Results 51 to 60 of about 3,537 (211)

Finding the Words: How Does the Aging Brain Process Language? A Focused Review of Brain Connectivity and Compensatory Pathways

Topics in Cognitive Science, EarlyView.
Abstract As people age, there is a natural decline in cognitive functioning and brain structure. However, the relationship between brain function and cognition in older adults is neither straightforward nor uniform. Instead, it is complex, influenced by multiple factors, and can vary considerably from one person to another.
Monica Baciu, Elise Roger
wiley   +1 more source

Long‐Tea‐CLIP: An Expert‐Level Multimodal AI Framework for Fine‐Grained Green Tea Grading Across Five Sensory Dimensions

Advanced Science, Volume 13, Issue 32, 9 June 2026.
Long‐Tea‐CLIP (Contrastive Language‐Image Pre‐training) presents a multimodal AI framework that integrates visual, metabolomic, and sensory knowledge to grade green tea across appearance, soup color, aroma, taste, and infused leaf. By combining expert‐guided modeling with CLIP‐supervised learning, the system delivers fine‐grained quality evaluation and
Yanqun Xu, Zhengfang Xue, Qing Luo, Xu Liu, Yongquan Xu, Jing Huang, Xingcai Zhang, Xinyuan Zhang, Zhonghua Liu, Zisheng Luo   +9 more
wiley   +1 more source

Multi-task learning with cross-task consistency for improved depth estimation in colonoscopy [PDF]

Colonoscopy screening is the gold standard procedure for assessing abnormalities in the colon and rectum, such as ulcers and cancerous polyps. Measuring the abnormal mucosal area and its 3D reconstruction can help quantify the surveyed area and ...
Subramanian, V., Ali, S., Bulpitt, A., Solano, P.E.C.   +3 more
core   +1 more source

Multi-task learning with a natural metric for quantitative structure activity relationship learning

Journal of Cheminformatics, 2019
The goal of quantitative structure activity relationship (QSAR) learning is to learn a function that, given the structure of a small molecule (a potential drug), outputs the predicted activity of the compound.
Noureddin Sadawi, Ivan Olier, Joaquin Vanschoren, Jan N. van Rijn, Jeremy Besnard, Richard Bickerton, Crina Grosan, Larisa Soldatova, Ross D. King   +8 more
doaj   +1 more source

Dopaminergic novelty detection and theta oscillations: Virtual reality‐based adaptive interventions for cognitive enhancement in aging

Ibrain, Volume 12, Issue 2, Page 169-189, Summer 2026.
Aging‐related cognitive decline is associated with reduced dopaminergic signaling and disrupted theta oscillations, which together impair novelty detection and memory formation. This review shows how VR environments can be used to deliver adaptive, novelty‐rich stimuli that engage dopaminergic circuits and entrain theta rhythms, thereby enhancing ...
Abraham Olufemi Asuku, Ayodeji Johnson Ajibare   +1 more
wiley   +1 more source

Dual-Balancing for Multi-Task Learning

, 2023
Multi-task learning (MTL), a learning paradigm to learn multiple related tasks simultaneously, has achieved great success in various fields. However, task balancing problem remains a significant challenge in MTL, with the disparity in loss/gradient ...
Lin, Baijiong, Zhang, Yu, Kwok, James T., Ye, Feiyang, Chen, Pengguang, Chen, Ying-Cong, Jiang, Weisen, Liu, Shu   +7 more
core  

A Bibliometric Analysis of Process Mining

WIREs Data Mining and Knowledge Discovery, Volume 16, Issue 2, June 2026.
Process mining studies with numbers. ABSTRACT Process mining (PM) has emerged as a pivotal discipline in data science, bridging traditional process analysis with data‐driven techniques to extract actionable insights from event logs. This study conducts a comprehensive bibliometric analysis of 1764 peer‐reviewed articles from the Web of Science database
Seyfullah Tokumaci, Onur Dogan, Oguzhan Ozturk   +2 more
wiley   +1 more source

Latent Multi-Task Architecture Learning

, 2019
Multi-task learning (MTL) allows deep neural networks to learn from related tasks by sharing parameters with other networks. In practice, however, MTL involves searching an enormous space of possible parameter sharing architectures to find (a) the layers
Bingel, Joachim, Ruder, Sebastian, Søgaard, Anders, Augenstein, Isabelle   +3 more
core   +1 more source

Unleashing the Power of Machine Learning in Nanomedicine Formulation Development

Advanced Functional Materials, Volume 36, Issue 38, 11 May 2026.
A random forest machine learning model is able to make predictions on nanoparticle attributes of different nanomedicines (i.e. lipid nanoparticles, liposomes, or PLGA nanoparticles) based on microfluidic formulation parameters. Machine learning models are based on a database of nanoparticle formulations, and models are able to generate unique solutions
Thomas L. Moore, Cristiano Pesce, Antonietta Greco, Claudia Pisante, Greta Avancini, Valentina Di Francesco, Yosi Shamay, Paolo Decuzzi   +7 more
wiley   +1 more source

Automatic identification of tokamak plasma confinement states (L-mode, ELM-free H-mode, and ELMy H-mode) with multi-task learning neural network

Nuclear Fusion
The identification of plasma confinement states (L-mode, ELM-free H-mode, and ELMy H-mode) is carried out using a multi-task learning neural network (MTL-NN) in EAST.
Guo-Hong Deng, Peng-Cheng Xie, You-Wen Sun, Hui-Hui Wang, Jian Xu, Qun Ma, Shuai Gu, Hui Sheng, Hua Yang, Gao-Ting Chen   +9 more
doaj   +1 more source