Results 91 to 100 of about 250,329 (273)

Cascaded Fast and Slow Models for Efficient Semantic Code Search

, 2021
12 ...
Gotmare, Akhilesh Deepak, Li, Junnan, Joty, Shafiq, Hoi, Steven C. H.   +3 more
openaire   +2 more sources

Consumer Acceptance of New Sustainable Food Technologies: Upcycling Technology, Biostimulants, and Artificial Intelligence

Agribusiness, EarlyView.
ABSTRACT Food systems have a significant impact on environmental sustainability, underscoring the need for innovative technologies to support more sustainable agricultural methods. However, the adoption of these technologies hinges on consumer acceptance, making the analysis of consumer perceptions essential.
Greta Castellini, Guendalina Graffigna
wiley   +1 more source

Deep Neighborhood-Aware Hashing via Class-Center Guiding for Multi-Label Image Retrieval

IEEE Access
Learning to hash effectively addresses the challenges posed by massive data due to its low storage cost and fast search speed. Under multi-label scenarios, pair-wise hashing is typically determined by roughly counting the number of shared labels. However,
Chunping Dong, Jiaqiang Zhao, Meiling Ge, Wenfeng Zhang, Congcong Zhu, Xin Wang   +5 more
doaj   +1 more source

AI in chemical engineering: From promise to practice

AIChE Journal, EarlyView.
Abstract Artificial intelligence (AI) in chemical engineering has moved from promise to practice: physics‐aware (gray‐box) models are gaining traction, reinforcement learning complements model predictive control (MPC), and generative AI powers documentation, digitization, and safety workflows.
Jia Wei Chew, Ronnie Andersson, Thomas Bierweiler, Patrik Ryttestal, Torsten Wik   +4 more
wiley   +1 more source

Learning Deep Semantic Model for Code Search using CodeSearchNet Corpus

, 2022
Semantic code search is the task of retrieving relevant code snippet given a natural language query. Different from typical information retrieval tasks, code search requires to bridge the semantic gap between the programming language and natural language, for better describing intrinsic concepts and semantics.
Wu, Chen, Yan, Ming
openaire   +2 more sources

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

Semantic Code Search in Software Repositories using Neural Machine Translation

, 2022
AbstractNowadays, software development is accelerated through the reuse of code snippets found online in question-answering platforms and software repositories. In order to be efficient, this process requires forming an appropriate query and identifying the most suitable code snippet, which can sometimes be challenging and particularly time-consuming ...
Evangelos Papathomas, Themistoklis Diamantopoulos, Andreas Symeonidis   +2 more
openaire   +1 more source

ChatCFD: A Large Language Model‐Driven Agent for End‐to‐End Computational Fluid Dynamics Automation with Structured Knowledge and Reasoning

Advanced Intelligent Discovery, EarlyView.
Chat computational fluid dynamics (CFD) introduces an large language model (LLM)‐driven agent that automates OpenFOAM simulations end‐to‐end, attaining 82.1% execution success and 68.12% physical fidelity across 315 benchmarks—far surpassing prior systems.
E Fan, Kang Hu, Zhuowen Wu, Jiangyang Ge, Jiawei Miao, Yuzhi Zhang, He Sun, Weizong Wang, Tianhan Zhang   +8 more
wiley   +1 more source

LLM‐Based Scientific Assistants for Knowledge Extraction: Which Design Choices Matter?

Advanced Intelligent Discovery, EarlyView.
A comprehensive framework for optimizing Large Language Models in domain‐specific applications is introduced. The LLM Playground integrates Prompt Engineering, knowledge augmentation, and advanced reasoning strategies to enable systematic comparison of architectures and base models.
David Exler, Manuel Raimann, Marc Münker, Maxim Rosin, Joaquin E. Urrutia Gómez, Christof M. Niemeyer, Markus Reischl, Luca Rettenberger   +7 more
wiley   +1 more source

Cell Segmentation Beyond 2D—A Review of the State‐of‐the‐Art

Advanced Intelligent Discovery, EarlyView.
Cell segmentation underpins many biological image analysis tasks, yet most deep learning methods remain limited to 2D despite the inherently 3D nature of cellular processes. This review surveys segmentation approaches beyond 2D, comparing 2.5D and fully 3D methods, analyzing 31 models and 32 volumetric datasets, and introducing a unified reference ...
Fabian Schmeisser, Haseeb Muhammad, Junaid Younas, Gillian Lovell, Bianca Migliori, Andreas Dengel, Sheraz Ahmed   +6 more
wiley   +1 more source