汉字形音义加工时间进程的事件相关电位（ERP）研究

The time-courses of orthographic, phonological and semantic processing of Chinese characters were investigated systematically with multi-channel event-related potentials (ERPs). New evidences concerning whether phonology or semantics is processed first and whether phonology mediates semantic access were obtained, supporting and developing the new concept of repetition, overlapping, and alternating processing in Chinese character recognition. Statistic parameter mapping based on physiological double dissociation has been developed. Seven experiments were conducted: I) deciding which type of structure, left-right or non-left-right, the character displayed on the screen was; 2) deciding whether or not there was a vowel/a/in the pronunciation of the character; 3) deciding which classification, natural object or non-natural object, the character was; 4) deciding which color, red or green, the character was; 5) deciding which color, red or green, the non-character was; 6) fixing on the non-character; 7) fixing on the crosslet. The main results are: 1. N240 and P240:N240 and P240 localized at occipital and prefrontal respectively were found in experiments 1, 2, 3, and 4, but not in experiments 5, 6, or 7. The difference between the former 4 and the latter 3 experiments was only their stimuli: the former's were true Chinese characters while the latter's were non-characters or crosslet. Thus Chinese characters were related to these two components, which reflected unique processing of Chinese characters peaking at about 240 msec. 2. Basic visual feature analysis: In comparison with experiment 7 there was a common cognitive process in experiments 1, 2, 4, and 6 - basic visual feature analysis. The corresponding ERP amplitude increase in most sites started from about 60 msec. 3. Orthography: The ERP differences located at the main processing area of orthography (occipital) between experiments 1, 2, 3, 4 and experiment 5 started from about 130 msec. This was the category difference between Chinese characters and non-characters, which revealed that orthographic processing started from about 130 msec. The ERP differences between the experiments 1, 2, 3 and the experiment 4 occurred in 210-250, 230-240, and 190-250 msec respectively, suggesting orthography was processed again. These were the differences between language and non-language tasks, which revealed a higher level processing than that in the above mentioned 130 msec. All the phenomena imply that the orthographic processing does not finished in one time of processing; the second time of processing is not a simple repetition, but a higher level one. 4. Phonology: The ERPs of experiment 2 (phonological task) were significantly stronger than those of experiment 3 (semantic task) at the main processing areas of phonology (temporal and left prefrontal) starting from about 270 msec, which revealed phonologic processing. The ERP differences at left frontal between experiment 2 and experiment 1 (orthographic task) started from about 250 msec. When comparing phonological task with experiment 4 (character color decision), the ERP differences at left temporal and prefrontal started from about 220 msec. Thus phonological processing may start before 220 msec. 5. Semantic: The ERPs of experiment 3 (semantic task) were significantly stronger than those of experiment 2 (phonological task) at the main processing areas of semantics (parietal and occipital) starting from about 290 msec, which revealed semantic processing. The ERP differences at these areas between experiment 3 and experiment 4 (character color decision) started from about 270 msec. The ERP differences between experiment 3 and experiment 1 (orthographic task) started from about 260 msec. Thus semantic processing may start before 260 msec. 6. Overlapping of phonological and semantic processing: From about 270 to 350 msec, the ERPs of experiment 2 (phonological task) were significantly larger than those of experiment 3 (semantic task) at the main processing areas of phonology (temporal and left prefrontal); while from about 290-360 msec, the ERPs of experiment 3 were significantly larger than those of experiment 2 at the main processing areas of semantics (frontal, parietal, and occipital). Thus phonological processing may start earlier than semantic and their time-courses may alternate, which reveals parallel processing. 7. Semantic processing needs part phonology: When experiment 1 (orthographic task) served as baseline, the ERPs of experiment 2 and 3 (phonological and semantic tasks) significantly increased at the main processing areas of phonology (left temporal and frontal) starting from about 250 msec. The ERPs of experiment 3, besides, increased significantly at the main processing areas of semantics (parietal and frontal) starting from about 260 msec. When experiment 4 (character color decision) served as baseline, the ERPs of experiment 2 and 3 significantly increased at phonological areas (left temporal and frontal) starting from about 220 msec. The ERPs of experiment 3, similarly, increased significantly at semantic areas (parietal and frontal) starting from about270 msec. Hence, before semantic processing, a part of phonological information may be required. The conclusion could be got from above results in the present experimental conditions: 1. The basic visual feature processing starts from about 60 msec; 2. Orthographic processing starts from about 130 msec, and repeats at about 240 msec. The second processing is not simple repetition of the first one, but a higher level processing; 3. Phonological processing begins earlier than semantic, and their time-courses overlap; 4. Before semantic processing, a part of phonological information may be required; 5. The repetition, overlapping, and alternating of the orthographic, phonological and semantic processing of Chinese characters could exist in cognition. Thus the problem of whether phonology mediates semantics access is not a simple, but a complicated issue.

