Self-Organizing Hierarchical Knowledge Discovery by an ARTMAP Image Fusion System

Classifying novel terrain or objects front sparse, complex data may require the resolution of conflicting information from sensors working at different times, locations, and scales, and from sources with different goals and situations. Information fusion methods can help resolve inconsistencies, as when evidence variously suggests that an object's class is car, truck, or airplane. The methods described here consider a complementary problem, supposing that information from sensors and experts is reliable though inconsistent, as when evidence suggests that an object's class is car, vehicle, and man-made. Underlying relationships among objects are assumed to be unknown to the automated system or the human user. The ARTMAP information fusion system used distributed code representations that exploit the neural network's capacity for one-to-many learning in order to produce self-organizing expert systems that discover hierarchical knowledge structures. The system infers multi-level relationships among groups of output classes, without any supervised labeling of these relationships.

Self-Organizing Information Fusion and Hierarchical Knowledge Discovery: A New Framework Using Artmap Neural Networks

Classifying novel terrain or objects from sparse, complex data may require the resolution of conflicting information from sensors woring at different times, locations, and scales, and from sources with different goals and situations. Information fusion methods can help resolve inconsistencies, as when eveidence variously suggests that and object's class is car, truck, or airplane. The methods described her address a complementary problem, supposing that information from sensors and experts is reliable though inconsistent, as when evidence suggests that an object's class is car, vehicle, and man-made. Underlying relationships among classes are assumed to be unknown to the autonomated system or the human user. The ARTMAP information fusion system uses distributed code representations that exploit the neural network's capacity for one-to-many learning in order to produce self-organizing expert systems that discover hierachical knowlege structures. The fusion system infers multi-level relationships among groups of output classes, without any supervised labeling of these relationships. The procedure is illustrated with two image examples, but is not limited to image domain.

Application of proton radiography in experiments of relevance to inertial confinement fusion

Multi-Mev proton beams generated by target normal sheath acceleration (TNSA) during the interaction of an ultra intense laser beam (Ia parts per thousand yen10(19) W/cm(2)) with a thin metallic foil (thickness of the order of a few tens of microns) are particularly suited as a particle probe for laser plasma experiments. The proton imaging technique employs a laser-driven proton beam in a point-projection imaging scheme as a diagnostic tool for the detection of electric fields in such experiments. The proton probing technique has been applied in experiments of relevance to inertial confinement fusion (ICF) such as laser heated gasbags and laser-hohlraum experiments. The data provides direct information on the onset of laser beam filamentation and on the plasma expansion in the hohlraum's interior, and confirms the suitability and usefulness of this technique as an ICF diagnostic.

Regulation of OPA1-mediated mitochondrial fusion by leucine zipper/EF-hand-containing transmembrane protein-1 plays a role in apoptosis

Carboxyl-terminal modulator protein (CTMP) is a tumor suppressor-like binding partner of Protein kinase B (PKB/Akt) that negative regulates this kinase. In the course of our recent work, we identified that CTMP is consistently associated with leucine zipper/EF-hand-containing transmembrane-1 (LETM1). Here, we report that adenovirus-LETM1 increased the sensitivity of HeLa cells to apoptosis, induced by either staurosporine or actinomycin D. As shown previously, LETM1 localized to the inner mitochondrial membrane. Electron-microscopy analysis of adenovirus-LETM1 transduced cells revealed that mitochondrial cristae were swollen in these cells, a phenotype similar to that observed in optic atrophy type-1 (OPA1)-ablated cells. OPA1 cleavage was increased in LETM1-overexpressing cells, and this phenotype was reversed by overexpression of OPA1 variant-7, a cleavage resistant form of OPA1. Taken together, these data suggest that LETM1 is a novel binding partner for CTMP that may play an important role in mitochondrial fragmentation via OPA1-cleavage. (C) 2009 Elsevier Inc. All rights reserved

Burkholderia cenocepacia requires RpoE for growth under stress conditions and delay of phagolysosomal fusion in macrophages

Burkholderia cenocepacia is an opportunistic pathogen causing serious infections in patients with cystic fibrosis. The widespread distribution of this bacterium in the environment suggests that it must adapt to stress to be able to survive. We identified in B. cenocepacia K56-2 a gene predicted to encode RpoE, the extra-cytoplasmic stress response regulator. The rpoE gene is the first gene of a predicted operon encoding proteins homologous to RseA, RseB, MucD and a protein of unknown function. The genomic organization and the co-transcription of these genes were confirmed by PCR and RT-PCR. The mucD and rpoE genes were mutated, giving rise to B. cenocepacia RSF24 and RSF25, respectively. While mutant RSF24 did not demonstrate any growth defects under the conditions tested, RSF25 was compromised for growth under temperature (44 degrees C) and osmotic stress (426 mM NaCl). Expression of RpoE in trans could complement the osmotic growth defect but exacerbated temperature sensitivity in both RSF25 and wild-type K56-2. Inactivation of rpoE altered the bacterial cell surface, as indicated by increased binding of the fluorescent dye calcofluor white and by an altered outer-membrane protein profile. These cell surface changes were restored by complementation with a plasmid encoding rpoE. Macrophage infections in which bacterial colocalization with fluorescent dextran was examined demonstrated that the rpoE mutant could not delay the fusion of B. cenocepacia-containing vacuoles with lysosomes, in contrast to the parental strain K56-2. These data show that B. cenocepacia rpoE is required for bacterial growth under certain stress conditions and for the ability of intracellular bacteria to delay phagolysosomal fusion in macrophages.

