Information Fusion and Hierarchical Knowledge Discovery by ARTMAP Neural Networks

Mapping novel terrain from sparse, complex data often requires the resolution of conflicting information from sensors working at different times, locations, and scales, and from experts with different goals and situations. Information fusion methods help resolve inconsistencies in order to distinguish correct from incorrect answers, as when evidence variously suggests that an object's class is car, truck, or airplane. The methods developed here consider a complementary problem, supposing that information from sensors and experts is reliable though inconsistent, as when evidence suggests that an objects class is car, vehicle, or man-made. Underlying relationships among objects are assumed to be unknown to the automated system of 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 hierarchial knowledge structures. The 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.

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.

A preliminary study on how fatigue affects scoring average in professional darts

The purpose of this preliminary study is to identify signs of fatigue in specific muscle groups that in turn directly influence accuracy in professional darts. Electromyography (EMG) sensors are employed to monitor the electrical activity produced by skeletal muscles of the trunk and upper limb during throw. It is noted that the Flexor Pollicis Brevis muscle which controls the critical release action during throw shows signs of fatigue. This is accompanied by an inherent increase in mean integral EMG amplitude for a number of other throw related muscles indicating an attempt to maintain constant applied throwing force. A strong correlation is shown to exist between average score and decrease in mean integral ECG amplitude for the Flexor Pollicis Brevis.

Women's help seeking behaviour and the associated influencing factors on self discovery of a breast symptom

To investigate women’s help seeking behavior (HSB) following self discovery of a breast symptom and determine the associated influencing factors. A descriptive correlation design was used to ascertain the help seeking behavior (HSB) and the associated influencing factors of a sample of women (n = 449) with self discovered breast symptoms. The study was guided by the ‘Help Seeking Behaviour and Influencing Factors” conceptual framework (Facione et al., 2002; Meechan et al., 2003, 2002; Leventhal, Brissette and Leventhal, 2003 and O’Mahony and Hegarty, 2009b). Data was collected using a researcher developed multi-scale questionnaire package to ascertain women’s help seeking behavior on self discovery of a breast symptom and determine the factors most associated with HSB. Factors examined include: socio-demographics, knowledge and beliefs (regarding breast symptom; breast changes associated with breast cancer; use of alternative help seeking behaviours and presence or absence of a family history of breast cancer),emotional responses, social factors, health seeking habits and health service system utilization and help seeking behavior. A convenience sample (n = 449 was obtained by the researcher from amongst women attending the breast clinics of two large urban hospitals within the Republic of Ireland. All participants had self-discovered breast symptoms and no previous history of breast cancer. The study identified that while the majority of women (69.9%; n=314) sought help within one month, 30.1% (n=135) delayed help seeking for more than one month following self discovery of their breast symptom. The factors most significantly associated with HSB were the presenting symptom of ‘nipple indrawn/changes’ (p = 0.005), ‘ignoring the symptom and hoping it would go away’ (p < 0.001), the emotional response of being ‘afraid@ on symptom discovery (p = 0.005) and the perception/belief in longer symptom duration (p = 0.023). It was found that women who presented with an indrawn/changed nipple were more likely to delay (OR = 4.81) as were women who ‘ignored the symptoms and hoped it would go away’ (OR = 10.717). Additionally, the longer women perceived that their symptom would last, they more likely they were to delay (OR = 1.18). Conversely, being afraid following symptom discovery was associated with less delay (OR = 0.37; p=0.005). This study provides further insight into the HSB of women who self discovered breast symptoms. It highlights the complexity of the help seeking process, indicating that is not a linear event but is influenced by multiple factors which can have a significant impact on the outcomes in terms of whether women delay or seek help promptly. The study further demonstrates that delayed HSB persists amongst women with self discovered breast symptoms. This has important implications for continued emphasis on the promotion of breast awareness, prompt help seeking for self discovered breast symptoms and early detection and treatment of breast cancer, amongst women of all ages.

