Reading hospitality mutually

This article addresses debates in geography regarding the nature and significance of hospitality. Despite increasingly inhospitable policy landscapes across the Global North, grassroots hospitality initiatives stubbornly persist, including various global travel-based initiatives and networks. Drawing from research with these travel networks, we argue that hospitality is fundamentally based on a pervasive, mutualistic sociality in a multitude of forms. Such initiatives, and hospitality more generally, can be better understood in terms of their relationship to these wider mutualities. we therefore use Peter Kropotkin’s anarchist-geographic concept of mutual aid – in conversation with Jacques Derrida and other thinkers – to reimagine hospitality as ‘mutual hospitableness’; systemic, spatio-temporally expansive, and underpinned by a conception of self that is constituted through, and gains its vitality from, intertwinement with the other

Flexible and efficient spatial extremes emulation via variational autoencoders

Many real-world processes have complex tail dependence structures that cannot be characterized using classical Gaussian processes. More flexible spatial extremes models exhibit appealing extremal dependence properties but are often exceedingly prohibitive to fit and simulate from in high dimensions. In this paper, we develop a new spatial extremes model that has flexible and non-stationary dependence properties, and we integrate it in the encoding-decoding structure of a variational autoencoder (XVAE), whose parameters are estimated via variational Bayes combined with deep learning. The XVAE can be used as a spatio-temporal emulator that characterizes the distribution of potential mechanistic model output states and produces outputs that have the same statistical properties as the inputs, especially in the tail. As an aside, our approach also provides a novel way of making fast inference with complex extreme-value processes. Through extensive simulation studies, we show that our XVAE is substantially more time-efficient than traditional Bayesian inference while also outperforming many spatial extremes models with a stationary dependence structure. To further demonstrate the computational power of the XVAE, we analyze a high-resolution satellite-derived dataset of sea surface temperature in the Red Sea, which includes 30 years of daily measurements at 16703 grid cells. We find that the extremal dependence strength is weaker in the interior of Red Sea and it has decreased slightly over time.Comment: 30 pages, 8 figure

Advances in Non-parametric Hypothesis Testing with Kernels

Non-parametric statistical hypothesis testing procedures aim to distinguish the null hypothesis against the alternative with minimal assumptions on the model distributions. In recent years, the maximum mean discrepancy (MMD) has been developed as a measure to compare two distributions, which is applicable to two-sample problems and independence tests. With the aid of reproducing kernel Hilbert spaces (RKHS) that are rich-enough, MMD enjoys desirable statistical properties including characteristics, consistency, and maximal test power. Moreover, MMD receives empirical successes in complex tasks such as training and comparing generative models. Stein’s method also provides an elegant probabilistic tool to compare unnormalised distributions, which commonly appear in practical machine learning tasks. Combined with rich-enough RKHS, the kernel Stein discrepancy (KSD) has been developed as a proper discrepancy measure between distributions, which can be used to tackle one-sample problems (or goodness-of-fit tests). The existing development of KSD applies to a limited choice of domains, such as Euclidean space or finite discrete sets, and requires complete data observations, while the current MMD constructions are limited by the choice of simple kernels where the power of the tests suffer, e.g. high-dimensional image data. The main focus of this thesis is on the further advancement of kernel-based statistics for hypothesis testings. Firstly, Stein operators are developed that are compatible with broader data domains to perform the corresponding goodness-of-fit tests. Goodness-of-fit tests for general unnormalised densities on Riemannian manifolds, which are of the non-Euclidean topology, have been developed. In addition, novel non-parametric goodness-of-fit tests for data with censoring are studied. Then the tests for data observations with left truncation are studied, e.g. times of entering the hospital always happen before death time in the hospital, and we say the death time is truncated by the entering time. We test the notion of independence beyond truncation by proposing a kernelised measure for quasi-independence. Finally, we study the deep kernel architectures to improve the two-sample testing performances

Crosstalk between Depression, Anxiety, and Dementia: Comorbidity in Behavioral Neurology and Neuropsychiatry

This Special Issue highlights the most recent research on depression, anxiety and dementia, with attention to comorbidity in a range of diseases. The symptoms of depression, anxiety and dementia are the most common comorbid manifestations present in patients suffering from neurodegenerative and psychiatric diseases. Together, these illnesses constitute an extremely complex and challenging research field due to their inherent multifactorial causative factors, heterogeneous pathogenesis, and mental and behavioral manifestations. This Special Issue covers laboratory, clinical and statistical studies on the crosstalk between depression, anxiety, dementia, Alzheimer’s disease, multiple sclerosis, schizophrenia, diabetes mellitus, Down’s syndrome, and/or compulsive disorders. It contains contributions from 71 authors, has been reviewed by 25 referees, and edited by three academic editors and one managing editor

