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Using a power law distribution to describe big data

Published in:
HPEC 2015: IEEE Conf. on High Performance Extreme Computing, 15-17 September 2015.

Summary

The gap between data production and user ability to access, compute and produce meaningful results calls for tools that address the challenges associated with big data volume, velocity and variety. One of the key hurdles is the inability to methodically remove expected or uninteresting elements from large data sets. This difficulty often wastes valuable researcher and computational time by expending resources on uninteresting parts of data. Social sensors, or sensors which produce data based on human activity, such as Wikipedia, Twitter, and Facebook have an underlying structure which can be thought of as having a Power Law distribution. Such a distribution implies that few nodes generate large amounts of data. In this article, we propose a technique to take an arbitrary dataset and compute a power law distributed background model that bases its parameters on observed statistics. This model can be used to determine the suitability of using a power law or automatically identify high degree nodes for filtering and can be scaled to work with big data.
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Summary

The gap between data production and user ability to access, compute and produce meaningful results calls for tools that address the challenges associated with big data volume, velocity and variety. One of the key hurdles is the inability to methodically remove expected or uninteresting elements from large data sets. This...

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Portable Map-Reduce utility for MIT SuperCloud environment

Summary

The MIT Map-Reduce utility has been developed and deployed on the MIT SuperCloud to support scientists and engineers at MIT Lincoln Laboratory. With the MIT Map-Reduce utility, users can deploy their applications quickly onto the MIT SuperCloud infrastructure. The MIT Map-Reduce utility can work with any applications without the need for any modifications. For improved performance, the MIT Map-Reduce utility provides an option to consolidate multiple input data files per compute task as a single stream of input with minimal changes to the target application. This enables users to reduce the computational overhead associated with the cost of multiple application starting up when dealing with more than one piece of input data per compute task. With a small change in a sample MATLAB image processing application, we have observed approximately 12x speed up by reducing the application startup overhead. Currently the MIT Map-Reduce utility can work with several schedulers such as SLURM, Grid Engine and LSF.
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Summary

The MIT Map-Reduce utility has been developed and deployed on the MIT SuperCloud to support scientists and engineers at MIT Lincoln Laboratory. With the MIT Map-Reduce utility, users can deploy their applications quickly onto the MIT SuperCloud infrastructure. The MIT Map-Reduce utility can work with any applications without the need...

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Big data strategies for data center infrastructure management using a 3D gaming platform

Summary

High Performance Computing (HPC) is intrinsically linked to effective Data Center Infrastructure Management (DCIM). Cloud services and HPC have become key components in Department of Defense and corporate Information Technology competitive strategies in the global and commercial spaces. As a result, the reliance on consistent, reliable Data Center space is more critical than ever. The costs and complexity of providing quality DCIM are constantly being tested and evaluated by the United States Government and companies such as Google, Microsoft and Facebook. This paper will demonstrate a system where Big Data strategies and 3D gaming technology is leveraged to successfully monitor and analyze multiple HPC systems and a lights-out modular HP EcoPOD 240a Data Center on a singular platform. Big Data technology and a 3D gaming platform enables the relative real time monitoring of 5000 environmental sensors, more than 3500 IT data points and display visual analytics of the overall operating condition of the Data Center from a command center over 100 miles away. In addition, the Big Data model allows for in depth analysis of historical trends and conditions to optimize operations achieving even greater efficiencies and reliability.
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Summary

High Performance Computing (HPC) is intrinsically linked to effective Data Center Infrastructure Management (DCIM). Cloud services and HPC have become key components in Department of Defense and corporate Information Technology competitive strategies in the global and commercial spaces. As a result, the reliance on consistent, reliable Data Center space is...

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ASR-9 Weather Systems Processor technology refresh and upgrade

Summary

The Weather Systems Processor (WSP) is an add-on system to the Airport Surveillance Radar-9 (ASR-9) that generates wind shear detection and storm tracking products for the terminal airspace. As the original system ages and pre-purchased replacement parts in the depot are used up, it becomes increasingly problematic to procure hardware components for repairs. Thus, a technical refresh is needed to sustain WSP operations into the future. This phase of the project targets the intermediate frequency digital receiver, the radar interface module, and the digital signal processor for replacement by updated hardware platforms. At the same time, the increase in computational capability allows for an upgrade in the signal processing algorithm, which will lead to data quality improvements.
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Summary

The Weather Systems Processor (WSP) is an add-on system to the Airport Surveillance Radar-9 (ASR-9) that generates wind shear detection and storm tracking products for the terminal airspace. As the original system ages and pre-purchased replacement parts in the depot are used up, it becomes increasingly problematic to procure hardware...

