检索结果-内蒙古大学图书馆

arXiv 2023年

作者： Li, Jinyang Wang, Zhepeng Hu, Zhirui Date, Prasanna Li, Ang Jiang, Weiwen George Mason University Department of Electrical and Computer Engineering VA United States George Mason University Quantum Science and Engineering Center VA United States Oak Ridge National Laboratory TN United States Pacific Northwest National Laboratory WA United States

Quantum computing presents a promising approach for machine learning with its capability for extremely parallel computation in high-dimension through superposition and entanglement. Despite its potential, existing quantum learning algorithms, such as Variational Quantum Circuits (VQCs), face challenges in handling more complex datasets, particularly those that are not linearly separable. What's more, it encounters the deployability issue, making the learning models suffer a drastic accuracy drop after deploying them to the actual quantum devices. To overcome these limitations, this paper proposes a novel spatial-temporal design, namely "ST-VQC", to integrate nonlinearity in quantum learning and improve the robustness of the learning model to noise. Specifically, ST-VQC can extract spatial features via a novel block-based encoding quantum sub-circuit coupled with a layer-wise computation quantum sub-circuit to enable temporal-wise deep learning. Additionally, a SWAP-Free physical circuit design is devised to improve robustness. These designs bring a number of hyperparameters. After a systematic analysis of the design space for each design component, an automated optimization framework is proposed to generate the ST-VQC quantum circuit. The proposed ST-VQC has been evaluated on two IBM quantum processors, ibm_cairo with 27 qubits and ibmq_lima with 7 qubits to assess its effectiveness. The results of the evaluation on the standard dataset for binary classification show that ST-VQC can achieve over 30% accuracy improvement compared with existing VQCs on actual quantum computers. Moreover, on a non-linear synthetic dataset, the ST-VQC outperforms a linear classifier by 27.9%, while the linear classifier using classical computing outperforms the existing VQC by 15.58%. Copyright © 2023, The Authors. All rights reserved.

关键词： Learning algorithms

来源：评论

学校读者我要写书评

暂无评论

All-passive upconversion of incoherent near-infrared light at intensities down to ~10-7 W/cm2

arXiv

引用

arXiv 2024年

作者： Hamid, Rabeeya Feng, Demeng Narayanan, Pournima Edwards, Justin S. Hu, Manchen Belliveau, Emma Kim, Minjeong Deshpande, Sanket Wan, Chenghao Pucurimay, Linda Czaplewski, David A. Congreve, Daniel N. Kats, Mikhail A. Department of Electrical and Computer Engineering University of Wisconsin-Madison Madison53706 United States Department of Chemistry Stanford University StanfordCA94305 United States Department of Physics University of Wisconsin-Madison Madison53706 United States Department of Electrical Engineering Stanford University StanfordCA94305 United States Department of Materials Science and Engineering Stanford University StanfordCA94305 United States Center for Nanoscale Materials Argonne National Laboratory ArgonneIL60439 United States

Frequency upconversion, which converts low-energy photons into higher-energy ones, typically requires intense coherent illumination to drive nonlinear processes or the use of externally driven optoelectronic devices. Here, we demonstrate an upconversion system that converts low-intensity (down to ~10-7 W/cm2) incoherent near-infrared (NIR) light into the visible, reaching intensities perceptible by the human eye, without the use of any external power input. Our upconverting element is enabled by the following ingredients: (1) photon upconversion via triplet-triplet annihilation in a bulk heterojunction of the organic semiconductors Y6 and rubrene;(2) plasmonic enhancement of absorption and field intensity in the heterojunction layer;(3) collection enhancement using a dichroic thin-film assembly. To enable high-resolution imaging, the upconverting element is inserted at an intermediate image plane of a dual-wavelength telescope system, which preserves the relative directionality of rays between the incident NIR light and output visible light. Our all-passive upconversion imaging system will enable NIR imaging and sensing in low-light environments under energy constraints. Copyright © 2024, The Authors. All rights reserved.

关键词： Heterojunctions

来源：评论

学校读者我要写书评

暂无评论

Assessing the Robustness of nnU-Net in the Detection of Prostate Lesions via Bi-Parametric MRI

Assessing the Robustness of nnU-Net in the Detection of Pros...

