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arXiv 2022年

作者： Wang, Yongwei Wang, Yuheng Lee, Tim K. Miao, Chunyan Wang, Z. Jane Joint NTU-WeBank Research Center on Fintech Nanyang Technological University Singapore Singapore Department of Electrical and Computer Engineering University of British Columbia VancouverBC Canada School of Biomedical Engineering University of British Columbia VancouverBC Canada Department of Dermatology and Skin Science University of British Columbia VancouverBC Canada Photomedicine Institute Vancouver Coast Health Research Institute VancouverBC Canada Cancer Control Research Program Bc Cancer VancouverBC Canada School of Computer Science and Engineering Nanyang Technological University Singapore Singapore

Skin cancer is one of the most common types of malignancy, affecting a large population and causing a heavy economic burdenworldwide. Over the last fewyears, computer-aided diagnosis has been rapidly developed and make great progress in healthcare and medical practices due to the advances in artificial intelligence, particularly with the adoption of convolutional neural networks. However, most studies in skin cancer detection keep pursuing high prediction accuracies without considering the limitation of computing resources on portable devices. In this case, the knowledge distillation (KD) method has been proven as an efficient tool to help improve the adaptability of lightweight models under limited resources, meanwhile keeping a high-level representation capability. To bridge the gap, this study specifically proposes a novel method, termed SSD-KD, that unifies diverse knowledge into a generic KD framework for skin diseases classification. Our method models an intra-instance relational feature representation and integrates it with existing KD research. A dual relational knowledge distillation architecture is self-supervisedly trained while the weighted softened outputs are also exploited to enable the student model to capture richer knowledge from the teacher model. To demonstrate the effectiveness of our method, we conduct experiments on ISIC 2019, a large-scale open-accessed benchmark of skin diseases dermoscopic images. Experiments show that our distilled lightweight model can achieve an accuracy as high as 85% for the classification tasks of 8 different skin diseases with minimal parameters and computing requirements. Ablation studies confirm the effectiveness of our intra- and inter-instance relational knowledge integration strategy. Compared with state-of-the-art knowledge distillation techniques, the proposed method demonstrates improved performances. To the best of our knowledge, this is the first deep knowledge distillation application for multi-diseases cl

关键词： Dermatology

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KartalOl: Transfer learning using deep neural network for iris segmentation and localization: New dataset for iris segmentation

arXiv

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arXiv 2021年

作者： Khiarak, Jalil Nourmohammadi Nasab, Samaneh Salehi Jaryani, Farhang Moafinejad, Seyed Naeim Pourmohamad, Rana Amini, Yasin Noshad, Morteza Institute of Control and Computation Engineering Warsaw University of Technology Warsaw Poland School of Engineering Lorestan University Aleshtar Campus Aleshtar Iran Faculty of Engineering Arak University Arak Iran Department of Physics Shahid Beheshti University Tehran Iran School of Computing Informatics and Decision Systems Engineering Arizona State University AZ United States Faculty of Electrical and Computer Engineering University of Kharazmi Iran Stanford University Stanford United States

Iris segmentation and localization in unconstrained environments is challenging due to long distances, illumination variations, limited user cooperation, and moving subjects. To address this problem, we present a U-Net with a pre-trained MobileNetV2 deep neural network method. We employ the pre-trained weights given with MobileNetV2 for the ImageNet dataset and fine-tune it on the iris recognition and localization domain. Further, we have introduced a new dataset, called KartalOl, to better evaluate detectors in iris recognition scenarios. To provide domain adaptation, we fine-tune the MobileNetV2 model on the provided data for NIR-ISL 2021 from the CASIA-Iris-Asia, CASIA-Iris-M1, and CASIA-Iris-Africa and our dataset. We also augment the data by performing left-right flips, rotation, zoom, and brightness. We chose the binarization threshold for the binary masks by iterating over the images in the provided dataset. The proposed method is tested and trained in CASIA-Iris-Asia, CASIA-Iris-M1, CASIA-Iris-Africa, along the KartalOl dataset. The experimental results highlight that our method surpasses state-of-the-art methods on mobile-based benchmarks. The codes and evaluation results are publicly available at https://***/Jalilnkh/KartalOl-NIR-ISL2021031301. Copyright © 2021, The Authors. All rights reserved.

