Stochastic blockmodel approximation of a graphon: Theory and consistent estimation

Non-parametric approaches for analyzing network data based on exchangeable graph models (ExGM) have recently gained interest. The key object that defines an ExGM is often referred to as a graphon. This non-parametric perspective on network modeling poses challenging questions on how to make inference on the graphon underlying observed network data. In this paper, we propose a computationally efficient procedure to estimate a graphon from a set of observed networks generated from it. This procedure is based on a stochastic blockmodel approximation (SBA) of the graphon. We show that, by approximating the graphon with a stochastic block model, the graphon can be consistently estimated, that is, the estimation error vanishes as the size of the graph approaches infinity.Comment: 20 pages, 4 figures, 2 algorithms. Neural Information Processing Systems (NIPS), 201

Predicting Intermediate Storage Performance for Workflow Applications

Configuring a storage system to better serve an application is a challenging task complicated by a multidimensional, discrete configuration space and the high cost of space exploration (e.g., by running the application with different storage configurations). To enable selecting the best configuration in a reasonable time, we design an end-to-end performance prediction mechanism that estimates the turn-around time of an application using storage system under a given configuration. This approach focuses on a generic object-based storage system design, supports exploring the impact of optimizations targeting workflow applications (e.g., various data placement schemes) in addition to other, more traditional, configuration knobs (e.g., stripe size or replication level), and models the system operation at data-chunk and control message level. This paper presents our experience to date with designing and using this prediction mechanism. We evaluate this mechanism using micro- as well as synthetic benchmarks mimicking real workflow applications, and a real application.. A preliminary evaluation shows that we are on a good track to meet our objectives: it can scale to model a workflow application run on an entire cluster while offering an over 200x speedup factor (normalized by resource) compared to running the actual application, and can achieve, in the limited number of scenarios we study, a prediction accuracy that enables identifying the best storage system configuration

Graphene-based spin-pumping transistor

We demonstrate with a fully quantum-mechanical approach that graphene can function as gate-controllable transistors for pumped spin currents, i.e., a stream of angular momentum induced by the precession of adjacent magnetizations, which exists in the absence of net charge currents. Furthermore, we propose as a proof of concept how these spin currents can be modulated by an electrostatic gate. Because our proposal involves nano-sized systems that function with very high speeds and in the absence of any applied bias, it is potentially useful for the development of transistors capable of combining large processing speeds, enhanced integration and extremely low power consumption

Graphene as a non-magnetic spin-current lens

In spintronics, the ability to transport magnetic information often depends on the existence of a spin current traveling between two different magnetic objects acting as source and probe. A large fraction of this information never reaches the probe and is lost because the spin current tends to travel omni-directionally. We propose that a curved boundary between a gated and a non-gated region within graphene acts as an ideal lens for spin currents despite being entirely of non-magnetic nature. We show as a proof of concept that such lenses can be utilized to redirect the spin current that travels away from a source onto a focus region where a magnetic probe is located, saving a considerable fraction of the magnetic information that would be otherwise lost.Comment: 9 pages, 3 figure

Light controlled magnetoresistance and magnetic field controlled photoresistance in CoFe film deposited on BiFeO3

We present a magnetoresistive-photoresistive device based on the interaction of a piezomagnetic CoFe thin film with a photostrictive BiFeO3 substrate that undergoes light-induced strain. The magnitude of the resistance and magnetoresistance in the CoFe film can be controlled by the wavelength of the incident light on the BiFeO3. Moreover, a light-induced decrease in anisotropic magnetoresistance is detected due to an additional magnetoelastic contribution to magnetic anisotropy of the CoFe film. This effect may find applications in photo-sensing systems, wavelength detectors and can possibly open a research development in light-controlled magnetic switching properties for next generation magnetoresistive memory devices.Comment: 5 pages, 4 figures, journal pape

Dataplane Specialization for High-performance OpenFlow Software Switching

OpenFlow is an amazingly expressive dataplane program- ming language, but this expressiveness comes at a severe performance price as switches must do excessive packet clas- sification in the fast path. The prevalent OpenFlow software switch architecture is therefore built on flow caching, but this imposes intricate limitations on the workloads that can be supported efficiently and may even open the door to mali- cious cache overflow attacks. In this paper we argue that in- stead of enforcing the same universal flow cache semantics to all OpenFlow applications and optimize for the common case, a switch should rather automatically specialize its dat- aplane piecemeal with respect to the configured workload. We introduce ES WITCH , a novel switch architecture that uses on-the-fly template-based code generation to compile any OpenFlow pipeline into efficient machine code, which can then be readily used as fast path. We present a proof- of-concept prototype and we demonstrate on illustrative use cases that ES WITCH yields a simpler architecture, superior packet processing speed, improved latency and CPU scala- bility, and predictable performance. Our prototype can eas- ily scale beyond 100 Gbps on a single Intel blade even with complex OpenFlow pipelines

Monte Carlo Simulations of Ultrathin Magnetic Dots

In this work we study the thermodynamic properties of ultrathin ferromagnetic dots using Monte Carlo simulations. We investigate the vortex density as a function of the temperature and the vortex structure in monolayer dots with perpendicular anisotropy and long-range dipole interaction. The interplay between these two terms in the hamiltonian leads to an interesting behavior of the thermodynamic quantities as well as the vortex density.Comment: 10 figure