Improving STEM Education in Research: Preliminary Report on the Development of a Computer-Assisted Student-Mentor Research Community

Research education in STEM disciplines currently suffers from 1) The inability to feasibly collect highly detailed data on both the student’s and mentor’s activities; 2) The lack of tools to assist students and mentors in organizing and managing their research activities and environments; and 3) The inability to correlate a student’s assessment results with their actual research activities. Together these three problems act to impede both the improvement and educational quality of student research experiences. We propose a computer-assisted student-mentor research community as a solution to these problems. Within this community setting, students and their mentors are provided tools to make their work easier, much like a word processor makes writing a letter easier. Through their use of these tools, details of student-mentor activities are automatically recorded in a relational database, without burdening users with the responsibility of archiving data. Equally important, student assessments of outcome can be directly related to student activity, allowing educators to identify practices resulting in successful research experiences. Community tools also facilitate the use of labor-intensive teaching laboratories involving real inquiry-based research. The community structure has the added benefit of allowing students to see, communicate and interact more freely with other students and their projects, thus enriching the student’s research experience. We provide herein a preliminary report on the development and testing of a prototype, student-mentor research community, and present its tools, an assessment of student interest in participating in the community, and discuss its further development into a nationally-available student-mentor research community

Atmospheric Escape Processes and Planetary Atmospheric Evolution

The habitability of the surface of any planet is determined by a complex evolution of its interior, surface, and atmosphere. The electromagnetic and particle radiation of stars drive thermal, chemical and physical alteration of planetary atmospheres, including escape. Many known extrasolar planets experience vastly different stellar environments than those in our Solar system: it is crucial to understand the broad range of processes that lead to atmospheric escape and evolution under a wide range of conditions if we are to assess the habitability of worlds around other stars. One problem encountered between the planetary and the astrophysics communities is a lack of common language for describing escape processes. Each community has customary approximations that may be questioned by the other, such as the hypothesis of H-dominated thermosphere for astrophysicists, or the Sun-like nature of the stars for planetary scientists. Since exoplanets are becoming one of the main targets for the detection of life, a common set of definitions and hypotheses are required. We review the different escape mechanisms proposed for the evolution of planetary and exoplanetary atmospheres. We propose a common definition for the different escape mechanisms, and we show the important parameters to take into account when evaluating the escape at a planet in time. We show that the paradigm of the magnetic field as an atmospheric shield should be changed and that recent work on the history of Xenon in Earth's atmosphere gives an elegant explanation to its enrichment in heavier isotopes: the so-called Xenon paradox

MLSys: The New Frontier of Machine Learning Systems

Machine learning (ML) techniques are enjoying rapidly increasing adoption. However, designing and implementing the systems that support ML models in real-world deployments remains a significant obstacle, in large part due to the radically different development and deployment profile of modern ML methods, and the range of practical concerns that come with broader adoption. We propose to foster a new systems machine learning research community at the intersection of the traditional systems and ML communities, focused on topics such as hardware systems for ML, software systems for ML, and ML optimized for metrics beyond predictive accuracy. To do this, we describe a new conference, MLSys, that explicitly targets research at the intersection of systems and machine learning with a program committee split evenly between experts in systems and ML, and an explicit focus on topics at the intersection of the two

Automated Duplicate Detection for Bug Tracking Systems

Bug tracking systems are important tools that guide the maintenance activities of software developers. The utility of these systems is hampered by an excessive number of duplicate bug reports–in some projects as many as a quarter of all reports are duplicates. Developers must manually identify duplicate bug reports, but this identification process is time-consuming and exacerbates the already high cost of software maintenance. We propose a system that automatically classifies duplicate bug reports as they arrive to save developer time. This system uses surface features, textual semantics, and graph clustering to predict duplicate status. Using a dataset of 29,000 bug reports from the Mozilla project, we perform experiments that include a simulation of a real-time bug reporting environment. Our system is able to reduce development cost by filtering out 8 % of duplicate bug reports while allowing at least one report for each real defect to reach developers. 1

Thermochemical cycle of a mixed metal oxide for augmentation of thermal energy storage in solid particles

Solid particle heat transfer and storage media have been shown to be able to operate at temperatures \u3e1000°C in concentrated solar power (CSP) applications, much higher than the operational limit of 600°C for current state-of-the-art molten nitrate salt heat transfer fluid. Solid particles can be endothermically reduced by direct exposure to concentrated solar energy, thus absorbing and storing thermal energy beyond that possible with sensible heating alone. The particles can then be oxidized exothermically at a later time, releasing the stored chemical heat and effectively augmenting the thermal energy storage capacity of the solid particles. A mixed metal oxide spinel material that reduces in the temperature range of interest (1000-1200°C) has been examined for applicability to this thermochemical energy storage concept. A description of this application, prospective materials, and details of the thermochemical cycle are presented. The heats of reduction and oxidation for the thermochemical cycle have been determined for various operating conditions to evaluate the amount of thermal energy that may be stored. Various possible implementations of this augmented storage concept are considered, and alternate means of controlling the thermochemical cycle are explored

Inhibition of VEGF-C modulates distal lymphatic remodeling and secondary metastasis.

Tumor-associated lymphatics are postulated to provide a transit route for disseminating metastatic cells. This notion is supported by preclinical findings that inhibition of pro-lymphangiogenic signaling during tumor development reduces cell spread to sentinel lymph nodes (SLNs). However, it is unclear how lymphatics downstream of SLNs contribute to metastatic spread into distal organs, or if modulating distal lymph transport impacts disease progression. Utilizing murine models of metastasis, longitudinal in vivo imaging of lymph transport, and function blocking antibodies against two VEGF family members, we provide evidence that distal lymphatics undergo disease course-dependent up-regulation of lymph transport coincidental with structural remodeling. Inhibition of VEGF-C activity with antibodies against VEGF-C or NRP2 prevented these disease-associated changes. Furthermore, utilizing a novel model of adjuvant treatment, we demonstrate that antagonism of VEGF-C or NRP2 decreases post SLN metastasis. These data support a potential therapeutic strategy for inhibiting distant metastatic dissemination via targeting tumor-associated lymphatic remodeling