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Reducing Share Size in JPEG Image Sharing over GF(2m) and GF(p) Galois Fields

IEEE Internet of Things Journal 2025年

作者： Yu, Chunqiang Cheng, Shichao Zhang, Xianquan Wang, Pengfei Yang, Ching-Nung Guangxi Normal University Key Lab of Education Blockchain and Intelligent Technology Ministry of Education Guilin541004 China Nanyang Technological University School of Electrical and Electronic Engineering 639798 Singapore National Dong Hwa University Department of Computer Science and Information Engineering Hualien974 Taiwan

Image sharing is a crucial security technique of protecting the original image. Most image sharing methods elaborate on the uncompressed formats and cannot be applied directly to compressed formats such as JPEG format. Recently, some related JPEG sharing methods were proposed. However, these methods are short in size reduction. To address this issue, we introduce a novel JPEG image sharing method that significantly reduces the file sizes of shared images while maintaining compatibility with the JPEG standard. Specifically, an original JPEG bitstream is parsed to obtain three components, namely, DC appended bits (DCAs), AC Huffman codes (ACHs), and AC appended bits (ACAs). Different sharing mechanisms are applied for these three components. The DCAs are shared over GF(2m) in a way that prevents overflow in the shared DC coefficients. The ACH sharing is performed by polynomials over GF(p) with an intermediate process by Huffman table mapping and the ACAs are shared by polynomials over GF(2m) with directed codes. Consequently, the ACH and ACA shares with smaller sizes are generated, respectively. To be compatible with the JPEG standard, the shared ACAs are adaptively modulated according to the shared ACHs. Finally, the shared JPEG images can be reconstructed by the shared DCAs, ACHs, ACAs and the original partial JPEG information. The experimental results demonstrate that the proposed method can efficiently reduce the file sizes of shared JPEG images and preserve the JPEG format for each shared image. © 2014 IEEE.

关键词： Polynomials

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Multi-View Partial (MVP) Point Cloud Challenge 2021 on Completion and Registration: Methods and Results

arXiv

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arXiv 2021年

作者： Pan, Liang Wu, Tong Cai, Zhongang Liu, Ziwei Yu, Xumin Rao, Yongming Lu, Jiwen Zhou, Jie Xu, Mingye Luo, Xiaoyuan Fu, Kexue Gao, Peng Wang, Manning Wang, Yali Qiao, Yu Zhou, Junsheng Wen, Xin Xiang, Peng Liu, Yu-Shen Han, Zhizhong Yan, Yuanjie An, Junyi Zhu, Lifa Lin, Changwei Liu, Dongrui Li, Xin Gómez-Fernández, Francisco Wang, Qinlong Yang, Yang S-Lab Nanyang Technological University Singapore SenseTime-CUHK Joint Lab The Chinese University of Hong Kong Hong Kong Sensetime Research Shanghai AI Laboratory China Department of Automation Tsinghua University China University of Chinese Academy of Sciences China ShenZhen Key Lab of Computer Vision and Pattern Recognition SIAT-SenseTime Joint Lab Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences China Digital Medical Research Center School of Basic Medical Science Fudan University China School of Software BNRist Tsinghua University China *** Wayne State University State Key Laboratory for Novel Software Technology Nanjing University China DeepGlint Shanghai Jiao Tong University China Sichuan University China Xi'an Jiaotong University China

As real-scanned point clouds are mostly partial due to occlusions and viewpoints, reconstructing complete 3D shapes based on incomplete observations becomes a fundamental problem for computer vision. With a single incomplete point cloud, it becomes the partial point cloud completion problem. Given multiple different observations, 3D reconstruction can be addressed by performing partial-to-partial point cloud registration. Recently, a large-scale Multi-View Partial (MVP) point cloud dataset has been released, which consists of over 100,000 high-quality virtual-scanned partial point clouds. Based on the MVP dataset, this paper reports methods and results in the Multi-View Partial Point Cloud Challenge 2021 on Completion and Registration. In total, 128 participants registered for the competition, and 31 teams made valid submissions. The top-ranked solutions will be analyzed, and then we will discuss future research directions. Copyright © 2021, The Authors. All rights reserved.

