The use of Neural Networks for the estimation of oceanic constituents based on the MERIS instrument

Artificial Neural Networks (NNs) are used in estimations of oceanic constituents from simulated data for the Mechron Resolution Imaging Spectrometer (MERIS) instrument system for Case II water applications. The simulation includes the effects of oceanic substances such as algal related chlorophyll, non-chlorophyllous suspended matter and DOM (dissolved organic matter). It is shown here that NNs can be used to estimate oceanic constituents based on simulated data which include the effects of realistic noise and variability models. The advantage of NNs is that they not only achieve higher retrieval accuracy than more traditional techniques such as band ratio algorithms, but they also allow the inclusion of usually superfluous or unused information, such as geometric parameters and atmospheric visibility.     

Real-time path planning in dynamic virtual environments using multiagent navigation graphs

We present a novel approach for efficient path planning and navigation of multiple virtual agents in complex dynamic scenes. We introduce a new data structure, Multi-agent Navigation Graph (MaNG), which is constructed using first- and second-order Voronoi diagrams. The MaNG is used to perform route planning and proximity computations for each agent in real time. Moreover, we use the path information and proximity relationships for local dynamics computation of each agent by extending a social force model [Helbing05]. We compute the MaNG using graphics hardware and present culling techniques to accelerate the computation. We also address undersampling issues and present techniques to improve the accuracy of our algorithm. Our algorithm is used for real-time multi-agent planning in pursuit-evasion, terrain exploration and crowd simulation scenarios consisting of hundreds of moving agents, each with a distinct goal.     

Solving multi-granularity temporal constraint networks

Many problems in scheduling, planning, and natural language understanding have been formulated in terms of temporal constraint satisfaction problems (TCSP). These problems have been extensively investigated in the AI literature providing effective solutions for some fragments of the general model. Independently, there has been an effort in the data and knowledge management research community for the formalization of the concept of time granularity and for its applications. This paper considers a framework for integrating the notion of time granularity into TCSP, and investigates the problems of consistency and network solution, which, in this context, involve complex manipulation of the periodic sets representing time granularities. A sound and complete algorithm for consistency checking and for deriving a solution is presented. The paper also investigates the algorithm's computational complexity and several optimization techniques specific to the multi-granularity context. An application to e-commerce workflows illustrates the benefits of the framework and the need for specific reasoning tools.     

Halogenated ligands and their interactions with amino acids: implications for structure-activity and structure-toxicity relationships

The properties of chemicals are rooted in their molecular structure. It follows that structural analysis of specific interactions between ligands and biomolecules at the molecular level is invaluable for defining structure-activity relationships (SARs) and structure-toxicity relationships (STRs). This study has elucidated the structural and molecular basis of interactions of biomolecules with alkyl and aryl halides that are extensively used as components in many commercial pesticides, disinfectants, and drugs. We analyzed the protein structures deposited in Protein Data Bank (PDB) for structural information associated with interactions between halogenated ligands and proteins. This analysis revealed distinct patterns with respect to the nature and structural characteristics of halogen interactions with specific types of atoms and groups in proteins. Fluorine had the highest propensity of interactions for glycine, while chlorine for leucine, bromine for arginine, and iodine for lysine. Chlorine, bromine and iodine had the lowest propensity of interactions for cysteine, while fluorine had a lowest propensity for proline. These trends for highest propensity shifted towards the hydrophobic residues for all the halogens when only interactions with the side chain were considered. Halogens had equal propensities of interaction for the halogen bonding partners (nitrogen and oxygen atoms), albeit with different geometries. The optimal angle for interactions with halogens was approximately 120 degrees for oxygen atoms, and approximately 96 degrees for nitrogen atoms. The distance distributions of halogens with various amino acids were mostly bimodal, and the angle distributions were unimodal. Insights gained from this study have implications for the rational design of safer drugs and commercially important chemicals.     

Computational system identification for Bayesian NARMAX modelling

In this contribution we derive a computational Bayesian approach to NARMAX model identification. The identification algorithm exploits continuing advances in computational processing power to numerically obtain posterior distributions for both model structure and parameters via sampling methods. The main advantage of this approach over other NARMAX identification algorithms is that for the first time model uncertainty is characterised as a byproduct of the identification procedure. The algorithm is based on the reversible jump Markov chain Monte Carlo (RJMCMC) procedure. Key features of the approach are (i) sampling of unselected model terms for testing for inclusion in the model (the birth move), which encourages global searching of the model term space, (ii) sampling of previously selected model terms for testing for exclusion from the model—a naturally incorporated pruning step (the death move), which leads to model parsimony, and (iii) estimation of model and parameter distributions, which are naturally generated in the Bayesian framework. We present a numerical example to demonstrate the algorithm and a comparison with a forward regression method: the results show that the RJMCMC approach is competitive and gives useful additional information regarding uncertainty in both model parameters and structure.     

