Results 201 to 210 of about 3,257,896 (285)

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

A Self-Supervised Approach for Hainan Gibbon Call Separation in Passive Acoustic Monitoring. [PDF]

Ecol Evol
Zi J, Xu N, Dong Y, Xia Y, Wang W, Chen F.   +5 more
europepmc   +1 more source

Composition‐Aware Cross‐Sectional Integration for Spatial Transcriptomics

Advanced Intelligent Discovery, EarlyView.
Multi‐section spatial transcriptomics demands coherent cell‐type deconvolution, domain detection, and batch correction, yet existing pipelines treat these tasks separately. FUSION unifies them within a composition‐aware latent framework, modeling reads as cell‐type–specific topics and clustering in embedding space.
Qishi Dong, Zijian Huang, Xuanwu Wang, Zhenghui Feng, Wei Liu, Bingding Huang   +5 more
wiley   +1 more source

First insights into oceanic manta ray movements in Japan using satellite telemetry. [PDF]

J Fish Biol
Ozaki R, Kamotani Y, Kamotani M, Chin A, Ravasi T, Erdmann MV.   +5 more
europepmc   +1 more source

Ecoefficiency Analysis and Regression in Data Conversion for Spiking Neural Network Training

Advanced Intelligent Systems, EarlyView.
The environmental footprint of spiking neural networks is quantified during dataset encoding and training for autonomous driving regression across three benchmarks. Temporal depth emerges as the dominant driver of energy consumption and CO2 emissions, while the accuracy–energy trade‐off proves dataset‐dependent. On conventional hardware, spiking models
Fernando S. Martínez, Raúl Parada, Jordi Casas‐Roma, Laia Subirats   +3 more
wiley   +1 more source

Comments from the Cell Genomics board. [PDF]

Cell Genom
Teichmann S, Shapiro B, Hanchard NA, Rehm H.   +3 more
europepmc   +1 more source

Designing Wire Mazes for Replicating Natural Echoes to Study Bat Biosonar Function

Advanced Intelligent Systems, EarlyView.
A validated framework combining efficient physical modeling (multiple scattering model) and deep learning is presented to guide wire‐maze design for bat biosonar studies. This approach rapidly generates large datasets to test acoustic distinguishability among wire arrangements.
Chunlin Jia, Gengkai Hu, Boo Cheong Khoo, Rolf Müller   +3 more
wiley   +1 more source