DevOps is Bigger than IT: Driving Digital Transformation in Libraries

The DevOps movement represents a paradigm shift in software development, but there are misconceptions about what it really means. It’s much more nuanced than simply adding a DevOps engineer to your team or asking systems administrators and developers to play well together. At its core, DevOps is about culture change. In this talk, we will define the CALMS framework of DevOps and the people, technical, and organizational factors that challenge its adoption. We will share specific examples of how DevOps is changing the way we work on digital library projects at Ohio State University Libraries and how its universal principles can drive large-scale digital transformation in libraries. Presented at Coalition for Networked Information (CNI) Spring 2020 Membership MeetingSlidesSpeaker notesNo embarg

The Agile Organization: What We Can Learn from Software Development

Agile planning and operations is one focus area of the strategic directions (2018) of University Libraries at Ohio State. To illuminate what it means to be an agile organization, the presenter outlines key milestones in the history of agile software development, its core values and common practices, and how it differs from the waterfall approach to software development. Seven characteristics of IT agility are identified with recommendations about how these characteristics can be extended to the organization at large to increase organizational agility.slides and speaker notesNo embarg

Thermal Protection Test Bed Pathfinder Development Project

In order to increase thermal protection capabilities for future reentry vehicles, a method to obtain relevant test data is required. Although arc jet testing can be used to obtain some data on materials, the best method to obtain these data is to actually expose them to an atmospheric reentry. The overprediction of the Orion EFT-1 flight data is an example of how the ground test to flight traceability is not fully understood. The RED-Data small reentry capsule developed by Terminal Velocity Aerospace is critical to understanding this traceability. In order to begin to utilize this technology, ES3 needs to be ready to build and integrate heat shields onto the RED-Data vehicle. Using a heritage Shuttle tile material for the heat shield will both allow valuable insight into the environment that the RED-Data vehicle can provide and give ES3 the knowledge and capability to build and integrate future heat shields for this vehicle

Generalizing smoothness constraints from discrete samples

We study how certain smoothness constraints, for example, piecewise continuity, can be generalized from a discrete set of analog-valued data, by modifying the error backpropagation, learning algorithm. Numerical simulations demonstrate that by imposing two heuristic objectives — (1) reducing the number of hidden units, and (2) minimizing the magnitudes of the weights in the network — during the learning process, one obtains a network with a response function that smoothly interpolates between the training data

Measuring sustainable intensification in smallholder agroecosystems: A review

In the sustainable intensification (SI) of smallholder agroecosystems, researchers and farmers collaborate to produce more food on land currently in cultivation, secure wellbeing in the present day, and bolster ecosystem services to sustain agricultural productivity into the future. In recent years there has been debate in the SI literature about the meaning and boundaries SI, accompanied by calls for clearly defined metrics to evaluate SI efforts. In this review, we present the current state of the literature in regards to SI metrics. We first survey the literature to identify key concepts and qualities associated with SI (referred to as SI indicators). We briefly discuss indicators that have been sources of contention in the SI literature, and highlight tradeoffs between certain SI indicators. The bulk of this review focuses on identifying measurable properties (referred to as SI metrics) associated with each SI indicator. We also identify metrics of broader system-level properties such as sustainability and intensification. We conclude by highlighting gaps in the current literature on SI metrics

On the Finite Sample Performance of the Nearest Neighbor Classifier

The finite sample performance of a nearest neighbor classifier is analyzed for a two-class pattern recognition problem. An exact integral expression is derived for the m-sample risk R_m given that a reference m-sample of labeled points, drawn independently from Euclidean n-space according to a fixed probability distribution, is available to the classifier. For a family of smooth distributions characterized by asymptotic expansions in general form, it is shown that the m-sample risk R_m has a complete asymptotic series expansion R_m ~ R_∞ + Σ^∞_(k=1) c_km^(-k/n) (m → ∞) where R_∞ denotes the nearest neighbor risk in the infinite-sample limit. Improvements in convergence rate are shown under stronger smoothness assumptions, and in particular, R_m = R_∞ + O(m^(-2/n)) if the class-conditional probability densities have uniformly bounded third derivatives on their probability one support. This analysis thus provides further analytic validation of Bellman's curse of dimensionality. Numerical simulations corroborating the formal results are included, and extensions of the theory discussed. The analysis also contains a novel application of Laplace's asymptotic method of integration to a multidimensional integral where the integrand attains its maximum on a continuum of points