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TechRxiv 2021年

作者： Trindade, Alessandro Galvão, Edilson Cordeiro, Lucas The Post-graduate Program in Electrical Engineering Manaus Brazil Department of Electricity Federal University of Amazonas Manaus Brazil School of Computer Science The University of Manchester United Kingdom

Annual global energy consumption growth is around 1.3% with forecasts until 2040. Photovoltaic systems became a suitable alternative to nuclear and fossil energy generation. In order to support this technology’s dissemination, we develop and evaluate an automated formal synthesis approach that assists in decision-making for off-grid systems. Our proposed approach, called PVz, is based on a variant of the counterexample-guided inductive synthesis;it has a multi-core feature, which can obtain the optimal sizing of photovoltaic systems focusing on Life Cycle Cost analysis. Given the electrical needs of a home, we seek a set of electrical equipment with the best possible combination of devices that meet the specified requirements. We calculate all costs related to maintenance over 20 years. The results presented are based on seven case studies;some of them are real ones from the Amazon region in Brazil. The same case studies were solved by a commercial optimization tool. Our technique and the commercial tool results were validated with popular simulation software to perform a fair comparison. Furthermore, we analyze some topics such as run-time, optimal solution, and configuration of the resulting systems. We claim that our technique is advantageous compared to the existing approaches in the literature. © 2021, CC BY-NC-SA.

关键词： Decision making

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Outcoupling Hyperbolic Modes from Aligned Carbon Nanotube Films

Outcoupling Hyperbolic Modes from Aligned Carbon Nanotube Fi...

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CLEO: QELS_Fundamental science, QELS 2022

作者： Jerome, Bryant Prasad, Ciril S. Doumani, Jacques Dewey, Oliver S. Baydin, Andrey Pasquali, Matteo Kono, Junichiro Gao, Weilu Alabastri, Alessandro Naik, Gururaj V. Department of Electrical and Computer Engineering Rice University HoustonTX77005 United States Applied Physics Graduate Program Smalley-Curl Institute Rice University HoustonTX77005 United States Department of Chemical and Biomolecular Engineering Rice University HoustonTX77005 United States Smalley-Curl Institute Rice University HoustonTX77005 United States Department of Chemistry Rice University HoustonTX77005 United States Department of Materials Science and NanoEngineering Rice University HoustonTX77005 United States Department of Physics and Astronomy Rice University HoustonTX77005 United States Department of Electrical and Computer Engineering University of Utah Salt Lake CityUT84112 United States

Aligned carbon nanotube films make an excellent hyperbolic material platform in the infrared. Here, we experimentally demonstrate the presence of high-k modes in aligned carbon nanotube films and outcouple them to fre... 详细信息

ISBN: (纸本)9781557528209

关键词： Carbon films

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Generating a fuzzy rule based classification system by genetic learning of granularity level using TOPSIS 11

Generating a fuzzy rule based classification system by genet...

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11th Conference of the European Society for Fuzzy Logic and Technology, EUSFLAT 2019

作者： de O. Araujo, Antonio E. Krohling, Renato A. Graduate Program in Computer Science - PPGI Federal University of Espirito Santo - UFES Vitoria ES Brazil Production Engineering Department Federal University of Espirito Santo - UFES Vitoria ES Brazil

ISBN: (纸本)9789462527706

Fuzzy Rule Based Classification Systems (FRBCSs) are widely used tools in classification problems. An important aspect in the design of a FRBCS is the number of fuzzy labels per variable (granularity level), which significantly influences the performance of the fuzzy system. Another relevant issue to be considered when generating a FRBCS is the accuracy-interpretability tradeoff, which can be addressed in the context of multi-objective optimization. Thus, in this work, we propose a new approach to design a FRBCS in which the accuracy and the interpretability (number of rules) of the FRBCS are considered objectives to be treated with the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). We applied our method to several well-known standard classification datasets and the results show the feasibility of the proposed approach. Copyright © 2019, the Authors. Published by Atlantis Press. This is an open access article under the CC BY-NC license (http://***/licenses/by-nc/4.0/).

