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Single-Shot Readout of a Solid-State Spin in a Decoherence-Free Subspace

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Physical Review Applied 2021年第3期15卷 L031002-L031002页

作者： D. Farfurnik R. M. Pettit Z. Luo E. Waks Department of Electrical and Computer Engineering Institute for Research in Electronics and Applied Physics and Joint Quantum Institute University of Maryland College Park Maryland 20742 USA Intelligence Community Postdoctoral Research Fellowship Program University of Maryland College Park Maryland 20742 USA

The efficient single-photon-emission capabilities of quantum dot molecules position them as promising platforms for quantum information processing. Furthermore, quantum dot molecules feature a “decoherence-free” subspace that enables spin qubits with long coherence times. To efficiently read out the spin state within this subspace requires the optical cycling of isolated transitions that originate from a triplet manifold within the quantum dot molecule. We propose and theoretically study a two-stage spin-readout protocol within this decoherence-free subspace that allows single-shot readout performance. The process incorporates a microwave π pulse and optical cycling of the isolated transitions, which induces fluorescence that allows us to identify the initial spin state. This protocol offers enhanced readout fidelity compared to previous schemes that rely on the excitation of transitions that strongly decay to multiple ground states or require long initialization via slow optically forbidden transitions. By simulating the performance of the protocol, we show that an optimal spin-readout fidelity of over 97% and single-shot readout performance are achievable for a photon-collection efficiency of just 0.12%. This high readout performance for such realistic photon-collection conditions within the decoherence-free subspace expands the potential of quantum dot molecules as building blocks for quantum networks.

关键词： Optoelectronics Quantum information with solid state qubits Quantum networks Single photon sources Double quantum dots Coherent control Microwave techniques Quantum master equation

Designing, Synthesizing and Modeling Active Fluids

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arXiv 2023年

作者： Essafri, Ilham Ghosh, Bappa Desgranges, Caroline Delhommelle, Jerome Department of Biomedical Engineering University of North Dakota 243 Centennial Dr Stop 8155 Grand ForksND58202 United States Tech Accelerator University of North Dakota Suite 2300 4201 James Ray Drive Grand ForksND58202 United States Department of Chemistry University of North Dakota 151 Cornell Street Stop 9024 Grand ForksND58202 United States School of Electrical Engineering and Computer Science University of North Dakota 243 Centennial Dr Stop 7165 Grand ForksND58202 United States

We review recent advances in the design, synthesis, and modeling of active fluids. Active fluids have been at the center of many technological innovations and theoretical advances over the past two decades. Research on this new class of fluids has been inspired by the fascinating and remarkably efficient strategies that biological systems employ, leading to the development of biomimetic nano- and micro-machines and -swimmers. The review encompasses active fluids on both the nano- and micro-scale. We start with examples of biological active systems before we discuss how experimentalists leverage novel propulsion mechanisms to power nano- and micro-machines. We then examine how the study of these far-from-equilibrium systems has prompted the development of new simulation methods and theoretical models in nonquilibrium physics to account for their mechanical, thermodynamic and emergent properties. Recent advances in the field have paved the way for the design, synthesis, and modeling of autonomous systems at the nano- and microscale and open the door to the development of soft matter robotics. © 2023, CC BY-SA.

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Digital twins: a new paradigm in oncology in the era of big data

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ESMO Real World Data and Digital Oncology

ESMO Real World Data and Digital Oncology 2024年 5卷 100056-100056页

作者： Mollica, L. Leli, C. Sottotetti, F. Quaglini, S. Locati, L.D. Marceglia, S. Medical Oncology Unit Istituti Clinici Scientifici Maugeri IRCCS Pavia Italy Department of Internal Medicine and Medical Therapy University of Pavia Pavia Italy Department of Electrical Computer and Biomedical Engineering University of Pavia Pavia Italy Department of Engineering and Architecture University of Trieste Trieste Italy

