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arXiv 2021年

作者： Xu, Chengyuan McCully, Curtis Dong, Boning Howell, D. Andrew Sen, Pradeep Media Arts and Technology University of California Santa BarbaraCA93106 United States Department of Computer Science University of California Santa BarbaraCA93106 United States Las Cumbres Observatory 6740 Cortona Drive Suite 102 GoletaCA93117-5575 United States Department of Electrical and Computer Engineering University of California Santa BarbaraCA93106 United States Department of Physics University of California Santa BarbaraCA93106 United States

Rejecting cosmic rays (CRs) is essential for the scientific interpretation of CCD-captured data, but detecting CRs in single-exposure images has remained challenging. Conventional CR detectors require experimental parameter tuning for different instruments, and recent deep learning methods only produce instrument-specific models that suffer from performance loss on telescopes not included in the training data. We present Cosmic-CoNN, a generic CR detector deployed for 24 telescopes at the Las Cumbres Observatory, which is made possible by the three contributions in this work: 1) We build a large and diverse ground-based CR dataset leveraging thousands of images from a global telescope network. 2) We propose a novel loss function and a neural network optimized for telescope imaging data to train generic CR detection models. At 95% recall, our model achieves a precision of 93.70% on Las Cumbres imaging data and maintains a consistent performance on new ground-based instruments never used for training. Specifically, the Cosmic-CoNN model trained on the Las Cumbres CR dataset maintains high precisions of 92.03% and 96.69% on Gemini GMOS-N/S 1x1 and 2x2 binning images, respectively. 3) We build a suite of tools including an interactive CR mask visualization and editing interface, console commands, and Python APIs to make automatic, robust CR detection widely accessible by the community of astronomers. Our dataset, open-source codebase, and trained models are available at https://***/cy-xu/cosmic-conn. Copyright © 2021, The Authors. All rights reserved.

关键词： Cosmic rays

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HapToes: Vibrotactile Numeric Information Delivery via Tactile Toe Display

HapToes: Vibrotactile Numeric Information Delivery via Tacti...

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International Symposium on Haptic Interfaces for Virtual Environment and Teleoperator Systems (HAPTICS)

作者： Preeti Vyas Feras Al Taha Jeffrey R. Blum Jeremy R. Cooperstock Centre for Interdisciplinary Research in Music Media and Technology McGill University MontrØal Canada Department of Electrical and Computer Engineering McGill University MontrØal Canada

ISBN: (数字)9781728102344

ISBN: (纸本)9781728102351

Tactile rendering of numeric information via a single actuator has been considered for such purposes as fitness progress tracking. However, multi-actuator designs, leveraging spatial mapping, may offer superior performance. Motivated to explore this approach without requiring hardware on the fingers or wrist, we designed HapToes, a novel ten-digit spatial mapping of numeric information to the toes, which overcomes inter-toe discrimination ambiguity. Compared to ActiVibe, a single-actuator wrist-based numeric rendering technique, under similar distraction conditions, HapToes demonstrates equivalent performance for single-value identification, and improved accuracy, response time, and cognitive load when conveying three values sequentially in a single message.

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Publisher Correction: Battery-free wireless imaging of underwater environments

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Nature communications 2022年第1期13卷 6011页

作者： Sayed Saad Afzal Waleed Akbar Osvy Rodriguez Mario Doumet Unsoo Ha Reza Ghaffarivardavagh Fadel Adib Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology Cambridge MA 02139 USA. MIT Media Lab Massachusetts Institute of Technology Cambridge MA 02139 USA. Program in Media Arts and Sciences Massachusetts Institute of Technology Cambridge MA 02139 USA. Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology Cambridge MA 02139 USA. fadel@mit.edu. MIT Media Lab Massachusetts Institute of Technology Cambridge MA 02139 USA. fadel@mit.edu. Program in Media Arts and Sciences Massachusetts Institute of Technology Cambridge MA 02139 USA. fadel@mit.edu. MIT Sea Grant Massachusetts Institute of Technology Cambridge MA 02139 USA. fadel@mit.edu.

