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Measurement of the surface-enhanced coherent anti-Stokes Raman scattering (SECARS) due to the 1574 cm^-1 surface-enhanced Raman scattering (SERS) mode of benzenethiol using low-power (<20 mW) CW diode lasers

Published in:
Appl. Spectrosc., Vol. 67, No. 2, February 2013, pp. 132-135.

Summary

The surface-enhanced coherent anti-Stokes Raman scattering (SECARS) from a self-assembled monolayer (SAM) of benzenethiol on a silver-coated surface-enhanced Raman scattering (SERS) substrate has been measured for the 1574 cm^-1 SERS mode. A value of 9.6 +- 1.7 x 10^-14 W was determined for the resonant component of the SECARS signal using 17.8 mW of 784.9 nm pump laser power and 7.1 mW of 895.5 nm Stokes laser power; the pump and Stokes lasers were polarized parallel to each other but perpendicular to the grooves of the diffraction grating in the spectrometer. The measured value of resonant component of the SECARS signal is in agreement with the calculated value of 9.3 x 10^-14 W using the measured value of 8.7 +- 0.5 cm^-1 for the SERS linewidth Gamma (full width at half-maximum) and the value of 5.7 +- 1.4 x 10^-7 for the product of the Raman cross section rSERS and the surface concentration Ns of the benzenethiol SAM. The xxxx component of the resonant part of the third-order nonlinear optical susceptibility |3X (3)R/xxxx| for the 1574 cm^-1 SERS mode has been determined to be 4.3 +- 1.1 x 10^-5 cm g^-1 s^2. The SERS enhancement factor for the 1574 cm^-1 mode was determined to be 3.6 +- 0.9 x 10^7 using the value of 1.8 x 10^15 molecules/cm^2 for Ns.
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Summary

The surface-enhanced coherent anti-Stokes Raman scattering (SECARS) from a self-assembled monolayer (SAM) of benzenethiol on a silver-coated surface-enhanced Raman scattering (SERS) substrate has been measured for the 1574 cm^-1 SERS mode. A value of 9.6 +- 1.7 x 10^-14 W was determined for the resonant component of the SECARS signal...

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Novel graph processor architecture

Published in:
Lincoln Laboratory Journal, Vol. 20, No. 1, 2013, pp. 92-104.

Summary

Graph algorithms are increasingly used in applications that exploit large databases. However, conventional processor architectures are hard-pressed to handle the throughput and memory requirements of graph computation. Lincoln Laboratory's graph-processor architecture represents a fundamental rethinking of architectures. It utilizes innovations that include high-bandwidth three-dimensional (3D) communication links, a sparse matrix-based graph instruction set, accelerator-based architecture, a systolic sorter, randomized communications, a cacheless memory system, and 3D packaging.
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Summary

Graph algorithms are increasingly used in applications that exploit large databases. However, conventional processor architectures are hard-pressed to handle the throughput and memory requirements of graph computation. Lincoln Laboratory's graph-processor architecture represents a fundamental rethinking of architectures. It utilizes innovations that include high-bandwidth three-dimensional (3D) communication links, a sparse matrix-based...

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Taming biological big data with D4M

Published in:
Lincoln Laboratory Journal, Vol. 20, No. 1, 2013, pp. 82-91.

Summary

The supercomputing community has taken up the challenge of "taming the beast" spawned by the massive amount of data available in the bioinformatics domain: How can these data be exploited faster and better? MIT Lincoln Laboratory computer scientists demonstrated how a new Laboratory-developed technology, the Dynamic Distributed Dimensional Data Model (D4M), can be used to accelerate DNA sequence comparison, a core operation in bioinformatics.
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Summary

The supercomputing community has taken up the challenge of "taming the beast" spawned by the massive amount of data available in the bioinformatics domain: How can these data be exploited faster and better? MIT Lincoln Laboratory computer scientists demonstrated how a new Laboratory-developed technology, the Dynamic Distributed Dimensional Data Model...