关键词： Surface measurement

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Investigating Bi-level optimization for learning and vision from a unified perspective: A survey and beyond

arXiv

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arXiv 2021年

作者： Liu, Risheng Gao, Jiaxin Zhang, Jin Meng, Deyu Lin, Zhouchen DUT-RU International School of Information Science & Engineering Dalian University of Technology Key Laboratory for Ubiquitous Network Service Software of Liaoning Province Dalian116024 China Department of Mathematics Southern University of Science and Technology National Center for Applied Mathematics Shenzhen China School of Mathematics and Statistics Ministry of Education Key Lab of Intelligent Networks and Network Security Xi’an Jiaotong University Xi’an Shaanxi China School of Electronics Engineering and Computer Science Peking University Beijing100871 China Cooperative Medianet Innovation Center Shanghai Jiao Tong University Shanghai200240 China

Bi-Level Optimization (BLO) is originated from the area of economic game theory and then introduced into the optimization community. BLO is able to handle problems with a hierarchical structure, involving two levels of optimization tasks, where one task is nested inside the other. In machine learning and computer vision fields, despite the different motivations and mechanisms, a lot of complex problems, such as hyper-parameter optimization, multi-task and meta learning, neural architecture search, adversarial learning and deep reinforcement learning, actually all contain a series of closely related subproblms. In this paper, we first uniformly express these complex learning and vision problems from the perspective of BLO. Then we construct a best-response-based single-level reformulation and establish a unified algorithmic framework to understand and formulate mainstream gradient-based BLO methodologies, covering aspects ranging from fundamental automatic differentiation schemes to various accelerations, simplifications, extensions and their convergence and complexity properties. Last but not least, we discuss the potentials of our unified BLO framework for designing new algorithms and point out some promising directions for future research. © 2021, CC BY.

关键词： Deep learning

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Context-Aware Point-of-Interest Recommendation Algorithm with Interpretability 15th

Context-Aware Point-of-Interest Recommendation Algorithm wit...

引用

15th EAI International Conference on Collaborative Computing: Networking, Applications and Worksharing, CollaborateCom 2019

作者： Zhang, Guoming Qi, Lianyong Zhang, Xuyun Xu, Xiaolong Dou, Wanchun State Key Laboratory for Novel Software Technology Nanjing University Nanjing China School of Information Science and Engineering Qufu Normal University Qufu China Department of Electrical Computer Engineering The University of Auckland Auckland New Zealand School of Computer and Software Nanjing University of Information Science and Technology Nanjing China Health Statistics and Information Center of Jiangsu Province Nanjing China

ISBN: (纸本)9783030301453

With the rapid development of mobile Internet, smart devices, and positioning technologies, location-based social networks (LBSNs) are growing rapidly. In LBSNs, point-of-interest (POI) recommendation is a crucial personalized location service that has become a research hotspot. To address extreme sparsity of user check-in data, a growing line of research exploits spatial-temporal information, social relationship, content information, popularity, and other factors to improve recommendation performance. However, the temporal and spatial transfers of user preferences are seldom mentioned in existing works, and interpretability, which is an important factor to enhance credibility of recommendation result, is overlooked. To cope with these issues, we propose a context-aware POI recommendation framework, which integrates users’ long-term static and time-varying preferences to improve recommendation performance and provide explanations. Experimental results over two real-world LBSN datasets demonstrate that the proposed solution has better performance than other advanced POI recommendation approaches. © 2019, ICST Institute for computer sciences, Social Informatics and Telecommunications Engineering.

关键词： Location

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Crosslink-net: Double-branch encoder segmentation network via fusing vertical and horizontal convolutions

arXiv

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arXiv 2021年

作者： Yu, Qian Qi, Lei Zhou, Luping Wang, Lei Yin, Yilong Shi, Yinghuan Wang, Wuzhang Gao, Yang School of Data and Computer Science Shandong Women's University Jinan250300 China The State Key Laboratory for Novel Software Technology Nanjing University Nanjing210023 China School of Computer Science and Engineering Southeast University Nanjing211189 China The School of Electrical and Information Engineering University of Sydney NSW2006 Australia The School of Computing and Information Technology University of Wollongong WollongongNSW2522 Australia The School of Soft Shandong University Jinan250100 China Department of Respiratory and Critical Care Medicine Shandong Chest Hospital Jinan250013 China

