Results 91 to 100 of about 8,170 (257)
On new multivariate cryptosystems based on hidden Eulerian equations
Доповiдi Нацiональної академiї наук УкраїниWe propose new multivariate cryptosystems over an n-dimensional free module over the arithmetical ring Zm based on the idea of hidden discrete logarithm for Zm∗. These cryptosystems are based on the hidden Eulerian equations.
V.A. Ustimenko
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Exact Discrete Stochastic Simulation With Deep‐Learning‐Scale Gradient Optimization
Advanced Science, EarlyView.A 203,796‐parameter gene regulatory network classifies handwritten digits with 98.4% accuracy using exact stochastic dynamics. The framework decouples forward simulation from backward differentiation, making continuous‐time Markov chain models compatible with deep‐learning optimization.
Jose M. G. Vilar, Leonor Saiz
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Finding discrete logarithms with a set orbit distinguisher
Journal of Mathematical Cryptology, 2012 We consider finding discrete logarithms in a group of prime order p when the help of an algorithm D that distinguishes certain subsets of from each other is available.
Gallant Robert P.
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Lower Bounds for Discrete Logarithms and Related Problems [PDF]
, 1997 This paper considers the computational complexity of the discrete logarithm and related problems in the context of "generic algorithms"--that is, algorithms which do not exploit any special properties of the encodings of group elements, other than the property that each group element is encoded as a unique binary string.
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A Generative Neuro‐Symbolic AI for Protein Sequence Design
Advanced Science, EarlyView.We introduce EffieDes, a neuro‐symbolic framework coupling deep learning‐based fitness landscape parameterization with exact automated reasoning. Unlike greedy sampling, EffieDes identifies sequences that globally optimize fitness while satisfying intricate design constraints.
Marianne Defresne +12 more
wiley +1 more source
Generic Hardness of the Multiple Discrete Logarithm Problem [PDF]
, 2015 We study generic hardness of the multiple discrete logarithm problem, where the solver has to solve \(n\) instances of the discrete logarithm problem simultaneously. There are known generic algorithms which perform \(O(\sqrt{np})\) group operations, where \(p\) is the group order, but no generic lower bound was known other than the trivial bound.
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StackingNet: Collective Inference Across Independent AI Foundation Models
Advanced Science, EarlyView.ABSTRACT Artificial intelligence (AI) built on large foundation models has transformed language understanding, computer vision, and reasoning, yet these systems remain isolated and cannot readily share their capabilities. Coordinating the complementary strengths of independently developed, black‐box foundation models is essential for trustworthy ...
Siyang Li +4 more
wiley +1 more source
Quantum Complexity for Discrete Logarithms and Related Problems
This paper studies the quantum computational complexity of the discrete logarithm (DL) and related group-theoretic problems in the context of generic algorithms -- that is, algorithms that do not exploit any properties of the group encoding. We establish a generic model of quantum computation for group-theoretic problems, which we call the quantum ...
Minki Hhan, Takashi Yamakawa, Aaram Yun
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Advanced Science, EarlyView.Using grilled lamb skewers as a model system, this work builds a multiscale coupling framework from oral processing to retronasal aroma perception, reveals dual‐kinetic release patterns and Electroencephalogram‐characterized central encoding features, and proposes an interpretable physics‐guided deep learning model validated by multiphysics simulation,
Che Shen +12 more
wiley +1 more source
Automation and Active Learning for the Multi‐Objective Optimization of Antibody Formulations
Advanced Science, EarlyView.Successful antibody formulation necessitates balancing factors such as thermal stability, colloidal stability, and viscosity across a vast excipient design space. This work integrates robotic liquid handling, high‐throughput biophysical characterization, and multi‐objective Bayesian optimization in an iterative closed‐loop Design‐Build‐Test‐Learn cycle.
D. Christopher Radford +3 more
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