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Estimating Causal Effects using a Multi-task Deep Ensemble

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arXiv 2023年

作者： Jiang, Ziyang Hou, Zhuoran Liu, Yiling Ren, Yiman Li, Keyu Carlson, David Department of Civil and Environmental Engineering Duke University DurhamNC United States Department of Biostatistics and Bioinformatics Duke University School of Medicine DurhamNC United States Program in Computational Biology and Bioinformatics Duke University School of Medicine DurhamNC United States Department of Economics University of Michigan Ross School of Business Ann ArborMI United States Department of Electrical and Computer Engineering Duke University DurhamNC United States Department of Computer Science Duke University DurhamNC United States

A number of methods have been proposed for causal effect estimation, yet few have demonstrated efficacy in handling data with complex structures, such as images. To fill this gap, we propose Causal Multi-task Deep Ensemble (CMDE), a novel framework that learns both shared and group-specific information from the study population. We provide proofs demonstrating equivalency of CDME to a multi-task Gaussian process (GP) with a coregionalization kernel a priori. Compared to multi-task GP, CMDE efficiently handles high-dimensional and multi-modal covariates and provides pointwise uncertainty estimates of causal effects. We evaluate our method across various types of datasets and tasks and find that CMDE outperforms state-of-the-art methods on a majority of these tasks. © 2023, CC BY.

关键词： Data handling

Spatially-varying microscope calibration from unstructured sparse inputs

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Spatially-varying microscope calibration from unstructured s...

Computational Optical Sensing and Imaging, COSI 2020 - Part of Imaging and Applied Optics Congress 2020

作者： Kuo, Grace Monakhova, Kristina Yanny, Kyrollos Ng, Ren Waller, Laura Department of Electrical Engineering and Computer Sciences University of California Berkeley BerkeleyCA94720 United States UCB/UCSF Joint Graduate Program in Bioengineering University of California Berkeley BerkeleyCA94720 United States

We propose a method based on blind deconvolution to calibrate the spatially-varying point spread functions of a coded-aperture microscope system. From easy-to-acquire measurements of unstructured fluorescent beads, we... 详细信息

ISBN: (纸本)9781557528209

关键词： Optical transfer function

Terahertz spin dynamics in rare-earth orthoferrites

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Photonics Insights 2022年第2期1卷 1-56页

作者： Xinwei Li Dasom Kim Yincheng Liu Junichiro Kono Department of Physics California Institute of TechnologyPasadenaUSA Department of Electrical and Computer Engineering Rice UniversityHoustonUSA Applied Physics Graduate Program Smalley-Curl InstituteRice UniversityHoustonUSA Department of Physics and Astronomy Rice UniversityHoustonUSA Department of Materials Science and NanoEngineering Rice UniversityHoustonUSA

Recent interest in developing fast spintronic devices and laser-controllable magnetic solids has sparked tremendous experimental and theoretical efforts to understand and manipulate ultrafast dynamics in *** of spin dynamics in the terahertz(THz)frequency range are particularly important for elucidating microscopic pathways toward novel device ***,we review THz phenomena related to spin dynamics in rare-earth orthoferrites,a class of materials promising for antiferromagnetic *** expand this topic into a description of four key elements.(1)We start by describing THz spectroscopy of spin excitations for probing magnetic phase transitions in thermal *** acoustic magnons are useful indicators of spin reorientation transitions,electromagnons that arise from dynamic magnetoelectric couplings serve as a signature of inversion-symmetry-breaking phases at low temperatures.(2)We then review the strong laser driving scenario,where the system is excited far from equilibrium and thereby subject to modifications to the free-energy *** pathways for ultrafast laser manipulation of magnetic order are discussed.(3)Furthermore,we review a variety of protocols to manipulate coherent THz magnons in time and space,which are useful capabilities for antiferromagnetic spintronic applications.(4)Finally,new insights into the connection between dynamic magnetic coupling in condensed matter and the Dicke superradiant phase transition in quantum optics are *** presenting a review on an array of THz spin phenomena occurring in a single class of materials,we hope to trigger interdisciplinary efforts that actively seek connections between subfields of spintronics,which will facilitate the invention of new protocols of active spin control and quantum phase engineering.

关键词： terahertz spin dynamics magnetism orthoferrite ultrafast spectroscopy magneto-optics

Multiscale Numerical Modeling for Near-Field Microwave Impedance Microscopy

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Multiscale Numerical Modeling for Near-Field Microwave Imped...

