Results 41 to 50 of about 217,920 (290)

Electronic Jury Voting Protocols

Theoretical Computer Science, 2002
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Kiwi, M., Hevia, A.
openaire   +3 more sources

Compliant Pneumatic Feet with Real‐Time Stiffness Adaptation for Humanoid Locomotion

Advanced Robotics Research, EarlyView.
A compliant pneumatic foot with real‐time variable stiffness enables humanoid robots to adapt to changing terrains. Using onboard vision and pressure control, the foot modulates stiffness within each gait cycle, reducing impact forces and improving balance. The design, cast in soft silicone with embedded air chambers and Kevlar wrapping, offers durable,
Irene Frizza, Hiroshi Kaminaga, Philippe Fraisse, Gentiane Venture   +3 more
wiley   +1 more source

Prerendered User Interfaces for Higher-Assurance Electronic Voting [PDF]

, 2006
We propose an electronic voting machine architecture in which the voting user interface is prerendered and published before election day. The prerendered user interface is a verifiable artifact—an electronic sample ballot—enabling public participation in
Bellovin, Steven Michael, Hearst, Marti, Wagner, David A., Yee, Ka-Ping   +3 more
core   +2 more sources

Reference‐Guided Chromosome‐by‐Chromosome de novo Assembly at Scale Using Low‐Coverage High‐Fidelity Long‐Reads with HiFiCCL

Advanced Science, EarlyView.
HiFiCCL, as the first assembly framework specifically designed for low‐coverage high‐fidelity reads, improves the assembly quality of existing assemblers and also enhances downstream applications such as large structural variant (SV) detection (>10 000 bp), synteny analysis, pangenome graph construction, and graph‐based individual‐specific germline SVs
Zhongjun Jiang, Weihua Pan, Runtian Gao, Heng Hu, Wentao Gao, Murong Zhou, Yu‐Hang Yin, Zhipeng Qian, Shuilin Jin, Guohua Wang   +9 more
wiley   +1 more source

eVoting is a good idea, but it won’t happen any time soon [PDF]

, 2013
The 2010 UK General Election vividly illustrated the plight of Britain’s electoral infrastructure. But despite the clear need for modernisation, there is an overwhelming suspicion of the reliability and integrity of electronic voting system.
Williamson, Andy
core  

His‐MMDM: Multi‐Domain and Multi‐Omics Translation of Histopathological Images with Diffusion Models

Advanced Science, EarlyView.
His‐MMDM is a diffusion model‐based framework for scalable multi‐domain and multi‐omics translation of histopathological images, enabling tasks from virtual staining, cross‐tumor knowledge transfer, and omics‐guided image editing. ABSTRACT Generative AI (GenAI) has advanced computational pathology through various image translation models.
Zhongxiao Li, Tianqi Su, Bin Zhang, Wenkai Han, Sibin Zhang, Guiyin Sun, Yuwei Cong, Xin Chen, Jiping Qi, Yujie Wang, Shiguang Zhao, Hongxue Meng, Peng Liang, Xin Gao   +13 more
wiley   +1 more source

Encapsulated Non‐Exchangeable Na+ Ions Determining the Upper Limit of Al Inclusion in FAU—A Multiscale Simulation

Angewandte Chemie, EarlyView.
DFT computation, AIMD simulations, machine learning, statistical analysis, and experimental characterization reveal that the upper limit of Al inclusion in FAU zeolite synthesized under hydrothermal conditions is determined by the encapsulated non‐exchangeable Na+ ions.
Qi Dong, Tao Zhang, Chuanhao Zhang, Tong Zhang, Yanze Du, Jinghong Ma, Ruifeng Li, Bo Qin, Haijun Jiao   +8 more
wiley   +2 more sources

From Distrust to Legitimization: The Difficult Path of Digital Electoral Technologies, an Evidence from Russia

RUDN Journal of Political Science, 2022
There are numerous issues surrounding digital technologies in elections: from ensuring the secrecy of voting to controlling the process of vote counting.
Nikolay A. Baranov
doaj   +1 more source

Back to Paper: A Case Study [PDF]

, 2008
Documents the developments in California, Colorado, Florida, New Mexico, and Ohio, where electronic voting machines were introduced after the 2000 election but are now being replaced by paper ballots.

core  

What to Make and How to Make It: Combining Machine Learning and Statistical Learning to Design New Materials

Advanced Intelligent Discovery, EarlyView.
Combining machine learning and probabilistic statistical learning is a powerful way to discover and design new materials. A variety of machine learning approaches can be used to identify promising candidates for target applications, and causal inference can help identify potential ways to make them a reality.
Jonathan Y. C. Ting, Amanda S. Barnard
wiley   +1 more source