The stochastic implicit Euler method - A stable coupling scheme for Monte Carlo burnup calculations

Existing Monte Carlo burnup codes use various schemes to solve the coupled criticality and burnup equations. Previous studies have shown that the coupling schemes of the existing Monte Carlo burnup codes can be numerically unstable. Here we develop the Stochastic Implicit Euler method - a stable and efficient new coupling scheme. The implicit solution is obtained by the stochastic approximation at each time step. Our test calculations demonstrate that the Stochastic Implicit Euler method can provide an accurate solution to problems where the methods in the existing Monte Carlo burnup codes fail. © 2013 Elsevier Ltd. All rights reserved.

Improvements in interface design through implicit modeling

Touchscreen devices are often limited by the complexity of their user interface design. In the past, iterative design processes using representative user groups to test prototypes were the standard method for increasing the inclusivity of a given design, but cognitive modeling has potential to be an alternative to rigorous user testing. However, these modeling approaches currently have many limitations, some of which are based on the assumptions made in translating a User Interface (UI) into a definition file that cognitive modeling frameworks can process. This paper discusses these issues and postulates potential approaches to improvements to the translation procedure. © 2013 Springer-Verlag Berlin Heidelberg.

Smoothing LDA Model for Text Categorization

Abstract. Latent Dirichlet Allocation (LDA) is a document level language model. In general, LDA employ the symmetry Dirichlet distribution as prior of the topic-words’ distributions to implement model smoothing. In this paper, we propose a data-driven smoothing strategy in which probability mass is allocated from smoothing-data to latent variables by the intrinsic inference procedure of LDA. In such a way, the arbitrariness of choosing latent variables'priors for the multi-level graphical model is overcome. Following this data-driven strategy,two concrete methods, Laplacian smoothing and Jelinek-Mercer smoothing, are employed to LDA model. Evaluations on different text categorization collections show data-driven smoothing can significantly improve the performance in balanced and unbalanced corpora.

Parallel Fully-Implicit Computation of Magnetohydrodynamics Acceleration Experiments

A three-dimensional MHD solver is described in the paper. The solver simulates reacting flows with nonequilibrium between translational-rotational, vibrational and electron translational modes. The conservation equations are discretized with implicit time marching and the second-order modified Steger-Warming scheme, and the resulted linear system is solved iteratively with Newton-Krylov-Schwarz method that is implemented by PETS,: package. The results of convergence tests arc plotted, which show good scalability and convergence around twice faster when compared with the DPLR method. Then five test runs are conducted simulating the experiments done at the NASA Ames MHD channel, and the calculated pressures, temperatures, electrical conductivity, back EMF, load factors and flow accelerations are shown to agree with the experimental data. Our computation shows that the electrical conductivity distribution is not uniform in the powered section of the MHD channel, and that it is important to include Joule heating in order to calculate the correct conductivity and the MHD acceleration.

中美大学生归因偏差的跨文化研究

This research systematically compared Chinese undergraduates with American undergraduates on four kinds of attributional bias: correspondent bias, siuational overattribution, intergroup attributional bias and self-serving attributional bias, and examined the effect of the implicit theories reflecting the cultures on attributional bias. First is analyzed three pairs of opposite implicit theories: dispositionalist theory and situationalist theory, generality and particularity, stressing the positive evaluation of self and despising the positive evaluation of self. It developed the Modern Implicit Theories Inventory and Traditional Implicit Theories Inventory to measure these implicit theories, and the results showed these inventories had satisfactory validity and reliability, and they were suitable for the group comparison of Chinese implicit theories with European-American. At the same time through the test it found Chinese undergraduates agreed all these opposite implicit theories more than American undergraduates. Second, it studied Chinese and American undergraduates' attributional accuracy on locus of causality. The results showed: Chinese and American undergraduates both had the correspondent bias under the different salient situational constraints, and the degree of Chinese and American undergraduates' correspondent bias under the different salient situational constraints had no significant difference' Chinese and American undergraduates both showed the situational overattribution; Chinese undergraduates had more the correspondent bias and situational overattribution than American undergraduates. Third, on the research of Chinese and American undergraduates' intergroup attributional bias, it found Chinese and American undergraduates both had no intergroup attributional bias among kin, friends and strangers, while they both show some favorable outcome effects for these three group actors. The favorable outcome effects were significant on the attributional dimensions of locus of causality and controllability for strangers' behavior, and stability for kin and friends' behavior rating by Chinese undergraduates, and stability for friends' behavior rating by American undergraduates. Fourth, it explored Chinese and American undergraduates' self-serving attributional bias, and the result indicated that Chinese and American undergraduates both showed significant self-serving attributional bias: for outcome effects, Chinese undergraduates' self-serving attributional bias were reflected on the attributional dimensions of locus of causality, stability, controllability and globality, and American undergraduates were reflected on the attributional dimensions of locus of causality, stability and globality; for categorization effects, both Chinese and American undergraduates' self-serving attributional bias were reflected on attributional difference between self's negative behavior and others', but Chinese undergraduates were embodied on the attributional dimensions of locus of causality, stability and globality while American undergraduates were reflected on the attributional dimensions of stability and globality. It also found Chinese undergraduates had more self-serving attributional bias than American undergraduates. This was reflected on the attributional dimensions of locus of causality, stability and controllability for outcome effects, and for categorization effects, locus of causality, stability and globality rating for self and others' negative behavior. All studies indicated that Chinese and American undergraduates' implicit theories had no significant effects on all their four attributional bias. These findings' potentially important implications were discussed and the further research was suggested.

