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We show that Nesterov acceleration arises from discretizing an ordinary differential equation with a semi-implicit Euler integration scheme. ; Li, F.; Desrosiers, C.; Zhang, C.M. 2, pp. From Jan 2008 to Jan 2015,he was with the National Sun Yat-sen University (NSYSU) as Xiwan Chair Professor.He was Head of the Department of Applied Mathematics from Nov 2010-July 2013 and Dean of College of Science from Feb 2014-Jan 2015 both at NSYSU. Thus, there are several ways in which these movements, that define a human action, can be recordedas a video clip (a set of RGB images), by recording a series of depth maps, or in the form of a data structure storing the positions of many joints for each time frame, either representing a time-dependant 3D mesh of the visible human body surface or even just a time-dependent graph of articulation points that describes a simplified model of a human skeleton or other combinations. A combination of source and site effects appears to be reason for the occurrence of such amplifications [36] during large magnitude earthquakes. [5][6][7][8][9] This method, sometimes called "FISTA", was further developed by Beck & Teboulle in their 2009 paper "A Fast Iterative Shrinkage-Thresholding Algorithm for Linear Inverse Problems". 137148, Springer, Berlin, Germany, 1999. On Nesterov acceleration for Landweber iteration of linear ill-posed and Ph.D. degrees from Zhejiang Normal University in 1982, Zhejiang University in 1985 and Xian Jiaotong University in 1988, respectively. Nesterov Acceleration for Riemannian Optimization Jungbin Kim, Insoon Yang In this paper, we generalize the Nesterov accelerated gradient (NAG) method to solve Riemannian optimization problems in a computationally tractable manner. Larger variabilities for the spectral accelerations computed for the seismic stations in Bucharest area were observed as compared to the variability of the peak ground velocity and displacement. Xu's research areas include nonlinear functional analysis, iterative methods for nonlinear equations, and inverse and ill-posed problems, convex and nonconvex optimization algorithms for big data problems, geometry of Banach spaces, and mathematical finance. He, K.; Zhang, X.; Ren, S.; Sun, J. Delving deep into rectifiers: Surpassing human-level performance on imagenet classification. Relation between observed and empirical values for (a) PGV (correlation coefficient=0.97) and (b) PGD (correlation coefficient=0.99). Paper should be a substantial original Article that involves several techniques or approaches, provides an outlook for 1, pp. [. NesterovMomentum - - NesterovMomentumMomentumNesterov v NesterovMomentum ; Chichung, A.K. Si, C.; Jing, Y.; Wang, W.; Wang, L.; Tan, T. Skeleton-based action recognition with spatial reasoning and temporal stack learning. We cannot increase the learning rate, because doing so might lead us to diverge along some dimensions or from some starting points. Comparison between Recurrent Networks and Temporal Convolutional - MDPI The mean and standard deviation necessary for computing the normal probability distribution functions shown in Figure 13 are given in Table 3. Acceleration Turns out we canaccelerateproximal gradient descent in order to achieve the optimal O(1= p ) convergence rate. those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). Disclaimer/Publishers Note: The statements, opinions and data contained in all publications are solely Song, S.; Lan, C.; Xing, J.; Zeng, W.; Liu, J. Spatio-temporal attention-based LSTM networks for 3D action recognition and detection. Zhang, L.; Shi, Z.; Han, J.; Shi, A.; Ma, D. FurcaNeXt: End-to-end monaural speech separation with dynamic gated dilated temporal convolutional networks. Biography Initially, the paper explores and compares different ways to extract the most relevant spatial and temporal characteristics for a sequence of frames describing an action. The idea being that it is better to correct a mistake after you have made it. These approaches are based on the two types of networks that are currently mostly used to determine temporal dependencies in sequence modelling problemsrecurrent neural networks and temporal convolutional networks. Biography:Hong Kun Xu is currently Distinguished Professor at Hangzhou Dianzi University in Hangzhou, China. 