The Ties That Bind Us: Ritual, Fusion, and Identification

Most social scientists endorse some version of the claim that participating in collective rituals promotes social cohesion. The systematic testing and evaluation of this claim, however, has been prevented by a lack of precision regarding the nature of both ‘ritual’and ‘social cohesion’ as well as a lack of integration between the theories and findings of the social and evolutionary sciences. By directly addressing these challenges, we argue that a systematic investigation and evaluation of the claim that ritual promotes social cohesion is achievable.

We present a general and testable theory of the relationship between ritual, cohesion, and cooperation that more precisely connects particular elements of ‘ritual,’ such as causal opacity and emotional arousal, to two particular forms
of ‘social cohesion’: group identification and identity fusion. Further, we ground this theory in an evolutionary account of why particular modes of ritual practice would be adaptive for societies with particular resource acquisition strategies. In setting out our conceptual framework we report numerous ongoing investigations that test our hypotheses against data from controlled psychological experiments as well as from theethnographic, archaeological, and historical records.

A Data Model for Content Modelling of Temporal Media

This paper describes a data model for content representation of temporal media in an IP based sensor network. The model is formed by introducing the idea of semantic-role from linguistics into the underlying concepts of formal event representation with the aim of developing a common event model. The architecture of a prototype system for a multi camera surveillance system, based on the proposed model is described. The important aspects of the proposed model are its expressiveness, its ability to model content of temporal media, and its suitability for use with a natural language interface. It also provides a platform for temporal information fusion, as well as organizing sensor annotations by help of ontologies.

Robust Multimodal Person Identification With Limited Training Data

This paper presents a novel method of audio-visual feature-level fusion for person identification where both the speech and facial modalities may be corrupted, and there is a lack of prior knowledge about the corruption. Furthermore, we assume there are limited amount of training data for each modality (e.g., a short training speech segment and a single training facial image for each person). A new multimodal feature representation and a modified cosine similarity are introduced to combine and compare bimodal features with limited training data, as well as vastly differing data rates and feature sizes. Optimal feature selection and multicondition training are used to reduce the mismatch between training and testing, thereby making the system robust to unknown bimodal corruption. Experiments have been carried out on a bimodal dataset created from the SPIDRE speaker recognition database and AR face recognition database with variable noise corruption of speech and occlusion in the face images. The system's speaker identification performance on the SPIDRE database, and facial identification performance on the AR database, is comparable with the literature. Combining both modalities using the new method of multimodal fusion leads to significantly improved accuracy over the unimodal systems, even when both modalities have been corrupted. The new method also shows improved identification accuracy compared with the bimodal systems based on multicondition model training or missing-feature decoding alone.

Assessment of Atomic Data: Problems and Solutions

For the reliable analysis and modeling of astrophysical, laser-produced, and fusion plasmas, atomic data are required for a number of parameters, including energy levels, radiative rates, and electron impact excitation rates. Such data are desired for a range of elements (H to W) and their many ions. However, measurements of atomic data, mainly for radiative and excitation rates, are not feasible for many species, and therefore, calculations are needed. For some ions (such as of C, Fe, and Kr), there is a variety of calculations available in the literature, but often, they differ significantly from one another. Therefore, there is a great demand from the user community to have data "assessed" for accuracy so that they can be confidently applied to the modeling of plasmas. In this paper we highlight the difficulties in assessing atomic data and offer some solutions for improving the accuracy of calculated results.

Fusion of Static and Temporal Information for Threat Evaluation in Sensor Networks

In many CCTV and sensor network based intelligent surveillance systems, a number of attributes or criteria are used to individually evaluate the degree of potential threat of a suspect. The outcomes for these attributes are in general from analytical algorithms where data are often pervaded with uncertainty and incompleteness. As a result, such individual threat evaluations are often inconsistent, and individual evaluations can change as time elapses. Therefore, integrating heterogeneous threat evaluations with temporal influence to obtain a better overall evaluation is a challenging issue. So far, this issue has rarely be considered by existing event reasoning frameworks under uncertainty in sensor network based surveillance. In this paper, we first propose a weighted aggregation operator based on a set of principles that constraints the fusion of individual threat evaluations. Then, we propose a method to integrate the temporal influence on threat evaluation changes. Finally, we demonstrate the usefulness of our system with a decision support event modeling framework using an airport security surveillance scenario.