关键词： Deep neural networks

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Optimal scheduling for electric-heat-gas systems: Harnessing nonlinear thermoelectricity of hydrogen fuel cells

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Renewable Energy 2025年 253卷

作者： Hao Fu Fang Kong Xiao Wu Kwang Y. Lee Feng Wu School of Electrical and Power Engineering Hohai University Nanjing 211100 China National Engineering Research Center of Power Generation Control and Safety School of Energy and Environment Southeast University Nanjing 210096 China Department of Electrical and Computer Engineering Baylor University One Bear Place #97356 Waco TX 76798-7356 USA Department of Convergence IT Engineering Pohang University of Science and Technology (POSTECH) Pohang Gyeongbuk 37673 South Korea

Hydrogen fuel cell, having features of high efficiency and zero carbon emission, has shown great application potential in integrated energy systems. To this end, a novel optimal scheduling strategy of electric-heat-gas (EHG) integrated energy system considering the heat and power coupling characters of hydrogen fuel cell is proposed in this paper. However, the intricate thermoelectric coupling characteristics of fuel cells need to be investigated to develop the refined scheduling modes. Based on the constructed mechanism model of hydrogen fuel cell, the thermoelectric coupling characteristics of hydrogen fuel cell cogeneration is analyzed among the feasible region, and further linearized as a series of constraints in the scheduling model. Considering the carbon trading cost, energy purchase cost and wind curtailment cost, the scheduling model of the EHG integrated energy system is developed. The results show that consideration of nonlinear thermoelectric coupling characteristics has improved the scheduling accuracy, which is more desirable in real projects. Using the established scheduling strategy, the available renewable resources can be fully absorbed. The carbon emissions and the total cost are significantly reduced, by 25.8 % and 16 %, respectively. Studies also indicate that the proposed optimal scheduling significantly enhances the operational flexibility and economic effect of the system.

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Prediction of influenza a virus infections in humans using an artificial neural network learning approach

arXiv

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arXiv 2021年

作者： Chrysostomou, Charalambos Partaourides, Harris Seker, Huseyin Computation-based Science and Technology Research Center The Cyprus Institute 20 Konstantinou Kavafi Street Aglantzia Nicosia2121 Cyprus Department of Electrical Engineering Computer Engineering and Informatics Cyprus University of Technology 30 Archbishop Kyprianou Str. Limassol3036 Cyprus Department of Computer and Information Sciences Faculty of Engineering and Environment The University of Northumbria NE1 8ST Newcastle-upon-Tyne United Kingdom

The Influenza type A virus can be considered as one of the most severe viruses that can infect multiple species with often fatal consequences to the hosts. The Haemagglutinin (HA) gene of the virus has the potential to be a target for antiviral drug development realised through accurate identification of its sub-types and possible the targeted hosts. In this paper, to accurately predict if an Influenza type A virus has the capability to infect human hosts, by using only the HA gene, is therefore developed and tested. The predictive model follows three main steps;(i) decoding the protein sequences into numerical signals using EIIP amino acid scale, (ii) analysing these sequences by using Discrete Fourier Transform (DFT) and extracting DFTbased features, (iii) using a predictive model, based on Artificial Neural Networks and using the features generated by DFT. In this analysis, from the Influenza Research Database, 30724, 18236 and 8157 HA protein sequences were collected for Human, Avian and Swine respectively. Given this set of the proteins, the proposed method yielded 97.36% (± 0.04%), 97.26% (± 0.26%), 0.978 (± 0.004), 0.963 (± 0.005) and 0.945 (± 0.005) for the training accuracy validation accuracy, precision, recall and Mathews Correlation Coefficient (MCC) respectively, based on a 10-fold cross-validation. The classification model generated by using one of the largest dataset, if not the largest, yields promising results that could lead to early detection of such species and help develop precautionary measurements for possible human infections. © 2021, CC BY.

