MCRapper: Monte-Carlo Rademacher Averages for Poset Families and Approximate Pattern Mining

We present MCRapper, an algorithm for efficient computation of Monte-Carlo Empirical Rademacher Averages (MCERA) for families of functions exhibiting poset (e.g., lattice) structure, such as those that arise in many pattern mining tasks. The MCERA allows us to compute upper bounds to the maximum deviation of sample means from their expectations, thus it can be used to find both statistically-significant functions (i.e., patterns) when the available data is seen as a sample from an unknown distribution, and approximations of collections of high-expectation functions (e.g., frequent patterns) when the available data is a small sample from a large dataset. This feature is a strong improvement over previously proposed solutions that could only achieve one of the two. MCRapper uses upper bounds to the discrepancy of the functions to efficiently explore and prune the search space, a technique borrowed from pattern mining itself. To show the practical use of MCRapper, we employ it to develop an algorithm TFP-R for the task of True Frequent Pattern (TFP) mining. TFP-R gives guarantees on the probability of including any false positives (precision) and exhibits higher statistical power (recall) than existing methods offering the same guarantees. We evaluate MCRapper and TFP-R and show that they outperform the state-of-the-art for their respective tasks

Elevational patterns of Polylepis tree height (Rosaceae) in the high Andes of Peru: role of human impact and climatic conditions

We studied tree height in stands of high-Andean&nbsp;Polylepis&nbsp;forests in two cordilleras near Cuzco (Peru) with respect to variations in human impact and climatic conditions, and compared air and soil temperatures between qualitatively defined dry and humid slopes. We studied 46 forest plots of 100 m2&nbsp;of five&nbsp;Polylepis&nbsp;species at 3560&ndash;4680 m. We measured diameter at breast height (dbh) and tree height in the stands (1229 trees in total), as well as air and soil temperatures in a subset of plots. The data was analyzed combining plots of given species from different sites at the same elevation (&plusmn;100 m). There was no elevational decrease of mean maximum tree height across the entire data set. On humid slopes, tree height decreased continuously with elevation, whereas on dry slopes it peaked at middle elevations. With mean maximum tree heights of 9 m at 4530 m on the humid slopes and of 13 m at 4650 m on the dry slopes, we here document the tallest high-elevation forests found so far worldwide. These highest stands grow under cold mean growing season air temperatures (3.6 and 3.8&deg;C on humid vs. dry slopes) and mean growing season soil temperatures (5.1 vs. 4.6&deg;C). Mean annual air and soil temperature both decreased with elevation. Dry slopes had higher mean and maximum growing season air temperatures than humid slopes. Mean annual soil temperatures did not significantly differ and mean annual air temperatures only slightly differed between slopes. However, maximum air temperatures differed on average by 6.6 K between dry and humid slopes. This suggests that the differences in tree height between the two slopes are most likely due to differences in solar radiation as reflected by maximum air temperatures. Our study furthermore provides evidence that alpine&nbsp;Polylepis&nbsp;treelines grow under lower temperature conditions than global high-elevation treelines on average, suggesting that&nbsp;Polylepis&nbsp;species may have evolved special physiological adaptations to low temperatures.</p

Global change synergies and trade-offs between renewable energy and biodiversity

Reliance on fossil fuels is causing unprecedented climate change and is accelerating environmental degradation and global biodiversity loss. Together, climate change and biodiversity loss, if not averted urgently, may inflict severe damage on ecosystem processes, functions and services that support the welfare of modern societies. Increasing renewable energy deployment and expanding the current protected area network represent key solutions to these challenges, but conflicts may arise over the use of limited land for energy production as opposed to biodiversity conservation. Here, we compare recently identified core areas for the expansion of the global protected area network with the renewable energy potential available from land-based solar photovoltaic, wind energy and bioenergy (in the form of Miscanthus 9 giganteus). We show that these energy sources have very different biodiversity impacts and net energy contributions. The extent of risks and opportunities deriving from renewable energy development is highly dependent on the type of renewable source harvested, the restrictions imposed on energy harvest and the region considered, with Central America appearing at particularly high potential risk from renewable energy expansion. Without restrictions on power generation due to factors such as production and transport costs, we show that bioenergy production is a major potential threat to biodiversity, while the potential impact of wind and solar appears smaller than that of bioenergy. However, these differences become reduced when energy potential is restricted by external factors including local energy demand. Overall, we found that areas of opportunity for developing solar and wind energy with little harm to biodiversity could exist in several regions of the world, with the magnitude of potential impact being particularly dependent on restrictions imposed by local energy demand. The evidence provided here helps guide sustainable development of renewable energy and contributes to the targeting of global efforts in climate mitigation and biodiversity conservation

