Neural networks in the real domain have been studied for a long time and achieved promising results in many vision tasks for recent years. However, the extensions of the neural network models in other number fields and their potential applications are not fully-investigated yet. Focusing on color images, which can be naturally represented as quaternion matrices, we propose a quaternion convolutional neural network (QCNN) model to obtain more representative features. In particular, we re-design the basic modules like convolution layer and fullyconnected layer in the quaternion domain, which can be used to establish fully-quaternion convolutional neural networks. Moreover, these modules are compatible with almost all deep learning techniques and can be plugged into traditional CNNs easily. We test our QCNN models in both color image classification and denoising tasks. Experimental results show that they outperform the real-valued CNNs with same structures.

We have previously reported that dopamine-1 receptor-mediated activation of phospholipase C is diminished in renal cortical slices of adult spontaneously hypertensive rats. To determine the potential consequences of this phenomenon, we performed the present studies in which renal proximal tubule suspensions obtained from spontaneously hypertensive and Wistar-Kyoto rats of 10-12 weeks of age were used. The tubule suspensions were incubated with dopamine in the presence or absence of dopamine receptor antagonists, and sodium, potassium adenosine trisphosphatase (sodium pump) activity was measured as the ouabain-sensitive adenosine trisphosphate hydrolysis. We found that dopamine produced a concentration-related inhibition of sodium pump activity in the normotensive rats but not in the hypertensive rats. Dopamine-induced inhibition of sodium pump activity in the normotensive rats was abolished by the phospholipase C inhibitor U-73122 or the protein kinase C inhibitor sphingosine, suggesting the involvement of a phospholipase C-coupled protein kinase C pathway in this response. Dopamine-induced inhibition in the normotensive rats was attenuated by the dopamine-1 receptor antagonist SCH 23390 but not by the dopamine-2 receptor antagonist domperidone. To identify possible sites of defect in dopamine-1 receptor-coupled signaling pathways in the hypertensive rats, we incubated the proximal tubules with phorbol 12,13-dibutyrate or the synthetic diacylglycerol analogue l-oleoyI-2-acetyl-rac-glycerol. The results showed that both compounds inhibited sodium pump activity as effectively in the hypertensive as in the normotensive rats, suggesting that the protein kinase C-coupled sodium pump pathway was not defective in the hypertensive animals. Failure of dopamine to inhibit sodium pump activity in the hypertensive rats could not be due to a defective dopamine-1 receptor adenylate cyclase coupling, because dopamine was still unable to inhibit sodium pump activity in the presence of dibutyryl cyclic adenosine monophosphate or forskolin. These results show that dopamine failed to inhibit sodium pump activity in the proximal tubules of adult hypertensive rats, which may be mainly due to a defect in the dopamine-1 receptor-mediated signal transduction pathway. The site of this defect is most likely proximal to the activation of protein kinase C and may involve a defect in the dopamine-1 receptor phospholipase C coupling process. (Hypertension 1993;21:364-372) KEY WORDS • receptors, dopamine • dopamine • sodium • phospholipase C • adenosine trisphosphatase, sodium, potassium A bnormal renal sodium handling has been known / \ to be one of the major factors involved in the -Z A . initiation and maintenance of high blood pressure in several models of hypertension, including genetic hypertension. 1 -2 Endogenous kidney dopamine plays an important role in regulation of renal sodium excretion, so it has been proposed that impaired renal sodium handling in spontaneously hypertensive rats (SHR) may partly be due to a malfunction or a d...

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Visual analytics for machine learning has recently evolved as one of the most exciting areas in the field of visualization. To better identify which research topics are promising and to learn how to apply relevant techniques in visual analytics, we systematically review 259 papers published in the last ten years together with representative works before 2010. We build a taxonomy, which includes three first-level categories: techniques before model building, techniques during modeling building, and techniques after model building. Each category is further characterized by representative analysis tasks, and each task is exemplified by a set of recent influential works. We also discuss and highlight research challenges and promising potential future research opportunities useful for visual analytics researchers.

Stress sensing is the basis of human-machine interface, biomedical engineering, and mechanical structure detection systems. Stress sensing based on mechanoluminescence (ML) shows significant advantages of distributed detection and remote response to mechanical stimuli and is thus expected to be a key technology of next-generation tactile sensors and stress recorders. However, the instantaneous photon emission in ML materials generally requires real-time recording with a photodetector, thus limiting their application fields to real-time stress sensing. In this paper, we report a force-induced charge carrier storage (FICS) effect in deep-trap ML materials, which enables storage of the applied mechanical energy in deep traps and then release of the stored energy as photon emission under thermal stimulation. The FICS effect was confirmed in five ML materials with piezoelectric structures, efficient emission centres and deep trap distributions, and its mechanism was investigated through detailed spectroscopic characterizations. Furthermore, we demonstrated three applications of the FICS effect in electronic signature recording, falling point monitoring and vehicle collision recording, which exhibited outstanding advantages of distributed recording, long-term storage, and no need for a continuous power supply. The FICS effect reported in this paper provides not only a breakthrough for ML materials in the field of stress recording but also a new idea for developing mechanical energy storage and conversion systems.