Visual Comfort Assessment for Stereoscopic Image Retargeting

In recent years, visual comfort assessment (VCA) for 3D/stereoscopic content has aroused extensive attention. However, much less work has been done on the perceptual evaluation of stereoscopic image retargeting. In this paper, we first build a Stereoscopic Image Retargeting Database (SIRD), which contains source images and retargeted images produced by four typical stereoscopic retargeting methods. Then, the subjective experiment is conducted to assess four aspects of visual distortion, i.e. visual comfort, image quality, depth quality and the overall quality. Furthermore, we propose a Visual Comfort Assessment metric for Stereoscopic Image Retargeting (VCA-SIR). Based on the characteristics of stereoscopic retargeted images, the proposed model introduces novel features like disparity range, boundary disparity as well as disparity intensity distribution into the assessment model. Experimental results demonstrate that VCA-SIR can achieve high consistency with subjective perception

The ultrastructural study situation of aging brain nerve tissue

本文简述了衰老脑组织的样本来源、取样部位及意义，重点阐述了电镜下衰老脑组织神经元及神经胶质细胞的退行性改变。脑衰老时神经元胞体的退变集中体现在细胞器、细胞核、染色质、细胞骨架等的形态结构改变之上，如线粒体的肿胀、脊和膜消失，出现空泡样变，粗面内质网扩张、脱颗粒，微丝、微管溶解数目减少，染色质凝固，核膜皱缩等；胞突与细胞体是相连通的，关系十分密切，因而上述细胞体所见病理变化有时在树突、轴突和突触亦可见到。神经元在退化的同时具有一定的可塑能力，但这一能力随脑衰老的进展而减退。胶质细胞在脑衰老时主要出现代偿性增生及上述失代偿性退变两种形态结构的改变。如何增强胶质细胞的代偿修复能力，以及如何促进树突棘及突触的再发育，改善神经元的可塑性，成为延缓脑衰老的研究热点。In this paper, the sample source, sampling location and significance of aging brain tissue were introduced, and the degeneration of neurons and glial cells in aging brain were reviewed. Aging brain degeneration of neuronal cell body embodies a concentrated reflection in the organelles and the nucleus, chromatin and cytoskeleton of morphological structure changes, such as mitochondria swelling disappeared, ridge and film, a cavity sample, rough endoplasmic reticulum and degranulation, reduced Numbers of microfilament, microtubule dissolved, chromatin solidification, nuclear membrane shrinkage, etc. Cytoplasmic processes is connected with the cell body, very close. The pathological changes of cell body can also be seen in the dendrites and axons and synapses. Neurons in the degradation also possess a certain ability of plasticity, but the ability decreased along with the progress of brain aging. Glial cells in brain aging and compensatory hyperplasia mainly appear at the time of the compensatory hyperplasia, and two forms of degenerative changes. How to enhance the capacity of compensatory of glial cells to repair, and how to promote dendritic spines and synaptic development, improve the plasticity of neurons, become the focus in the slow brain aging

Finding coverage using incremental attribute combinations

Coverage is the range that covers only positive samples in attribute (or feature) space. Finding coverage is the kernel problem in induction algorithms because of the fact that coverage can be used as rules to describe positive samples. To reflect the characteristic of training samples, it is desirable that the large coverage that cover more positive samples. However, it is difficult to find large coverage, because the attribute space is usually very high dimensionality. Many heuristic methods such as ID3, AQ and CN2 have been proposed to find large coverage. A robust algorithm also has been proposed to find the largest coverage, but the complexities of time and space are costly when the dimensionality becomes high. To overcome this drawback, this paper proposes an algorithm that adopts incremental feature combinations to effectively find the largest coverage. In this algorithm, the irrelevant coverage can be pruned away at early stages because potentially large coverage can be found earlier. Experiments show that the space and time needed to find the largest coverage has been significantly reduced.<br /

Finding rule groups to classify high dimensional gene expression datasets

Microarray data provides quantitative information about the transcription profile of cells. To analyze microarray datasets, methodology of machine learning has increasingly attracted bioinformatics researchers. Some approaches of machine learning are widely used to classify and mine biological datasets. However, many gene expression datasets are extremely high dimensionality, traditional machine learning methods can not be applied effectively and efficiently. This paper proposes a robust algorithm to find out rule groups to classify gene expression datasets. Unlike the most classification algorithms, which select dimensions (genes) heuristically to form rules groups to identify classes such as cancerous and normal tissues, our algorithm guarantees finding out best-k dimensions (genes), which are most discriminative to classify samples in different classes, to form rule groups for the classification of expression datasets. Our experiments show that the rule groups obtained by our algorithm have higher accuracy than that of other classification approaches <br /

Finding short patterns to classify text documents

Many classification methods have been proposed to find patterns in text documents. However, according to Occam\u27s razor principle, &quot;the explanation of any phenomenon should make as few assumptions as possible&quot;, short patterns usually have more explainable and meaningful for classifying text documents. In this paper, we propose a depth-first pattern generation algorithm, which can find out short patterns from text document more effectively, comparing with breadth-first algorithm <br /

Concept learning of text documents

Concept learning of text documents can be viewed as the problem of acquiring the definition of a general category of documents. To definite the category of a text document, the Conjunctive of keywords is usually be used. These keywords should be fewer and comprehensible. A na&iuml;ve method is enumerating all combinations of keywords to extract suitable ones. However, because of the enormous number of keyword combinations, it is impossible to extract the most relevant keywords to describe the categories of documents by enumerating all possible combinations of keywords. Many heuristic methods are proposed, such as GA-base, immune based algorithm. In this work, we introduce pruning power technique and propose a robust enumeration-based concept learning algorithm. Experimental results show that the rules produce by our approach has more comprehensible and simplicity than by other methods. <br /

Keyword extraction for text categorization

Text categorization (TC) is one of the main applications of machine learning. Many methods have been proposed, such as Rocchio method, Naive bayes based method, and SVM based text classification method. These methods learn labeled text documents and then construct a classifier. A new coming text document\u27s category can be predicted. However, these methods do not give the description of each category. In the machine learning field, there are many concept learning algorithms, such as, ID3 and CN2. This paper proposes a more robust algorithm to induce concepts from training examples, which is based on enumeration of all possible keywords combinations. Experimental results show that the rules produced by our approach have more precision and simplicity than that of other methods.<br /

Resolve negative cross section of quarkonium hadroproduction using soft gluon factorization

It was found that, using NRQCD factorization, the predicted $\chi_{cJ}$ hadroproduction cross section at large $p_T$ can be negative. The negative cross sections originate from terms proportional to plus function in ${^{3}\hspace{-0.6mm}P_{J}^{[1]}}$ channels, which are remnants of the infrared subtraction in matching the ${^{3}\hspace{-0.6mm}P_{J}^{[1]}}$ short-distance coefficients. In this article, we find that the above terms can be factorized into the nonperturbative ${^{3}\hspace{-0.6mm}S_{1}^{[8]}}$ soft gluon distribution function in the soft gluon factorization (SGF) framework. Therefore, the problem can be naturally resolved in SGF. With an appropriate choice of nonperturbative parameters, the SGF can indeed give positive predictions for $\chi_{cJ}$ production rates within the whole $p_T$ region. The production of $\psi(2S)$ is also discussed, and there is no negative cross section problem.Comment: 11 pages, 7 figure