Distance Metric Between 3D Models and 3D Images for Recognition and Classification

Similarity measurements between 3D objects and 2D images are useful for the tasks of object recognition and classification. We distinguish between two types of similarity metrics: metrics computed in image-space (image metrics) and metrics computed in transformation-space (transformation metrics). Existing methods typically use image and the nearest view of the object. Example for such a measure is the Euclidean distance between feature points in the image and corresponding points in the nearest view. (Computing this measure is equivalent to solving the exterior orientation calibration problem.) In this paper we introduce a different type of metrics: transformation metrics. These metrics penalize for the deformatoins applied to the object to produce the observed image. We present a transformation metric that optimally penalizes for "affine deformations" under weak-perspective. A closed-form solution, together with the nearest view according to this metric, are derived. The metric is shown to be equivalent to the Euclidean image metric, in the sense that they bound each other from both above and below. For Euclidean image metric we offier a sub-optimal closed-form solution and an iterative scheme to compute the exact solution.

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.

Permutation Tests for Classification

We introduce and explore an approach to estimating statistical significance of classification accuracy, which is particularly useful in scientific applications of machine learning where high dimensionality of the data and the small number of training examples render most standard convergence bounds too loose to yield a meaningful guarantee of the generalization ability of the classifier. Instead, we estimate statistical significance of the observed classification accuracy, or the likelihood of observing such accuracy by chance due to spurious correlations of the high-dimensional data patterns with the class labels in the given training set. We adopt permutation testing, a non-parametric technique previously developed in classical statistics for hypothesis testing in the generative setting (i.e., comparing two probability distributions). We demonstrate the method on real examples from neuroimaging studies and DNA microarray analysis and suggest a theoretical analysis of the procedure that relates the asymptotic behavior of the test to the existing convergence bounds.

Complex System Classification

The use of terms such as “Engineering Systems”, “System of systems” and others have been coming into greater use over the past decade to denote systems of importance but with implied higher complexity than for the term systems alone. This paper searches for a useful taxonomy or classification scheme for complex Systems. There are two aspects to this problem: 1) distinguishing between Engineering Systems (the term we use) and other Systems, and 2) differentiating among Engineering Systems. Engineering Systems are found to be differentiated from other complex systems by being human-designed and having both significant human complexity as well as significant technical complexity. As far as differentiating among various engineering systems, it is suggested that functional type is the most useful attribute for classification differentiation. Information, energy, value and mass acted upon by various processes are the foundation concepts underlying the technical types.

The Grasping Problem: Toward Task-Level Programming for an Articulated Hand

This report presents a system for generating a stable, feasible, and reachable grasp of a polyhedral object. A set of contact points on the object is found that can result in a stable grasp; a feasible grasp is found in which the robot contacts the object at those contact points; and a path is constructed from the initial configuration of the robot to the stable, feasible final grasp configuration. The algorithm described in the report is designed for the Salisbury hand mounted on a Puma 560 arm, but a similar approach could be used to develop grasping systems for other robots.

Automatic diagnosis of task faults in flexible manufacturing systems

Hardy, N. W., Barnes, D. P., Lee, L. H. (1989). Automatic diagnosis of task faults in flexible manufacturing systems. Robotica, 7 (1):25-35

Hierarchical Multi-classification with Predictive Clustering Trees in Functional Genomics

Struyf, J., Dzeroski, S. Blockeel, H. and Clare, A. (2005) Hierarchical Multi-classification with Predictive Clustering Trees in Functional Genomics. In proceedings of the EPIA 2005 CMB Workshop