Representation of perfectly reconstructed octave decomposition filter banks with set of decimators {2,4,4} via tree structure

In this letter, we prove that a filter bank with set of decimators {2,4,4} achieves perfect reconstruction if and only if it can be represented via a tree structure and each branch of the tree structure achieves perfect reconstruction

Coronal heating in multiple magnetic threads

Context. Heating the solar corona to several million degrees requires the conversion of magnetic energy into thermal energy. In this paper, we investigate whether an unstable magnetic thread within a coronal loop can destabilise a neighbouring magnetic thread. Aims. By running a series of simulations, we aim to understand under what conditions the destabilisation of a single magnetic thread can also trigger a release of energy in a nearby thread. Methods. The 3D magnetohydrodynamics code, Lare3d, is used to simulate the temporal evolution of coronal magnetic fields during a kink instability and the subsequent relaxation process. We assume that a coronal magnetic loop consists of non-potential magnetic threads that are initially in an equilibrium state. Results. The non-linear kink instability in one magnetic thread forms a helical current sheet and initiates magnetic reconnection. The current sheet fragments, and magnetic energy is released throughout that thread. We find that, under certain conditions, this event can destabilise a nearby thread, which is a necessary requirement for starting an avalanche of energy release in magnetic threads. Conclusions. It is possible to initiate an energy release in a nearby, non-potential magnetic thread, because the energy released from one unstable magnetic thread can trigger energy release in nearby threads, provided that the nearby structures are close to marginal stability

Wau: Location-Based Photo-Sharing and Friend-Finding Android Application

Wau (Where are u?) is a location-based photo-sharing and friend-finding Android application. The basic concept of my app is to help people avoid the hassle of having to call/text their friends in order to know where they are. The ability to simply tap on a name and visually see their location in reference to your own would be a huge convenience. Another neat feature is that it allows users to take and upload pictures wherever they are, and the only way for anyone to see the picture is to be within the same vicinity that it was taken. I imagine people using this functionality for scavenger hunts, geo-caching, or even for looking back at old photos in a specific area. The possibilities are endless

Clustering High Dimensional Data Using SVM

The Web contains massive amount of documents from across the globe to the point where it has become impossible to classify them manually. This project’s goal is to find a new method for clustering documents that are as close to humans’ classification as possible and at the same time to reduce the size of the documents. This project uses a combination of Latent Semantic Indexing (LSI) with Singular Value Decomposition (SVD) calculation as well as Support Vector Machine (SVM) classification. With SVD, data sets are decomposed and can be truncated to reduce the data sets size. The reduced data set will then be used to cluster. With SVM, clustered data set is used for training to allow new data to be classified based on SVM’s prediction. The project’s result show that the method of combining SVD and SVM is able to reduce data size and classifies documents reasonably compared to humans’ classification

Gamma-ray emission from globular clusters

Over the last few years, the data obtained using the Large Area Telescope (LAT) aboard the Fermi Gamma-ray Space Telescope has provided new insights on high-energy processes in globular clusters, particularly those involving compact objects such as Millisecond Pulsars (MSPs). Gamma-ray emission in the 100 MeV to 10 GeV range has been detected from more than a dozen globular clusters in our galaxy, including 47 Tucanae and Terzan 5. Based on a sample of known gamma-ray globular clusters, the empirical relations between gamma-ray luminosity and properties of globular clusters such as their stellar encounter rate, metallicity, and possible optical and infrared photon energy densities, have been derived. The measured gamma-ray spectra are generally described by a power law with a cut-off at a few gigaelectronvolts. Together with the detection of pulsed gamma-rays from two MSPs in two different globular clusters, such spectral signature lends support to the hypothesis that gamma-rays from globular clusters represent collective curvature emission from magnetospheres of MSPs in the clusters. Alternative models, involving Inverse-Compton (IC) emission of relativistic electrons that are accelerated close to MSPs or pulsar wind nebula shocks, have also been suggested. Observations at >100 GeV by using Fermi/LAT and atmospheric Cherenkov telescopes such as H.E.S.S.-II, MAGIC-II, VERITAS, and CTA will help to settle some questions unanswered by current data.Comment: 11 pages, 7 figures, 2 tables, J. Astron. Space Sci., in pres

Outward Influence and Cascade Size Estimation in Billion-scale Networks

Estimating cascade size and nodes' influence is a fundamental task in social, technological, and biological networks. Yet this task is extremely challenging due to the sheer size and the structural heterogeneity of networks. We investigate a new influence measure, termed outward influence (OI), defined as the (expected) number of nodes that a subset of nodes $S$ will activate, excluding the nodes in S. Thus, OI equals, the de facto standard measure, influence spread of S minus |S|. OI is not only more informative for nodes with small influence, but also, critical in designing new effective sampling and statistical estimation methods. Based on OI, we propose SIEA/SOIEA, novel methods to estimate influence spread/outward influence at scale and with rigorous theoretical guarantees. The proposed methods are built on two novel components 1) IICP an important sampling method for outward influence, and 2) RSA, a robust mean estimation method that minimize the number of samples through analyzing variance and range of random variables. Compared to the state-of-the art for influence estimation, SIEA is $\Omega(\log^4 n)$ times faster in theory and up to several orders of magnitude faster in practice. For the first time, influence of nodes in the networks of billions of edges can be estimated with high accuracy within a few minutes. Our comprehensive experiments on real-world networks also give evidence against the popular practice of using a fixed number, e.g. 10K or 20K, of samples to compute the "ground truth" for influence spread.Comment: 16 pages, SIGMETRICS 201