Analysis of a selected sample of RR Lyrae stars in LMC from OGLE III

A systematic study of RR Lyrae stars is performed based on a selected sample of 655 objects in the Large Magellanic Cloud with observation of long span and numerous measurements by the Optical Gravitational Lensing Experiment III project. The Phase Dispersion Method and linear superposition of the harmonic oscillations are used to derive the pulsation frequency and variation properties. It is found that there exists an Oo I and Oo II dichotomy in the LMC RR Lyrae stars. Due to our strict criteria to identify a frequency, a lower limit of the incidence rate of Blazhko modulation in LMC is estimated in various subclasses of RR Lyrae stars. For fundamental-mode RR Lyrae stars, the rate 7.5% is smaller than previous result. In the case of the first-overtone RR Lyr variables, the rate 9.1% is relatively high. In addition to the Blazhko variables, fifteen objects are identified to pulsate in the fundamental/first-overtone double mode. Furthermore, four objects show a period ratio around 0.6 which makes them very likely the rare pulsators in the fundamental/second-overtone double-mode.Comment: 25 pages and 11 figures, accepted for publication in RA

The dose response for sprint interval training interventions may affect the time course of aerobic training adaptations

Low vs. high volume sprint-interval training (SIT) sessions have shown similar physiological benefits after 8 weeks. However, the dose response and residual effects of shorter SIT bouts (<10 s) are unknown. Following a 6-wk control period, 13 healthy inactive males were assigned to a low dose (LDG: n = 7) or high dose (HDG: n = 6) supervised 6-wk intervention: ×2/wk of SIT (LDG = 2 sets of 5 × 6 s ON: 18 s OFF bouts; HDG = 4–6 sets); ×1/wk resistance training (3 exercises at 3 × 10 reps). Outcome measures were tested pre and post control (baseline (BL) 1 and 2), 72 h post (0POST), and 3-wk post (3POST) intervention. At 0POST, peak oxygen uptake (VO2peak) increased in the LDG (+16%) and HDG (+11%) vs. BL 2, with no differences between groups (p = 0.381). At 3POST, VO2peak was different between LDG (−11%) and HDG (+3%) vs. 0POST. Positive responses for the intervention’s perceived enjoyment (PE) and rate of perceived exertion (RPE) were found for both groups. Blood pressure, blood lipids, or body composition were not different between groups at any time point. Conclusion: LDG and HDG significantly improved VO2peak at 0POST. However, findings at 3POST suggest compromised VO2peak at 0POST in the HDG due to the delayed time course of adaptations. These findings should be considered when implementing high-dose SIT protocols for non-athletic population

ERBlox: Combining Matching Dependencies with Machine Learning for Entity Resolution

Entity resolution (ER), an important and common data cleaning problem, is about detecting data duplicate representations for the same external entities, and merging them into single representations. Relatively recently, declarative rules called "matching dependencies" (MDs) have been proposed for specifying similarity conditions under which attribute values in database records are merged. In this work we show the process and the benefits of integrating four components of ER: (a) Building a classifier for duplicate/non-duplicate record pairs built using machine learning (ML) techniques; (b) Use of MDs for supporting the blocking phase of ML; (c) Record merging on the basis of the classifier results; and (d) The use of the declarative language "LogiQL" -an extended form of Datalog supported by the "LogicBlox" platform- for all activities related to data processing, and the specification and enforcement of MDs.Comment: Final journal version, with some minor technical corrections. Extended version of arXiv:1508.0601