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Pre-discovery observations of disrupting asteroid P/2010 A2

Published in:
Astronom. J., Vol. 142, No. 29, July 2011.

Summary

Solar system object P/2010 A2 is the first-noticed example of the aftermath of a recently disrupted asteroid, probably resulting from a collision. Nearly a year elapsed between its inferred initiation in early 2009 and its eventual detection in early 2010. Here, we use new observations to assess the factors underlying the visibility, especially to understand the delayed discovery. We present pre-discovery observations from the LINEAR telescope and set limits to the early-time brightness from SOHO and STEREO satellite coronagraphic images. Consideration of the circumstances of discovery of P/2010 A2 suggests that similar objects must be common, and that future all-sky surveys will reveal them in large numbers.
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Summary

Solar system object P/2010 A2 is the first-noticed example of the aftermath of a recently disrupted asteroid, probably resulting from a collision. Nearly a year elapsed between its inferred initiation in early 2009 and its eventual detection in early 2010. Here, we use new observations to assess the factors underlying...

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Accounting for state uncertainty in collision avoidance

Published in:
J. Guidance, Control, and Dynamics, Vol. 34, No. 4, July-August 2011, pp. 951-960.

Summary

An important consideration in the development of aircraft collision avoidance systems is how to account for state uncertainty due to sensor limitations and noise. However, many collision avoidance systems simply use point estimates of the state instead of leveraging the full posterior state distribution. Recently, there has been work on applying decision-theoretic methods to collision avoidance, but the importance of accommodating state uncertainty has not yet been well studied. This paper presents a computationally efficient framework for accounting for state uncertainty based on dynamic programming. Examination of characteristic encounters and Monte Carlo simulations demonstrates that properly handling state uncertainty rather than simply using point estimates can significantly enhance safety and improve robustness to sensor error.
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Summary

An important consideration in the development of aircraft collision avoidance systems is how to account for state uncertainty due to sensor limitations and noise. However, many collision avoidance systems simply use point estimates of the state instead of leveraging the full posterior state distribution. Recently, there has been work on...

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Noise spectroscopy through dynamical decoupling with a superconducting flux qubit

Summary

Quantum coherence in natural and artificial spin systems is fundamental to applications ranging from quantum information science to magnetic-resonance imaging and identification. Several multipulse control sequences targeting generalized noise models have been developed to extend coherence by dynamically decoupling a spin system from its noisy environment. In any particular implementation, however, the efficacy of these methods is sensitive to the specific frequency distribution of the noise, suggesting that these same pulse sequences could also be used to probe the noise spectrum directly. Here we demonstrate noise spectroscopy by means of dynamical decoupling using a superconducting qubit with energy-relaxation time T1 D12 us. We first demonstrate that dynamical decoupling improves the coherence time T2 in this system up to the T2 D2 T1 limit (pure dephasing times exceeding 100 us), and then leverage its filtering properties to probe the environmental noise over a frequency (f) range 0.2-20 MHz, observing a 1=fa distribution with a < 1. The characterization of environmental noise has broad utility for spin-resonance applications, enabling the design of optimized coherent-control methods, promoting device and materials engineering, and generally improving coherence.
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Summary

Quantum coherence in natural and artificial spin systems is fundamental to applications ranging from quantum information science to magnetic-resonance imaging and identification. Several multipulse control sequences targeting generalized noise models have been developed to extend coherence by dynamically decoupling a spin system from its noisy environment. In any particular implementation...

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Cryogenic Yb3+ -doped materials for pulsed solid-state laser applications

Published in:
Opt. Mat. Expr., Vol. 1, No. 3, 1 July 2011, pp. 434-450.

Summary

We review recent progress in pulsed lasers using cryogenically-cooled Yb3+ -doped gain media, with an emphasis on high average power. Recent measurements of thermo-optic properties for various host material at both room and cryogenic temperature are presented, including themral conductivity, coefficient of thermal expansion and refractive index. Host materials reviewed include Y2O3, Lu2O3, Sc2O3, YLF, YSO, GSAG, and YVO4. We report on performance of several cryogenic Yb lasers operating at 5-kHz pulse repetition frequency (PRF) a Q-switched Yb:YAG laser is shwon to operate at 114-W average power, with 16-ns pulse duration. A chirped pulse amplifier achieves 115-W output using a composite Yb:YAG/Yb:GSAG amplifier, with pulses that compress to 1.6 ps. Finally, a high-average-power femtosecond laser based on Yb:YLF is discussed, with results for a 10-W regenerative amplifier at 10-kHZ PRF.
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Summary

We review recent progress in pulsed lasers using cryogenically-cooled Yb3+ -doped gain media, with an emphasis on high average power. Recent measurements of thermo-optic properties for various host material at both room and cryogenic temperature are presented, including themral conductivity, coefficient of thermal expansion and refractive index. Host materials reviewed...

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Collision avoidance system optimization with probabilistic pilot response models

Published in:
2011 American Control Conf., 29 June-1 July 2011, pp. 2765-2770.

Summary

All large transport aircraft are required to be equipped with a collision avoidance system that instructs pilots how to maneuver to avoid collision with other aircraft. Uncertainty in the compliance of pilots to advisories makes designing collision avoidance logic challenging. Prior work has investigated formulating the problem as a Markov decision process and solving for the optimal collision avoidance strategy using dynamic programming. The logic was optimized to a pilot response model in which the pilot responds deterministically to all alerts. Deviation from this model during flight can degrade safety. This paper extends the methodology to include probabilistic pilot response models that capture the variability in pilot behavior in order to enhance robustness.
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Summary

All large transport aircraft are required to be equipped with a collision avoidance system that instructs pilots how to maneuver to avoid collision with other aircraft. Uncertainty in the compliance of pilots to advisories makes designing collision avoidance logic challenging. Prior work has investigated formulating the problem as a Markov...