关键词： Genetic algorithms

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Strong nonreciprocal acoustic extinction and asymmetric audibility from spinning fluid scatterers

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Physical Review B 2023年第16期108卷 165129-165129页

作者： Yao-Ting Wang Heedong Goh Sander A. Mann Andrea Alù Photonics Initiative Advanced Science Research Center City University of New York New York New York 10031 USA Department of Photonics National Sun Yat-Sen University Kaohsiung 804201 Taiwan Department of Electrical and Computer Engineering The University of Texas at Austin Austin Texas 78712 USA Physics Program Graduate Center City University of New York New York New York 10026 USA

Lorentz reciprocity places important constraints on the response of systems to wave excitations, and surpassing these constraints is of both fundamental and applied interest. In the context of scattering, reciprocity requires that the total extinction of an object is identical when excited from opposite directions, regardless of the asymmetry of the scatterer. Here, we demonstrate that, by combining multiple scatterers that break time-reversal symmetry, this extinction symmetry can be largely violated. We consider spinning cylinders, demonstrating that nonreciprocity must be paired with broken parity symmetry to enable large extinction contrast. As a dramatic example, we show a system that strongly scatters when excited from one side, but it is cloaked for the opposite excitation. Our results pave the way for approaches to asymmetric audibility and scattering manipulation, and they may readily translate to other wave domains.

关键词： Acoustic metamaterials Acoustics Non-reciprocal propagation

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Giant Deep-Ultraviolet Circular Dichroism in Synthetic Architectures of Ordered Carbon Nanotubes

Giant Deep-Ultraviolet Circular Dichroism in Synthetic Archi...

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2023 Conference on Lasers and Electro-Optics, CLEO 2023

作者： Doumani, Jacques Lou, Minhan Dewey, Oliver Hong, Nina Fan, Jichao Baydin, Andrey Pasquali, Matteo Yomogida, Yohei Yanagi, Kazuhiro Saito, Riichiro Kono, Junichiro Gao, Weilu Department of Electrical and Computer Engineering Rice University HoustonTX United States Applied Physics Graduate Program Smalley-Curl Institute Rice University HoustonTX United States Department of Electrical and Computer Engineering The University of Utah Salt Lake CityUT United States Carbon Hub Rice University HoustonTX United States Department of Chemical and Biomolecular Engineering Rice University HoustonTX United States J.A. Woollam Co. LincolnNE United States The Smalley-Curl Institute Rice University HoustonTX United States Department of Chemistry Rice University HoustonTX United States Department of Materials Science and NanoEngineering Rice University HoustonTX United States Department of Physics and Astronomy Tokyo Metropolitan University Tokyo Japan Department Physics Tohoku University Sendai Japan Department Physics and Astronomy Rice University HoustonTX United States

We demonstrate tunable, giant, and structure-induced deep-ultraviolet circular dichroism in macroscopically chiral assemblies of racemic carbon nanotubes prepared using two approaches: mechanical-rotation-assisted vac... 详细信息

ISBN: (纸本)9781957171258

关键词： Dichroism

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Expectation-Maximization Contrastive Learning for Compact Video-and-Language Representations

arXiv

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arXiv 2022年

作者： Jin, Peng Huang, Jinfa Liu, Fenglin Wu, Xian Ge, Shen Song, Guoli Clifton, David A. Chen, Jie School of Electronic and Computer Engineering Peking University China Peng Cheng Laboratory Shenzhen China -Preferred Program Peking University Shenzhen Graduate School China Department of Engineering Science University of Oxford United Kingdom Tencent JARVIS Lab China Oxford-Suzhou Centre for Advanced Research Suzhou China

Most video-and-language representation learning approaches employ contrastive learning, e.g., CLIP [53], to project the video and text features into a common latent space according to the semantic similarities of text-video pairs. However, such learned shared latent spaces are not often optimal, and the modality gap between visual and textual representation can not be fully eliminated. In this paper, we propose Expectation-Maximization Contrastive Learning (EMCL) to learn compact video-and-language representations. Specifically, we use the Expectation-Maximization algorithm to find a compact set of bases for the latent space, where the features could be concisely represented as the linear combinations of these bases. Such feature decomposition of video-and-language representations reduces the rank of the latent space, resulting in increased representing power for the semantics. Extensive experiments on three benchmark text-video retrieval datasets prove that our EMCL can learn more discriminative video-and-language representations than previous methods, and significantly outperform previous state-of-the-art methods across all metrics. More encouragingly, the proposed method can be applied to boost the performance of existing approaches either as a jointly training layer or an out-of-the-box inference module with no extra training, making it easy to be incorporated into any existing methods‡ Copyright © 2022, The Authors. All rights reserved.

