心算年老化的认知心理机制及功能磁共振成像研究

Mechanisms underlying cognitive psychology and cerebral physiological of mental arithmetic with increasing are were studied by using behavioral methods and functional magnetic resonance imaging (fMRI). I. Studies on mechanism underlying cognitive psychology of mental arithmetic with increasing age These studies were accomplished in 172 normal subjects ranging from 20 to 79 years of age with above 12 years of education (Mean = 1.51, SD = 1.5). Five mental arithmetic tasks, "1000-1", "1000-3", "1000-7", "1000-13", "1000-17", were designed with a serial calculation in which subjects sequentially subtracted the same prime number (1, 3, 7, 13, 17) from another number 1000. The variables studied were mental arithmetic, age, working memory, and sensory-motor speed, and four studies were conducted: (1) Aging process of mental arithmetic with different difficulties, (2) mechanism of aging of mental arithmetic processing. (3) effects of working memory and sensory-motor speed on aging process of mental arithmetic, (4) model of cognitive aging of mental arithmetic, with statistical methods such as MANOVA, hierarchical multiple regression, stepwise regression analysis, structural equation modelling (SEM). The results were indicated as following: Study 1: There was an obvious interaction between age and mental arithmetic, in which reaction time (RT) increased with advancing age and more difficult mental arithmetic, and mental arithmetic efficiency (the ratio of accuracy to RT) deceased with advancing age and more difficult mental arithmetic; Mental arithmetic efficiency with different difficulties decreased in power function: Study 2: There were two mediators (latent variables) in aging process of mental arithmetic, and age had an effect on mental arithmetic with different difficulties through the two mediators; Study 3: There were obvious interactions between age and working memory, working memory and mental arithmetic; Working memory and sensory-motor speed had effects on aging process of mental arithmetic, in which the effect of working memory on aging process of mental arithmetic was about 30-50%, and the effect of sensory-motor speed on aging process of mental arithmetic was above 35%. Study 4: Age, working memory, and sensory-motor speed had effects on two latent variables (factor 1 and factor 2), then had effects on mental arithmetic with different difficulties through factor 1 which was relative to memory component, and factor 2 which relative to speed component and had an effect on factor 1 significantly. II. Functional magnetic resonance imaging study on metal arithmetic with increasing age This study was accomplished in 14 normal right-handed subjects ranging from 20 to 29 (7 subjects) and 60 to 69 (7 subjects) years of age by using functional magnetic resonance imaging apparatus, a superconductive Signa Horizon 1.5T MRI system. Two mental arithmetic tasks, "1000-3" and "1000-17", were designed with a serial calculation in which subjects sequentially subtracted the same prime number (3 or 17) from another number 1000 silently, and controlling task, "1000-0", in which subjects continually rehearsed number 1000 silently, was regarded as baseline, based on current "baseline-task" OFF-ON subtraction pattern. Original data collected by fMRI apparatus, were analyzed off-line in SUN SPARC working station by using current STIMULATE software. The analytical steps were composed of within-subject analysis, in which brain activated images about mental arithmetic with two difficulties were obtained by using t-test, and between-subject analysis, in which features of brain activation about mental arithmetic with two difficulties, the relationship between left and right hemisphere during mental arithmetic, and age differences of brain activation in young and elderly adults were examined by using non-parameter Wilcoxon test. The results were as following:

Some Extensions of the K-Means Algorithm for Image Segmentation and Pattern Classification

In this paper we present some extensions to the k-means algorithm for vector quantization that permit its efficient use in image segmentation and pattern classification tasks. It is shown that by introducing state variables that correspond to certain statistics of the dynamic behavior of the algorithm, it is possible to find the representative centers fo the lower dimensional maniforlds that define the boundaries between classes, for clouds of multi-dimensional, mult-class data; this permits one, for example, to find class boundaries directly from sparse data (e.g., in image segmentation tasks) or to efficiently place centers for pattern classification (e.g., with local Gaussian classifiers). The same state variables can be used to define algorithms for determining adaptively the optimal number of centers for clouds of data with space-varying density. Some examples of the applicatin of these extensions are also given.

Pattern Motion Perception: Feature Tracking or Integration of Component Motions?

A key question regarding primate visual motion perception is whether the motion of 2D patterns is recovered by tracking distinctive localizable features [Lorenceau and Gorea, 1989; Rubin and Hochstein, 1992] or by integrating ambiguous local motion estimates [Adelson and Movshon, 1982; Wilson and Kim, 1992]. For a two-grating plaid pattern, this translates to either tracking the grating intersections or to appropriately combining the motion estimates for each grating. Since both component and feature information are simultaneously available in any plaid pattern made of contrast defined gratings, it is unclear how to determine which of the two schemes is actually used to recover the plaid"s motion. To address this problem, we have designed a plaid pattern made with subjective, rather than contrast defined, gratings. The distinguishing characteristic of such a plaid pattern is that it contains no contrast defined intersections that may be tracked. We find that notwithstanding the absence of such features, observers can accurately recover the pattern velocity. Additionally we show that the hypothesis of tracking "illusory features" to estimate pattern motion does not stand up to experimental test. These results present direct evidence in support of the idea that calls for the integration of component motions over the one that mandates tracking localized features to recover 2D pattern motion. The localized features, we suggest, are used primarily as providers of grouping information - which component motion signals to integrate and which not to.

