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The Journal of Machine Learning Research 2024年第1期25卷 19070-19118页

作者： Jordan Richards Matthew Sainsbury-Dale Andrew Zammit-Mangion Raphaël Huser School of Mathematics and Maxwell Institute for Mathematical Sciences University of Edinburgh Edinburgh UK and Statistics Program Computer Electrical and Mathematical Sciences and Engineering Division King Abdullah University of Science and Technology (KAUST) Thuwal Saudi Arabia Statistics Program Computer Electrical and Mathematical Sciences and Engineering Division King Abdullah University of Science and Technology (KAUST) Thuwal Saudi Arabia and School of Mathematics and Applied Statistics University of Wollongong Wollongong Australia School of Mathematics and Applied Statistics University of Wollongong Wollongong Australia Statistics Program Computer Electrical and Mathematical Sciences and Engineering Division King Abdullah University of Science and Technology (KAUST) Thuwal Saudi Arabia

Making inference with spatial extremal dependence models can be computationally burdensome since they involve intractable and/or censored likelihoods. Building on recent advances in likelihood-free inference with neural Bayes estimators, that is, neural networks that approximate Bayes estimators, we develop highly efficient estimators for censored peaks-over-threshold models that use augmented data to encode censoring information in the neural network input. Our new method provides a paradigm shift that challenges traditional censored likelihood-based inference methods for spatial extremal dependence models. Our simulation studies highlight significant gains in both computational and statistical efficiency, relative to competing likelihood-based approaches, when applying our novel estimators to make inference with popular extremal dependence models, such as max-stable, r-Pareto, and random scale mixture process models. We also illustrate that it is possible to train a single neural Bayes estimator for a general censoring level, precluding the need to retrain the network when the censoring level is changed. We illustrate the efficacy of our estimators by making fast inference on hundreds-of-thousands of high-dimensional spatial extremal dependence models to assess extreme particulate matter 2.5 microns or less in diameter (PM2:5) concentration over the whole of Saudi Arabia.

关键词： censored data convolutional neural network likelihood-free inference neural estimation spatial extremes threshold exceedance

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Cyclodextrin-modified layered double hydroxide thin-film nanocomposite desalination membrane for boron removal

Cyclodextrin-modified layered double hydroxide thin-film nan...

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2023 AIChE Annual Meeting

作者： Ee, Liang Ying Zhao, Qipeng Gao, Jie Lim, Chit Kai Xue, Kai Chin, Sze Yuet Li, Sam Fong Yau Chung, Tai-Shung Chen, Shing Bor Department of Chemical and Biomolecular Engineering College of Design and Engineering National University of Singapore 4 Engineering Drive 4 Singapore117585 Singapore Department of Chemistry College of Humanities and Sciences National University of Singapore 3 Science Drive 3 Singapore117543 Singapore Chemical Engineering Program Physical Science and Engineering Division King Abdullah University of Science and Technology Thuwal23955-6900 Saudi Arabia Centre of High Field NMR Spectroscopy and Imaging School of Physical and Mathematical Sciences College of Science Nanyang Technological University 21 Nanyang Link Singapore637371 Singapore NUS Environmental Research Institute National University of Singapore T-Lab Building 5A Engineering Drive 1 Singapore117411 Singapore Graduate Institute of Applied Science and Technology National Taiwan University of Science and Technology Taipei106335 Taiwan

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Economic and Sensitivity Analyses for an Optimal Hybrid Power Generation for Stand-Alone Power Systems: Case of Klongrua, Phato, Chumphon, Thailand

Economic and Sensitivity Analyses for an Optimal Hybrid Powe...

