189,562 research outputs found

    The cusp: a window for particle exchange between the radiation belt and the solar wind

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    International audienceThe study focuses on a single particle dynamics in the cusp region. The topology of the cusp region in terms of magnetic field iso-B contours has been studied using the Tsyganenko 96 model (T96) as an example, to show the importance of an off-equatorial minimum on particle trapping. We carry out test particle simulations to demonstrate the bounce and drift motion. The "cusp trapping limit" concept is introduced to reflect the particle motion in the high latitude magnetospheric region. The spatial distribution of the "cusp trapping limit" shows that only those particles with near 90° pitch-angles can be trapped and drift around the cusp. Those with smaller pitch angles may be partly trapped in the iso-B contours, however, they will eventually escape along one of the magnetic field lines. There exist both open field lines and closed ones within the same drift orbit, indicating two possible destinations of these particles: those particles being lost along open field lines will be connected to the surface of the magnetopause and the solar wind, while those along closed ones will enter the equatorial radiation belt. Thus, it is believed that the cusp region can provide a window for particle exchange between these two regions. Some of the factors, such as dipole tilt angle, magnetospheric convection, IMF and the Birkeland current system, may influence the cusp's trapping capability and therefore affect the particle exchanging mechanism. Their roles are examined by both the analysis of cusp magnetic topology and test particle simulations

    Integrating Species Traits into Species Pools

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    Despite decades of research on the species‐pool concept and the recent explosion of interest in trait‐based frameworks in ecology and biogeography, surprisingly little is known about how spatial and temporal changes in species‐pool functional diversity (SPFD) influence biodiversity and the processes underlying community assembly. Current trait‐based frameworks focus primarily on community assembly from a static regional species pool, without considering how spatial or temporal variation in SPFD alters the relative importance of deterministic and stochastic assembly processes. Likewise, species‐pool concepts primarily focus on how the number of species in the species pool influences local biodiversity. However, species pools with similar richness can vary substantially in functional‐trait diversity, which can strongly influence community assembly and biodiversity responses to environmental change. Here, we integrate recent advances in community ecology, trait‐based ecology, and biogeography to provide a more comprehensive framework that explicitly considers how variation in SPFD, among regions and within regions through time, influences the relative importance of community assembly processes and patterns of biodiversity. First, we provide a brief overview of the primary ecological and evolutionary processes that create differences in SPFD among regions and within regions through time. We then illustrate how SPFD may influence fundamental processes of local community assembly (dispersal, ecological drift, niche selection). Higher SPFD may increase the relative importance of deterministic community assembly when greater functional diversity in the species pool increases niche selection across environmental gradients. In contrast, lower SPFD may increase the relative importance of stochastic community assembly when high functional redundancy in the species pool increases the influence of dispersal history or ecological drift. Next, we outline experimental and observational approaches for testing the influence of SPFD on assembly processes and biodiversity. Finally, we highlight applications of this framework for restoration and conservation. This species‐pool functional diversity framework has the potential to advance our understanding of how local‐ and regional‐scale processes jointly influence patterns of biodiversity across biogeographic regions, changes in biodiversity within regions over time, and restoration outcomes and conservation efforts in ecosystems altered by environmental change

    Dynamic feature selection for clustering high dimensional data streams

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    open access articleChange in a data stream can occur at the concept level and at the feature level. Change at the feature level can occur if new, additional features appear in the stream or if the importance and relevance of a feature changes as the stream progresses. This type of change has not received as much attention as concept-level change. Furthermore, a lot of the methods proposed for clustering streams (density-based, graph-based, and grid-based) rely on some form of distance as a similarity metric and this is problematic in high-dimensional data where the curse of dimensionality renders distance measurements and any concept of “density” difficult. To address these two challenges we propose combining them and framing the problem as a feature selection problem, specifically a dynamic feature selection problem. We propose a dynamic feature mask for clustering high dimensional data streams. Redundant features are masked and clustering is performed along unmasked, relevant features. If a feature's perceived importance changes, the mask is updated accordingly; previously unimportant features are unmasked and features which lose relevance become masked. The proposed method is algorithm-independent and can be used with any of the existing density-based clustering algorithms which typically do not have a mechanism for dealing with feature drift and struggle with high-dimensional data. We evaluate the proposed method on four density-based clustering algorithms across four high-dimensional streams; two text streams and two image streams. In each case, the proposed dynamic feature mask improves clustering performance and reduces the processing time required by the underlying algorithm. Furthermore, change at the feature level can be observed and tracked