Learning and Example Selection for Object and Pattern Detection

This thesis presents a learning based approach for detecting classes of objects and patterns with variable image appearance but highly predictable image boundaries. It consists of two parts. In part one, we introduce our object and pattern detection approach using a concrete human face detection example. The approach first builds a distribution-based model of the target pattern class in an appropriate feature space to describe the target's variable image appearance. It then learns from examples a similarity measure for matching new patterns against the distribution-based target model. The approach makes few assumptions about the target pattern class and should therefore be fairly general, as long as the target class has predictable image boundaries. Because our object and pattern detection approach is very much learning-based, how well a system eventually performs depends heavily on the quality of training examples it receives. The second part of this thesis looks at how one can select high quality examples for function approximation learning tasks. We propose an {em active learning} formulation for function approximation, and show for three specific approximation function classes, that the active example selection strategy learns its target with fewer data samples than random sampling. We then simplify the original active learning formulation, and show how it leads to a tractable example selection paradigm, suitable for use in many object and pattern detection problems.

Some Aspects of Pattern Recognition by Computer

A computer may gather a lot of information from its environment in an optical or graphical manner. A scene, as seen for instance from a TV camera or a picture, can be transformed into a symbolic description of points and lines or surfaces. This thesis describes several programs, written in the language CONVERT, for the analysis of such descriptions in order to recognize, differentiate and identify desired objects or classes of objects in the scene. Examples are given in each case. Although the recognition might be in terms of projections of 2-dim and 3-dim objects, we do not deal with stereoscopic information. One of our programs (Polybrick) identifies parallelepipeds in a scene which may contain partially hidden bodies and non-parallelepipedic objects. The program TD works mainly with 2-dimensional figures, although under certain conditions successfully identifies 3-dim objects. Overlapping objects are identified when they are transparent. A third program, DT, works with 3-dim and 2-dim objects, and does not identify objects which are not completely seen. Important restrictions and suppositions are: (a) the input is assumed perfect (noiseless), and in a symbolic format; (b) no perspective deformation is considered. A portion of this thesis is devoted to the study of models (symbolic representations) of the objects we want to identify; different schemes, some of them already in use, are discussed. Focusing our attention on the more general problem of identification of general objects when they substantially overlap, we propose some schemes for their recognition, and also analyze some problems that are met.

Changes in innovation styles - comprehensive study of the changes in innovation styles to identify the causes and effects of different influencing factors and capabilities to create a general innovation pattern.

An investigation in innovation management and entrepreneurial management is conducted in this thesis. The aim of the research is to explore changes of innovation styles in the transformation process from a start-up company to a more mature phase of business, to predict in a second step future sustainability and the probability of success. As businesses grow in revenue, corporate size and functional complexity, various triggers, supporters and drivers affect innovation and company's success. In a comprehensive study more than 200 innovative and technology driven companies have been examined and compared to identify patterns in different performance levels. All of them have been founded under the same formal requirements of the Munich Business Plan Competition -a research approach which allowed a unique snapshot that only long-term studies would be able to provide. The general objective was to identify the correlation between different factors, as well as different dimensions, to incremental and radical innovations realised. The 12 hypothesis were formed to prove have been derived from a comprehensive literature review. The relevant academic and practitioner literature on entrepreneurial, innovation, and knowledge management as well as social network theory revealed that the concept of innovation has evolved significantly over the last decade. A review of over 15 innovation models/frameworks contributed to understand what innovation in context means and what the dimensions are. It appears that the complex theories of innovation can be described by the increasing extent of social ingredients in the explanation of innovativeness. Originally based on tangible forms of capital, and on the necessity of pull and technology push, innovation management is today integrated in a larger system. Therefore, two research instruments have been developed to explore the changes in innovations styles. The Innovation Management Audits (IMA Start-up and IMA Mature) provided statements related to product/service development, innovativeness in various typologies, resources for innovations, innovation capabilities in conjunction to knowledge and management, social networks as well as the measurement of outcomes to generate high-quality data for further exploration. In obtaining results the mature companies have been clustered in the performance level low, average and high, while the start-up companies have been kept as one cluster. Firstly, the analysis exposed that knowledge, the process of acquiring knowledge, interorganisational networks and resources for innovations are the most important driving factors for innovation and success. Secondly, the actual change of the innovation style provides new insights about the importance of focusing on sustaining success and innovation ii 16 key areas. Thirdly, a detailed overview of triggers, supporters and drivers for innovation and success for each dimension support decision makers in putting their company in the right direction. Fourthly, a critical review of contemporary strategic management in conjunction to the findings provides recommendation of how to apply well-known management tools. Last but not least, the Munich cluster is analysed providing an estimation of the success probability of the different performance cluster and start-up companies. For the analysis of the probability of success of the newly developed as well as statistically and qualitative validated ICP Model (Innovativeness, Capabilities & Potential) has been developed and applied. While the model was primarily developed to evaluate the probability of success of companies; it has equal application in the situation to measure innovativeness to identify the impact of various strategic initiatives within small or large enterprises. The main findings of the model are that competitor, and customer orientation and acquiring knowledge important for incremental and radical innovation. Formal and interorganisation networks are important to foster innovation but informal networks appear to be detrimental to innovation. The testing of the ICP model h the long term is recommended as one subject of further research. Another is to investigate some of the more intangible aspects of innovation management such as attitude and motivation of mangers. IV

