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Photonics for Quantum 2021

作者： Eldridi, Asma Gueddana, Amor Lakshminarayanan, Vasudevan University of Carthage Department of Electrical Engineering National Engineering School of Carthage ENICarthage Tunis2035 Tunisia University of Carthage Higher School of Communication of Tunis Sup'Com Green and Smart Communication Systems Lab Gres'Com Ariana2083 Tunisia University of Waterloo School of Optometry and Vision Science Theoretical and Experimental Epistemology Lab TEEL 200 University Avenue West WaterlooONN2l 3G1 Canada University of Waterloo Department of Physics Department of Electrical and Computer Engineering Department of Systems Design Engineering 200 University Avenue West WaterlooONN2l 3G1 Canada

ISBN: (纸本)9781510645264

We use the IBMQ machine to implement a quantum database circuit. The database is made of a single multi-column table with multiple superposed records using Toffoli, CNOT and single quantum qubit gates. We present an optimized quantum circuit which allows us to select an item from the table, according to a specific oracle. Our circuit is based only on a single auxiliary qubit to perform the Selective Inversion (SI) and the Inversion Around the Average (IAA), which constitute the Grover operator. We analyze the difference between the generated transpired circuits while executing the original circuit on different IBMQ machines, namely the Melbourne and Athens quantum machines. We discuss the physical constraints causing the difference between the simulation on the composer and the executions of the circuits on the real IBMQ devices. © 2021 Copyright SPIE.

关键词： Timing circuits

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Converged reconfigurable intelligent surface and mobile edge computing for space information networks

arXiv

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arXiv 2021年

作者： Cao, Xuelin Yang, Bo Huang, Chongwen Yuen, Chau Zhang, Yan Niyato, Dusit Han, Zhu Engineering Product Development Pillar Singapore University of Technology and Design Singapore Singapore College of Information Science and Electronic Engineering Zhejiang Provincial Key Lab of information processing communication and networking International Joint Innovation Center Zhejiang University China Department of Informatics University of Oslo Norway School of Computer Science and Engineering Nanyang Technological University Singapore Singapore Department of Electrical and Computer Engineering University of Houston United States

Space information networks (SIN) are facing an ever-increasing thirst for high-speed and high-capacity seamless data transmission due to the integration of ground, air, and space communications. However, this imposes a new paradigm on the architecture design of the integrated SIN. Recently, reconfigurable intelligent surfaces (RISs) and mobile edge computing (MEC) are the most promising techniques, conceived to improve communication and computation capability by reconfiguring the wireless propagation environment and offloading. Hence, converging RISs and MEC in SIN is becoming an effort to reap the double benefits of computation and communication. In this article, we propose an RIS-assisted collaborative MEC architecture for SIN and discuss its implementation. Then we present its potential benefits, major challenges, and feasible applications. Subsequently, we study different cases to evaluate the system data rate and latency. Finally, we conclude with a list of open issues in this research area. Copyright © 2021, The Authors. All rights reserved.

关键词： Mobile edge computing

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Machine Learning-Assisted design of Al-Zn-Mg-Cu Alloys with Dramatically Enhanced Combination of Mechanical Strength and Plasticity

SSRN

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SSRN 2022年

作者： Juan, Yongfei Niu, Guoshuai Jiang, Haitao Yang, Jian Tang, Wenqi Liang, Enquan Yang, Yang Dai, Yongbing Zhang, Jiao Sun, Baode Shanghai Key Lab of Advanced High-Temperature Materials and Precision Forming State Key Lab of Metal Matrix Composites School of Materials Science and Engineering Shanghai Jiao Tong University Shanghai200240 China Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai200240 China Shanghai Aircraft Design and Research Institute Commercial Aircraft Corporation of China Shanghai201210 China Collaborative Innovation Center for Advanced Ship and Deep-sea Exploration Shanghai Jiao Tong University Shanghai200240 China

