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Two-factor authentication (2FA) defends against password compromise by a remote attacker. We surveyed 4,275 students, faculty, and staff at Brigham Young University to measure user sentiment about Duo 2FA one year after the university adopted it. The results were mixed. A majority of the participants felt more secure using Duo and felt it was easy to use. About half of all participants reported at least one instance of being locked out of their university account because of an inability to authenticate with Duo. We found that students and faculty generally had more negative perceptions of Duo than staff. The survey responses reveal some pain points for Duo users. In response, we offer recommendations that reduce the frequency of 2FA for users. We also suggest UI changes that draw more attention to 2FA methods that do not require WiFi, the “Remember Me” setting, and the help utility. 

As the era of high frequency, single core processors have come to a close, the new paradigm of many core processors has come to dominate. In response to these systems, asynchronous multitasking runtime systems have been developed as a promising solution to efficiently utilize these newly available hardware. Asynchronous multitasking runtime systems work by dividing a problem into a large number of fine grained tasks. However, as the number of tasks created increase, the overheads associated with task creation and management cannot be ignored. Task inlining, a method where the parent thread consumes a child thread, enables the runtime system to achieve the balance between parallelism and its overhead. As largely impacted by different processor architectures, the decision of task inlining is dynamic in nature. In this research, we present adaptive techniques for deciding, at runtime, whether a particular task should be inlined or not. We present two policies, a baseline policy that makes inlining decision based on a fixed threshold and an adaptive policy which decides the threshold dynamically at runtime. We also evaluate and justify the performance of these policies on different processor architectures. To the best of our knowledge, this is the first study of the impacts of adaptive policy at runtime for task inlining in an asynchronous multitasking runtime system on different processor architectures. From experimentation, we find that the baseline policy improves the execution time from 7.61% to 54.09%. Furthermore, the adaptive policy improves over the baseline policy by up to 74%. 

Our analytically based technique for coarse-graining (CG) polymer simulations dramatically improves spatial and temporal scaling while preserving thermodynamic quantities and bulk properties. The purpose of CG codes is to run more efficient molecular dynamics simulations, yet the research field generally lacks thorough analysis of how such codes scale with respect to full-atom representations. This paper conducts an in-depth performance study of highly realistic polymer melts on modern supercomputing systems. We also present a workflow that integrates our analytical solution for calculating CG forces with new high-performance techniques for mapping back and forth between the atomistic and CG descriptions in LAMMPS. The workflow benefits from the performance of CG, while maintaining full-atom accuracy. Our results show speedups up to 12x faster than atomistic simulations. 

Secondary production, the growth of new heterotrophic biomass, is a key process in aquatic and terrestrial ecosystems that has been carefully measured in many flowing water ecosystems. We combine structural equation modeling with the first worldwide dataset on annual secondary production of stream invertebrate communities to reveal core pathways linking air temperature and precipitation to secondary production. In the United States, where the most extensive set of secondary production estimates and covariate data were available, we show that precipitation-mediated, low–stream flow events have a strong negative effect on secondary production. At larger scales (United States, Europe, Central America, and Pacific), we demonstrate the significance of a positive two-step pathway from air to water temperature to increasing secondary production. Our results provide insights into the potential effects of climate change on secondary production and demonstrate a modeling framework that can be applied across ecosystems. 

Valid interpretation of preclinical animal models in immunology-related clinical challenges is important to solve outstanding clinical needs. Given the overall complexity of the immune system and both species- and tissue-specific immune peculiarities, the selection and design of appropriate immune-relevant animal models is, however, not following a straightforward path. The topics in this issue of the ILAR Journal provide assessments of immune-relevant animal models used in oncology, hematopoietic-, CAR-T cell- and xenotransplantation, adjuvants and infectious diseases, and immune privileged inflammation that are providing key insights into unmet human clinical needs.