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

KINETIC PROPERTIES AND EMG ACTIVITY OF NORMAL AND OVER-SPEED PEDALING IN TRACK SPRINT CYCLISTS: A CASE STUDY

Track sprint cycling requires unique skills. We investigate the pedaling kinetics and muscle coordination of a male track sprinter (170cm, 65kg, peak power 1513W) to see if they differ from that of a non-sprinter, and if the subject’s own technique vary from normal riding to an under-load maximal cadence sprint. Two trials were collected using 3D motion capture technology. EMG signals of 8 leg muscles were recorded. Joint torque and power of each trial were calculated using a subject specific musculoskeletal model, with realistic pedal forces as input to our dynamic simulation. Flexion torque appears at the knee during its extension, different from the non-sprinters. Joint torque and power appears similar for both trials, but 6 of the 8 muscles showed differences in EMG patterns. These findings could potentially benefit the evolvement of training methods

Solid Pseudopapillary Neoplasm of the Pancreas: Report of Two Cases and Review of the Literature

AbstractSolid pseudopapillary neoplasm (SPN) of the pancreas is a rare low-grade malignant-potential epithelial tumor that predominantly affects young women aged 20–35 years with a mean age of 22 years. It is currently categorized in the World Health Organization classification under exocrine pancreatic tumor. Here, we present two cases of SPN with initial presentation of large intra-abdominal masses. Both patients underwent successful en bloc distal pancreatectomy and splenectomy. Local recurrence and distant metastasis were not detected at the follow up at 21 months and 9 years respectively. In summary, a large, well-encapsulated cystic mass in the pancreas of a young woman should raise suspicion of SPN

Image Deraining via Self-supervised Reinforcement Learning

The quality of images captured outdoors is often affected by the weather. One factor that interferes with sight is rain, which can obstruct the view of observers and computer vision applications that rely on those images. The work aims to recover rain images by removing rain streaks via Self-supervised Reinforcement Learning (RL) for image deraining (SRL-Derain). We locate rain streak pixels from the input rain image via dictionary learning and use pixel-wise RL agents to take multiple inpainting actions to remove rain progressively. To our knowledge, this work is the first attempt where self-supervised RL is applied to image deraining. Experimental results on several benchmark image-deraining datasets show that the proposed SRL-Derain performs favorably against state-of-the-art few-shot and self-supervised deraining and denoising methods

The crystal structure of the DNase domain of colicin E7 in complex with its inhibitor Im7 protein

AbstractBackground: Colicin E7 (ColE7) is one of the bacterial toxins classified as a DNase-type E-group colicin. The cytotoxic activity of a colicin in a colicin-producing cell can be counteracted by binding of the colicin to a highly specific immunity protein. This biological event is a good model system for the investigation of protein recognition.Results: The crystal structure of a one-to-one complex between the DNase domain of colicin E7 and its cognate immunity protein Im7 has been determined at 2.3 Å resolution. Im7 in the complex is a varied four-helix bundle that is identical to the structure previously determined for uncomplexed Im7. The structure of the DNase domain of ColE7 displays a novel α/β fold and contains a Zn2+ ion bound to three histidine residues and one water molecule in a distorted tetrahedron geometry. Im7 has a V-shaped structure, extending two arms to clamp the DNase domain of ColE7. One arm (α1∗–loop12–α2∗; where ∗ represents helices in Im7) is located in the region that displays the greatest sequence variation among members of the immunity proteins in the same subfamily. This arm mainly uses acidic sidechains to interact with the basic sidechains in the DNase domain of ColE7. The other arm (loop 23–α3∗–loop 34) is more conserved and it interacts not only with the sidechain but also with the mainchain atoms of the DNase domain of ColE7.Conclusions: The protein interfaces between the DNase domain of ColE7 and Im7 are charge-complementary and charge interactions contribute significantly to the tight and specific binding between the two proteins. The more variable arm in Im7 dominates the binding specificity of the immunity protein to its cognate colicin. Biological and structural data suggest that the DNase active site for ColE7 is probably near the metal-binding site

A Retrospective Cohort Study Comparing Stroke Recurrence Rate in Ischemic Stroke Patients With and Without Acupuncture Treatment.

Little was known about the effects of acupuncture on stroke recurrence. The aim of this study is to investigate whether ischemic stroke patients receiving acupuncture treatment have a decreased risk of stroke recurrence. A retrospective cohort study of 30,058 newly diagnosed cases of ischemic stroke in 2000 to 2004 was conducted based on the claims of Taiwan National Health Insurance Research Database. The use of acupuncture treatment and stroke recurrence were identified during the follow-up period from 2000 to 2009. This study compared the risk of stroke recurrence between ischemic stroke cohorts with and without acupuncture treatment by calculating adjusted hazard ratios (HRs) and 95% confidence intervals (CIs) of acupuncture associated with stroke recurrence in the Cox proportional hazard model. The stroke recurrence rate per 1000 person-years decreased from 71.4 without to 69.9 with acupuncture treatment (P < 0.001). Acupuncture treatment was associated with reduced risk of stroke recurrence (HR 0.88; 95% CI 0.84-0.91). The acupuncture effect was noted in patients with or without medical treatment for stroke prevention but its impact decreased with aging of stroke patients. Compared with stroke patients without acupuncture treatment and medication therapy, the hazard ratios of stroke recurrence for those had medication therapy only, acupuncture only, and both were 0.42 (95% CI 0.38-0.46), 0.50 (95% CI 0.43-0.57), and 0.39 (95% CI 0.35-0.43), respectively. This study raises the possibility that acupuncture might be effective in lowering stroke recurrence rate even in those on medications for stroke prevention. Results suggest the need of prospective sham-controlled and randomized trials to establish the efficacy of acupuncture in preventing stroke

Local and Systemic Cardiovascular Effects from Monochromatic Infrared Therapy in Patients with Knee Osteoarthritis: A Double-Blind, Randomized, Placebo-Controlled Study

Infrared (IR) therapy is used for pain relief in patients with knee osteoarthritis (OA). However, IR’s effects on the cardiovascular system remain uncertain. Therefore, we investigated the local and systemic cardiovascular effects of monochromatic IR therapy on patients with knee OA in a double-blind, randomized, placebo-controlled study. Seventy-one subjects with knee OA received one session of 40 min of active or placebo monochromatic IR treatment (with power output of 6.24 W, wavelength of 890 nm, power density of 34.7 mW/cm2 for 40 min, total energy of 41.6 J/cm2 per knee per session) over the knee joints. Heart rate, blood pressure, and knee arterial blood flow velocity were periodically assessed at the baseline, during, and after treatment. Data were analyzed by repeated-measure analysis of covariance. Compared to baseline, there were no statistically significant group x time interaction effects between the 2 groups for heart rate (P=0.160), blood pressure (systolic blood pressure: P=0.861; diastolic blood pressure: P=0.757), or mean arterial blood flow velocity (P=0.769) in follow-up assessments. The present study revealed that although there was no increase of knee arterial blood flow velocity, monochromatic IR therapy produced no detrimental systemic cardiovascular effects