Materials development is stuck in vast untapped composition space and traditional trial-and-error methods, efficiently discovering novel alloys remains a significant challenge. Herein, we propose a machine learning-based alloy optimal property design system (OPDS) to facilitate the rational design of high-performance aluminum alloy. A database comprising element contents, heat treatment parameters, and material properties was preliminarily constructed. Notably, five knowledge-based material descriptors, including absolute electronegativity ( ), atomic size difference ( ), mixing enthalpy ( ), mixing entropy ( ), and valence electron concentration (VEC), were employed to enhance the model interpretability. After feature engineering, eight key features associated with the material properties were screened out. Subsequently, five ML algorithms, including Support Vector Regression (SVR), Random Forest (RF), eXtreme Gradient Boosting (XGB), Gaussian Process Regression (GPR), and Multilayer Perceptron (MLP), were respectively employed to construct the prediction models. More crucially, we employed the optimum search method, Particle Swarm Optimization (PSO), to boost the mining ability of OPDS;meanwhile, the multi-objective prediction for tensile strength, yield strength, and elongation was conducted by calculating the Pareto front. According to the compositions and heat treatment strategy predicted by OPDS, a novel Al-Zn-Mg-Cu alloy (OPDS-1) with the average properties of 834 MPa for UTS, 798 MPa for YS, and 7.2% for EL was successfully synthesized. Furthermore, the strengthening mechanisms in this alloy were established quantitatively, accounting for solid-solution, grain-boundary, dislocation, precipitation, and dispersoid strengthening contributions. The calculations confirmed that the precipitation strengthening ( 439 MPa) was most critical in the final strength increment, agreeing with the microstructure analysis. © 2022, The Authors. All rights reserved.

关键词： Machine learning

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Accurate Leukocyte Detection Based on Deformable-DETR and Multi-Level Feature Fusion for Aiding Diagnosis of Blood Diseases

arXiv

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arXiv 2024年

作者： Chen, Yifei Zhang, Chenyan Chen, Ben Huang, Yiyu Sun, Yifei Wang, Changmiao Fu, Xianjun Dai, Yuxing Qin, Feiwei Peng, Yong Gao, Yu HDU-ITMO Joint Institute Hangzhou Dianzi University Hangzhou310018 China Medical Big Data Lab Shenzhen Research Institute of Big Data Shenzhen518172 China School of Artificial Intelligence Zhejiang College of Security Technology Wenzhou325016 China National-local Joint Engineering Research Center for Digitalized Electrical Design Technology Wenzhou University Wenzhou325035 China School of Computer Science and Technology Hangzhou Dianzi University Hangzhou310018 China Department of Hematology Zhejiang Hospital Hangzhou310018 China

In standard hospital blood tests, the traditional process requires doctors to manually isolate leukocytes from microscopic images of patients’ blood using microscopes. These isolated leukocytes are then categorized via automatic leukocyte classifiers to determine the proportion and volume of different types of leukocytes present in the blood samples, aiding disease diagnosis. This methodology is not only time-consuming and labor-intensive, but it also has a high propensity for errors due to factors such as image quality and environmental conditions, which could potentially lead to incorrect subsequent classifications and misdiagnosis. Contemporary leukocyte detection methods exhibit limitations in dealing with images with fewer leukocyte features and the disparity in scale among different leukocytes, leading to unsatisfactory results in most instances. To address these issues, this paper proposes an innovative method of leukocyte detection: the Multi-level Feature Fusion and Deformable Self-attention DETR (MFDS-DETR). To tackle the issue of leukocyte scale disparity, we designed the High-level Screening-feature Fusion Pyramid (HS-FPN), enabling multi-level fusion. This model uses high-level features as weights to filter low-level feature information via a channel attention module and then merges the screened information with the high-level features, thus enhancing the model’s feature expression capability. Further, we address the issue of leukocyte feature scarcity by incorporating a multi-scale deformable self-attention module in the encoder and using the self-attention and cross-deformable attention mechanisms in the decoder, which aids in the extraction of the global features of the leukocyte feature maps. The effectiveness, superiority, and generalizability of the proposed MFDS-DETR method are confirmed through comparisons with other cutting-edge leukocyte detection models using the private WBCDD, public LISC and BCCD datasets. Our source code and private WBCCD

关键词： Object detection

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DCET-Net: Dual-Stream Convolution Expanded Transformer for Breast Cancer Histopathological Image Classification

DCET-Net: Dual-Stream Convolution Expanded Transformer for B...

