Results 121 to 130 of about 893 (280)

Large Language Model in Materials Science: Roles, Challenges, and Strategic Outlook

Advanced Intelligent Discovery, EarlyView.
Large language models (LLMs) are reshaping materials science. Acting as Oracle, Surrogate, Quant, and Arbiter, they now extract knowledge, predict properties, gauge risk, and steer decisions within a traceable loop. Overcoming data heterogeneity, hallucinations, and poor interpretability demands domain‐adapted models, cross‐modal data standards, and ...
Jinglan Zhang, Xinyi Chen, Xu Ye, Yulin Yang, Bin Ai   +4 more
wiley   +1 more source

Forward Private Searchable Symmetric Encryption with Optimized I/O Efficiency [PDF]

Recently, several practical attacks raised serious concerns over the security of searchable encryption. The attacks have brought emphasis on forward privacy, which is the key concept behind solutions to the adaptive leakage-exploiting attacks, and will ...
Yuan D, Zhao M, Xu Q, Song X, Dong C
core  

A Physics Constrained Machine Learning Pipeline for Young's Modulus Prediction in Multimaterial Hyperelastic Cylinders Guided by Contact Mechanics

Advanced Intelligent Discovery, EarlyView.
A physics‐guided machine learning framework estimates Young's modulus in multilayered multimaterial hyperelastic cylinders using contact mechanics. A semiempirical stiffness law is embedded into a custom neural network, ensuring physically consistent predictions. Validation against experimental and numerical data on C.
Christoforos Rekatsinas, Panagiotis Krokidas, Vasileios Vavourakis, Clara Essmann, George Giannakopoulos   +4 more
wiley   +1 more source

Quantifying Security in Volume-Hiding Searchable Symmetric Encryption Schemes With a Novel Scoring Metric

IEEE Access
Size pattern leakage remains a critical issue in oblivious RAM (ORAM)-based Searchable Symmetric Encryption (SSE) schemes. Despite efforts to define security notions against size pattern leakage, existing studies either overly restrict analysis by ...
Kangmo Ahn, Chaewon Kwak, Dongyoung Koo, Changhee Hahn, Junbeom Hur   +4 more
doaj   +1 more source

FIRE‐GNN: Force‐Informed, Relaxed Equivariance Graph Neural Network for Rapid and Accurate Prediction of Surface Properties

Advanced Intelligent Discovery, EarlyView.
This study introduces FIRE‐GNN, a force‐informed, relaxed equivariant graph neural network for predicting surface work functions and cleavage energies from slab structures. By incorporating surface‐normal symmetry breaking and machine learning interatomic potential‐derived force information, the approach achieves state‐of‐the‐art accuracy and enables ...
Circe Hsu, Claire Schlesinger, Karan Mudaliar, Jordan Leung, Robin Walters, Peter Schindler   +5 more
wiley   +1 more source

SWiSSSE: System-Wide Security for Searchable Symmetric Encryption

Proceedings on Privacy Enhancing Technologies
This paper initiates a new direction in the design and analysis of searchable symmetric encryption (SSE) schemes. We provide the first comprehensive security model and definition for SSE that takes into account leakage from the entirety of the SSE system, including not only from access to encrypted indices but also from access to the encrypted database
Zichen Gui, Kenneth G. Paterson, Sikhar Patranabis, Bogdan Warinschi   +3 more
openaire   +3 more sources

A Unifying Approach to Self‐Organizing Systems Interacting via Conservation Laws

Advanced Intelligent Discovery, EarlyView.
The article develops a unified way to model and analyze self‐organizing systems whose interactions are constrained by conservation laws. It represents physical/biological/engineered networks as graphs and builds projection operators (from incidence/cycle structure) that enforce those constraints and decompose network variables into constrained versus ...
F. Barrows, G. Zhang, S. Anand, Z. Chen, J. Lin, A. Desai, S. Martiniani, F. Caravelli   +7 more
wiley   +1 more source

Gaussian Process Regression–Neural Network Hybrid with Optimized Redundant Coordinates: A New Simple Yet Potent Tool for Scientist's Machine Learning Toolbox

Advanced Intelligent Discovery, EarlyView.
A machine learning method, opt‐GPRNN, is presented that combines the advantages of neural networks and kernel regressions. It is based on additive GPR in optimized redundant coordinates and allows building a representation of the target with a small number of terms while avoiding overfitting when the number of terms is larger than optimal.
Sergei Manzhos, Manabu Ihara
wiley   +1 more source

Multi-Client Symmetric Searchable Encryption with Forward Privacy [PDF]

, 2019
Symmetric Searchable encryption (SSE) is an encryption technique that allows users to search directly on their outsourced encrypted data, in a way that the privacy of both the files and the search queries is preserved. Naturally, with every search query,
Antonis Michalas, Alexandros Bakas
core  

No-Dictionary Searchable Symmetric Encryption

IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences, 2019
Wakaha Ogata, Kaoru Kurosawa
openaire   +2 more sources