Tootfinder

This is sickening…
It’s ⭐️Jordan Wood,
Susan Collins’ only Democratic opponent.
When Susan Collins confirmed RFK Jr. and Tulsi Gabbard
after expressing her *deepest concerns*
she left Mainers stunned.
But now, I’m learning that days before the votes:
…Susan Collins jetted down to Mar-a-Lago
…she DINED with Donald Trump 
…the MAGA establishment vowed 💥over $400 MILLION would be spent on her race
I won’t mince words:
👉 Susan Col…

Chip in now to elect Jordan Wood >>
Help Jordan defeat Susan Collins

Private Memorization Editing: Turning Memorization into a Defense to Strengthen Data Privacy in Large Language Models
Elena Sofia Ruzzetti, Giancarlo A. Xompero, Davide Venditti, Fabio Massimo Zanzotto
https://arxiv.org/abs/2506.10024

Private Memorization Editing: Turning Memorization into a Defense to Strengthen Data Privacy in Large Language Models
Large Language Models (LLMs) memorize, and thus, among huge amounts of uncontrolled data, may memorize Personally Identifiable Information (PII), which should not be stored and, consequently, not leaked. In this paper, we introduce Private Memorization Editing (PME), an approach for preventing private data leakage that turns an apparent limitation, that is, the LLMs' memorization ability, into a powerful privacy defense strategy. While attacks against LLMs have been performed exploiting previou…

Spin Correlations in Recirculating Multipass Alkali Cells for Advancing Quantum Magnetometry
Qian Ling Kee, Lingyi Zhao, Ruvi Lecamwasam, Biveen Shajilal, Xinan Liang, Joel K Jose, Yao Chen, Ping Koy Lam, Tao Wang
https://arxiv.org/abs/2506.14160

Spin Correlations in Recirculating Multipass Alkali Cells for Advancing Quantum Magnetometry
Multipass cells are critical components in a variety of quantum technologies, including atomic magnetometers and optical quantum memories, due to their ability to achieve long optical paths within compact volumes. In optical magnetometry, increasing the optical depth through multipass geometries enhances sensitivity by reducing photon shot noise and enabling quantum nondemolition (QND) detection. In this work, we present a novel recirculating multipass alkali cell that improves the active-to-ce…

Safe-Child-LLM: A Developmental Benchmark for Evaluating LLM Safety in Child-AI Interactions
Junfeng Jiao, Saleh Afroogh, Kevin Chen, Abhejay Murali, David Atkinson, Amit Dhurandhar
https://arxiv.org/abs/2506.13510

Safe-Child-LLM: A Developmental Benchmark for Evaluating LLM Safety in Child-AI Interactions
As Large Language Models (LLMs) increasingly power applications used by children and adolescents, ensuring safe and age-appropriate interactions has become an urgent ethical imperative. Despite progress in AI safety, current evaluations predominantly focus on adults, neglecting the unique vulnerabilities of minors engaging with generative AI. We introduce Safe-Child-LLM, a comprehensive benchmark and dataset for systematically assessing LLM safety across two developmental stages: children (7-12…

Preprocessing Methods for Memristive Reservoir Computing for Image Recognition
Rishona Daniels, Duna Wattad, Ronny Ronen, David Saad, Shahar Kvatinsky
https://arxiv.org/abs/2506.05588

Preprocessing Methods for Memristive Reservoir Computing for Image Recognition
Reservoir computing (RC) has attracted attention as an efficient recurrent neural network architecture due to its simplified training, requiring only its last perceptron readout layer to be trained. When implemented with memristors, RC systems benefit from their dynamic properties, which make them ideal for reservoir construction. However, achieving high performance in memristor-based RC remains challenging, as it critically depends on the input preprocessing method and reservoir size. Despite …

This https://arxiv.org/abs/2209.09443 has been replaced.
link: https://scholar.google.com/scholar?q=a

Cryogenic in-memory computing using magnetic topological insulators
Machine learning algorithms have been proven effective for essential quantum computation tasks such as quantum error correction and quantum control. Efficient hardware implementation of these algorithms at cryogenic temperatures is essential. Here, we utilize magnetic topological insulators as memristors (termed magnetic topological memristors) and introduce a cryogenic in-memory computing scheme based on the coexistence of the chiral edge state and the topological surface state. The memristive…