Results 91 to 100 of about 699 (280)

Distributed Shared Memory and Compiler-Induced Scalable Locality for Scalable Cluster Performance

, 2012
This was a two-page overview of my NSF-funded project Supercomputing on a Cluster of Workstations via Scalable Locality and Scalable Parallelism , presented as a poster session to the research community that focuses on Cluster Computing ; most of my ...
Wonnacott, David
core  

CrossMatAgent: AI‐Assisted Design of Manufacturable Metamaterial Patterns via Multi‐Agent Generative Framework

Advanced Intelligent Discovery, EarlyView.
CrossMatAgent is a multi‐agent framework that combines large language models and diffusion‐based generative AI to automate metamaterial design. By coordinating task‐specific agents—such as describer, architect, and builder—it transforms user‐provided image prompts into high‐fidelity, printable lattice patterns.
Jie Tian, Martin Taylor Sobczak, Dhanush Patil, Jixin Hou, Lin Pang, Arunachalam Ramanathan, Libin Yang, Xianyan Chen, Yuval Golan, Xiaoming Zhai, Hongyue Sun, Kenan Song, Xianqiao Wang   +12 more
wiley   +1 more source

Supervised data extraction from transformer representation of Lambda-terms

Радіоелектронні і комп'ютерні системи
The object of this research is the process of compiler optimization, as it is essential in modern software development, particularly in functional programming languages like Lambda Calculus.
Oleksandr Deineha
doaj   +1 more source

Data‐Guided Photocatalysis: Supervised Machine Learning in Water Splitting and CO2 Conversion

Advanced Intelligent Discovery, EarlyView.
This review highlights recent advances in supervised machine learning (ML) for photocatalysis, emphasizing methods to optimize photocatalyst properties and design materials for solar‐driven water splitting and CO2 reduction. Key applications, challenges, and future directions are discussed, offering a practical framework for integrating ML into the ...
Paul Rossener Regonia, Christian Mark Pelicano   +1 more
wiley   +1 more source

Toward Predictable Nanomedicine: Current Forecasting Frameworks for Nanoparticle–Biology Interactions

Advanced Intelligent Discovery, EarlyView.
Predictive models successfully screen nanoparticles for toxicity and cellular uptake. Yet, complex biological dynamics and sparse, nonstandardized data limit their accuracy. The field urgently needs integrated artificial intelligence/machine learning, systems biology, and open‐access data protocols to bridge the gap between materials science and safe ...
Mariya L. Ivanova, Michael Nicholls, Nicola Russo, Gueorgui Mihaylov, Konstantin Nikolic   +4 more
wiley   +1 more source

ToolPhet: Inference of Compiler Provenance From Stripped Binaries With Emerging Compilation Toolchains

IEEE Access
Identifying compiler toolchain provenance serves as a basis for both benign and malicious binary analyses. A wealth of prior studies mostly focuses on the inference of a popular compiler toolchain for C and C++ languages from stripped ...
Hohyeon Jang, Nozima Murodova, Hyungjoon Koo   +2 more
doaj   +1 more source

A Multimodal Intelligent System for Human Digital Twin Simulation with Continuous Kinematic Data Tracking, Biometric Prognosis, and Cognitive State Feedback in Industrial Environments

Advanced Intelligent Discovery, EarlyView.
This article implements a unified human digital twin framework that integrates cutting edge actuation, sensing, simulation, and bidirectional feedback capability. The approach includes integrating multimodal sensing, AI, and biomechanical simulation into one compact system.
Tajbeed Ahmed Chowdhury, Sebastian Gratz‐Kelly, Eric Wagner, Paul Motzki, Martina Lehser   +4 more
wiley   +1 more source

Error Classification and Static Detection Methods in Tri-Programming Models: MPI, OpenMP, and CUDA

Computers
The growing adoption of supercomputers across various scientific disciplines, particularly by researchers without a background in computer science, has intensified the demand for parallel applications.
Saeed Musaad Altalhi, Fathy Elbouraey Eassa, Sanaa Abdullah Sharaf, Ahmed Mohammed Alghamdi, Khalid Ali Almarhabi, Rana Ahmad Bilal Khalid   +5 more
doaj   +1 more source

Simplifying Design of Wireless Sensor Networks with Programming Languages, Compilers, and Synthesis

, 2011
I am heartily thankful to my advisor, Robert Dick, for his guidance and support through-out my Ph.D. He sparked my interests in wireless sensor networks and provided valuable advice in the course of this dissertation.
Lan Bai
core  

Harnessing Machine Learning to Understand and Design Disordered Solids

Advanced Intelligent Discovery, EarlyView.
This review maps the dynamic evolution of machine learning in disordered solids, from structural representations to generative modeling. It explores how deep learning and model explainability transform property prediction into profound physical insight.
Muchen Wang, Yue Fan
wiley   +1 more source