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We apply the statistical sparse jump model, a recently developed, interpretable and robust regime-switching model, to infer key features that drive the return dynamics of the largest cryptocurrencies. The algorithm jointly performs feature selection, parameter estimation, and state classification. Our large set of candidate features are based on cryptocurrency, sentiment and financial market-based time series that have been identified in the emerging literature to affect cryptocurrency returns, while others are new. In our empirical work, we demonstrate that a three-state model best describes the dynamics of cryptocurrency returns. The states have natural market-based interpretations as they correspond to bull, neutral, and bear market regimes, respectively. Using the data-driven feature selection methodology, we are able to determine which features are important and which ones are not. In particular, out of the set of candidate features, we show that first moments of returns, features representing trends and reversal signals, market activity and public attention are key drivers of crypto market dynamics.

Tilltal (Tillgängligt kulturarv för forskning i tal, 'Accessible cultural heritage for speech research') is a multidisciplinary and methodological project undertaken by the Institute of Language and Folklore, KTH Royal Institute of Technology, and The Swedish National Archives in cooperation with the National Language Bank and SWE-CLARIN [1]. It aims to provide researchers better access to archival audio recordings using methods from language technology. The project comprises three case studies and one activity and usage study. In the case studies, actual research agendas from three different fields (ethnology, sociolinguistics and interaction analysis) serve as a basis for identifying procedures that may be simplified with the aid of digital tools. In the activity and usage study, we are applying an activity-theoretical approach with the aim of involving researchers and investigating how they use - and would like to be able to use - the archival resources at ISOF. Involving researchers in participatory design ensures that digital solutions are suggested and evaluated in relation to the requirements expressed by researchers engaged in specific research tasks [2]. In this paper we focus on one of the case studies, which investigates the process by which personal experience narratives are transformed into cultural heritage [3], and account for our results in exploring how different types of text material from the archives can be used to find relevant sections of the audio recordings. Finally, we discuss what lessons can be learned, and what conclusions can be drawn, from our experiences of interdisciplinary collaboration in the project., QC 20210322

QC 20200310

Microorganisms are vital in mediating the earth’s biogeochemical cycles; yet, despite our rapidly increasing ability to explore complex environmental microbial communities, the relationship between microbial community structure and ecosystem processes remains poorly understood. Here, we address a fundamental and unanswered question in microbial ecology: ‘When do we need to understand microbial community structure to accurately predict function?’ We present a statistical analysis investigating the value of environmental data and microbial community structure independently and in combination for explaining rates of carbon and nitrogen cycling processes within 82 global datasets. Environmental variables were the strongest predictors of process rates but left 44% of variation unexplained on average, suggesting the potential for microbial data to increase model accuracy. Although only 29% of our datasets were significantly improved by adding information on microbial community structure, we observed improvement in models of processes mediated by narrow phylogenetic guilds via functional gene data, and conversely, improvement in models of facultative microbial processes via community diversity metrics. Our results also suggest that microbial diversity can strengthen predictions of respiration rates beyond microbial biomass parameters, as 53% of models were improved by incorporating both sets of predictors compared to 35% by microbial biomass alone. Our analysis represents the first comprehensive analysis of research examining links between microbial community structure and ecosystem function. Taken together, our results indicate that a greater understanding of microbial communities informed by ecological principles may enhance our ability to predict ecosystem process rates relative to assessments based on environmental variables and microbial physiology.

The aim of the BENCHOP project is to provide the finance community with a common suite of benchmark problems for option pricing. We provide a detailed description of the six benchmark problems together with methods to compute reference solutions. We have implemented fifteen different numerical methods for these problems, and compare their relative performance. All implementations are available on line and can be used for future development and comparisons