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Customized slicing for 6G: Enforcing artificial intelligence on resource management

arXiv

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arXiv 2021年

作者： Guan, Wanqing Zhang, Haijun Leung, Victor C.M. Beijing Engineering and Technology Research Center for Convergence Networks and Ubiquitous Services Institute of Artificial Intelligence University of Science and Technology Beijing Beijing China State Key Laboratory of Media Convergence and Communication Communication University of China Beijing China Department of Electrical and Computer Engineering University of British Columbia VancouverBCV6T 1Z4 Canada

Next generation wireless networks are expected to support diverse vertical industries and offer countless emerging use cases. To satisfy stringent requirements of diversified services, network slicing is developed, which enables service-oriented resource allocation by tailoring the infrastructure network into multiple logical networks. However, there are still some challenges in cross-domain multi-dimensional resource management for end-to-end (E2E) slices under the dynamic and uncertain environment. Trading off the revenue and cost of resource allocation while guaranteeing service quality is significant to tenants. Therefore, this article introduces a hierarchical resource management framework, utilizing deep reinforcement learning in admission control of resource requests from different tenants and resource adjustment within admitted slices for each tenant. Particularly, we first discuss the challenges in customized resource management of 6G. Second, the motivation and background are presented to explain why artificial intelligence (AI) is applied in resource customization of multi-tenant slicing. Third, E2E resource management is decomposed into two problems, multi-dimensional resource allocation decision based on slice-level feedback and real-time slice adaption aimed at avoiding service quality degradation. Simulation results demonstrate the effectiveness of AI-based customized slicing. Finally, several significant challenges that need to be addressed in practical implementation are investigated. Copyright © 2021, The Authors. All rights reserved.

关键词： Resource allocation

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Stable THz wave out put power using laser chaos

Stable THz wave out put power using laser chaos

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International Conference on Infrared and Millimeter Waves

作者： Fumiyoshi Kuwashima Mona Jarrahi Semih Cakmakyapan Kenji Wada Masanobu Haraguchi Yuki Kawakami Takeshi Moriyasu Osamu Morikawa Kazuyoshi Kurihara Hideaki Kitahara Takashi Furuya Makoto Nakajima Masahiko Tani Dep. of Electr. and Electron. Eng. Fukui University of Technology Fukui Japan Electrical and Computer Eng. UCLA Los Angeles CA USA Dep. of Physics and Electronics Osaka Metropolitan University Osaka Japan Dep. of Elec. and Inf. Eng. Natl. Inst. of Tech. Fukui Col. Fukui Japan Faculty of Engineering University of Fukui Fukui Japan Chair of Liberal Arts Japan Coast Guard Academy Kure Japan School of Education University. of Fukui Fukui Japan FIR-UF University of Fukui Fukui Japan Inst. of Laser Engineering Osaka Univ. Osaka Japan

We generate stable optical beats using laser chaos. This optical beat is converted to THz waves through the photoconductive antenna. The output of the THz wave was roughly estimated by comparison with the output of th... 详细信息

ISBN: (数字)9798350370324

ISBN: (纸本)9798350370331

关键词： Chaos Power lasers Laser stability Laser excitation Antennas Oscillators

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An Enhanced Exploration of Sentimental Analysis in Health Care

Research Square

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Research Square 2021年

作者： Chakrapani, Kannan Kempanna, Muniyegowda Safa, Mohamed Iqubal Kavitha, Thiyagarajan Ramachandran, Manikandan Bhaskar, Vidhyacharan Kumar, Ambeshwar School of Computing SASTRA Deemed University Thanjavur613401 India Computer science &Engineering Department Bangalore Institute of Technology Bangalore India Department of IT SRM Institute of Science and Technology Kattankulathur Chennai India Department of Computer Science & Engineering Koneru Lakshmaiah Education Foundation Greenfields Vaddeswaram Guntur522502 India School of Computing SASTRA Deemed University Shanmugha Arts Science Technology and Research Academy Tamil-Nadu Thanjavur India Dept. of Electrical and Computer Engineering San Francisco State University San FranciscoCA94132 United States

