DEVELOPING, MODELLING AND MAPPING OF CRITICAL LOADS AND THEIR INPUT DATA STATUS REPORT ON THE CALL FOR EUROPEAN CRITICAL LOADS ON ACIDIFICATION AND EUTROPHICATION

Programme (ICP) on the Modelling and Mapping of Critical Levels and Loads and Air Pollution Effects, Risks and Trends, with the assistance of the secretaria

UA12/11/2 Scrapbook

Scrapbook created by students involved in the Dynamic Leadership Institute and includes: Ericka Bardin, Kayla Caudle, Hailey Burke, Jamiesha Sandifo, Hannah Jones, Molly Kirks, Jeremy Matteoli, Lydia Hall, Lynlee Jackson, Sue Kafoglis, Alecia Natale, Michael Bush, Zachary, Ritchey, Cory Weikel, Megan Weedman, Hannalore Clause, Jeremy Webb, Elaine Burchett, Samantha Burnett, Katie Pay, Madeline Beath, Chris Hancock, Josh Rodriguez, Sierra Rhodes, Ashely Tutt, J.E. Greene, Kayla Tyson, Jennifer Palmer, Benjamin Kemble, Alicia Beach, Cody Hutchins, Kaitlyn Henderson, Allison Feikes, Samuel Knott, Mackenzie Farrar, Michelle Porter, Amanda Pursell, Sarah Nikolai, Darren Tinker, Katie Honadle, Scott Lanter, Allison Parks, Sabrina Heinrich, Barley Mack, Amber Buchanan, Scott Vennell, Hillary Asberry, Maddey Gates, Autumn Ward, Ryan Vennell, Olivia Estill, Aly Badinger, Becky Thieman, Elizabeth Pickens, Hannah Holtz, Amanda Ridley, Shar-na Willis, Ryan Kappler, Brittany Besserman, Sarah Badgley, Kathryn Drye, Emily Ahnquist, Evan Conder, Alex Manglaris and Michael Warren

Dynamic Low-Stretch Trees via Dynamic Low-Diameter Decompositions

Spanning trees of low average stretch on the non-tree edges, as introduced by Alon et al. [SICOMP 1995], are a natural graph-theoretic object. In recent years, they have found significant applications in solvers for symmetric diagonally dominant (SDD) linear systems. In this work, we provide the first dynamic algorithm for maintaining such trees under edge insertions and deletions to the input graph. Our algorithm has update time $n^{1/2 + o(1)}$ and the average stretch of the maintained tree is $n^{o(1)}$, which matches the stretch in the seminal result of Alon et al. Similar to Alon et al., our dynamic low-stretch tree algorithm employs a dynamic hierarchy of low-diameter decompositions (LDDs). As a major building block we use a dynamic LDD that we obtain by adapting the random-shift clustering of Miller et al. [SPAA 2013] to the dynamic setting. The major technical challenge in our approach is to control the propagation of updates within our hierarchy of LDDs: each update to one level of the hierarchy could potentially induce several insertions and deletions to the next level of the hierarchy. We achieve this goal by a sophisticated amortization approach. We believe that the dynamic random-shift clustering might be useful for independent applications. One of these applications is the dynamic spanner problem. By combining the random-shift clustering with the recent spanner construction of Elkin and Neiman [SODA 2017]. We obtain a fully dynamic algorithm for maintaining a spanner of stretch $2k - 1$ and size $O (n^{1 + 1/k} \log{n})$ with amortized update time $O (k \log^2 n)$ for any integer $2 \leq k \leq \log n$. Compared to the state-of-the art in this regime [Baswana et al. TALG '12], we improve upon the size of the spanner and the update time by a factor of $k$.Comment: To be presented at the 51st Annual ACM Symposium on the Theory of Computing (STOC 2019); abstract shortened to respect the arXiv limit of 1920 character

COVID-Dynamic: A Large-Scale Longitudinal Study of Socioemotional and Behavioral Change Across the Pandemic

The COVID-19 pandemic has caused enormous societal upheaval globally. In the US, beyond the devastating toll on life and health, it triggered an economic shock unseen since the great depression and laid bare preexisting societal inequities. The full impacts of these personal, social, economic, and public-health challenges will not be known for years. To minimize societal costs and ensure future preparedness, it is critical to record the psychological and social experiences of individuals during such periods of high societal volatility. Here, we introduce, describe, and assess the COVID-Dynamic dataset, a within-participant longitudinal study conducted from April 2020 through January 2021, that captures the COVID-19 pandemic experiences of \u3e1000 US residents. Each of 16 timepoints combines standard psychological assessments with novel surveys of emotion, social/political/moral attitudes, COVID-19-related behaviors, tasks assessing implicit attitudes and social decision-making, and external data to contextualize participants’ responses. This dataset is a resource for researchers interested in COVID-19-specific questions and basic psychological phenomena, as well as clinicians and policy-makers looking to mitigate the effects of future calamities

Effect of Cargo Shifting on Vehicle Handling

DOT-FH-11-9195The objective of the program was to determine in full-scale testing how dynamic cargo shifting affects the stability of articulated trucks and establish the severity of the problem for two cases: (1) sloshing of liquid cargo and (2) swinging of hanging meat. Two tasks (A and B) were accomplished. Task A, Planning and Preparation, was devoted to obtaining information on hanging meat and liquid cargo loading procedures; identifying representative test vehicles on the basis of industry usage, accident exposure, and mileage accumulation; and developing the test plans, procedures, and instrumentation. Task B, Conduct of Tests, was devoted to preparing the vehicles and instrumentation; mounting the instrumentation and performing necessary calibrations; driver selection, training, and system orientation; conducting the tests; and data acquisition and analysis