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Artist as reporter : Weegee, AD Reinhardt, and the PM news picture
\"Active from 1940 to 1948, PM was a progressive New York City daily tabloid newspaper committed to the politics of labor, social justice, and antifascism--and it prioritized the intelligent and critical deployment of both pictures and their perception as paramount in these campaigns. With PM as its main focus, Artist as Reporter offers a substantial intervention into the literature on American journalism, photography, and modern art. The book considers the journalistic contributions to PM of such signal American modernists as the curator Holger Cahill, the abstract painter Ad Reinhardt, the photographers Weegee and Lisette Model, and the filmmaker, photographer, and editor Ralph Steiner. Each of its five chapters explores one dimension of the tabloid's complex journalistic activation of modernism's potential, showing how PM inserted into daily print journalism the most innovative critical thinking in the fields of painting, illustration, cartooning, and the lens-based arts. Artist as Reporter promises to revise our own understanding of midcentury American modernism and the nature of its relationship to the wider media and public culture.\"--Provided by publisher.
Book
Interpretation of machine learning models using shapley values: application to compound potency and multi-target activity predictions
Difficulties in interpreting machine learning (ML) models and their predictions limit the practical applicability of and confidence in ML in pharmaceutical research. There is a need for agnostic approaches aiding in the interpretation of ML models regardless of their complexity that is also applicable to deep neural network (DNN) architectures and model ensembles. To these ends, the SHapley Additive exPlanations (SHAP) methodology has recently been introduced. The SHAP approach enables the identification and prioritization of features that determine compound classification and activity prediction using any ML model. Herein, we further extend the evaluation of the SHAP methodology by investigating a variant for exact calculation of Shapley values for decision tree methods and systematically compare this variant in compound activity and potency value predictions with the model-independent SHAP method. Moreover, new applications of the SHAP analysis approach are presented including interpretation of DNN models for the generation of multi-target activity profiles and ensemble regression models for potency prediction.
Journal Article
Exploring the possibility space: taking stock of the diverse capabilities and gaps in integrated assessment models
Integrated assessment models (IAMs) have emerged as key tools for building and assessing long term climate mitigation scenarios. Due to their central role in the recent IPCC assessments, and international climate policy analyses more generally, and the high uncertainties related to future projections, IAMs have been critically assessed by scholars from different fields receiving various critiques ranging from adequacy of their methods to how their results are used and communicated. Although IAMs are conceptually diverse and evolved in very different directions, they tend to be criticised under the umbrella of ‘IAMs’. Here we first briefly summarise the IAM landscape and how models differ from each other. We then proceed to discuss six prominent critiques emerging from the recent literature, reflect and respond to them in the light of IAM diversity and ongoing work and suggest ways forward. The six critiques relate to (a) representation of heterogeneous actors in the models, (b) modelling of technology diffusion and dynamics, (c) representation of capital markets, (d) energy-economy feedbacks, (e) policy scenarios, and (f) interpretation and use of model results.
Journal Article
A translucent box
Machine learning has become popular in ecology but its use has remained restricted to predicting, rather than understanding, the natural world. Many researchers consider machine learning algorithms to be a black box. These models can, however, with careful examination, be used to inform our understanding of the world. They are translucent boxes. Furthermore, the interpretation of these models can be an important step in building confidence in a model or in a specific prediction from a model. Here I review a number of techniques for interpreting machine learning models at the level of the system, the variable, and the individual prediction as well as methods for handling non-independent data. I also discuss the limits of interpretability for different methods and demonstrate these approaches using a case example of understanding litter sizes in mammals.
