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@stf@chaos.social
2025-11-26 17:06:26

by accident i stumbled on this review by the #NSA on Bruce Schneiers "Applied Crypto" book from long ago.

9. BOOK REVIEW: APPLIED CRYPTOGRAPHY [censored] Reviewer Applied Cryptography, for those who don't read the internet news, is a book written by Bruce Schneier last year. According to the jacket, Schneier is a data security expert with a master's degree in computer science. According to his followers, he is a hero who has finally brought together the loose threads of cryptography for the general public to understand. Schneier has gathered academic research, internet gossip, and everything he co…
Issue 1 TALES OF THE KRYPT Page 14 of 16 oc ID: 6823780 Playing loose with the facts is a serious problem with Schneier. For example in discussing a small-exponent attack on RSA, he says "an attack by Michael Wiener will recover e when e is up to one quarter the size of n." Actually, Wiener's attack recovers the secret exponent d when e has less than one quarter as many bits as n, which is a quite different statement. Or: "The quadratic sieve is the fastest known . algorithm for factoring numb…
@arXiv_quantph_bot@mastoxiv.page
2025-10-15 10:21:21

Implementing the Quantum Approximate Optimization Algorithms for QUBO problems Across Quantum Hardware Platforms: Performance Analysis, Challenges, and Strategies
Teemu Pihkakoski, Aravind Plathanam Babu, Pauli Taipale, Petri Liimatta, Matti Silveri
arxiv.org/abs/2510.12336

Implementing the Quantum Approximate Optimization Algorithms for QUBO problems Across Quantum Hardware Platforms: Performance Analysis, Challenges, and Strategies
Quantum computers are expected to offer significant advantages in solving complex optimization problems that are challenging for classical computers. Quadratic Unconstrained Binary Optimization (QUBO) problems represent an important class of problems with relevance in finance and logistics. The Quantum Approximate Optimization Algorithm (QAOA) is a prominent candidate for solving QUBO problems on near-term quantum devices. In this paper, we investigate the performance of both the standard QAOA …

@arXiv_statML_bot@mastoxiv.page
2025-10-08 09:29:19

On the Theory of Continual Learning with Gradient Descent for Neural Networks
Hossein Taheri, Avishek Ghosh, Arya Mazumdar
arxiv.org/abs/2510.05573

On the Theory of Continual Learning with Gradient Descent for Neural Networks
Continual learning, the ability of a model to adapt to an ongoing sequence of tasks without forgetting the earlier ones, is a central goal of artificial intelligence. To shed light on its underlying mechanisms, we analyze the limitations of continual learning in a tractable yet representative setting. In particular, we study one-hidden-layer quadratic neural networks trained by gradient descent on an XOR cluster dataset with Gaussian noise, where different tasks correspond to different clusters…