Results 31 to 40 of about 22,610 (167)

Artificial intelligence in enzyme catalysis: Emerging trends and applications in biocatalyst engineering

The Canadian Journal of Chemical Engineering, EarlyView.
Schematic representation of artificial intelligence approaches in enzyme catalysis, integrating bibliometric analysis, emerging research trends, and machine learning tools for enzyme design, prediction, and industrial biocatalytic applications. Abstract This study systematically explores the applications of artificial intelligence (AI) in enzyme ...
Misael Bessa Sales, Francisco Izaias da Silva Aires, Paulo Gonçalves de Sousa Junior, Calebe Da Rocha Silva, Emanuel Lino Rodrigues, Maria Cristiane Martins de Souza, José C. S. dos Santos   +6 more
wiley   +1 more source

Attentional Encoder Network for Targeted Sentiment Classification

, 2019
Targeted sentiment classification aims at determining the sentimental tendency towards specific targets. Most of the previous approaches model context and target words with RNN and attention.
Jiang, Tao, Liu, Zhiyue, Rao, Yanghui, Song, Youwei, Wang, Jiahai   +4 more
core   +1 more source

Abnormal functional connectivity patterns in temporal lobe epilepsy—An international ENIGMA‐epilepsy study

Epilepsia Open, EarlyView.
Abstract Objectives Temporal lobe epilepsy (TLE) impacts multiple brain networks. Aberrant functional connectivity has been demonstrated in resting‐state networks (RSNs) that mediate higher brain functions in TLE. This study aimed to identify the reproducible patterns of altered functional connectivity in TLE in a large, international cohort through ...
Victoria Ives‐Deliperi, Jonathan Ipser, James T. Butler, Heath Pardoe, Hamid Soltanian‐Zadeh, Christian Rummel, Roland Wiest, Simon S. Keller, Barbara A. K. Kreilkamp, Anna Elisabetta Vaudano, Alice Ballerini, Stefano Meletti, Gerard Hall, Peter Taylor, Luis Concha, Alfonso Fajardo‐Valdez, Raphael Fernandes Casseb, Fernando Cendes, Clarissa L. Yasuda, Louis Lemieux, John S. Duncan, Esmaeil Davoodi‐Bodj, Orrin Devinsky, Sophia I. Thomopoulos, Dan J. Stein, Paul M. Thompson, Sanjay M. Sisodiya, Taha Gholipour, Carrie R. McDonald   +28 more
wiley   +1 more source

Linguistics as Metaphor in Organizational Regularization and Decay

American Journal of Management, 2020
Linguists have found that the twin tendencies of regularization and decay lead to the development of language structures that are simultaneously complex and unstable. This serves as a metaphor to illuminate the parallel processes of regularization and decay in organizational structure. Both the evolution of languages and the evolution of organizational
openaire   +2 more sources

Deep learning cone‐beam computed tomography image segmentation for the 3D visualization of mandibular infraosseous periodontal defects

Journal of Periodontology, EarlyView.
Abstract Background The accurate assessment of infraosseous periodontal defects is crucial for effective diagnosis and treatment planning. Cone‐beam computed tomography (CBCT) enables detailed imaging of these defects; however, to leverage their full potential, CBCT images must be reconstructed in 3 dimensions (3D).
Daniel Palkovics, Balint Molnar, Csaba Pinter, David García‐Mato, Andres Diaz‐Pinto, Attila Tanacs, Andrea Dobos, Peter Windisch, Christoph A. Ramseier   +8 more
wiley   +1 more source

SelExNet: A Self‐Supervised Physics‐Informed Framework for Multi‐Channel Joint RF and Gradient Waveform Optimization in 2D Spatially Selective Excitation

Magnetic Resonance in Medicine, EarlyView.
ABSTRACT Purpose To introduce SelExNet: a self‐supervised framework for two‐dimensional spatially selective excitation that jointly optimizes radiofrequency (RF) pulses and gradient waveforms, and extends to multi‐channel transmission MRI. Methods Building on prior RF‐only and joint RF‐gradient optimization approaches, SelExNet couples neural RF and ...
Yuliang Xiao, Jason Rock, Zhe Wu, Jamie Near, Mark Chiew, Simon J. Graham   +5 more
wiley   +1 more source

CRF Autoencoder for Unsupervised Dependency Parsing

, 2017
Unsupervised dependency parsing, which tries to discover linguistic dependency structures from unannotated data, is a very challenging task. Almost all previous work on this task focuses on learning generative models.
Cai, Jiong, Jiang, Yong, Tu, Kewei
core   +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

Dropout Training as Adaptive Regularization [PDF]

, 2013
Dropout and other feature noising schemes control overfitting by artificially corrupting the training data. For generalized linear models, dropout performs a form of adaptive regularization.
Liang, Percy, Wager, Stefan, Wang, Sida
core   +3 more sources

Selective Neural Entrainment Reveals Hierarchical Tuning to Linguistic Regularities in Reading

Neurobiology of Language, 2023
Abstract Reading is both a visual and a linguistic task, and as such it relies on both general-purpose, visual mechanisms and more abstract, meaning-oriented processes. Disentangling the roles of these resources is of paramount importance in reading research.
Mara De Rosa, Lorenzo Vignali, Anna D’Urso, Maria Ktori, Roberto Bottini, Davide Crepaldi   +5 more
openaire   +5 more sources