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Neural network learning (MLR-NNLR)

MODEL BASED DEEP LEARNING IN FREE BREATHING, UNGATED, CARDIAC MRI RECOVERY


We introduce a model-based reconstruction
framework with deep learned (DL) and smoothness regularization
on manifolds (STORM) priors to recover free
breathing and ungated (FBU) cardiac MRI from highly undersampled
measurements. The DL priors enable us to exploit
the local correlations, while the STORM prior enables
us to make use of the extensive non-local similarities that are
subject dependent. We introduce a novel model-based formulation
that allows the seamless integration of deep learning

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Authors:
Sampurna Biswas, Hemant K. Aggarwal, Sunrita Poddar, Mathews Jacob
Submitted On:
14 April 2018 - 1:52pm
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icassp_poster_final.pptx

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[1] Sampurna Biswas, Hemant K. Aggarwal, Sunrita Poddar, Mathews Jacob, "MODEL BASED DEEP LEARNING IN FREE BREATHING, UNGATED, CARDIAC MRI RECOVERY ", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2845. Accessed: Jul. 23, 2019.
@article{2845-18,
url = {http://sigport.org/2845},
author = {Sampurna Biswas; Hemant K. Aggarwal; Sunrita Poddar; Mathews Jacob },
publisher = {IEEE SigPort},
title = {MODEL BASED DEEP LEARNING IN FREE BREATHING, UNGATED, CARDIAC MRI RECOVERY },
year = {2018} }
TY - EJOUR
T1 - MODEL BASED DEEP LEARNING IN FREE BREATHING, UNGATED, CARDIAC MRI RECOVERY
AU - Sampurna Biswas; Hemant K. Aggarwal; Sunrita Poddar; Mathews Jacob
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2845
ER -
Sampurna Biswas, Hemant K. Aggarwal, Sunrita Poddar, Mathews Jacob. (2018). MODEL BASED DEEP LEARNING IN FREE BREATHING, UNGATED, CARDIAC MRI RECOVERY . IEEE SigPort. http://sigport.org/2845
Sampurna Biswas, Hemant K. Aggarwal, Sunrita Poddar, Mathews Jacob, 2018. MODEL BASED DEEP LEARNING IN FREE BREATHING, UNGATED, CARDIAC MRI RECOVERY . Available at: http://sigport.org/2845.
Sampurna Biswas, Hemant K. Aggarwal, Sunrita Poddar, Mathews Jacob. (2018). "MODEL BASED DEEP LEARNING IN FREE BREATHING, UNGATED, CARDIAC MRI RECOVERY ." Web.
1. Sampurna Biswas, Hemant K. Aggarwal, Sunrita Poddar, Mathews Jacob. MODEL BASED DEEP LEARNING IN FREE BREATHING, UNGATED, CARDIAC MRI RECOVERY [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2845

AN UPPER BOUND ON THE REQUIRED SIZE OF A NEURAL NETWORK CLASSIFIER


There is growing interest in understanding the impact of architectural parameters such as depth, width, and the type of
activation function on the performance of a neural network. We provide an upper-bound on the number of free parameters
a ReLU-type neural network needs to exactly fit the training data. Whether a net of this size generalizes to test data will
be governed by the fidelity of the training data and the applicability of the principle of Occam’s Razor. We introduce the

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Authors:
Hossein Valavi, Peter J. Ramadge
Submitted On:
19 April 2018 - 7:01pm
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AN UPPER-BOUND ON THE REQUIRED SIZE OF A NEURAL NETWORK CLASSIFIER

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[1] Hossein Valavi, Peter J. Ramadge, "AN UPPER BOUND ON THE REQUIRED SIZE OF A NEURAL NETWORK CLASSIFIER", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2843. Accessed: Jul. 23, 2019.
@article{2843-18,
url = {http://sigport.org/2843},
author = {Hossein Valavi; Peter J. Ramadge },
publisher = {IEEE SigPort},
title = {AN UPPER BOUND ON THE REQUIRED SIZE OF A NEURAL NETWORK CLASSIFIER},
year = {2018} }
TY - EJOUR
T1 - AN UPPER BOUND ON THE REQUIRED SIZE OF A NEURAL NETWORK CLASSIFIER
AU - Hossein Valavi; Peter J. Ramadge
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2843
ER -
Hossein Valavi, Peter J. Ramadge. (2018). AN UPPER BOUND ON THE REQUIRED SIZE OF A NEURAL NETWORK CLASSIFIER. IEEE SigPort. http://sigport.org/2843
Hossein Valavi, Peter J. Ramadge, 2018. AN UPPER BOUND ON THE REQUIRED SIZE OF A NEURAL NETWORK CLASSIFIER. Available at: http://sigport.org/2843.
Hossein Valavi, Peter J. Ramadge. (2018). "AN UPPER BOUND ON THE REQUIRED SIZE OF A NEURAL NETWORK CLASSIFIER." Web.
1. Hossein Valavi, Peter J. Ramadge. AN UPPER BOUND ON THE REQUIRED SIZE OF A NEURAL NETWORK CLASSIFIER [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2843

