Adaptive Representations for Image Restoration

In the �eld of image processing, building good representation models for natural images is crucial for various applications, such as image restora- tion, sampling, segmentation, etc. Adaptive image representation models are designed for describing the intrinsic structures of natural images. In the classical Bayesian inference, this representation is often known as the prior of the intensity distribution of the input image. Early image priors have forms such as total variation norm, Markov Random Fields (MRF), and wavelets. Recently, image priors obtained from machine learning tech- niques tend to be more adaptive, which aims at capturing the natural image models via learning from larger databases. In this thesis, we study adaptive representations of natural images for image restoration. The purpose of image restoration is to remove the artifacts which degrade an image. The degradation comes in many forms such as image blurs, noises, and artifacts from the codec. Take image denoising for an example. There are several classic representation methods which can generate state- of-the-art results. The �rst one is the assumption of image self-similarity. However, this representation has the issue that sometimes the self-similarity assumption would fail because of high noise levels or unique image contents. The second one is the wavelet based nonlocal representation, which also has a problem in that the �xed basis function is not adaptive enough for any arbitrary type of input images. The third is the sparse coding using over- complete dictionaries, which does not have the hierarchical structure that is similar to the one in human visual system and is therefore prone to denoising artifacts. My research started from image denoising. Through the thorough review and evaluation of state-of-the-art denoising methods, it was found that the representation of images is substantially important for the denoising tech- nique. At the same time, an improvement on one of the nonlocal denoising method was proposed, which improves the representation of images by the integration of Gaussian blur, clustering and Rotationally Invariant Block Matching. Enlightened by the successful application of sparse coding in compressive sensing, we exploited the image self-similarity by using a sparse representation based on wavelet coe�cients in a nonlocal and hierarchical way, which generates competitive results compared to the state-of-the-art denoising algorithms. Meanwhile, another adaptive local �lter learned by Genetic Programming (GP) was proposed for e�cient image denoising. In this work, we employed GP to �nd the optimal representations for local im- age patches through training on massive datasets, which yields competitive results compared to state-of-the-art local denoising �lters. After success- fully dealt with the denoising part, we moved to the parameter estimation for image degradation models. For instance, image blur identi�cation uses deep learning, which has recently been proposed as a popular image repre- sentation approach. This work has also been extended to blur estimation based on the fact that the second step of the framework has been replaced with general regression neural network. In a word, in this thesis, spatial cor- relations, sparse coding, genetic programming, deep learning are explored as adaptive image representation models for both image restoration and parameter estimation. We conclude this thesis by considering methods based on machine learning to be the best adaptive representations for natural images. We have shown that they can generate better results than conventional representation mod- els for the tasks of image denoising and deblurring

Ginkgolide B Reduces Atherogenesis and Vascular Inflammation in ApoE−/− Mice

To investigate whether ginkgolide B (a platelet-activating factor inhibitor) affects vascular inflammation in atherosclerosis-prone apolipoprotein E-deficient (ApoE(-/-)) mice.Human platelets were used to evaluate the effects of ginkgolide B on platelet aggregation and signal transduction. Ginkgolide B attenuated platelet aggregation and inhibited phosphatidylinositol 3 kinase (PI3K) activation and Akt phosphorylation in thrombin- and collagen-activated platelets. ApoE(-/-) mice were administered a high-cholesterol diet for 8 weeks. Plasma platelet factor 4 (PF4) and RANTES (regulated upon activation, normal T-cell expressed, and secreted protein) were then measured using an enzyme-linked immunosorbent assay. Scanning electron microscopy and immunohistochemistry were used to determine atherosclerotic lesions. Ginkgolide B decreased plasma PF4 and RANTES levels in ApoE(-/-) mice. Scanning electron microscopic examination showed that ginkgolide B reduced aortic plaque in ApoE(-/-) mice. Immunohistochemistry analysis demonstrated that ginkgolide B diminished P-selectin, PF4, RANTES, and CD40L expression in aortic plaque in ApoE(-/-) mice. Moreover, ginkgolide B suppressed macrophage and vascular cell adhesion protein 1 (VCAM-1) expression in aorta lesions in ApoE(-/-) mice. Similar effects were observed in aspirin-treated ApoE(-/-) mice.Ginkgolide B significantly reduced atherosclerotic lesions and P-selectin, PF4, RANTES, and CD40L expression in aortic plaque in ApoE-/- mice. The efficacy of ginkgolide B was similar to aspirin. These results provide direct evidence that ginkgolide B inhibits atherosclerosis, which may be associated with inhibition of the PI3K/Akt pathway in activated platelets

Cloning and molecular characterization of two novel LMW-m type glutenin genes from Triticum spelta L.