Conference on Lasers and Electro-Optics (CLEO)

作者： Diego Tami Douglas A. A. Ohlberg Jhonattan C. Ramirez Cássio Gonçalves Do Rego Gilberto Medeiros-Ribeiro Graduate Program in Electrical Engineering Universidade Federal de Minas Gerais Brazil Microscopy Center Universidade Federal de Minas Gerais Brazil Department of Electronic Engineering Universidade Federal de Minas Gerais Brazil Computer Science Department Universidade Federal de Minas Gerais Brazil

In this work, we analyzed numerically a multiscale nanosystem based on sMIM on TBG. Spontaneous formation of a water-meniscus by the approximation between the tip-sample concentrates the microwave fields, reaching res... 详细信息

ISBN: (数字)9781957171050

ISBN: (纸本)9781665466660

关键词： Microscopy Masers Lasers and electrooptics Laser modes Numerical models Impedance Electrooptical waveguides

Heterogeneous Oblique Double Random Forest

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arXiv 2023年

作者： Ganaie, M.A. Tanveer, M. Beheshti, I. Ahmad, N. Suganthan, P.N. Department of Robotics University of Michigan Ann ArborMI United States Department of Mathematics Indian Institute of Technology Indore Simrol Indore453552 India Department of Human Anatomy and Cell Science Rady Faculty of Health Sciences Max Rady College of Medicine University of Manitoba WinnipegMB Canada Department of International Program of Electrical Engineering and Computer Science National Taipei University of Technology Taipei Taiwan KINDI Center for Computing Research College of Engineering Qatar University Doha Qatar

The decision tree ensembles use a single data feature at each node for splitting the data. However, splitting in this manner may fail to capture the geometric properties of the data. Thus, oblique decision trees generate the oblique hyperplane for splitting the data at each non-leaf node. Oblique decision trees capture the geometric properties of the data and hence, show better generalization. The performance of the oblique decision trees depends on the way oblique hyperplanes are generate and the data used for the generation of those hyperplanes. Recently, multiple classifiers have been used in a heterogeneous random forest (RaF) classifier, however, it fails to generate the trees of proper depth. Moreover, double RaF studies highlighted that larger trees can be generated via bootstrapping the data at each non-leaf node and splitting the original data instead of the bootstrapped data recently. The study of heterogeneous RaF lacks the generation of larger trees while as the double RaF based model fails to take over the geometric characteristics of the data. To address these shortcomings, we propose heterogeneous oblique double RaF. The proposed model employs several linear classifiers at each non-leaf node on the bootstrapped data and splits the original data based on the optimal linear classifier. The optimal hyperplane corresponds to the models based on the optimized impurity criterion. The experimental analysis indicates that the performance of the introduced heterogeneous double random forest is comparatively better than the baseline models. To demonstrate the effectiveness of the proposed heterogeneous double random forest, we used it for the diagnosis of Schizophrenia disease. The proposed model predicted the disease more accurately compared to the baseline models. © 2023, CC BY-NC-ND.

关键词： Decision trees

Observation of Photonic Chiral Flatbands

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arXiv 2024年

作者： Choi, Minho Alù, Andrea Majumdar, Arka Department of Electrical and Computer Engineering University of Washington SeattleWA98195 United States Photonics Initiative Advanced Science Research Center City University of New York New YorkNY10031 United States Physics Program Graduate Center City University of New York New YorkNY10031 United States Department of Physics University of Washington SeattleWA98195 United States

Distinct selectivity to the spin angular momenta of photons have garnered significant attention in recent years, for their relevance in basic science and for imaging and sensing applications. While nonlocal metasurfaces with strong chiral responses to the incident light have been reported, these responses are typically limited to a narrow range of incident angles. In this study, we demonstrate a nonlocal metasurface that showcases strong chirality, characterized by circular dichroism larger than 0.6, over a wide range of incident angles ±5o. Its quality factor, circular dichroism and resonant frequency can be optimized by design. These findings pave the way to further advance the development of valley-selective optical cavities and augmented reality applications. Copyright © 2024, The Authors. All rights reserved.

关键词： Natural frequencies

Scalable privacy-preserving cancer type prediction with homomorphic encryption

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arXiv 2022年

作者： Sarkar, Esha Chielle, Eduardo Gursoy, Gamze Chen, Leo Gerstein, Mark Maniatakos, Michail Tandon School of Engineering New York University Brooklyn United States Department of Electrical and Computer Engineering New York University Abu Dhabi Abu Dhabi United Arab Emirates Program in Computational Biology and Bioinformatics Yale University New Haven United States Department of Computer Science Yale University New Haven United States

