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High Performance Big data and Intelligent Systems (HPBD&IS)

作者： Qi Zhou Kai Yang Zunwang Ke Gang Wang Yugui Zhang Yizhen Jia Fengcai Cao Junxiao Ma Changlin Liu Kaijie Zhang Min Wu School of Semiconductor Science and Technology South China Normal University GuangZhou China AnnLab Institute of Semiconductors Chinese Academy of Sciences Beijing China Beijing Key Laboratory of Semiconductor Neural Network Intelligent Sensing and Computing Technology Beijing China CGNPC Uranium Industry Development Co. Ltd. Beijing China School of Software Xinjiang University Urumqi China School of Computing and Data Engineering NingboTech University Ningbo China School of Integrated Circuits University of Chinese Academy of Sciences Beijing China Zhongke Shangyi Health Technology (Beijing) Co. Ltd. Beijing China School of Information Beijing Forestry University Beijing China

In real-world scenarios, capturing scenes with excessive dynamic range often leads to the partial loss of highlight or dark area information due to irradiance variations and limitations in the capture capabilities of imaging devices caused by their size and material constraints. This phenomenon gives rise to anomalous exposure problems and triggers the loss of crucial initial details, thereby becoming a significant obstacle to achieving high image quality. Despite numerous researchers' integration of exposure compensation techniques into the imaging process, the anomalous exposure problem remains unsolved, hindering progress in image processing. Consequently, considerable research is valuable in implementing exposure calibration techniques on visual perception and multimedia devices. This dissertation proposes FARDBUNet, a novel end-to-end exposure calibration model designed to address the anomalous exposure problem. A key innovation of FARDBUNet is the incorporation of a fused attention mechanism, enabling the model to prioritize regions needing enhanced correction while accounting for the global exposure characteristics of the image. Additionally, to tackle the challenge of inadequate information availability in the localized areas due to exposure issues, we introduce an atrous residual dense block that efficiently captures local information by expanding the receptive field. Comprehensive tests validate the effectiveness of our suggested model, which performs on par with leading techniques on expansive exposure datasets.

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A Solution-Based Tabu Search Algorithm for the Resource-Constrained Project Scheduling Problem with Step Deterioration

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Mathematical Problems in Engineering 2023年第1期2023卷

作者： Dai, Huafeng Li, Lei Mao, Run Liu, Xingyue Zhou, Kun School of Mechanical Engineering Chengdu University Chengdu610105 China Technology and Equipment of Rail Transit Operation and Maintenance Key Laboratory of Sichuan Province Chengdu610031 China CNPC National Engineering Research Center for Oil & Gas Drilling Equipment Co. Ltd. Chengdu610052 China School of Information Science and Engineering Chengdu University Chengdu610105 China Key Lab. of Intelligent Manufacturing Quality Big Data Tracing and Analysis of Zhejiang Province China Jiliang University Hangzhou310018 China

This paper integrates the classical resource-constrained project scheduling problem with step deterioration and accordingly presents a solution-based tabu search (SB-TS) algorithm to solve it with challenging computations. First, we choose successor list size as the priority rule to generate the initial solution of the presented methods. Next, the corresponding solution decoding and calculation ways are defined to update the related configuration at each iteration. Compared with the state-of-the-art methods, in terms of the known datasets, the proposed SB-TS finds the optimal solution for each instance in J30 and shows strongly competitive performance in J60, J90, and J120. Then, on newly generated datasets, where the step deterioration is attached to, the comparison experimental data state that the SB-TS with high-quality solution is superior to the move-based tabu search (MB-TS) algorithm. In addition, two key components are investigated to emphasize their attributions to the proposed algorithm. © 2023 Huafeng Dai et al.

关键词： Deterioration

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Larger Receptive Field and Context Information for Pose Estimation: Larger Gaussian Kernel

Larger Receptive Field and Context Information for Pose Esti...

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High Performance Big data and Intelligent Systems (HPBD&IS)

作者： Junxiao Ma Kai Yang Zunwang Ke Gang Wang Yugui Zhang Fengcai Cao Qi Zhou Yang Fei School of Information Beijing Forestry University Beijing China AnnLab Institute of Semiconductors Chinese Academy of Sciences Beijing China CGNPC Uranium Industry Development Co. Ltd. Beijing China School of Software Xinjiang University Urumqi China School of Computing and Data Engineering NingboTech University Ningbo China Beijing Key Laboratory of Semiconductor Neural Network Intelligent Sensing and Computing Technology Beijing China School of Integrated Circuits University of Chinese Academy of Sciences Beijing China Zhongke Shangyi Health Technology (Beijing) Co. Ltd. Beijing China School of Semiconductor Science and Technology South China Normal University GuangZhou China