Accurate image segmentation plays a crucial role in medical image analysis, yet it faces great challenges of various shapes, diverse sizes, and blurry boundaries. To address these difficulties, square kernel-based encoder-decoder architecture has been proposed and widely used, but its performance remains still unsatisfactory. To further cope with these challenges, we present a novel double-branch encoder architecture. Our architecture is inspired by two observations: 1) Since the discrimination of features learned via square convolutional kernels needs to be further improved, we propose to utilize non-square vertical and horizontal convolutional kernels in the double-branch encoder, so features learned by the two branches can be expected to complement each other. 2) Considering that spatial attention can help models to better focus on the target region in a large-sized image, we develop an attention loss to further emphasize the segmentation on small-sized targets. Together, the above two schemes give rise to a novel double-branch encoder segmentation framework for medical image segmentation, namely Crosslink-Net. The experiments validate the effectiveness of our model on four datasets. The code is released at https://***/Qianyu1226/*** Codes 68T07 Copyright © 2021, The Authors. All rights reserved.

关键词： Convolution

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lMFF: Efficient and Scalable layered Materials Force Field on Heterogeneous Many-Core Processors

lMFF: Efficient and Scalable layered Materials Force Field o...

引用

Supercomputing Conference

作者： Ping Gao Xiaohui Duan Jiaxu Guo Jin Wang Zhenya Song Lizhen Cui Xiangxu Meng Xin Liu Wusheng Zhang Ming Ma Guohui Li Dexun Chen Haohuan Fu Wei Xue Weiguo Liu Guangwen Yang School of Software Shandong University Jinan China National Supercomputing Center in Wuxi Wuxi China Department of Computer Science and Technology Tsinghua University Beijing China College of Computer Science and Technology Jilin University Changchun China Department of Engineering Mechanics Center for Nano and Micro Mechanics Tsinghua University Beijing China Ministry of Natural Resources First Institute of Oceanography and Key Laboratory of Marine Science and Numerical Modeling Qingdao China Joint SDU-NTU Centre for Artificial Intelligence Research (C-FAIR) Jinan China Engineering Research Center of Digital Media Technology Ministry of Education Jinan China Dalian Institute of Chemical Physics Chinese Academy of Sciences Dalian China Department of Earth System Science Ministry of Education Key Lab for Earth System Modeling Tsinghua University Beijing China

ISBN: (数字)9781450384421

ISBN: (纸本)9781665483902

LAMMPS is one of the most popular Molecular Dynamic (MD) packages and is widely used in the field of physics, chemistry and materials simulation. Layered Materials Force Field (LMFF) is our expansion of the LAMMPS potential function based on the Tersoff potential and inter-layer potential (ILP) in LAMMPS. LMFF is designed to study layered materials such as graphene and boron hexanitride. It is universal and does not depend on any platform. We have also carried out a series of optimizations on LMFF and the optimization work is carried out on the new generation of Sunway supercomputer, called SWLMFF. Experiments show that our implementation is efficient, scalable and portable. When generic LMFF is ported to Intel Xeon Gold 6278C, $2\times$ performance improvement is achieved. For the optimized SWLMFF, the overall performance improvement is nearly $200-330\times$ compared to the original ILP and Tersoff potentials. And SWLMFF has good parallel efficiency of 95%-100% under weak scaling with 2.7 million atoms on a single process. The maximum atomic system simulated by SWLMFF is close to $2^{31}$ atoms. And nanosecond simulations in one day can be realized.

关键词： Program processors Atomic layer deposition High performance computing Force Graphene Supercomputers Optimization

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A knowledge-enhanced pretraining model for commonsense story generation

arXiv

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arXiv 2020年

作者： Guan, Jian Huang, Fei Zhao, Zhihao Zhu, Xiaoyan Huang, Minlie Institute for Artificial Intelligence State Key Lab of Intelligent Technology and Systems Tsinghua University Beijing100084 China Beijing National Research Center for Information Science and Technology Tsinghua University Beijing100084 China Department of Computer Science and Technology Tsinghua University Beijing100084 China School of Software Beihang University Beijing China

Story generation, namely generating a reasonable story from a leading context, is an important but challenging task. In spite of the success in modeling fluency and local coherence, existing neural language generation models (e.g., GPT-2) still suffer from repetition, logic conflicts, and lack of long-range coherence in generated stories. We conjecture that this is because of the difficulty of associating relevant commonsense knowledge, understanding the causal relationships, and planning entities and events with proper temporal order. In this paper, we devise a knowledge-enhanced pretraining model for commonsense story generation. We propose to utilize commonsense knowledge from external knowledge bases to generate reasonable stories. To further capture the causal and temporal dependencies between the sentences in a reasonable story, we employ multi-task learning which combines a discriminative objective to distinguish true and fake stories during fine-tuning. Automatic and manual evaluation shows that our model can generate more reasonable stories than state-of-the-art baselines, particularly in terms of logic and global coherence. Copyright © 2020, The Authors. All rights reserved.

