Image reconstruction under visual disruption caused by rain

This thesis contributes to single-image reconstruction under visual disruption caused by rain in the following areas: 1. Parameterization of a Convolutional Autoencoder (CAE) for small images [1] 2. Generation of a rain-free image using Cycle-Consistent Generative Adversarial Network (CycleGAN) [2] 3. Rain removal across spatial frequencies using the Multi-Scale CycleGANs (MS-CycleGANs) 4. Rain removal at spatial frequency’s sub-bands using theWavelet-CycleGANs (W-CycleGANs) Image reconstruction or restoration refers to reproducing a clean or disruption-free image from an original image corrupted with some form of noise or unwanted disturbance. The goal of image reconstruction is to remove such disruption from the original corrupted image while preserving the original detail of the image scene. In recent years, deep learning techniques have been proposed for removal of rain disruption, or rain removal. They were devised using the Convolutional Neural Network (CNN) [3], and a more recent type of deep learning network called the Generative Adversarial Network (GAN) [4]. Current state-of the-art deep learning rain removal method, called the Image De-raining Conditional Generative Adversarial Network (ID-CGAN) [5], has been shown to be unable to remove rain disruption completely, or preserving the original scene detail [2]. The focus of this research is to remove rain corruption from images without sacrificing the content of the scene, starting from the collection of real rain images to the testing methodologies developed for our Generative Adversarial Network (GAN) networks. This image rain removal or reconstruction research area has attracted much interest in the past decade as it forms an important aspect of outdoor vision systems where many computer vision algorithms could be affected by rain disruption, especially if only a single image is captured. The first contribution of this thesis in the area of image reconstruction or restoration is the parameterization of a Convolutional Autoencoder (CAE). A framework for deriving an optimum set of CAE parameters for the reconstruction of small input images based on the standard Modified National Institute of Standards and Technology (MNIST) and Street View House Numbers (SVHN) data sets are proposed, using the quantitative mean squared error (MSE) and the qualitative 2Ds’ visualization of the neurons’ activation statistics and entropy at the hidden layers of the CAE. This methodology’s results show that for small 32x32 pixels’ input images, having 2560 neurons at the hidden layer (bottleneck layer) and 32 convolutional feature maps can result in optimum reconstruction performance or good representations of the input image in the latent space for the CAE [1]. The second contribution of this thesis is the generation of a rain-free image using the proposed CycleGAN [2]. Its network model was trained on the same set of 700 rain and rainfree image-pairs used by the recent ID-CGAN work [5]. In the ID-CGAN paper, there was a thorough comparison with other existing techniques like sparse dictionary-based method, convolutional-coding based method, etc. The results using synthetic rain training images have shown that the ID-CGAN method has outperformed all other existing techniques. Hence, our first proposed algorithm, the CycleGAN, is only compared to the ID-CGAN, using the same set of real rain images provided by the authors. The CycleGAN is a practical image’s style transfer approach that falls into the unpaired category, which is capable of transferring an image with rain to an image that is rain-free, without the use of training image-pairs. This is important as natural or real rain images don’t have their corresponding image-pairs that are rain-free. For comparison purpose, a real rain image data set was created. The real rain’s physical properties and phenomena [6] were used to streamline our testing conditions into five broad types of real rain disruption. This testing methodology covers most of the different outdoor rain distortion scenarios captured in the real rain image data set. Hence, we can compare both ID-CGAN and CycleGAN networks using only real rain images. The comparison results using both real and synthetic rain has shown that the CycleGAN method has outperformed the ID-CGAN which represents the state-of-the-art techniques for rain removal [2]. The Natural Image Quality Evaluator (NIQE) is also introduced as a quantitative measure [7] to analyze rain removal results as it can predict the quality of an image without relying on any prior knowledge of the image’s distortions. The results are presented in Chapter 6. Subsequently, from the CycleGAN technique, the third contribution of the thesis is proposed based on the multi-scale representation of the CycleGAN, called the MS-CycleGANs technique. This proposed technique was built on the remaining gaps on rain removal using the CycleGAN. As highlighted in the rain removal paper using CycleGAN [2], the CycleGAN results could be further improved as its reconstructed output was still unable to remove the rain components at low frequency band and preserved as much original details of the scenes as possible. Hence, the MS-CycleGANs was introduced as a better algorithm than the CycleGAN, as it could train multiple CycleGANs to remove rain components at different spatial frequency bands. The implementation of the MS-CycleGANs is discussed after the CycleGAN, and its rain removal results are also compared to the CycleGAN. The results of the MS-CycleGANs framework has shown that the MS-CycleGANs can learn the characteristics between the rain and rain-free domain at different spatial frequency scales, which is essential for removing the individual frequency components of rain while preserving the scene details. In the final contribution towards image reconstruction for removal of visual disruptions caused by rain across spatial frequency’s sub-bands, the W-CycleGANs is proposed and implemented to exploit the properties of wavelet transform such as orthogonality and signal localization, to improve the CycleGAN results. For a fair comparison with the CycleGAN, both the proposed multi-scale representations of CycleGAN networks, namely the MS-CycleGANs and the W-CycleGANs, were trained and tested on the same set of rain images used by the ID-CGAN work [5]. A qualitative visual comparison of rain-removed images, especially at the enlarged rain-removed regions, is performed for the ID-CGAN, CycleGAN, MS-CycleGANs and W-CycleGANs. The comparison results among them has demonstrated the superiority of both the MS-CycleGANs and W-CycleGANs in removing rain distortions

