905 research outputs found
On exploiting human domain workflows in cyber-physical systems
No full textIn this thesis, we describe a general methodology for enhancing sensing accuracy in cyber-physical systems that involve human domain workflows in noisy physical environment. A novel workflow-aware sensing model is proposed to jointly correct unreliable sensor data and keep track of states in a workflow. We also propose a new inference algorithm to handle cases with partially known states and objects as supervision. Our model is evaluated with extensive simulations. As a concrete application, we develop a novel log service called Emergency Transcriber, which can automatically document operational procedures followed by teams of first responders in emergency response scenarios. Evaluation shows that our system has significant improvement over commercial off-the-shelf (COTS) sensors and keeps track of workflow states with high accuracy in noisy physical environment.Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2018-12-01The student, Hongwei Wang, accepted the attached license on 2016-12-06 at 15:52.The student, Hongwei Wang, submitted this Thesis for approval on 2016-12-06 at 16:57.This Thesis was approved for publication on 2016-12-07 at 09:47.DSpace SAF Submission Ingestion Package generated from Vireo submission #10458 on 2017-02-28 at 14:43:19Made available in DSpace on 2017-03-01T17:02:05Z (GMT). No. of bitstreams: 2
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Quantum scattering code to simulate photodetachment process
No full text<p>Quantum scattering code to simulate photodetachment process of penta-atomic system in the AB + CDE Jacobi coordinates.</p>
<p>Users are required to cite the following papers:</p>
<ol>
<li>Phys. Chem. Chem. Phys. 2014, 16, 17770-17776;</li>
<li>J. Chem. Phys. 2016, 144, 244311;</li>
<li>Phys. Chem. Chem. Phys. 2021, 23, 22298-22304.</li>
</ol>
<p>Corresponding author : Hongwei Song Email: <a href="mailto:[email protected]">[email protected]</a></p>
<p>Affiliation: State Key Laboratory of Magnetic Resonance and Atomic and Molecular Physics, Innovation Academy for Precision Measurement Science and Technology, Chinese Academy of Sciences, Wuhan, China</p>
<p>All Copyrights Reserved by the Original Authors.</p>
Unsupervised Deep-learning Methods for Low-dose Computed Tomography Reconstruction
No full textComputed tomography (CT) has become an indispensable imaging technique in medical diagnostics and industrial applications, owing to its non-invasive nature and high resolution in visualizing object internal structures. While X-ray CT (X-ray computed tomography) significantly enhances lesion detection capabilities, excessive exposure to X-ray radiation raises substantial health concerns, including elevated cancer risks and potential genetic damage. Although low-dose CT (LDCT) protocols reduce radiation safety concerns, they inevitably introduce severe noise and artifacts, which might compromise diagnostic accuracy. Recent advances in supervised deep learning approaches have demonstrated remarkable success in LDCT reconstruction. However, the reliance on paired training data severely limits their deployment in practical CT applications. This fundamental constraint highlights the critical importance of developing unsupervised reconstruction methods. Although existing unsupervised LDCT methods have made notable progress, they still face challenges requiring systematic solutions. This thesis makes three contributions to advance the field of unsupervised LDCT reconstruction:
1. Current dual-domain self-supervised LDCT denoising methods typically neglect the heterogeneity of the non-stationary Gaussian noise levels in low-dose sinograms and usually treat them as common images without appropriately controlling the denoising strength. The denoisers employed by them, which are based on classic convolutional neural networks (CNN), will lead to blurring artifacts in the reconstructed images if directly used for sinogram denoising. In addition, the denoising strength in the sinogram and image domains must be well-balanced to avoid introducing over-blurring or secondary artifacts in the reconstructed images, but existing approaches do not focus on this crucial point. To address these limitations, this thesis proposes a novel end-to-end dual-domain self-supervised framework for LDCT denoising. It employs Dropblock layers to adaptively localize the effect of convolution for sinogram denoising and sets a weighted average between the denoised sinograms and their noisy counterparts to better control the denoising strength, thus effectively reducing the blurring artifacts and leading to a well-balanced dual-domain denoising. Numerical experiments demonstrate the effectiveness and superior performance of the proposed method.
