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"Higher Order Polynomial Transformer for Fine-Grained Freezing of Gait " by Renfei Sun, Kun Hu et al.

Freezing of Gait (FoG) is a common symptom of Parkinson’s disease (PD), manifesting as a brief, episodic absence, or marked reduction in walking, despite a patient’s intention to move. Clinical assessment of FoG events from manual observations by experts is both time-consuming and highly subjective. Therefore, machine learning-based FoG identification methods would be desirable. In this article, we address this task as a fine-grained human action recognition problem based on vision inputs. A...
Deep Learning Feature Extraction Igher Order Attention Olynomial Transformation Self Attention Patiotemporal Phenomena
Source: uow.edu.au

FOXNEWS America Reports With John Roberts Sandra Smith June 4, 2024 17:45:00

, shes getting away with this because lets be clear , because of her race and her gender. if you attack or your either racist or sexist, or in my case im a sellout. kendra henderson won the silver medal. black americans were draped in the american flag. who are the oppressed people sh is talking about? first and last names, she doesnt have anybody. its nothing more than a stunt i order to basically threaten people and play the race card and the gender card. >> what is the golden do you think? wh...
Kendra Henderson Black Americans American Flag Silver Medal Rules Don T Gender Card

"SATP-GAN: self-attention based generative adversarial network for traf" by Liang Zhang, Jianqing Wu et al.

Abstract Traffic flow prediction, one of the essential problems in traffic control and guidance systems, is still drawing increasing attention in recent years with the new methods tipped by the success of AI. In this paper, we propose a novel model, namely self-attention generative adversarial networks for time-series prediction (SATP-GAN). The SATP-GAN method is based on self-attention and generative adversarial networks (GAN) mechanisms, which are composed of the GAN module and reinforcement ...
Reinforcement Learning Self Attention Time Series Traffic Flow
Source: uow.edu.au

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