The field of pose estimation has a wide range of application prospects in various industries in the current era. With the continuous development of deep learning techniques, the effects in the field of human pose estimation are constantly being optimized. However, due to the influence of factors such as occlusion and human posture variability, it is easy to lead to inaccurate identification of keypoints and even keypoints' missing and matching errors, there is still a lot of optimization space for the effect of pose estimation. In this paper, we propose an improvement to generate heatmaps in the training process: increase the radius of the Gaussian kernel and adjust the smoothness of Gaussian distribution to get a larger receptive field, encourage the model to use more context information in prediction, and control other parameters unchanged on the premise of using heatmap regression. Improve keypoint detection performance by optimizing this item. In the experiment, for the COCO-keypoint data set, we selected different methods published in recent years, and after our improvement, we obtained better accuracy and performance than the original method, which proves the validity of our method.

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Measurement-Device-Independent Quantum Secure Direct Communication

arXiv

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arXiv 2018年

作者： Zhou, Zeng-Rong Sheng, Yu-Bo Niu, Peng-Hao Yin, Liu-Guo Long, Gui-Lu State Key Laboratory of Low-dimensional Quantum Physics Beijing100084 China Department of Physics Tsinghua University Beijing100084 China Beijing National Research Center for Information Science and Technology Beijing100084 China School of Information and Technology Tsinghua University Beijing100084 China Collaborative Innovation Center of Quantum Matter Beijing100084 China Beijing Academy of Quantum Information Beijing100084 China Institute of Quantum Information and Technology Nanjing University of Posts and Telecommunications Nanjing210003 China College of Telecommunications & Information Engineering Nanjing University of Posts and Telecommunications Nanjing210003 China Key Lab of Broadband Wireless Communication and Sensor Network Technology Nanjing University of Posts and Telecommunications Ministry of Education Nanjing210003 China

Quantum secure direct communication (QSDC) is the technology to transmit secret information directly through a quantum channel without neither key nor ciphertext. It provides us with a secure communication structure that is fundamentally different from the one that we use today. In this Letter, we report the first measurement-device-independent(MDI) QSDC protocol with sequences of entangled photon pairs and single photons. It eliminates security loopholes associated with the measurement device. In addition, the MDI technique doubles the communication distance compared to those without using the technique. We also give a protocol with linear optical Bell-basis measurement, where only two of the four Bell-basis states could be measured. When the number of qubit in a sequence reduces to 1, the MDI-QSDC protocol reduces to a deterministic MDI quantum key distribution protocol, which is also presented in the Letter. Copyright © 2018, The Authors. All rights reserved.

关键词： Quantum communication

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Revisiting Cloud Migration: Strategies and Methods

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Journal of Physics: Conference Series 2020年第1期1575卷

作者： Zhixiao Wang Wenyao Yan Wendong Wang School of Computer Science and Engineering Xi'an University of Technology Xi'an 710048 China Shaanxi Key Laboratory Xi'an of Network Computing and Security Technology China School of Data Science and Computer Xi'an innovation college of Yan'an University Xi'an 710100 China Yan'an University Xi'an 716000 China

Cloud computing attracts users with its advantage of unlimited resource supply where resources can be elastically expanded on demand and balanced-load at the same time. This means that the application in the cloud environment should run in the way of elastic expansion cluster. At present, most of the elastic expansion and load balancing technologies provided by IaaS level are oriented to virtual machines (VM), with little consideration of application level, which does not fundamentally meet the needs of users. Based on this, starting from the motivation of cloud migration, we first analyse the cloud migration technology from three aspects: migration object, migration means and migration objectives, then explore the cloud migration criteria and four cloud migration strategies at the application level in the cloud environment, and point out that cloud migration should be optimized and maintained in terms of elastic expansion, load balancing, security, etc.

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Ultra-compact beam steering nanolasers

arXiv

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arXiv 2024年

作者： Chen, Xinghong Gu, Mingxuan Tang, Jiankai Sang, Yungang Xiang, Bingrui Zhang, Kong Zhang, Guanjie Wang, Xingyuan Guo, Xuhan Zhou, Linjie Wu, Wengang Mao, Yifei School of Sensing Science and Engineering School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai200240 China SJTU-Pinghu Institute of Intelligent Optoelectronics Pinghu314200 China Institute of Microelectronics Chinese Academy of Sciences Beijing100029 China College of Mathematics and Physics Beijing University of Chemical Technology Beijing100029 China State Key Laboratory of Advanced Optical Communication Systems and Networks Department of Electronic Engineering Shanghai Jiao Tong University Shanghai200240 China State Key Laboratory of Advanced Optical Communication Systems and Networks Shanghai Key Lab of Navigation and Location Services Shanghai Institute for Advanced Communication and Data Science Department of Electronic Engineering Shanghai Jiao Tong University Shanghai200240 China National Key Laboratory of Science and Technology on Micro/Nano Fabrication School of Integrated Circuits Peking University Beijing100871 China Beijing Advanced Innovation Center for Integrated Circuits Beijing100871 China Frontiers Science Center for Nano-optoelectronics Peking University Beijing100871 China

