Tootfinder

Radiation damage study of Belle II silicon strip sensors with 90 MeV electron irradiation
K. Adamczyk (Belle II SVD Collaboration), H. Aihara (Belle II SVD Collaboration), K. Amos (Belle II SVD Collaboration), S. Bacher (Belle II SVD Collaboration), S. Bahinipati (Belle II SVD Collaboration), J. Baudot (Belle II SVD Collaboration), P. K. Behera (Belle II SVD Collaboration), S. Bettarini (Belle II SVD Collaboration), L. Bosisio (Belle II SVD Collaboration), A. Bozek (Belle II SVD Collab…

Radiation damage study of Belle II silicon strip sensors with 90 MeV electron irradiation
The silicon strip sensors of the Belle II silicon vertex detector were irradiated with 90 MeV electron beams up to an equivalent 1-MeV-neutron fluence of $3.0\times 10^{13}~{\rm n}_{\rm eq}/{\rm cm^2}$. We measure changes in sensor properties induced by radiation damage in the semiconductor bulk. Electrons around this energy are a major source of beam-induced background during Belle II operation. We discuss observed changes in full depletion voltage, sensor leakage current, noise, and charge co…

from my link log —
Rendezvous hashing explained.
https://randorithms.com/2020/12/26/rendezvous-hashing.html
saved 2025-09-16 https://

SDGO: Self-Discrimination-Guided Optimization for Consistent Safety in Large Language Models
Peng Ding, Wen Sun, Dailin Li, Wei Zou, Jiaming Wang, Jiajun Chen, Shujian Huang
https://arxiv.org/abs/2508.15648

SDGO: Self-Discrimination-Guided Optimization for Consistent Safety in Large Language Models
Large Language Models (LLMs) excel at various natural language processing tasks but remain vulnerable to jailbreaking attacks that induce harmful content generation. In this paper, we reveal a critical safety inconsistency: LLMs can more effectively identify harmful requests as discriminators than defend against them as generators. This insight inspires us to explore aligning the model's inherent discrimination and generation capabilities. To this end, we propose SDGO (Self-Discrimination-Guide…

Safe Guaranteed Dynamics Exploration with Probabilistic Models
Manish Prajapat, Johannes K\"ohler, Melanie N. Zeilinger, Andreas Krause
https://arxiv.org/abs/2509.16650 htt…

Safe Guaranteed Dynamics Exploration with Probabilistic Models
Ensuring both optimality and safety is critical for the real-world deployment of agents, but becomes particularly challenging when the system dynamics are unknown. To address this problem, we introduce a notion of maximum safe dynamics learning via sufficient exploration in the space of safe policies. We propose a $\textit{pessimistically}$ safe framework that $\textit{optimistically}$ explores informative states and, despite not reaching them due to model uncertainty, ensures continuous online…

SABER: Uncovering Vulnerabilities in Safety Alignment via Cross-Layer Residual Connection
Maithili Joshi, Palash Nandi, Tanmoy Chakraborty
https://arxiv.org/abs/2509.16060 https…

SABER: Uncovering Vulnerabilities in Safety Alignment via Cross-Layer Residual Connection
Large Language Models (LLMs) with safe-alignment training are powerful instruments with robust language comprehension capabilities. These models typically undergo meticulous alignment procedures involving human feedback to ensure the acceptance of safe inputs while rejecting harmful or unsafe ones. However, despite their massive scale and alignment efforts, LLMs remain vulnerable to jailbreak attacks, where malicious users manipulate the model to produce harmful outputs that it was explicitly t…

Explainable Knowledge Distillation for Efficient Medical Image Classification
Aqib Nazir Mir, Danish Raza Rizvi
https://arxiv.org/abs/2508.15251 https://ar…

Explainable Knowledge Distillation for Efficient Medical Image Classification
This study comprehensively explores knowledge distillation frameworks for COVID-19 and lung cancer classification using chest X-ray (CXR) images. We employ high-capacity teacher models, including VGG19 and lightweight Vision Transformers (Visformer-S and AutoFormer-V2-T), to guide the training of a compact, hardware-aware student model derived from the OFA-595 supernet. Our approach leverages hybrid supervision, combining ground-truth labels with teacher models' soft targets to balance accuracy…

That's how our hike at the #mummelsee started ... and how it was when we were up there. - But the walk was quite okay.
We checked the rain radar and decided to go back down rather quickly. We leveraged our rain gear and it did not disappoint us! 🙂
So we didn't see the area atop - the so called

Security guarantees for Ukraine explained: What's on the table and what's realistic?: https://benborges.xyz/2025/08/19/security-guarantees-for-ukraine-explained.html

Morphologies of a sagging elastica with intrinsic sensing and actuation
Vishnu Deo Mishra, S Ganga Prasath
https://arxiv.org/abs/2509.17572 https://arxiv.o…

Morphologies of a sagging elastica with intrinsic sensing and actuation
The morphology of a slender soft-robot can be modified by sensing its shape via sensors and exerting moments via actuators embedded along its body. The actuating moments required to morph these soft-robots to a desired shape are often difficult to compute due to the geometric non-linearity associated with the structure, the errors in modeling the experimental system, and the limitations in sensing and feedback/actuation capabilities. In this article, we explore the effect of a simple feedback s…

White House to fire explosive artillery over major roadway in Southern California, I-5 to be temporarily shut down on Saturday due to life safety risk (Governor of California)
https://www.gov.ca.gov/2025/10/18/white-house-to-fire-explosive-artillery-over-major-roadway-in-southern-california-i-5-to-be-temporarily-shut-down-on-saturday-due-to-life-safety-risk/
http://www.memeorandum.com/251018/p47#a251018p47