Recurrent event data arise in diverse fields such as medicine, public health, insurance, social science, economics, manufacturing and reliability. The purpose of this book is to present models and ...statistical methods for the analysis of recurrent event data. No single comprehensive treatment of these areas currently exists. The authors provide broad but detailed coverage of the major approaches to analysis, while also emphasizing the modeling assumptions that they are based on. Thus, they consider important models such as Poisson and renewal processes, with extensions to incorporate covariates or random effects.More general intensity-based models are also considered, as well as simpler models that focus on rate or mean functions. Parametric, nonparametric and semiparametric methodologies areall covered, with clear descriptions of procedures for estimation, testing and model checking. Important practical topics such as variations in observation schemes or selection of individuals for study, the planning of randomized experiments, events of several types, and the prediction of future events are considered.Methods of modeling and analysis are illustrated through many examples taken from health research and industry. The objectives and interpretations of different analyses are discussed in detail, and issues of robustness are addressed. Statistical analysis of the examples is carried out with S-PLUS software and code is given for some examples.This book is directed at graduate students, researchers, and applied statisticians working in industry, government or academia. Some familiarity with survival analysis is beneficial since survival software is used to carry out many of the analyses considered. This book can be used as a textbook for a graduate course on the analysis of recurrent events or as a reference for a more general course on event history analysis. Problems are given at the end of chapters to reinforce the material presented and to provide additional background or extensions to certain topics.
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Deep learning has produced state-of-the-art results for a variety of tasks. While such approaches for supervised learning have performed well, they assume that training and testing data are drawn ...from the same distribution, which may not always be the case. As a complement to this challenge, single-source unsupervised domain adaptation can handle situations where a network is trained on labeled data from a source domain and unlabeled data from a related but different target domain with the goal of performing well at test-time on the target domain. Many single-source and typically homogeneous unsupervised deep domain adaptation approaches have thus been developed, combining the powerful, hierarchical representations from deep learning with domain adaptation to reduce reliance on potentially-costly target data labels. This survey will compare these approaches by examining alternative methods, the unique and common elements, results, and theoretical insights. We follow this with a look at application areas and open research directions.
Climate change plays a key role in changing vegetation productivity dynamics, which ultimately affect the hydrological cycle of a watershed through evapotranspiration (ET). Trends and correlation ...analysis were conducted to investigate vegetation responses across the whole Upper Jhelum River Basin (UJRB) in the northeast of Pakistan using the normalized difference vegetation index (NDVI), climate variables, and river flow data at inter-annual/monthly scales between 1982 and 2015. The spatial variability in trends calculated with the Mann-Kendall (MK) trend test on NDVI and climate data was assessed considering five dominant land use/cover types. The inter-annual NDVI in four out of five vegetation types showed a consistent increase over the 34-year study period; the exception was for herbaceous vegetation (HV), which increased until the end of the 1990s and then decreased slightly in subsequent years. In spring, significant (p<0.05) increasing trends were found in the NDVI of all vegetation types. Minimum temperature (Tmin) showed a significant increase during spring, while maximum temperature (Tmax) decreased significantly during summer. Average annual increase in Tmin (1.54°C) was much higher than Tmax (0.37°C) over 34 years in the UJRB. Hence, Tmin appears to have an enhancing effect on vegetation productivity over the UJRB. A significant increase in NDVI, Tmin and Tmax during spring may have contributed to reductions in spring river flow by enhancing evapotranspiration observed in the watershed of UJRB. These findings provide valuable information to improve our knowledge and understanding about the interlinkages between vegetation, climate and river flow at a watershed scale.
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Change points are abrupt variations in time series data. Such abrupt changes may represent transitions that occur between states. Detection of change points is useful in modelling and prediction of ...time series and is found in application areas such as medical condition monitoring, climate change detection, speech and image analysis, and human activity analysis. This survey article enumerates, categorizes, and compares many of the methods that have been proposed to detect change points in time series. The methods examined include both supervised and unsupervised algorithms that have been introduced and evaluated. We introduce several criteria to compare the algorithms. Finally, we present some grand challenges for the community to consider.
ABSTRACT
Acute myeloid leukemia (AML) is an uncommon but potentially catastrophic diagnosis with historically high mortality rates. The standard of care treatment remained unchanged for decades; ...however, recent discoveries of molecular drivers of leukemogenesis and disease progression have led to novel therapies for AML. Ongoing research and clinical trials are actively seeking to personalize therapy by identifying molecular targets, discovering patient specific and disease specific risk factors, and identifying effective combinations of modalities and drugs. This review focuses on important updates in diagnostic and disease classifications that reflect new understanding of the biology of AML, its mutational heterogeneity, some important genetic and environmental risk factors, and new treatment options including cytotoxic chemotherapy, novel targeted agents, and cellular therapies.
Defines the notion of an activity model learned from sensor data and presents key algorithms that form the core of the field Activity Learning: Discovering, Recognizing and Predicting Human Behavior ...from Sensor Data provides an in-depth look at computational approaches to activity learning from sensor data. Each chapter is constructed to provide practical, step-by-step information on how to analyze and process sensor data. The book discusses techniques for activity learning that include the following: * Discovering activity patterns that emerge from behavior-based sensor data * Recognizing occurrences of predefined or discovered activities in real time * Predicting the occurrences of activities The techniques covered can be applied to numerous fields, including security, telecommunications, healthcare, smart grids, and home automation. An online companion site enables readers to experiment with the techniques described in the book, and to adapt or enhance the techniques for their own use. With an emphasis on computational approaches, Activity Learning: Discovering, Recognizing, and Predicting Human Behavior from Sensor Data provides graduate students and researchers with an algorithmic perspective to activity learning.
Abstract We offer a cosmological model based on conventional general relativity (no speculative physics) that may resolve the Hubble tension. A reanalysis of the foundation of the Lambda-CDM model ...shows that general relativity alone does not specify what fraction of the mass density acts as the source term in Friedmann’s equation and what fraction acts as the source of the gravitational potential of condensed objects. This observation opens the way to alternative cosmological models within conventional general relativity, and it proves that the ΛCDM model is not the unique solution of Einstein’s equations for the usual cosmological sources of gravitation. We emphasize that the source of the gravitation potential in the ΛCDM model is the deviation δ ρ m of the mass density away from its average value, and not the total density of condensed masses as in Newtonian theory. Though not often stated, this is a modification of Newtonian gravitation within the ΛCDM model. The ΛCDM-NG model uses the freedom to move matter between source terms to restore the source of gravitational potential to its Newtonian form. There is no Hubble tension in the ΛCDM-NG model if the gravitational potential of condensed objects (stars, galaxies, and dark matter clouds) falls in a certain range, a range that does not seem unreasonable for the actual Universe. The deceleration parameter in the ΛCDM-NG model differs from that in the ΛCDM model, suggesting a test to distinguish between the two models.
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