关键词： Proteins

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Measurement device-independent quantum key distribution with passive, time-dependent source side-channels

arXiv

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arXiv 2021年

作者： Bourassa, J. Eli Gnanapandithan, Amita Qian, Li Lo, Hoi-Kwong Department of Physics University of Toronto Toronto Canada Department of Electrical and Computer Engineering University of Toronto Toronto Canada Center for Quantum Information and Quantum Control University of Toronto Toronto Canada Department of Physics University of Hong Kong Hong Kong

While measurement-device-independent (MDI) quantum key distribution (QKD) allows two trusted parties to establish a shared secret key from a distance without needing to trust a central detection node, their quantum sources must be well-characterized, with side-channels at the source posing the greatest loophole to the protocol's security. In this paper, we identify a time-dependent side-channel in a common polarization-based QKD source that employs a Faraday mirror for phase stabilization. We apply the recently developed numerical proof technique from [Phys. Rev. A 99, 062332 (2019)] to quantify the sensitivity of the secret key rate to the quantum optical model for the side-channel, and to develop strategies to mitigate the information leakage. In particular, we find that the MDI three-state and BB84 protocols, while yielding the same key rate under ideal conditions, have diverging results in the presence of a side-channel, with BB84 proving more advantageous. While we consider only a representative case example, we expect the strategies developed and key rate analysis method to be broadly applicable to other leaky sources. © 2021, CC BY.

关键词： Quantum cryptography

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Quantum error correction with metastable states of trapped ions using erasure conversion

arXiv

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arXiv 2022年

作者： Kang, Mingyu Campbell, Wesley C. Brown, Kenneth R. Duke Quantum Center Duke University DurhamNC27701 United States Department of Physics Duke University DurhamNC27708 United States Department of Physics and Astronomy University of California Los AngelesCA90095 United States Challenge Institute for Quantum Computation University of California Los AngelesCA90095 United States Center for Quantum Science and Engineering University of California Los AngelesCA90095 United States Department of Electrical and Computer Engineering Duke University DurhamNC27708 United States Department of Chemistry Duke University DurhamNC27708 United States

Erasures, or errors with known locations, are a more favorable type of error for quantum error-correcting codes than Pauli errors. Converting physical noise into erasures can significantly improve the performance of quantum error correction. Here we apply the idea of performing erasure conversion by encoding qubits into metastable atomic states, proposed by Wu, Kolkowitz, Puri, and Thompson [Nat. Comm. 13, 4657 (2022)], to trapped ions. We suggest an erasure-conversion scheme for metastable trapped-ion qubits and develop a detailed model of various types of errors. We then compare the logical performance of ground and metastable qubits on the surface code under various physical constraints, and conclude that metastable qubits may outperform ground qubits when the achievable laser power is higher for metastable qubits. Copyright © 2022, The Authors. All rights reserved.

关键词： Trapped ions

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Energy-Constrained computation Offloading in Space-Air-Ground Integrated Networks using Distributionally Robust Optimization

arXiv

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arXiv 2022年

作者： Chen, Yali Ai, Bo Niu, Yong Zhang, Hongliang Han, Zhu The State Key Laboratory of Rail Traffic Control and Safety Beijing Jiaotong University Beijing100044 China The Frontiers Science Center for Smart High-Speed Railway System Beijing Jiaotong University Beijing100044 China Henan Joint International Research Laboratory of Intelligent Networking and Data Analysis Zhengzhou University Zhengzhou450001 China Research Center of Networks and Communications Peng Cheng Laboratory Shenzhen China The National Mobile Communications Research Laboratory Southeast University Nanjing211189 China The Department of Electrical Engineering Princeton University PrincetonNJ08544 United States The Department of Electrical and Computer Engineering The University of Houston HoustonTX77004 United States The Department of Computer Science and Engineering Kyung Hee University Seoul446-701 Korea Republic of