Comparing Student Software Designs Using Semantic Categorization

Eckerdal, A. McCartney, R. Mostr?m, J.E. Ratcliffe, M. Zander, C. Comparing Student Software Designs Using Semantic Categorization. Proceedings of the Fifth Finnish/Baltic Sea Conference on Computer Science Education, 2005

Fuzzy-Rough Attribute Reduction with Application to Web Categorization.

R. Jensen and Q. Shen, 'Fuzzy-Rough Attribute Reduction with Application to Web Categorization,' Fuzzy Sets and Systems, vol. 141, no. 3, pp. 469-485, 2004.

Variable Rate Working Memories for Phonetic Categorization and Invariant Speech Perception

Speech can be understood at widely varying production rates. A working memory is described for short-term storage of temporal lists of input items. The working memory is a cooperative-competitive neural network that automatically adjusts its integration rate, or gain, to generate a short-term memory code for a list that is independent of item presentation rate. Such an invariant working memory model is used to simulate data of Repp (1980) concerning the changes of phonetic category boundaries as a function of their presentation rate. Thus the variability of categorical boundaries can be traced to the temporal in variance of the working memory code.

Fuzzy ART: Fast Stable Learning and Categorization of Analog Patterns by an Adaptive Resonance System

A Fuzzy ART model capable of rapid stable learning of recognition categories in response to arbitrary sequences of analog or binary input patterns is described. Fuzzy ART incorporates computations from fuzzy set theory into the ART 1 neural network, which learns to categorize only binary input patterns. The generalization to learning both analog and binary input patterns is achieved by replacing appearances of the intersection operator (n) in AHT 1 by the MIN operator (Λ) of fuzzy set theory. The MIN operator reduces to the intersection operator in the binary case. Category proliferation is prevented by normalizing input vectors at a preprocessing stage. A normalization procedure called complement coding leads to a symmetric theory in which the MIN operator (Λ) and the MAX operator (v) of fuzzy set theory play complementary roles. Complement coding uses on-cells and off-cells to represent the input pattern, and preserves individual feature amplitudes while normalizing the total on-cell/off-cell vector. Learning is stable because all adaptive weights can only decrease in time. Decreasing weights correspond to increasing sizes of category "boxes". Smaller vigilance values lead to larger category boxes. Learning stops when the input space is covered by boxes. With fast learning and a finite input set of arbitrary size and composition, learning stabilizes after just one presentation of each input pattern. A fast-commit slow-recode option combines fast learning with a forgetting rule that buffers system memory against noise. Using this option, rare events can be rapidly learned, yet previously learned memories are not rapidly erased in response to statistically unreliable input fluctuations.

An Integrated Neural Network-Event-Related Potentials Model of Temporal and Probability Context Effects on Event Categorization

We present a neural network that adapts and integrates several preexisting or new modules to categorize events in short term memory (STM), encode temporal order in working memory, evaluate timing and probability context in medium and long term memory. The model shows how processed contextual information modulates event recognition and categorization, focal attention and incentive motivation. The model is based on a compendium of Event Related Potentials (ERPs) and behavioral results either collected by the authors or compiled from the classical ERP literature. Its hallmark is, at the functional level, the interplay of memory registers endowed with widely different dynamical ranges, and at the structural level, the attempt to relate the different modules to known anatomical structures.

An investigation of implicit measurement techniques amongst low risk and forensic samples

There are difficulties with utilising self- report and physiological measures of assessment amongst forensic populations. This study investigates implicit based measures amongst sexual offenders, nonsexual offenders and low risk samples. Implicit measurement is a term applied to measurement methods that makes it difficult to influence responses through conscious control. The test battery includes the Implicit Association Test (IAT), Rapid Serial Visual Presentation (RSVP), Viewing Time (VT) and the Structured Clinical interview for disorders. The IAT proposes that people will perform better on a task when they depend on well-practiced cognitive associations. The RSVP task requires participants to identify a single target image that is presented amongst a series of rapidly presented visual images. RSVP operates on the premise that if two target images are presented within 500milliseconds of each other, the possibility that the participant will recognize the second target is significantly reduced when the first target is of salience to the individual. This is the attentional blink phenomenon. VT is based on the principle that people will look longer at images that are of salience. Results showed that on the VT task, child sexual offenders took longer to view images of children than low risk groups. Nude over clothed images induced a greater attentional blink amongst low risk and offending samples on the RSVP task. Sexual offenders took longer than low risk groups on word pairing tasks where sexual words were paired with adult words on the IAT. The SCID highlighted differences between the offending and non offending groups on the sub scales for personality disorders. More erotic stimulus items on the VT and RSVP measures is recommended to better differentiate sexual preference between offending and non offending samples. A pictorial IAT is recommended. Findings provide the basis for further development of implicit measures within the assessment of sexual offenders.