Research on the Cognitive Comprehension Logic and Its Application in Understanding of Metaphor

In this article,a novel logic which concerns with the natural property of comprehension is presented,and the cognitive state of the agent is also considered. The cognitive comprehension operator is put forward,the logical system is established,and then the axioms and the properties of the system are discussed. Finally,the application of the cognitive comprehension logic in the understanding of the metaphor is showed.

Non-pharmacological cognitive intervention for aging and dementia:Current perspectives

In recent years, cognitive difficulties associated with normal aging and dementia have been receiving increased attention from both public and scientific communities. With an increase in overall lifespan, promoting healthy cognition has become a priority and a necessity for minimizing and preventing individual and societal burdens associated with cognitive dysfunctions in the elderly. The general awareness concerning the efficacy of preventive(e.g., lifestyles) and palliative treatment strategies of cognitive impairments, related to either healthy or unhealthy trajectories in cognitive aging, is continuously rising. There are several therapeutic strategies which can be broadly classified as either pharmacological or non-pharmacological/psychosocial. In face of the modest evidence for success of pharmacological treatments, especially for dementia related impairments, psychosocial interventions are progressively considered as a complementary treatment. Despite the relative spread of psychosocial interventions in clinical settings, research in this area is rather scarce with evidence for success of these therapies remaining controversial. In this work we provide an evidence based perspective on cognitive intervention(s) for healthy aging, pre-dementia(mild cognitive impairment), and dementia populations. Current evidence and future directions for improving cognitive functions in the elderly are discussed as well.

Automatic Partition of Chinese Sentence Group

Automatic partition of Chinese sentence group is very important to the statistical machine translation system based on discourse. This paper presents an approach to this issue： first, each sentence in a discourse is expressed as a feature vector; second, a special hierarchical clustering algorithm is applied to present a discourse as a sentence group tree. In this paper, local reoccurrence measure is proposed to the selection of key phras and the evaluation of the weight of key phrases. Experimental results show our approach promising.

An Automatic Segmentation of Kidney in Serial Abdominal CT Scans Using Region Growing Approach

Automatic kidney segmentation from abdominal CT images is a key step in computer-aided diagnosis for kidney CT as well as computeraided surgery. However, kidney segmentation from CT images is generally performed manually or semi-autornatically because of gray levels similarities of adjacent organs/tissues in abdominal CT images. This paper presents an efficient algorithm for segmenting kidney from serials of abdominal CT images. First, we extracted estimated kidney position （EKP） according to the statistical geometric location of kidney within the abdomen. Second, we analyzed the intensity distribution of EKP for several abdominal CT images and exploit an adaptive threshold searching algorithm to eliminate many other organs/tissues in the EKP. Finally, a novel region growing approach based on labeling is used to obtain the fine kidney regions. Experimental results are comparable to those of manual tracing radiologist and shown to be efficient.

Auto Composition System for Two Voice Part Inventions Based on Soft Computational Methods

Algorithmic composition is a very popular research field today. Bach's "two voice part invention" is the research object in this paper. The grammar and compositional rules of "invention" are introduced first. Then two soft computational methods,genetic algorithms and back propagation (BP) neural network technology,are combined to the experiment on assisting in composing "two voice part inventions". The system presented in this paper is quite effective and satisfactory.