Comparison of Ventricular Refractory Periods Determined by Incremental and Decremental Scanning of an Extrastimulus

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/73345/1/j.1540-8159.1989.tb02699.x.pd

Synergies and trade-offs between renewable energy extraction and biodiversity conservation - a cross-national multi-factor analysis

Increased deployment of renewable energy can contribute towards mitigating climate change and improving air quality, wealth and development. However, renewable energy technologies are not free of environmental impacts; thus, it is important to identify opportunities and potential threats from the expansion of renewable energy deployment. Currently, there is no cross-national comprehensive analysis linking renewable energy potential simultaneously to socio-economic and political factors and biodiversity priority locations. Here, we quantify the relationship between the fraction of land-based renewable energy (including solar photovoltaic, wind and bioenergy) potential available outside the top biodiversity areas (i.e. outside the highest ranked 30% priority areas for biodiversity conservation) within each country, with selected socio-economic and geopolitical factors as well as biodiversity assets. We do so for two scenarios that identify priority areas for biodiversity conservation alternatively in a globally coordinated manner vs. separately for individual countries. We show that very different opportunities and challenges emerge if the priority areas for biodiversity protection are identified globally or designated nationally. In the former scenario, potential for solar, wind and bioenergy outside the top biodiversity areas is highest in developing countries, in sparsely populated countries and in countries of low biodiversity potential but with high air pollution mortality. Conversely, when priority areas for biodiversity protection are designated nationally, renewable energy potential outside the top biodiversity areas is highest in countries with good governance but also in countries with high biodiversity potential and population density. Overall, these results identify both clear opportunities but also risks that should be considered carefully when making decisions about renewable energy policies

Laterality of motor activity during normal and disturbed sleep

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New approaches to model and study social networks

We describe and develop three recent novelties in network research which are particularly useful for studying social systems. The first one concerns the discovery of some basic dynamical laws that enable the emergence of the fundamental features observed in social networks, namely the nontrivial clustering properties, the existence of positive degree correlations and the subdivision into communities. To reproduce all these features we describe a simple model of mobile colliding agents, whose collisions define the connections between the agents which are the nodes in the underlying network, and develop some analytical considerations. The second point addresses the particular feature of clustering and its relationship with global network measures, namely with the distribution of the size of cycles in the network. Since in social bipartite networks it is not possible to measure the clustering from standard procedures, we propose an alternative clustering coefficient that can be used to extract an improved normalized cycle distribution in any network. Finally, the third point addresses dynamical processes occurring on networks, namely when studying the propagation of information in them. In particular, we focus on the particular features of gossip propagation which impose some restrictions in the propagation rules. To this end we introduce a quantity, the spread factor, which measures the average maximal fraction of nearest neighbors which get in contact with the gossip, and find the striking result that there is an optimal non-trivial number of friends for which the spread factor is minimized, decreasing the danger of being gossiped.Comment: 16 Pages, 9 figure

Critical parameters and performance tests for the evaluation of digital data acquisition hardware

Recent developments of digital data acquisition systems allow real-time pre-processing of detector signals at a high count rate. These so-called pulse processing digitizers are powerful and versatile instruments offering techniques which are important for nuclear security, critical infrastructure protection, nuclear physics and radiation metrology. Certain aspects of digital data acquisition affect the performance of the total system in a critical way and therefore require special attention. This report presents a short introduction to digital data acquisition, followed by a discussion of the critical parameters which affect the performance in the lab and in the field. For some of the parameters, tests are proposed to assess the performance of digital data acquisition systems. Good practices are offered to guide the selection and evaluation of digital data acquisition systems. More general performance criteria which are not specifically related to digital data acquisition systems are discussed separately.JRC.D.4-Standards for Nuclear Safety, Security and Safeguard