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Intracerebral hemorrhage (ICH) is an important public health problem with high rates of mortality, morbidity, and disability, but no clinically proven treatment strategy is available to date. Scalp acupuncture (SA) refers to a therapy for treating diseases by needling and stimulating the specific areas of the scalp. The evidence from clinical studies suggested that SA therapy may produce significant benefits for patients with acute ICH. However, the therapeutic mechanisms are yet not well addressed. Therefore, in this paper, we provide a comprehensive overview on the history and mechanisms of SA therapy on acute ICH. Although SA has been practiced for thousands of years in China and could date back to 5 BC, SA therapy for acute ICH develops only in the recent 30 years. The possible mechanisms associated with the therapeutic effects of SA on ICH include the influence on hematoma, brain edema, and blood brain barrier, the products released from haematoma, the immune and inflammatory reaction, focal perihemorrhagic hypoperfusion and hemorheology, neuroelectrophysiology, and so on. At last, the existence of instant effect of SA on acute ICH and its possible mechanisms are presented.

Electroencephalogram (EEG)-based neurofeedback has been widely studied for tinnitus therapy in recent years. Most existing research relies on experts' cognitive prediction, and studies based on machine learning and deep learning are either data-hungry or not well generalizable to new subjects. In this paper, we propose a robust, data-efficient model for distinguishing tinnitus from the healthy state based on EEGbased tinnitus neurofeedback. We propose trend descriptor, a feature extractor with lower fineness, to reduce the effect of electrode noises on EEG signals, and a siamese encoder-decoder network boosted in a supervised manner to learn accurate alignment and to acquire high-quality transferable mappings across subjects and EEG signal channels. Our experiments show the proposed method significantly outperforms state-of-the-art algorithms when analyzing subjects' EEG neurofeedback to 90dB and 100dB sound, achieving an accuracy of 91.67%-94.44% in predicting tinnitus and control subjects in a subject-independent setting. Our ablation studies on mixed subjects and parameters show the method's stability in performance.

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Conventional multi-view clustering methods seek for a view consensus through minimizing the pairwise discrepancy between the consensus and subviews. However, the pairwise comparison cannot portray the inter-view relationship precisely if some of the subviews can be further agglomerated. To address the above challenge, we propose the agglomerative analysis to approximate the optimal consensus view, thereby describing the subview relationship within a view structure. We present Agglomerative Neural Network (ANN) based on Constrained Laplacian Rank to cluster multi-view data directly while avoiding a dedicated postprocessing step (e.g., using K-means). We further extend ANN with learnable data space to handle data of complex scenarios. Our evaluations against several state-of-theart multi-view clustering approaches on four popular datasets show the promising view-consensus analysis ability of ANN. We further demonstrate ANN's capability in analyzing complex view structures and extensibility in our case study and explain its robustness and the effectiveness of data-driven modifications.