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IEEE International Conference on Artificial Intelligence and Virtual Reality (AIVR)

作者： Yu-Hung Hsu Iuan-Kai Fang Chun Wang Jui-Chien Chen Min-Chun Hu Department of Computer Science National Tsing Hua University Hsinchu Taiwan International Intercollegiate Ph.D. Program National Tsing Hua University Hsinchu Taiwan Department of Electrical Engineering National Taiwan University Taipei Taiwan School of Information University of Texas at Austin USA

Wheelchair basketball for people with disabilities is a niche market with limited attention and resources due to its smaller scale. We utilize the characteristics of VR technology, combining wearable devices with physical wheelchair seating, to create a virtual and real interactive game of wheelchair basketball. This enables people with disabilities to exercise at home and allows the general public to experience the challenges of disability sports, fostering empathy and emphasizing the importance of social justice.

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Visualizing cortical blood perfusion after photothrombotic stroke in vivo by needle-shaped beam optical coherence tomography angiography

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PhotoniX 2024年第1期5卷 605-621页

作者： Xiangyu Guo Jingjing Zhao Liqun Sun Varun Gupta Lin Du Komal Sharma Aidan Van Vleck Kaitlyn Liang Liangcai Cao Lingjie Kong Yuanmu Yang Yong Huang Adam de la Zerda Guofan Jin State Key Laboratory of Precision Measurement Technology and Instruments Department of Precision InstrumentTsinghua UniversityBeijing 100084China Department of Structural Biology Stanford University School of MedicineStanford UniversityStanfordCA 94305USA Department of Neurosurgery Stanford University School of MedicineStanford UniversityStanfordCA 94305USA Department of Radiology Stanford University School of MedicineStanford UniversityStanfordCA 94305USA Department of Electrical Engineering and Computer Sciences University of CaliforniaBerkeley 94720USA School of Optics and Photonics Beijing Institute of TechnologyNo.5 South Zhongguancun StreetHaidianBeijing 100081China Biophysics Program Molecular Imaging Programand Bio-X Program at Stanford UniversityStanfordCA 94305USA Chan Zuckerberg Biohub San FranciscoCA 94110USA Institute of Medical Equipment Science and Engineering Huazhong University of Science and TechnologyWuhan 430074China

Optical imaging techniques provide low-cost,non-radiative images with high spatiotemporal resolution,making them advantageous for long-term dynamic observation of blood perfusion in stroke research and other brain studies compared to non-optical ***,high-resolution imaging in optical microscopy fundamentally requires a tight optical focus,and thus a limited depth of field(DOF).Consequently,large-scale,non-stitched,high-resolution images of curved surfaces,like brains,are difficult to acquire without z-axis *** overcome this limitation,we developed a needle-shaped beam optical coherence tomography angiography(NB-OCTA)system,and for the first time,achieved a volumetric resolution of less than 8μm in a non-stitched volume space of 6.4 mm×4 mm×620μm in *** system captures the distribution of blood vessels at 3.4-times larger depths than normal OCTA equipped with a Gaussian beam(GB-OCTA).We then employed NB-OCTA to perform long-term observation of cortical blood perfusion after stroke in vivo,and quantitatively analyzed the vessel area density(VAD)and the diameters of representative vessels in different regions over 10 days,revealing different spatiotemporal dynamics in the acute,sub-acute and chronic phase of post-ischemic *** from our NB-OCTA,we revealed that the recovery process is not only the result of spontaneous reperfusion,but also the formation of new *** study provides visual and mechanistic insights into strokes and helps to deepen our understanding of the spontaneous response of brain after stroke.

关键词： Optical coherence tomography angiography Diffractive optical elements Needle-shaped beam Brain imaging Stroke Cortical blood perfusion

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SARA: Semantic-assisted Reinforced Active Learning for Entity Alignment

SARA: Semantic-assisted Reinforced Active Learning for Entit...

