Results 251 to 260 of about 56,042 (290)

Lightweight deep learning model for gastrointestinal precancerous lesion screening with attention enhancement. [PDF]

Front Med (Lausanne)
Chen S, Cai J, Wu Z, Liu X, Wang Q, Zhou L.   +5 more
europepmc   +1 more source

Standardized patient training for neurological examination: providing precise, technique-oriented feedback. [PDF]

BMC Med Educ
Xu D, Zhou L, Zhang Z, Fan H, Qian M, Huang X.   +5 more
europepmc   +1 more source

On the Hardness of Module Learning with Errors with Short Distributions [PDF]

Journal of Cryptology, 2022
International audienceThe Module Learning With Errors (M-LWE) problem is a core computational assumption of lattice-based cryptography which offers an interesting trade-off between guaranteed security and concrete efficiency. The problem is parameterized
Katharina Boudgoust, Adeline Roux-Langlois, Boudgoust Katharina   +2 more
exaly   +5 more sources

Module Learning with Errors with Truncated Matrices [PDF]

Lecture Notes in Computer Science
International audienceThe Module Learning with Errors (MLWE) problem is one of the most commonly used hardness assumption in lattice-based cryptography. In its standard version, a matrix A is sampled uniformly at random over a quotient ring R q , as well
Katharina Boudgoust
exaly   +6 more sources

Ring/Module Learning with Errors Under Linear Leakage – Hardness and Applications [PDF]

Lecture Notes in Computer Science
This paper studies the hardness of decision Module Learning with Errors (\MLWE) under linear leakage, which has been used as a foundation to derive more efficient lattice-based zero-knowledge proofs in a recent paradigm of Lyubashevsky, Nguyen, and ...
Zhedong Wang, Feng-Hao Liu, Wang Zhedong   +2 more
exaly   +4 more sources

Automatic Parallelism Tuning for Module Learning with Errors Based Post-Quantum Key Exchanges on GPUs [PDF]

2021 IEEE International Symposium on Circuits and Systems (ISCAS), 2021
The module learning with errors (MLWE) problem is one of the most promising candidates for constructing quantum-resistant cryptosystems. In this work, we propose an open-source framework to automatically adjust the level of parallelism for MLWE-based key
Song Bian
exaly   +4 more sources