StoryDroid: Automated Generation of Storyboard for Android Apps

Mobile apps are now ubiquitous. Before developing a new app, the development team usually endeavors painstaking efforts to review many existing apps with similar purposes. The review process is crucial in the sense that it reduces market risks and provides inspiration for app development. However, manual exploration of hundreds of existing apps by different roles (e.g., product manager, UI/UX designer, developer) in a development team can be ineffective. For example, it is difficult to completely explore all the functionalities of the app in a short period of time. Inspired by the conception of storyboard in movie production, we propose a system, StoryDroid, to automatically generate the storyboard for Android apps, and assist different roles to review apps efficiently. Specifically, StoryDroid extracts the activity transition graph and leverages static analysis techniques to render UI pages to visualize the storyboard with the rendered pages. The mapping relations between UI pages and the corresponding implementation code (e.g., layout code, activity code, and method hierarchy) are also provided to users. Our comprehensive experiments unveil that StoryDroid is effective and indeed useful to assist app development. The outputs of StoryDroid enable several potential applications, such as the recommendation of UI design and layout code

Multi-Camera Action Dataset for Cross-Camera Action Recognition Benchmarking

Action recognition has received increasing attention from the computer vision and machine learning communities in the last decade. To enable the study of this problem, there exist a vast number of action datasets, which are recorded under controlled laboratory settings, real-world surveillance environments, or crawled from the Internet. Apart from the "in-the-wild" datasets, the training and test split of conventional datasets often possess similar environments conditions, which leads to close to perfect performance on constrained datasets. In this paper, we introduce a new dataset, namely Multi-Camera Action Dataset (MCAD), which is designed to evaluate the open view classification problem under the surveillance environment. In total, MCAD contains 14,298 action samples from 18 action categories, which are performed by 20 subjects and independently recorded with 5 cameras. Inspired by the well received evaluation approach on the LFW dataset, we designed a standard evaluation protocol and benchmarked MCAD under several scenarios. The benchmark shows that while an average of 85% accuracy is achieved under the closed-view scenario, the performance suffers from a significant drop under the cross-view scenario. In the worst case scenario, the performance of 10-fold cross validation drops from 87.0% to 47.4%

GEOSTATISTICAL INFERENCE UNDER PREFERENTIAL SAMPLING

Geostatistics involves the fitting of spatially continuous models to spatially discrete data (Chil`es and Delfiner, 1999). Preferential sampling arises when the process that determines the data-locations and the process being modelled are stochastically dependent. Conventional geostatistical methods assume, if only implicitly, that sampling is non-preferential. However, these methods are often used in situations where sampling is likely to be preferential. For example, in mineral exploration samples may be concentrated in areas thought likely to yield high-grade ore. We give a general expression for the likelihood function of preferentially sampled geostatistical data and describe how this can be evaluated approximately using Monte Carlo methods. We present a model for preferential sampling, and demonstrate through simulated examples that ignoring preferential sampling can lead to seriously misleading inferences. We describe an application of the model to a set of bio-monitoring data from Galicia, northern Spain, in which making allowance for preferential sampling materially changes the inferences

Point Process Methodology for On-line Spatio-temporal Disease Surveillance

The AEGISS (Ascertainment and Enhancement of Gastrointestinal Infection Surveillance and Statistics) project aims to use spatio-temporal statistical methods to identify anomalies in the space-time distribution of non-specific, gastrointestinal infections in the UK, using the Southampton area in southern England as a test-case. In this paper, we use the AEGISS project to illustrate how spatio-temporal point process methodology can be used in the development of a rapid-response, spatial surveillance system. Current surveillance of gastroenteric disease in the UK relies on general practitioners reporting cases of suspected food-poisoning through a statutory notification scheme, voluntary laboratory reports of the isolation of gastrointestinal pathogens and standard reports of general outbreaks of infectious intestinal disease by public health and environmental health authorities. However, most statutory notifications are made only after a laboratory reports the isolation of a gastrointestinal pathogen. As a result, detection is delayed and the ability to react to an emerging outbreak is reduced. For more detailed discussion, see Diggle et al. (2003). A new and potentially valuable source of data on the incidence of non-specific gastro-enteric infections in the UK is NHS Direct, a 24-hour phone-in clinical advice service. NHS Direct data are less likely than reports by general practitioners to suffer from spatially and temporally localized inconsistencies in reporting rates. Also, reporting delays by patients are likely to be reduced, as no appointments are needed. Against this, NHS Direct data sacrifice specificity. Each call to NHS Direct is classified only according to the general pattern of reported symptoms (Cooper et al, 2003). The current paper focuses on the use of spatio-temporal statistical analysis for early detection of unexplained variation in the spatio-temporal incidence of non-specific gastroenteric symptoms, as reported to NHS Direct. Section 2 describes our statistical formulation of this problem, the nature of the available data and our approach to predictive inference. Section 3 describes the stochastic model. Section 4 gives the results of fitting the model to NHS Direct data. Section 5 shows how the model is used for spatio-temporal prediction. The paper concludes with a short discussion

A Structured Framework and Resources to Use to Get Your Medical Education Work Published.

