Designing Network Design Strategies Through Gradient Path Analysis

Designing a high-efficiency and high-quality expressive network architecture has always been the most important research topic in the field of deep learning. Most of today's network design strategies focus on how to integrate features extracted from different layers, and how to design computing units to effectively extract these features, thereby enhancing the expressiveness of the network. This paper proposes a new network design strategy, i.e., to design the network architecture based on gradient path analysis. On the whole, most of today's mainstream network design strategies are based on feed forward path, that is, the network architecture is designed based on the data path. In this paper, we hope to enhance the expressive ability of the trained model by improving the network learning ability. Due to the mechanism driving the network parameter learning is the backward propagation algorithm, we design network design strategies based on back propagation path. We propose the gradient path design strategies for the layer-level, the stage-level, and the network-level, and the design strategies are proved to be superior and feasible from theoretical analysis and experiments.Comment: 12 pages, 9 figure

Fast Video Retrieval via the Statistics of Motion

[[abstract]]Due to the popularity of the Internet and the powerful computing capability of computers, efficient processing/retrieval of multimedia data has become an important issue. In this paper, we propose a fast video retrieval algorithm that bases its search core on the statistics of object motion. The algorithm starts with extracting object motions from a shot and then transform/quantize them into the form of probability distributions. By choosing the shot that has the largest entropy value among the constituent shots of an unknown query video clip, we execute the first stage video search.By comparing two shots with different lengths, their corresponding motion probability distributions are compared by a discrete Bhattacharyya distance which is designed to measure the similarity between any two distribution functions. In the second stage, we add an adjacent shot(either preceding or subsequent) to perform a finer comparison. Experimental results demonstrate that our fast video retrieval algorithm is powerful in terms of accuracy and efficiency.[[fileno]]2030144030026[[department]]電機工程學

Automatic Key Posture Selection for Human Behavior Analysis

[[abstract]]A novel human posture analysis framework that can perform automatic key posture selection and template matching for human behavior analysis is proposed. The entropy measurement, which is commonly adopted as an important feature to describe the degree of disorder in thermodynamics, is used as an underlying feature for identifying key postures. First, we use cumulative entropy change as an indicator to select an appropriate set of key postures from a human behavior video sequence and then conduct a cross entropy check to remove redundant key postures. With the key postures detected and stored as human posture templates, the degree of similarity between a query posture and a database template is evaluated using a modified Hausdorff distance measure. The experiment results show that the proposed system is highly efficient and powerful[[fileno]]2030144030013[[department]]電機工程學

Differential cross section measurements for the production of a W boson in association with jets in proton–proton collisions at √s = 7 TeV

Measurements are reported of differential cross sections for the production of a W boson, which decays into a muon and a neutrino, in association with jets, as a function of several variables, including the transverse momenta (pT) and pseudorapidities of the four leading jets, the scalar sum of jet transverse momenta (HT), and the difference in azimuthal angle between the directions of each jet and the muon. The data sample of pp collisions at a centre-of-mass energy of 7 TeV was collected with the CMS detector at the LHC and corresponds to an integrated luminosity of 5.0 fb[superscript −1]. The measured cross sections are compared to predictions from Monte Carlo generators, MadGraph + pythia and sherpa, and to next-to-leading-order calculations from BlackHat + sherpa. The differential cross sections are found to be in agreement with the predictions, apart from the pT distributions of the leading jets at high pT values, the distributions of the HT at high-HT and low jet multiplicity, and the distribution of the difference in azimuthal angle between the leading jet and the muon at low values.United States. Dept. of EnergyNational Science Foundation (U.S.)Alfred P. Sloan Foundatio