引用

Data Science and engineering in Healthcare, Medicine and Biology, IEEE EMBS Special Topic Conference on

作者： Dimitrios I. Zaridis Eugenia Mylona Nikolaos Tachos Charalampos Kalantzopoulos Vasileios C. Pezoulas Dimitrios D. Koutsouris George K. Matsopoulos Kostas Marias Manolis Tsiknakis Dimitrios I. Fotiadis Biomedical Engineering Laboratory School of Electrical and Computer Engineering National Technical University of Athens GR15773 Athens Greece Biomedical Research Institute FORTH Ioannina Greece University of Ioannina Ioannina Greece Institute of Computer Science FORTH Heraklion Greece Department of Electrical and Computer Engineering Hellenic Mediterranean University Heraklion Crete Greece FORTH-IMBB Ioannina Greece Materials Science and Engineering Department Unit of Medical Technology and Intelligent Information Systems University of Ioannina Ioannina GR 45110 Greece

Within the scope of prostate cancer diagnostic imaging, distinguishing lesions is challenging due to their subtle appearance and the prostate gland’s complexity. This study employed the nnU-Net, a state-of-the-art medical image segmentation model, on a dataset of 301 patients from 2 openly available datasets to identify whether lesion sizes are affecting the model’s performance. By applying t-SNE dimensionality reduction algorithm among dice score and respective lesion sizes, we found that nnU-Net behaves differently for lesions smaller than 9mm compared to lesions larger than 15mm. These insights can inform specialized training approaches for future deep learning models in prostate lesion *** Relevance: The main contributions of this study is, to investigate whether lesion sizes affects nnU-Net’s deficiency leading to the development of more specialized models in respect to these factors.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Authentication through residual attention-based processing of tampered optical responses

引用

Advanced Photonics 2024年第5期 45-54页

作者： Blake A.Wilson Yuheng Chen Daksh Kumar Singh Rohan Ojha Jaxon Pottle Michael Bezick Alexandra Boltasseva Vladimir M.Shalaev Alexander V.Kildishev Purdue University Elmore Family School of Electrical and Computer Engineering Quantum Science Center Oak Ridge National Laboratory Purdue University School of Aeronautics and Astronautics Purdue University Department of Computer Science

The global chip industry is grappling with dual challenges: a profound shortage of new chips and a surge of counterfeit chips valued at $75 billion, introducing substantial risks of malfunction and unwanted surveillance. To counteract this, we propose an optical anti-counterfeiting detection method for semiconductor devices that is robust under adversarial tampering features, such as malicious package abrasions,compromised thermal treatment, and adversarial tearing. Our new deep-learning approach uses a RAPTOR(residual, attention-based processing of tampered optical response) discriminator, showing the capability of identifying adversarial tampering to an optical, physical unclonable function based on randomly patterned arrays of gold nanoparticles. Using semantic segmentation and labeled clustering, we efficiently extract the positions and radii of the gold nanoparticles in the random patterns from 1000 dark-field images in just 27 ms and verify the authenticity of each pattern using RAPTOR in 80 ms with 97.6% accuracy under difficult adversarial tampering conditions. We demonstrate that RAPTOR outperforms the state-ofthe-art Hausdorff, Procrustes, and average Hausdorff distance metrics, achieving a 40.6%, 37.3%, and6.4% total accuracy increase, respectively.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Cross-Modal Few-Shot Learning with Second-Order Neural Ordinary Differential Equations 39

Cross-Modal Few-Shot Learning with Second-Order Neural Ordin...

引用

39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Zhang, Yi Cheng, Chun-Wun He, Junyi He, Zhihai Schönlieb, Carola-Bibiane Chen, Yuyan Aviles-Rivero, Angelica I. Harbin Institute of Technology China Department of Electrical and Electronic Engineering Southern University of Science and Technology China Department of Applied Mathematics and Theoretical Physics University of Cambridge United Kingdom Pengcheng Laboratory China Shanghai Key Laboratory of Data Science School of Computer Science Fudan University China Yau Mathematical Sciences Center Tsinghua University China

ISBN: (纸本)157735897X

We introduce SONO, a novel method leveraging Second-Order Neural Ordinary Differential Equations (Second-Order NODEs) to enhance cross-modal few-shot learning. By employing a simple yet effective architecture consisting of a Second-Order NODEs model paired with a cross-modal classifier, SONO addresses the significant challenge of overfitting, which is common in few-shot scenarios due to limited training examples. Our second-order approach can approximate a broader class of functions, enhancing the model's expressive power and feature generalization capabilities. We initialize our cross-modal classifier with text embeddings derived from class-relevant prompts, streamlining training efficiency by avoiding the need for frequent text encoder processing. Additionally, we utilize text-based image augmentation, exploiting CLIP's robust image-text correlation to enrich training data significantly. Extensive experiments across multiple datasets demonstrate that SONO outperforms existing state-of-the-art methods in few-shot learning performance. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Differential equations

来源：评论

学校读者我要写书评

暂无评论

Short-Term Power Load Forecasting Using MOGOA and ConvBiLSTM During COVID-19 Pandemic

Short-Term Power Load Forecasting Using MOGOA and ConvBiLSTM...

