Whitmeyer, S J; Kellett, D A; Tikoff, B; Williams, M L. Laurentia: turning points in the evolution of a continent; Geological Society of America, Memoir vol. 220, 2023 p. 7-16, https://doi.org/10.1130/2022.1220(001)
<a href="https://geoscan.nrcan.gc.ca/images/geoscan/20210545.jpg"><img src="https://geoscan.nrcan.gc.ca/images/geoscan/20210545.jpg" title="Laurentia: turning points in the evolution of a continent; Geological Society of America, Memoir vol. 220, 2023 p. 7-16, https://doi.org/10.1130/2022.1220(001)" height="150" border="1" /></a>

The post Notice of Special Interest (NOSI): Analysis of Existing Linked Datasets to Understand the Relationship between Housing Program Participation and Risk for Chronic Diseases and Other Conditions (R01-Clinical Trial Not Allowed) [First Available Due Date: Oct 07] was curated by information for practice.

The post Notice of Special Interest (NOSI): Analysis of Existing Linked Datasets to Understand the Relationship between Housing Program Participation and Risk for Chronic Diseases and Other Conditions (R01-Clinical Trial Not Allowed) was curated by information for practice.

The objective of this analysis is to gain more insight into the coping behavior of households in Mali when facing covariate shocks and stressors of different kinds Source: IFPRI Africa Regional Office (AFR)

The objective of this analysis is to gain more insight into the coping behavior of households in Mali when facing covariate shocks and stressors of different kinds Source: IFPRI Africa Regional Office (AFR)

The aim of this study was to validate a previously developed deep learning model in 5 independent clinical trials. The predictive performance of this model was compared with the international prognostic index (IPI) and 2 models incorporating radiomic PET/CT features (clinical PET and PET models). Methods: In total, 1,132 diffuse large B-cell lymphoma patients were included: 296 for training and 836 for external validation. The primary outcome was 2-y time to progression. The deep learning model was trained on maximum-intensity projections from PET/CT scans. The clinical PET model included metabolic tumor volume, maximum distance from the bulkiest lesion to another lesion, SUV_peak, age, and performance status. The PET model included metabolic tumor volume, maximum distance from the bulkiest lesion to another lesion, and SUV_peak. Model performance was assessed using the area under the curve (AUC) and Kaplan–Meier curves. Results: The IPI yielded an AUC of 0.60 on all external data. The deep learning model yielded a significantly higher AUC of 0.66 (P < 0.01). For each individual clinical trial, the model was consistently better than IPI. Radiomic model AUCs remained higher for all clinical trials. The deep learning and clinical PET models showed equivalent performance (AUC, 0.69; P > 0.05). The PET model yielded the highest AUC of all models (AUC, 0.71; P < 0.05). Conclusion: The deep learning model predicted outcome in all trials with a higher performance than IPI and better survival curve separation. This model can predict treatment outcome in diffuse large B-cell lymphoma without tumor delineation but at the cost of a lower prognostic performance than with radiomics.

A global cropland percentage map and a global field size map have been created for the first time to guide scientists and policymakers interested in global agricultural modelling and assessment. Both maps are for the baseline year 2005 and combined multiple data sets from global, regional and national levels to achieve a high level of accuracy and 1 km2 resolution.

Willems et al.'s fundamental lemma asserts that all trajectories of a linear system can be obtained from a single given one, assuming that a persistency of excitation condition holds. This result has profound implications for system identification and data-driven control, and has seen a revival over the last few years. The purpose of this paper is to extend Willems' lemma to the situation where multiple (possibly short) system trajectories are given instead of a single long one. To this end, we introduce a notion of collective persistency of excitation. We will then show that all trajectories of a linear system can be obtained from a given finite number of trajectories, as long as these are collectively persistently exciting. We will demonstrate that this result enables the identification of linear systems from data sets with missing data samples. Additionally, we show that the result is of practical significance in data-driven control of unstable systems.

Our world is becoming increasingly intelligent, interconnected, and instrumented, resulting in massive amounts of data being collected. This data is a treasure trove of information that can be mined to improve service, increase sales, determine risk, or make operations more efficient.

Analysis of such large amounts of data, often called analytics, is increasingly desired by governments and businesses alike.

The machine learning techniques scientists use to predict outcomes from large datasets may fall short when it comes to projecting the outcomes of people’s lives, according to a large-scale mass collaboration led by researchers at Princeton.

Jonathan R. Bradley, Noel Cressie, Tao Shi.

Source: Statistics Surveys, Volume 10, 100--131.

Abstract:
In this article, we review and compare a number of methods of spatial prediction, where each method is viewed as an algorithm that processes spatial data. To demonstrate the breadth of available choices, we consider both traditional and more-recently-introduced spatial predictors. Specifically, in our exposition we review: traditional stationary kriging, smoothing splines, negative-exponential distance-weighting, fixed rank kriging, modified predictive processes, a stochastic partial differential equation approach, and lattice kriging. This comparison is meant to provide a service to practitioners wishing to decide between spatial predictors. Hence, we provide technical material for the unfamiliar, which includes the definition and motivation for each (deterministic and stochastic) spatial predictor. We use a benchmark dataset of $mathrm{CO}_{2}$ data from NASA’s AIRS instrument to address computational efficiencies that include CPU time and memory usage. Furthermore, the predictive performance of each spatial predictor is assessed empirically using a hold-out subset of the AIRS data.

In many Machine Learning domains, datasets are characterized by highly imbalanced and overlapping classes. Particularly in the medical domain, a specific list of symptoms can be labeled as one of various different conditions. Some of these conditions may be more prevalent than others by several orders of magnitude. Here we present a novel unsupervised Domain Adaptation scheme for such datasets. The scheme, based on a specific type of Quantification, is designed to work under both label and conditional shifts. It is demonstrated on datasets generated from Electronic Health Records and provides high quality results for both Quantification and Domain Adaptation in very challenging scenarios. Potential benefits of using this scheme in the current COVID-19 outbreak, for estimation of prevalence and probability of infection, are discussed.

Hayden Library - QB817.5.I58 2018

Francis, Anthony