Long-term Leap Attention, Short-term Periodic Shift for Video Classification

Video transformer naturally incurs a heavier computation burden than a static vision transformer, as the former processes $T$ times longer sequence than the latter under the current attention of quadratic complexity $(T^2N^2)$. The existing works treat the temporal axis as a simple extension of spatial axes, focusing on shortening the spatio-temporal sequence by either generic pooling or local windowing without utilizing temporal redundancy. However, videos naturally contain redundant information between neighboring frames; thereby, we could potentially suppress attention on visually similar frames in a dilated manner. Based on this hypothesis, we propose the LAPS, a long-term ``\textbf{\textit{Leap Attention}}'' (LA), short-term ``\textbf{\textit{Periodic Shift}}'' (\textit{P}-Shift) module for video transformers, with $(2TN^2)$ complexity. Specifically, the ``LA'' groups long-term frames into pairs, then refactors each discrete pair via attention. The ``\textit{P}-Shift'' exchanges features between temporal neighbors to confront the loss of short-term dynamics. By replacing a vanilla 2D attention with the LAPS, we could adapt a static transformer into a video one, with zero extra parameters and neglectable computation overhead ($\sim$2.6\%). Experiments on the standard Kinetics-400 benchmark demonstrate that our LAPS transformer could achieve competitive performances in terms of accuracy, FLOPs, and Params among CNN and transformer SOTAs. We open-source our project in \sloppy \href{https://github.com/VideoNetworks/LAPS-transformer}{\textit{\color{magenta}{https://github.com/VideoNetworks/LAPS-transformer}}} .Comment: Accepted by ACM Multimedia 2022, 10 pages, 4 figure

Analyses of representative elementary volume for coal using X-ray μ-CT and FIB-SEM and its application in permeability predication model

We acknowledge financial support from the National Natural Science Foundation of China (41872123; 41830427), the Petro China Innovation Foundation (2018D-5007-0101), the Key research and development project of Xinjiang Uygur Autonomous Region (2017B03019-1), the Royal Society Edinburgh through the international cost share scheme and National Natural Science Foundation China (NSFC 41711530129).Peer reviewedPostprin

Imaged based fractal characterization of micro-fracture structure in coal

We acknowledge financial support from the National Natural Science Foundation of China (41830427; 41472137), the Petro China Innovation Foundation (2018D-5007-0101), the Key research and development project of Xinjiang Uygur Autonomous Region (2017B03019-1), the Royal Society Edinburgh and National Natural Science Foundation China (NSFC 41711530129), and the Foreign Experts’ Recruiting Program from the State Administration of Foreign Experts Affairs P.R. China.Peer reviewedPostprin

Masked Collaborative Contrast for Weakly Supervised Semantic Segmentation

This study introduces an efficacious approach, Masked Collaborative Contrast (MCC), to emphasize semantic regions in weakly supervised semantic segmentation. MCC adroitly incorporates concepts from masked image modeling and contrastive learning to devise Transformer blocks that induce keys to contract towards semantically pertinent regions. Unlike prevalent techniques that directly eradicate patch regions in the input image when generating masks, we scrutinize the neighborhood relations of patch tokens by exploring masks considering keys on the affinity matrix. Moreover, we generate positive and negative samples in contrastive learning by utilizing the masked local output and contrasting it with the global output. Elaborate experiments on commonly employed datasets evidences that the proposed MCC mechanism effectively aligns global and local perspectives within the image, attaining impressive performance. The source code is available at \url{https://github.com/fwu11/MCC}

