A comparative survey of integrated learning systems

This paper presents the duction framework for unifying the three basic forms of inference - deduction, abduction, and induction - by specifying the possible relationships and influences among them in the context of integrated learning. Special assumptive forms of inference are defined that extend the use of these inference methods, and the properties of these forms are explored. A comparison to a related inference-based learning frame work is made. Finally several existing integrated learning programs are examined in the perspective of the duction framework

Theory-driven learning : using intra-example relationships to constrain learning

We describe an incremental learning algorithm, called theory-driven learning, that creates rules to predict the effect of actions. Theory-driven learning exploits knowledge of regularities among rules to constrain the learning problem. We demonstrate that this knowledge enables the learning system to rapidly converge on accurate predictive rules and to tolerate more complex training data. An algorithm for incrementally learning these regularities is described and we provide evidence that the resulting regularities are sufficiently general to facilitate learning in new domains

Finding New Rules for Incomplete Theories: Induction with Explicit Biases in Varying Contexts

Many AI problem solvers possess explicitly encoded knowledge - a domain theory ““ that they use to solve problems. If these problem solvers are to be autonomous, they must be able to detect and to fill gaps in their own knowledge. The field of machine learning addresses this issue. Recently two disparate machine learning approaches have emerged as predominant in the field: explanation-based learning (EBL) and similarity-based learning (SBL), EBL and SBL have been applied to problems in a variety of domains. Both methods have clear problems, however, EBL assumes that a system is given an explicit theory of the domain that is complete, correct, and tractable. These assumptions are clearly unrealistic for most complex, real-world problems. SBL suffers because of its lack of an explicit theory of the domain. The simplicity of the method requires that human intervention playa large role in tailoring input examples and the features describing them in such a way as to allow a system to choose an appropriate set of features to define a concept. Biasing a system in this way may result in its being unable to discover all concepts in even a Single domain. Less tailoring of the examples leaves a system open to the possibility of not converging on the best definition for a concept, or any at all, due to the computational complexity. The research described in this proposal addresses a number of the problems found in explanation-based and similarity-based learning. The major focus of the research is the elimination of the assumption that the domain theory of an EBL system is complete. In particular, it considers the problem of working with an incomplete theory by suggesting a method by which gaps in an EBL system's knowledge can be detected and filled. We suggest that when EBL cannot derive a complete explanation, the partial explanation focus a context in which learning takes place. Information extracted from partial explanations, as well as from complete explanations, can be exploited by SBL to do better induction of the missing domain knowledge. The extracted information constitutes an explicit bias for similarity-based learning. A second problem to be addressed is that of making the biases of SBL explicit. Finally, all testing of the claims made in this proposal is to be done in the Gemini learning system. The development of the system addresses the goal of constructing an integrated learning architecture utilizing both EBL and SBL

A Survey of Machine Learning Systems Integrating Explanation-Based and Similarity-Based Methods

Two disparate machine learning approaches have received considerable attention. These are explanation-based and similarity-based learning. The basic goal of an explanation-based learning system is to more efficiently recognize concepts that it is already capable of recognizing. The learning process involves a knowledge-intensive analysis of an environment-provided example of a concept in order to extract its characteristic features. The basic goal of a similarity-based system, on the other hand, is to acquire descriptions that allow the system to recognize concepts it does not yet know. Although they have been applied with some success to problems in a variety of domains, both methods have clear deficiencies. Explanation-based learning assumes that a system will be provided with an explicit domain theory that is complete, correct, and tractable. This assumption is unrealistic for many complex, real-world domains. Similarity-based learning suffers because of its lack of an explicit theory. Since the two methods are complementary in nature, an obvious solution is to augment systems using one approach with techniques from the other. This survey discusses machine learning systems that integrate explanation-based and similarity-based learning methods such that one is incorporated primarily to handle a deficiency of the other. Although sufficient background material is provided that the reader need not be familiar with machine learning, general knowledge of AI is assumed

Integrative microRNA and mRNA deep-sequencing expression profiling in endemic Burkitt lymphoma