Parametric embedding for class visualization

We propose a new method, parametric embedding (PE), that embeds objects with the class structure into a low-dimensional visualization space. PE takes as input a set of class conditional probabilities for given data points and tries to preserve the structure in an embedding space by minimizing a sum of Kullback-Leibler divergences, under the assumption that samples are generated by a gaussian mixture with equal covariances in the embedding space. PE has many potential uses depending on the source of the input data, providing insight into the classifier's behavior in supervised, semisupervised, and unsupervised settings. The PE algorithm has a computational advantage over conventional embedding methods based on pairwise object relations since its complexity scales with the product of the number of objects and the number of classes. We demonstrate PE by visualizing supervised categorization of Web pages, semisupervised categorization of digits, and the relations of words and latent topics found by an unsupervised algorithm, latent Dirichlet allocation.     

Arthemis: annotation software in an integrated capturing and analysis system for colonoscopy

Colonoscopy is an endoscopic technique that allows physicians to inspect the inside of the human colon. During a colonoscopic procedure, a tiny video camera at the tip of the endoscope generates a video signal of the internal mucosa of the colon. In current practice, the entire colonoscopic procedure is not routinely captured. Software tools providing easy access to important contents of videos that are digitally captured during colonoscopy are not available. Hence, it is very time consuming to review an entire video, locate important contents, annotate them, and extract the annotated contents for research, teaching, and training purposes. Arthemis, a software application, was developed to facilitate this process. For convenient data sharing, Arthemis allows annotation according to the European Gastrointestinal Society for Endoscopy (ESGE) Minimal Standard Terminology (MST), an internationally accepted standard for digestive endoscopy. Arthemis is part of our integrated capturing and content analysis system for colonoscopy called Endoscopic Multimedia Information System (EMIS). This paper presents Arthemis as a component of EMIS, the design and implementation of Arthemis, and key lessons learned from the development process.     

Ontology-based feature mapping and verification between CAD systems

Data interoperability between computer-aided design (CAD) systems remains a major obstacle in the information integration and exchange in a collaborative engineering environment. The use of CAD data exchange standards causes the loss of design intent such as construction history, features, parameters, and constraints, whereas existing research on feature-based data exchange only focuses on class-level feature definitions and does not support instance-level verification, which causes ambiguity in data exchange. In this paper, a hybrid ontology approach is proposed to allow for the full exchange of both feature definition semantics and geometric construction data. A shared base ontology is used to convey the most fundamental elements of CAD systems for geometry and topology, which is to both maximize flexibility and minimize information loss. A three-branch hybrid CAD feature model that includes feature operation information at the boundary representation level is constructed. Instance-level feature information in the form of the base ontology is then translated to local ontologies of individual CAD systems during the rule-based mapping and verification process. A combination of the Ontology Web Language (OWL) and Semantic Web Rule Language (SWRL) is used to represent feature classes and properties and automatically classify them by a reasoner in the target system, which requires no knowledge about the source system.     

Gender differences in peer relations of children with neurodevelopmental conditions.

Objective: To examine gender differences in the peer relations of children with congenital neurodevelopmental conditions (NDC). Participants: Thirty-four children with cerebral palsy or spina bifida and 41 typically developing (TD) children, ages 6-12. Measures: Personality Inventory for Children-Second Edition (PIC-2), Social Network Inventory for Children-Child Version, and the Friendship Quality Questionnaire-Revised (FQQ-R). Results: Children with NDC report smaller social networks and lower quality of friendships than TD peers. Group × Gender interaction effects were not significant. Conclusion: Prior to adolescence, the social differences and risks associated with NDC do not appear to be gender specific. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Effects of 42 hr of total sleep deprivation on component processes of verbal working memory.

The current investigation examined changes in working memory (WM) component processes following total sleep deprivation (TSD) in a sample of healthy young persons. Forty subjects were administered a verbal form of a continuous recognition test (CRT) before and after 42 hr of TSD. Parameters of a computational model of the CRT reflecting attention, WM span, and rate of episodic memory encoding were estimated for each individual. Subjects made more errors on the test following sleep deprivation. Analysis of model parameters revealed statistically independent declines in both the attention and WM span parameters, with a larger effect observed for the decline in WM span. Examination of individual profiles suggested that the effects of TSD on verbal WM component processes vary from person to person. Declines in global verbal WM functioning appear to be primarily driven by reduced WM span and attention; however, these effects may be individual-specific. Further applications of the computational model for examining WM component processes with sleep deprivation and other clinical populations are discussed. (PsycINFO Database Record (c) 2010 APA, all rights reserved)