Multi-output programmable quantum processor

By combining telecloning and programmable quantum gate array presented by Nielsen and Chuang [Phys.Rev.Lett. 79 :321(1997)], we propose a programmable quantum processor which can be programmed to implement restricted set of operations with several identical data outputs. The outputs are approximately-transformed versions of input data. The processor successes with certain probability.Comment: 5 pages and 2 PDF figure

Generating entanglement of photon-number states with coherent light via cross-Kerr nonlinearity

We propose a scheme for generating entangled states of light fields. This scheme only requires the cross-Kerr nonlinear interaction between coherent light-beams, followed by a homodyne detection. Therefore, this scheme is within the reach of current technology. We study in detail the generation of the entangled states between two modes, and that among three modes. In addition to the Bell states between two modes and the W states among three modes, we find plentiful new kinds of entangled states. Finally, the scheme can be extend to generate the entangled states among more than three modes.Comment: 2 figure

Phycocyanin relieves myocardial ischemia-reperfusion injury in rats by inhibiting oxidative stress

Purpose: To investigate the effect of phycocyanin on myocardial ischemia-reperfusion injury, and the possible mechanisms involved. Methods: Twenty-four Sprague-Dawley (SD) rats were randomly divided into Sham group (only threading without ligation), IRI group (myocardial ischemia-reperfusion injury group) and phycocyanin group (phycocyanin pretreatment + myocardial ischemia-reperfusion injury group). The heart was harvested and cardiomyocytes were isolated. Colorimetry was used to determine the contents of cardiomyocyte serum creatine phospho-MB (CK-MB), lactate dehydrogenase (LDH) and malondialdehyde (MDA), and the activities of total antioxidant capacity (T-AOC), catalase (CAT), glutathione (GSH), total superoxide dismutase (SOD) and other related oxidative stress indicators. Furthermore, apoptosis was evaluated using TUNEL staining. Protein levels of cardiac factor E2 related factor 2 (Nrf2), heme oxygenase-1 (HO-1), human NADPH dehydrogenase 1 (NQO1) and nuclear factor-κB (NF-κB) were evaluated by Western blot and immunohistochemistry. Results: Compared with the myocardial IRI group, the contents of CK-MB, LDH, MAD and ROS in the treated group were significantly decreased (p < 0.05), but the activities of SOD, GSH, SOD, CAT, and T-AOC in the myocardial tissues were significantly enhanced (p < 0.05). Moreover, the pathological changes in myocardial tissue were significantly reduced. In addition, the expression levels of Nrf2, HO-1 and NQO-1 were significantly up-regulated after phycocyanin pretreatment, while expression of NF-κB was significantly down-regulated (p < 0.05). Conclusion: Phycocyanin improves myocardial anti-oxidative stress via activation of Nrf2 signaling pathway, and also protects rats from myocardial ischemia-reperfusion injury by reducing inflammatory response via inhibition of NF-κB signaling pathway

Ultrafast Video Attention Prediction with Coupled Knowledge Distillation

Large convolutional neural network models have recently demonstrated impressive performance on video attention prediction. Conventionally, these models are with intensive computation and large memory. To address these issues, we design an extremely light-weight network with ultrafast speed, named UVA-Net. The network is constructed based on depth-wise convolutions and takes low-resolution images as input. However, this straight-forward acceleration method will decrease performance dramatically. To this end, we propose a coupled knowledge distillation strategy to augment and train the network effectively. With this strategy, the model can further automatically discover and emphasize implicit useful cues contained in the data. Both spatial and temporal knowledge learned by the high-resolution complex teacher networks also can be distilled and transferred into the proposed low-resolution light-weight spatiotemporal network. Experimental results show that the performance of our model is comparable to ten state-of-the-art models in video attention prediction, while it costs only 0.68 MB memory footprint, runs about 10,106 FPS on GPU and 404 FPS on CPU, which is 206 times faster than previous models