On the Feature Discovery for App Usage Prediction in Smartphones

With the increasing number of mobile Apps developed, they are now closely integrated into daily life. In this paper, we develop a framework to predict mobile Apps that are most likely to be used regarding the current device status of a smartphone. Such an Apps usage prediction framework is a crucial prerequisite for fast App launching, intelligent user experience, and power management of smartphones. By analyzing real App usage log data, we discover two kinds of features: The Explicit Feature (EF) from sensing readings of built-in sensors, and the Implicit Feature (IF) from App usage relations. The IF feature is derived by constructing the proposed App Usage Graph (abbreviated as AUG) that models App usage transitions. In light of AUG, we are able to discover usage relations among Apps. Since users may have different usage behaviors on their smartphones, we further propose one personalized feature selection algorithm. We explore minimum description length (MDL) from the training data and select those features which need less length to describe the training data. The personalized feature selection can successfully reduce the log size and the prediction time. Finally, we adopt the kNN classification model to predict Apps usage. Note that through the features selected by the proposed personalized feature selection algorithm, we only need to keep these features, which in turn reduces the prediction time and avoids the curse of dimensionality when using the kNN classifier. We conduct a comprehensive experimental study based on a real mobile App usage dataset. The results demonstrate the effectiveness of the proposed framework and show the predictive capability for App usage prediction.Comment: 10 pages, 17 figures, ICDM 2013 short pape

Light Flicker Detector

Light flickering can be detrimental to humans even if imperceptible. Headaches, migraine, and/or eye strain can result from exposure to flickering light. This disclosure describes techniques to detect and monitor light flicker. An ambient light sensor detects flickering in environmental lighting. Environmental light intensity is measured over a wide spectrum covering the human perceptive range. The peak flicker frequencies (and their magnitudes), flicker percentages, etc. are determined. If a substantial amount of flicker (greater than a threshold) is detected, an alert is provided. Furthermore, auto-generated instructions enable users to determine the light source that is the source of flickering

The Effectiveness of Traditional Chinese Medicine in Treating Patients with Leukemia

Leukemia is the most common malignancy among all childhood cancers and is associated with a low survival rate in adult patients. Since 1995, the National Health Insurance (NHI) program in Taiwan has been offering insurance coverage for Traditional Chinese Medicine (TCM), along with conventional Western medicine (WM). This study analyzes the status of TCM utilization in Taiwan, in both pediatric and adult patients with leukemia. A retrospective cohort study was conducted using population-based National Health Insurance Research Database of Registry of Catastrophic Illness, involving patient data from 2001 to 2010 and follow-up data through 2011. The effectiveness of TCM use was evaluated. Relevant sociodemographic data showed that both pediatric and adult patients who were TCM users one year prior to leukemia diagnosis were more likely to utilize TCM services for cancer therapy. A greater part of medical expenditure of TCM users was lower than that of TCM nonusers, except little discrepancy in drug fee of adult patients. The survival rate is also higher in TCM users. Altogether, these data show that TCM has the potential to serve as an adjuvant therapy when combined with conventional WM in the treatment of patients with leukemia

Deep ocean mineral supplementation enhances the cerebral hemodynamic response during exercise and decreases inflammation postexercise in men at two age levels.

Background: Previous studies have consistently shown that oral supplementation of deep ocean minerals (DOM) improves vascular function in animals and enhances muscle power output in exercising humans. Purpose: To examine the effects of DOM supplementation on the cerebral hemodynamic response during physical exertion in young and middle-aged men. Design: Double-blind placebo-controlled crossover studies were conducted in young (N = 12, aged 21.2 ± 0.4 years) and middle-aged men (N = 9, aged 46.8 ± 1.4 years). The counter-balanced trials of DOM and Placebo were separated by a 2-week washout period. DOM and Placebo were orally supplemented in drinks before, during, and after cycling exercise. DOM comprises desalinated minerals and trace elements from seawater collected ~618 m below the earth's surface. Methods: Cerebral hemodynamic response (tissue hemoglobin) was measured during cycling at 75% VO2max using near infrared spectroscopy (NIRS). Results: Cycling time to exhaustion at 75% VO2max and the associated plasma lactate response were similar between the Placebo and DOM trials for both age groups. In contrast, DOM significantly elevated cerebral hemoglobin levels in young men and, to a greater extent, in middle-aged men compared with Placebo. An increased neutrophil to lymphocyte ratio (NLR) was observed in middle-aged men, 2 h after exhaustive cycling, but was attenuated by DOM. Conclusion: Our data suggest that minerals and trace elements from deep oceans possess great promise in developing supplements to increase the cerebral hemodynamic response against a physical challenge and during post-exercise recovery for middle-aged men.This work was supported by Pacific Deep Ocean Biotech (Taipei,Taiwan) and University of Taipei (Taipei, Taiwan). The funding sponsors had no role in the design of the study; in the of the manuscript, and in the decision to publish the results. We declare that the results of the study are presented clearly, honestly, and without fabrication, falsification, or inappropriate data manipulation

MetaSquare: An integrated metadatabase of 16S rRNA gene amplicon for microbiome taxonomic classification

MOTIVATION: Taxonomic classification of 16S ribosomal RNA gene amplicon is an efficient and economic approach in microbiome analysis. 16S rRNA sequence databases like SILVA, RDP, EzBioCloud and HOMD used in downstream bioinformatic pipelines have limitations on either the sequence redundancy or the delay on new sequence recruitment. To improve the 16S rRNA gene-based taxonomic classification, we merged these widely used databases and a collection of novel sequences systemically into an integrated resource. RESULTS: MetaSquare version 1.0 is an integrated 16S rRNA sequence database. It is composed of more than 6 million sequences and improves taxonomic classification resolution on both long-read and short-read methods. AVAILABILITY AND IMPLEMENTATION: Accessible at https://hub.docker.com/r/lsbnb/metasquare_db and https://github.com/lsbnb/MetaSquare. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online

Inflammatory Marker but Not Adipokine Predicts Mortality among Long-Term Hemodialysis Patients

Aims: chronic inflammation contributes significantly to the morbidity and mortality of chronic hemodialysis patients. A recent research has shown that adipokines were associated with inflammation in these patients. We aim to investigate whether biomarkers of inflammation, adipokines, and clinical features can predict the outcome of hemodialysis patients. Materials and methods: we enrolled 181 hemodialysis patients (men: 97, mean age: 56.3±13.6) and analyzed predictors of long-term outcomes. Results: during the 3-year followup period, 41 patients died; the main causes of death were infection and cardiovascular disease. Elevated serum levels of hsCRP and albumin and advanced age were highly associated with death (all P<.001). Leptin and adiponectin levels were not significantly different between deceased patients and survivors. Cox-regression analysis indicated that age, diabetes, albumin level, and hsCRP were independent factors predicting mortality. Conclusion: the presence of underlying disease, advanced age, and markers of chronic inflammation is strongly related to survival rate in long-term hemodialysis patients