Research on the development of carrier intelligent cloud network under the background of IPv6+

With the increasingly mature 5G technology in our country, the government has comprehensively promoted IPv6 scale deployment, the rapid improvement of network quality of the three operators, and gradually transformed to IPv6+, the carrying network is more fl exible, and the user opening service is more convenient, which has promoted the development of intelligent cloud network of China’s carriers. Operators should actively respond to the challenges of IPv6+ era, based on their own intelligent cloud network development needs, the use of SRv6 technology, promote cloud network integration, carrying a variety of online services; Provide integrated cloud network products and services, build an intelligent operation and maintenance system, and improve user satisfaction; To build IPv6 networking capability of the whole network and build intelligent cloud network; Do a good job in the construction of IPv6 network information security, improve the security defense capability of intelligent cloud network, ensure the smooth operation of network, and inject new vitality into the 2B industry market for operators

Refined Temporal Pyramidal Compression-and-Amplification Transformer for 3D Human Pose Estimation

Accurately estimating the 3D pose of humans in video sequences requires both accuracy and a well-structured architecture. With the success of transformers, we introduce the Refined Temporal Pyramidal Compression-and-Amplification (RTPCA) transformer. Exploiting the temporal dimension, RTPCA extends intra-block temporal modeling via its Temporal Pyramidal Compression-and-Amplification (TPCA) structure and refines inter-block feature interaction with a Cross-Layer Refinement (XLR) module. In particular, TPCA block exploits a temporal pyramid paradigm, reinforcing key and value representation capabilities and seamlessly extracting spatial semantics from motion sequences. We stitch these TPCA blocks with XLR that promotes rich semantic representation through continuous interaction of queries, keys, and values. This strategy embodies early-stage information with current flows, addressing typical deficits in detail and stability seen in other transformer-based methods. We demonstrate the effectiveness of RTPCA by achieving state-of-the-art results on Human3.6M, HumanEva-I, and MPI-INF-3DHP benchmarks with minimal computational overhead. The source code is available at https://github.com/hbing-l/RTPCA.Comment: 11 pages, 5 figure

PoSynDA: Multi-Hypothesis Pose Synthesis Domain Adaptation for Robust 3D Human Pose Estimation

Existing 3D human pose estimators face challenges in adapting to new datasets due to the lack of 2D-3D pose pairs in training sets. To overcome this issue, we propose \textit{Multi-Hypothesis \textbf{P}ose \textbf{Syn}thesis \textbf{D}omain \textbf{A}daptation} (\textbf{PoSynDA}) framework to bridge this data disparity gap in target domain. Typically, PoSynDA uses a diffusion-inspired structure to simulate 3D pose distribution in the target domain. By incorporating a multi-hypothesis network, PoSynDA generates diverse pose hypotheses and aligns them with the target domain. To do this, it first utilizes target-specific source augmentation to obtain the target domain distribution data from the source domain by decoupling the scale and position parameters. The process is then further refined through the teacher-student paradigm and low-rank adaptation. With extensive comparison of benchmarks such as Human3.6M and MPI-INF-3DHP, PoSynDA demonstrates competitive performance, even comparable to the target-trained MixSTE model\cite{zhang2022mixste}. This work paves the way for the practical application of 3D human pose estimation in unseen domains. The code is available at https://github.com/hbing-l/PoSynDA.Comment: Accepted to ACM Multimedia 2023; 10 pages, 4 figures, 8 tables; the code is at https://github.com/hbing-l/PoSynD

KeyPosS: Plug-and-Play Facial Landmark Detection through GPS-Inspired True-Range Multilateration

In the realm of facial analysis, accurate landmark detection is crucial for various applications, ranging from face recognition and expression analysis to animation. Conventional heatmap or coordinate regression-based techniques, however, often face challenges in terms of computational burden and quantization errors. To address these issues, we present the KeyPoint Positioning System (KeyPosS) - a groundbreaking facial landmark detection framework that stands out from existing methods. The framework utilizes a fully convolutional network to predict a distance map, which computes the distance between a Point of Interest (POI) and multiple anchor points. These anchor points are ingeniously harnessed to triangulate the POI's position through the True-range Multilateration algorithm. Notably, the plug-and-play nature of KeyPosS enables seamless integration into any decoding stage, ensuring a versatile and adaptable solution. We conducted a thorough evaluation of KeyPosS's performance by benchmarking it against state-of-the-art models on four different datasets. The results show that KeyPosS substantially outperforms leading methods in low-resolution settings while requiring a minimal time overhead. The code is available at https://github.com/zhiqic/KeyPosS.Comment: Accepted to ACM Multimedia 2023; 10 pages, 7 figures, 6 tables; the code is at https://github.com/zhiqic/KeyPos

26th Annual Computational Neuroscience Meeting (CNS*2017): Part 3 - Meeting Abstracts - Antwerp, Belgium. 15–20 July 2017

This work was produced as part of the activities of FAPESP Research,\ud Disseminations and Innovation Center for Neuromathematics (grant\ud 2013/07699-0, S. Paulo Research Foundation). NLK is supported by a\ud FAPESP postdoctoral fellowship (grant 2016/03855-5). ACR is partially\ud supported by a CNPq fellowship (grant 306251/2014-0)

China: strengthening the quality of education in rural communities

[About the book]: Everyone remembers a good teacher. But across the world, in developing country contexts, millions of children, seventy five million at the last estimate, have no access to schooling and no opportunity to engage with any teachers at all. And of the lucky ones in schools the chances of meeting a teacher to remember are dropping. In some parts of the world qualified teachers are a rarity with millions of untrained adults taking over the role of teacher. Schools and teachers in many parts of the world face significant challenges. Enrolment is expanding to meet the millennium targets to have every child in school by 2015. Yet the supply of good quality teachers is falling behind. Not the least because the image of the teaching profession is deterring many from entering the profession. Poor status, low salaries and inadequate working conditions characterise perceptions of teachers in many countries. There are strong critiques of the one dimensional, didactic approach to pedagogic practice. Despite this, millions of teachers are carrying out, often heroically, the task of educating a newly enfranchised generation of learners. This book focuses on the teacher role. It examines the problems of finding and retaining teachers and also on how these teachers can be supported, trained and educated. The book examines the ways in which teachers can help in raising achievement levels and contributing to poverty alleviation. The main argument of this book is that existing policy structures around teachers, whilst barely adequate in the twentieth century, are not adequate at all in the twenty-first. The book identifies the global pressures on teaching and shows how these are particularly acute in developing economies. In summarising the key policy and research issues and analysing innovatory approaches to teacher supply, retention, training, education and career enhancement, the book provides a key text for policy makers, researchers and others working in this important development area