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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 novel deep learning architecture, namely, higher order polynomial transformer (HP-Transformer), is proposed to incorporate pose and appearance feature sequences to formulate fine-grained FoG patterns. In particular, a higher order self-attention mechanism is proposed based on higher order polynomials. To this end, linear, bilinear, and trilinear transformers are formulated in pursuit of discriminative fine-grained representations. These representations are treated as multiple streams and furthe
, she s getting away with this because let s be clear , because of her race and her gender. if you attack or your either racist or sexist, or in my case i m a sellout. kendra henderson won the silver medal. black americans we re draped in the american flag. who are the oppressed people sh is talking about? first and last names, she doesn t 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? why don t you become a social activist someone say? exactly. why don t you. you have to ask yourself the most important question, why? self attention, branding, that 15 minutes of fame. if your going to honestly do something about the oppressed, get into the community. work with groups, work with organizations. was a civil rights attorney for 30 years i follow lawsuits, i
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 learning (RL) module. In the GAN module, we apply the self-attention layer to capture the pattern of time-series data instead of RNNs (recurrent neural networks). In the RL module, we apply the RL algorithm to tune the parameters of our SATP-GAN model. We evaluate the framework on the real-world traffic dataset and obtain a consistent improvement of 6.5% over baseline methods. The SATP-GAN framework proves the GAN mechanism is also available for time-series prediction after fine-tuning the par