The miniaturization and integration of beam steering devices have consistently been the focus of the field. Conventional methods alter the eigenmode of the optical cavity by regulating the refractive index. Due to the weak nonlinear effect of the optical system, the device must be sufficiently large to achieve sufficient light modulation. The effective method for miniaturizing beam steering devices currently in use is based on metasurfaces. However, this type of device necessitates the input of a laser source, which precludes the simultaneous generation and control of light in a single device. Here we propose a miniaturized beam steering device that employs mode selection between different bound states in the continuum (BIC) states through phase change material. The device is capable of simultaneously achieving both light generation and beam steering (33°) in a single device with a size of only 25 μm and with a low threshold of 8.9 kW cm-2. Furthermore, it is possible to achieve a significant degree of dynamic wavelength tunability, with a range extending up to 296 nm. This method achieves high-efficient regulation of light properties by dynamically controlling the system’s topological charge, circumventing the problem of weak nonlinearity in traditional methods. Furthermore, the integration of phase change materials with nanolasers enables the direct alteration of lasing properties, which provides a novel idea for dynamic light control. The device process scheme based on phase change materials is straightforward, direct, and highly compatible, which will be advantageous for its intended application. Copyright © 2024, The Authors. All rights reserved.

关键词： Phase change materials

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GSMorph: Gradient Surgery for cine-MRI Cardiac Deformable Registration

arXiv

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arXiv 2023年

作者： Dou, Haoran Bi, Ning Han, Luyi Huang, Yuhao Mann, Ritse Yang, Xin Ni, Dong Ravikumar, Nishant Frangi, Alejandro F. Huang, Yunzhi University of Leeds Leeds United Kingdom Department of Radiology and Nuclear Medicine Radboud University Medical Centre Nijmegen Netherlands Department of Radiology Netherlands Cancer Institute Amsterdam Netherlands National-Regional Key Technology Engineering Laboratory for Medical Ultrasound School of Biomedical Engineering Health Science Center Shenzhen University China Lab Shenzhen University China Marshall Laboratory of Biomedical Engineering Shenzhen University China Shenzhen RayShape Medical Technology Co. Ltd China Division of Informatics Imaging and Data Science Schools of Computer Science and Health Sciences University of Manchester Manchester United Kingdom Electrical Engineering and Cardiovascular Sciences Departments KU Leuven Leuven Belgium Alan Turing Institute London United Kingdom Institute for AI in Medicine School of Artificial Intelligence Nanjing University of Information Science and Technology Nanjing China

Deep learning-based deformable registration methods have been widely investigated in diverse medical applications. Learning-based deformable registration relies on weighted objective functions trading off registration accuracy and smoothness of the deformation field. Therefore, they inevitably require tuning the hyperparameter for optimal registration performance. Tuning the hyperparameters is highly computationally expensive and introduces undesired dependencies on domain knowledge. In this study, we construct a registration model based on the gradient surgery mechanism, named GSMorph, to achieve a hyperparameter-free balance on multiple losses. In GSMorph, we reformulate the optimization procedure by projecting the gradient of similarity loss orthogonally to the plane associated with the smoothness constraint, rather than additionally introducing a hyperparameter to balance these two competing terms. Furthermore, our method is model-agnostic and can be merged into any deep registration network without introducing extra parameters or slowing down inference. In this study, We compared our method with state-of-the-art (SOTA) deformable registration approaches over two publicly available cardiac MRI datasets. GSMorph proves superior to five SOTA learning-based registration models and two conventional registration techniques, SyN and Demons, on both registration accuracy and smoothness. © 2023, CC BY.

关键词： Medical applications

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AI exposure predicts unemployment risk

arXiv

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arXiv 2023年

作者： Frank, Morgan R. Ahn, Yong-Yeol Moro, Esteban Department of Informatics and Networked Systems University of Pittsburgh PittsburghPA15216 United States Digital Economy Lab Institute for Human-Centered Artificial Intelligence Stanford University StanfordCA94305 United States Media Laboratory Massachusetts Institute of Technology CambridgeMA02139 United States Connection Science Massachusetts Institute of Technology CambridgeMA United States Center for Complex Networks and Systems Research Luddy School of Informatics Computing and Engineering Indiana University BloomingtonIN United States Indiana University Network Science Institute Indiana University BloomingtonIN United States Department of Mathematics & GISC Universidad Carlos III de Madrid Leganes28911 Spain Institute for Data Systems and Society Massachusetts Institute of Technology CambridgeMA United States