引用

International Conference on Power System Technology (POWERCON)

作者： Da Xu Bowen Liu Chi-Seng Lam Zhangyou Huang State Key Laboratory of Analog and Mixed-Signal VLSI University of Macau Macao China Institute of Microelectronics University of Macau Macao China School of Automation China University of Geosciences Wuhan China School of Automation China University of Geosciences Wuhan China Department of Electrical and Computer Engineering FST University of Macau Macao China

Due to the high restrictions of the global COVID-19 on society, economy and production, the electrical supply-demand balance is facing a great challenge. This paper proposes a short-term load forecasting (STLF) method based on multi-factor fusion during the COVID-19. After fully analyzing and screening the nonlinear coupling interactions among cross-domain weather and public health safety features, a convolutional bi-directional long short-term memory neural network (ConvBiLSTM) is developed based on the convolutional and bidirectional loop enhancements of long short-term memory (LSTM). To avoid early termination of training the global optimal solution, multi-objective grasshopper optimization algorithm (MOGOA) is adopted to select the inherent hyperparameters of the forecasting network. Attention mechanism is further introduced to enhance the learning ability of the key features in temporal data, improving the forecasting accuracy and robustness of ConvBiLSTM. Comparisons on cross-domain COVID-19 dataset over classical methods are performed to verify its effective and superior performances.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Investigation of functional integration of cortical organoids transplanted in vivo towards future neural prosthetics applications

Investigation of functional integration of cortical organoid...

引用

International IEEE/EMBS Conference on Neural engineering, CNE

作者： Madison N. Wilson Martin Thunemann Francesca Puppo Emily Martin Rebeca Blanch Fred H. Gage Alysson R. Muotri Anna Devor Duygu Kuzum Department of Electrical and Computer Engineering University of California La Jolla CA USA Department of Biomedical Engineering Boston University Boston MA USA Department of Pediatrics University of California La Jolla CA USA Laboratory of Genetics The Salk Institute La Jolla CA USA

Cortical organoids are rapidly emerging as models for brain development and dysfunction because they bridge the benefits of physiological manipulability of in vitro cultures with the complexity of in vivo brains. Organoids offer a host of benefits including the ability to perform patient-specific drug screening, study brain development, and investigate disease. Another promising application of organoids is as neural prosthetics, where if a brain region is damaged or degenerated, an organoid can be grown from the patient's stem cells and be implanted to restore lost function. Here, we implant hiPSC-derived cortical organoids in the retrosplenial cortices (RSC) of mice and to see if the organoids can form functional connections to the surrounding host cortices. Using transparent graphene microelectrode arrays (MEA) for electrophysiology and two-photon imaging of organoids, we observe a response to the external light stimuli, suggesting functional synaptic connections had formed between organoids and cortex. Bursts of cell population calcium activity are observed with two-photon imaging and suggest a mature internal network. Post-mortem histology and vasculature tracing suggests morphological integration of organoid cells into mouse cortex and mouse vessels and synapses into organoid. Overall, our multimodal platform offers a way to study organoids in vivo longitudinally and investigate their development and integration with host cortex and their potential use for cell replacement therapy.

关键词： In vivo Microelectrodes Calcium Imaging Implants Light emitting diodes Mice

来源：评论

学校读者我要写书评

暂无评论

Vertical‐organic‐nanocrystal‐arrays for crossbar memristors with tuning switching dynamics toward neuromorphic computing

引用

SmartMat 2021年第1期2卷 99-108页

作者： Fangxu Yang Lingjie Sun Qingxi Duan Huanli Dong Zhaokun Jing Yuchao Yang Rongjin Li Xiaotao Zhang Wenping Hu Leon Chua Key Laboratory of Molecular Optoelectronic Sciences School of ScienceTianjin UniversityTianjinChina Beijing National Research Center for Molecular Sciences Chinese Academy of SciencesBeijingChina Joint School of National University of Singapore and Tianjin University International Campus of Tianjin UniversityBinhai New CityFuzhouChina Key Laboratory of Microelectronic Devices and Circuits(MOE) Institute of MicroelectronicsPeking UniversityBeijingChina Department of Electrical Engineering and Computer Sciences University of CaliforniaBerkeleyCaliforniaUSA