Mapping impacts of education for wilderness management planning

Thesis (Ph.D.) University of Alaska Fairbanks, 1998Wilderness education is considered a key response to abate physical impacts caused by wilderness recreationists, but education's impacts upon the psychological values of wilderness are unknown. This investigation used a wilderness purism scale to measure how minimum impact instruction affects the intensity and quality of a student's wilderness experience and the relation of these expectations and preferences to appreciation, knowledge, and concern for the environment as a whole, i.e., environmental literacy. A wilderness purism scale, a spatial scale, and wilderness management scale measured how wilderness education affects recreationists' limits of unacceptability in wilderness conditions. Effects of wilderness education on multiple perceptions of wilderness specific to particular groups, are explained. Methods of how these can be collected, organized, and mapped using a GIS approach are demonstrated and techniques to build a wilderness experience typology are outlined. The investigation determined that environmental literacy is correlated with wilderness purism. Student's expectations and ethical perspectives toward wilderness became stronger following wilderness leadership education courses, specifically, their perceptions of wildness, experiential factors, and ethical perspectives of the wilderness experience. Educational programs increased respondents' wilderness perceptions and their desired spatial buffer distances from unacceptable conditions in wilderness. Distances from sights and sounds were found to be critical to wildemess recreationists' wilderness experience relating to sensing unacceptable conditions inside wilderness boundaries and "knowing" that unacceptable (human-made) conditions do not exist. Educators may use the findings to better design and assess their program's effectiveness. Results of the methodology could aid Limits of Acceptable Change (LAC) process for wilderness planning. Wilderness managers may use the protocol to plan for the maintenance of wilderness opportunities to meet increasing demands brought about by education. Management must be prepared to protect suitable conditions for this potentially growing population. If managers zone wilderness accordingly to wilderness purism groups, they can protect vast areas from bio/physical impacts by using the processes described in this study. It is a tool for managing wilderness areas for a range of wilderness experiences which will aid in insuring protection of wildlife, ecosystem integrity, and native biodiversity

Applied and Computational Statistics

Research without statistics is like water in the sand; the latter is necessary to reap the benefits of the former. This collection of articles is designed to bring together different approaches to applied statistics. The studies presented in this book are a tiny piece of what applied statistics means and how statistical methods find their usefulness in different fields of research from theoretical frames to practical applications such as genetics, computational chemistry, and experimental design. This book presents several applications of the statistics: A new continuous distribution with five parameters—the modified beta Gompertz distribution; A method to calculate the p-value associated with the Anderson–Darling statistic; An approach of repeated measurement designs; A validated model to predict statement mutations score; A new family of structural descriptors, called the extending characteristic polynomial (EChP) family, used to express the link between the structure of a compound and its properties. This collection brings together authors from Europe and Asia with a specific contribution to the knowledge in regards to theoretical and applied statistics

Survival analysis of bank loans and credit risk prognosis

A dissertation submitted to the Faculty of Science, University of the Witwatersrand, Johannesburg, in fulfilment of requirements for the degree of Master of Science. Johannesburg, 2015.Standard survival analysis methods model lifetime data where cohorts are tracked from the point of origin, until the occurrence of an event. If more than one event occurs, a special model is chosen to handle competing risks. Moreover, if the events are defined such that most subjects are not susceptible to the event(s) of interest, standard survival methods may not be appropriate. This project is an application of survival analysis in a consumer credit context. The data used in this study was obtained from a major South African financial institution covering a five year observation period from April 2009 to March 2014. The aim of the project was to follow up on cohorts from the point where vehicle finance loans originated to either default or early settlement events and compare survival and logistic modeling methodologies. As evidenced by the empirical Kaplain Meier survival curve, the data typically had long term survivors with heavy censoring as at March 2014. Cause specific Cox regression models were fitted and an adjustment was made for each model, to accommodate a proportion p of long term survivors. The corresponding Cumulative Incidence Curves were calculated per model, to determine probabilities at a fixed horizon of 48 months. Given the complexity of the consumer credit lifetime data at hand, we investigated how logistic regression methods would compare. Logistic regression models were fitted per event type. The models were assessed for goodness of fit. Their ability to differentiate risk were determined using the model Gini Statistics. Model assessment results were satisfactory. Methodologies were compared for each event type using Receiver Operating Characteristic curves and area under the curves. The Results show that survival methods perform better than logistic regression methods when modelling lifetime data in the presence of competing risks and long term survivors

Towards Sustainable Global Food Systems

environment; food; agriculture; policy; global food system