Results 81 to 90 of about 215,679 (256)

A Systematic Evaluation of Recurrent Neural Network Models for Edge Intelligence and Human Activity Recognition Applications

Algorithms
The Recurrent Neural Networks (RNNs) are an essential class of supervised learning algorithms. Complex tasks like speech recognition, machine translation, sentiment classification, weather prediction, etc., are now performed by well-trained RNNs.
Varsha S. Lalapura, Veerender Reddy Bhimavarapu, J. Amudha, Hariram Selvamurugan Satheesh   +3 more
doaj   +1 more source

Adult‐Onset Subacute Sclerosing Panencephalitis Presenting With Subacute Cognitive Deficits

Annals of Clinical and Translational Neurology, EarlyView.
ABSTRACT We describe the case of a 41‐year‐old man diagnosed with adult‐onset subacute sclerosing panencephalitis (SSPE). The patient presented with subacute progressive cognitive deficits and a neuropsychological profile indicating predominant frontoparietal dysfunction. MRI showed only mild parietal‐predominant cerebral atrophy.
Dennis Yeow, Nicola McKern, Vincent Oxenham, Kate Ahmad, Omar Ahmad   +4 more
wiley   +1 more source

Sparsity Forcing: Reinforcing Token Sparsity of MLLMs

Sparse attention mechanisms aim to reduce computational overhead with minimal accuracy loss by selectively processing salient tokens. Despite their effectiveness, most methods merely exploit a model's inherent sparsity and thus plateau at moderate budgets (about 50\% token reduction), with little headroom to push budget lower without hurting accuracy ...
Chen, Feng, He, Yefei, Lin, Lequan, Gou, Chenhui, Liu, Jing, Zhuang, Bohan, Wu, Qi   +6 more
openaire   +2 more sources

Activity Sparsity Complements Weight Sparsity for Efficient RNN Inference

, 2023
Artificial neural networks open up unprecedented machine learning capabilities at the cost of ever growing computational requirements. Sparsifying the parameters, often achieved through weight pruning, has been identified as a powerful technique to compress the number of model parameters and reduce the computational operations of neural networks.
Mukherji, Rishav, Schöne, Mark, Nazeer, Khaleelulla Khan, Mayr, Christian, Subramoney, Anand   +4 more
openaire   +2 more sources

Whole‐Body Pattern of Muscle Degeneration and Progression in Sarcoglycanopathies

Annals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Objective To characterize whole‐body intramuscular fat distribution pattern in patients with sarcoglycanopathies and explore correlations with disease severity, duration and age at onset. Methods Retrospective, cross‐sectional, multicentric study enrolling patients with variants in one of the four sarcoglycan genes who underwent whole‐body ...
Laura Costa‐Comellas, Mauro Monforte, Angel Sanchez‐Montañez, Penélope Romero‐Duque, Elena Pegoraro, Jordi Díaz‐Manera, Dmitry Vlodavets, Lorenzo Maggi, Marco Moscatelli, Adele D‘Amico, Montse Olivé, Jorge Alonso‐Pérez, Giacomo Comi, José Miguel Escudero‐Fernández, Gabriela S. Urcuyo, Anna Pichiecchio, Angela Berardinelli, Kristl G. Claeys, Claudio Bruno, Chiara Panicucci, Sara Bortolani, Eleonora Torchia, Enzo Ricci, Soledad Monges, Jorge A. Bevilacqua, Jorge Diaz‐Jara, Maggie C. Walter, Simone Thiele, Nicoline Løkken, John Vissing, Susana Quijano‐Roy, Robert Y. Carlier, Nicol C. Voermans, Chiara Marini‐Bettolo, Michela Guglieri, Volker Straub, Lea Leonardis, Francina Munell, David Gómez‐Andrés, Giorgio Tasca   +39 more
wiley   +1 more source

Development of a Prediction Model for Progression Risk in High‐Grade Gliomas Based on Habitat Radiomics and Pathomics

Annals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Objective To investigate the value of constructing models based on habitat radiomics and pathomics for predicting the risk of progression in high‐grade gliomas. Methods This study conducted a retrospective analysis of preoperative magnetic resonance (MR) images and pathological sections from 72 patients diagnosed with high‐grade gliomas (52 ...
Yuchen Zhu, Yuxi Gong, Weilin Xu, Xingjian Sun, Gefei Jiang, Lei Qiu, Kexin Shi, Mengxing Wu, Yinjiao Fei, Jinling Yuan, Jinyan Luo, Yurong Li, Yuandong Cao, Minhong Pan, Shu Zhou   +14 more
wiley   +1 more source

Brainstem and Cerebellar Volume Loss and Associated Clinical Features in Progressive Supranuclear Palsy

Annals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Introduction Progressive Supranuclear Palsy (PSP) is a neurodegenerative ‘tauopathy’ with predominating pathology in the basal ganglia and midbrain. Caudal tau spread frequently implicates the cerebellum; however, the pattern of atrophy remains equivocal.
Chloe Spiegel, Timothy P. Siejka, Cassandra Marotta, Josh J. Y. Lee, Kelly Bertram, Terence J. O'Brien, Meng Law, Lucy Vivash, Ian H. Harding   +8 more
wiley   +1 more source

A 73‐Year‐Old Man With Several Years of Difficulty Climbing Stairs and Frequent Tripping

Annals of Clinical and Translational Neurology, EarlyView.
ABSTRACT A 73‐year‐old man presented with progressive weakness and atrophy predominantly affecting the distal finger flexors and quadriceps muscles. Electrophysiological studies demonstrated mixed myogenic and neurogenic features. Muscle MRI showed inflammatory changes, and muscle biopsy revealed granulomatous myositis with histologic features ...
Mehmet Can Sari, Arens Taga, Sonya Ulrike Steele, Ahmet Hoke   +3 more
wiley   +1 more source

Structure–Function Decoupling of the Sensorimotor and Default Mode Networks in Black Americans With MS

Annals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Background and Objectives Multiple sclerosis (MS) exhibits racially disparate rates of disease progression. Black people with MS (B‐PwMS) experience a more severe disease course than non‐Hispanic White people with MS (NHW‐PwMS). Here we investigated structural and functional connectivity as well as structure–function decoupling in the ...
Emilio Cipriano, Giacomo Boffa, Maria Petracca, Marta Ponzano, Nicole Graziano, Claire Wigley, Claire Riley, Jonathan Howard, Pietro Bontempi, Sylvia Klineova, Fred Lublin, Matilde Inglese   +11 more
wiley   +1 more source

Learning with structured sparsity

Proceedings of the 26th Annual International Conference on Machine Learning, 2009
This paper investigates a new learning formulation called structured sparsity, which is a natural extension of the standard sparsity concept in statistical learning and compressive sensing. By allowing arbitrary structures on the feature set, this concept generalizes the group sparsity idea that has become popular in recent years.
Huang, Junzhou, Zhang, Tong, Metaxas, Dimitris   +2 more
openaire   +2 more sources