Effects of healthy aging on human primary visual cortex

Aging often results in reduced visual acuity from changes in both the eye and neural circuits [1-4]. In normally aging subjects, primary visual cortex has been shown to have reduced responses to visual stimulation [5]. It is not known, however, to what extent aging affects visual field repre-sentations and population receptive sizes in human primary visual cortex. Here we use func-tional MRI (fMRI) and population receptive field (pRF) modeling [6] to measure angular and ec-centric retinotopic representations and population receptive fields in primary visual cortex in healthy aging subjects ages 57 - 70 and in healthy young volunteers ages 24 - 36 (n = 9). Retinotopic stimuli consisted of black and white, drifting checkerboards comprising moving bars 11 deg in radius. Primary visual cortex (V1) was clearly identifiable along the calcarine sulcus in all hemispheres. There was a significant decrease in the surface area of V1 from 0 to 3 deg eccentricity in the aging subjects with respect to the young subjects (p = 0.039). The coherence of the fMRI% BOLD modulation was significantly decreased in the aging subjects compared to the young subjects in the more peripheral eccentricity band from 7 to 10 deg (p = 0.029). Finally, pRF sizes were significantly increased within the 0 to 3 deg foveal representation of V1 in the aging subjects compared to the young subjects (p = 0.019). Understanding the extent of changes that occur in primary visual cortex during normal aging is essential both for understanding the normal aging process and for comparisons of healthy, aging subjects with aging patients suffering from age-related visual and cortical disorders.

Recognition of Chinese Organization Name Using Co-training

Chinese organization name recognition is hard and important in natural language processing. To reduce tagged corpus and use untagged corpus,we presented combing Co-training with support vector machines (SVM) and conditional random fields (CRF) to improve recognition results. Based on principles of uncorrelated and compatible,we constructed different classifiers from different views within SVM or CRF alone and combination of these two models. And we modified a heuristic untagged samples selection algorithm to reduce time complexity. Experimental results show that under the same tagged data,Co-training has 10% F-measure higher than using SVM or CRF alone; under the same F-measure,Co-training saves at most 70% of tagged data to achieve the same performance.

A Compression Algorithm of Hyperspectral Remote Sensing Image Based on 3-D Wavelet-Fractal Coder

In this paper, the 3-D Wavelet-Fractal coder was used to compress the hyperspectral remote sensing image, which is a combination of 3-D improved set partitioning in hierarchical trees （SPIHT） coding and 3-D fractal coding. Hyperspectral image date cube was first translated by 3-D wavelet and the 3-D fractal compression ceding was applied to lowest frequency subband. The remaining coefficients of higher frequency sub-bands were encoding by 3-D improved SPIHT. We used the block set instead of the hierarchical trees to enhance SPIHT＇s flexibility. The classical eight kinds of affme transformations in 2-D fractal image compression were generalized to nineteen for the 3-D fractal image compression. The new compression method had been tested on MATLAB. The experiment results indicate that we can gain high compression ratios and the information loss is acceptable.

Mining User Role in Social Community Application of Web 2.0

With the development of web 2.0, more and more social community applications appeared. The classical type of this kind of application is blog and facebook. The most important feature of these applications is that it is a self-media and users can post their own ideas in Internet. By using these social community applications, a big social network is formed. To study the feature of social network, it is important to mine the individual information at the beginning. In this paper, we propose a User Role based method to mine the relation between the user and object thing. First, we extract the User Role from the semantic dictionary Wordnet. Then, the feature of User Role is also mined by considering the hypemymy and hyponymy relation. Finally, we can use these features to deduce the User Role. In our experiments, we use a big corpus from TREC 2006 to test the mining performance. The experiment results show that the User Role effectively explores the feature of user.

Effects of Different Feedback Conditions on Sensorimotor Adaptation Revealed in a Mirror Reversal Paradigm

Humans are able to overcome sensory perturbations imposed on their movements through motor learning. One of the key mechanisms to accomplish this is sensorimotor adaptation, an implicit, error-driven learning mechanism. Past work on sensorimotor adaptation focused mainly on adaptation to rotated visual feedback—A paradigm known as visuomotor rotation. Recent studies have shown that sensorimotor adaptation can also occur under mirror-reversed visual feedback. In visuomotor rotation, sensorimotor adaptation can be driven by both endpoint and online feedback [1] [2]. However, it’s not been clear whether both kinds of feedback can similarly drive adaptation under a mirror reversed perturbation. We performed a study to establish what kinds of feedback can drive adaptation under mirror reversal. In the first two conditions, the participants were asked to ignore visual feedback. In the first condition, we provided mirror reversed online feedback and endpoint feedback. We reproduced previous findings showing that online feedback elicited adaptation under mirror reversal. In a second condition, we provided mirror reversed endpoint feedback. However, in the second condition, we found that endpoint feedback alone failed to elicit adaptation. In a third condition, we provided both types of feedback at the same time, but in a conflicting way: endpoint feedback was non-reversed while online feedback was mirror reversed. The participants were asked to ignore online visual feedback and try to hit the target with help from veridical endpoint feedback. In the third condition, in which veridical endpoint feedback and mirror reversed online feedback were provided, adaptation still occurred. Our results showed that endpoint feedback did not elicit adaptation under mirror reversal but online feedback did. This dissociation between effects of endpoint feedback and online feedback on adaptation under mirror reversal suggests that adaptation under these different kinds of feedback might in fact operate via distinct mechanisms.

