YOWOv2: A Stronger yet Efficient Multi-level Detection Framework for Real-time Spatio-temporal Action Detection

Designing a real-time framework for the spatio-temporal action detection task is still a challenge. In this paper, we propose a novel real-time action detection framework, YOWOv2. In this new framework, YOWOv2 takes advantage of both the 3D backbone and 2D backbone for accurate action detection. A multi-level detection pipeline is designed to detect action instances of different scales. To achieve this goal, we carefully build a simple and efficient 2D backbone with a feature pyramid network to extract different levels of classification features and regression features. For the 3D backbone, we adopt the existing efficient 3D CNN to save development time. By combining 3D backbones and 2D backbones of different sizes, we design a YOWOv2 family including YOWOv2-Tiny, YOWOv2-Medium, and YOWOv2-Large. We also introduce the popular dynamic label assignment strategy and anchor-free mechanism to make the YOWOv2 consistent with the advanced model architecture design. With our improvement, YOWOv2 is significantly superior to YOWO, and can still keep real-time detection. Without any bells and whistles, YOWOv2 achieves 87.0 % frame mAP and 52.8 % video mAP with over 20 FPS on the UCF101-24. On the AVA, YOWOv2 achieves 21.7 % frame mAP with over 20 FPS. Our code is available on https://github.com/yjh0410/YOWOv2

Analysis of the Research Trend of Global Value Chain Based on Literature Metrology and Visualization Technology

With the improvement of international division of labor and cooperation and the refinement of production process division, the global value chain division of labor system is becoming the latest mode of product division. In order to deeply analyze and explore the research status and development trend in the field of global value chain, based on the Web of Science core collection SCIE and SSCI database, this paper uses the literature metrology method and CiteSpace software to conduct a visual analysis of the literature in the field of global value chain. The study discovered research hotspots, research processes, development frontiers, research distribution, and citation status of global value chains. We have comprehensively grasped the literature status of global value chain research, providing scholars with new research directions, and also providing strong reference and guidance for investment decision-making of multinational corporations and trade policy formulation of countries and international organizations

Development of Mining Technology and Equipment for Seafloor Massive Sulfide Deposits

Seafloor massive sulfide(SMS) deposits which consist of Au, Ag, Cu, and other metal elements, have been a target of commercial mining in recent decades. The demand for established and reliable commercial mining system for SMS deposits is increasing within the marine mining industry. The current status and progress of mining technology and equipment for SMS deposits are introduced. First, the mining technology and other recent developments of SMS deposits are comprehensively explained and analyzed. The seafloor production tools manufactured by Nautilus Minerals and similar mining tools from Japan for SMS deposits are compared and discussed in turn. Second, SMS deposit mining technology research being conducted in China is described, and a new SMS deposits mining tool is designed according to the environmental requirement. Finally, some new trends of mining technology of SMS deposits are summarized and analyzed. All of these conclusions and results have reference value and guiding significance for the research of SMS deposit mining in China

New Noise-Tolerant ZNN Models With Predefined-Time Convergence for Time-Variant Sylvester Equation Solving

Sylvester equation is often applied to various fields, such as mathematics and control systems due to its importance. Zeroing neural network (ZNN), as a systematic design method for time-variant problems, has been proved to be effective on solving Sylvester equation in the ideal conditions. In this paper, in order to realize the predefined-time convergence of the ZNN model and modify its robustness, two new noise-tolerant ZNNs (NNTZNNs) are established by devising two novelly constructed nonlinear activation functions (AFs) to find the accurate solution of the time-variant Sylvester equation in the presence of various noises. Unlike the original ZNN models activated by known AFs, the proposed two NNTZNN models are activated by two novel AFs, therefore, possessing the excellent predefined-time convergence and strong robustness even in the presence of various noises. Besides, the detailed theoretical analyses of the predefined-time convergence and robustness ability for the NNTZNN models are given by considering different kinds of noises. Simulation comparative results further verify the excellent performance of the proposed NNTZNN models, when applied to online solution of the time-variant Sylvester equation

catena-Poly[[[(1,10-phenanthroline)copper(I)]-μ-cyanido] ethanol hemisolvate]

In the title coordination polymer, {[Cu(CN)(C12H10N2)]·0.5C2H5OH}n, there are two CuI ions, two 1,10-phenanthroline (phen) ligands and two cyanide ions in the asymmetric unit along with a highly disordered ethanol solvent mol­ecule, which was modelled as being disordered over two sets of sites in a 0.829 (7):0.171 (7) ratio. The orientation/ordering of the C and N atoms of the cyanide ions could not be determined in the present refinement and they were modelled as being statistically disordered. Both copper ions are coordinated by an N,N′-bidentate phen ligand and two cyanide ligands, generating distorted tetra­hedral CuN2 Q 2 (Q = C or N) tetra­hedra. The μ-cyanide ligands link the metal ions, forming a zigzag chain propagating in [001]. The chains are cross-linked by numerous aromatic π–π stacking contacts between adjacent phen rings [minimum centroid–centroid separation = 3.620 (3) Å]

Design and Comprehensive Analysis of a Noise-Tolerant ZNN Model With Limited-Time Convergence for Time-Dependent Nonlinear Minimization

Zeroing neural network (ZNN) is a powerful tool to address the mathematical and optimization problems broadly arisen in the science and engineering areas. The convergence and robustness are always co-pursued in ZNN. However, there exists no related work on the ZNN for time-dependent nonlinear minimization that achieves simultaneously limited-time convergence and inherently noise suppression. In this article, for the purpose of satisfying such two requirements, a limited-time robust neural network (LTRNN) is devised and presented to solve time-dependent nonlinear minimization under various external disturbances. Different from the previous ZNN model for this problem either with limited-time convergence or with noise suppression, the proposed LTRNN model simultaneously possesses such two characteristics. Besides, rigorous theoretical analyses are given to prove the superior performance of the LTRNN model when adopted to solve time-dependent nonlinear minimization under external disturbances. Comparative results also substantiate the effectiveness and advantages of LTRNN via solving a time-dependent nonlinear minimization problem

A Noise-Tolerant Zeroing Neural Network for Time-Dependent Complex Matrix Inversion Under Various Kinds of Noises

Complex-valued time-dependent matrix inversion (TDMI) is extensively exploited in practical industrial and engineering fields. Many current neural models are presented to find the inverse of a matrix in an ideal noise-free environment. However, the outer interferences are normally believed to be ubiquitous and avoidable in practice. If these neural models are applied to complex-valued TDMI in a noise environment, they need to take a lot of precious time to deal with outer noise disturbances in advance. Thus, a noise-suppression model is urgent to be proposed to address this problem. In this article, a complex-valued noise-tolerant zeroing neural network (CVNTZNN) on the basis of an integral-type design formula is established and investigated for finding complex-valued TDMI under a wide variety of noises. Furthermore, both convergence and robustness of the CVNTZNN model are carefully analyzed and rigorously proved. For comparison and verification purposes, the existing zeroing neural network (ZNN) and gradient neural network (GNN) have been presented to address the same problem under the same conditions. Numerical simulation consequences demonstrate the effectiveness and excellence of the proposed CVNTZNN model for complex-valued TDMI under various kinds of noises, by comparing the existing ZNN and GNN models

N-Benzyl­pyridin-2-amine

In the title compound, C12H12N2, the dihedral angle between the benzene and pyridine rings is 67.63 (8)°. Mol­ecules are linked into centrosymmetric dimers by a simple inter­molecular N—H⋯N hydrogen bond with graph-set motif R 2 2(8)