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Anomalous subgraph detection via sparse principal component analysis

Published in:
Proc. 2011 IEEE Statistical Signal Processing Workshop (SSP), 28-30 June 2011, pp. 485-488.

Summary

Network datasets have become ubiquitous in many fields of study in recent years. In this paper we investigate a problem with applicability to a wide variety of domains - detecting small, anomalous subgraphs in a background graph. We characterize the anomaly in a subgraph via the well-known notion of network modularity, and we show that the optimization problem formulation resulting from our setup is very similar to a recently introduced technique in statistics called Sparse Principal Component Analysis (Sparse PCA), which is an extension of the classical PCA algorithm. The exact version of our problem formulation is a hard combinatorial optimization problem, so we consider a recently introduced semidefinite programming relaxation of the Sparse PCA problem. We show via results on simulated data that the technique is very promising.
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Summary

Network datasets have become ubiquitous in many fields of study in recent years. In this paper we investigate a problem with applicability to a wide variety of domains - detecting small, anomalous subgraphs in a background graph. We characterize the anomaly in a subgraph via the well-known notion of network...

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Graph relational features for speaker recognition and mining

Published in:
Proc. 2011 IEEE Statistical Signal Processing Workshop (SSP), 28-30 June 2011, pp. 525-528.

Summary

Recent advances in the field of speaker recognition have resulted in highly efficient speaker comparison algorithms. The advent of these algorithms allows for leveraging a background set, consisting a large numbers of unlabeled recordings, to improve recognition. In this work, a relational graph, where nodes represent utterances and links represent speaker similarity, is created from the background recordings in which the recordings of interest, train and test, are then embedded. Relational features computed from the embedding are then used to obtain a match score between the recordings of interest. We show the efficacy of these features in speaker verification and speaker mining tasks.
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Summary

Recent advances in the field of speaker recognition have resulted in highly efficient speaker comparison algorithms. The advent of these algorithms allows for leveraging a background set, consisting a large numbers of unlabeled recordings, to improve recognition. In this work, a relational graph, where nodes represent utterances and links represent...

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Efficient reconstruction of block-sparse signals

Published in:
IEEE Statistical Signal Processing Workshop, 28-30 June 2011.

Summary

In many sparse reconstruction problems, M observations are used to estimate K components in an N dimensional basis, where N > M ¿ K. The exact basis vectors, however, are not known a priori and must be chosen from an M x N matrix. Such underdetermined problems can be solved using an l2 optimization with an l1 penalty on the sparsity of the solution. There are practical applications in which multiple measurements can be grouped together, so that K x P data must be estimated from M x P observations, where the l1 sparsity penalty is taken with respect to the vector formed using the l2 norms of the rows of the data matrix. In this paper we develop a computationally efficient block partitioned homotopy method for reconstructing K x P data from M x P observations using a grouped sparsity constraint, and compare its performance to other block reconstruction algorithms.
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Summary

In many sparse reconstruction problems, M observations are used to estimate K components in an N dimensional basis, where N &gt; M ¿ K. The exact basis vectors, however, are not known a priori and must be chosen from an M x N matrix. Such underdetermined problems can be solved...

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Matched filtering for subgraph detection in dynamic networks

Published in:
2011 IEEE Statistical Signal Processing Workshop (SSP), 28-30 June 2011, pp. 509-512.

Summary

Graphs are high-dimensional, non-Euclidean data, whose utility spans a wide variety of disciplines. While their non-Euclidean nature complicates the application of traditional signal processing paradigms, it is desirable to seek an analogous detection framework. In this paper we present a matched filtering method for graph sequences, extending to a dynamic setting a previous method for the detection of anomalously dense subgraphs in a large background. In simulation, we show that this temporal integration technique enables the detection of weak subgraph anomalies than are not detectable in the static case. We also demonstrate background/foreground separation using a real background graph based on a computer network.
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Summary

Graphs are high-dimensional, non-Euclidean data, whose utility spans a wide variety of disciplines. While their non-Euclidean nature complicates the application of traditional signal processing paradigms, it is desirable to seek an analogous detection framework. In this paper we present a matched filtering method for graph sequences, extending to a dynamic...

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Unmanned aircraft collision avoidance using continuous-state POMDPs

Published in:
2011 Robotics: Science and Systems, 27-30 June 2011.

Summary

An effective collision avoidance system for unmanned aircraft will enable them to fly in civil airspace and greatly expand their applications. One promising approach is to model aircraft collision avoidance as a partially observable Markov decision process (POMDP) and automatically generate the threat resolution logic for the collision avoidance system by solving the POMDP model. However, existing discrete-state POMDP algorithms cannot cope with the high-dimensional state space in collision avoidance POMDPs. Using a recently developed algorithm called Monte Carlo Value Iteration (MCVI), we constructed several continuous-state POMDP models and solved them directly, without discretizing the state space. Simulation results show that our 3-D continuous-state models reduce the collision risk by up to 70 times, compared with earlier 2-D discrete-state POMDP models. The success demonstrates both the benefits of continuous-state POMDP models for collision avoidance systems and the latest algorithmic progress in solving these complex models.
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Summary

An effective collision avoidance system for unmanned aircraft will enable them to fly in civil airspace and greatly expand their applications. One promising approach is to model aircraft collision avoidance as a partially observable Markov decision process (POMDP) and automatically generate the threat resolution logic for the collision avoidance system...

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