Olfactory dysfunction in dementia

The natural aging process brings about some inevitable consequences, such as olfactory dysfunction, which is also frequently linked to numerous neurodegenerative disorders. Many age-related dementia, such as Alzheimer's disease, Vascular dementia, Parkinson's disease, and Frontotemporal Dementia often display olfactory dysfunction. Despite the overwhelming evidence of above mentioned facts, the symptomatic relevance and potential clinical and pre-clinical value of olfactory dysfunction remains overlooked by many clinicians and public alike. Olfactory dysfunction has strong practical implications on daily activities and, although not as prominent as in other mammals, olfaction is still an evolutionarily relevant sense involved in human survival(e.g., smelling gas; bad food). In this work, we provide a brief review of current research related to the olfactory dysfunction profiles in different types of dementia. Additionally, we present a compilation of accessible, easy to use olfaction assessment tools; and highlight future directions in terms of improving clinical diagnosis in patient care and research.

Rodent models for experimental heat stroke research

Because of the aggressive threaten of heat stroke and a lack of understanding of the mechanism of action, mammal animal models for experimental heat stroke were well developed. During the past 5 decades, anesthetized mouse, rat, rabbit, dog, baboon and monkey were used as animal model for experimental heat stroke. However, anesthetized mammals models have some limitations, such as neuroprotective effect of anesthetic agents, possible disturbance on injury and recovery of stroke animals by anesthetic agents, difficulty of discussing animal behavior before and after heat stroke, it was also difficult for the models to evaluate cognitive function of animal under hot environment. Considering humanitarian, only awaked and unrestrained mouse heat stroke model was accepted so far. Therefore, we also developed an awaked and unrestrained rat heat stroke model, and found it was helpful to evaluate drug effectiveness for animal behavior and cognitive function under hot environment.

Gender Recognition with Face Images Based on Partially Connected Neural Evolutionary

In this paper,a new type of neural network model - Partially Connected Neural Evolutionary (PARCONE) was introduced to recognize a face gender. The neural network has a mesh structure in which each neuron didn't connect to all other neurons but maintain a fixed number of connections with other neurons. In training,the evolutionary computation method was used to improve the neural network performance by change the connection neurons and its connection weights. With this new model,no feature extraction is needed and all of the pixels of a sample image can be used as the inputs of the neural network. The gender recognition experiment was made on 490 face images (245 females and 245 males from Color FERET database),which include not only frontal faces but also the faces rotated from-40°-40° in the direction of horizontal. After 300-600 generations' evolution,the gender recognition rate,rejection rate and error rate of the positive examples respectively are 96.2%,1.1%,and 2.7%. Furthermore,a large-scale GPU parallel computing method was used to accelerate neural network training. The experimental results show that the new neural model has a better pattern recognition ability and may be applied to many other pattern recognitions which need a large amount of input information.

Vitality Condition Judgment through Eye Quantification in Traditional Chinese Medicine

There are four main vitality states of human body in Traditional Chinese Medicine (TCM). Eye is the most important part on face for TCM doctors to diagnose patient. In this paper,we present several methods of eye quantification. We quantify eye movement,venation in white part of eye,tears in eye and the shape of upper eyelid,and then use cloud model to deduce volunteers' vitality condition by these measurements. We collect more than one thousand face images and dozens of face videos of patients',and some rules we mined with the highest support and interesting scores are right correspond to TCM diagnosis.