GENERALIZED END-TO-END LOSS FOR SPEAKER VERIFICATION


In this paper, we propose a new loss function called generalized end-to-end (GE2E) loss, which makes the training of speaker verification models more efficient than our previous tuple-based end-to-end (TE2E) loss function. Unlike TE2E, the GE2E loss function updates the network in a way that emphasizes examples that are difficult to verify at each step of the training process. Additionally, the GE2E loss does not require an initial stage of example selection.

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Authors:
Li Wan, Quan Wang, Alan Papir, Ignacio Lopez Moreno
Submitted On:
18 April 2018 - 11:00am
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ICASSP 2018 GE2E.pptx

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[1] Li Wan, Quan Wang, Alan Papir, Ignacio Lopez Moreno, "GENERALIZED END-TO-END LOSS FOR SPEAKER VERIFICATION", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2778. Accessed: Jul. 23, 2019.
@article{2778-18,
url = {http://sigport.org/2778},
author = {Li Wan; Quan Wang; Alan Papir; Ignacio Lopez Moreno },
publisher = {IEEE SigPort},
title = {GENERALIZED END-TO-END LOSS FOR SPEAKER VERIFICATION},
year = {2018} }
TY - EJOUR
T1 - GENERALIZED END-TO-END LOSS FOR SPEAKER VERIFICATION
AU - Li Wan; Quan Wang; Alan Papir; Ignacio Lopez Moreno
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2778
ER -
Li Wan, Quan Wang, Alan Papir, Ignacio Lopez Moreno. (2018). GENERALIZED END-TO-END LOSS FOR SPEAKER VERIFICATION. IEEE SigPort. http://sigport.org/2778
Li Wan, Quan Wang, Alan Papir, Ignacio Lopez Moreno, 2018. GENERALIZED END-TO-END LOSS FOR SPEAKER VERIFICATION. Available at: http://sigport.org/2778.
Li Wan, Quan Wang, Alan Papir, Ignacio Lopez Moreno. (2018). "GENERALIZED END-TO-END LOSS FOR SPEAKER VERIFICATION." Web.
1. Li Wan, Quan Wang, Alan Papir, Ignacio Lopez Moreno. GENERALIZED END-TO-END LOSS FOR SPEAKER VERIFICATION [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2778

A Random Matrix and Concentration Inequalities framework for Neural Networks Analysis


Our article provides a theoretical analysis of the asymptotic performance of a regression or classification task performed by a simple random neural network. This result is obtained by leveraging a new framework at the crossroads between random matrix theory and the concentration of measure theory. This approach is of utmost interest for neural network analysis at large in that it naturally dismisses the difficulty induced by the non-linear activation functions, so long that these are Lipschitz functions.

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Authors:
Romain Couillet
Submitted On:
13 April 2018 - 5:38pm
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conc_measure_NN_ICASSP18(3).pdf

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[1] Romain Couillet, "A Random Matrix and Concentration Inequalities framework for Neural Networks Analysis", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2767. Accessed: Jul. 23, 2019.
@article{2767-18,
url = {http://sigport.org/2767},
author = {Romain Couillet },
publisher = {IEEE SigPort},
title = {A Random Matrix and Concentration Inequalities framework for Neural Networks Analysis},
year = {2018} }
TY - EJOUR
T1 - A Random Matrix and Concentration Inequalities framework for Neural Networks Analysis
AU - Romain Couillet
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2767
ER -
Romain Couillet. (2018). A Random Matrix and Concentration Inequalities framework for Neural Networks Analysis. IEEE SigPort. http://sigport.org/2767
Romain Couillet, 2018. A Random Matrix and Concentration Inequalities framework for Neural Networks Analysis. Available at: http://sigport.org/2767.
Romain Couillet. (2018). "A Random Matrix and Concentration Inequalities framework for Neural Networks Analysis." Web.
1. Romain Couillet. A Random Matrix and Concentration Inequalities framework for Neural Networks Analysis [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2767