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International Joint Conference on Neural Networks (IJCNN)

作者： Ching-Hsuan Liu Chih-Ming Chen Jing-Kai Lou Ming-Feng Tsai Jiun-Lang Huang Chuan-Ju Wang Data Science Degree Program National Taiwan University Academia Sinica Taiwan Research Center for Information Technology Innovation Academia Sinica KKCompany Taiwan Department of Computer Science National Chengchi University Taiwan Department of Electrical Engineering National Taiwan University Taiwan Research Center for Information Technology Innovation Academia Sinica Taiwan

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

This paper introduces SARA, a semantic-assisted reinforced active learning framework for enhancing entity alignment (EA) under limited supervision scenarios. SARA addresses the challenges of EA in real-world scenarios, including knowledge graph heterogeneity and limited training ground truth. SARA effectively selects valuable entity pairs with limited labeled data by combining reinforced active learning and semantic information. It utilizes a pair-wise language model based on Sentence-BERT to learn informative name embeddings that capture entity name semantics. These embeddings are combined with structural embeddings and trained using a novel semantic-assisted alignment loss. Extensive experiments on benchmark datasets and a real-world dataset demonstrate the superiority of SARA over existing approaches, particularly in limited labeled data scenarios. The paper also provides insights into fine-tuning strategies, presents ablation studies, and conducts sensitivity analyses to validate the effectiveness of SARA.

关键词： Training Sensitivity analysis Semantics Neural networks Knowledge graphs Benchmark testing Data models

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Virtual histological staining of label-free autopsy tissue sections via deep learning

Virtual histological staining of label-free autopsy tissue s...

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2024 Conference on Lasers and Electro-Optics, CLEO 2024

作者： Li, Yuzhu Pillar, Nir Li, Jingxi Liu, Tairan Wu, Di Sun, Songyu Ma, Guangdong de Haan, Kevin Huang, Luzhe Zhang, Yijie Hamidi, Sepehr Urisman, Anatoly Haran, Tal Keidar Wallace, William Dean Zuckerman, Jonathan E. 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 Computer Science Department University of California Los AngelesCA90095 United States School of Physics Xi'an Jiaotong University Shaanxi Xi'an710049 China Department of Pathology and Laboratory Medicine David Geffen School of Medicine University of California Los AngelesCA90095 United States Department of Pathology University of California San FranciscoCA94143 United States Department of Pathology Hadassah Hebrew University Medical Center Jerusalem91120 Israel Department of Pathology Keck School of Medicine University of Southern California Los AngelesCA90033 United States Department of Surgery University of California Los AngelesCA90095 United States

We present deep learning-based virtual staining of label-free autopsy tissue sections, eliminating severe autolysis-induced artifacts caused by delayed fixation inherent in traditional histochemical H&E staining. ... 详细信息

ISBN: (纸本)9781957171395

关键词： Electrooptical materials

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Spin-orbit-locked hyperbolic polariton vortices carrying reconfigurable topological charges

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eLight 2022年第1期2卷 143-153页

作者： Mingsong Wang Guangwei Hu Saroj Chand Michele Cotrufo Yohannes Abate Kenji Watanabe Takashi Taniguchi Gabriele Grosso Cheng‑Wei Qiu Andrea Alù Photonics Initiative Advanced Science Research CenterCity University of New YorkNew York CityNY 10031USA Department of Electrical and Computer Engineering National University of SingaporeSingapore 116083Singapore Department of Physics and Astronomy The University of GeorgiaAthensGA 30602USA Research Center for Functional Materials National Institute for Materials ScienceTsukubaIbaraki 305‑0044Japan International Center for Materials Nanoarchitectonics National Institute for Materials ScienceTsukubaIbaraki 305‑0044Japan Physics Program Graduate CenterCity University of New YorkNew YorkNY 10016USA

The topological features of optical vortices have been opening opportunities for free-space and on-chip photonic technologies,e.g.,for multiplexed optical communications and robust information *** a parallel but disjoint effort,polar anisotropic van der Waals nanomaterials supporting hyperbolic phonon polaritons(HP2s)have been leveraged to drastically boost light-matter *** far HP2 studies have been mainly focusing on the control of their amplitude and scale *** we report the generation and observation of mid-infrared hyperbolic polariton vortices(HP2Vs)associated with reconfigurable topological ***-shaped gold disks coated with a flake of hexagonal boron nitride are exploited to tailor spin-orbit interactions and realise deeply subwavelength *** complex interplay between excitation spin,spiral geometry and HP2 dispersion enables robust reconfigurability of the associated topological *** results reveal unique opportunities to extend the application of HP2s into topological photonics,quantum information processing by integrating these phenomena with single-photon emitters,robust on-chip optical applications,sensing and nanoparticle manipulation.

关键词： Hyperbolic polariton vortex Light-matter interactions Spin-orbit interaction Topological charge Reconfigurability

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Virtual histological staining of label-free autopsy tissue sections via deep learning

Virtual histological staining of label-free autopsy tissue s...