Journal Article
Cross‐validated permutation feature importance considering correlation between features
In molecular design, material design, process design, and process control, it is important not only to construct a model with high predictive ability between explanatory features x and objective features y using a dataset but also to interpret the constructed model. An index of feature importance in x is permutation feature importance (PFI), which can be combined with any regressors and classifiers. However, the PFI becomes unstable when the number of samples is low because it is necessary to divide a dataset into training and validation data when calculating it. Additionally, when there are strongly correlated features in x, the PFI of these features is estimated to be low. Hence, a cross‐validated PFI (CVPFI) method is proposed. CVPFI can be calculated stably, even with a small number of samples, because model construction and feature evaluation are repeated based on cross‐validation. Furthermore, by considering the absolute correlation coefficients between the features, the feature importance can be evaluated appropriately even when there are strongly correlated features in x. Case studies using numerical simulation data and actual compound data showed that the feature importance can be evaluated appropriately using CVPFI compared to PFI. This is possible when the number of samples is low, when linear and nonlinear relationships are mixed between x and y when there are strong correlations between features in x, and when quantised and biased features exist in x. Python codes for CVPFI are available at https://github.com/hkaneko1985/dcekit.
Journal Article
Learning Forecasts of Rare Stratospheric Transitions from Short Simulations
Rare events arising in nonlinear atmospheric dynamics remain hard to predict and attribute. We address the problem of forecasting rare events in a prototypical example, sudden stratospheric warmings (SSWs). Approximately once every other winter, the boreal stratospheric polar vortex rapidly breaks down, shifting midlatitude surface weather patterns for months. We focus on two key quantities of interest: the probability of an SSW occurring, and the expected lead time if it does occur, as functions of initial condition. These optimal forecasts concretely measure the event’s progress. Direct numerical simulation can estimate them in principle but is prohibitively expensive in practice: each rare event requires a long integration to observe, and the cost of each integration grows with model complexity. We describe an alternative approach using integrations that are short compared to the time scale of the warming event. We compute the probability and lead time efficiently by solving equations involving the transition operator, which encodes all information about the dynamics. We relate these optimal forecasts to a small number of interpretable physical variables, suggesting optimal measurements for forecasting. We illustrate the methodology on a prototype SSW model developed by Holton and Mass and modified by stochastic forcing. While highly idealized, this model captures the essential nonlinear dynamics of SSWs and exhibits the key forecasting challenge: the dramatic separation in time scales between a single event and the return time between successive events. Our methodology is designed to fully exploit high-dimensional data from models and observations, and has the potential to identify detailed predictors of many complex rare events in meteorology.
Journal Article
Using Explainable Machine Learning Forecasts to Discover Subseasonal Drivers of High Summer Temperatures in Western and Central Europe
Reliable subseasonal forecasts of high summer temperatures would be very valuable for society. Although state-of-the-art numerical weather prediction (NWP) models have become much better in representing the relevant sources of predictability like land and sea surface states, the subseasonal potential is not fully realized. Complexities arise because drivers depend on the state of other drivers and on interactions over multiple time scales. This study applies statistical modeling to ERA5 data, and explores how nine potential drivers, interacting on eight time scales, contribute to the subseasonal predictability of high summer temperatures in western and central Europe. Features and target temperatures are extracted with two variations of hierarchical clustering, and are fitted with a machine learning (ML) model based on random forests. Explainable AI methods show that the ML model agrees with physical understanding. Verification of the forecasts reveals that a large part of predictability comes from climate change, but that reliable and valuable subseasonal forecasts are possible in certain windows, like forecasting monthly warm anomalies with a lead time of 15 days. Contributions of each driver confirm that there is a transfer of predictability from the land and sea surface state to the atmosphere. The involved time scales depend on lead time and the forecast target. The explainable AI methods also reveal surprising driving features in sea surface temperature and 850 hPa temperature, and rank the contribution of snow cover above that of sea ice. Overall, this study demonstrates that complex statistical models, when made explainable, can complement research with NWP models, by diagnosing drivers that need further understanding and a correct numerical representation, for better future forecasts.