The medical dataset replicates the patient's crucial information, such as important details regarding the patient's health. It includes disease diagnoses, interventions, and descriptions of the examined results. Also, detecting the mindset of an acute disease-affected patient is a primary challenging task. Though sentiment analysis plays a role in seeing their perspective, the significant broad medical application does not yet meet the analysis of patient mindset. So here we identified major shortcoming exists while studies the diversified disease-affected people mindset. Hence, we introduce a practical framework to analyse patients' perspectives using a socio-medical dataset that contains various reviews and feedback of critical diseases-affected people—initially, we applied a pre-processing technique, including Lowercase Conversion, removing special characters, removing stop words, Number to word conversion, Stemming, and lemmatization over dataset. Next, N-gram tokenization methodology is used to extract the valuable features followed by assigning polarity score to each sentiment we extract and calculate the overall polarity of the context. Finally, a probabilistic LDA model was employed to combine the review. Furthermore, various machine learning classifiers are explored to evaluate the performance of the proposed framework. © 2021, CC BY.

关键词： Medical applications

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On the Judgement of Full-Period Sequences and a Novel Congruential Map with Double Modulus on Z(p^n)

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China Communications 2019年第5期16卷 189-196页

作者： Yongkui Li Jingbo Hu Yiyang Yu Center for Nonlinear Complex Systems Department of PhysicsSchool of Physics and AstronomyYunnan University Kunming 650091China School of Computer Science and Technology Hubei University of Science and TechnologyXianning 437100China School of Electronic and Electrical Engineering Baoji University of Arts and SciencesBaojiShaanxi 721016China

This paper studies the judgement problem of full-period maps on Z(p^n) and proposes a novel congruential map with double modulus on Z(p^n). The full-period properties of the sequences generated by the novel map are studied completely. We prove some theorems including full-period judgement theorem on Z(p^n) and validate them by some numerical experiments. In the experiments, full-period sequences are generated by a full-period map on Z(p^n). By the binarization, full-period sequences are transformed into binary sequences. Then, we test the binary sequences with the NIST SP 800-22 software package and make the poker test. The passing rates of the statistical tests are high in NIST test and the sequences pass the poker test. So the randomness and statistic characteristics of the binary sequences are good. The analysis and experiments show that these full-period maps can be applied in the pseudo-random number generation(PRNG), cryptography, spread spectrum communications and so on.

关键词： congruential map full-period maps generating sequences residue class rings chaos

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Decoupled Pyramid Correlation Network for Liver Tumor Segmentation from CT images

arXiv

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arXiv 2022年

作者： Zhang, Yao Yang, Jiawei Liu, Yang Tian, Jiang Wang, Siyun Zhong, Cheng Shi, Zhongchao Zhang, Yang He, Zhiqiang Institute of Computing Technology Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China Electrical and Computer Engineering University of California Los Angeles United States Dornsife College of Letters Arts and Sciences University of Southern California Los Angeles United States AI Lab Lenovo Research Beijing China Lenovo Research Beijing China Lenovo Ltd. Beijing China

