Selection Effects in Identifying Magnetic Clouds and the Importance of the Closest Approach Parameter

This study is motivated by the unusually low number of magnetic clouds (MCs) that are strictly identified within interplanetary coronal mass ejections (ICMEs), as observed at 1 AU; this is usually estimated to be around 30% or lower. But a looser definition of MCs may significantly increase this percentage. Another motivation is the unexpected shape of the occurrence distribution of the observers' "closest approach distances" (measured from a MC's axis, and called CA) which drops off somewhat rapidly as |CA| (in % of MC radius) approaches 100%, based on earlier studies. We suggest, for various geometrical and physical reasons, that the |CA|-distribution should be somewhere between a uniform one and the one actually observed, and therefore the 30% estimate should be higher. So we ask, When there is a failure to identify a MC within an ICME, is it occasionally due to a large |CA| passage, making MC identification more difficult, i.e., is it due to an event selection effect? In attempting to answer this question we examine WIND data to obtain an accurate distribution of the number of MCs vs. |CA| distance, whether the event is ICME-related or not, where initially a large number of cases (N=98) are considered. This gives a frequence distribution that is far from uniform, confirming earlier studies. This along with the fact that there are many ICME identification-parameters that do not depend on |CA| suggest that, indeed an MC event selection effect may explain at least part of the low ratio of (No. MCs)/(No. ICMEs). We also show that there is an acceptable geometrical and physical consistency in the relationships for both average "normalized" magnetic field intensity change and field direction change vs. |CA| within a MC, suggesting that our estimates of |CA|, B(sub 0) (magnetic field intensity on the axis), and choice of a proper "cloud coordinate" system (all needed in the analysis) are acceptably accurate. Therefore the MC fitting model (Lepping et al., 1990) is adequate, on average, for our analysis. However, this selection effect is not likely to completely answer our original question, on the unexpected ratio of MCs to ICMEs, so we must look for other factors, such as peculiarities of CME birth conditions. As a by-product of this analysis, we determine that the first order structural effects within a MC due to its interaction with the solar wind, plus the MC's usual expansion at 1 AU (i.e., the non-force free components of the MC's field) are, on average, weakly dependent on radial distance from the MC's axis; that is, in the outer reaches of a typical MC the non-force free effects show up, but even there they are rather weak. Finally, we show that it is not likely that a MC's size distribution statistically controls the occurrence distribution of the estimated |CA|s

Comparisons of Characteristics of Magnetic Clouds and Cloud-Like Structures During 1995-2012

Using eighteen years (1995 - 2012) of solar wind plasma and magnetic field data (observed by the Wind spacecraft), solar activity (e.g. sunspot number: SSN), and the geomagnetic activity index (Dst), we have identified 168 magnetic clouds (MCs) and 197 magnetic cloud - like structures (MCLs), and we have made relevant comparisons. The following features are found during seven different periods (TP: Total period during 1995 - 2012, P1 and P2: first and second half period during 1995 - 2003 and 2004 - 2012, Q1 and Q2: quiet periods during 1995 - 1997 and 2007 - 2009, A1 and A2: active periods during 1998 - 2006 and 2010 - 2012). (1) During the total period the yearly occurrence frequency is 9.3 for MCs and 10.9 for MCLs. (2) In the quiet periods Q1 > Q1 and Q2 > Q2, but in the active periods A1 A1 and A2 A2. (3) The minimum Bz (Bzmin) inside of a MC is well correlated with the intensity of geomagnetic activity, Dstmin (minimum Dst found within a storm event) for MCs (with a Pearson correlation coefficient, c.c. = 0.75, and the fitting function is Dstmin = 0.90+7.78Bzmin), but Bzmin for MCLs is not well correlated with the Dst index (c.c. = 0.56, and the fitting function is Dstmin = -9.40+ 4.58 Bzmin). (4) MCs play a major role in producing geomagnetic storms: the absolute value of the average Dstmin (MC = -70 nT) for MCs associated geomagnetic storms is two times stronger than that for MCLs (MCL = -35 nT), due to the difference in the IMF (interplanetary magnetic field) strength. (5) The SSN is not correlated with MCs (TP, c.c. = 0.27), but is well associated with MCLs (TP, c.c. = 0.85). Note that the c.c. for SSN vs. P2 is higher than that for SSN vs. P2. (6) Averages of IMF, solar wind speed, and density inside of the MCs are higher than those inside of the MCLs. (7) The average of MC duration (approx. = 18.82 hours) is approx. = 20 % longer than the average of MCL duration (approx. = 15.69 hours). (8) There are more MCs than MCLs in the quiet solar period, and more MCLs than MCs in the active solar period, probably due to the interaction between a MC and another significant interplanetary disturbance (including another MC) which could obviously change the character of a MC, but we speculate that some MCLs are no doubt due to other factors such as complex birth conditions at the Sun