Memristors proposed by Leon Chua provide a new type of memory device for novel neuromorphic computing ***,the approaching of distinct multi‐intermediate states for tunable switching dynamics,the con-trolling of conducting filaments(CFs)toward high device repeatability and reproducibility,and the ability for large‐scale preparation devices,remain full of ***,we show that vertical‐organic‐nanocrystal‐arrays(VONAs)could make a way toward the *** perfect one‐dimensional structure of the VONAs could confine the CFs accurately with fine‐tune resistance states in a broad range of 103 *** availability of large‐area VONAs makes the fabrication of large‐area crossbar memristor arrays facilely,and the analog switching characteristic of the memristors is to effectively imitate different kinds of synaptic plasticity,indicating their great potential in future applications.

关键词： conducting filament memristor organic electronics organic single crystal

来源：评论

学校读者我要写书评

暂无评论

Slow-Wave Hybrid Magnonics

引用

Physical Review Letters 2024年第11期132卷 116701-116701页

作者： Jing Xu Changchun Zhong Shihao Zhuang Chen Qian Yu Jiang Amin Pishehvar Xu Han Dafei Jin Josep M. Jornet Bo Zhen Jiamian Hu Liang Jiang Xufeng Zhang Center for Nanoscale Materials Argonne National Laboratory Lemont Illinois 60439 USA Pritzker School of Molecular Engineering University of Chicago Chicago Illinois 60637 USA Department of Materials Science and Engineering University of Wisconsin—Madison Madison Wisconsin 53706 USA Department of Physics and Astronomy University of Pennsylvania Philadelphia Pennsylvania 19104 USA Department of Electrical and Computer Engineering Northeastern University Boston Massachusetts 02115 USA Department of Physics and Astronomy University of Notre Dame Notre Dame Indiana 46556 USA Department of Physics Northeastern University Boston Massachusetts 02115 USA

Cavity magnonics is an emerging research area focusing on the coupling between magnons and photons. Despite its great potential for coherent information processing, it has been long restricted by the narrow interaction bandwidth. In this Letter, we theoretically propose and experimentally demonstrate a novel approach to achieve broadband photon-magnon coupling by adopting slow waves on engineered microwave waveguides. To the best of our knowledge, this is the first time that slow wave is combined with hybrid magnonics. Its unique properties promise great potentials for both fundamental research and practical applications, for instance, by deepening our understanding of the light-matter interaction in the slow wave regime and providing high-efficiency spin wave transducers. The device concept can be extended to other systems such as optomagnonics and magnomechanics, opening up new directions for hybrid magnonics.

关键词： Magnons Spin waves Surface plasmon polariton Waveguides

来源：评论

学校读者我要写书评

暂无评论

AC Breakdown Performance of A12O3 and SiC Natural Ester Based Nanofluids

AC Breakdown Performance of A12O3 and SiC Natural Ester Base...

引用

2020 IEEE International Conference on Environment and electrical engineering and 2020 IEEE Industrial and Commercial Power Systems Europe, EEEIC / I and CPS Europe 2020

作者： Koutras, Konstantinos Pyrgioti, Eleftheria Naxakis, Ioannis Charalampakos, Vassilios Peppas, Georgios High Voltage Laboratory University of Patras Department of Electrical and Computer Engineering Patras Greece

ISBN: (纸本)9781728174532

In this paper, dielectric Al2O3and semi conductive SiC nanoparticles were added to a certain volume of natural ester oil. The two types of nanoparticles were dispersed into the base oil in the same weight % ratio (0.004% w/w) with the assistance of ultrasonication and magnetic stirring. AC breakdown voltage of the nano-oil samples was measured according to IEC 60156 standard using a Baur DTA 100C, while Normal and Weibull distributions were used to calculate the breakdown probability for the samples in question in comparison to the corresponding ones of the base oil. The results prove the effect of the integration of these specific types of nanoparticles in the same concentrations in terms of AC breakdown voltage in order to draw a conclusion on the reliability of them as an alternative insulating medium of a power transformer. © 2020 IEEE.

关键词： Electric breakdown

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：