Revisit Two Memoryless State-Recovery Cryptanalysis Methods on A5/1

At ASIACRYPT 2019, Zhang proposed a near collision attack on A5/1 claiming to recover the 64-bit A5/1 state with a time complexity around $2^{32}$ cipher ticks with negligible memory requirements. Soon after its proposal, Zhang\u27s near collision attack was severely challenged by Derbez \etal who claimed that Zhang\u27s attack cannot have a time complexity lower than Golic\u27s memoryless guess-and-determine attack dating back to EUROCRYPT 1997. In this paper, we study both the guess-and-determine and the near collision attacks for recovering A5/1 states with negligible memory complexities. Firstly, we propose a new guessing technique called the \emph{move guessing technique} that can construct linear equation filters in a more efficient manner. Such a technique can be applied to both guess-and-determine and collision attacks for efficiency improvements. Secondly, we take the filtering strength of the linear equation systems into account for complexity analysis. Such filtering strength are evaluated with practical experiments making the complexities more convincing. Based on such new techniques, we are able to give 2 new guess-and-determine attacks on A5/1: the 1st attack recovers the internal state $\vec{s}^0$ with time complexity $2^{43.92}$; the 2nd one recovers a different state $\vec{s}^1$ with complexity $2^{43.25}$. We also revisit Golic\u27s guess-and-determine attack and Zhang\u27s near collision attacks. According to our detailed analysis, the complexity of Golic\u27s $\vec{s}^1$ recovery attack is no lower than $2^{46.04}$, higher than the previously believed $2^{43}$. On the other hand, Zhang\u27s near collision attack recovers $\vec{s}^0$ with the time complexity $2^{53.19}$: such a complexity can be further lowered to $2^{50.78}$ with our move guessing technique

Aggregated Multi-GANs for Controlled 3D Human Motion Prediction

Human motion prediction from historical pose sequence is at the core of many applications in machine intelligence. However, in current state-of-the-art methods, the predicted future motion is confined within the same activity. One can neither generate predictions that differ from the current activity, nor manipulate the body parts to explore various future possibilities. Undoubtedly, this greatly limits the usefulness and applicability of motion prediction. In this paper, we propose a generalization of the human motion prediction task in which control parameters can be readily incorporated to adjust the forecasted motion. Our method is compelling in that it enables manipulable motion prediction across activity types and allows customization of the human movement in a variety of fine-grained ways. To this aim, a simple yet effective composite GAN structure, consisting of local GANs for different body parts and aggregated via a global GAN is presented. The local GANs game in lower dimensions, while the global GAN adjusts in high dimensional space to avoid mode collapse. Extensive experiments show that our method outperforms state-of-the-art. The codes are available at https://github.com/herolvkd/AM-GAN

The associations between body dissatisfaction, exercise intensity, sleep quality, and depression in university students in southern China

BackgroundIn recent years, depression in early adulthood has become an urgent global public health concern. The university years mark a transitional period from adolescence to adulthood. Young people are required to face academic and life pressures independently, which increases the risk of mental health problems in university.PurposeThe main goal of the current study was to explore the sex differences in depression, body dissatisfaction, sleep quality, and exercise intensity among university students in southern China and to analyze the factors affecting the level of depression among university students.MethodsIn total, 1,258 university students aged 18–23 years were recruited for this study. All participants completed anthropometric measurements, the Self-rating Depression Scale, Physical Activity Rating Scale, and Pittsburgh Sleep Quality Index. Body dissatisfaction levels were measured using sex-appropriate silhouettes.ResultsCompared with young women, young men had higher exercise intensity and sleep quality, whereas young women’s body dissatisfaction and depression levels were significantly higher than those of young men. Sleep quality score (β = 0.34, p < 0.01), sex (β = 0.15, p < 0.01), physical activity score (β = −0.14, p < 0.01), and body dissatisfaction (β = 0.14, p < 0.01) were significant predictive factors of the Self-rating Depression Scale score.ConclusionLow levels of physical dissatisfaction have a positive effect on depression, and high levels of physical activity and quality sleep can also improve depressive symptoms. At the same time, increasing body satisfaction has the effect of increasing physical activity and improving sleep quality. Therefore, there is great potential to prevent and ameliorate depression by reducing body dissatisfaction