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

CFAD: A Chinese Dataset for Fake Audio Detection

Fake audio detection is a growing concern and some relevant datasets have been designed for research. However, there is no standard public Chinese dataset under complex conditions.In this paper, we aim to fill in the gap and design a Chinese fake audio detection dataset (CFAD) for studying more generalized detection methods. Twelve mainstream speech-generation techniques are used to generate fake audio. To simulate the real-life scenarios, three noise datasets are selected for noise adding at five different signal-to-noise ratios, and six codecs are considered for audio transcoding (format conversion). CFAD dataset can be used not only for fake audio detection but also for detecting the algorithms of fake utterances for audio forensics. Baseline results are presented with analysis. The results that show fake audio detection methods with generalization remain challenging. The CFAD dataset is publicly available at: https://zenodo.org/record/8122764.Comment: FAD renamed as CFA

Expanding Language-Image Pretrained Models for General Video Recognition

Contrastive language-image pretraining has shown great success in learning visual-textual joint representation from web-scale data, demonstrating remarkable "zero-shot" generalization ability for various image tasks. However, how to effectively expand such new language-image pretraining methods to video domains is still an open problem. In this work, we present a simple yet effective approach that adapts the pretrained language-image models to video recognition directly, instead of pretraining a new model from scratch. More concretely, to capture the long-range dependencies of frames along the temporal dimension, we propose a cross-frame attention mechanism that explicitly exchanges information across frames. Such module is lightweight and can be plugged into pretrained language-image models seamlessly. Moreover, we propose a video-specific prompting scheme, which leverages video content information for generating discriminative textual prompts. Extensive experiments demonstrate that our approach is effective and can be generalized to different video recognition scenarios. In particular, under fully-supervised settings, our approach achieves a top-1 accuracy of 87.1% on Kinectics-400, while using 12 times fewer FLOPs compared with Swin-L and ViViT-H. In zero-shot experiments, our approach surpasses the current state-of-the-art methods by +7.6% and +14.9% in terms of top-1 accuracy under two popular protocols. In few-shot scenarios, our approach outperforms previous best methods by +32.1% and +23.1% when the labeled data is extremely limited. Code and models are available at https://aka.ms/X-CLIPComment: Accepted by ECCV2022, Ora

Efficient perpendicular magnetization switching by a magnetic spin Hall effect in a noncollinear antiferromagnet

Current induced spin-orbit torques driven by the conventional spin Hall effect are widely used to manipulate the magnetization. This approach, however, is nondeterministic and inefficient for the switching of magnets with perpendicular magnetic anisotropy that are demanded by the high-density magnetic storage and memory devices. Here, we demonstrate that this limitation can be overcome by exploiting a magnetic spin Hall effect in noncollinear antiferromagnets, such as Mn3Sn. The magnetic group symmetry of Mn3Sn allows generation of the out-of-plane spin current carrying spin polarization collinear to its direction induced by an in-plane charge current. This spin current drives an out-of-plane anti-damping torque providing the deterministic switching of the perpendicular magnetization of an adjacent Ni/Co multilayer. Due to being odd with respect to time reversal symmetry, the observed magnetic spin Hall effect and the resulting spin-orbit torque can be reversed with reversal of the antiferromagnetic order. Contrary to the conventional spin-orbit torque devices, the demonstrated magnetization switching does not need an external magnetic field and requires much lower current density which is useful for low power spintronics

Aberrant Expression of N-Methylpurine-DNA Glycosylase Influences Patient Survival in Malignant Gliomas

Aim. To examine the expression of N-methylpurine-DNA glycosylase (MPG) gene and protein in glioma samples with different WHO grades and its association with patients' survival. Methods. Immunohistochemistry assay, quantitative real-time PCR and Western blot analysis were carried out to investigate the expression of MPG gene and protein in 128 glioma and 10 non-neoplastic brain tissues. Results. MPG gene expression level in glioma tissues was significantly higher than that in non-neoplastic brain tissues (P < 0.001). Immunohistochemistry also showed that MPG protein was over-expressed in glioma tissues, which was consistent with the resutls of Western blot analysis. Additionally, the expression levels of MPG gene and protein both increase from grade I to grade IV glioma according to the results of real-time PCR, immunohistochemistry and western blot analysis. Moreover, the survival rate of MPG-positive patients was significantly lower than that of MPG-negative patients (P < 0.001). We further confirmed that the over-expression of MPG was a significant and independent prognostic indicator in glioma by multivariate analysis (P < 0.001). Conclusions. Our data showed the over-expression of MPG gene and protein in human gliomas, and also suggested for the first time that MPG be an unfavorable independent prognostic indicator for glioma patients

Unraveling the Role of the rssC Gene of Serratia marcescens by Atomic Force Microscopy

100學年度研究獎補助論文[[abstract]]The product and direct role of the rssC gene of Serratia marcescens is unknown. For unraveling the role of the rssC gene, atomic force microscopy has been used to identify the surfaces of intact S. marcescens wild-type CH-1 cells and rssC mutant CH-1ΔC cells. The detailed surface topographies were directly visualized, and quantitative measurements of the physical properties of the membrane structures were provided. CH-1 and CH-1ΔC cells were observed before and after treatment with lysozyme, and their topography-related parameters, e.g., a valley-to-peak distance, mean height, surface roughness, and surface root-mean-square values, were defined and compared. The data obtained suggest that the cellular surface topography of mutant CH-1ΔC becomes rougher and more precipitous than that of wild-type CH-1 cells. Moreover, it was found that, compared with native wild-type CH-1, the cellular surface topography of lysozyme-treated CH-1 was not changed profoundly. The product of the rssC gene is thus predicted to be mainly responsible for fatty-acid biosynthesis of the S. marcescens outer membrane. This study represents the first direct observation of the structural changes in membranes of bacterial mutant cells and offers a new prospect for predicting gene expression in bacterial cells.[[journaltype]]國外[[incitationindex]]SCI[[booktype]]紙本[[countrycodes]]GB