Predicting Multi-actor collaborations using Hypergraphs

Social networks are now ubiquitous and most of them contain interactions involving multiple actors (groups) like author collaborations, teams or emails in an organizations, etc. Hypergraphs are natural structures to effectively capture multi-actor interactions which conventional dyadic graphs fail to capture. In this work the problem of predicting collaborations is addressed while modeling the collaboration network as a hypergraph network. The problem of predicting future multi-actor collaboration is mapped to hyperedge prediction problem. Given that the higher order edge prediction is an inherently hard problem, in this work we restrict to the task of predicting edges (collaborations) that have already been observed in past. In this work, we propose a novel use of hyperincidence temporal tensors to capture time varying hypergraphs and provides a tensor decomposition based prediction algorithm. We quantitatively compare the performance of the hypergraphs based approach with the conventional dyadic graph based approach. Our hypothesis that hypergraphs preserve the information that simple graphs destroy is corroborated by experiments using author collaboration network from the DBLP dataset. Our results demonstrate the strength of hypergraph based approach to predict higher order collaborations (size>4) which is very difficult using dyadic graph based approach. Moreover, while predicting collaborations of size>2 hypergraphs in most cases provide better results with an average increase of approx. 45% in F-Score for different sizes = {3,4,5,6,7}

Quantifying the Benefits of Resource Multiplexing in On-Demand Data Centers

On-demand data centers host multiple applications on server farms by dynamically provisioning resources in response to workload variations. The efficiency of such dynamic provisioning on the required server farm capacity is dependent on several factors — the granularity and frequency of reallocation, the number of applications being hosted, the amount of resource overprovisioning and the accuracy of workload prediction. In this paper, we quantify the effect of these factors on the multiplexing benefits achievable in an on-demand data center. Using traces of real e-commerce workloads, we demonstrate that the ability to allocate fractional server resources at fine time-scales of tens of seconds to a few minutes can increase the multiplexing benefits by 162-188% over coarsegrained reallocation. Our results also show that these benefits increase in the presence of large number of hosted applications as a result of high level of multiplexing. In addition, we demonstrate that such fine-grained multiplexing is achievable even in the presence of real-world (inaccurate) workload predictors and allows overprovisioning slack of nearly 35-70% over coarse-grained multiplexing

Observations of Multiple Surges Associated with Magnetic Activities in AR10484 on 25 October 2003

We present a multiwavelength study of recurrent surges observed in H{\alpha}, UV (SOHO/EIT) and Radio (Learmonth, Australia) from the super-active region NOAA 10484 on 25 October, 2003. Several bright structures visible in H{\alpha} and UV corresponding to subflares are also observed at the base of each surge. Type III bursts are triggered and RHESSI X-ray sources are evident with surge activity. The major surge consists of the bunches of ejective paths forming a fan-shape region with an angular size of (\approx 65\degree) during its maximum phase. The ejection speed reaches upto \sim200 km/s. The SOHO/MDI magnetograms reveal that a large dipole emerges east side of the active region on 18-20 October 2003, a few days before the surges. On October 25, 2003, the major sunspots were surrounded by "moat regions" with moving magnetic features (MMFs). Parasitic fragmented positive polarities were pushed by the ambient dispersion motion of the MMFs and annihilated with negative polarities at the borders of the moat region of the following spot to produce flares and surges. A topology analysis of the global Sun using PFSS shows that the fan structures visible in the EIT 171 A images follow magnetic field lines connecting the present AR to a preceding AR in the South East. Radio observations of type III bursts indicate that they are coincident with the surges, suggesting that magnetic reconnection is the driver mechanism. The magnetic energy released by reconnection is transformed into plasma heating and provides the kinetic energy for the ejections. A lack of a radio signature in the high corona suggests that the surges are confined to follow the closed field lines in the fans. We conclude that these cool surges may have some local heating effects in the closed loops, but probably play a minor role in global coronal heating and the surge material does not escape to the solar wind.Comment: Accepted for the Publication in ApJ; 25 pages, 10 Figures, and 1 Tabl

Method of lines solution of Richards' equation with spatially and temporally adaptive discretization techniques

Efficient, robust simulation of groundwater flow in the unsaturated zone remains difficult for problems characterized by sharp fronts in both space and time. Employing uniform spatial and temporal discretizations for the numerical solution of these problems can lead to inefficient and expensive simulations. In this work, we solve Richards' equation using the method of lines with both temporally and spatially adaptive approximations. The time discretization adapts both the approximation order and step-size based on formal error control to satisfy user-specified error tolerances. For the spatial discretization, we use h-refinement with a Lagrangian prediction of the fluid front location to determine when and where to refine. We evaluate our method in comparison with uniform spatial discretizations for several test problems. The numerical results demonstrate that this method provides a robust and efficient alternative to standard approaches for simulating variably saturated flow.Master of Science in Environmental Engineerin

Optimizing MapReduce for Highly Distributed Environments

MapReduce, the popular programming paradigm for large-scale data processing, has traditionally been deployed over tightly-coupled clusters where the data is already locally available. The assumption that the data and compute resources are available in a single central location, however, no longer holds for many emerging applications in commercial, scientific and social networking domains, where the data is generated in a geographically distributed manner. Further, the computational resources needed for carrying out the data analysis may be distributed across multiple data centers or community resources such as Grids. In this paper, we develop a modeling framework to capture MapReduce execution in a highly distributed environment comprising distributed data sources and distributed computational resources. This framework is flexible enough to capture several design choices and performance optimizations for MapReduce execution. We propose a model-driven optimization that has two key features: (i) it is end-to-end as opposed to myopic optimizations that may only make locally optimal but globally suboptimal decisions, and (ii) it can control multiple MapReduce phases to achieve low runtime, as opposed to single-phase optimizations that may control only individual phases. Our model results show that our optimization can provide nearly 82% and 64% reduction in execution time over myopic and single-phase optimizations, respectively. We have modified Hadoop to implement our model outputs, and using three different MapReduce applications over an 8-node emulated PlanetLab testbed, we show that our optimized Hadoop execution plan achieves 31-41% reduction in runtime over a vanilla Hadoop execution. Our model-driven optimization also provides several insights into the choice of techniques and execution parameters based on application and platform characteristics

Phonon-Induced Collective Modes in Spin-Orbit Coupled Polar Metals

We study the interplay between collective electronic and lattice modes in polar metals in an applied magnetic field aligned with the polar axis. Static spin-orbit coupling leads to the appearance of a particle-hole spin-flip continuum that is gapped at low energies in a finite field. We find that a weak spin-orbit assisted coupling between electrons and polar phonons leads to the appearance of new electronic collective modes. The strength of the applied magnetic field tunes the number of modes and their energies, which can lie both above and below the particle-hole continuum. For a range of field values, we identify Fano-like interference between the electronic continuum and phonons. We show that signatures of these collective modes can be observed in electron spin resonance experiments, and we provide the corresponding theoretical predictions.Comment: 24 pages, 8 figure