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CLEO: Applications and Technology in CLEO 2024, CLEO: A and T 2024 - Part of Conference on Lasers and Electro-Optics

作者： Li, Yuzhu Pillar, Nir Li, Jingxi Liu, Tairan Wu, Di Sun, Songyu Ma, Guangdong de Haan, Kevin Huang, Luzhe Zhang, Yijie Hamidi, Sepehr Urisman, Anatoly Haran, Tal Keidar Wallace, William Dean Zuckerman, Jonathan E. 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 Computer Science Department University of California Los AngelesCA90095 United States School of Physics Xi an Jiaotong University Shaanxi Xi'an710049 China Department of Pathology and Laboratory Medicine David Geffen School of Medicine University of California Los AngelesCA90095 United States Department of Pathology University of California San FranciscoCA94143 United States Department of Pathology Hadassah Hebrew University Medical Center Jerusalem91120 Israel Department of Pathology Keck School of Medicine University of Southern California Los AngelesCA90033 United States Department of Surgery University of California Los AngelesCA90095 United States

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Guiding Brain-to-Vocalization Decoder Design Using Structured Generalization Error

Guiding Brain-to-Vocalization Decoder Design Using Structure...

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Annual International Conference of the IEEE engineering in Medicine and Biology Society (EMBC)

作者： Jingya Huang Pablo Tostado-Marcos Sowmya Manojna Narasimha Aashish N. Patel Ezequiel. M. Arneodo Timothy Q. Gentner Gal Mishne Vikash Gilja Department of Electrical and Computer Engineering University of California San Diego CA Department of Bioengineering University of California San Diego CA Institute for Neural Computation University of California San Diego CA Neuroscience Graduate Program University of California San Diego CA Department of Physics University of California San Diego CA Department of Psychology University of California San Diego CA Halıcıoğlu Data Science Institute University of California San Diego CA

ISBN: (数字)9798350371499

ISBN: (纸本)9798350371505

State-of-the-art intracortical neuroprostheses currently enable communication at 60+ words per minute for anarthric individuals by training on over 10K sentences to account for phoneme variability in different word contexts. There is limited understanding about whether this performance can be maintained in decoding naturalistic speech with 40K+ word vocabularies across elicited, spontaneous, and conversational speech contexts. We introduce a vocal-unit-level generalization test to explicitly evaluate neural decoder performance with an expanded and more diverse behavioral repertoire. Tested on neural decoders modeling zebra finch vocalization, an analog to human vocal production, we compare three decoders with different input types: spike trains, neural factors, and firing rates. The factors and rates are latent neural features inferred using trained Latent Factor Analysis via Dynamical Systems (LFADS) models that capture the population neural dynamics during vocal production. While the conventional random holdout generalization error measure is similar for all three decoders, factor- and rate-based decoders outperform spike-based decoders when testing vocal-unit-holdout generalization error. These results suggest the later models better adapt to flexible vocalization inference when trained with partial observation of data variation, motivating further exploration of decoders incorporating latent neural and vocalization dynamics.

关键词： Training Adaptation models Vocabulary Firing Measurement uncertainty Neuroprostheses Production Decoding engineering in medicine and biology Testing

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Meta-optic accelerators for object classifier

Meta-optic accelerators for object classifier

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Conference on Lasers and Electro-Optics (CLEO)

作者： Hanyu Zheng Quan Liu You Zhou Ivan I. Kravchenko Yuankai Huo Jason Valentine Department of Electrical and Computer Engineering Vanderbilt University Nashville TN USA Department of Computer Science Vanderbilt University Nashville TN USA Interdisciplinary Materials Science Program Vanderbilt University Nashville TN USA Center for Nanophase Materials Sciences Oak Ridge National Laboratory Oak Ridge TN USA Department of Mechanical Engineering Vanderbilt University Nashville TN USA

A meta-optic platform for accelerating object classification is demonstrated. End-to-end design is used to co-optimize the optical and digital systems resulting in a high-speed and robust classifier with 93.1% accurac... 详细信息

ISBN: (纸本)9781957171258

关键词： Image analysis Image resolution Machine vision Neural networks Optical systems Signal processing

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A Large Reservoir Computing Forecasting Method Based on Randomized Fuzzy Cognitive Maps

A Large Reservoir Computing Forecasting Method Based on Rand...