BACKGROUND: Burkitt lymphoma (BL) is characterized by overexpression of the c-myc oncogene, which in the vast majority of cases is a consequence of an IGH/MYC translocation. While myc is the seminal event, BL is a complex amalgam of genetic and epigenetic changes causing dysregulation of both coding and non-coding transcripts. Emerging evidence suggest that abnormal modulation of mRNA transcription via miRNAs might be a significant factor in lymphomagenesis. However, the alterations in these miRNAs and their correlations to their putative mRNA targets have not been extensively studied relative to normal germinal center (GC) B cells. METHODS: Using more sensitive and specific transcriptome deep sequencing, we compared previously published small miRNA and long mRNA of a set of GC B cells and eBL tumors. MiRWalk2.0 was used to identify the validated target genes for the deregulated miRNAs, which would be important for understanding the regulatory networks associated with eBL development. RESULTS: We found 211 differentially expressed (DE) genes (79 upregulated and 132 downregulated) and 49 DE miRNAs (22 up-regulated and 27 down-regulated). Gene Set enrichment analysis identified the enrichment of a set of MYC regulated genes. Network propagation-based method and correlated miRNA-mRNA expression analysis identified dysregulated miRNAs, including miR-17~95 cluster members and their target genes, which have diverse oncogenic properties to be critical to eBL lymphomagenesis. Central to all these findings, we observed the downregulation of ATM and NLK genes, which represent important regulators in response to DNA damage in eBL tumor cells. These tumor suppressors were targeted by multiple upregulated miRNAs (miR-19b-3p, miR-26a-5p, miR-30b-5p, miR-92a-5p and miR-27b-3p) which could account for their aberrant expression in eBL. CONCLUSION: Combined loss of p53 induction and function due to miRNA-mediated regulation of ATM and NLK, together with the upregulation of TFAP4, may be a central role for human miRNAs in eBL oncogenesis. This facilitates survival of eBL tumor cells with the IGH/MYC chromosomal translocation and promotes MYC-induced cell cycle progression, initiating eBL lymphomagenesis. This characterization of miRNA-mRNA interactions in eBL relative to GC B cells provides new insights on miRNA-mediated transcript regulation in eBL, which are potentially useful for new improved therapeutic strategies

Eosinophilia in children with endemic Burkitt lymphoma in Malawi as a prognostic factor for survival

In dieser Arbeit wurde gezielt die Frage untersucht, ob Eosinophilie bei Diagnosestellung eines endemischen Burkitt Lymphoms in Malawi einen prognostisch positiven Faktor für das Überleben darstellt. In diese retrospektive Studie wurden 479 Patienten, zwischen 1997 und 2009 in Malawi behandelt, eingeschlossen. Mittels uni- und multivariater Analyse wurde versucht unabhängige Einflussvariablen auf das 1-Jahres Ereignis-freie Überleben zu erfassen. Das mediane Alter lag bei 7.0 Jahre (M:F Ratio 1.8:1, Stadium I 14.4%, II 22.3%, III 46.8%, IV 15.2%). Der mediane Eosinophilen-Wert betrug 0.10 x 10³/µl. 25.5% der Kinder präsentierten initial mit Eosinophilie. Innerhalb der 12 Monate erlitten 36.3% der Patienten ein Rezidiv oder Tod, 16.1% blieben Tumor-frei. Die multivariate Analyse ergab, dass Patienten mit Stadien III, IV ein signifikant erhöhtes Risiko für ein Ereignis haben (chi² 0.019, exp(B) 1.579). Eosinophilie hatte keinen signifikanten Einfluss auf das ereignisfreie Überleben.In this dissertation we analysed the hypothesis that eosinophilia in children with newly diagnosed endemic Burkitt lymphoma represents a positive prognostic factor for survival. In this retrospective study, data of 479 patients treated in Malawi from 1997 to 2009 was analysed. By means of uni- and multivariate statistical analyses we tried to identify independent variables influencing 1 year event free survival (1-EFS). Median age was 7.0 years (M:F ratio 1.8:1, Stage I 14.4%, II 22.3%, III 46.8%, IV 15.2%). Median eosinophilic count was 0.10 x 10³/µl. 25.5% of the patients presented initially with eosinophilia. Within the 12 months follow-up 36.6% of the children died or suffered relapse, 16.1% remaind in tumor-free status. Multivariate analysis showed patients with stage III,IV disease to have a significantly increased risk to suffer an event (chi² 0.019, exp(B) 1.579). Eosinophilia was not shown to correlate significantly with 1-EFS. Thus, our hypothesis was not proofed.<br

Final draft : Dallas transportation system plan

370 pp. Includes maps and figures. Published June 2005. Received from ODOT January 2, 2007.The City of Dallas (City), in association with the Oregon Department of Transportation (ODOT), has prepared a Transportation System Plan (TSP) that addresses the transportation issues and system needs within the City’s Urban Growth Boundary (UGB) over a 20-year timeframe. This is the first TSP for the City of Dallas, though the City has prepared several fragmented documents in the past decade that address portions of the area’s transportation system.... The purpose of the TSP is to develop a plan that addresses the transportation issues and needs for all users of Dallas’s transportation network over a 20-year planning horizon. The TSP provides for a safe, efficient, multi-modal transportation network. It has been prepared to be compliant with requirements specified in the state Transportation Planning Rule (TPR) and to be consistent with state, regional, and local plans and policies, including the Oregon Highway Plan (OHP) and the City of Dallas Comprehensive Plan. [From the Plan]"This project is partially funded by a grant from the Transportation and Growth Management (TGM) Program, a joint program of the Oregon Department of Transportation and Oregon Department of Land Conservation & Development. This TGM grant is financed, in part, by federal Transportation Equity Act for the 21st Century (TEA-21), local government, and the State of Oregon funds.