Results 111 to 120 of about 83,974 (281)

A Relational View of Uncertainty

Strategic Change, EarlyView.
ABSTRACT There is significant confusion and debate in entrepreneurship and strategy research about the nature and locus of uncertainty. Does uncertainty reside internally in the agent or externally in the environment? This article introduces a relational view of uncertainty (RVU) to help reframe this issue.
Daniel Leunbach
wiley   +1 more source

A Lightweight and Small Sample Bearing Fault Diagnosis Algorithm Based on Probabilistic Decoupling Knowledge Distillation and Meta-Learning

Sensors
Rolling bearings play a crucial role in industrial equipment, and their failure is highly likely to cause a series of serious consequences. Traditional deep learning-based bearing fault diagnosis algorithms rely on large amounts of training data ...
Hao Luo, Tongli Ren, Ying Zhang, Li Zhang   +3 more
doaj   +1 more source

Pupil Plane Multiplexing for Vectorial Fourier Ptychography

Laser &Photonics Reviews, EarlyView.
This study proposes a cost‐effective, modality‐adaptive multichannel microscopy framework using pupil‐plane multiplexing. A custom pupil aperture at the Fourier plane encodes channel‐specific transfer functions with spectral or polarization filters, and model‐based reconstruction with channel‐dependent priors decodes them.
Hyesuk Chae, Kyungwon Lee, Yong Guk Kang, Hansol Yoon, Kyung Chul Lee, Seung Ah Lee   +5 more
wiley   +1 more source

The Feasibility and Practical Utility of Virtual Visits for Patients with Parkinson's Disease in Different World Regions

Movement Disorders Clinical Practice, EarlyView.
Abstract Background Overcoming existing access barriers is crucial for better‐specialized health care of patients with Parkinson's disease (PD). Objective The aim of the study was to compare the access and visit quality/acceptability between in‐office and virtual telemedicine visits.
Álvaro García‐Bustillo, Jinyoung Youn, Jong Hyeon Ahn, Oluwadamilola Ojo, Njideka Okubadejo, Zakiyah Aldaajani, Mohamed Essam, Ali Shalash, Adriana Cardozo, Meredith Spindler, Zoltan Mari, Esther Cubo   +11 more
wiley   +1 more source

Model-Agnostic Meta-Learning for Digital Pathology

, 2020
The performance of conventional deep neural networks tends to degrade when a domain shift is introduced, such as collecting data from a new site. Model-Agnostic Meta-Learning, or MAML, has achieved state-of-the-art performance in few-shot learning by finding initial parameters that adapt easily for new tasks.
openaire   +1 more source

Machine Learning for Predictive Modeling in Nanomedicine‐Based Cancer Drug Delivery

Med Research, EarlyView.
The integration of AI/ML into nanomedicine offers a transformative approach to therapeutic design and optimization. Unlike conventional empirical methods, AI/ML models (such as classification, regression, and neural networks) enable the analysis of complex clinical and formulation datasets to predict optimal nanoparticle characteristics and therapeutic
Rohan Chand Sahu, Sanchit Arora, Dinesh Kumar, Ashish Kumar Agrawal   +3 more
wiley   +1 more source

Recovering manifold representations via unsupervised meta-learning

Frontiers in Computer Science
Manifold representation learning holds great promise for theoretical understanding and characterization of deep neural networks' behaviors through the lens of geometries.
Yunye Gong, Jiachen Yao, Ruyi Lian, Xiao Lin, Chao Chen, Ajay Divakaran, Yi Yao   +6 more
doaj   +1 more source

Opening the Black Box of Nonprofit Reputation and Volunteer Attraction With Supervised Machine Learning

Nonprofit Management and Leadership, EarlyView.
ABSTRACT With the aim to explore the potential of machine learning for nonprofit research, this article contrasts traditional linear regression with four contemporary supervised machine learning approaches. Concretely, we predict (1) reputation ratings and (2) the total number of volunteers for 4021 non‐profit organizations in the U.S.
Moritz Schmid, Hannes Lampe, Jurgen Willems   +2 more
wiley   +1 more source

Adaptive Position Control of Pneumatic Continuum Manipulator Based on MAML Meta-Reinforcement Learning

Applied Sciences
Reinforcement learning algorithms usually focus on a specific task, which often performs well only in the training environment. When the task changes, its performance drops significantly, with the algorithm lacking the ability to adapt to new ...
Lina Hao, Qiang Cheng, Hongshuai Liu, Ying Zhang   +3 more
doaj   +1 more source

High-Resolution PM10 Estimation Using Satellite Data and Model-Agnostic Meta-Learning

Remote Sensing
Characterizing the spatial distribution of particles smaller than 10 μm (PM10) is of great importance for air quality management yet is very challenging because of the sparseness of air quality monitoring stations.
Yue Yang, Jan Cermak, Xu Chen, Yunping Chen, Xi Hou   +4 more
semanticscholar   +1 more source