An official website of the United States government
Teens are a unique population with needs and communication styles that differ from adults and children. Methods in humancentered design were initially conceptualized with adults in mind, but these methods should be reexamined to include the needs of teens. In this experience report, we reflect on a project introducing teens to human-centered design and methods. As part of the project, our team created a website and series of videos. We conducted a usability evaluation on the videos and an accompanying website with teens to understand what worked well and how to make improvements. In this report, we discuss how we modified traditional usability methods and tailored them for a teen audience. We share takeaways including keep methods and tools lightweight and facilitation styles engaging and casual. We assert that modifying methods is a key consideration for conducting usability testing with any unique group of users.
more »
« less
Stratification and duality for unipotent finite supergroup schemes.
We survey some methods developed in a series of papers, for classifying localising subcategories of tensor triangulated categories. We illustrate these methods by proving a new theorem, providing such a classification in the case of the stable module category of a unipotent finite supergroup scheme.
more »
« less
- Award ID(s):
- 1901854
- PAR ID:
- 10341255
- Date Published:
- Journal Name:
- London Mathematical Society lecture note series
- Volume:
- 474
- ISSN:
- 2634-3681
- Page Range / eLocation ID:
- 241-275
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
Anomaly detection methods abound and are used extensively in streaming settings in a wide variety of domains. But a strength can also be a weakness; given the vast number of methods, how can one select the best method for their application? Unfortunately, there is no one best way for all domains. Existing literature is focused on creating new anomaly detection methods or creating large frameworks for experimenting with multiple methods at the same time. As the literature continues to grow, extensive evaluation of every available anomaly detection method is not feasible. To reduce this evaluation burden, in this paper we present a framework to intelligently choose the optimal anomaly detection methods based on the characteristics the time series displays. We provide a comprehensive experimental validation of multiple anomaly detection methods over different time series characteristics to form guidelines. Applying our framework can save time and effort by surfacing the most promising anomaly detection methods instead of experimenting extensively with a rapidly expanding library of anomaly detection methods.more » « less
-
Models of reaction networks within interacting compartments (RNIC) are a generalization of stochastic reaction networks. It is most natural to think of the interacting compartments as “cells” that can appear, degrade, split, and even merge, with each cell containing an evolving copy of the underlying stochastic reaction network. Such models have a number of parameters, including those associated with the internal chemical model and those associated with the compartment interactions, and it is natural to want efficient computational methods for the numerical estimation of sensitivities of model statistics with respect to these parameters. Motivated by the extensive work on computational methods for parametric sensitivity analysis in the context of stochastic reaction networks over the past few decades, we provide a number of methods in the basic RNIC setting. Provided methods include the (unbiased) Girsanov transformation method (also called the likelihood ratio method) and a number of coupling methods for the implementation of finite differences, each motivated by methods from previous work related to stochastic reaction networks. We provide several numerical examples comparing the various methods in the new setting. We find that the relative performance of each method is in line with its analog in the “standard” stochastic reaction network setting. We have made all the MATLAB codes used to implement the various methods freely available for download.more » « less
-
Abstract The superior accuracy isogeometric analysis (IGA) brought to computations in fluid and solid mechanics has been yielding higher fidelity in computational aerodynamics. The increased accuracy we achieve with the IGA is in the flow solution, in representing the problem geometry, and, when we use the IGA basis functions also in time in a space–time (ST) framework, in representing the motion of solid surfaces. It is of course as part of a set of methods that the IGA has been very effective in computational aerodynamics, including complex-geometry aerodynamics. The set of methods we have been using can be categorized into those that serve as a core method, those that increase the accuracy, and those that widen the application range. The core methods are the residual-based variational multiscale (VMS), ST-VMS and arbitrary Lagrangian–Eulerian VMS methods. The IGA and ST-IGA are examples of the methods that increase the accuracy. The complex-geometry IGA mesh generation method is an example of the methods that widen the application range. The ST Topology Change method is another example of that. We provide an overview of these methods for IGA-based computational aerodynamics and present examples of the computations performed. In computational flow analysis with moving solid surfaces and contact between the solid surfaces, it is a challenge to represent the boundary layers with an accuracy attributed to moving-mesh methods and represent the contact without leaving a mesh protection gap.more » « less
-
null (Ed.)Typical model reduction methods for parametric partial differential equations construct a linear space V n which approximates well the solution manifold M consisting of all solutions u ( y ) with y the vector of parameters. In many problems of numerical computation, nonlinear methods such as adaptive approximation, n -term approximation, and certain tree-based methods may provide improved numerical efficiency over linear methods. Nonlinear model reduction methods replace the linear space V n by a nonlinear space Σ n . Little is known in terms of their performance guarantees, and most existing numerical experiments use a parameter dimension of at most two. In this work, we make a step towards a more cohesive theory for nonlinear model reduction. Framing these methods in the general setting of library approximation, we give a first comparison of their performance with the performance of standard linear approximation for any compact set. We then study these methods for solution manifolds of parametrized elliptic PDEs. We study a specific example of library approximation where the parameter domain is split into a finite number N of rectangular cells, with affine spaces of dimension m assigned to each cell, and give performance guarantees with respect to accuracy of approximation versus m and N .more » « less
- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1017/9781108942874.008