Robust bimodal person identification using face and speech with limited training data and corruption of both modalities

This paper presents a novel method of audio-visual fusion for person identification where both the speech and facial modalities may be corrupted, and there is a lack of prior knowledge about the corruption. Furthermore, we assume there is a limited amount of training data for each modality (e.g., a short training speech segment and a single training facial image for each person). A new representation and a modified cosine similarity are introduced for combining and comparing bimodal features with limited training data as well as vastly differing data rates and feature sizes. Optimal feature selection and multicondition training are used to reduce the mismatch between training and testing, thereby making the system robust to unknown bimodal corruption. Experiments have been carried out on a bimodal data set created from the SPIDRE and AR databases with variable noise corruption of speech and occlusion in the face images. The new method has demonstrated improved recognition accuracy.

Electron-impact excitation and ionization of W 3+ for the determination of tungsten influx in a fusion plasma

Tungsten will be employed as a plasma facing material in the ITER fusion reactor under construction in Cadarache, France; therefore, there is a significant need for accurate electron-impact excitation and ionization data for the ions of tungsten. We report on the results of extensive calculations of ionization and excitation for W 3+ that are intended to provide the atomic data needed for the determination of impurity influx diagnostics of tungsten in several existing tokamak reactors. The electron-impact excitation rate coefficients for this study were determined using the relativistic R -matrix method. The contribution to direct electron-impact ionization was determined using the distorted-wave approximation, the accuracy of which was verified by an R -matrix with pseudo states calculation. Contributions to total ionization from excitation autoionization were also generated from the relativistic R -matrix method. These results were then employed to calculate values of ionization per emitted photon, or SXB ratios, for four carefully selected spectral lines; these data will allow the determination of impurity influx from tungsten facing surfaces. For the range of densities of importance in the edge region of a tokamak reactor, these SXB ratios are found to be nearly independent of electron density but vary significantly with electron temperature.

The application of atomic physics within impurity diagnostics for fusion plasmas

With the focus of ITER on the transport and emission properties of tungsten, generating atomic data for complex species has received much interest. Focusing on impurity influx diagnostics, we discuss recent work on heavy species. Perturbative approaches do not work well for near neutral systems so non-perturbative data are required, presenting a particular challenge for these influx diagnostics. Recent results on Mo+ are given as an illustration of how the diagnostic applications can guide the theoretical calculations for such systems.

A radiation-damped R -matrix approach to the electron-impact excitation of helium-like ions for diagnostic application to fusion and astrophysical plasmas

Electron-impact excitation collision strengths for transitions between all singly excited levels up to the n = 4 shell of helium-Eke argon and the n = 4 and 5 shells of helium-like iron have been calculated using a radiation-damped R-matrix approach. The theoretical collision strengths have been examined and associated with their infinite-energy limit values to allow the preparation of Maxwell-averaged effective collision strengths. These are conservatively considered to be accurate to within 20% at all temperatures, 3 x 10(5)-3 x 10(8) K forAr(16+) and 10(6)-10(9) K for Fe24+. They have been compared with the results of previous studies, where possible, and we find a broad accord. The corresponding rate coefficients are required for use in the calculation of derived, collisional-radiative, effective emission coefficients for helium-like lines for diagnostic application to fusion and astrophysical plasmas. The uncertainties in the fundamental collision data have been used to provide a critical assessment of the expected resultant uncertainties in such derived data, including redistributive and cascade collisional-radiative effects. The consequential uncertainties in the parts of the effective emission coefficients driven by excitation from the ground levels for the key w, x, y and z lines vary between 5% and 10%. Our results remove an uncertainty in the reaction rates of a key class of atomic processes governing the spectral emission of helium-like ions in plasmas.

Generalized collisional radiative model for light elements. Part B: Data for the Be isonuclear sequence

A first-stage collision database is assembled which contains electron-impact excitation, ionization, and recombination rate coefficients for Be, Be+, Be2+, and Be3+. The first-stage database is constructed using the R-matrix with pseudo-states, time-dependent close-coupling, and perturbative, distorted-wave methods. A second-stage collision database is then assembled which contains generalized collisional-radiative and radiated power loss coefficients. The second-stage database is constructed by solution of collisional-radiative equations in the quasi-static equilibrium approximation using the first-stage database. Both collision database stages reside in electronic form at the ORNL Controlled Fusion Atomic Data Center and in the ADAS database, and are easily accessed over the worldwide internet. © 2007 Elsevier Inc. All rights reserved.