Wang R., J. Zhang, F. Luo, N Liu, S. Prodanovic, Y. Yan (2021). Cloning and molecular characterization of two novel LMW-m type glutenin genes from Triticum spelta L.- Genetika, Vol 53, No.1,141 -155. Spelt wheat (Triticum spelta L., 2n=6x=42, AABBDD), as a hexaploid wheat species, is important sources of food and feed in Europe. It also serves as an important genetic resource for improvement of wheat quality and resistance. In this study, two novel m-type low molecularmolecularmolecular molecular glutenin subunit (LMW -GS) genes, named as TsLMW-m1and TsLMW-m2were cloned by allelic specific polymerase chain reaction (AS -PCR) from German spelt wheat cultivars Rochbergers fruher Dinke and Schwabenkorn, respectively. The complete open reading frames (ORFs) of both genes contained contained contained contained contained contained 873 bp encoding 290 amino acid residues, and had typical LMW-GS structural features. Two same deletions with 24 bp at the position of 707-730 bp were present in both genes, while TsLMW-m1had two nonsynonymous single-nucleotide polymorphism (SNP) variations at the positions of 434 bp (C-A transversion) and 857 bp (G-A transition). Phylogenic analysis revealed that both LMW-m genes were closely related to those from wheat A genome, suggesting that both subunits are encoded by the Glu-A3 locus. Secondary structure prediction showed that TsLMW-m1and TsLMW-m2subunits had more α-helices than other wheat LMW-GS including superior quality subunit EU369717, which would benefit to form superior gluten structures and dough properties. The authenticity and expression activity of TsLMW-m1 and TsLMW-m2 genes were verified by prokaryotic expression in E. coli. Our results indicated that two newly cloned TsLMW-m genes could have potential values for wheat quality improvement

A global portrait of expressed mental health signals towards COVID-19 in social media space

Globally, the COVID-19 pandemic has induced a mental health crisis. Social media data offer a unique oppor- tunity to track the mental health signals of a given population and quantify their negativity towards COVID-19. To date, however, we know little about how negative sentiments differ across countries and how these relate to the shifting policy landscape experienced through the pandemic. Using 2.1 billion individual-level geotagged tweets posted between 1 February 2020 and 31 March 2021, we track, monitor and map the shifts in negativity across 217 countries and unpack its relationship with COVID-19 policies. Findings reveal that there are important geographic, demographic, and socioeconomic disparities of negativity across continents, different levels of a nation’s income, population density, and the level of COVID-19 infection. Countries with more stringent policies were associated with lower levels of negativity, a relationship that weakened in later phases of the pandemic. This study provides the first global and multilingual evaluation of the public’s real-time mental health signals to COVID-19 at a large spatial and temporal scale. We offer an empirical framework to monitor mental health signals globally, helping international authorizations, including the United Nations and World Health Organi- zation, to design smart country-specific mental health initiatives in response to the ongoing pandemic and future public emergencies

JAKARTA NON-CHINESE ANCESTRY INDONESIAN STUDENTS’MOTIVATION OF CHINESE STUDYING

随着中国综合国力的增强，中文在国际上的地位不断提升，因而催生了世界范围的“中 文热”。印度尼西亚华人众多，随着中文限制的解除，中文学习者人数与日俱增。近年来，印 尼非华裔汉语学习者的数量也大幅增加，甚至有不少非华裔大学生选择中文作为自己的专业。 华裔学生学习中文总是与身为华人的使命感紧密相连。非华裔学生学习中文动机是什么？带着 这一疑问，笔者选取了雅加达地区四所具有代表性的大学，对其中文系非华裔学生学习中文动 机进行调查。文章结合了雅加达主要大学中文系介绍，依托学习动机理论对问卷结果进行整理 分析。调查结果表明，非华裔大学生学习中文多为内部动机，兴趣是学习中文的主要推动力。 这与华裔学生受使命感驱使有较大不同

