Read, Watch, and Move: Reinforcement Learning for Temporally Grounding Natural Language Descriptions in Videos

The task of video grounding, which temporally localizes a natural language description in a video, plays an important role in understanding videos. Existing studies have adopted strategies of sliding window over the entire video or exhaustively ranking all possible clip-sentence pairs in a pre-segmented video, which inevitably suffer from exhaustively enumerated candidates. To alleviate this problem, we formulate this task as a problem of sequential decision making by learning an agent which regulates the temporal grounding boundaries progressively based on its policy. Specifically, we propose a reinforcement learning based framework improved by multi-task learning and it shows steady performance gains by considering additional supervised boundary information during training. Our proposed framework achieves state-of-the-art performance on ActivityNet'18 DenseCaption dataset and Charades-STA dataset while observing only 10 or less clips per video.Comment: AAAI 201

Relationship of rock microscopic parameters with the elastic modulus and strength

The microscopic damage of materials will induce changes in the macroscopic mechanical characteristics of rock material. When simulating engineering problems using the discrete element method, to explore the macroscopic mechanical response of rock material, the microscopic parameters that match the macro material characteristics must be obtained. In this paper, the influence of macroscopic mechanical properties of rock materials is studied through the variation of microscopic parameters, and the quantitative relation between macroscopic parameters of rock material is discussed. The results show that, (1) In accordance with the order of influencing factors, the parameters affecting the elastic modulus of the specimen are parallel bond elastic modulus, particle contact modulus, and parallel bond stiffness ratio. (2) The Poisson’s ratio of the specimen was most influenced by the parallel bond stiffness ratio, and their relation was nonlinear. The influence of parallel bond modulus and friction factor on the Poisson’s ratio was negatively correlated. (3) The effect of particle contact stiffness ratio, parallel bond stiffness ratio, and particle contact modulus on the uniaxial compressive strength was less than that of the particle friction factor

Rolling bearing fault diagnosis by a novel fruit fly optimization algorithm optimized support vector machine

Based on the nonlinear and non-stationary characteristics of rotating machinery vibration, a FOA-SVM model is established by Fruit Fly Optimization Algorithm (FOA) and combining the Support Vector Machine (SVM) to realize the optimization of the SVM parameters. The mechanism of this model is imitating the foraging behavior of fruit flies. The smell concentration judgment value of the forage is used as the parameter to construct a proper fitness function in order to search the optimal SVM parameters. The FOA algorithm is proved to be convergence fast and accurately with global searching ability by optimizing the analog signal of rotating machinery fault. In order to improve the classification accuracy rate, built FOA-SVM model, and then to extract feature value for training and testing, so that it can recognize the fault rolling bearing and the degree of it. Analyze and diagnose actual signals, it prove the validity of the method, and the improved method had a good prospect for its application in rolling bearing diagnosis