关键词： Semantics

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Generating Lymphoma Variability Through Adversarial Generation Models: A Limited Resource Approach for Synthetic Histopathology Image Generation and Classification

SSRN

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SSRN 2024年

作者： Tiwari, Ashutosh Saudarshan, Harsh Krisnawati, Dyah Ika Khafid, Muhamad Wardani, Erika Martining Mutalik, Chinmaya Chen, Chi-Long Kuo, Tsung-Rong International Ph.D. Program in Biomedical Engineering College of Biomedical Engineering Taipei Medical University Taipei11031 Taiwan Department of Computer Science and Engineering Guru Ghasidas Vishwavidyalaya Chhattisgarh Bilaspur495509 India Department of Nursing Faculty of Nursing and Midwifery Universitas Nahdlatul Ulama Surabaya East Java Surabaya60237 Indonesia Graduate Institute of Nanomedicine and Medical Engineering College of Biomedical Engineering Taipei Medical University Taipei11031 Taiwan Department of Pathology School of Medicine College of Medicine and Taipei Medical University Hospital Taipei Medical University Taipei11031 Taiwan International Master/Ph.D. Program in Medicine College of Medicine Taipei Medical University Taipei11031 Taiwan Precision Medicine and Translational Cancer Research Center Taipei Medical University Hospital Taipei11031 Taiwan Tulane University United States Graduate Institute of Clinical Medicine Taiwan

Lymphomas, a diverse group of blood cancers originating from the lymphatic system, present significant challenges for diagnosis and treatment due to their morphological and genetic differences. These variability obscures the development of personalized therapies and impacts disease outcomes. The scarcity of high-quality, diverse images of lymphoma cells is further hindered by patient privacy concerns, the rarity of certain types, and logistical challenges of collecting and processing biomedical images. Additionally, the costs and technical difficulties of microscopic imaging limit the scope of research. This paper addresses these challenges by utilizing generative adversarial networks (GANs) to generate synthetic yet realistic images of lymphoma histopathology and classify them in a low resource setting. Focusing specifically on chronic lymphocytic leukemia (CLL), follicular lymphoma (FL), and mantle cell lymphoma (MCL), we employed GANs to enhance the understanding of lymphoma variability and support advancements in digital cancer biology and pathology. This dual approach of image generation and classification aims to improve disease diagnosis and prognosis, ultimately contributing to better patient outcomes. © 2024, The Authors. All rights reserved.

关键词： Lung cancer

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The 2020 power blackout in the brazilian state of amapá: Lessons learned and opportunities

The 2020 power blackout in the brazilian state of amapá: Le...

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2021 IEEE PES Innovative Smart Grid Technologies Conference - Latin America, ISGT Latin America 2021

作者： Do Prado, Josue Campos Santos, Marcos Vinicius Ramos, Lucas Feksa Canha, Luciane Neves Konzen, Gabriel School of Engineering Computer Science Washington State University VancouverWA98686 United States School of Engineering Newcastle University Newcastle Upon TyneNE1 7RU United Kingdom Federal University of Rondônia Department of Electrical Engineering Porto Velho Brazil Graduate Program in Electrical Engineering Federal University of Santa Maria Santa Maria Brazil Division of Energy Economics and Environmental Studies Rio de Janeiro Brazil

ISBN: (纸本)9781665444217

The Brazilian state of Amapa=-notable for having 70% of its area consisting of unexplored Amazonian forests-experienced a massive power blackout in November 2020. An internal short-circuit in a transformer led to an explosion followed by a fire at the Macapa substation. This event left about 90% of the Amapa's population without power for several days in the middle of the COVID-19 pandemic, disrupting the lives of hundreds of thousands of people and raising concerns from several electricity sector stakeholders. This paper discusses select lessons learned from the power blackout and presents future opportunities to improve power system reliability and resilience for the grids in Amapá, and other similar Brazilian states, with a special focus on the increasingly important role of of distributed energy resources and smart grid technologies. © 2021 IEEE.