Delivering mobile cloud services to the user: description, discovery, and consumption

The mobile cloud computing paradigm can offer relevant and useful services to the users of smart mobile devices. Such public services already exist on the web and in cloud deployments, by implementing common web service standards. However, these services are described by mark-up languages, such as XML, that cannot be comprehended by non-specialists. Furthermore, the lack of common interfaces for related services makes discovery and consumption difficult for both users and software. The problem of service description, discovery, and consumption for the mobile cloud must be addressed to allow users to benefit from these services on mobile devices. This paper introduces our work on a mobile cloud service discovery solution, which is utilised by our mobile cloud middleware, Context Aware Mobile Cloud Services (CAMCS). The aim of our approach is to remove complex mark-up languages from the description and discovery process. By means of the Cloud Personal Assistant (CPA) assigned to each user of CAMCS, relevant mobile cloud services can be discovered and consumed easily by the end user from the mobile device. We present the discovery process, the architecture of our own service registry, and service description structure. CAMCS allows services to be used from the mobile device through a user's CPA, by means of user defined tasks. We present the task model of the CPA enabled by our solution, including automatic tasks, which can perform work for the user without an explicit request.

Population dynamics of Asterias rubens under contrasted environmental conditions: Preliminary results

info:eu-repo/semantics/published

Development of a new blood test based on the helper lymphocytes Th-17 response for the diagnosis of allergies to dental metals: Preliminary results

info:eu-repo/semantics/nonPublished

Purification, crystallization and preliminary X-ray diffraction studies of a complex between G protein-coupled receptor kinase 2 and Gbeta1gamma2.

G protein-coupled receptor kinase 2 (GRK2) phosphorylates activated G protein-coupled receptors (GPCRs), which ultimately leads to their desensitization and/or downregulation. The enzyme is recruited to the plasma membrane via the interaction of its carboxyl-terminal pleckstrin-homology (PH) domain with the beta and gamma subunits of heterotrimeric G proteins (Gbetagamma). An improved purification scheme for GRK2 has been developed, conditions under which GRK2 forms a complex with Gbeta(1)gamma(2) have been determined and the complex has been crystallized in CHAPS detergent micelles. Crystals of the GRK2-Gbetagamma complex belong to space group C2 and have unit-cell parameters a = 187.0, b = 72.1, c = 122.0 A, beta = 115.2 degrees. A complete data set has been collected to 3.2 A resolution with Cu Kalpha radiation.

Real-Time and Data-Driven Operation Optimization and Knowledge Discovery for an Enterprise Information System

An enterprise information system (EIS) is an integrated data-applications platform characterized by diverse, heterogeneous, and distributed data sources. For many enterprises, a number of business processes still depend heavily on static rule-based methods and extensive human expertise. Enterprises are faced with the need for optimizing operation scheduling, improving resource utilization, discovering useful knowledge, and making data-driven decisions.

This thesis research is focused on real-time optimization and knowledge discovery that addresses workflow optimization, resource allocation, as well as data-driven predictions of process-execution times, order fulfillment, and enterprise service-level performance. In contrast to prior work on data analytics techniques for enterprise performance optimization, the emphasis here is on realizing scalable and real-time enterprise intelligence based on a combination of heterogeneous system simulation, combinatorial optimization, machine-learning algorithms, and statistical methods.

On-demand digital-print service is a representative enterprise requiring a powerful EIS.We use real-life data from Reischling Press, Inc. (RPI), a digit-print-service provider (PSP), to evaluate our optimization algorithms.

In order to handle the increase in volume and diversity of demands, we first present a high-performance, scalable, and real-time production scheduling algorithm for production automation based on an incremental genetic algorithm (IGA). The objective of this algorithm is to optimize the order dispatching sequence and balance resource utilization. Compared to prior work, this solution is scalable for a high volume of orders and it provides fast scheduling solutions for orders that require complex fulfillment procedures. Experimental results highlight its potential benefit in reducing production inefficiencies and enhancing the productivity of an enterprise.

