Epistemic Logic Programs with World View Constraints

An epistemic logic program is a set of rules written in the language of Epistemic Specifications, an extension of the language of answer set programming that provides for more powerful introspective reasoning through the use of modal operators K and M. We propose adding a new construct to Epistemic Specifications called a world view constraint that provides a universal device for expressing global constraints in the various versions of the language. We further propose the use of subjective literals (literals preceded by K or M) in rule heads as syntactic sugar for world view constraints. Additionally, we provide an algorithm for finding the world views of such programs

Measurement of the B0-anti-B0-Oscillation Frequency with Inclusive Dilepton Events

The $B^0$-$\bar B^0$ oscillation frequency has been measured with a sample of 23 million \B\bar B pairs collected with the BABAR detector at the PEP-II asymmetric B Factory at SLAC. In this sample, we select events in which both B mesons decay semileptonically and use the charge of the leptons to identify the flavor of each B meson. A simultaneous fit to the decay time difference distributions for opposite- and same-sign dilepton events gives $\Delta m_d = 0.493 \pm 0.012{(stat)}\pm 0.009{(syst)}$ ps$^{-1}$.Comment: 7 pages, 1 figure, submitted to Physical Review Letter

Measurement of D-s(+) and D-s(*+) production in B meson decays and from continuum e(+)e(-) annihilation at √s=10.6 GeV

This is the pre-print version of the Article. The official published version can be accessed from the links below. Copyright @ 2002 APSNew measurements of Ds+ and Ds*+ meson production rates from B decays and from qq̅ continuum events near the Υ(4S) resonance are presented. Using 20.8 fb-1 of data on the Υ(4S) resonance and 2.6 fb-1 off-resonance, we find the inclusive branching fractions B(B⃗Ds+X)=(10.93±0.19±0.58±2.73)% and B(B⃗Ds*+X)=(7.9±0.8±0.7±2.0)%, where the first error is statistical, the second is systematic, and the third is due to the Ds+→φπ+ branching fraction uncertainty. The production cross sections σ(e+e-→Ds+X)×B(Ds+→φπ+)=7.55±0.20±0.34pb and σ(e+e-→Ds*±X)×B(Ds+→φπ+)=5.8±0.7±0.5pb are measured at center-of-mass energies about 40 MeV below the Υ(4S) mass. The branching fractions ΣB(B⃗Ds(*)+D(*))=(5.07±0.14±0.30±1.27)% and ΣB(B⃗Ds*+D(*))=(4.1±0.2±0.4±1.0)% are determined from the Ds(*)+ momentum spectra. The mass difference m(Ds+)-m(D+)=98.4±0.1±0.3MeV/c2 is also measured.This work was supported by DOE and NSF (USA), NSERC (Canada), IHEP (China), CEA and CNRS-IN2P3 (France), BMBF (Germany), INFN (Italy), NFR (Norway), MIST (Russia), and PPARC (United Kingdom). Individuals have received support from the Swiss NSF, A. P. Sloan Foundation, Research Corporation, and Alexander von Humboldt Foundation

An Introspective Comparison of Random Forest-Based Classifiers for the Analysis of Cluster-Correlated Data by Way of RF++

Many mass spectrometry-based studies, as well as other biological experiments produce cluster-correlated data. Failure to account for correlation among observations may result in a classification algorithm overfitting the training data and producing overoptimistic estimated error rates and may make subsequent classifications unreliable. Current common practice for dealing with replicated data is to average each subject replicate sample set, reducing the dataset size and incurring loss of information. In this manuscript we compare three approaches to dealing with cluster-correlated data: unmodified Breiman's Random Forest (URF), forest grown using subject-level averages (SLA), and RF++ with subject-level bootstrapping (SLB). RF++, a novel Random Forest-based algorithm implemented in C++, handles cluster-correlated data through a modification of the original resampling algorithm and accommodates subject-level classification. Subject-level bootstrapping is an alternative sampling method that obviates the need to average or otherwise reduce each set of replicates to a single independent sample. Our experiments show nearly identical median classification and variable selection accuracy for SLB forests and URF forests when applied to both simulated and real datasets. However, the run-time estimated error rate was severely underestimated for URF forests. Predictably, SLA forests were found to be more severely affected by the reduction in sample size which led to poorer classification and variable selection accuracy. Perhaps most importantly our results suggest that it is reasonable to utilize URF for the analysis of cluster-correlated data. Two caveats should be noted: first, correct classification error rates must be obtained using a separate test dataset, and second, an additional post-processing step is required to obtain subject-level classifications. RF++ is shown to be an effective alternative for classifying both clustered and non-clustered data. Source code and stand-alone compiled versions of command-line and easy-to-use graphical user interface (GUI) versions of RF++ for Windows and Linux as well as a user manual (Supplementary File S2) are available for download at: http://sourceforge.org/projects/rfpp/ under the GNU public license

The process of recovery of people with mental illness: The perspectives of patients, family members and care providers: Part 1

<p>Abstract</p> <p>Background</p> <p>It is a qualitative design study that examines points of divergence and convergence in the perspectives on recovery of 36 participants or 12 triads. Each triad comprising a patient, a family member/friend, a care provider and documents the procedural, analytic of triangulating perspectives as a means of understanding the recovery process which is illustrated by four case studies. Variations are considered as they relate to individual characteristics, type of participant (patient, family, member/friend and care provider), and mental illness. This paper which is part of a larger study and is based on a qualitative research design documents the process of recovery of people with mental illness: Developing a Model of Recovery in Mental Health: A middle range theory.</p> <p><b>Methods</b></p> <p>Data were collected in field notes through semi-structured interviews based on three interview guides (one for patients, one for family members/friends, and one for caregivers). Cross analysis and triangulation methods were used to analyse the areas of convergence and divergence on the recovery process of all triads.</p> <p>Results</p> <p>In general, with the 36 participants united in 12 triads, two themes emerge from the cross-analysis process or triangulation of data sources (12 triads analysis in 12 cases studies). Two themes emerge from the analysis process of the content of 36 interviews with participants: (1) <it>Revealing dynamic context</it>, situating patients in their dynamic context; and (2) <it>Relationship issues in a recovery process</it>, furthering our understanding of such issues. We provide four case studies examples (among 12 cases studies) to illustrate the variations in the way recovery is perceived, interpreted and expressed in relation to the different contexts of interaction.</p> <p>Conclusion</p> <p>The perspectives of the three participants (patients, family members/friends and care providers) suggest that recovery depends on constructing meaning around mental illness experiences and that the process is based on each person's dynamic context (e.g., social network, relationship), life experiences and other social determinants (e.g., symptoms, environment). The findings of this study add to existing knowledge about the determinants of the recovery of persons suffering with a mental illness and significant other utilizing public mental health services in Montreal, Canada.</p

The Physics of the B Factories

This work is on the Physics of the B Factories. Part A of this book contains a brief description of the SLAC and KEK B Factories as well as their detectors, BaBar and Belle, and data taking related issues. Part B discusses tools and methods used by the experiments in order to obtain results. The results themselves can be found in Part C