引用

IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Ying Zou Shannan Chen Qiule Sun Bin Liu Jianxin Zhang Key Lab of Advanced Design and Intelligent Computing Ministry of Education Dalian University Dalian China School of Information and Communication Engineering Dalian University of Technology Dalian China International School of Information Science and Engineering (DUT-RUISE) Dalian University of Technology Dalian China School of Computer Science and Engineering Dalian Minzu University Dalian China

ISBN: (纸本)9781665429825

Researches on breast cancer histopathological image classification have achieved a great breakthrough using deep backbones of Convolutional Neural Networks (CNNs) in recent years. However, due to the inductive bias of locality, CNNs are unable to effectively extract the global feature information of breast cancer histopathological images, limiting the improvement of the classification results. To overcome this shortcoming, this paper reasonably introduces an extra backbone stream of a pure transformer, which consists of a self-attention mechanism to capture global receptive fields of histopathological images, thereby compensating the locality characteristic of CNNs backbone. Based on two backbone streams of CNN and transformer, a dual-stream network called DCET-Net is proposed, which considers local features and global ones simultaneously, and progressively combines them from these two streams to form the final representations for classification. DCET-Net is extensively evaluated on the representative BreakHis histopathological image dataset, and experimental results demonstrate that it is highly competitive with the state-of-the-art CNN methods in breast cancer histopathological image classification task.

关键词： Limiting Biological system modeling Streaming media Transformers Feature extraction Breast cancer Convolutional neural networks

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design4Health bootcamp: A design thinking approach to improve the 21stcentury skills of health, engineering and design students

Design4Health bootcamp: A design thinking approach to improv...

引用

2020 Advances in science and engineering Technology International Conferences, ASET 2020

作者： Boillat, Thomas Tuffnell, Christopher Rivas, Homero Aloul, Fadi Montana, Carlos Design Lab Mohammed Bin Rashid University of Medicine and Health Sciences Dubai United Arab Emirates Mohammed Bin Rashid University of Medicine and Health Sciences Dubai United Arab Emirates Computer Science Engineering American University of Sharjah Sharjah United Arab Emirates Dubai Institute of Design and Innovation Dubai United Arab Emirates

ISBN: (纸本)9781728146409

Global industries are acknowledging that professions as we know them will not require the same skills as they did in the 20th Century. Therefore how can universities prepare students for challenges that have not been fully identified? This paper will present the design4Health Bootcamp, an initiative that intends to close the so-called '21st century skills gap'. We present its course curriculum, quantitative and qualitative evaluation and conclude with a set of principles that support the implementation of such initiatives. © 2020 IEEE.

关键词： Students

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What Matters in Professional Drone Pilots’ Practice? An Interview Study to Understand the Complexity of Their Work and Inform Human-Drone Interaction Research 21

What Matters in Professional Drone Pilots’ Practice? An Int...

引用

Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems

作者： Sara Ljungblad Yemao Man Mehmet Aydın Baytaş Mafalda Gamboa Mohammad Obaid Morten Fjeld Interaction Design Computer Science and Engineering (CSE) University of Gothenburg and Chalmers University of Technology Gothenburg Sweden Interaction Design CSE University of Gothenburg and Chalmers University of Technology Gothenburg Sweden t2i lab Interaction Design CSE Chalmers University of Technology Gothenburg Sweden Interaction Design CSE Chalmers University of Technology Gothenburg Sweden t2i lab Interaction Design CSE Chalmers University of Technology Gothenburg Sweden and University of Bergen Bergen Norway

ISBN: (纸本)9781450380966

Human-drone interaction is a growing topic of interest within HCI research. Researchers propose many innovative concepts for drone applications, but much of this research does not incorporate knowledge on existing applications already adopted by professionals. This limits the validity of said research. To address this limitation, we present our findings from an in-depth interview study with 10 professional drone pilots. Our participants were armed with significant experience and qualifications – pertinent to both drone operations and a set of applications covering diverse industries. Our findings have resulted in design recommendations that should inform both ends and means of human-drone interaction research. These include, but are not limited to: safety-related protocols, insights from domain-specific use cases, and relevant practices outside of hands-on flight.

关键词： pilots piloting quadcopters professionals Drones interview study

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Lessons Learnt from designing a Smart Clothing Telehealth System for Hospital Use 32

Lessons Learnt from Designing a Smart Clothing Telehealth Sy...