A New Word Detection Method for Chinese Based on Local Context Information

Finding out out-of-vocabulary words is an urgent and difficult task in Chinese words segmentation. To avoid the defect causing by offline training in the traditional method, the paper proposes an improved prediction by partical match （PPM） segmenting algorithm for Chinese words based on extracting local context information, which adds the context information of the testing text into the local PPM statistical model so as to guide the detection of new words. The algorithm focuses on the process of online segmentatien and new word detection which achieves a good effect in the close or opening test, and outperforms some well-known Chinese segmentation system to a certain extent.

Quinacrine pretreatment reduces microwave-induced neuronal damage by stabilizing the cell membrane

Quinacrine, widely used to treat parasitic diseases, binds to cell membranes. We previously found that quinacrine pretreatment reduced microwave radiation damage in rat hippocampal neurons, but the molecular mechanism remains poorly understood. Considering the thermal effects of microwave radiation and the protective effects of quinacrine on heat damage in cells, we hypothesized that quinacrine would prevent microwave radiation damage to cells in a mechanism associated with cell membrane stability. To test this, we used retinoic acid to induce PC12 cells to differentiate into neuron-like cells. We then pretreated the neurons with quinacrine （20 and 40 mM） and irradiated them with 50 mW/cm^2 microwaves for 3 or 6 hours. Flow cytometry, atomic force microscopy and western blot assays revealed that irradiated cells pretreated with quinacrine showed markedly less apoptosis, necrosis, and membrane damage, and greater expression of heat shock protein 70, than cells exposed to microwave irradiation alone. These results suggest that quinacrine stabilizes the neuronal membrane structure by upregulating the expression of heat shock protein 70, thus reducing neuronal injury caused by microwave radiation.

Song Ci Style Automatic Identification

To identify Song Ci style automatically,we put forward a novel stylistic text categorization approach based on words and their semantic in this paper. And a modified special word segmentation method,a new semantic relativity computing method based on HowNet along with the corresponding word sense disambiguation method are proposed to extract words and semantic features from Song Ci. Experiments are carried out and the results show that these methods are effective.

Auto-Harmonizing Chinese Folk Song with Piano Accompaniment

This paper approaches melody harmonization with piano accompaniment as a machine learning task, in a probabilistic framework. An existing Sample score set of Chinese folk song with piano accompaniment is used to build a model of piano accomimniment process. The mcdel can then be used to harmonize new mclody with piano accompaniment.

Improving Phrase-Based Statistical Machine Translation Models by Incorporating Syntax-Based Language Models

This paper proposed a method to incorporate syntax-based language models in phrase-based statistical machine translation (SMT) systems. The syntax-based language model used in this paper is based on link grammar,which is a high lexical formalism. In order to apply language models based on link grammar in phrase-based models,the concept of linked phrases,an extension of the concept of traditional phrases in phrase-based models was brought out. Experiments were conducted and the results showed that the use of syntax-based language models could improve the performance of the phrase-based models greatly.

Graphic Processing Unit-Accelerated Neural Network Model for Biological Species Recognition

A graphic processing unit (GPU)-accelerated biological species recognition method using partially connected neural evolutionary network model is introduced in this paper. The partial connected neural evolutionary network adopted in the paper can overcome the disadvantage of traditional neural network with small inputs. The whole image is considered as the input of the neural network, so the maximal features can be kept for recognition. To speed up the recognition process of the neural network, a fast implementation of the partially connected neural network was conducted on NVIDIA Tesla C1060 using the NVIDIA compute unified device architecture (CUDA) framework. Image sets of eight biological species were obtained to test the GPU implementation and counterpart serial CPU implementation, and experiment results showed GPU implementation works effectively on both recognition rate and speed, and gained 343 speedup over its counterpart CPU implementation. Comparing to feature-based recognition method on the same recognition task, the method also achieved an acceptable correct rate of 84.6% when testing on eight biological species.