关键词： Modeling languages

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NTIRE 2022 Challenge on Efficient Super-Resolution: Methods and Results

arXiv

引用

arXiv 2022年

作者： Li, Yawei Zhang, Kai Timofte, Radu Van Gool, Luc Kong, Fangyuan Li, Mingxi Liu, Songwei Du, Zongcai Liu, Ding Zhou, Chenhui Chen, Jingyi Han, Qingrui Li, Zheyuan Liu, Yingqi Chen, Xiangyu Cai, Haoming Qiao, Yu Dong, Chao Sun, Long Pan, Jinshan Zhu, Yi Zong, Zhikai Liu, Xiaoxiao Hui, Zheng Yang, Tao Ren, Peiran Xie, Xuansong Hua, Xian-Sheng Wang, Yanbo Ji, Xiaozhong Lin, Chuming Luo, Donghao Tai, Ying Wang, Chengjie Zhang, Zhizhong Xie, Yuan Cheng, Shen Luo, Ziwei Yu, Lei Wen, Zhihong Wu, Qi Li, Youwei Fan, Haoqiang Sun, Jian Liu, Shuaicheng Huang, Yuanfei Jin, Meiguang Huang, Hua Liu, Jing Zhang, Xinjian Wang, Yan Long, Lingshun Li, Gen Zhang, Yuanfan Cao, Zuowei Sun, Lei Alexander, Panaetov Wang, Yucong Cai, Minjie Wang, Li Tian, Lu Wang, Zheyuan Ma, Hongbing Liu, Jie Chen, Chao Cai, Yidong Tang, Jie Wu, Gangshan Wang, Weiran Huang, Shirui Lu, Honglei Liu, Huan Wang, keyan Chen, Jun Chen, Shi Miao, Yuchun Huang, Zimo Zhang, Lefei Ayazoglu, Mustafa Xiong, Wei Xiong, Chengyi Wang, Fei Li, Hao Wen, Ruimian Yang, Zhijing Zou, Wenbin Zheng, Weixin Ye, Tian Zhang, Yuncheng Kong, Xiangzhen Arora, Aditya Zamir, Syed Waqas Khan, Salman Hayat, Munawar Khan, Fahad Shahbaz Gao, Dandan Zhou, Dengwen Ning, Qian Tang, Jingzhu Huang, Han Wang, Yufei Peng, Zhangheng Li, Haobo Guan, Wenxue Gong, Shenghua Li, Xin Liu, Jun Wang, Wanjun Zeng, Kun Lin, Hanjiang Chen, Xinyu Fang, Jinsheng Zhang, Shuhao Zhang, Yuhao Sinha, Abhishek Kumar Moorthi, S. Manthira Dhar, Debajyoti Yang, Hao-Hsiang Huang, Zhi-Kai Chen, Wei-Ting Chang, Hua-En Kuo, Sy-Yen Tan, Wei Chen, Hao Xu, Qian Narang, Pratik Singh, Usneek Sameen, Syed Khaitan, Harsh Yinghua, Liu Tianlin, Zhang Xiaoming, Zhang Meng, Dingxuan Tian, Chunwei Morshed, Mashrur M. Ahsan, Ahmad Omar Computer Vision Lab ETH Zurich Switzerland University of Würzburg Germany ByteDance Shenzhen China State Key Laboratory for Novel Software Technology Nanjing University China ByteDance Inc China NetEase Inc. China Shenzhen Institutes of Advanced Technology CAS China University of Macau China Shanghai AI Lab Shanghai China Nanjing University of Science and Technology China Amazon Web Services United States China Alibaba DAMO Academy EFC Yuhang District Zhejiang Hangzhou China East China Normal University China Youtu Lab Tencent China Megvii Technology China University of Electronic Science and Technology of China China School of Artificial Intelligence Beijing Normal University China Alibaba Group China Bilibili AI China Nankai-Baidu Joint Lab Nankai University Tianjin China Platform Technologies Tencent Online Video China Higher School of Economics Russia Huawei Moscow Research Center Russia Hunan University China Xidian University China Xilinx Technology Beijing Limited China College of Information Science and Engineering Xinjiang University Urumqi China Department of Electronic Engineering Tsinghua University Beijing China Nanjing University China McMaster University Canada School of Telecommunication Engineering Xidian University Xi’an China School of Computer Science Wuhan University Wuhan China School of Mathematical Science University of Electronic Science and Technology of China Chengdu China School of Computer Science The University of Sydney Sydney Australia Aselsan Research Ankara Turkey School of Electronic and Information Engineering South-Central University for Nationalities Wuhan China Guangdong University of Technology China Fujian Normal University Fuzhou University Jimei University China Design Group China Abu Dhabi United Arab Emirates Monash University Melbourne Australia Mohamed bin Zayed University of AI United Arab Emirates North China Electric Power University Changping District Beijing China The School of Art