Joint Verification-Identification in End-to-End Multi-Scale CNN Framework for Topic Identification


We present an end-to-end multi-scale Convolutional Neural
Network (CNN) framework for topic identification (topic ID).
In this work, we examined multi-scale CNN for classification
using raw text input. Topical word embeddings are learnt at
multiple scales using parallel convolutional layers. A technique
to integrate verification and identification objectives is
examined to improve topic ID performance. With this approach,
we achieved significant improvement in identification
task. We evaluated our framework on two contrasting

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Authors:
Raghavendra Pappagari, Jesus Villalba, Najim Dehak
Submitted On:
13 April 2018 - 4:16pm
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Final.pdf

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[1] Raghavendra Pappagari, Jesus Villalba, Najim Dehak, "Joint Verification-Identification in End-to-End Multi-Scale CNN Framework for Topic Identification", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2758. Accessed: Jul. 23, 2019.
@article{2758-18,
url = {http://sigport.org/2758},
author = {Raghavendra Pappagari; Jesus Villalba; Najim Dehak },
publisher = {IEEE SigPort},
title = {Joint Verification-Identification in End-to-End Multi-Scale CNN Framework for Topic Identification},
year = {2018} }
TY - EJOUR
T1 - Joint Verification-Identification in End-to-End Multi-Scale CNN Framework for Topic Identification
AU - Raghavendra Pappagari; Jesus Villalba; Najim Dehak
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2758
ER -
Raghavendra Pappagari, Jesus Villalba, Najim Dehak. (2018). Joint Verification-Identification in End-to-End Multi-Scale CNN Framework for Topic Identification. IEEE SigPort. http://sigport.org/2758
Raghavendra Pappagari, Jesus Villalba, Najim Dehak, 2018. Joint Verification-Identification in End-to-End Multi-Scale CNN Framework for Topic Identification. Available at: http://sigport.org/2758.
Raghavendra Pappagari, Jesus Villalba, Najim Dehak. (2018). "Joint Verification-Identification in End-to-End Multi-Scale CNN Framework for Topic Identification." Web.
1. Raghavendra Pappagari, Jesus Villalba, Najim Dehak. Joint Verification-Identification in End-to-End Multi-Scale CNN Framework for Topic Identification [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2758

Speaker Invariant Feature Extraction for Zero-Resource Languages with Adversarial Learning


We introduce a novel type of representation learning to obtain a speaker invariant feature for zero-resource languages. Speaker adaptation is an important technique to build a robust acoustic model. For a zero-resource language, however, conventional model-dependent speaker adaptation methods such as constrained maximum likelihood linear regression are insufficient because the acoustic model of the target language is not accessible. Therefore, we introduce a model-independent feature extraction based on a neural network.

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Authors:
Taira Tsuchiya, Naohiro Tawara, Tetsuji Ogawa, Tetsunori Kobayashi
Submitted On:
13 April 2018 - 10:12am
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speaker-invariant-feature-extraction-for-zero-resource-languages-with-adversarial-learning.pdf

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[1] Taira Tsuchiya, Naohiro Tawara, Tetsuji Ogawa, Tetsunori Kobayashi, "Speaker Invariant Feature Extraction for Zero-Resource Languages with Adversarial Learning", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2709. Accessed: Jul. 23, 2019.
@article{2709-18,
url = {http://sigport.org/2709},
author = {Taira Tsuchiya; Naohiro Tawara; Tetsuji Ogawa; Tetsunori Kobayashi },
publisher = {IEEE SigPort},
title = {Speaker Invariant Feature Extraction for Zero-Resource Languages with Adversarial Learning},
year = {2018} }
TY - EJOUR
T1 - Speaker Invariant Feature Extraction for Zero-Resource Languages with Adversarial Learning
AU - Taira Tsuchiya; Naohiro Tawara; Tetsuji Ogawa; Tetsunori Kobayashi
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2709
ER -
Taira Tsuchiya, Naohiro Tawara, Tetsuji Ogawa, Tetsunori Kobayashi. (2018). Speaker Invariant Feature Extraction for Zero-Resource Languages with Adversarial Learning. IEEE SigPort. http://sigport.org/2709
Taira Tsuchiya, Naohiro Tawara, Tetsuji Ogawa, Tetsunori Kobayashi, 2018. Speaker Invariant Feature Extraction for Zero-Resource Languages with Adversarial Learning. Available at: http://sigport.org/2709.
Taira Tsuchiya, Naohiro Tawara, Tetsuji Ogawa, Tetsunori Kobayashi. (2018). "Speaker Invariant Feature Extraction for Zero-Resource Languages with Adversarial Learning." Web.
1. Taira Tsuchiya, Naohiro Tawara, Tetsuji Ogawa, Tetsunori Kobayashi. Speaker Invariant Feature Extraction for Zero-Resource Languages with Adversarial Learning [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2709