Parameterization of a Convolutional Autoencoder for Reconstruction of Small Images

The following topics are dealt with: mobile robots; control system synthesis; learning (artificial intelligence); feature extraction; robot vision; autonomous aerial vehicles; feedback; feedforward neural nets; nonlinear control systems; multi-robot systems

Transcription factors NF-YB involved in embryogenesis and hormones responses in Dimocarpus Longan Lour

IntroductionNF-YB transcription factor is an important regulatory factor in plant embryonic development.ResultsIn this study, 15 longan NF-YB (DlNF-YB) family genes were systematically identified in the whole genome of longan, and a comprehensive bioinformatics analysis of DlNF-YB family was performed. Comparative transcriptome analysis of DlNF-YBs expression in different tissues, early somatic embryogenesis (SE), and under different light and temperature treatments revealed its specific expression profiles and potential biological functions in longan SE. The qRT-PCR results implied that the expression patterns of DlNF-YBs were different during SE and the zygotic embryo development of longan. Supplementary 2,4-D, NPA, and PP333 in longan EC notably inhibited the expression of DlNF-YBs; ABA, IAA, and GA3 suppressed the expressions of DlNF-YB6 and DlNF-YB9, but IAA and GA3 induced the other DlNF-YBs. Subcellular localization indicated that DlNF-YB6 and DlNF-YB9 were located in the nucleus. Furthermore, verification by the modified 5'RNA Ligase Mediated Rapid Amplification of cDNA Ends (5' RLM-RACE) method demonstrated that DlNF-YB6 was targeted by dlo-miR2118e, and dlo-miR2118e regulated longan somatic embryogenesis (SE) by targeting DlNF-YB6. Compared with CaMV35S- actuated GUS expression, DlNF-YB6 and DlNF-YB9 promoters significantly drove GUS expression. Meanwhile, promoter activities were induced to the highest by GA3 but suppressed by IAA. ABA induced the activities of the promoter of DlNF-YB9, whereas it inhibited the promoter of DlNF-YB6.DiscussionHence, DlNF-YB might play a prominent role in longan somatic and zygotic embryo development, and it is involved in complex plant hormones signaling pathways

The short-term associations of chronic obstructive pulmonary disease hospitalizations with meteorological factors and air pollutants in Southwest China: a time-series study

