Optical Flow Guided Feature: A Fast and Robust Motion Representation for Video Action Recognition

Motion representation plays a vital role in human action recognition in videos. In this study, we introduce a novel compact motion representation for video action recognition, named Optical Flow guided Feature (OFF), which enables the network to distill temporal information through a fast and robust approach. The OFF is derived from the definition of optical flow and is orthogonal to the optical flow. The derivation also provides theoretical support for using the difference between two frames. By directly calculating pixel-wise spatiotemporal gradients of the deep feature maps, the OFF could be embedded in any existing CNN based video action recognition framework with only a slight additional cost. It enables the CNN to extract spatiotemporal information, especially the temporal information between frames simultaneously. This simple but powerful idea is validated by experimental results. The network with OFF fed only by RGB inputs achieves a competitive accuracy of 93.3% on UCF-101, which is comparable with the result obtained by two streams (RGB and optical flow), but is 15 times faster in speed. Experimental results also show that OFF is complementary to other motion modalities such as optical flow. When the proposed method is plugged into the state-of-the-art video action recognition framework, it has 96:0% and 74:2% accuracy on UCF-101 and HMDB-51 respectively. The code for this project is available at https://github.com/kevin-ssy/Optical-Flow-Guided-Feature.Comment: CVPR 2018. code available at https://github.com/kevin-ssy/Optical-Flow-Guided-Featur

Design verification of stress and sag for 500 kV transmission line

Transmission line stress directly affects the tower sag force structure and a safe distance from the safe operation of the transmission lines play a crucial role. Calculation method for the preparation of the design process were complied, simply enter transmission liner parameters can be automatically obtained stress and sag. According to a 500 kV transmission line tests carried out stress sag design verification, checking ground found stress design errors and were corrected to ensure the safety of the project. Calculation program can be developed for different voltage levels for line stress on cables sag design verification

Understanding the Generalization Performance of Spectral Clustering Algorithms

The theoretical analysis of spectral clustering mainly focuses on consistency, while there is relatively little research on its generalization performance. In this paper, we study the excess risk bounds of the popular spectral clustering algorithms: \emph{relaxed} RatioCut and \emph{relaxed} NCut. Firstly, we show that their excess risk bounds between the empirical continuous optimal solution and the population-level continuous optimal solution have a $\mathcal{O}(1/\sqrt{n})$ convergence rate, where $n$ is the sample size. Secondly, we show the fundamental quantity in influencing the excess risk between the empirical discrete optimal solution and the population-level discrete optimal solution. At the empirical level, algorithms can be designed to reduce this quantity. Based on our theoretical analysis, we propose two novel algorithms that can not only penalize this quantity, but also cluster the out-of-sample data without re-eigendecomposition on the overall sample. Experiments verify the effectiveness of the proposed algorithms

VIGraph: Self-supervised Learning for Class-Imbalanced Node Classification

Class imbalance in graph data poses significant challenges for node classification. Existing methods, represented by SMOTE-based approaches, partially alleviate this issue but still exhibit limitations during imbalanced scenario construction. Self-supervised learning (SSL) offers a promising solution by synthesizing minority nodes from the data itself, yet its potential remains unexplored. In this paper, we analyze the limitations of SMOTE-based approaches and introduce VIGraph, a novel SSL model based on the self-supervised Variational Graph Auto-Encoder (VGAE) that leverages Variational Inference (VI) to generate minority nodes. Specifically, VIGraph strictly adheres to the concept of imbalance when constructing imbalanced graphs and utilizes the generative VGAE to generate minority nodes. Moreover, VIGraph introduces a novel Siamese contrastive strategy at the decoding phase to improve the overall quality of generated nodes. VIGraph can generate high-quality nodes without reintegrating them into the original graph, eliminating the "Generating, Reintegrating, and Retraining" process found in SMOTE-based methods. Experiments on multiple real-world datasets demonstrate that VIGraph achieves promising results for class-imbalanced node classification tasks

Successful radiofrequency ablation of a right posteroseptal accessory pathway through an anomalous inferior vena cava and azygos continuation in a patient with incomplete situs inversus

We present a 43-year-old patient with paroxysmal supraventricular tachycardia. In the process of catheter ablation, we found interruption of the inferior vena cava with azygos continuation with incomplete situs inversus. In this patient, we adopted the lower approach via the anomalous inferior vena cava and azygos continuation to achieve stability of radiofrequency catheter for right posteroseptal accessory pathway, and successfully abolished the preexcitation

Numerical investigation on hydrodynamic performance of a novel shaftless rim-driven counter-rotating thruster considering gap fluid

Shaftless rim-driven thruster (RDT) has recently become the research focus for marine propulsion, primarily due to low vibration, low noise, and energy saving as its advantage. This study is based on CFD theory and used the Ansys-Fluent software to examine the hydrodynamic performance of a novel rim-driven counter-rotating thruster (RDCRT). It takes a No.19A+Ka4-70 duct propeller and a 20 kW RDT as examples, as it verifies the feasibility of the simulation method. It establishes three geometric models for RDCRT's hydrodynamic performance to determine whether it is necessary to consider the motor stator/rotor gap. It examines the flow distribution characteristics of the gap fluid friction force and flow channel and investigates the gap's influence on the hydrodynamic performance. Relevant case studies indicate that, when considering the gap, the calculation outcomes of the simulation model are between the stationary model and the rotational model of the rotor inner wall when ignoring the gap. In the Forward and Aft regions, the total frictional power of the gap channel correspondingly accounts for 1.7% and 1.35% of the rated power. Additionally, compared to situations with a gap, the pressure coefficient of the inner surface of the Forward and Aft rim without a gap is more significant. Thus, the hydrodynamic simulation model should not ignore the gap. For the RDCRT, the thrust coefficient, the torque coefficient, and the maximum efficiency value are more significant than those of the single-propeller RDT, hence validating its advantages

Short-range interaction of strongly nonlocal spatial optical solitons

A novel phenomenon is discovered that the short-range interaction between strongly nonlocal spatial solitons depends sinusoidally on their phase difference. The two neighbouring solitons at close proximate can be inter-trapped via the strong nonlocality, and propagate together as a whole. The trajectory of the propagation is a straight line with its slope controlled by the phase difference. The experimental results carried out in nematic liquid crystals agree quantitatively with the prediction. Our study suggests that the phenomenon to steer optical beams by controlling the phase difference could be used in all-optical information processing.Comment: 4 pages 6 figure