Results 111 to 120 of about 12,964 (262)

Hardware acceleration of number theoretic transform for zk‐SNARK

Engineering Reports, EarlyView., 2023
An FPGA‐based hardware accelerator with a multi‐level pipeline is designed to support the large‐bitwidth and large‐scale NTT tasks in zk‐SNARK. It can be flexibly scaled to different scales of FPGAs and has been equipped in the heterogeneous acceleration system with the help of HLS and OpenCL.
Haixu Zhao, Dong Ding, Feng Wang, Pengcheng Hua, Ning Wang, Qin Wu, Zhilei Chai   +6 more
wiley   +1 more source

so(3) ⊂ su(3) revisited

Acta Polytechnica
This paper reproduces the result of Elliot, namely that the irreducible finite dimensional representation of the Lie algebra su(3) of highest weight (m, n) is decomposed according to the embedding so(3) ⊂ su(3). First, a realisation (a representation in
Čestmír Burdík, Severin Pošta, Erik Rapp   +2 more
doaj   +1 more source

Factorization Machine‐Based Active Learning for Functional Materials Design with Optimal Initial Data

Advanced Intelligent Discovery, EarlyView.
This work investigates the optimal initial data size for surrogate‐based active learning in functional material optimization. Using factorization machine (FM)‐based quadratic unconstrained binary optimization (QUBO) surrogates and averaged piecewise linear regression, we show that adequate initial data accelerates convergence, enhances efficiency, and ...
Seongmin Kim, In‐Saeng Suh
wiley   +1 more source

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

A granularity data method for power frequency electric and electromagnetic fields forecasting based on T–S fuzzy model

Complex & Intelligent Systems
The impact of electromagnetic radiation generated by signal transmission base stations and power stations to meet the needs of communication equipment and energy consumption on the environment has caused people concerns.
Peng Nie, Qiang Yu, Zhenkun Li, Xiguo Yuan   +3 more
doaj   +1 more source

Identification of soybean planting gaps using machine learning

Smart Agricultural Technology
The identification of planting gaps is essential for optimizing crop management in precision agriculture. Traditional methods, such as manual scouting, are limited in scale and precision.
Flávia Luize Pereira de Souza, Maurício Acconcia Dias, Tri Deri Setiyono, Sérgio Campos, Luciano Shozo Shiratsuchi, Haiying Tao   +5 more
doaj   +1 more source

Comparison of DeePMD, MTP, GAP, ACE and MACE Machine‐Learned Potentials for Radiation‐Damage Simulations: A User Perspective

Advanced Intelligent Discovery, EarlyView.
The authors evaluated six machine‐learned interatomic potentials for simulating threshold displacement energies and tritium diffusion in LiAlO2 essential for tritium production. Trained on the same density functional theory data and benchmarked against traditional models for accuracy, stability, displacement energies, and cost, Moment Tensor Potential ...
Ankit Roy, Ram Devanathan, Sarah Allec, Giridhar Nandipati, Andrew M. Casella, David J. Senor, Duane D. Johnson, Ganesh Balasubramanian, Ayoub Soulami   +8 more
wiley   +1 more source

Generic Antiholomorphic Polynomial Vector Fields

24 pages, 13 ...
Godin, Jonathan, Perazzelli, Jérémy
openaire   +2 more sources

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

Interpretability and Representability of Commutative Algebra, Algebraic Topology, and Topological Spectral Theory for Real‐World Data

Advanced Intelligent Discovery, EarlyView.
This article investigates how persistent homology, persistent Laplacians, and persistent commutative algebra reveal complementary geometric, topological, and algebraic invariants or signatures of real‐world data. By analyzing shapes, synthetic complexes, fullerenes, and biomolecules, the article shows how these mathematical frameworks enhance ...
Yiming Ren, Guo‐Wei Wei
wiley   +1 more source