引用

32nd Australian computer-Human Interaction Conference, OzCHI 2020

作者： Aggarwal, Deepti Hoang, Thuong Ploderer, Bernd Vetere, Frank Khot, Rohit Ashok Bradford, Mark Hafp Research Lab Rmit University Australia School of Information Technology Deakin University Australia School of Electrical Engineering and Computer Science Queensland University of Technology Australia Interaction Design Lab School of Computing and Information Systems The University of Melbourne Australia Dept. of Anaesthesia and Pain Management Royal Children's Hospital Australia

ISBN: (纸本)9781450389754

In this paper, we describe the design journey of a smart clothing system, SoPhy from the research laboratory to finally being evaluated in the hospital setting. SoPhy is a smart socks-based system, designed to make physiotherapy video consultations effective for assessing lower limb issues. SoPhy is the result of a 3-year journey of development, study and refinement of a research prototype done in collaboration with a physiotherapist. Drawing on this journey, we present seven lessons that emphasize on the importance of fostering strong collaborations with clinicians to move beyond laboratory studies and reach the target health setting. We provide contextual narratives on how we designed a comfortable smart clothing for patients;how we created an intuitive mapping of sensor data for clinicians;how we integrated hospital practice in the system design, and how we managed the ethics clearance for the field evaluation. We hope that these lessons are useful for HCI and health researchers who aim to innovate the field of health but feel restricted with the little knowledge of design and electronics. © 2020 ACM.

关键词： Research laboratories

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Second-order Attention Guided Convolutional Activations for Visual Recognition

Second-order Attention Guided Convolutional Activations for ...

引用

International Conference on Pattern Recognition

作者： Shannan Chen Qian Wang Qiule Sun Bin Liu Jianxin Zhang Qiang Zhang Key Lab of Advanced Design and Intelligent Computing (Ministry of Education) Dalian University Dalian China School of Information and Communication Engineering Dalian University of Technology Dalian China International School of Information Science and Engineering (DUT-RUISE) Dalian University of Technology Dalian China School of Computer Science and Technology Dalian University of Technology Dalian China

Recently, modeling deep convolutional activations by the global second-order pooling has shown great advance on visual recognition tasks. However, most of the existing deep second-order statistical models mainly compute second-order statistics of activations of the last convolutional layer as image representations, and they seldom introduce second-order statistics into earlier layers to better fit network topology, thus limiting the representational ability to a certain extent. Motivated by the flexibility of attention blocks that are commonly plugged into intermediate layers of deep convolutional networks (ConvNets), this work makes an attempt to combine deep second-order statistics with attention mechanisms in ConvNets, and further proposes a novel Second-order Attention Guided Network (SoAG-Net) for visual recognition. More specifically, SoAG-Net involves several SoAG modules seemingly inserted into intermediate layers of the network, in which SoAG collects second-order statistics of convolutional activations by polynomial kernel approximation to predict channel-wise attention maps utilized for guiding the learning of convolutional activations through tensor scaling along channel dimension. SoAG improves the nonlinearity of ConvNets and enables ConvNets to fit more complicated distribution of convolutional activations. Experiment results on three commonly used datasets illuminate that SoAG-Net outperforms its counterparts and achieves competitive performance with state-of-the-art models under the same backbone.

关键词： Visualization Tensors Limiting Network topology Computational modeling Image representation Pattern recognition

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Rapid non-contact visual measurement method for key dimensions of revolving workpieces

引用

International Journal of Metrology and Quality engineering 2021年第1期12卷

作者： Gao, Peipei Liu, Feng Sun, Xizhi Wang, Fang Li, Jiajun Nankai University College of Computer Science Tianjin300071 China Tianjin University State Key Lab Precision Measuring Technology and Instruments Tianjin300072 China Brunel University London College of Engineering Design and Physical Sciences UxbridgeUB8 3PH United Kingdom

Aimed at the rapid non-contact measurement problem of a revolving workpiece's radial and axial dimensions, a fast and high-precision visual inspection method has been presented in this paper. For the workpiece with large axial size, the proposed method established the measurement transformation chain using the object-image and object-object transformations, thus realizing the rapid axial dimensional measurements. For the workpiece with large radial size, this method determined the measurement transformation model based on two-dimensional target and measurement correspondence relationship, and further achieved rapid radial dimensional measurements. The experimental results have shown that the method is effective and can be applied to in situ dimensional measurement of revolving workpieces on high quality production lines. © 2021 P. Gao et al., Published by EDP sciences.

关键词： computer vision

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