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A unified deep neural network for speaker and language recognition

Published in:
INTERSPEECH 2015: 15th Annual Conf. of the Int. Speech Communication Assoc., 6-10 September 2015.

Summary

Significant performance gains have been reported separately for speaker recognition (SR) and language recognition (LR) tasks using either DNN posteriors of sub-phonetic units or DNN feature representations, but the two techniques have not been compared on the same SR or LR task or across SR and LR tasks using the same DNN. In this work we present the application of a single DNN for both tasks using the 2013 Domain Adaptation Challenge speaker recognition (DAC13) and the NIST 2011 language recognition evaluation (LRE11) benchmarks. Using a single DNN trained on Switchboard data we demonstrate large gains in performance on both benchmarks: a 55% reduction in EER for the DAC13 out-of-domain condition and a 48% reduction in Cavg on the LRE11 30s test condition. Score fusion and feature fusion are also investigated as is the performance of the DNN technologies at short durations for SR.
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Summary

Significant performance gains have been reported separately for speaker recognition (SR) and language recognition (LR) tasks using either DNN posteriors of sub-phonetic units or DNN feature representations, but the two techniques have not been compared on the same SR or LR task or across SR and LR tasks using the...

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Estimating lower vocal tract features with closed-open phase spectral analyses

Published in:
INTERSPEECH 2015: 15th Annual Conf. of the Int. Speech Communication Assoc., 6-10 September 2015.

Summary

Previous studies have shown that, in addition to being speaker-dependent yet context-independent, lower vocal tract acoustics significantly impact the speech spectrum at mid-to-high frequencies (e.g 3-6kHz). The present work automatically estimates spectral features that exhibit acoustic properties of the lower vocal tract. Specifically aiming to capture the cyclicity property of the epilarynx tube, a novel multi-resolution approach to spectral analyses is presented that exploits significant differences between the closed and open phases of a glottal cycle. A prominent null linked to the piriform fossa is also estimated. Examples of the feature estimation on natural speech of the VOICES multi-speaker corpus illustrate that a salient spectral pattern indeed emerges between 3-6kHz across all speakers. Moreover, the observed pattern is consistent with that canonically shown for the lower vocal tract in previous works. Additionally, an instance of a speaker's formant (i.e. spectral peak around 3kHz that has been well-established as a characteristic of voice projection) is quantified here for the VOICES template speaker in relation to epilarynx acoustics. The corresponding peak is shown to be double the power on average compared to the other speakers (20 vs 10 dB).
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Summary

Previous studies have shown that, in addition to being speaker-dependent yet context-independent, lower vocal tract acoustics significantly impact the speech spectrum at mid-to-high frequencies (e.g 3-6kHz). The present work automatically estimates spectral features that exhibit acoustic properties of the lower vocal tract. Specifically aiming to capture the cyclicity property of...

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A near-quantum-limited Josephson traveling-wave parametric amplifier

Published in:
Sci., Vol. 350, No. 6258, 16 October 2015,pp. 307-10.

Summary

Detecting single photon level signals--carriers of both classical and quantum information--is particularly challenging for low-energy microwave frequency excitations. Here we introduce a superconducting amplifier based on a Josephson junction transmission line. Unlike current standing-wave parametric amplifiers, this traveling wave architecture robustly achieves high gain over a bandwidth of several gigahertz with sufficient dynamic range to read out 20 superconducting qubits. To achieve this performance, we introduce a sub-wavelength resonant phase matching technique that enables the creation of nonlinear microwave devices with unique dispersion relations. We benchmark the amplifier with weak measurements, obtaining a high quantum efficiency of 75% (70% including following amplifier noise). With a flexible design based on compact lumped elements, this Josephson amplifier has broad applicability to microwave metrology and quantum optics.
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Summary

Detecting single photon level signals--carriers of both classical and quantum information--is particularly challenging for low-energy microwave frequency excitations. Here we introduce a superconducting amplifier based on a Josephson junction transmission line. Unlike current standing-wave parametric amplifiers, this traveling wave architecture robustly achieves high gain over a bandwidth of several gigahertz...