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2022 International electrical engineering Congress, iEECON 2022

作者： Jeenthanom, Pathumwadee Saelao, Jeerawan Muangjai, Worrajak Thongporn, Jutturit Panlawan, Nattawat Somsak, Teerasak Rajamangala University of Technology Lanna Electrical Engineering Program Faculty of Engineering Chiang Mai Thailand Maejo University Division of Mathematics Faculty of Science Chiangmai Thailand Rajamangala University of Technology Lanna Collage of Integrated of Science and Technology Chiang Mai Thailand

ISBN: (纸本)9781665402064

This paper analyzes a hybrid power system for an independent power system for Baan Khlongrua community, which is a village far from the transmission line system so that the community has electricity available all the time. The designed system consists of solar cells, batteries, inverters and small power turbines. Therefore, this research assesses the economic value and analyzes the system's sensitivity. It is assumed that the electricity demand will increase by 4.25% per year. The analysis data found that in the first year, the cost of energy (COE) was 0.137 /kWh, the second year was 0.132 /kWh, and the third year was 0.127 /kWh. Year 1 has a high COE due to system design being approximately 10% larger than current demand, and in years 4, 5, 6, COE will increase. As a result of the designed system, it will not produce enough electricity to meet the electrical load of the village. Therefore, a diesel generator compensates during the power shortage, causing the CO2 value in the year 4,5,6 to 3,780 kg/yr 2,898 kg/yr 3,265 kg/yr increased. This CO2 value, therefore, affects the COE increase, and COE decreases from 0.137 to 0.127 /kWh. the payback period of the system can be quickly achieved. © 2022 IEEE.

关键词： Sensitivity analysis

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Heterogeneous Integration of Lithium Niobate and Silicon Photonics for Nonlinear Optics

Heterogeneous Integration of Lithium Niobate and Silicon Pho...

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2024 Conference on Lasers and Electro-Optics, CLEO 2024

作者： Palici, Alexandra Maria Lu, Xiyuan Srinivasan, Kartik Lu, Xinda Labbe, Fabien Ding, Yunhong Laroque, Hugo Hamerly, Ryan Errando-Herranz, Carlos Pu, Minhao Englund, Dirk Heuck, Mikkel Department of Electrical and Photonics Engineering Technical University of Denmark Lyngby2800 Denmark Microsystems and Nanotechnology Division Physical Measurement Laboratory National Institute of Standards and Technology GaithersburgMD20899 United States Joint Quantum Institute NIST University of Maryland College ParkMD20742 United States Research Laboratory of Electronics Massachusetts Institute of Technology CambridgeMA02139 United States Electrical Engineering Mathematics and Computer Science Delft University of Technology Delft Netherlands

We demonstrate a hybrid device consisting of a thin film lithium niobate membrane transfer-printed onto a silicon nitride ring resonator. We measure quality factors in the 105 range at telecom wavelengths. CLEO 2024 &... 详细信息

ISBN: (纸本)9781957171395

关键词： Silicon nitride

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Heterogeneous Integration of Lithium Niobate and Silicon Photonics for Nonlinear Optics

Heterogeneous Integration of Lithium Niobate and Silicon Pho...

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

关键词： Silicon photonics

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Directly and instantly seeing through random diffusers by self-imaging in scattering speckles

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PhotoniX 2023年第1期4卷 674-687页

作者： Jietao Liu Wenhong Yang Guofeng Song Qiaoqiang Gan Laboratory of Photonic Integrated Circuits Xiong’an Institute of InnovationChinese Academy of SciencesXiong’an New AreaBeijing 071700Hebei ProvinceChina 2 Sustainability&Photonics Energy Research LaboratoryMaterial Science Engineering programDivision of Physical Science&EngineeringKing Abdullah University of Science and TechnologyThuwalSaudi Arabia

Imaging through scattering medium is challenging but important for different *** advances rely on computational image reconstruction from scattering *** these conventional investigations,speckles were always treated as scrambled grainy *** seeing through scattering diffusers has never been ***,we report a new strategy to see through random diffusers directly using self-imaging of *** analyzing the physics,a direct observation strategy through scattering media is reported with improved image *** this method,we experimentally demonstrated reconstruction-free real-time imaging of static and moving objects with their actual orientation information under single-wavelength and white light *** also proposed a modified speckle autocorrelation imaging(SAI)method inspired by the self-imaging ***,our strategy requires no pre-calibration or acquisition of point-spread-function,no active control of wavefronts or complicated equipment,nor iterations or carefully adjusted parameters,paving the way towards rapid and high-quality imaging through scattering diffusers.