Machine Learning (ML) alleviates the challenges of high-dimensional data analysis and improves decision making in critical applications like healthcare. Effective cancer/tumor classification from high-dimensional genetic mutation data can be useful for cancer diagnosis and treatment, if the distinguishable patterns between cancer/tumor types are identified. At the same time, analysis of high-dimensional data is computationally expensive and is often outsourced to cloud services. Privacy concerns in outsourced ML, especially in the field of genetics, motivate the use of encrypted computation, like Homomorphic Encryption (HE). But restrictive overheads of encrypted computation deter its usage. In this work, we explore the challenges of privacy preserving cancer detection using a real-world dataset consisting of more than 2 million genetic information for several cancer types. Since the data is inherently high-dimensional, we explore smaller ML models for cancer prediction to enable fast inference in the privacy preserving domain. We develop a solution for privacy preserving cancer inference which first leverages the domain knowledge on somatic mutations to efficiently encode genetic mutations and then uses statistical tests for feature selection. Our logistic regression model, built using our novel encoding scheme, achieves 0.98 micro-average area under curve with 13% higher test accuracy than similar studies. We exhaustively test our model’s predictive capabilities by analyzing the genes used by the model. Furthermore, we propose a fast matrix multiplication algorithm that can efficiently handle high-dimensional data. Experimental results show that, even with 40,000 features, our proposed matrix multiplication algorithm can speed up concurrent inference of multiple individuals by approximately 10x and inference of a single individual by approximately 550x, in comparison to standard matrix multiplication. © 2022, CC BY.

关键词： Clustering algorithms

Ultrastrong Spin-Photon Coupling in Gadolinium Gallium Garnet

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Ultrastrong Spin-Photon Coupling in Gadolinium Gallium Garne...

2023 Conference on Lasers and Electro-Optics, CLEO 2023

作者： Kritzell, T. Elijah Bao, Junzhe Doumani, Jacques Lee, Jae Joon Xu, Hongjing Tay, Fuyang Nojiri, Hiroyuki Bamba, Motoaki Baydin, Andrey Kono, Junichiro 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 Physics and Astronomy Rice University HoustonTX77005 United States Institute for Materials Research Tohoku University Sendai980-8577 Japan The Hakubi Center for Advanced Research Kyoto University Kyoto606-8502 Japan Department of Physics I Kyoto University Kyoto606-8502 Japan Smalley-Curl Institute Rice University HoustonTX77005 United States Department of Material Science and NanoEngineering Rice University HoustonTX77005 United States

We examine ultrastrong coupling of an ensemble of paramagnetic spins in Gd3Ga5O12 with terahertz photons in the presence of a high external magnetic field both in the bulk and thin-film limits. © 2023 The Author(s)

ISBN: (纸本)9781957171258

关键词： Photons

Naked-eye observation of water-forming reaction on palladium etalon:transduction of gas-matter reaction into light-matter interaction

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PhotoniX 2023年第1期4卷 349-363页

作者： Jongsu Lee Eui-Sang Yu Taehyun Kim In Soo Kim Seok Chung Seung Jae Kwak Won Bo Lee Yusin Pak Yong-Sang Ryu Sensor System Research Center Korea Institute of Science and TechnologySeoul 02792Republic of Korea Department of Electrical and Computer Engineering Seoul National UniversitySeoul 08826Republic of Korea Smart Materials Research Section Electronics and Telecommunications Research InstituteDaejeon 34129Republic of Korea Department of Micro/Nano Systems Korea University02841 SeoulRepublic of Korea Nanophotonics Research Center Korea Institute of Science and TechnologySeoul 02792Republic of Korea School of Mechanical Engineering Korea UniversitySeoul 02841Republic of Korea School of Chemical and Biological Engineering Seoul National UniversitySeoul 08826Republic of Korea Interdisciplinary Program in Precision Public Health Korea UniversitySeoul 02481Republic of Korea School of Biomedical Engineering Korea UniversitySeoul 02481Republic of Korea

Palladium is the most prominent material in both scientific and industrial research on gas storage,purification,detection,and catalysis due to its unique properties as a catalyst and hydrogen *** the dynamic optical phenomena of palladium reacting with hydrogen,transduction of the gas-matter reaction into light-matter interaction is attempted to visualize the dynamic surface chemistry and reaction *** simple geometry of the metal-dielectric-metal structure,Fabry-Perot etalon,is employed for a colorimetric reactor,to display the catalytic reaction of the exposed gas via water-film/bubble formation at the dielectric/palladium *** adsorption/desorption behavior and catalytic reaction of hydrogen and oxygen on the palladium surface display highly repeatable and dramatic color changes based on two distinct water formation trends:the foggy effect by water bubbles and the whiteout effect by water film *** and experiments demonstrate the robustness of the proposed Fabry-Perot etalon as an excellent platform for monitoring the opto-physical phenomena driven by heterogeneous catalysis.

关键词： Palladium Hydrogen Oxygen Fabry-Perot resonators Colorimetric sensors Water-forming reaction