535562, 2015. "Comparison between Recurrent Networks and Temporal Convolutional Networks Approaches for Skeleton-Based Action Recognition" Sensors 21, no. [. We give a theoretical analysis that . [25, 26], Calarasu [27], Calarasu et al. Unfortunately, no ground motion was recorded during the largest seismic event of the past century, the November 1940 Vrancea earthquake, and thus, the above-made observation cannot be validated. Zhang, P.; Lan, C.; Xing, J.; Zeng, W.; Xue, J.; Zheng, N. View adaptive neural networks for high performance skeleton-based human action recognition. Mean and standard deviation of the shear wave velocities at depths of 10m, 30m, and 50m. Mean and standard deviation of the shear wave velocities in the upper 30m and 50m of soil deposits. Coefficients of variation for the spectral accelerations as a function of the earthquake magnitude. MomentumNesterov Accelerated Gradient - In Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany, 814 September 2018; pp. These methods are based on the . The draft of the future version of the Eurocode 8 [30] proposes a new site classification and new site-amplification factors [13] as compared to the current version [10]. Based on this comparative analysis, we show how a TCN type unit can be extended to work even on the characteristics extracted from the spatial domain. For most exciting work published in the various research areas of the journal. Bai, S.; Kolter, J.Z. 11101118. This is the sort of situation where Nesterov-type acceleration helps. Comparison between Recurrent Networks and Temporal Convolutional Networks Approaches for Skeleton-Based Action Recognition. [. He received B.S., M.S. A. Bala, B. Grecu, V. Ciugudean, and V. Raileanu, Dynamic properties of the Quaternary sedimentary rocks and their influence on seismic site effects. [11]. Also, it is relevant to notice that this representation can compress a large amount of information. Thus, we obtained a smaller neural network, but with a performance comparable to the previous ones. In Proceedings of the IEEE International Conference on Computer Vision, Santiago, Chile, 713 December 2015; pp. ; Investigation: M.N. [, For the bone-branch, we also have 6 features that include the 3 lengths and the 3 values of the angles for the, To extract spatial dependencies, we proposed two variants of reorganizing the joints: one 2D (shown in, The 2D variant was proposed earlier in our paper [, The second proposed reorganization is a linear one and is inspired by Yang et al. and A.M.F. The main purpose of using this spatial rearrangement technique is to help the classification module work with relevant spatial features. [, Li, W.; Zhang, Z.; Liu, Z. [. Yurii Nesterov is a Russian mathematician, an internationally recognized expert in convex optimization, especially in the development of efficient algorithms and numerical optimization analysis. ; Koltun, V. An empirical evaluation of generic convolutional and recurrent networks for sequence modeling. 28422850, 2014. Data used in this study are available upon request to the author (e-mail: [emailprotected]). [12] has shown that the median site amplifications decrease with the increase of the input peak ground acceleration for spectral periods of up to 2.0s, while for longer periods, the median site amplifications increase denoting nonlinear soil behaviour. Optimal-order convergence of Nesterov acceleration for linear ill-posed Geng, X.; Li, Y.; Wang, L.; Zhang, L.; Yang, Q.; Ye, J.; Liu, Y. Spatiotemporal multi-graph convolution network for ride-hailing demand forecasting. 17, no. It is well-known since the pioneering work of Nesterov that the rate of convergence O(1/t2) is optimal for the class of convex functions with Lipschitz gradient. 12271236. The two variables continuously mix following a linear ordinary differential equation and take gradient steps at random times. In this paper, we study the behavior of solutions of the ODE associated to Nesterov acceleration. Yang, Z.; Li, Y.; Yang, J.; Luo, J. We give a theoretical analysis. Jia, J.G. This gives them a greater degree of generalization in terms of reuse for other types of problems. 6, pp. Actionactions are single-person activities that may be composed of multiple gestures organized temporally. [. Li, C.; Zhong, Q.; Xie, D.; Pu, S. Skeleton-based Action Recognition with Convolutional Neural Networks. He is currently a professor at the University of Louvain (UCLouvain). 