关键词： speckle random instant

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Transformation of ambient CO₂ in air into cyclic carbonates mediated by a phosphorus-nitrogen PN³-pincer iron complex

Green Synthesis and Catalysis

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Green Synthesis and Catalysis 2024年

作者： Yang, Li Chakraborty, Priyanka Das, Pradip K. Zhang, Dongya Huang, Kuo-Wei Chemistry Program Division of Physical Science and Engineering and KAUST Catalysis Center King Abdullah University of Science and Technology Thuwal 23955 Saudi Arabia

The atom-economical reaction of carbon dioxide (CO2) with epoxides possesses the potential to utilize captured CO₂ for synthesizing useful chemicals. Among a wide range of metal complexes employed for this transformat... 详细信息

The atom-economical reaction of carbon dioxide (CO₂) with epoxides possesses the potential to utilize captured CO₂ for synthesizing useful chemicals. Among a wide range of metal complexes employed for this transformation, iron (Fe) stands out because of its low cost, ready availability, and stability. In this work, a pyridine-based pincer PN³-Fe(II) complex was synthesized and used as an efficient catalyst for the CO₂ epoxide cycloaddition. More importantly, this complex enabled the direct capture of CO₂ in the atmosphere and conversion into cyclic carbonates in excellent yields. © 2024 Fudan University

关键词： Carbon dioxide Cyclocarbonates Epoxides Iron PN³ pincer

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Organic-inorganic hybrid piezotronic bipolar junction transistor for pressure sensing

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Microsystems & Nanoengineering 2024年第3期10卷 229-244页

作者： Emad Iranmanesh Zihao Liang Weiwei Li Congwei Liao Shunyu Jin Chuan Liu Kai Wang Shengdong Zhang Charalampos Doumanidis Gehan A.J.Amaratunga Hang Zhou Guangdong Provincial Key Laboratory of In-Memory Computing Chips School of Electronic and Computer EngineeringPeking University Shenzhen Graduate SchoolShenzhen 518055P.R.China School of Mechanical Engineering Guangdong Technion-Israel Institute of TechnologyShantou 515063P.R.China State Key Laboratory of Microelectronics Device and Integrated Technology Institute of MicroelectronicsChinese Academy of SciencesBeijing 100029P.R.China Hefei National Research Center for Physical Sciences at the Microscale University of Science and Technology of ChinaHefei 23000PR China School of Electronics and Information Technology Sun Yat-sen UniversityNo.132 East Waihuan RoadGuangzhou 510006P.R.China Department of Mechanical Aerospaceand Biomedical EngineeringUniversity of South AlabamaShelby Hall3128MobileAL 36688USA Electrical Engineering Division Department of EngineeringUniversity of Cambridge9 JJ Thomson AvenueCambridge CB30FAUnited Kingdom Zhejiang University International CampusHainingChina

With the rapid development of the Internet of Things(IoTs),wearable sensors are playing an increasingly important role in daily monitoring of personal health and *** signal-to-noise-ratio has become the most critical performance factor to *** enhance it,on the one hand,good sensing materials/devices have been employed;on the other hand,signal amplification and noise reduction circuits have been ***,most of these devices and circuits work in an active sampling mode,requiring frequent data acquisition and hence,entailing high-power *** this scenario,a flexible and wearable event-triggered sensor with embedded signal amplification without an external power supply is of great ***,we report a flexible two-terminal piezotronic n-p-n bipolar junction transistor(PBJT)that acts as an autonomous and highly sensitive,current-and/or voltage-mediated pressure *** PBJT is formed by two back-to-back piezotronic diodes which are defined as emitter-base and collectorbase *** force exertion on the emitter side,as a result of the piezoelectric effect,the emitter-base diode is forward biased while the collector-base diode is reverse *** to the inherent BJT amplification effect,the PBJT achieves record-high sensitivities of 139.7 kPa^(-1)(current-based)and 88.66 kPa^(-1)(voltage-based)in sensing *** PBJT also has a fast response time of<110 ms under exertion of dynamic stimuli ranging from a flying butterfly to a gentle finger ***,the PBJT advances the state of the art not only in terms of sensitivity but also in regard to being self-driven and autonomous,making it promising for pressure sensing and other IoT applications.

