Earth System Modeling 2.0: A Blueprint for Models That Learn From Observations and Targeted High-Resolution Simulations

Climate projections continue to be marred by large uncertainties, which originate in processes that need to be parameterized, such as clouds, convection, and ecosystems. But rapid progress is now within reach. New computational tools and methods from data assimilation and machine learning make it possible to integrate global observations and local high-resolution simulations in an Earth system model (ESM) that systematically learns from both. Here we propose a blueprint for such an ESM. We outline how parameterization schemes can learn from global observations and targeted high-resolution simulations, for example, of clouds and convection, through matching low-order statistics between ESMs, observations, and high-resolution simulations. We illustrate learning algorithms for ESMs with a simple dynamical system that shares characteristics of the climate system; and we discuss the opportunities the proposed framework presents and the challenges that remain to realize it.Comment: 32 pages, 3 figure

Evaluating Visitor Experience in the Department of Coins and Medals

The Department of Coins and Medals at the British Museum sponsored our evaluation of two specific galleries in order to discover new ways to improve their exhibits for visitors. This evaluation included Cases 3 and 10 of Gallery 68 as well as the entire Gallery 69a. To collect data efficiently, we employed tracking studies and surveys as determined by the British Museum Evaluation Framework. In addition, we created new and effective ways for future researchers to display data visually. Of these tools, the most important was the creation of macros in Excel that tabulate data and create heat maps of the galleries

A learning hypothesis of the term structure of interest rates

Recent empirical results about the US term structure are difficult to reconcile with the classical hypothesis of rational expectations even if time-varying but stationary term premia are allowed for. A hypothesis of rational learning about the conditional variance of the log pricing kernel is put forward. In a simple, illustrative consumption-based asset pricing model the long-term interest rate turns out to have an economic meaning distinct from both price stability and full employment, namely to measure the market perception of aggregate level of future risk in the economy. Implications for economic modeling and monetary policy are explored.term structure; interest rate; learning; uncertainty; monetary policy

The Relationship Between Students’ Applied Mathematics Skills and Students’ Attitudes Towards Mathematics

Mathematics is a subject with which many students struggle. It has been noted that students’ attitudes towards mathematics can often affect their performance in related courses. The goal of this research is to explore the relationship between students’ basic applied mathematics skill and students’ attitudes towards mathematics. That is, do students, as they learn how to use mathematics in the real world, tend to develop a more favorable outlook towards mathematics? Or, on the other hand, do the attitudes towards mathematics of students remain unaffected as their ability to use mathematics in the real world increases? The current research seeks to clarify these propositions in an effort to improve mathematics instruction by providing educators with a better understanding of students’ attitudes towards mathematics. Multiple linear regression analysis found that attitude toward mathematics was indeed significantly related to students’ basic applied mathematics skill. Attitude toward mathematics explained 29.7% of the variance observed in basic applied mathematics skill. Attitudinal subscales were also analyzed. Student self-confidence and motivation were both significant predictors of basic applied mathematics skill. In a separate analysis, attitude toward mathematics was found not to be significantly related to mathematical achievement in the college classroom

Interpretable Diabetic Retinopathy Diagnosis based on Biomarker Activation Map

Deep learning classifiers provide the most accurate means of automatically diagnosing diabetic retinopathy (DR) based on optical coherence tomography (OCT) and its angiography (OCTA). The power of these models is attributable in part to the inclusion of hidden layers that provide the complexity required to achieve a desired task. However, hidden layers also render algorithm outputs difficult to interpret. Here we introduce a novel biomarker activation map (BAM) framework based on generative adversarial learning that allows clinicians to verify and understand classifiers decision-making. A data set including 456 macular scans were graded as non-referable or referable DR based on current clinical standards. A DR classifier that was used to evaluate our BAM was first trained based on this data set. The BAM generation framework was designed by combing two U-shaped generators to provide meaningful interpretability to this classifier. The main generator was trained to take referable scans as input and produce an output that would be classified by the classifier as non-referable. The BAM is then constructed as the difference image between the output and input of the main generator. To ensure that the BAM only highlights classifier-utilized biomarkers an assistant generator was trained to do the opposite, producing scans that would be classified as referable by the classifier from non-referable scans. The generated BAMs highlighted known pathologic features including nonperfusion area and retinal fluid. A fully interpretable classifier based on these highlights could help clinicians better utilize and verify automated DR diagnosis.Comment: 12 pages, 8 figure

The Evolving Cataloging Department

The shrinking of traditional cataloging departments is not news to library technical services staff. Nor is it news that digital projects that require standardized metadata are being created and supported by the same libraries that employ traditional catalogers. What may be less apparent is the ease with which a traditional cataloging unit can be transformed to incorporate metadata creation in the regular workflow of these units. IUPUI University Library’s Bibliographic and Metadata Services Team (BAMS) has made this transition and provides one example of how libraries can capitalize on the wealth of skilled employees already in place. This article discusses the full range of ideologies already in place and tactics used, including hiring a metadata cataloger, collaborating with digital initiatives groups in and outside the library, outsourcing some of the traditional cataloging, and training copy catalogers to create metadata to increase the viability and currency of the skills of a traditional cataloging unit

Mapping Europe into local climate zones

Cities are major drivers of environmental change at all scales and are especially at risk from the ensuing effects, which include poor air quality, flooding and heat waves. Typically, these issues are studied on a city-by-city basis owing to the spatial complexity of built landscapes, local topography and emission patterns. However, to ensure knowledge sharing and to integrate local-scale processes with regional and global scale modelling initiatives, there is a pressing need for a world-wide database on cities that is suited for environmental studies. In this paper we present a European database that has a particular focus on characterising urbanised landscapes. It has been derived using tools and techniques developed as part of the World Urban Database and Access Portal Tools (WUDAPT) project, which has the goal of acquiring and disseminating climate-relevant information on cities worldwide. The European map is the first major step toward creating a global database on cities that can be integrated with existing topographic and natural land-cover databases to support modelling initiatives