IntroductionMedical educators often have great ideas for medical education scholarship but have difficulty converting their educational abstract or project into a published manuscript.MethodsDuring this workshop, participants addressed common challenges in developing an educational manuscript. In small-group case scenarios, participants discovered the importance of the "So what?" in making the case for their project. Incorporating conceptual frameworks, participants chose appropriate outcome metrics, discussed how to frame the discussion section, and ensured appropriate journal fit. After each small-group exercise, large-group discussions allowed the small groups to report back so that facilitators could highlight and reinforce key learning points. At the conclusion of the workshop, participants left with a checklist for creating an educational manuscript and an additional resources document to assist them in avoiding common pitfalls when turning their educational abstract/project into a publishable manuscript.ResultsThis workshop was presented in 2016 and 2017. Presenter evaluations were completed by 33 participants; 11 completed conference evaluations. The mean overall rating on presenter evaluations was 4.55 out of 5, while the conference evaluations mean was 3.73 out of 4. Comments provided on both evaluation tools highlighted the perceived effectiveness of the delivery and content. More than 50% of respondents stated that they planned to incorporate the use of conceptual frameworks in future work.DiscussionThis workshop helped participants address common challenges by providing opportunities for hands-on practice as well as tips and resources for use when submitting a medical education manuscript for publication

Vector Magnetic Fields and Current Helicities in Coronal Holes and Quiet Regions

In the solar photosphere, many properties of coronal holes (CHs) are not known, especially vector magnetic fields. Using observations from \emph{Hinode}, we investigate vector magnetic fields, current densities and current helicities in two CHs and compare them with two normal quiet regions (QRs) for the first time. We find that, in the CHs and QRs, the areas where large current helicities are located are mainly co-spatial with strong vertical and horizontal field elements both in shape and location. In the CHs, horizontal magnetic fields, inclination angles, current densities and current helicities are larger than those in the QRs. The mean vertical current density and current helicity, averaged over all the observed areas including the CHs and QRs, are approximately 0.008 A m$^{-2}$ and 0.005 G$^{2}$ m$^{-1}$, respectively. The mean current density in magnetic flux concentrations where the vertical fields are stronger than 100 G is as large as 0.012 $\pm$ 0.001 A m$^{-2}$, consistent with that in the flare productive active regions. Our results imply that the magnetic fields, especially the strong fields, both in the CHs and QRs are nonpotential.Comment: 21 pages, 1 table, 9 figures, ApJ (accepted for publication

Kondo Signatures of a Quantum Magnetic Impurity in Topological Superconductors

We study the Kondo physics of a quantum magnetic impurity in two-dimensional topological superconductors (TSCs), either intrinsic or induced on the surface of a bulk topological insulator, using a numerical renormalization group technique. We show that, despite sharing the p+ip pairing symmetry, intrinsic and extrinsic TSCs host different physical processes that produce distinct Kondo signatures. Extrinsic TSCs harbor an unusual screening mechanism involving both electron and orbital degrees of freedom that produces rich and prominent Kondo phenomena, especially an intriguing pseudospin Kondo singlet state in the superconducting gap and a spatially anisotropic spin correlation. In sharp contrast, intrinsic TSCs support a robust impurity spin doublet ground state and an isotropic spin correlation. These findings advance fundamental knowledge of novel Kondo phenomena in TSCs and suggest experimental avenues for their detection and distinction

Research in Pediatric Residency: National Experience of Pediatric Chief Residents

Objective To determine factors associated with increased research productivity, satisfaction, and perceived barriers to research within residency from the experience of pediatric chief residents. Methods An online cross-sectional survey was administered to academic year 2014–15 chief residents. Topics assessed included program demographic characteristics, career intentions, research productivity, satisfaction with research training and opportunities, and research barriers. Chi-square and Fisher exact tests were used for descriptive statistics. Multivariable logistic regression analysis was used to determine factors associated with productivity and research satisfaction. Results The response rate was 63% (165 of 261). Half (82 of 165) were productive in research. Most were satisfied with their quality of research training (55%; 90 of 165) and research opportunities (69%; 114 of 165). Chiefs reporting interest in research were 5 times more likely to be productive than those who did not (odds ratio [OR] = 5.2; 95% confidence interval [CI], 2.3–11.8). Productive chiefs were more likely to report including research time in future careers (P = .003). Most (83%; 137 of 165) thought their programs were supportive of resident research, but lack of time was frequently cited as a major barrier. Those satisfied with research opportunities were less likely to find lack of training (OR = 0.3; 95% CI, 0.1–0.7) or faculty mentorship (OR = 0.2; 95% CI, 0.0–0.9) as a major barrier. Conclusions Pediatric chief resident interest in research is strongly associated with research productivity during residency, and research productivity is strongly associated with career plans including research time. By cultivating research interest through faculty mentorship, research training, and dedicated time, pediatric residency programs might help foster early research success and, potentially lead to continued engagement with research in trainees' future careers