With the rapid development of connecting massive devices to the Internet, especially for remote areas without cellular network infrastructures, space-air-ground integrated networks (SAGINs) emerge and offload computation-intensive tasks. In this paper, we consider a SAGIN, where multiple low-earthorbit (LEO) satellites providing connections to the cloud server, an unmanned aerial vehicle (UAV), and nearby base stations (BSs) providing edge computing services are included. The UAV flies along a fixed trajectory to collect tasks generated by Internet of Things (IoT) devices, and forwards these tasks to a BS or the cloud server for further processing. To facilitate efficient processing, the UAV needs to decide where to offload as well as the proportion of offloaded tasks. However, in practice, due to the variability of environment and actual demand, the amount of arrival tasks is uncertain. If the deterministic optimization is utilized to develop offloading strategy, unnecessary system overhead or higher task drop rate may occur, which severely damages the system robustness. To address this issue, we characterize the uncertainty with a data-driven approach, and formulate a distributionally robust optimization problem to minimize the expected energy-constrained system latency under the worst-case probability distribution. Furthermore, the distributionally robust latency optimization algorithm is proposed to reach the suboptimal solution. Finally, we perform simulations on the realworld data set, and compare with other benchmark schemes to verify the efficiency and robustness of our proposed algorithm. Copyright © 2022, The Authors. All rights reserved.

关键词： Probability distributions

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Virtual Staining of Label-Free Tissue in Imaging Mass Spectrometry

arXiv

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arXiv 2024年

作者： Zhang, Yijie Huang, Luzhe Pillar, Nir Li, Yuzhu Migas, Lukasz G. Van de Plas, Raf Spraggins, Jeffrey M. Ozcan, Aydogan Electrical and Computer Engineering Department University of California Los AngelesCA90095 United States Bioengineering Department University of California Los AngelesCA90095 United States University of California Los AngelesCA90095 United States Mass Spectrometry Research Center Vanderbilt University NashvilleTN37232 United States Delft Center for Systems and Control Delft University of Technology Delft2628 Netherlands Department of Cell and Developmental Biology Vanderbilt University NashvilleTN37232 United States Department of Chemistry Vanderbilt University NashvilleTN37232 United States Department of Biochemistry Vanderbilt University NashvilleTN37232 United States Department of Pathology Microbiology and Immunology Vanderbilt University Medical Center NashvilleTN37232 United States Department of Surgery University of California Los AngelesCA90095 United States

Imaging mass spectrometry (IMS) is a powerful tool for untargeted, highly multiplexed molecular mapping of tissue in biomedical research. IMS offers a means of mapping the spatial distributions of molecular species in biological tissue with unparalleled chemical specificity and sensitivity. However, most IMS platforms are not able to achieve microscopy-level spatial resolution and lack cellular morphological contrast, necessitating subsequent histochemical staining, microscopic imaging and advanced image registration steps to enable molecular distributions to be linked to specific tissue features and cell types. Here, we present a virtual histological staining approach that enhances spatial resolution and digitally introduces cellular morphological contrast into mass spectrometry images of label-free human tissue using a diffusion model. Blind testing on human kidney tissue demonstrated that the virtually stained images of label-free samples closely match their histochemically stained counterparts (with Periodic Acid-Schiff staining), showing high concordance in identifying key renal pathology structures despite utilizing IMS data with 10-fold larger pixel size. Additionally, our approach employs an optimized noise sampling technique during the diffusion model’s inference process to reduce variance in the generated images, yielding reliable and repeatable virtual staining. We believe this virtual staining method will significantly expand the applicability of IMS in life sciences and open new avenues for mass spectrometry-based biomedical research. Copyright © 2024, The Authors. All rights reserved.