Chronic obstructive pulmonary disease (COPD) is the fourth major cause of mortality and morbidity worldwide and is projected to be the third by 2030. However, there is little evidence available on the associations of COPD hospitalizations with meteorological factors and air pollutants in developing countries/regions of Asia. In particular, no study has been done in western areas of China considering the nonlinear and lagged effects simultaneously. This study aims to evaluate the nonlinear and lagged associations of COPD hospitalizations with meteorological factors and air pollutants using time-series analysis. The modified associations by sex and age were also investigated. The distributed lag nonlinear model was used to establish the association of daily COPD hospitalizations of all 441 public hospitals in Chengdu, China from Jan/2015–Dec/2017 with the ambient meteorological factors and air pollutants. Model parameters were optimized based on quasi Akaike Information Criterion and model diagnostics was conducted by inspecting the deviance residuals. Subgroup analysis by sex and age was also performed. Temperature, relative humidity, wind and Carbon Monoxide (CO) have statistically significant and consistent associations with COPD hospitalizations. The cumulative relative risk (RR) was lowest at a temperature of 19℃ (relative humidity of 67%). Both extremely high and low temperature (and relative humidity) increase the cumulative RR. An increase of wind speed above 4 mph (an increase of CO above 1.44 mg/m3) significantly decreases (increases) the cumulative RR. Female populations were more sensitive to low temperature and high CO level; elderly (74+) populations are more sensitive to high relative humidity; younger populations (< = 74) are more susceptible to CO higher than 1.44 mg/m3. Therefore, people with COPD should avoid exposure to adverse environmental conditions of extreme temperatures and relative humidity, low wind speed and high CO level, especially for female and elderly patients who were more sensitive to extreme temperatures and relative humidity

Depressive Neurosis Treated by Acupuncture for Regulating the Liver —A Report of 176 Cases

ObjectiveTo observe therapeutic effect of acupuncture for regulating the liver on depressive neurosis.MethodsIn a multi-center randomized controlled trial, 440 patients were divided into 3 groups: Acupuncture group for regulating the liver (Acup., 176 cases) was treated by acupuncture at Siguan Points, i.e. bilateral Hegu (LI 4) and Taichong (LR 3), Baihui (GV 20) and Yintang (EX-HN3) plus ear-acupuncture, Prozac group (P., 176 cases) by oral administration of Prozac, and Non-acupoint needling group (NAN, 88 cases) by acupuncture at non-acupoints as acupuncture placebo. Self-rating Depression Scale (SDS) was examined before treatment, and one month, two and three months after treatment respectively to evaluate therapeutic effect, and Rating Scale for Side Effects (SERS) was used to evaluate the safety.ResultsAfter one month of treatment, SDS scores in Acup. Group were significantly lower than that in P. Group (P<0.05) and than that in NAN Group (P<0.01), and SDS scores in P. Group were lower than that in NAN Group (P<0.05), showing the SDS scores in Acup. Group <P. Group <NAN Group. After 2 months of treatment, SDS scores in Acup. Group were also significantly lower than that in P. Group (P<0.01) and than that in NAN Group (P<0.01), and SDS scores in P. Group were also lower than that in NAN Group (P<0.05), showing the SDS scores in Acup. Group <P. Group <NAN Group. After 3 months of treatment, SDS scores in Acup. Group were also significantly lower than that in P. Group (P<0.01) and than that in NAN Group (P<0.01), and SDS scores in P. Group were also lower than that in NAN Group (P<0.01), showing the SDS score in Acup. Group <P. Group <NAN Group. After treatment, SERS scores were 0.16±0.95, 6.51±5.09 and 0.23±1.36 in Acup. Group, P. Group and NAN Group respectively. A significant difference existed between Acup. Group and P. Group (P<0.05), but no significant difference between Acup. Group and NAN Group (P>0.05), showing the SERS scores in Acup. Group <NAN Group <P. Group. No side effect was found in Acup. and NAN groups.ConclusionThe therapeutic effect of acupuncture on depressive neurosis is better than or similar to that of Prozac but with less side effect

