University of Virginia

About: University of Virginia is a education organization based out in Charlottesville, Virginia, United States. It is known for research contribution in the topics: Population & Poison control. The organization has 52543 authors who have published 113268 publications receiving 5220506 citations. The organization is also known as: U of V & UVa.

Emotional experience improves with age: evidence based on over 10 years of experience sampling.

Abstract: Recent evidence suggests that emotional well-being improves from early adulthood to old age. This study used experience-sampling to examine the developmental course of emotional experience in a representative sample of adults spanning early to very late adulthood. Participants (N = 184, Wave 1; N = 191, Wave 2; N = 178, Wave 3) reported their emotional states at five randomly selected times each day for a one week period. Using a measurement burst design, the one-week sampling procedure was repeated five and then ten years later. Cross-sectional and growth curve analyses indicate that aging is associated with more positive overall emotional well-being, with greater emotional stability and with more complexity (as evidenced by greater co-occurrence of positive and negative emotions). These findings remained robust after accounting for other variables that may be related to emotional experience (personality, verbal fluency, physical health, and demographic variables). Finally, emotional experience predicted mortality; controlling for age, sex, and ethnicity, individuals who experienced relatively more positive than negative emotions in everyday life were more likely to have survived over a 13 year period. Findings are discussed in the theoretical context of socioemotional selectivity theory.

The Fourteenth Data Release of the Sloan Digital Sky Survey: First Spectroscopic data from the extended Baryon Oscillation Spectroscopic Survey and from the second phase of the Apache Point Observatory Galactic Evolution Experiment

Abstract: The fourth generation of the Sloan Digital Sky Survey (SDSS-IV) has been in operation since 2014 July. This paper describes the second data release from this phase, and the 14th from SDSS overall (making this Data Release Fourteen or DR14). This release makes the data taken by SDSS-IV in its first two years of operation (2014-2016 July) public. Like all previous SDSS releases, DR14 is cumulative, including the most recent reductions and calibrations of all data taken by SDSS since the first phase began operations in 2000. New in DR14 is the first public release of data from the extended Baryon Oscillation Spectroscopic Survey; the first data from the second phase of the Apache Point Observatory (APO) Galactic Evolution Experiment (APOGEE-2), including stellar parameter estimates from an innovative data-driven machine-learning algorithm known as "The Cannon"; and almost twice as many data cubes from the Mapping Nearby Galaxies at APO (MaNGA) survey as were in the previous release (N = 2812 in total). This paper describes the location and format of the publicly available data from the SDSS-IV surveys. We provide references to the important technical papers describing how these data have been taken (both targeting and observation details) and processed for scientific use. The SDSS web site (www.sdss.org) has been updated for this release and provides links to data downloads, as well as tutorials and examples of data use. SDSS-IV is planning to continue to collect astronomical data until 2020 and will be followed by SDSS-V.

DeepTest: automated testing of deep-neural-network-driven autonomous cars

Abstract: Recent advances in Deep Neural Networks (DNNs) have led to the development of DNN-driven autonomous cars that, using sensors like camera, LiDAR, etc., can drive without any human intervention. Most major manufacturers including Tesla, GM, Ford, BMW, and Waymo/Google are working on building and testing different types of autonomous vehicles. The lawmakers of several US states including California, Texas, and New York have passed new legislation to fast-track the process of testing and deployment of autonomous vehicles on their roads. However, despite their spectacular progress, DNNs, just like traditional software, often demonstrate incorrect or unexpected corner-case behaviors that can lead to potentially fatal collisions. Several such real-world accidents involving autonomous cars have already happened including one which resulted in a fatality. Most existing testing techniques for DNN-driven vehicles are heavily dependent on the manual collection of test data under different driving conditions which become prohibitively expensive as the number of test conditions increases. In this paper, we design, implement, and evaluate DeepTest, a systematic testing tool for automatically detecting erroneous behaviors of DNN-driven vehicles that can potentially lead to fatal crashes. First, our tool is designed to automatically generated test cases leveraging real-world changes in driving conditions like rain, fog, lighting conditions, etc. DeepTest systematically explore different parts of the DNN logic by generating test inputs that maximize the numbers of activated neurons. DeepTest found thousands of erroneous behaviors under different realistic driving conditions (e.g., blurring, rain, fog, etc.) many of which lead to potentially fatal crashes in three top performing DNNs in the Udacity self-driving car challenge.

Phosphorylation of the translational repressor PHAS-I by the mammalian target of rapamycin

Abstract: The immunosuppressant rapamycin interferes with G1-phase progression in lymphoid and other cell types by inhibiting the function of the mammalian target of rapamycin (mTOR). mTOR was determined to be a terminal kinase in a signaling pathway that couples mitogenic stimulation to the phosphorylation of the eukaryotic initiation factor (eIF)-4E-binding protein, PHAS-I. The rapamycin-sensitive protein kinase activity of mTOR was required for phosphorylation of PHAS-I in insulin-stimulated human embryonic kidney cells. mTOR phosphorylated PHAS-I on serine and threonine residues in vitro, and these modifications inhibited the binding of PHAS-I to eIF-4E. These studies define a role for mTOR in translational control and offer further insights into the mechanism whereby rapamycin inhibits G1-phase progression in mammalian cells.