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IEEE Workshop on Evolving and Adaptive Intelligent Systems (EAIS)

作者： Omid Orang Fabricio J. Erazo-Costa Petrônio C. L. Silva Guilherme de Alencar Barreto Frederico Gadelha Guimarães Department of Computer Science Universidade Federal de Minas Gerais Belo Horizonte Brazil Graduate Program in Electrical Engineering Universidade Federal de Minas Gerais Belo Horizonte Brazil Federal Institute of Northern Minas Gerais Januária Campus Brazil Department of Teleinformatics Engineering Universidade Federal do Ceará Fortaleza Brazil

ISBN: (数字)9798350366235

ISBN: (纸本)9798350366242

This paper introduces a novel forecastings technique based on randomized fuzzy cognitive maps (FCM), called LRHFCM (or large reservoir of randomized high-order FCM) for predicting univariate time series. LR-HFCM is a hybrid method combining fuzzy time series (FTS), FCMs, and reservoir computing. It is a type of echo state network (ESN) consisting of the input layer, intermediate (or large reservoir) layer, and output layer, where LASSO regression is applied to train the output layer. The novelty of this approach is that the internal layer includes a very large reservoir, considering different combinations from the sets of concepts and order using a certain number of sub-reservoirs to capture different dynamics of input time series. It is important to highlight that the weights within each sub-reservoir are chosen randomly and remain constant throughout the training process. The validity of the LR-HFCM approach is evaluated across 15 different time series datasets. The results highlight the outperformance of the LR-HFCM technique in comparison to various baseline models.

关键词： Training Adaptation models Adaptive systems Computational modeling Time series analysis Fuzzy cognitive maps Reservoirs

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A Genetic Algorithm approach for optimization of geothermal power plant production: Case studies of direct steam cycle in Kamojang

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South African Journal of Chemical engineering 2023年 45卷 1-9页

作者： Rudiyanto, Bayu Birri, Mochammad Syahrul Widjonarko Avian, Cries Kamal, Dianta Mustofa Hijriawan, Miftah Energy Engineering Laboratory Department of Renewable Energy Engineering Politeknik Negeri Jember Jl. Mastrip No.164 Krajan Timur Jember 68121 Indonesia Graduate School of Department of Renewable Energy Engineering Politeknik Negeri Jember Jl. Mastrip No.164 Krajan Timur Jember 68121 Indonesia Department of Electrical Engineering Universitas Jember Jl. Kalimantan 37 Tegalboto Jember 68121 Indonesia Departement of Electronic and Computer Engineering National Taiwan University of Science and Technology No.43 Keelung Rd. Sec.4 Da'an Dist. Taipei City 106335 Taiwan Department of Mechanical Engineering Politeknik Negeri Jakarta Jl. Prof. Dr. G. A. Siwabessy Depok 16425 Indonesia Graduate Program of Mechanical Engineering Universitas Sebelas Maret Jl. Ir. Sutami 36 Surakarta 57126 Indonesia

Indonesia has enormous geothermal potential, but it only contributes 5% to Indonesia's energy matrix. During 37 years of operation, PT. Pertamina Geothermal Energy Kamojang area has been operating to produce electricity and is currently capable of supplying and distributing electricity to the Java-Bali area with a capacity of 60 MWe. However, this can run into a decrease in the efficiency and effectiveness of system performance due to energy losses in several geothermal power plant components during energy conversion. In this case, exergy analysis at PT. Pertamina Geothermal Energy Kamojang area Unit 4 direct-dry steam cycle was done on each component and state. This aims to know the energy and exergy stream and where it happened irreversibly at the component. The biggest irreversibility value occurred at the turbine and main condenser, with a value of 21,693.890 kW and 21,688.148 kW. The total irreversibility of all systems is 58,326.201 kW, while the total exergy inlet systems is 119,308.457 kW, so the value efficiency exergy obtained is 51.13%. Based on the environment as dead state analysis, an efficiency exergy value is inversely proportional to the irreversibility value and ascending environment temperature. System optimization was done with the genetic algorithm method, with variable values at the pressure wellhead and inlet turbine for the overall exergy efficiency value. The value obtained from optimization is 11.98 bar at the wellhead and 10.023 bar at the inlet turbine, and the overall efficiency exergy increased by 51.22%. © 2023 The Author(s)

关键词： Dry steam cycle Exergy Genetic Algorithm Geothermal power plant Optimization

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