关键词： Tissue

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Master Equation Emulation and Coherence Preservation with Classical control of a Superconducting Qubit

arXiv

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arXiv 2022年

作者： Vlachos, Evangelos Zhang, Haimeng Maurya, Vivek Marshall, Jeffrey Albash, Tameem Levenson-Falk, E.M. Department of Physics & Astronomy Dornsife College of Letters Arts & Sciences University of Southern California Los AngelesCA90089 United States Center for Quantum Information Science & Technology University of Southern California Los AngelesCA90089 United States Department of Electrical Engineering Viterbi School of Engineering University of Southern California Los AngelesCA90089 United States NASA Ames Research Center Moffett FieldCA94035 United States Mountain ViewCA94043 United States Department of Electrical and Computer Engineering University of New Mexico AlbuquerqueNM87131 United States Department of Physics and Astronomy Center for Quantum Information and Control University of New Mexico AlbuquerqueNM87131 United States

Open quantum systems are a topic of intense theoretical research. The use of master equations to model a system's evolution subject to an interaction with an external environment is one of the most successful theoretical paradigms. General experimental tools to study different open system realizations have been limited, and so it is highly desirable to develop experimental tools which emulate diverse master equation dynamics and give a way to test open systems theories. In this paper we demonstrate a systematic method for engineering specific system-environment interactions and emulating master equations of a particular form using classical stochastic noise in a superconducting transmon qubit. We also demonstrate that non-Markovian noise can be used as a resource to extend the coherence of a quantum system and counteract the adversarial effects of Markovian environments. © 2022, CC BY.

关键词： Stochastic systems

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Behavior and properties of water in silicate melts under deep mantle conditions

Research Square

引用

Research Square 2021年

作者： Karki, Bijaya B. Ghosh, Dipta B. Karato, Shun-Ichiro School of Electrical Engineering and Computer Science Department of Geology and Geophysics Center for Computation and Technology Louisiana State University Baton RougeLA70803 United States Dpeartment of Earth and Planetary Sciences Yale University New HavenCT06520 United States

Water (H2O) as one of the most abundant fluids present in Earth plays crucial role in the generation and transport of magmas in the interior. Though hydrous silicate melts have been studied extensively, the experimental data are confined to relatively low pressures and the computational results are still rare. Moreover, these studies imply large differences in the way water influences the physical properties of silicate magmas, such as density and electrical conductivity. Here, we invesigate the equation of state, speciation, and transport properties of water dissolved in Mg1-xFexSiO3 and Mg2(1-x)Fe2xSiO4 melts (for x = 0 and 0.25) as well as in its bulk (pure) fluid state over the entire mantle pressure regime at 2000 to 4000 K using first-principles molecular dynamics. The simulation results allow us to constrain the partial molar volume of the water component in melts along with the molar volume of pure water. The predicted volume of silicate melt+water solution is negative at low pressures but becomes zero above 15 GPa implying ideal mixing at higher pressures. Consequently, the hydrous component tends to lower the melt density to similar extent over much of the mantle pressure regime irrespective of composition. Our results also show that hydrogen diffuses fast in silicate melts and enhances the melt electrical conductivity in a way that differs from electrical conduction in the bulk water. The speciation of the water component varies considerably from the bulk water structure as well. Water is dissolved in melts mostly as hydroxyls at low pressure and as -O-H-O-, -O-H-O-H- and other extended species with increasing pressure. On the other hand, the pure water behaves as a molecular fluid below 15 GPa, gradually becoming a dissociated fluid with further compression. On the basis of modeled density and conductivity results, we suggest that partial melts containing a few percent of water may be gravitationally trapped both above and below the upper mantle-transiti

关键词： Iron compounds

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