    Heuristics Miners for Streaming Event Data

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    More and more business activities are performed using information systems. These systems produce such huge amounts of event data that existing systems are unable to store and process them. Moreover, few processes are in steady-state and due to changing circumstances processes evolve and systems need to adapt continuously. Since conventional process discovery algorithms have been defined for batch processing, it is difficult to apply them in such evolving environments. Existing algorithms cannot cope with streaming event data and tend to generate unreliable and obsolete results. In this paper, we discuss the peculiarities of dealing with streaming event data in the context of process mining. Subsequently, we present a general framework for defining process mining algorithms in settings where it is impossible to store all events over an extended period or where processes evolve while being analyzed. We show how the Heuristics Miner, one of the most effective process discovery algorithms for practical applications, can be modified using this framework. Different stream-aware versions of the Heuristics Miner are defined and implemented in ProM. Moreover, experimental results on artificial and real logs are reported

    Concepts of Drift and Selection in “The Great Snail Debate” of the 1950s and Early 1960s

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    Recently, much philosophical discussion has centered on the best way to characterize the concepts of random drift and natural selection, and, in particular, on the question of whether selection and drift can be conceptually distinguished (Beatty 1984; Brandon 2005; Hodge 1983, 1987; Millstein 2002, 2005; Pfeifer 2005; Shanahan 1992; Stephens 2004). These authors all contend, to a greater or lesser degree, that their concepts make sense of biological practice. So, it should be instructive to see how the concepts of drift and selection were distinguished by the disputants in a high-profile debate; debates such as these often force biologists to take a more philosophical turn, discussing the concepts at issue in greater detail than usual. A prime candidate for just such a case study is what William Provine (1986) has termed “The Great Snail Debate,” that is, the debate over the highly polymorphic land snails Cepaea nemoralis and Cepaea hortensis in the 1950s and early 1960s. This study will reveal that much of the present-day confusion over the concepts of drift and selection is rooted in confusions of the past. Nonetheless, there are lessons that can be learned about nonadaptiveness, indiscriminate sampling, and causality with respect to these two concepts. In particular, this paper will shed light on the following questions: 1) What is “drift”? Is “drift” a purely mathematical construct, a physical process analogous to the indiscriminate sampling of balls from an urn, or the outcome of a sampling process? 2) What is “nonadaptiveness,” and is a proponent of drift committed to claims that organisms’ traits are nonadaptive? 3) Can disputes concerning selection and drift be settled by statistics alone, or is causal information essential? If causal information is essential, what does that say about the concepts of “drift” and “selection” themselves

    Reservoir of Diverse Adaptive Learners and Stacking Fast Hoeffding Drift Detection Methods for Evolving Data Streams

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    The last decade has seen a surge of interest in adaptive learning algorithms for data stream classification, with applications ranging from predicting ozone level peaks, learning stock market indicators, to detecting computer security violations. In addition, a number of methods have been developed to detect concept drifts in these streams. Consider a scenario where we have a number of classifiers with diverse learning styles and different drift detectors. Intuitively, the current 'best' (classifier, detector) pair is application dependent and may change as a result of the stream evolution. Our research builds on this observation. We introduce the \mbox{Tornado} framework that implements a reservoir of diverse classifiers, together with a variety of drift detection algorithms. In our framework, all (classifier, detector) pairs proceed, in parallel, to construct models against the evolving data streams. At any point in time, we select the pair which currently yields the best performance. We further incorporate two novel stacking-based drift detection methods, namely the \mbox{FHDDMS} and \mbox{FHDDMS}_{add} approaches. The experimental evaluation confirms that the current 'best' (classifier, detector) pair is not only heavily dependent on the characteristics of the stream, but also that this selection evolves as the stream flows. Further, our \mbox{FHDDMS} variants detect concept drifts accurately in a timely fashion while outperforming the state-of-the-art.Comment: 42 pages, and 14 figure
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