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Although first-episode drug naive patients with schizophrenia are known to show cognitive impairment, the cognitive performances of these patients, who suffer deficit syndrome, compared with those who suffer non-deficit syndrome is undetermined. The aim of this study was to compare cognitive performances in first-episode drug-naive schizophrenia with(More)
Most instance search systems are based on modeling local features. It remains a challenge to apply deep learning techniques into this task because of the asymmetrical similarity between the query region and dataset images. In this paper, we propose ALADDIN, A Locality Aligned Deep moDel for INstance search. This model deals with the asymmetrical similarity(More)
Mental fatigue has a pernicious influence on road and work place safety as well as a negative symptom of many acute and chronic illnesses, since the ability of concentrating, responding and judging quickly decreases during the fatigue or drowsiness stage. Electroencephalography (EEG) has been proven to be a robust physiological indicator of human cognitive(More)
Object retrieval is still an open question. A promising approach is based on the matching of visual phrases. However, this routine is often corrupted by visual phrase burstiness, i.e., the repetitive occurrence of some certain visual phrases. Burstiness leads to over-counting the co-occurring visual patterns between two images, thus would deteriorate the(More)
Accurate pedestrian detection in highly crowded surveillance videos is a challenging task, since the regions of pedestrians in the videos may be largely occluded by other pedestrians. In this paper, we propose an effective part-based deep network cascade (HsNet) to solve this problem. In this model, the part-based scheme effectively restrains the appearance(More)
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