We next discuss analysis and prediction of different attributes involved in hierarchical components of an enterprise. We start from a study of the fundamental processes related to real-time prediction. Our process-execution time and process status prediction models integrate statistical methods with machine-learning algorithms. In addition to improved prediction accuracy compared to stand-alone machine-learning algorithms, it also performs a probabilistic estimation of the predicted status. An order generally consists of multiple series and parallel processes. We next introduce an order-fulfillment prediction model that combines advantages of multiple classification models by incorporating flexible decision-integration mechanisms. Experimental results show that adopting due dates recommended by the model can significantly reduce enterprise late-delivery ratio. Finally, we investigate service-level attributes that reflect the overall performance of an enterprise. We analyze and decompose time-series data into different components according to their hierarchical periodic nature, perform correlation analysis,

and develop univariate prediction models for each component as well as multivariate models for correlated components. Predictions for the original time series are aggregated from the predictions of its components. In addition to a significant increase in mid-term prediction accuracy, this distributed modeling strategy also improves short-term time-series prediction accuracy.

In summary, this thesis research has led to a set of characterization, optimization, and prediction tools for an EIS to derive insightful knowledge from data and use them as guidance for production management. It is expected to provide solutions for enterprises to increase reconfigurability, accomplish more automated procedures, and obtain data-driven recommendations or effective decisions.

Component-wise gradient boosting and false discovery control in survival analysis with high-dimensional covariates.

MOTIVATION: Technological advances that allow routine identification of high-dimensional risk factors have led to high demand for statistical techniques that enable full utilization of these rich sources of information for genetics studies. Variable selection for censored outcome data as well as control of false discoveries (i.e. inclusion of irrelevant variables) in the presence of high-dimensional predictors present serious challenges. This article develops a computationally feasible method based on boosting and stability selection. Specifically, we modified the component-wise gradient boosting to improve the computational feasibility and introduced random permutation in stability selection for controlling false discoveries. RESULTS: We have proposed a high-dimensional variable selection method by incorporating stability selection to control false discovery. Comparisons between the proposed method and the commonly used univariate and Lasso approaches for variable selection reveal that the proposed method yields fewer false discoveries. The proposed method is applied to study the associations of 2339 common single-nucleotide polymorphisms (SNPs) with overall survival among cutaneous melanoma (CM) patients. The results have confirmed that BRCA2 pathway SNPs are likely to be associated with overall survival, as reported by previous literature. Moreover, we have identified several new Fanconi anemia (FA) pathway SNPs that are likely to modulate survival of CM patients. AVAILABILITY AND IMPLEMENTATION: The related source code and documents are freely available at https://sites.google.com/site/bestumich/issues. CONTACT: yili@umich.edu.

Preliminary experience of ovarian tissue cryopreservation procedure: alternatives, perspectives and feasibility

Chemotherapy and radiotherapy induce premature ovarian failure in many patients treated for oncological or benign diseases. The present paper reviews the risk of developing premature ovarian failure according to the type of treatment and the different options to preserve fertility, focusing on the cryopreservation of ovarian tissue. This technique constitutes a promising approach to preserve the fertility of young patients and offers the advantage of storing a large number of follicles that could be subsequently transplanted or cultured in vitro to obtain mature oocytes. Based on 34 requests, from which 19 were performed, the feasibility of the ovarian cryopreservation procedure is evaluated. The medical and ethical approaches of this protocol are also discussed. Cryopreservation of ovarian tissue constitutes new hope for many patients, but must still be kept for selected cases, with a significant risk of premature ovarian failure after treatments such as bone marrow transplantation.