Searching and Ranking the Suitable Web Services with the Ontology-Based Measurements

One of the major problems for seamlessly electronic business is how to find a suitable web services. Only the syntax and semantic comparison do not precisely find the suitable web services for they are procedures embedded with a complicated thought. In this paper, we propose an effective approach based on the ontology to solve this problem. With the help of ontology-based metrics, we can measure a web service matching degree to a given request and determine the rank in which the advertisement matches the request. Simulations are also performed, and the results show that our method can have a good precision and recall rate

Trapping effects on inflation

We develop a Lagrangian approach based on the influence functional method so as to derive self-consistently the Langevin equation for the inflaton field in the presence of trapping points along the inflaton trajectory. The Langevin equation exhibits the backreaction and the fluctuation-dissipation relation of the trapping. The fluctuation is induced by a multiplicative colored noise that can be identified as the the particle number density fluctuations and the dissipation is a new effect that may play a role in the trapping with a strong coupling. In the weak coupling regime, we calculate the power spectrum of the noise-driven inflaton fluctuations for a single trapping point and studied its variation with the trapping location. We also consider a case with closely spaced trapping points and find that the resulting power spectrum is blue.Comment: 13 pages, 2 figure

THE EFFECTS OF PUSH-PULL-MOORING ON THE SWITCHING MODEL FOR SOCIAL NETWORK SITES MIGRATION

Although the number of users has been growing exponentially in SNSs, some SNSs are facing a financial crisis and might be shut down in the near future. Therefore, understand users\u27 incentives to switch to another SNS has great influence on operators\u27 business performance. The study extended Push-Pull-Mooring migratory theory to explain the switching behaviors of users in SNS. Structural equation modeling will applied to analyze data collected from a filed survey. The result can construct a solid switching framework and help operators to understand their customer better

Distributed Training Large-Scale Deep Architectures

Scale of data and scale of computation infrastructures together enable the current deep learning renaissance. However, training large-scale deep architectures demands both algorithmic improvement and careful system configuration. In this paper, we focus on employing the system approach to speed up large-scale training. Via lessons learned from our routine benchmarking effort, we first identify bottlenecks and overheads that hinter data parallelism. We then devise guidelines that help practitioners to configure an effective system and fine-tune parameters to achieve desired speedup. Specifically, we develop a procedure for setting minibatch size and choosing computation algorithms. We also derive lemmas for determining the quantity of key components such as the number of GPUs and parameter servers. Experiments and examples show that these guidelines help effectively speed up large-scale deep learning training

Phylo-mLogo: an interactive and hierarchical multiple-logo visualization tool for alignment of many sequences

BACKGROUND: When aligning several hundreds or thousands of sequences, such as epidemic virus sequences or homologous/orthologous sequences of some big gene families, to reconstruct the epidemiological history or their phylogenies, how to analyze and visualize the alignment results of many sequences has become a new challenge for computational biologists. Although there are several tools available for visualization of very long sequence alignments, few of them are applicable to the alignments of many sequences. RESULTS: A multiple-logo alignment visualization tool, called Phylo-mLogo, is presented in this paper. Phylo-mLogo calculates the variabilities and homogeneities of alignment sequences by base frequencies or entropies. Different from the traditional representations of sequence logos, Phylo-mLogo not only displays the global logo patterns of the whole alignment of multiple sequences, but also demonstrates their local homologous logos for each clade hierarchically. In addition, Phylo-mLogo also allows the user to focus only on the analysis of some important, structurally or functionally constrained sites in the alignment selected by the user or by built-in automatic calculation. CONCLUSION: With Phylo-mLogo, the user can symbolically and hierarchically visualize hundreds of aligned sequences simultaneously and easily check the changes of their amino acid sites when analyzing many homologous/orthologous or influenza virus sequences. More information of Phylo-mLogo can be found at URL