This paper reviews the NTIRE 2022 challenge on efficient single image super-resolution with focus on the proposed solutions and results. The task of the challenge was to super-resolve an input image with a magnification factor of ×4 based on pairs of low and corresponding high resolution images. The aim was to design a network for single image super-resolution that achieved improvement of efficiency measured according to several metrics including runtime, parameters, FLOPs, activations, and memory consumption while at least maintaining the PSNR of 29.00dB on DIV2K validation set. IMDN is set as the baseline for efficiency measurement. The challenge had 3 tracks including the main track (runtime), sub-track one (model complexity), and sub-track two (overall performance). In the main track, the practical runtime performance of the submissions was evaluated. The rank of the teams were determined directly by the absolute value of the average runtime on the validation set and test set. In sub-track one, the number of parameters and FLOPs were considered. And the individual rankings of the two metrics were summed up to determine a final ranking in this track. In sub-track two, all of the five metrics mentioned in the description of the challenge including runtime, parameter count, FLOPs, activations, and memory consumption were considered. Similar to sub-track one, the rankings of five metrics were summed up to determine a final ranking. The challenge had 303 registered participants, and 43 teams made valid submissions. They gauge the state-of-the-art in efficient single image super-resolution. © 2022, CC BY.

关键词： Chemical activation

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二维材料最新研究进展

引用

物理化学学报 2021年第12期37卷 1-151页

作者：常诚陈伟陈也陈永华陈雨丁峰樊春海范红金范战西龚成宫勇吉何其远洪勋胡晟胡伟达黄维黄元季威李德慧李连忠李强林立凌崇益刘鸣华刘楠刘庄 Kian Ping Loh 马建民缪峰彭海琳邵明飞宋礼苏邵孙硕谭超良唐智勇王定胜王欢王金兰王欣王欣然 Andrew T.S.Wee 魏钟鸣吴宇恩吴忠帅熊杰熊启华徐伟高尹鹏曾海波曾志远翟天佑张晗张辉张其春张铁锐张翔赵立东赵美廷赵伟杰赵运宣周凯歌周兴周喻朱宏伟张华刘忠范 Institute of Science and Technology Austria Am Campus 13400 KlosterneuburgAustria Department of Chemistry National University of Singapore3 Science Drive 3117543Singapore Department of Chemistry The Chinese University of Hong KongShatinNew TerritoriesHong KongChina Key Laboratory of Flexible Electronics and Institute of Advanced Materials Nanjing Tech UniversityNanjing 211816China School of Life Sciences Shanghai UniversityShanghai 200444China Centre for Multidimensional Carbon Materials Institute for Basic ScienceUlsan 44919Korea School of Chemistry and Chemical Engineering Shanghai Jiao Tong UniversityShanghai 200240China School of Physical and Mathematical Sciences Nanyang Technological UniversitySingapore 637371Singapore Department of Chemistry City University of Hong KongKowloonHong KongChina Department of Electrical and Computer Engineering and Quantum Technology Center University of MarylandCollege ParkMaryland 20742USA School of Materials Science and Engineering Beihang UniversityBeijing 100191China Department of Materials Science and Engineering City University of Hong KongKowloonHong KongChina Center of Advanced Nanocatalysis(CAN) Hefei National Laboratory for Physical Sciences at the MicroscaleDepartment of Applied ChemistryUniversity of Science and Technology of ChinaHefei 230026China College of Chemistry and Chemical Engineering State Key Laboratory of Physical Chemistry of Solid SurfacesCollaborative Innovation Center of Chemistry for Energy Materials(iChEM)Xiamen UniversityXiamen361005Fujian ProvinceChina State Key Laboratory of Infrared Physics Shanghai Institute of Technical PhysicsChinese Academy of SciencesShanghai 200083China Advanced Research Institute of Multidisciplinary Science Beijing Institute of TechnologyBeijing100081China Beijing Key Laboratory of Optoelectronic Functional Materials&Micro-Nano Devices Department of PhysicsRenmin University of ChinaBeijing 100872China School of Optical and Electronic Information Wuhan National Laboratory