2. Existing normalizing flows (NFs)-based unsupervised LDCT reconstruction methods face challenges in both image quality and computational efficiency. On one hand, they typically utilize a two-way transformation strategy between noisy images and latent variables, which could easily lead to detail loss and secondary artifacts in the reconstructed images. On the other hand, training NFs on high-resolution CT images is computationally intensive. Although conditional normalizing flows (CNFs) can mitigate computational costs by learning conditional probabilities, existing approaches rely on labeled data for conditionalization, leaving unsupervised CNFs-based LDCT reconstruction a challenge. To tackle these issues, this thesis proposes a novel unsupervised LDCT iterative reconstruction algorithm based on CNFs. The proposed method employs a strict one-way transformation strategy during the alternating optimization in the dual spaces to prevent detail loss and secondary artifacts, and proposes a novel unsupervised conditionalization strategy for CNFs, thus achieving efficient training and inference on high-resolution images. The proposed method illustrates high-quality and relatively fast unsupervised reconstruction. Experiments across two datasets demonstrate the superior performance of the proposed method by rivaling several state-of-the-art methods.
3. Current diffusion model-based LDCT reconstruction methods solely employ the prior distributions learned from normal-dose CT data but neglect the valuable priors in low-dose data, which contain information about the characteristics of low-dose noise and artifacts. In addition, their reconstruction speed suffers from multiple sampling steps, presenting low efficiency. Breaking new ground in prior utilization, this thesis proposes a novel unsupervised LDCT iterative reconstruction algorithm based on dual denoising diffusion probabilistic models (DDPM), which leverages both normal-dose and low-dose priors. Specifically, the proposed method employs two DDPMs to learn the prior distributions from normal-dose and low-dose images, respectively, and incorporates them into a joint iterative reconstruction framework. To accelerate the reconstruction, partial diffusion sampling and single-pass deterministic reconstruction strategy are utilized in the proposed method, as well as the spaced OS-SART approaches. Experiments at different dose levels demonstrate the outstanding performance and fast speed of the proposed reconstruction algorithm.
Through these innovations and contributions, this thesis addresses some significant challenges in current deep-learning-based unsupervised LDCT reconstruction methods, and proposes three novel unsupervised reconstruction algorithms. The proposed methods effectively improve the performance and efficiency of LDCT reconstruction under different unsupervised learning conditions, and provide some new perspectives and inspirations for future research
Genetic redundancy of senescence-associated transcription factors in Arabidopsis
No full textLeaf senescence is a genetically programmed process that constitutes the last stage of leaf development, and involves massive changes in gene expression. As a result of the intensive efforts that have been made to elucidate the molecular genetic mechanisms underlying leaf senescence, 184 genes that alter leaf senescence phenotypes when mutated or overexpressed have been identified in Arabidopsis thaliana over the past two decades. Concurrently, experimental evidence on functional redundancy within senescence-associated genes (SAGs) has increased. In this review, we focus on transcription factors that play regulatory roles in Arabidopsis leaf senescence, and describe the relationships among gene duplication, gene expression level, and senescence phenotypes. Previous findings and our re-analysis demonstrate the widespread existence of duplicate SAG pairs and a correlation between gene expression levels in duplicate genes and senescence-related phenotypic severity of the corresponding mutants. We also highlight effective and powerful tools that are available for functional analyses of redundant SAGs. We propose that the study of duplicate SAG pairs offers a unique opportunity to understand the regulation of leaf senescence and can guide the investigation of the functions of redundant SAGs via reverse genetic approaches
© The Author(s) 2017. Published by Oxford University Press on behalf of the Society for Experimental Biology. All rights reserved.1
Correction to: Physics-informed deep learning for three-dimensional transient heat transfer analysis of functionally graded materials