Recent advancements in health care digitalization opened the collection and availability of big data, whose analysis requires artificial intelligence-based technologies to facilitate the development of predictive tools supporting decision making in clinical practice. In this context, the idea of constructing ‘digital worlds’ to evaluate the performance of such novel tools becomes more attractive. Digital twins (DTs) are ‘digital objects’ characterized by a bi-directional interaction with their ‘real-world counterparts’. DTs aim to enhance predictions further by leveraging both the predictive capabilities of digital simulations and the continuous updating of real-life data—ideally incorporating clinical records, multiomics data, and patient-reported outcomes. DTs can potentially integrate these diverse data into virtual models applicable across pre-clinical to clinical studies. Running simulations in silico on cancer cells or cancer patients’ DTs can provide valuable insights into cancer biology, clinical practice, and health care education, with the added value of reducing costs and overcoming many common limitations of current studies (limited number of variables, challenges in recruiting patients with rare tumors, lack of real-life feedback). Despite their significant potential, DTs are still in their infancy, facing numerous unsolved technical and ethical challenges that hinder their application in clinical practice.

关键词： digital twins clinical oncology big data artificial intelligence cancer patient

VOLUMETRIC LANDMARK DETECTION WITH A MULTI-SCALE SHIFT EQUIVARIANT NEURAL NETWORK

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arXiv 2020年

作者： Ma, Tianyu Gupta, Ajay Sabuncu, Mert R. School of Electrical and Computer Engineering Meinig School of Biomedical Engineering Cornell University Department of Radiology Weill Cornell Medical College

Deep neural networks yield promising results in a wide range of computer vision applications, including landmark detection. A major challenge for accurate anatomical landmark detection in volumetric images such as clinical CT scans is that large-scale data often constrain the capacity of the employed neural network architecture due to GPU memory limitations, which in turn can limit the precision of the output. We propose a multi-scale, end-to-end deep learning method that achieves fast and memory-efficient landmark detection in 3D images. Our architecture consists of blocks of shift-equivariant networks, each of which performs landmark detection at a different spatial scale. These blocks are connected from coarse to fine-scale, with differentiable resampling layers, so that all levels can be trained together. We also present a noise injection strategy that increases the robustness of the model and allows us to quantify uncertainty at test time. We evaluate our method for carotid artery bifurcations detection on 263 CT volumes and achieve a better than state-of-the-art accuracy with mean Euclidean distance error of 2.81mm. Copyright © 2020, The Authors. All rights reserved.

关键词： Deep neural networks

Synergetic Effect of Nonthermal Plasma and Supported Cobalt Catalyst in Plasma-Enhanced Co2 Hydrogenation

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SSRN

SSRN 2022年

作者： Song, Chunshan Wang, Jiajie Wang, Xiaoxing AlQahtani, Mohammad S. Knecht, Sean D. Bilén, Sven G. Chu, Wei Department of Chemical Engineering Sichuan University Chengdu610065 China Clean Fuels & Catalysis Program EMS Energy Institute Department of Energy and Mineral Engineering The Pennsylvania State University University ParkPA16802 United States Department of Chemical Engineering The Pennsylvania State University University ParkPA16802 United States School of Engineering Design Technology and Professional Programs The Pennsylvania State University University ParkPA16802 United States School of Electrical Engineering and Computer Science The Pennsylvania State University University ParkPA16802 United States Department of Chemistry Faculty of Science The Chinese University of Hong Kong NT Hong Kong Shatin China