Site Report for Phase III Archaeological Investigations at Reynolds Tavern (18AP23), 4 Church Circle, Annapolis, Maryland. 1982-1984

This report details the archaeology completed at Reynolds Tavern in the years 1982,1983, and 1984. It was completed in 2013, nearly 30 years after the excavation took place, using archival materials such as the draft interim reports, unit summary forms, original notes and photographs which are currently stored in the University Archives at Hornbake Library, at the University of Maryland, College Park. This report has been a collaboration across time and space, drawing from preliminary reports written by Anne Yenstch and Susan Mira in 1982 and Joe Dent and Beth Ford in 1983, as well as original notes from students of the field schools held there during those years, various analyses by scholars from many universities (including the University of Maryland, University of Georgia, and the College of William and Mary), and historical research by Nancy Baker. Thomas Cuddy began the writing of this report in 2002, completing the first three chapters in addition to the artifact analysis that led to the postexcavation identification of the African bundles in the Reynolds Tavern basement. This remarkable discovery was made along with Mark Leone of the University of Maryland, founder and director of Archaeology in Annapolis, who also served as the Principle Investigator during all three years of the Reynolds Tavern excavations. Dr. Leone contributed the fifth and final chapter to this report, the Conclusions and Recommendations, during its final compilation in 2013. The final report, including the fourth chapter on the archaeology itself, was written in part and compiled by Patricia Markert of the University of Maryland in the spring of 2013. Reynolds Tavern has been part of the landscape of Annapolis for two-hundred and fifty five years (at the time of the publication of this report). It sits on Church Circle facing St. Anne’s Church, and is a beautiful example of 18th century Georgian architecture as well one of the defining features of Historic Annapolis today. It currently operates as a popular restaurant and pub, but has served variously as a hat shop, a tavern, an inn, a library and a bank over time, among other things. Its long history contributes to its significance as an archaeological site, and also as a historic marker in present day Annapolis. The archaeology conducted at Reynolds Tavern shed light on life in 18th and 19th century Annapolis, illuminating details of the occupants’ lives through the material traces they left behind. These include an 18th century cobblestone road that ran diagonally through the Tavern’s yard, telling of the movement through early Annapolis; a large and intact well, which was found ii to contain a 19 foot wooden pipe; a large, ovular privy containing many of the objects used on a day to day basis at the Tavern or the structures around it; a subterranean brick storage feature in the basement of the Tavern, which may have been used by Reynolds during his days operating a hat shop; and also in the basement, two African caches of objects, providing a glimpse into West African spiritual practices alive in historic Annapolis and the presence of African American individuals at the Tavern in the 18th and 19th centuries. The purpose of this report is to detail these archaeological investigations and their findings, so that a public record will be available and the archaeology completed at Reynolds Tavern can continue to contribute to the history of Annapolis.

Interrogation of individual intratumoral B lymphocytes from lung cancer patients for molecular target discovery.

Intratumoral B lymphocytes are an integral part of the lung tumor microenvironment. Interrogation of the antibodies they express may improve our understanding of the host response to cancer and could be useful in elucidating novel molecular targets. We used two strategies to explore the repertoire of intratumoral B cell antibodies. First, we cloned VH and VL genes from single intratumoral B lymphocytes isolated from one lung tumor, expressed the genes as recombinant mAbs, and used the mAbs to identify the cognate tumor antigens. The Igs derived from intratumoral B cells demonstrated class switching, with a mean VH mutation frequency of 4%. Although there was no evidence for clonal expansion, these data are consistent with antigen-driven somatic hypermutation. Individual recombinant antibodies were polyreactive, although one clone demonstrated preferential immunoreactivity with tropomyosin 4 (TPM4). We found that higher levels of TPM4 antibodies were more common in cancer patients, but measurement of TPM4 antibody levels was not a sensitive test for detecting cancer. Second, in an effort to focus our recombinant antibody expression efforts on those B cells that displayed evidence of clonal expansion driven by antigen stimulation, we performed deep sequencing of the Ig genes of B cells collected from seven different tumors. Deep sequencing demonstrated somatic hypermutation but no dominant clones. These strategies may be useful for the study of B cell antibody expression, although identification of a dominant clone and unique therapeutic targets may require extensive investigation.