose a significant challenge to healthcare systems worldwide. Accurate identification of progression events, such as hospitalization and reinfection, is essential for effective patient management and resource allocation. However, traditional models trained on structured data struggle to capture the nuanced progression of PASC. In this study, we introduce the first publicly available cohort of 18 PASC patients, with text t…

High-Layer Attention Pruning with Rescaling
Songtao Liu, Peng Liu
https://arxiv.org/abs/2507.01900 https://arxiv.org/pdf/2507.01900…

High-Layer Attention Pruning with Rescaling
Pruning is a highly effective approach for compressing large language models (LLMs), significantly reducing inference latency. However, conventional training-free structured pruning methods often employ a heuristic metric that indiscriminately removes some attention heads across all pruning layers, without considering their positions within the network architecture. In this work, we propose a novel pruning algorithm that strategically prunes attention heads in the model's higher layers. Since t…

Stacking the Deck: Gambling on a Light QCD Axion
Abhishek Banerjee, Manuel A. Buen-Abad, Anson Hook
https://arxiv.org/abs/2507.02049 https://

Stacking the Deck: Gambling on a Light QCD Axion
We consider axions lighter than what their QCD couplings might otherwise suggest. Starting with a $\mathbb{Z}_N$-axion, we introduce a small explicit $\mathbb{Z}_N$ symmetry-breaking coupling between the Standard Model Higgs boson and a reheaton. This small explicit breaking allows us to populate a large portion of the light axion $m_a$-$f_a$ plane, removes the $1/N$ tuning in the $\mathbb{Z}_N$-axion, and explains why only our sector was reheated. Due to finite temperature effects, axions of t…