No full textIn the original publication of the article, the author wanted to correct the authors group and affiliation as it was wrongly updated. The correct authors group and affiliation should be: Hongwei Guo1,2, Xiaoying Zhuang1,2, Xiaolong Fu3, Yunzheng Zhu4 and Timon Rabczuk5 1 Department of Geotechnical Engineering,Tongji University,Shanghai, 200092, P.R. China. 2 Chair of Computational Science and Simulation Technology, Leibniz Universitat Hannover, Hannover, Germany. 3 Xi’an Modern Chemistry Research Institute, Xi’an, China. 4 Department of Electrical and Computer Engineering, UCLA, 420 Westwood Plaza, Los Angeles, CA 90095, USA. 5 Institute of Structural Mechanics, Bauhaus Universität Weimar, Weimar, Germany Now, the original article has been updated
Reduction of nitroaromatic compounds on supported gold nanoparticles by visible and ultraviolet light
No full textShedding light: Nitroaromatic compounds on gold nanoparticles (3 wt %) supported on ZrO2 can be reduced directly to the corresponding azo compounds when illuminated with visible light or ultraviolet light at 40 °C (see picture). The process occurs with high selectivity and at ambient temperature and pressure, and enables the selection of intermediates that are unstable in thermal reactions.\ud
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Love Your Films and Love Your Life: An Interview with Fan Popo
No full textInvited by the Institute for Screen Industries Research, the University of Nottingham, Chinese queer filmmaker and activist Fan Popo visited Nottingham in February 2018 for a series of events titled “‘Queer Cinema as Art, Activism and Industry’,” including research workshops, student seminars, and film screenings. Dr Bao Hongwei, Assistant Professor in Media Studies, interviewed Fan about the latter’s filmmaking career and his participation in transnational screen industries. This interview focuses on the status quo of queer independent filmmaking in a transnational context, with an emphasis on the opportunities and challenges that creative professionals face in increasingly commercialised and competitive work environment.Fan Popo is an independent filmmaker and queer activist from Beijing. He studied screenwriting at the Beijing Film Academy. After his graduation in 2007 he became a leading figure in China’s queer filmmaking and activist communities. His documentaries on LGBTQ and gender issues have been screened at film festivals around the world. Fan Popo is the author of Happy Together: A Complete Record of a Hundred Queer Films. He is also an organizer of the Beijing Queer Film Festival and the China Queer Film Festival Tour. In 2015, he sued China’s censorship authority, the State Administration of Radio, Film, and Television (SARFT), for banning his film Mama Rainbow from online video streaming platforms, and this became a landmark event for China’s queer activism. Fan’s films include: New Beijing New Marriage, Be a Woman, Chinese Closet, Mama Rainbow, The VaChina Monologues, and Papa Rainbow. Fan is currently based in Berlin, writing scripts and making new films
Current concepts on oxidative/carbonyl stress, inflammation and epigenetics in pathogenesis of chronic obstructive pulmonary disease
No full textChronic obstructive pulmonary disease (COPD) is a global health problem. The current therapies for COPD are poorly effective and the mainstays of pharmacotherapy are bronchodilators. A better understanding of the pathobiology of COPD is critical for the development of novel therapies. In the present review, we have discussed the roles of oxidative/aldehyde stress, inflammation/immunity, and chromatin remodeling in the pathogenesis of COPD. An imbalance of oxidants/antioxidants caused by cigarette smoke and other pollutants/biomass fuels plays an important role in the pathogenesis of COPD by regulating redox-sensitive transcription factors (e.g., NF-κB), autophagy and unfolded protein response leading to chronic lung inflammatory response. Cigarette smoke also activates canonical/alternative NF-κB pathways and their upstream kinases leading to sustained inflammatory response in lungs. Recently, epigenetic regulation has been shown to be critical for the development of COPD because the expression/activity of enzymes that regulate these epigenetic modifications have been reported to be abnormal in airways of COPD patients. Hence, the significant advances made in understanding the pathophysiology of COPD as described herein will identify novel therapeutic targets for intervention in COPD
Stability of subsea pipelines during large storms
No full textpipeline stability, scour, offshore hydrodynamics Author for correspondence
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