Query Learning with Exponential Query Costs

In query learning, the goal is to identify an unknown object while minimizing the number of "yes" or "no" questions (queries) posed about that object. A well-studied algorithm for query learning is known as generalized binary search (GBS). We show that GBS is a greedy algorithm to optimize the expected number of queries needed to identify the unknown object. We also generalize GBS in two ways. First, we consider the case where the cost of querying grows exponentially in the number of queries and the goal is to minimize the expected exponential cost. Then, we consider the case where the objects are partitioned into groups, and the objective is to identify only the group to which the object belongs. We derive algorithms to address these issues in a common, information-theoretic framework. In particular, we present an exact formula for the objective function in each case involving Shannon or Renyi entropy, and develop a greedy algorithm for minimizing it. Our algorithms are demonstrated on two applications of query learning, active learning and emergency response.Comment: 15 page

Web log analysis panel

No Abstract.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/61334/1/1450440124_ftp.pd

Network analysis of genes regulated in renal diseases: implications for a molecular-based classification

Abstract Background Chronic renal diseases are currently classified based on morphological similarities such as whether they produce predominantly inflammatory or non-inflammatory responses. However, such classifications do not reliably predict the course of the disease and its response to therapy. In contrast, recent studies in diseases such as breast cancer suggest that a classification which includes molecular information could lead to more accurate diagnoses and prediction of treatment response. This article describes how we extracted gene expression profiles from biopsies of patients with chronic renal diseases, and used network visualizations and associated quantitative measures to rapidly analyze similarities and differences between the diseases. Results The analysis revealed three main regularities: (1) Many genes associated with a single disease, and fewer genes associated with many diseases. (2) Unexpected combinations of renal diseases that share relatively large numbers of genes. (3) Uniform concordance in the regulation of all genes in the network. Conclusion The overall results suggest the need to define a molecular-based classification of renal diseases, in addition to hypotheses for the unexpected patterns of shared genes and the uniformity in gene concordance. Furthermore, the results demonstrate the utility of network analyses to rapidly understand complex relationships between diseases and regulated genes.http://deepblue.lib.umich.edu/bitstream/2027.42/112463/1/12859_2009_Article_3354.pd

Strategy-based instruction: Lessons Learned in Teaching the Effective and Efficient Use of Computer Applications

________________________________________________________________________ Numerous studies have shown that many users do not acquire the knowledge necessary for the effective and efficient use of computer applications such as spreadsheets and web-authoring tools. While many cognitive, cultural, and social reasons have been offered to explain this phenomenon, there have been few systematic attempts to address it. This article describes how we identified a framework to organize effective and efficient strategies to use computer applications, and used an approach called strategy-based instruction to teach those strategies over five years to almost 400 students. Controlled experiments demonstrated that the instructional approach (1) enables students to learn strategies without harming command knowledge, (2) benefits students from technical and non-technical majors, an

Strategy-based instruction: Lessons Learned in Teaching the Effective and Efficient Use of Computer Applications

________________________________________________________________________ Numerous studies have shown that many users do not acquire the knowledge necessary for the effective and efficient use of computer applications such as spreadsheets and web-authoring tools. While many cognitive, cultural, and social reasons have been offered to explain this phenomenon, there have been few systematic attempts to address it. This article describes how we identified a framework to organize effective and efficient strategies to use computer applications, and used an approach called strategy-based instruction to teach those strategies over five years to almost 400 students. Controlled experiments demonstrated that the instructional approach (1) enables students to learn strategies without harming command knowledge, (2) benefits students from technical and non-technical majors, an

Prognostic Performance of Peripheral Blood Biomarkers in Identifying Seropositive Individuals at Risk of Developing Clinically Symptomatic Chagas Cardiomyopathy

Biomarkers for prognosis-based detection of Trypanosoma cruzi-infected patients presenting no clinical symptoms to cardiac Chagas disease (CD) are not available. In this study, we examined the performance of seven biomarkers in prognosis and risk of symptomatic CD development. T.cruzi-infected patients clinically asymptomatic (C/A; n = 30) or clinically symptomatic (C/S; n = 30) for cardiac disease and humans who were noninfected and healthy (N/H; n = 24) were enrolled (1 − β = 80%, α = 0.05). Serum, plasma, and peripheral blood mononuclear cells (PBMCs) were analyzed for heterogeneous nuclear ribonucleoprotein A1 (hnRNPA1), vimentin, poly(ADP-ribose) polymerase (PARP1), 8-hydroxy-2-deoxyguanosine (8-OHdG), copeptin, endostatin, and myostatin biomarkers by enzyme-linked immunosorbent assay (ELISA) and Western blotting. Secreted hnRNPA1, vimentin, PARP1, 8-OHdG, copeptin, and endostatin were increased by 1.4- to 7.0-fold in CD subjects versus N/H subjects (P < 0.001) and showed excellent predictive value in identifying the occurrence of infection (area under the receiver operating characteristic [ROC] curve [AUC], 0.935 to 0.999). Of these, vimentin, 8-OHdG, and copeptin exhibited the best performance in prognosis of C/S (versus C/A) CD, determined by binary logistic regression analysis with the Cox and Snell test (R2C&S = 0.492 to 0.688). A decline in myostatin and increase in hnRNPA1 also exhibited good predictive value in identifying C/S and C/A CD status, respectively. Furthermore, circulatory 8-OHdG (Wald x2 = 15.065), vimentin (Wald x2 = 14.587), and endostatin (Wald x2 = 17.902) levels exhibited a strong association with changes in left ventricular ejection fraction and diastolic diameter (P = 0.001) and predicted the risk of cardiomyopathy development in CD patients. We have identified four biomarkers (vimentin, 8-OHdG, copeptin, and endostatin) that offer excellent value in prognosis and risk of symptomatic CD development. Decline in these four biomarkers and increase in hnRNPA1 wouldbeuseful in monitoring the efficacy of therapies and vaccines in halting CD. IMPORTANCE There is a lack of validated biomarkers for diagnosis of T. cruzi-infected individuals at risk of developing heart disease. Of the seven potential biomarkers that were screened, vimentin, 8-OHdG, copeptin, and endostatin exhibited excellent performance in distinguishing the clinical severity of Chagas disease. A decline in these four biomarkers can also be used for monitoring the therapeutic responses of infected patients to established or newly developed drugs and vaccines and precisely inform the patients about their progress. These biomarkers can easily be screened using the readily available plasma/serum samples in the clinical setting by an ELISA that is inexpensive, fast, and requires low-tech resources at the facility, equipment, and personnel levels.Fil: Choudhuri, Subhadip. University of Texas Medical Branch; Estados UnidosFil: Bhavnani, Suresh K.. Institute For Human Infections And Immunity ; University Of Texas Medical Branch; . University of Texas Medical Branch; Estados UnidosFil: Zhang, Weibin. University of Texas Medical Branch; Estados UnidosFil: Botelli, Valentina. Gobierno de la Provincia de Salta. Hospital San Bernardo.; ArgentinaFil: Barrientos, Natalia Mariel. Gobierno de la Provincia de Salta. Hospital San Bernardo.; ArgentinaFil: lñiguez, Facundo. Gobierno de la Provincia de Salta. Hospital San Bernardo.; ArgentinaFil: Zago, María Paola. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Salta. Instituto de Patología Experimental. Universidad Nacional de Salta. Facultad de Ciencias de la Salud. Instituto de Patología Experimental; ArgentinaFil: Garg, Nisha Jain. Institute For Human Infections And Immunity ; University Of Texas Medical Branch