One-Class Classification: Taxonomy of Study and Review of Techniques

One-class classification (OCC) algorithms aim to build classification models when the negative class is either absent, poorly sampled or not well defined. This unique situation constrains the learning of efficient classifiers by defining class boundary just with the knowledge of positive class. The OCC problem has been considered and applied under many research themes, such as outlier/novelty detection and concept learning. In this paper we present a unified view of the general problem of OCC by presenting a taxonomy of study for OCC problems, which is based on the availability of training data, algorithms used and the application domains applied. We further delve into each of the categories of the proposed taxonomy and present a comprehensive literature review of the OCC algorithms, techniques and methodologies with a focus on their significance, limitations and applications. We conclude our paper by discussing some open research problems in the field of OCC and present our vision for future research.Comment: 24 pages + 11 pages of references, 8 figure

Explaining extreme events of 2013 from a climate perspective

Attribution of extreme events is a challenging science and one that is currently undergoing considerable evolution. In this paper, 20 different research groups explored the causes of 16 different events that occurred in 2013. The findings indicate that human-caused climate change greatly increased the risk for the extreme heat waves assessed in this report. How human influence affected other types of events such as droughts, heavy rain events, and storms was less clear, indicating that natural vari- ability likely played a much larger role in these extremes. Multiple groups chose to look at both the Australian heat waves and the California drought, providing an opportunity to compare and contrast the strengths and weak- nesses of various methodologies. There was considerable agreement about the role anthropogenic climate change played in the events between the different assessments. This year three analyses were of severe storms and none found an anthropogenic signal. However, attribution assessments of these types of events pose unique challenges due to the often limited observational record. When human-influence for an event is not identified with the scientific tools available to us today, this means that if there is a human contribution, it cannot be distinguished from natural climate variability.&nbsp

Learning from the 2018 heatwave in the context of climate change: Are high-temperature extremes important for adaptation in Scotland?

To understand whether high temperatures and temperature extremes are important for climate change adaptation in Scotland, we place the 2018 heatwave in the context of past, present, and future climate, and provide a rapid but comprehensive impact analysis. The observed hottest day, 5-day, and 30-day period of 2018 and the 5-day period with the warmest nights had return periods of 5-15 years for 1950-2018. The warmest night and the maximum 30-day average nighttime temperature were more unusual with return periods of >30 years. Anthropogenic climate change since 1850 has made all these high-temperature extremes more likely. Higher risk ratios are found for experiments from the CMIP6-generation global climate model HadGEM3-GA6 compared to those from the very-large ensemble system weather@home. Between them, the best estimates of the risk ratios for daytime extremes range between 1.2-2.4, 1.2-2.3, and 1.4-4.0 for the 1-, 5-, and 30-day averages. For the corresponding nighttime extremes, the values are higher and the ranges wider (1.5->50, 1.5-5.5, and 1.6->50). The short-period nighttime extremes were more likely in 2018 than in 2017, suggesting a contribution from year-to-year climate variability to the risk enhancement of extreme temperatures due to anthropogenic effects. Climate projections suggest further substantial increases in the likelihood of 2018 temperatures between now and 2050, and that towards the end of the century every summer might be as hot as 2018. Major negative impacts occurred, especially on rural sectors, while transport and water infrastructure alleviated most impacts by implementing costly special measures. Overall, Scotland could cope with the impacts of the 2018 heatwave. However, given the likelihood increase of high-temperature extremes, uncertainty about consequences of even higher temperatures and/or repeated heatwaves, and substantial costs of preventing negative impacts, we conclude that despite its cool climate, high-temperature extremes are important to consider for climate change adaptation in Scotland

A Survey of Location Prediction on Twitter

Locations, e.g., countries, states, cities, and point-of-interests, are central to news, emergency events, and people's daily lives. Automatic identification of locations associated with or mentioned in documents has been explored for decades. As one of the most popular online social network platforms, Twitter has attracted a large number of users who send millions of tweets on daily basis. Due to the world-wide coverage of its users and real-time freshness of tweets, location prediction on Twitter has gained significant attention in recent years. Research efforts are spent on dealing with new challenges and opportunities brought by the noisy, short, and context-rich nature of tweets. In this survey, we aim at offering an overall picture of location prediction on Twitter. Specifically, we concentrate on the prediction of user home locations, tweet locations, and mentioned locations. We first define the three tasks and review the evaluation metrics. By summarizing Twitter network, tweet content, and tweet context as potential inputs, we then structurally highlight how the problems depend on these inputs. Each dependency is illustrated by a comprehensive review of the corresponding strategies adopted in state-of-the-art approaches. In addition, we also briefly review two related problems, i.e., semantic location prediction and point-of-interest recommendation. Finally, we list future research directions.Comment: Accepted to TKDE. 30 pages, 1 figur