Purpose: Automated liver tumor segmentation from Computed Tomography (CT) images is a necessary prerequisite in the interventions of hepatic abnormalities and surgery planning. However, accurate liver tumor segmentation remains challenging due to the large variability of tumor sizes and inhomogeneous texture. Recent advances based on Fully Convolutional Network (FCN) for medical image segmentation drew on the success of learning discriminative pyramid features. In this paper, we propose a Decoupled Pyramid Correlation Network (DPC-Net) that exploits attention mechanisms to fully leverage both low- and high-level features embedded in FCN to segment liver tumor. Methods: We first design a powerful Pyramid Feature Encoder (PFE) to extract multi-level features from input images. Then we decouple the characteristics of features concerning spatial dimension (i.e., height, width, depth) and semantic dimension (i.e., channel). On top of that, we present two types of attention modules, Spatial Correlation (SpaCor) and Semantic Correlation (SemCor) modules, to recursively measure the correlation of multi-level features. The former selectively emphasizes global semantic information in low-level features with the guidance of high-level ones. The latter adaptively enhance spatial details in high-level features with the guidance of low-level ones. Results: We evaluate the DPC-Net on MICCAI 2017 LiTS Liver Tumor Segmentation (LiTS) challenge dataset. Dice Similarity Coefficient (DSC) and Average Symmetric Surface Distance (ASSD) are employed for evaluation. The proposed method obtains a DSC of 76.4% and an ASSD of 0.838 mm for liver tumor segmentation, outperforming the state-of-the-art methods. It also achieves a competitive results with a DSC of 96.0% and an ASSD of 1.636 mm for liver segmentation. Conclusions: The experimental results show promising performance of DPC-Net for liver and tumor segmentation from CT images. Furthermore, the proposed SemCor and SpaCor can effectivel

关键词： Semantics

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STS MICCAI 2023 Challenge: Grand challenge on 2D and 3D semi-supervised tooth segmentation

arXiv

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arXiv 2024年

作者： Wang, Yaqi Zhang, Yifan Chen, Xiaodiao Wang, Shuai Qian, Dahong Ye, Fan Xu, Feng Zhang, Hongyuan Zhang, Qianni Wu, Chengyu Li, Yunxiang Cui, Weiwei Luo, Shan Wang, Chengkai Li, Tianhao Liu, Yi Feng, Xiang Zhou, Huiyu Liu, Dongyun Wang, Qixuan Lin, Zhouhao Song, Wei Li, Yuanlin Wang, Bing Wang, Chunshi Chen, Qiupu Li, Mingqian College of Media Engineering Communication University of Zhejiang Hangzhou China State Key Laboratory of Oral Diseases National Clinical Research Center for Oral Diseases West China Hospital of Stomatology Sichuan University Chengdu China Lishui University School of Medicine Hangzhou Geriatric Stomatology Hospital Hangzhou Dental Hospital Group Hangzhou China School of Computer Science and Technology Hangzhou Dianzi University Hangzhou China School of Cyberspace Hangzhou Dianzi University Hangzhou China Suzhou Research Institute of Shandong University Suzhou China School of Biomedical Engineering Shanghai Jiao Tong University Shanghai China School of Biomedical Engineering Medical School Shenzhen University Shenzhen China School of Electronic Engineering and Computer Science Queen Mary University of London London United Kingdom Department of Mechanical Electrical and Information Engineering Shandong University Weihai China School of Management Hangzhou Dianzi University Hangzhou China Department of Stomatology Sichuan Provincial People's Hospital University of Electronic Science and Technology of China Chengdu China School of Computing and Mathematical Sciences University of Leicester Leicester United Kingdom Zeta Technology Co. Ltd. No. 1158 Zhangdong Road Pudong New Area Shanghai China China Academy of Information and Communications Technology Beijing China HangZhou Dianzi University Xiasha Higher Education Zone Hangzhou China Southeast University Sipailou Xuanwu District Nanjing China Shanghai Ninth People's Hospital Shanghai Jiao Tong University School of Medicine Shanghai Jiao Tong University Shanghai China School of Computer Science and Technology Changchun University of Science and Technology Changchun China School of Artificial Intelligence Guilin University of Electronic Technology Guilin China University of Science and Technology of China Hefei China Hefei Institutes of Physical Science Chinese Academy of Sciences No. 350 Shushan