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Detection theory for graphs

Summary

Graphs are fast emerging as a common data structure used in many scientific and engineering fields. While a wide variety of techniques exist to analyze graph datasets, practitioners currently lack a signal processing theory akin to that of detection and estimation in the classical setting of vector spaces with Gaussian noise. Using practical detection examples involving large, random "background" graphs and noisy real-world datasets, the authors present a novel graph analytics framework that allows for uncued analysis of very large datasets. This framework combines traditional computer science techniques with signal processing in the context of graph data, creating a new research area at the intersection of the two fields.
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Summary

Graphs are fast emerging as a common data structure used in many scientific and engineering fields. While a wide variety of techniques exist to analyze graph datasets, practitioners currently lack a signal processing theory akin to that of detection and estimation in the classical setting of vector spaces with Gaussian...

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Social network analysis with content and graphs

Published in:
Lincoln Laboratory Journal, Vol. 20, No. 1, 2013, pp. 62-81.

Summary

Social network analysis has undergone a renaissance with the ubiquity and quantity of content from social media, web pages, and sensors. This content is a rich data source for constructing and analyzing social networks, but its enormity and unstructured nature also present multiple challenges. Work at Lincoln Laboratory is addressing the problems in constructing networks from unstructured data, analyzing the community structure of a network, and inferring information from networks. Graph analytics have proven to be valuable tools in solving these challenges. Through the use of these tools, Laboratory researchers have achieved promising results on real-world data. A sampling of these results are presented in this article.
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Summary

Social network analysis has undergone a renaissance with the ubiquity and quantity of content from social media, web pages, and sensors. This content is a rich data source for constructing and analyzing social networks, but its enormity and unstructured nature also present multiple challenges. Work at Lincoln Laboratory is addressing...

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Improving quantum gate fidelities by using a qubit to measure microwave pulse distortions

Published in:
Phys. Rev. Lett., Vol. 110, No. 4, 24 January 2013.

Summary

We present a new method for determining pulse imperfections and improving the single-gate fidelity in a superconducting qubit. By applying consecutive positive and negative pi pulses, we amplify the qubit evolution due to microwave pulse distortions, which causes the qubit state to rotate around an axis perpendicular to the intended rotation axis. Measuring these rotations as a function of pulse period allows us to reconstruct the shape of the microwave pulse arriving at the sample. Using the extracted response to predistort the input signal, we are able to reduce the average error per gate by 37%, which enables us to reach an average single-qubit gate fidelity higher than 0.998.
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Summary

We present a new method for determining pulse imperfections and improving the single-gate fidelity in a superconducting qubit. By applying consecutive positive and negative pi pulses, we amplify the qubit evolution due to microwave pulse distortions, which causes the qubit state to rotate around an axis perpendicular to the intended...

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Convective initiation forecasts through the use of machine learning methods

Published in:
11th Conf. on Artificial and Computational Intelligence and its Applications to the Environmental Sciences, 9 January 2013.

Summary

Storm initiation is a very challenging aspect of nowcasting. Rapidly forming storms that appear in areas of little to no pre-existing convection can pose a danger to aircraft, and have the potential to cause unforeseen delays in the national airspace system (NAS). As such, detection and prediction of the initial development of convective storms is critical to NAS operations and planning. The Corridor Integrated Weather System (CIWS) currently provides deterministic 0-2 hour storm forecasts over the NAS, and represents the 0-2 hour portion of the 0-8 hour deterministic CoSPA storm forecasts. CIWS includes a convective initiation (CI) module, however this module has difficulty initiating convection in areas of little or no pre-existing convection. In this study, we seek to improve the capabilities of the CI module using machine learning methods to detect regions of imminent convection and enhance the storm initiation to the 0-2 hour forecast. Improvements to the current CI detection capabilities will prove to be a benefit in the short term, as well in the longer term plans of the Federal Aviation Administration's (FAA) Next Generation Air Transportation System (NextGen). In order to improve the capabilities of the CI module in CIWS, data from a variety of sources are fused together to produce a forecast of CI. Data incorporated into the CI algorithm include: Satellite fields from NASA's Satellite Convective Analysis and Tracking (SATCAST), convective instability fields, and a collection of numerical models which includes NOAA's North America Rapid Refresh Ensemble Time Lag System (NARRE-TL), the Localized Aviation MOS Program (LAMP), Short Range Ensemble Forecasts (SREF), and High Resolution Rapid Refresh (HRRR) model forecasts. These fields are brought together in a machine learning framework to create a probabilistic model which is used to initiate new growth in the deterministic CIWS 0-2 hour forecast. A variety of machine learning classifiers, including logistic regression, neural networks, support vector machines, and random forests, are used to investigate which technique works best with the data available. The skill of this updated CI capability is being assessed over the summer of 2012 using multiple skill metrics including CSI, bias and fraction skill score.
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Summary