Nonthermal plasma can provide an alternative to CO 2 utilization under mild conditions. Understanding the effect of plasma on catalysis is highly desired. Herein, a comparative study was performed on CO 2 hydrogenation under thermal catalytic and plasma catalytic conditions using an alumina-supported cobalt catalyst. Significant plasma-catalyst synergy was attained at the low-temperature range between 423 K and 498 K where the CO 2 conversion increased up to approximately 3.6 times of the sum of that using plasma or catalyst individually. The plasma-catalytic reaction showed a significantly lower activation energy (~40 kJ/mol), which is only half of that in the thermal catalytic reaction (~80 kJ/mol). Kinetic analysis and DRIFTS results indicate that CO 2 activation is promoted by plasma with the assistance of active H species from gas phase. Furthermore, the total H 2 adsorption capacity and the coverage of unrecyclable adsorbed H species on the metal surface were decreased under plasma. The promotion effect is very likely due to a new reaction pathway introduced by plasma. In addition, the structure and morphology changes of spent catalysts after thermal or plasma reaction were not significant, suggesting they contribute little to the synergetic effect observed under the conditions. © 2022, The Authors. All rights reserved.

关键词： Carbon dioxide

Adaptive thresholding skin lesion segmentation with gabor filters and principal component analysis 4th

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Adaptive thresholding skin lesion segmentation with gabor fi...

4th International Conference on Research in Intelligent and Computing in engineering, RICE 2019

作者： Thanh, Dang N. H. Hien, Nguyen Ngoc Surya Prasath, V.B. Erkan, Uğur Khamparia, Aditya Department of Information Technology Hue College of Industry Hue Viet Nam Center of Occupational Skills Development Dong Thap University Cao Lanh Viet Nam Division of Biomedical Informatics Cincinnati Children’s Hospital Medical Center Cincinnati United States Department of Pediatrics University of Cincinnati CincinnatiOH United States Department of Biomedical Informatics College of Medicine University of Cincinnati Cincinnati United States Department of Electrical Engineering and Computer Science University of Cincinnati Cincinnati United States Computer Engineering Faculty of Engineering Karamanoğlu Mehmetbey University Karaman Turkey School of Computer Science and Engineering Lovely Professional University PhagwaraPunjab India

ISBN: (纸本)9789811527791

In this article, we study and propose an adaptive thresholding segmentation method for dermoscopic images with Gabor filters and Principal Component Analysis. The Gabor filters is used for extracting statistical features of image and the Principal Component Analysis is applied for transforming features to various bases. In experiments, we implement tests with the ISIC dataset. Segmentation results are assessed by the Dice and the Jaccard similarities. We also compare the proposed method to other similar methods to prove its own effectiveness. © Springer Nature Singapore Pte Ltd. 2020.

关键词： Gabor filters

Estimating Potential Outcome Distributions with Collaborating Causal Networks

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arXiv 2021年

作者： Zhou, Tianhui Carson, William E. Carlson, David Department of Biostatistics and Bioinformatics Duke University DurhamNC27705 United States Department of Biomedical Engineering Duke University DurhamNC27705 United States Department of Civil and Environmental Engineering Duke University DurhamNC27705 United States Department of Computer Science Duke University DurhamNC27705 United States Department of Electrical and Computer Engineering Duke University DurhamNC27705 United States

Traditional causal inference approaches leverage observational study data to estimate the difference in observed (factual) and unobserved (counterfactual) outcomes for a potential treatment, known as the Conditional Average Treatment Effect (CATE). However, CATE corresponds to the comparison on the first moment alone, and as such may be insufficient in reflecting the full picture of treatment effects. As an alternative, estimating the full potential outcome distributions could provide greater insights. However, existing methods for estimating treatment effect potential outcome distributions often impose restrictive or overly-simplistic assumptions about these distributions. Here, we propose Collaborating Causal Networks (CCN), a novel methodology which goes beyond the estimation of CATE alone by learning the full potential outcome distributions. Estimation of outcome distributions via the CCN framework does not require restrictive assumptions of the underlying data generating process (e.g. Gaussian errors). Additionally, our proposed method facilitates estimation of the utility of each possible treatment and permits individual-specific variation through utility functions (e.g. risk tolerance variability). CCN not only extends outcome estimation beyond traditional risk difference, but also enables a more comprehensive decision making process through definition of flexible comparisons. Under assumptions commonly made in the causal inference literature, we show that CCN learns distributions that asymptotically capture the correct potential outcome distributions. Furthermore, we propose an adjustment approach that is empirically effective in alleviating sample imbalance between treatment groups in observational studies. Finally, we evaluate the performance of CCN in multiple experiments on both synthetic and semi-synthetic data. We demonstrate that CCN learns improved distribution estimates compared to existing Bayesian and deep generative methods as well as improved de