computer-aided design (CAD) tools are increasingly popular in modern dental practice, particularly for treatment planning or comprehensive prognosis evaluation. In particular, the 2D panoramic X-ray image efficiently detects invisible caries, impacted teeth and supernumerary teeth in children, while the 3D dental cone beam computed tomography (CBCT) is widely used in orthodontics and endodontics due to its low radiation dose. However, there is no open-access 2D public dataset for children's teeth and no open 3D dental CBCT dataset, which limits the development of automatic algorithms for segmenting teeth and analyzing diseases. The Semi-supervised Teeth Segmentation (STS) Challenge, a pioneering event in tooth segmentation, was held as a part of the MICCAI 2023 ToothFairy Workshop on the Alibaba Tianchi platform. This challenge aims to investigate effective semi-supervised tooth segmentation algorithms to advance the field of dentistry. In this challenge, we provide two modalities including the 2D panoramic X-ray images and the 3D CBCT tooth volumes. In Task 1, the goal was to segment tooth regions in panoramic X-ray images of both adult and pediatric teeth. Task 2 involved segmenting tooth sections using CBCT volumes. Limited labelled images with mostly unlabelled ones were provided in this challenge prompt using semi-supervised algorithms for training. In the preliminary round, the challenge received registration and result submission by 434 teams, with 64 advancing to the final round. This paper summarizes the diverse methods employed by the top-ranking teams in the STS MICCAI 2023 Challenge. Copyright © 2024, The Authors. All rights reserved.

关键词： Image segmentation

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Surveying Cyber Threat Intelligence and Collaboration: A Concise Analysis of Current Landscape and Trends

Surveying Cyber Threat Intelligence and Collaboration: A Con...

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IEEE International Conference on Cloud Computing technology and Science (CloudCom)

作者： Panagiotis Radoglou-Grammatikis Elisavet Kioseoglou Dimitrios Asimopoulos Miltiadis Siavvas Ioannis Nanos Thomas Lagkas Vasileios Argyriou Konstantinos E. Psannis Sotirios Goudos Panagiotis Sarigiannidis Department of Electrical and Computer Engineering Campus ZEP Kozani University of Western Macedonia Kozani Greece William Gladstone 31 K3Y Ltd Sofia Bulgaria MetaMind Innovations P.C. Kila Kozani Greece Centre for Research and Technology Hellas/Information Technologies Institute Thessaloniki Greece Sidroco Holdings Ltd Nicosia Cyprus Department of Computer Science Kavala Campus International Hellenic University Kavala Greece Department of Networks and Digital Media Kingston University London Surrey UK Department of Applied Informatics School of Information Sciences University of Macedonia Thessaloniki Greece School of Physics Aristotle University of Thessaloniki Thessaloniki Greece

The evolution of cyberattacks has been significantly impacted by the rise of Artificial Intelligence (AI). In particular, AI-driven attacks leverage Machine Learning (ML) and Deep Learning (DL) methods to automate tasks like identifying vulnerabilities, crafting convincing phishing emails, and evading conventional security measures. These cyberattacks can adapt in real time, making them more elusive and challenging to detect. Furthermore, AI has enabled the development of AI-powered malware that can learn and evolve, making it even more dangerous. As AI continues to evolve, both attackers and defenders are engaged in a relentless arms race, with cybersecurity professionals striving to harness AI for threat detection and response while cybercriminals seek to exploit AI’s capabilities for their malicious purposes. This ongoing battle underscores the need for proactive and adaptive cybersecurity strategies to mitigate the evolving threats posed by AI-driven cyberattacks. Based on the aforementioned remarks, it is evident that efficient and adaptable countermeasures are necessary. In this paper, we focus our attention on Cyber Threat Intelligence (CTI) mechanisms. CTI is the process of collecting, analysing, and sharing information about potential cybersecurity threats to help organisations proactively defend against cyberattacks. In particular, after providing an overview of the CTI use cases, a brief analysis of existing solutions follows, highlighting the current trends and directions for future work in this research field.

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