Grounded cognition suggests that conceptual processing shares cognitive resources with perceptual processing. Hence, conceptual processing should be affected by perceptual processing, and vice versa. The current study explored the relationship between conceptual and perceptual processing of size. Within a pair of words, we manipulated the font size of each word, which was either congruent or incongruent with the actual size of the referred object. In Experiment 1a, participants compared object sizes that were referred to by word pairs. Higher accuracy was observed in the congruent condition (e.g., word pairs referring to larger objects in larger font sizes) than in the incongruent condition. This is known as the size-congruency effect. In Experiments 1b and 2, participants compared the font sizes of these word pairs. The size-congruency effect was not observed. In Experiments 3a and 3b, participants compared object and font sizes of word pairs depending on a task cue. Results showed that perceptual processing affected conceptual processing, and vice versa. This suggested that the association between conceptual and perceptual processes may be bidirectional but further modulated by semantic processing. Specifically, conceptual processing might only affect perceptual processing when semantic information is activated. The current study

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This research investigates two competing strategies for managing the interaction between the optimization and the fidelity of the approximation models. Effective management ensures that the process converges to a solution of the original design problem. Two trust region managed approximate optimization approaches are studied in this research: a response surface based concurrent subspace optimization (RS-CSSO) strategy and a commercially available software package LANCELOT. A detailed performance comparison is conducted to evaluate how the two methods perform on different classes of optimization problems. A series of optimization problems ranging from simple analytic codes to multidisciplinary coupled engineering test problems are used. Both methods sequentially optimize approximate models of the augmented Lagrangian function subject to a trust region constraint. The RS-CSSO method builds a cumulative response surface approximation constructed from variable fidelity design data generated during CSSO's, while LANCELOT uses a Broyden-Fletcher-Goldfarb-£ Product Design Engineer † Associate Professor, Associate Fellow AIAA Copyright ©2001 by John E. Renaud. Published by the American Institute of Aeronautics and Astronautics, Inc. with permission. Shanno (BFGS) quadratic approximation. The performances of both optimizers are compared using the optima obtained and the number of optimization iterations as metrics. Results indicate that the response surface based CSSO strategy exhibits superior performance in application to multidisciplinary design optimization test problems.

Abstract-Bacterial meningitis is still a life-threatening disease, and early diagnosis of pathogen can be crucial to improving survival rate. Using the surface-enhanced Raman scattering (SERS) platform developed by our group, the pathogens can be differentiated on the basis of their SERS spectra which are believed to related to their surface chemical components. We collected the SERS spectra of ten pathogens: Streptococcus pneumoniae(Spn), Streptococcus agalactiae (group B streptococcus, GBS), Staphylococcus aureus (Sa), Pseudomonas aeruginosae (Psa), Acinetobacter baumannii (Ab), Klebsiella pneumoniae (Kp), Neisseria meningitidis (Nm), Listeria monocytogenes (Lm), Haemophilus influenzae (Hi), and Escherichia coli (E.coli). These samples were obtained from patients in National Taiwan University Hospital, and were believed to represent the real diversity of clinical pathogens. Using the support vector machine (SVM) method, the classification accuracy can achieve around 88%. However, we noted that SVM cannot distinguish between [E.coli, Kp] and [Sa, Hi] due to the fact that the global features of these two groups of pathogens are very similar. We therefore incorporated a classification tree method that can focus on local differences in classification rules. This improved the accuracy to 90%. To get a better understanding of the SERS signals, we also compared several other classification methods. In addition, rule extraction method which attempts to explain why classifier fail or succeed is also discussed. Our preliminary results are interesting, encouraging, and await more thorough investigation.

Improved ant colony optimization (ACO) algorithms for continuous-domain optimization have been widely applied in recent years, but these improved methods have a weak perception of environmental information changes and only rely on the residues of the pheromones in the path to guide colony evolution. In this paper, we propose an ant colony algorithm based on the reinforcement learning model (RLACO). RLACO can acquire more environmental information by calculating the diversity of the ant colony, and, uses the diversity and other basic information of the ant colony to establish a reinforcement learning model. At different stages of evolution, the algorithm chooses an optimal strategy that can maximize the reward to improve the global search ability and convergence speed of the colony. The experimental results on CEC 2017 test functions show that the proposed algorithm is superior to other algorithms for continuous-domain optimization in convergence speed, accuracy and global search ability.