Is artificial intelligence (AI) disrupting jobs and creating unemployment? Despite many attempts to quantify occupations' exposure to AI, inconsistent validation obfuscates the relative benefits of each approach. A lack of disaggregated labor outcome data, including unemployment data, further exacerbates the issue. Here, we assess which models of AI exposure predict job separations and unemployment risk using new occupation-level unemployment data by occupation from each US state's unemployment insurance office spanning 2010 through 2020. Although these AI exposure scores have been used by governments and industry, we find that individual AI exposure models are not predictive of unemployment rates, unemployment risk, or job separation rates. However, an ensemble of those models exhibits substantial predictive power suggesting that competing models may capture different aspects of AI exposure that collectively account for AI's variable impact across occupations, regions, and time. Our results also call for dynamic, context-aware, and validated methods for assessing AI exposure. Interactive visualizations for this study are available at https://***/~mrfrank/uiRiskDemo/. © 2023, CC BY.

关键词： Visualization

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Deterministic realization of collective measurements via photonic quantum walks

arXiv

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arXiv 2017年

作者： Hou, Zhibo Tang, Jun-Feng Shang, Jiangwei Zhu, Huangjun Li, Jian Yuan, Yuan Wu, Kang-Da Xiang, Guo-Yong Li, Chuan-Feng Guo, Guang-Can Key Laboratory of Quantum Information University of Science and Technology of China CAS Hefei230026 China Synergetic Innovation Center of Quantum Information and Quantum Physics University of Science and Technology of China Hefei230026 China Naturwissenschaftlich-Technische Fakultät Universität Siegen Siegen57068 Germany Beijing Key Laboratory of Nanophotonics Ultrafine Optoelectronic Systems School of Physics Beijing Institute of Technology Beijing100081 China Institute for Theoretical Physics University of Cologne Cologne50937 Germany Department of Physics Center for Field Theory and Particle Physics Fudan University Shanghai200433 China Institute for Nanoelectronic Devices and Quantum Computing Fudan University Shanghai200433 China State Key Laboratory of Surface Physics Fudan University Shanghai200433 China Collaborative Innovation Center of Advanced Microstructures Nanjing210093 China Institute of Signal Processing Transmission Nanjing University of Posts and Telecommunications Nanjing210003 China Key Lab of Broadband Wireless Communication and Sensor Network Technology Nanjing University of Posts and Telecommunications Ministry of Education Nanjing210003 China

Collective measurements on identically prepared quantum systems can extract more information than local measurements, thereby enhancing information-processing efficiency. Although this nonclassical phenomenon has been known for two decades, it has remained a challenging task to demonstrate the advantage of collective measurements in experiments. Here we introduce a general recipe for performing deterministic collective measurements on two identically prepared qubits based on quantum walks. Using photonic quantum walks, we realize experimentally an optimized collective measurement with fidelity 0.9946 without post selection. As an application, we achieve the highest tomographic efficiency in qubit state tomography to date. Our work offers an effective recipe for beating the precision limit of local measurements in quantum state tomography and metrology. In addition, our study opens an avenue for harvesting the power of collective measurements in quantum information processing and for exploring the intriguing physics behind this power. Copyright © 2017, The Authors. All rights reserved.

关键词： Quantum optics

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Transformers in Medical Image Analysis: A Review

arXiv

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arXiv 2022年

作者： He, Kelei Gan, Chen Li, Zhuoyuan Rekik, Islem Yin, Zihao Ji, Wen Gao, Yang Wang, Qian Zhang, Junfeng Shen, Dinggang Medical School of Nanjing University Nanjing China National Institute of Healthcare Data Science Nanjing University Nanjing China State Key Laboratory for Novel Software Technology Nanjing University Nanjing China BASIRA Lab Faculty of Computer and Informatics Engineering Istanbul Technical University Istanbul Turkey School of Science and Engineering Computing University of Dundee United Kingdom School of Biomedical Engineering ShanghaiTech University Shanghai China Department of Research and Development Shanghai United Imaging Intelligence Co. Ltd. Shanghai China

Transformers have dominated the field of natural language processing, and recently impacted the computer vision area. In the field of medical image analysis, Transformers have also been successfully applied to full-stack clinical applications, including image synthesis/reconstruction, registration, segmentation, detection, and diagnosis. Our paper aims to promote awareness and application of Transformers in the field of medical image analysis. Specifically, we first overview the core concepts of the attention mechanism built into Transformers and other basic components. Second, we review various Transformer architectures tailored for medical image applications and discuss their limitations. Within this review, we investigate key challenges revolving around the use of Transformers in different learning paradigms, improving the model efficiency, and their coupling with other techniques. We hope this review can give a comprehensive picture of Transformers to the readers in the field of medical image analysis. © 2022, CC BY-SA.

关键词： Diagnosis

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