Journal Article
Explaining the Mechanism of Multiscale Groundwater Drought Events: A New Perspective From Interpretable Deep Learning Model
This study presents a new approach to understand the causes of groundwater drought events with interpretable deep learning (DL) models. As prerequisites, accurate long short‐term memory (LSTM) models for simulating groundwater are built for 16 regions representing three types of spatial scales in the southeastern United States, and standardized groundwater index is applied to identify 233 groundwater drought events. Two interpretation methods, expected gradients (EG) and additive decomposition (AD), are adopted to decipher the DL‐captured patterns and inner workings of LSTM networks. The EG results show that: (a) temperature‐related features were the primary drivers of large‐scale groundwater droughts, with their importance increasing from 56.1% to 63.1% as the drought events approached from 6 months to 15 days. Conversely, precipitation‐related features were found to be the dominant factors in the formation of groundwater drought in small‐scale catchments, with the overall importance ranging from 59.8% to 53.3%; (b) Seasonal variations in the importance of temperature‐related factors are inversely related between large and small spatial scales, being more significant in summer for larger regions and in winter for catchments; and (c) temperature‐related factors exhibited an overall “trigger effect” on causing groundwater drought events in the studying areas. The AD method unveiled how the LSTM network behaved differently in retaining and discarding information when emulating different groundwater droughts. In summary, this study provides a new perspective for the causes of groundwater drought events and highlights the potential and prospect of interpretable DL in enhancing our understanding of hydrological processes. Key Points An interpretable deep learning model was proposed to explain the mechanisms of groundwater droughts at different scales The interpretation analysis of 233 groundwater droughts revealed spatial‐temporal variations in their dominant factors and trigger effects The proposed model could successfully capture the confluence effect of precipitation and long‐term memory characteristics of groundwater
Journal Article
A Machine Learning Tutorial for Operational Meteorology. Part I: Traditional Machine Learning
Recently, the use of machine learning in meteorology has increased greatly. While many machine learning methods are not new, university classes on machine learning are largely unavailable to meteorology students and are not required to become a meteorologist. The lack of formal instruction has contributed to perception that machine learning methods are “black boxes” and thus end-users are hesitant to apply the machine learning methods in their everyday workflow. To reduce the opaqueness of machine learning methods and lower hesitancy toward machine learning in meteorology, this paper provides a survey of some of the most common machine learning methods. A familiar meteorological example is used to contextualize the machine learning methods while also discussing machine learning topics using plain language. The following machine learning methods are demonstrated: linear regression, logistic regression, decision trees, random forest, gradient boosted decision trees, naïve Bayes, and support vector machines. Beyond discussing the different methods, the paper also contains discussions on the general machine learning process as well as best practices to enable readers to apply machine learning to their own datasets. Furthermore, all code (in the form of Jupyter notebooks and Google Colaboratory notebooks) used to make the examples in the paper is provided in an effort to catalyze the use of machine learning in meteorology.
Journal Article
An Explainable Artificial Intelligence Framework for the Deterioration Risk Prediction of Hepatitis Patients
In recent years, artificial intelligence-based computer aided diagnosis (CAD) system for the hepatitis has made great progress. Especially, the complex models such as deep learning achieve better performance than the simple ones due to the nonlinear hypotheses of the real world clinical data. However,complex model as a black box, which ignores why it make a certain decision, causes the model distrust from clinicians. To solve these issues, an explainable artificial intelligence (XAI) framework is proposed in this paper to give the global and local interpretation of auxiliary diagnosis of hepatitis while retaining the good prediction performance. First, a public hepatitis classification benchmark from UCI is used to test the feasibility of the framework. Then, the transparent and black-box machine learning models are both employed to forecast the hepatitis deterioration. The transparent models such as logistic regression (LR), decision tree (DT)and k-nearest neighbor (KNN) are picked. While the black-box model such as the eXtreme Gradient Boosting (XGBoost), support vector machine (SVM), random forests (RF) are selected. Finally, the SHapley Additive exPlanations (SHAP), Local Interpretable Model-agnostic Explanations (LIME) and Partial Dependence Plots (PDP) are utilized to improve the model interpretation of liver disease. The experimental results show that the complex models outperform the simple ones. The developed RF achieves the highest accuracy (91.9%) among all the models. The proposed framework combining the global and local interpretable methods improves the transparency of complex models, and gets insight into the judgments from the complex models, thereby guiding the treatment strategy and improving the prognosis of hepatitis patients. In addition, the proposed framework could also assist the clinical data scientists to design a more appropriate structure of CAD.
Journal Article