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In a field experiment we constructed two different communities using both annual and perennial plant species, in which species diversity is experimentally manipulated. We want to test the relationships between species diversity and biomass production and invasibility and the possible mechanisms driving this relationships, especially, whether the identical mechanisms drive both diversity-production and diversity-invasibility relationships. Our results indicated that a positive diversity-production relationship and negative diversity-invasibility and production-invasibility relationships emerged in two different communities. However, the mechanisms underlying are different in two communities. In the annual communities, the observed positive diversity-production and negative diversity-invasibility relationships are linked by the sampling effect. In the perennial communities, however, the mechanism responsible for these observed relationships are the complementarity effect. Our results also found that, in addition to species diversity, species composition also play an important role in governing the observed relationship. The results of our study suggest that because species in different communities may differ in their life history, biological and physiological traits, mechanisms responsible for the observed relationship between diversity and biomass production and invasibiltiy are likely different. [ABSTRACT FROM AUTHOR]

Even if viewed from different angles, one can identify an object from similar distractors after learning. To investigate the changes in neuronal activity accompanying to the object recognition learning, human subjects were asked to train themselves with novel objects in a task, in which the images of an object had to be discriminated from images of other objects regardless of the viewpoint. EEGs were recorded at the beginning of the task and after extensive training. Comparison of the ERP component - N1, the first early negative peak at posterior electrodes, showed a significant increase in the variation of N1 amplitude over objects during the learning process. The variation across views decreased. It suggests, at least at the level of N1, that the neuronal network responding to different views of the same object share largely after training, whereas before training it was based on the stimulus image similarity. [ABSTRACT FROM AUTHOR]

Simplified fuzzy ARTMAP(SFAM) is a simplification of fuzzy ARTMAP(FAM) in reducing architectural redundancy and computational overhead. The performance of individual SFAM depends on the ordering of training sample presentation. A multiple classifier combination scheme is proposed in order to overcome the problem. The sum rule voting algorithm combines the results from several SFAM's and generates reliable and accurate recognition conclusion. A confidence vector is assigned to each SFAM. The confidence element value can be dynamically adjusted according to the historical achievements. Experiments of recognizing mechanical workpieces have been conducted to verify the proposed method. The experimental results have shown that the fusion approach can achieve reliable recognition. Keywords: ARTMAP, Neural network, workpiece recognition. [ABSTRACT FROM AUTHOR]

Fisher Linear Discriminant (FLD) is a popular method for feature extraction in face recognition. However, It often suffers from the small sample size, bias and overfitting problems when dealing with the high dimensional face image data. In this paper, a framework of Ensemble Learning for Diversified Fisher Linear Discriminant (EnL-DFLD) is proposed to improve the current FLD based face recognition algorithms. Firstly, the classifier ensemble in EnL-DFLD is composed of a set of diversified component FLD classifiers, which are selected intentionally by computing the diversity between the candidate component classifiers. Secondly, the candidate component classifiers are constructed by coupling the random subspace and adaboost methods, and it can also be shown that such a coupling scheme will result in more suitable component classifiers so as to increase the generalization performance of EnL-DFLD. Experiments on two common face databases verify the superiority of the proposed EnL-DFLD over the state-of-the-art algorithms in recognition accuracy. [ABSTRACT FROM AUTHOR]

This paper presents a novel extended timed Petri Net model called PNPM — Petri Net based Representation for Pipeline Modeling, and a verification scheme based on PNPM called PPL-MC — PNPM and Lambda Calculus based Model Checker They focus on formal modeling and verification especially for ASIP architecture with pipeline structure. In this paper, PNPM elements have been defined, and their validity and usage are demonstrated. Also, the scheme of PPL-MC is introduced. [ABSTRACT FROM AUTHOR]

Protein folds prediction is one of the most important problems in computational biology. In previous works, local optimization algorithms were used mostly. A new global optimization algorithm is presented in this paper. Compared with previous works, our algorithm obtains much lower energy states in all examples with a lower complexity. [ABSTRACT FROM AUTHOR]

In this paper, an investigation is carried out regarding combination of 3D and 2D information for face recognition. A two-stage method, PCA and a Reduced Multivariate Polynomial Model (RMPM), is developed to fuse the appearance and depth information of face images in feature level where simplicity (number of polynomial coefficients increases linearly with model-order and input-dimension, i.e. no dimension explosion as in the case of full multivariate polynomials)and ease of use (can be easily formulated into recursive learning fashion) are major concerns. To cater for fast on-line registration capability when a new user arrives, the learning is formulated into recursive form. The improvement of the face recognition rate using this combination is quantified. The recognition rate by the combination is better than either appearance alone or depth alone. The performance of the algorithm is verified on both XM2VTS database and a real-time stereo vision system, showing that it is able to detect, track and recognize a person walking towards a stereo camera within reasonable time. [ABSTRACT FROM AUTHOR]