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We describe a mobile app for helping people manage their mental health, keeping in mind the unique challenges offered by the Indian context. One of the biggest challenges in India is the stigma around mental health and consequently the concerns and misconceptions about reaching out for professional help. Therefore, the proposed app focuses on tackling the barriers that people experience when reaching out for help and provides a free-of-cost platform for interactions with mental health professionals to alleviate any concerns and obtain an informed view. All content is developed by clinical psychologists and rather than replacing professional help, the app provides an example of a technology intervention for mental health that supplements existing infrastructures of mental healthcare.

The goal of the SunPy project is to facilitate and promote the use and development of community-led, free, and open source data analysis software for solar physics based on the scientific Python environment. The project achieves this goal by developing and maintaining the sunpy core package and supporting an ecosystem of affiliated packages. This paper describes the first official stable release (version 1.0) of the core package, as well as the project organization and infrastructure. This paper concludes with a discussion of the future of the SunPy project. NSFNational Science Foundation (NSF) [AST-1715122]; DIRAC Institute in the Department of Astronomy at the University of Washington; STFC studentshipScience & Technology Facilities Council (STFC) [ST/N504336/1]; STFC grantScience & Technology Facilities Council (STFC) [ST/N000692/1]; Google; NumFocus; Solar Physics Division of the American Astronomical Society; Space program The following individuals recognize support for their personal contributions. B.M.S. is supported by the NSF grant AST-1715122 and acknowledges support from the DIRAC Institute in the Department of Astronomy at the University of Washington. The DIRAC Institute is supported through generous gifts from the Charles and Lisa Simonyi Fund for Arts and Sciences, and the Washington Research Foundation. D.S. was supported by STFC studentship ST/N504336/1 and STFC grant ST/N000692/1.; We acknowledge financial contributions from Google as part of the Google Summer of Code program and from the Space program. We acknowledge financial contributions from NumFocus for improving the usability of SunPy's Data Downloader. Additionally, we acknowledge current and future funding from the Solar Physics Division of the American Astronomical Society for SunPy workshops and tutorials at annual meetings.; This work has made use of data from the European Space Agency (ESA) mission Gaia,80 processed by the Gaia Data Processing and Analysis Consortium (DPAC).81 Funding for the DPAC has been provided by national institutions, in particular, the institutions participating in the Gaia Multilateral Agreement.

The aim of the project is to see how group formation is taking place, how a particular group evolves over time, how the group breaks over time and correlation with the users of the group with their gpa. Also it aims to finding those users who are not participating in group formation and we can check whether the student has some issues, thus helping us to introspect into the matter. Recognition of group activities is fundamentally different from single, or multi-user activity recognition in that the goal is to recognize the behavior of the group as an entity, rather than the activities of the individual members within it. Group behavior is emergent in nature, meaning that the properties of the behavior of the group are fundamentally different then the properties of the behavior of the individuals within it, or any sum of that behavior. The main challenges are in modeling the behavior of the individual group members, as well as the roles of the individual within the group dynamic and their relationship to emergent behavior of the group in parallel. Challenges which must still be addressed include quantification of the behavior and roles of individuals who join the group, integration of explicit models for role description into inference algorithms, and scalability evaluations for very large groups and crowds. Group activity recognition has applications for crowd management and response in emergency situations, as well as for social networking and Quantified Self applications.

The sinking of the RMS Titanic is one of the most infamous shipwrecks in history. On April 15, 1912, during her maiden voyage, the Titanic sank after colliding with an iceberg, killing 1502 out of 2224 passengers and crew. This sensational tragedy shocked the international community and led to better safety regulations for ships. One of the reasons that the shipwreck led to such loss of life was that there were not enough lifeboats for the passengers and crew. Although there was some element of luck involved in surviving the sinking, some groups of people were more likely to survive than others, such as women, children, and the upper-class. In this problem, we complete the analysis of what sorts of people were likely to survive. In thisproject, you will create decision functions that attempt to predict survival outcomes from the 1912 Titanic disaster based on each passenger’s features, such as sex and age. &nbsp