关键词： bipolar transistor IoT

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Fish weight prediction using empirical and data-driven models in aquaculture systems

Fish weight prediction using empirical and data-driven model...

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Control Technology and Applications (CCTA),

作者： Xiao Chen Ibrahima N’Doye Fahad Aljehani Taous-Meriem Laleg-Kirati Computer Electrical and Mathematical Science and Engineering Division (CEMSE) King Abdullah University of Science and Technology (KAUST) Saudi Arabia Environmental Science and Engineering Program Biological and Environmental Science and Engineering Division King Abdullah University of Science and Technology (KAUST) Saudi Arabia National Institute for Research in Digital Science and Technology (INRIA) Paris-Saclay France

ISBN: (数字)9798350370942

ISBN: (纸本)9798350370959

Predicting fish growth trajectory is crucial in achieving better control for the growing phase of the fish in aquaculture systems. However, fish biomass sensors that directly measure the fish’s weight are currently unavailable. Additionally, the fish growth model that relies on the fish weight is affected by environmental and feeding management factors, raising the difficulty of predicting the weight. This paper proposes comparative empirical and data-driven models for fish weight prediction. The empirical fish growth model relies directly on the growth ratio, an essential component for optimizing fish growth, while the data-driven model is based on long short-term memory (LSTM). Since the exact daily food ratio, proportional to the body weight of a growing fish, cannot be prescribed, we optimize the growth ratio of the empirical model in a sliding window framework based on the previous growth periods and then predict the fish weights a few days ahead through a nonlinear exponential regression approach. The LSTM method uses a normal distribution to generate multiple datasets over the experimental fish weight range and interpolate these data to provide a good prediction. To this end, we propose a weighted sum optimization method that combines the individual cost of the LSTM and empirical methods to achieve better short-term prediction performance. The simulation results demonstrate the effectiveness of the proposed models in predicting the fish weight and show that the combined weighted sum approach reduces the testing errors by 61.3% and 70.9% compared to the empirical and LSTM methods, respectively. Furthermore, an increasing specific growth rate (SGR) for both LSTM and combined models was observed in the short-term predictions.

关键词： Weight measurement Costs Biological system modeling Simulation Predictive models Fish Trajectory

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Joint Modeling of Multivariate Longitudinal and Survival Outcomes with the R package INLAjoint

arXiv

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arXiv 2024年

作者： Rustand, Denis van Niekerk, Janet Krainski, Elias Teixeira Rue, Håvard Statistics Program Computer Electrical and Mathematical Sciences and Engineering Division King Abdullah University of Science and Technology Thuwal23955-6900 Saudi Arabia

This paper introduces the R package INLAjoint, designed as a toolbox for fitting a diverse range of regression models addressing both longitudinal and survival outcomes. INLAjoint relies on the computational efficiency of the integrated nested Laplace approximations methodology, an efficient alternative to Markov chain Monte Carlo for Bayesian inference, ensuring both speed and accuracy in parameter estimation and uncertainty quantification. The package facilitates the construction of complex joint models by treating individual regression models as building blocks, which can be assembled to address specific research questions. Joint models are relevant in biomedical studies where the collection of longitudinal markers alongside censored survival times is common. They have gained significant interest in recent literature, demonstrating the ability to rectify biases present in separate modeling approaches such as informative censoring by a survival event or confusion bias due to population heterogeneity. We provide a comprehensive overview of the joint modeling framework embedded in INLAjoint with illustrative examples. Through these examples, we demonstrate the practical utility of INLAjoint in handling complex data scenarios encountered in biomedical research. Copyright © 2024, The Authors. All rights reserved.

关键词： Computational efficiency

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