香港居家安老面對的挑戰 : 服務提供者及使用者之經驗

隨著人口老化，居家安老成為香港社會重大的挑戰。政府多年來提倡「居家安老為本，院舍照顧為後援」的政策方針，透過加強社區照顧服務，以減少院舍入住率。然而，政策需要由家居環境、以至社區支援互相配合，創造可供市民居家安老的先天條件方能成事。本研究旨在探討香港推行居家安老時所面對的困難與挑戰。研究團隊訪問了30名60歲或以上、曾經或正在使用長者服務的使用者和19名從事長者社區照顧及支援服務行業的服務提供者，從不同的角度探討長者居家安老的狀況和社區照顧及支援服務的成效。 研究團隊綜合了長者居家安老的狀況，提出了幾方面的改進建議。在家居環境和社區設施方面，建議 (1) 政府應主動協助長者改善「居住環境」和安裝「緊急呼叫系統」，並(2) 建設合適長者的公共交通工具和道路設施和交通配套。在醫療層面方面，建議 (3) 資助有緊急需要的長者使用私營醫療服務、(4) 擴展醫療券計劃至購買坊間藥物和 (5) 改善普通科門診醫療預約系統和公開長者預約專籌的數量及其使用狀況。在長者社區照顧及支援服務方面，建議 (6) 檢視社區照顧服務券的宣傳和使用狀況、(7) 對長者家庭進行家訪，及早識別有需要個案和 (8) 檢視未來各區長者比例，規劃長者設施服務。 研究團隊分析了社福機構人員在提供長者服務過程中所遇到的困難後，提出了四方面的建議，分別爲 (1) 改進安老服務人員資歷架構和服務外包、(2) 簡化並加強服務資訊的宣傳、(3) 優化一站式服務平台及個案管理和 (4) 制訂長遠的安老政策方向

High-Performance Flexible Quasi-Solid-State Supercapacitors Realized by Molybdenum Dioxide@Nitrogen-Doped Carbon and Copper Cobalt Sulfide Tubular Nanostructures

Flexible quasi‐/all‐solid‐state supercapacitors have elicited scientific attention to fulfill the explosive demand for portable and wearable electronic devices. However, the use of electrode materials faces several challenges, such as intrinsically slow kinetics and volume change upon cycling, which impede the energy output and electrochemical stability. This study presents well‐aligned molybdenum dioxide@nitrogen‐doped carbon (MoO2@NC) and copper cobalt sulfide (CuCo2S4) tubular nanostructures grown on flexible carbon fiber for use as electrode materials in supercapacitors. Benefiting from the chemically stable interfaces, affluent active sites, and efficient 1D electron transport, the MoO2@NC and CuCo2S4 nanostructures integrated on conductive substrates deliver excellent electrochemical performance. A flexible quasi‐solid‐state asymmetric supercapacitor composed of MoO2@NC as the negative electrode and CuCo2S4 as the positive electrode achieves an ultrahigh energy density of 65.1 W h kg−1 at a power density of 800 W kg−1 and retains a favorable energy density of 27.6 W h kg−1 at an ultrahigh power density of 12.8 kW kg−1. Moreover, it demonstrates good cycling performance with 90.6% capacitance retention after 5000 cycles and excellent mechanical flexibility by enabling 92.2% capacitance retention after 2000 bending cycles. This study provides an effective strategy to develop electrode materials with superior electrochemical performance for flexible supercapacitors

Measurements of the Mass and Full-Width of the ηc\eta_c Meson

In a sample of 58 million $J/\psi$ events collected with the BES II detector, the process J/$\psi\to\gamma\eta_c$ is observed in five different decay channels: $\gamma K^+K^-\pi^+\pi^-$, $\gamma\pi^+\pi^-\pi^+\pi^-$, $\gamma K^\pm K^0_S \pi^\mp$ (with $K^0_S\to\pi^+\pi^-$), $\gamma \phi\phi$ (with $\phi\to K^+K^-$) and $\gamma p\bar{p}$. From a combined fit of all five channels, we determine the mass and full-width of $\eta_c$ to be $m_{\eta_c}=2977.5\pm1.0 ({stat.})\pm1.2 ({syst.})$ MeV/$c^2$ and $\Gamma_{\eta_c} = 17.0\pm3.7 ({stat.})\pm7.4 ({syst.})$ MeV/$c^2$.Comment: 9 pages, 2 figures and 4 table. Submitted to Phys. Lett.