2021. Comparison of the coefficients of variation for the peak ground velocity and displacement as a function of the earthquake magnitude. and A.M.F. This section focuses on the assessment of the variability of the characteristics of the soil profiles within Bucharest area, namely, the variability in the shear wave velocities. Shahroudy, A.; Liu, J.; Ng, T.; Wang, G. NTU RGB+D: A Large Scale Dataset for 3D Human Activity Analysis. In Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition-Workshops, San Francisco, CA, USA, 1318 June 2010; pp. Subsequently, he joined East China University of Science and Technology as Lecturer and was promoted to Associate Professor in 1990. This change is made in order to ensure the capture of spatial dependencies, in addition to temporal ones. 23, no. F. Pavel, V. Popa, and R. Vacareanu, Impact of Long-Period Ground Motions on Structural Design: A Case Study for Bucharest, Romania, Springer International Publishing, New York, NY, USA, 2018. ; Florea, A.M. https://doi.org/10.3390/s21062051, Subscribe to receive issue release notifications and newsletters from MDPI journals, You can make submissions to other journals. Plizzari, C.; Cannici, M.; Matteucci, M. Spatial temporal transformer network for skeleton-based action recognition. Many approaches have been proposed to this problem, but most of the time, they have only been tested on certain benchmarks as it is very difficult to implement a model that can generalize and function in any conditions. [4] His main novel contribution is an accelerated version of gradient descent that converges considerably faster than ordinary gradient descent (commonly referred as Nesterov momentum, Nesterov Acceleration or Nesterov accelerated gradient, in short NAG). In both cases, this results in a signicant speedup in practice, see Figure1. Since it is a crustal event, it was not mentioned in Table 1. [, Liu, J.; Shahroudy, A.; Xu, D.; Wang, G. Spatio-temporal lstm with trust gates for 3D human action recognition. S. Akkar and J. J. Bommer, Influence of long-period filter cut-off on elastic spectral displacements, Earthquake Engineering & Structural Dynamics, vol. The large variability associated with the long periods may also be due to the processing of the ground motion recordings. Starting from the coordinates that are provided in the dataset for each joint and applying the pre-processing methods proposed by Song et al. In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, Snowmass Village, CO, USA, 15 March 2020; pp. This is extended to the case of composite optimization by Beck and Teboulle in 2009. But the beauty of Nesterov's method is that despite this exploration, it does not diverge: even if a particular iteration overshoots and gets us far from the optimum, we'll get back close to the optimum in a few more iterations. Stay informed on the latest trending ML papers with code, research developments, libraries, methods, and datasets. Variation with the earthquake magnitude of the mean peak ground velocity and peak ground displacement. https://doi.org/10.3390/s21062051, Nan, Mihai, Mihai Trscu, Adina Magda Florea, and Cezar Ctlin Iacob. These methods are based on the most widely used techniques for this problemGraph Convolutional Networks (GCNs), Temporal Convolutional Networks (TCNs) and Recurrent Neural Networks (RNNs). In Proceedings of the 27th ACM International Conference on Multimedia, Nice, France, 2125 October 2019; pp. In this paper, we propose improvements to some methods for human action recognition from videos that work with data represented in the form of skeleton poses. We analyze both the underlying differential equation as well as the discretization to obtain insights into the phenomenon of acceleration. Song, Y.F. In this case, the format of the input received by the LSTM cell should be changed. NesterovSGD. future research directions and describes possible research applications. 659694, 2011. [10], His work with Arkadi Nemirovski in their 1994 book[11] is the first to point out that the interior point method can solve convex optimization problems, and the first to make a systematic study of semidefinite programming (SDP). Please note that many of the page functionalities won't work as expected without javascript enabled. Si et al. Matlab-Implementation-of-Nesterov-s-Accelerated-Gradient-Method- This is extended to the case of composite optimization by Beck and Teboulle in 2009. Variability of Ground Motion Amplitudes Recorded in Bucharest Area The position of the available 41 boreholes and of the recording seismic stations is illustrated in Figure 11. 