Research on two-dimensional(2D) materials has been explosively increasing in last seventeen years in varying subjects including condensed matter physics, electronic engineering, materials science, and chemistry since the mechanical exfoliation of graphene in 2004. Starting from graphene, 2D materials now have become a big family with numerous members and diverse categories. The unique structural features and physicochemical properties of 2D materials make them one class of the most appealing candidates for a wide range of potential applications. In particular, we have seen some major breakthroughs made in the field of 2D materials in last five years not only in developing novel synthetic methods and exploring new structures/properties but also in identifying innovative applications and pushing forward commercialisation. In this review, we provide a critical summary on the recent progress made in the field of 2D materials with a particular focus on last five years. After a brief backgroundintroduction, we first discuss the major synthetic methods for 2D materials, including the mechanical exfoliation, liquid exfoliation, vapor phase deposition, and wet-chemical synthesis as well as phase engineering of 2D materials belonging to the field of phase engineering of nanomaterials(PEN). We then introduce the superconducting/optical/magnetic properties and chirality of 2D materials along with newly emerging magic angle 2D superlattices. Following that, the promising applications of 2D materials in electronics, optoelectronics, catalysis, energy storage, solar cells, biomedicine, sensors, environments, etc. are described sequentially. Thereafter, we present the theoretic calculations and simulations of 2D materials. Finally, after concluding the current progress, we provide some personal discussions on the existing challenges and future outlooks in this rapidly developing field.

关键词： Two-dimensional materials Transition metal dichalcogenides Phase engineering of nanomaterials Electronics Optoelectronics Catalysis Energy storage and conversion

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Reusing Discriminators for Encoding: Towards Unsupervised Image-to-Image Translation

Reusing Discriminators for Encoding: Towards Unsupervised Im...

引用

Conference on computer Vision and Pattern Recognition (CVPR)

作者： Runfa Chen Wenbing Huang Binghui Huang Fuchun Sun Bin Fang Department of Computer Science and Technology Tsinghua University Institute for Artificial Intelligence Tsinghua University (THUAI) Beijing National Research Center for Information Science and Technology (BNRist) State Key Lab on Intelligent Technology and Systems Beijing P.R.China

ISBN: (数字)9781728171685

ISBN: (纸本)9781728171692

Unsupervised image-to-image translation is a central task in computer vision. Current translation frameworks will abandon the discriminator once the training process is completed. This paper contends a novel role of the discriminator by reusing it for encoding the images of the target domain. The proposed architecture, termed as NICE-GAN, exhibits two advantageous patterns over previous approaches: First, it is more compact since no independent encoding component is required; Second, this plug-in encoder is directly trained by the adversary loss, making it more informative and trained more effectively if a multi-scale discriminator is applied. The main issue in NICE-GAN is the coupling of translation with discrimination along the encoder, which could incur training inconsistency when we play the min-max game via GAN. To tackle this issue, we develop a decoupled training strategy by which the encoder is only trained when maximizing the adversary loss while keeping frozen otherwise. Extensive experiments on four popular benchmarks demonstrate the superior performance of NICE-GAN over state-of-the-art methods in terms of FID, KID, and also human preference. Comprehensive ablation studies are also carried out to isolate the validity of each proposed component. Our codes are available at https://***/alpc91/NICE-GAN-pytorch.

关键词： Training Encoding Gallium nitride Generators Image coding Task analysis Benchmark testing

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