Nonlocal Hierarchical Dictionary Learning Using Wavelets for Image Denoising

Exploiting the sparsity within representation models for images is critical for image denoising. The best currently available denoising methods take advantage of the sparsity from image self-similarity, pre-learned, and fixed representations. Most of these methods, however, still have difficulties in tackling high noise levels or noise models other than Gaussian. In this paper, the multiresolution structure and sparsity of wavelets are employed by nonlocal dictionary learning in each decomposition level of the wavelets. Experimental results show that our proposed method outperforms two state-of-the-art image denoising algorithms on higher noise levels. Furthermore, our approach is more adaptive to the less extensively researched uniform noise

Image Blur Classification and Parameter Identification Using Two-stage Deep Belief Networks

Image blur kernel classification and parameter estimation are critical for blind image deblurring. Current dominant approaches use handcrafted blur features that are optimized for a certain type of blur, which is not applicable in real blind deconvolution application when the Point Spread Function (PSF) of the blur is unknown. In this paper, a Two-stage system using Deep Belief Networks (TDBN) is proposed to first classify the blur type and then identify its parameters. To the best of our knowledge, this is the first time that Deep Belief Network (DBN) has been applied to the problem of blur analysis. In the blur type classification, our method attempts to identify the blur type from mixed input of various blurs with different parameters, rather than blur estimation based on the assumption of a single blur type in current methodology. To this aim, a semi-supervised DBN is trained to project the input samples in a discriminative feature space, and then classify those features. Moreover, in the parameter identification, the proposed edge detection on logarithm spectrum helps DBN to identify the blur parameters with very high accuracy. Experiments demonstrate the effectiveness of the proposed methods with better results compared to the state-of-the-art on the Berkeley segmentation dataset and the Pascal VOC 2007 dataset

Freedom of Expression and Hate Speech on the Internet: Rules of responsibility in Norway in light of international human rights law

The paper focus on the material rules and circumstances in Norway in light of Norway’s international obligations. The balancing of the right to freedom of expression and the protection against hate speech is central. In addition, the rules of responsibility when hate speech is set forth on the Internet is addressed. In this regard, the possible responsibility for third-party content on the Internet is of particular interest. Furthermore, the difference between “hate speech” as a legal term and “hate speech” as a concept in the public debate is included

From Heuristic Optimization to Dictionary Learning: A Review and Comprehensive Comparison of Image Denoising Algorithms

Image denoising is a well explored topic in the field of image processing. In the past several decades, the progress made in image denoising has benefited from the improved modeling of natural images. In this paper, we introduce a new taxonomy based on image representations for a better understanding of state-of-the-art image denoising techniques. Within each category, several representative algorithms are selected for evaluation and comparison. The experimental results are discussed and analyzed to determine the overall advantages and disadvantages of each category. In general, the nonlocal methods within each category produce better denoising results than local ones. In addition, methods based on overcomplete representations using learned dictionaries perform better than others. The comprehensive study in this paper would serve as a good reference and stimulate new research ideas in image denoising

Natural image denoising using evolved local adaptive filters

The coefficients in previous local filters are mostly heuristically optimized, which leads to artifacts in the denoised image when the optimization is not adaptive enough to the image content. Compared to parametric filters, learning-based denoising methods are more capable of tackling the conflicting problem of noise reduction and artifact suppression. In this paper, a patch-based Evolved Local Adaptive (ELA) filter is proposed for natural image denoising. In the training process, a patch clustering is used and the genetic programming (GP) is applied afterwards for determining the optimal filter (linear or nonlinear in a tree structure) for each cluster. In the testing stage, the optimal filter trained beforehand by GP will be retrieved and employed on the input noisy patch. In addition, this adaptive scheme can be used for different noise models. Extensive experiments verify that our method can compete with and outperform the state-of-the-art local denoising methods in the presence of Gaussian or salt-and-pepper noise. Additionally, the computational efficiency has been improved significantly because of the separation of the offline training and the online testing processes