关键词： Outages

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Intrinsic Parameter Fluctuation and Process Variation Effect of Vertically Stacked Silicon Nanosheet Complementary Field-Effect Transistors

Intrinsic Parameter Fluctuation and Process Variation Effect...

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IEEE International Symposium on Quality Electronic Design

作者： Sekhar Reddy Kola Yiming Li Min-Hui Chuang Parallel and Scientific Computing Laboratory Hsinchu Taiwan Electrical Engineering and Computer Science International Graduate Program Hsinchu Taiwan Institute of Communications Engineering Hsinchu Taiwan Institute of Biomedical Engineering Hsinchu Taiwan Institute of Pioneer Semiconductor Innovation Hsinchu Taiwan Institute of Artificial Intelligence Innovation Hsinchu Taiwan Department of Electronics and Electrical Engineering National Yang Ming Chiao Tung University Hsinchu Taiwan Center for mmWave Smart Radar System and Technologies National Yang Ming Chiao Tung University Hsinchu Taiwan

We study the variability of vertically stacked gate-all-around silicon nanosheet (GAA Si NS) complementary field-effect transistors (CFETs). The process variation effect (PVE), the work function fluctuation (WKF), and the random dopant fluctuation (RDF) of CFETs are statistically estimated using an experimentally validated device simulation technique. Among five factors of PVE, the channel thickness (T Nch /T Pch ), the channel width (W ch ), and the gate length (L G ) are significant. Owing to superior GAA channel control and increased effective gate area, both WKF and RDF are suppressed. Notably, the PVE on both N-/P-FETs of GAA Si CFET induce the largest off-state current fluctuations of 80% and 278%, respectively, because the device characteristic is very sensitive to the layer thickness and width of channel.

关键词： Fluctuations Gallium arsenide Logic gates Electric variables Silicon Resource description framework Semiconductor process modeling

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Computational design of serine hydrolases

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science (New York, N.Y.) 2025年第6744期388卷 eadu2454页

作者： Lauko, Anna Pellock, Samuel J. Sumida, Kiera H. Anishchenko, Ivan Juergens, David Ahern, Woody Jeung, Jihun Shida, Alexander F. Hunt, Andrew Kalvet, Indrek Norn, Christoffer Humphreys, Ian R. Jamieson, Cooper Krishna, Rohith Kipnis, Yakov Kang, Alex Brackenbrough, Evans Bera, Asim K. Sankaran, Banumathi Houk, K.N. Baker, David Department of Biochemistry University of Washington Seattle WA United States Institute for Protein Design University of Washington Seattle WA United States Graduate Program in Biological Physics Structure and Design University of Washington Seattle WA United States Department of Chemistry University of Washington Seattle WA United States Graduate Program in Molecular Engineering University of Washington Seattle WA United States Paul G. Allen School of Computer Science and Engineering University of Washington Seattle WA United States Medical Scientist Training Program University of Washington Seattle WA United States Howard Hughes Medical Institute University of Washington Seattle WA United States Department of Chemistry and Biochemistry University of California Los Angeles CA United States

The design of enzymes with complex active sites that mediate multistep reactions remains an outstanding challenge. With serine hydrolases as a model system, we combined the generative capabilities of RFdiffusion with an ensemble generation method for assessing active site preorganization at each step in the reaction to design enzymes starting from minimal active site descriptions. Experimental characterization revealed catalytic efficiencies (kcat/Km) up to 2.2 × 105 M-1 s-1 and crystal structures that closely match the design models (Cα root mean square deviations <1 angstrom). Selection for structural compatibility across the reaction coordinate enabled identification of new catalysts remove with five different folds distinct from those of natural serine hydrolases. Our de novo approach provides insight into the geometric basis of catalysis and a roadmap for designing enzymes that catalyze multistep transformations.

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