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Reduction of trapped-ion anomalous heating by in situ surface plasma cleaning

Published in:
Phys. Rev. A, At. Mol. Opt. Phys., Vol. 92, No. 2, 2015, 020302.

Summary

Anomalous motional heating is a major obstacle to scalable quantum information processing with trapped ions. Although the source of this heating is not yet understood, several previous studies suggest that noise due to surface contaminants is the limiting heating mechanism in some instances. We demonstrate an improvement by a factor of 4 in the room-temperature heating rate of a niobium surface electrode trap by in situ plasma cleaning of the trap surface. This surface treatment was performed with a simple homebuilt coil assembly and commercially available matching network and is considerably gentler than other treatments, such as ion milling or laser cleaning, that have previously been shown to improve ion heating rates. We do not see an improvement in the heating rate when the trap is operated at cryogenic temperatures, pointing to a role of thermally activated surface contaminants in motional heating whose activity may freeze out at low temperatures.
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Summary

Anomalous motional heating is a major obstacle to scalable quantum information processing with trapped ions. Although the source of this heating is not yet understood, several previous studies suggest that noise due to surface contaminants is the limiting heating mechanism in some instances. We demonstrate an improvement by a factor...

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A spectral framework for anomalous subgraph detection

Published in:
IEEE Trans. Signal Process., Vol. 63, No. 16, 15 August 2015, 4191-4206.

Summary

A wide variety of application domains is concerned with data consisting of entities and their relationships or connections, formally represented as graphs. Within these diverse application areas, a common problem of interest is the detection of a subset of entities whose connectivity is anomalous with respect to the rest of the data. While the detection of such anomalous subgraphs has received a substantial amount of attention, no application-agnostic framework exists for analysis of signal detectability in graph-based data. In this paper, we describe a framework that enables such analysis using the principal eigenspace of a graph's residuals matrix, commonly called the modularity matrix in community detection. Leveraging this analytical tool, we show that the framework has a natural power metric in the spectral norm of the anomalous subgraph's adjacency matrix (signal power) and of the background graph's residuals matrix (noise power). We propose several algorithms based on spectral properties of the residuals matrix, with more computationally expensive techniques providing greater detection power. Detection and identification performance are presented for a number of signal and noise models, including clusters and bipartite foregrounds embedded into simple random backgrounds, as well as graphs with community structure and realistic degree distributions. The trends observed verify intuition gleaned from other signal processing areas, such as greater detection power when the signal is embedded within a less active portion of the background. We demonstrate the utility of the proposed techniques in detecting small, highly anomalous subgraphs in real graphs derived from Internet traffic and product co-purchases.
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Summary

A wide variety of application domains is concerned with data consisting of entities and their relationships or connections, formally represented as graphs. Within these diverse application areas, a common problem of interest is the detection of a subset of entities whose connectivity is anomalous with respect to the rest of...

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Super-resolution microscopy by movable thin-films with embedded microspheres: resolution analysis

Summary

Microsphere-assisted imaging has emerged as an extraordinary simple technique of obtaining optical super-resolution. This work addresses two central problems in developing this technology: i) methodology of the resolution measurements and ii) limited field-of-view provided by each sphere. It is suggested that a standard method of resolution analysis in far-field microscopy based on convolution with the point-spread function can be extended into the superresolution area. This allows developing a unified approach to resolution measurements, which can be used for comparing results obtained by different techniques. To develop the surface scanning functionality, the high-index (n ~ 2) barium titanate glass microspheres were embedded in polydimethylsiloxane (PDMS) thin-films. It is shown that such films adhere to the surface of nanoplasmonic structures so that the tips of embedded spheres experience the objects' optical near-fields. Based on rigorous criteria, the resolution ~lambda/6-lambda/7 (where lambda is the illumination wavelength) is demonstrated for arrays of Au dimers and bowties. Such films can be translated along the surface of investigated samples after liquid lubrication. It is shown that just after lubrication the resolution is diffraction limited, however the super-resolution gradually recovers as the lubricant evaporates.
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Summary

Microsphere-assisted imaging has emerged as an extraordinary simple technique of obtaining optical super-resolution. This work addresses two central problems in developing this technology: i) methodology of the resolution measurements and ii) limited field-of-view provided by each sphere. It is suggested that a standard method of resolution analysis in far-field microscopy...

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