Purpose
The purpose of this study was to investigate the local spatiotemporal consistency of spontaneous brain activity in patients with frontal lobe epilepsy (FLE).
Method
Eyes closed resting‐state ...functional magnetic resonance imaging (fMRI) data were collected from 19 FLE patients and 19 age‐ and gender‐matched healthy controls. A novel measure, named FOur‐dimensional (spatiotemporal) Consistency of local neural Activities (FOCA) was used to assess the spatiotemporal consistency of local spontaneous activity (emphasizing both local temporal homogeneity and regional stability of brain activity states). Then, two‐sample t test was performed to detect the FOCA differences between two groups. Partial correlations between the FOCA values and durations of epilepsy were further analyzed.
Key Findings
Compared with controls, FLE patients demonstrated increased FOCA in distant brain regions including the frontal and parietal cortices, as well as the basal ganglia. The decreased FOCA was located in the temporal cortex, posterior default model regions, and cerebellum. In addition, the FOCA measure was linked to the duration of epilepsy in basal ganglia.
Significance
Our study suggested that alterations of local spontaneous activity in frontoparietal cortex and basal ganglia was associated with the pathophysiology of FLE; and the abnormality in frontal and default model regions might account for the potential cognitive impairment in FLE. We also presumed that the FOCA measure had potential to provide important insights into understanding epilepsy such as FLE.
In our work, alterations of local spontaneous activity in frontoparietal cortex and basal ganglia were found in FLE patients using a novel resting‐state measure, named FOur‐dimensional (spatiotemporal) Consistency of local neural Activities (FOCA).
Along with quickly emerging applications, including 5G, IOT (Internet of Things), Auto Piloting, etc., in industry, demands for local large-scale data analysis and AI (Artificial Intelligence) ...processing on edge is growing rapidly. As a result, hardware power consumption for edge computing is increasing fast. At the same time, high-power edge computing products are supposed to fit into non-data center scenarios such as 5G edge network, Roads Side Control Unit (RSCU), smart factory, etc. This paper introduces a typical edge server, investigates the environmental requirements for non-data center scenarios and studies on technical feasibility of further extending working temperature, dustproof, waterproof, anti-condensation, etc., and proposed a completed, environmental reliable solution for edge hardware products. The proposed Cloud Edge Server also extends AI server products and environmental adaptability on edge. The same PCB (Printed Circuit Board) can support flexible AI hardware accelerators and storage configurations for diverse applications. And, it can fit into different outdoor and indoor scenarios with only changing chassis design which helps dramatically reducing total deployment cost and achieve better ROI (Return on Investment).
Sentiment classification is an important data mining task. Previous researches tried various machine learning techniques while didn't make fully use of the difference among features. This paper ...proposes a novel method for improving sentiment classification by fully exploring the different contribution of features. The method consists of two parts. First, we highlight sentimental features by increasing their weight. Second, we use bagging to construct multiple classifiers on different feature spaces and combine them into an aggregating classifier. Extensive experiments show that the method can evidently improve the performance of sentiment classification.