Advanced interatrial block: A well-defined electrocardiographic pattern with clinical arrhythmological implications

3.050 JCR (2013) Q2, 44/125 Cardiac & cardiovascular systems

A Phylogenetic Mixture Model for Detecting Pattern-Heterogeneity in Gene Sequence or Character-State Data

Mark Pagel, Andrew Meade (2004). A phylogenetic mixture model for detecting pattern-heterogeneity in gene sequence or character-state data. Systematic Biology, 53(4), 571-581. RAE2008

Integrating Symbolic and Neural Processing in a Self-Organizing Architechture for Pattern Recognition and Prediction

British Petroleum (89A-1204); Defense Advanced Research Projects Agency (N00014-92-J-4015); National Science Foundation (IRI-90-00530); Office of Naval Research (N00014-91-J-4100); Air Force Office of Scientific Research (F49620-92-J-0225)

An Active Pattern Recognition Architecture for Mobile Robots

An active, attentionally-modulated recognition architecture is proposed for object recognition and scene analysis. The proposed architecture forms part of navigation and trajectory planning modules for mobile robots. Key characteristics of the system include movement planning and execution based on environmental factors and internal goal definitions. Real-time implementation of the system is based on space-variant representation of the visual field, as well as an optimal visual processing scheme utilizing separate and parallel channels for the extraction of boundaries and stimulus qualities. A spatial and temporal grouping module (VWM) allows for scene scanning, multi-object segmentation, and featural/object priming. VWM is used to modulate a tn~ectory formation module capable of redirecting the focus of spatial attention. Finally, an object recognition module based on adaptive resonance theory is interfaced through VWM to the visual processing module. The system is capable of using information from different modalities to disambiguate sensory input.

Learning Temporal Contexts and Priming-Preparation Modes for Pattern Recognition

The system presented here is based on neurophysiological and electrophysiological data. It computes three types of increasingly integrated temporal and probability contexts, in a bottom-up mode. To each of these contexts corresponds an increasingly specific top-down priming effect on lower processing stages, mostly pattern recognition and discrimination. Contextual learning of time intervals, events' temporal order or sequential dependencies and events' prior probability results from the delivery of large stimuli sequences. This learning gives rise to emergent properties which closely match the experimental data.

A Neural Pattern Generator that Exhibits Arousal-Dependant Human Gait Transitions

A neural pattern generator based upon a non-linear cooperative-competitive feedback neural network is presented. It can generate the two standard human gaits: the walk and the run. A scalar arousal or GO signal causes a bifurcation from one gait to the next. Although these two gaits are qualitatively different, they both have the same limb order and may exhibit oscillation frequencies that overlap. The model simulates the walk and the run via qualitatively different waveform shapes. The fraction of cycle that activity is above threshold distinguishes the two gaits, much as the duty cycles of the feet are longer in the walk than in the run.

Quadruped Gait Transitions from a Neural Pattern Generator with Arousal Modulated Interactions

A four-channel neural pattern generator is described in which both the frequency and the relative phase of oscillations are controlled by a scalar arousal or GO signal. The generator is used to simulate quadruped gaits; in particular, rapid transitions are simulated in the order - walk, trot, pace, and gallop - that occurs in the cat. Precise switching control is achieved by using an arousal dependent modulation of the model's inhibitory interactions. This modulation generates a different functional connectivity in a single network at different arousal levels.

Orientational Harmonic Model for Rotation, Translation, and Scale Invariant Pattern Representation in Biological Vision

An extension to the orientational harmonic model is presented as a rotation, translation, and scale invariant representation of geometrical form in biological vision.

Orientational Harmonic Model for Rotation, Translation, and Scale Invariant Pattern Representation in Biological Vision

The proposed model, called the combinatorial and competitive spatio-temporal memory or CCSTM, provides an elegant solution to the general problem of having to store and recall spatio-temporal patterns in which states or sequences of states can recur in various contexts. For example, fig. 1 shows two state sequences that have a common subsequence, C and D. The CCSTM assumes that any state has a distributed representation as a collection of features. Each feature has an associated competitive module (CM) containing K cells. On any given occurrence of a particular feature, A, exactly one of the cells in CMA will be chosen to represent it. It is the particular set of cells active on the previous time step that determines which cells are chosen to represent instances of their associated features on the current time step. If we assume that typically S features are active in any state then any state has K^S different neural representations. This huge space of possible neural representations of any state is what underlies the model's ability to store and recall numerous context-sensitive state sequences. The purpose of this paper is simply to describe this mechanism.