Storm initiation is a very challenging aspect of nowcasting. Rapidly forming storms that appear in areas of little to no pre-existing convection can pose a danger to aircraft, and have the potential to cause unforeseen delays in the national airspace system (NAS). As such, detection and prediction of the initial...

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Time-reversal symmetry and universal conductance fluctuations in a driven two-level system

Published in:
Phys. Rev. Lett., Vol. 110, No. 1, 2 January 2013, 016603.

Summary

In the presence of time-reversal symmetry, quantum interference gives strong corrections to the electric conductivity of disordered systems. The self-interference of an electron wave function traveling time-reversed paths leads to effects such as weak localization and universal conductance fluctuations. Here, we investigate the effects of broken time-reversal symmetry in a driven artificial two-level system. Using a superconducting flux qubit, we implement scattering events as multiple Landau-Zener transitions by driving the qubit periodically back and forth through an avoided crossing. Interference between different qubit trajectories gives rise to a speckle pattern in the qubit transition rate, similar to the interference patterns created when coherent light is scattered off a disordered potential. Since the scattering events are imposed by the driving protocol, we can control the time-reversal symmetry of the system by making the drive waveform symmetric or asymmetric in time. We find that the fluctuations of the transition rate exhibit a sharp peak when the drive is time symmetric, similar to universal conductance fluctuations in electronic transport through mesoscopic systems.
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Summary

In the presence of time-reversal symmetry, quantum interference gives strong corrections to the electric conductivity of disordered systems. The self-interference of an electron wave function traveling time-reversed paths leads to effects such as weak localization and universal conductance fluctuations. Here, we investigate the effects of broken time-reversal symmetry in a...

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Dwell scheduling algorithms for phased array antenna

Published in:
IEEE Trans. Aerosp. Electron. Syst., Vol. 49, No. 1, January 2013, pp. 42-54.

Summary

In a multifunctional radar performing searching and tracking operations, the maximum number of targets that can be managed is an important measure of performance. One way a radar can maximize tracking performance is to optimize its dwell scheduling. The problem of designing efficient dwell scheduling algorithms for various tracking and searching scenarios with respect to various objective functions has been considered many times in the past and many solutions have been proposed. We consider the dwell scheduling problem for two different scenarios where the only objective is to maximize the number of dwells scheduled during a scheduling period. We formulate the problem as a distributed and a nondistributed bin packing problem and present optimal solutions using an integer programming formulation. Obtaining an optimal solution gives the limit of radar performance. We also present a more computationally friendly but less optimal solution using a greedy approach.
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Summary

In a multifunctional radar performing searching and tracking operations, the maximum number of targets that can be managed is an important measure of performance. One way a radar can maximize tracking performance is to optimize its dwell scheduling. The problem of designing efficient dwell scheduling algorithms for various tracking and...

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Graph embedding for speaker recognition

Published in:
Chapter in Graph Embedding for Pattern Analysis, 2013, pp. 229-60.

Summary

This chapter presents applications of graph embedding to the problem of text-independent speaker recognition. Speaker recognition is a general term encompassing multiple applications. At the core is the problem of speaker comparison-given two speech recordings (utterances), produce a score which measures speaker similarity. Using speaker comparison, other applications can be implemented-speaker clustering (grouping similar speakers in a corpus), speaker verification (verifying a claim of identity), speaker identification (identifying a speaker out of a list of potential candidates), and speaker retrieval (finding matches to a query set).
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Summary

This chapter presents applications of graph embedding to the problem of text-independent speaker recognition. Speaker recognition is a general term encompassing multiple applications. At the core is the problem of speaker comparison-given two speech recordings (utterances), produce a score which measures speaker similarity. Using speaker comparison, other applications can be...

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