189,562 research outputs found
The cusp: a window for particle exchange between the radiation belt and the solar wind
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
Despite decades of research on the speciesâpool concept and the recent explosion of interest in traitâbased frameworks in ecology and biogeography, surprisingly little is known about how spatial and temporal changes in speciesâpool functional diversity (SPFD) influence biodiversity and the processes underlying community assembly. Current traitâbased 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, speciesâpool 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 functionalâtrait diversity, which can strongly influence community assembly and biodiversity responses to environmental change. Here, we integrate recent advances in community ecology, traitâbased 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 speciesâpool functional diversity framework has the potential to advance our understanding of how localâ and regionalâscale 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
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 âdensityâ 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
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 âThe Great Snail Debateâ of the 1950s and Early 1960s
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 âThe Great Snail Debate,â 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 âdriftâ? Is âdriftâ 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 ânonadaptiveness,â and is a proponent of drift committed to claims that organismsâ 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 âdriftâ and âselectionâ themselves
Reservoir of Diverse Adaptive Learners and Stacking Fast Hoeffding Drift Detection Methods for Evolving Data Streams
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
- âŠ