79127921. View The Strong Romanian Earthquakes of 10.11.1940 and 4.03.1977. The aim is to provide a snapshot of some of the [, Feng, L.; Yuan, Q.; Liu, Y.; Huang, Q.; Liu, S.; Li, Y. In other words, this problem is a many-to-one sequence modelling problem. 5, pp. [2103.06494] Nesterov Acceleration for Equality-Constrained Convex A comparison between the variability of the spectral accelerations observed at seismic stations within Bucharest area during the Vrancea intermediate-depth earthquake of April 2009 (MW=5.4 and h=110km) and during the Vrancea crustal earthquake of November 2014 (MW=5.4 and h=41km) is shown in Figure 10. Multiple requests from the same IP address are counted as one view. 18. ResGCN-TCN (v3)This architectural model is similar to the previous one, the difference being the size of the temporal window. This category presents the smallest challenge, being about a single part of the body. [, Shi, L.; Zhang, Y.; Cheng, J.; Lu, H. Skeleton-based action recognition with directed graph neural networks. The 2014 event is the largest event which occurred in the Vrancea crustal seismic zone (which overlaps the Vrancea intermediate-depth zone) in the past 60 years. [31] for rock conditions and the site-amplification factors of Paolucci et al. An important observation that needs to be clarified, before presenting the difficulties and challenges of the problem, is related to the difference between the different types of human movements. The results of the comparisons are illustrated in Figure 14. The value is a member of a predetermined sequence of real numbers in the interval that is independent of the specific problem. . Language links are at the top of the page across from the title. Thus, the current site amplifications proposed for the F site should be further revised and adapted for Bucharest in order to ensure the necessary level of seismic safety of new buildings. How to implement the Nesterov Momentum optimization algorithm from scratch and apply it to an objective function and evaluate the results. (This article belongs to the Special Issue. B. Grecu, M. Popa, and M. Radulian, Seismic ground motion characteristics in the Bucharest area: sedimentary cover versus seismic source control, Romanian Reports in Physics n.d, vol. 25, no. [, Li, C.; Cui, Z.; Zheng, W.; Xu, C.; Yang, J. Spatio-Temporal Graph Convolution for Skeleton Based Action Recognition. To solve such a composite optimization problem, the proximal algorithm is prevailingly applied. The following abbreviations are used in this manuscript: Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. Liu, Z.; Zhang, H.; Chen, Z.; Wang, Z.; Ouyang, W. Disentangling and Unifying Graph Convolutions for Skeleton-Based Action Recognition. 221258, Springer, Berlin, Germany, 2014. Thakkar, K.; Narayanan, P. Part-based graph convolutional network for action recognition. In contrast, in terms of performance, the best results were obtained for TCN-based approaches. 104, no. The general scheme of the proposed TCN-based architectures is presented in. He took a Visiting Professor position at the University of Seville (Spain) from October 1992 to July 1993, and was a Postdoctoral Fellow at Dalhousie University (Canada) for the 1993/1994 academic year. [, The approaches proposed by us differ from the existing ones by using a simpler architectural model that can obtain satisfactory performances and a high inference speed. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA, 1620 June 2019; pp. The problem of recognizing peoples actions is very complex because it depends on many factors. CEN Eurocode 8, Design of Structures for Earthquake Resistance. PDF Nesterov's Acceleration for Approximate Newton The variability in the shear wave velocities decreases with the depth of the soil profile. In these cases, we set a small learning rate, obtained as the reciprocal of an upper bound on the global curvature across dimensions. However, for the medium- and long-period range, the spectral accelerations from the recorded ground motions are well below the design values. This fact makes them suitable for use in real-time scenarios. In 1977, Yurii Nesterov graduated in applied mathematics at Moscow State University. While spectral accelerations are important for the evaluation of the seismic fragility and risk of structures and buildings, the peak ground velocity and displacement are employed in the seismic risk assessment of distributed pipe networks (gas, water, and sewage) [20].