关键词： Risk perception

Aerogel from sustainably grown bacterial cellulose pellicle as thermally insulative film for building envelope

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arXiv 2022年

作者： Fleury, Blaise Abraham, Eldho De La Cruz, Joshua A. Chandrasekar, Varun S. Senyuk, Bohdan Liu, Qingkun Cherpak, Vladyslav Park, Sungoh ten Hove, Jan Bart Smalyukh, Ivan I. Department of Physics University of Colorado Boulder CO80309 United States Materials Science and Engineering Program University of Colorado Boulder CO80309 United States Soft Materials Research Center and Department of Electrical Computer and Energy Engineering University of Colorado Boulder CO80309 United States Renewable and Sustainable Energy Institute National Renewable Energy Laboratory University of Colorado Boulder CO80303 United States

Improving building energy performance requires the development of new highly insulative materials. An affordable retrofitting solution comprising a thin film could improve the resistance to heat flow in both residential and commercial buildings and reduce overall energy consumption. Here we propose cellulose aerogel films formed from pellicles produced by the bacteria Gluconacetobacter hansenii as insulation materials. We studied the impact of density and nanostructure on the aerogels' thermal properties. Thermal conductivity as low as 13 mW/(K·m) was measured for native pellicle-based aerogels dried as-is with minimal post-treatment. The use of waste from the beer brewing industry as a solution to grow the pellicle maintained the cellulose yield obtained with standard Hestrin-Schramm medium, making our product more affordable and sustainable. In the future, our work can be extended through further diversification of the sources of substrate among food wastes, facilitating larger potential production and applications. © 2022, CC BY-NC-ND.

关键词： Aerogels

Ramsey envelope modulation in NV diamond magnetometry

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Physical Review B 2022年第5期106卷 054110-054110页

作者： Jner Tzern Oon Jiashen Tang Connor A. Hart Kevin S. Olsson Matthew J. Turner Jennifer M. Schloss Ronald L. Walsworth Department of Physics University of Maryland College Park Maryland 20742 USA Quantum Technology Center University of Maryland College Park Maryland 20742 USA Department of Electrical Engineering and Computer Science University of Maryland College Park Maryland 20742 USA Intelligence Community Postdoctoral Research Fellowship Program University of Maryland College Park Maryland 20742 USA Lincoln Laboratory Massachusetts Institute of Technology Lexington Massachusetts 02421 USA

Nitrogen-vacancy (NV) spin ensembles in diamond provide an advanced magnetic sensing platform, with applications in both the physical and life sciences. The development of isotopically engineered NV15 diamond offers advantages over naturally occurring NV14 for magnetometry, due to its simpler hyperfine structure. However, for sensing modalities requiring a bias magnetic field not aligned with the sensing NV axis, the absence of a quadrupole moment in the N15 nuclear spin leads to pronounced envelope modulation effects in time-dependent measurements of NV15 spin evolution. While such behavior in spin echo experiments are well studied, analogous effects in Ramsey measurements and the implications for magnetometry remain underexplored. Here, we derive the modulated NV15 Ramsey response to a misaligned bias field, using a simple vector description of the effective magnetic field on the nuclear spin. The predicted modulation properties are then compared to experimental results, revealing significant magnetic sensitivity loss if unaddressed. We demonstrate that double-quantum coherences of the NV S=1 electronic spin states dramatically suppress these envelope modulations, while additionally proving resilient to other parasitic effects such as strain heterogeneity and temperature shifts.

关键词： NV centers Quantum metrology Quantum sensing Nitrogen vacancy centers in diamond Magnetization measurements Numerical approximation & analysis Perturbation theory