A MULTI-CAMERA DEEP NEURAL NETWORK FOR DETECTING ELEVATED ALERTNESS IN DRIVERS


We present a system for the detection of elevated levels of driver alertness in driver-facing video captured from multiple viewpoints. This problem is important in automotive safety as a helpful feedback signal to determine driver engagement and as a means of automatically flagging anomalous driving events. We generated a dataset of videos from 25 participants overseeing an hour each of driving sequences in a simulator consisting of a mixture of normal and near-miss driving events.

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Authors:
John Gideon, Simon Stent, Luke Fletcher
Submitted On:
13 April 2018 - 9:57am
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[1] John Gideon, Simon Stent, Luke Fletcher, "A MULTI-CAMERA DEEP NEURAL NETWORK FOR DETECTING ELEVATED ALERTNESS IN DRIVERS", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2706. Accessed: Jul. 23, 2019.
@article{2706-18,
url = {http://sigport.org/2706},
author = {John Gideon; Simon Stent; Luke Fletcher },
publisher = {IEEE SigPort},
title = {A MULTI-CAMERA DEEP NEURAL NETWORK FOR DETECTING ELEVATED ALERTNESS IN DRIVERS},
year = {2018} }
TY - EJOUR
T1 - A MULTI-CAMERA DEEP NEURAL NETWORK FOR DETECTING ELEVATED ALERTNESS IN DRIVERS
AU - John Gideon; Simon Stent; Luke Fletcher
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2706
ER -
John Gideon, Simon Stent, Luke Fletcher. (2018). A MULTI-CAMERA DEEP NEURAL NETWORK FOR DETECTING ELEVATED ALERTNESS IN DRIVERS. IEEE SigPort. http://sigport.org/2706
John Gideon, Simon Stent, Luke Fletcher, 2018. A MULTI-CAMERA DEEP NEURAL NETWORK FOR DETECTING ELEVATED ALERTNESS IN DRIVERS. Available at: http://sigport.org/2706.
John Gideon, Simon Stent, Luke Fletcher. (2018). "A MULTI-CAMERA DEEP NEURAL NETWORK FOR DETECTING ELEVATED ALERTNESS IN DRIVERS." Web.
1. John Gideon, Simon Stent, Luke Fletcher. A MULTI-CAMERA DEEP NEURAL NETWORK FOR DETECTING ELEVATED ALERTNESS IN DRIVERS [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2706

CATSEYES: Categorizing Seismic structures with tessellated scattering wavelet networks


As field seismic data sizes are dramatically increasing toward exabytes, automating the labeling of ``structural monads'' --- corresponding to geological patterns and yielding subsurface interpretation --- in a huge amount of available information would drastically reduce interpretation time. Since customary designed features may not account for gradual deformations observable in seismic data, we propose to adapt the wavelet-based scattering network methodology with a tessellation of geophysical images.

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Authors:
Yash BHALGAT, Jean CHARLETY
Submitted On:
13 April 2018 - 9:50am
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Supervised seismic structure classification clustering with wavelet scattering networks

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[1] Yash BHALGAT, Jean CHARLETY, "CATSEYES: Categorizing Seismic structures with tessellated scattering wavelet networks", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2704. Accessed: Jul. 23, 2019.
@article{2704-18,
url = {http://sigport.org/2704},
author = {Yash BHALGAT; Jean CHARLETY },
publisher = {IEEE SigPort},
title = {CATSEYES: Categorizing Seismic structures with tessellated scattering wavelet networks},
year = {2018} }
TY - EJOUR
T1 - CATSEYES: Categorizing Seismic structures with tessellated scattering wavelet networks
AU - Yash BHALGAT; Jean CHARLETY
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2704
ER -
Yash BHALGAT, Jean CHARLETY. (2018). CATSEYES: Categorizing Seismic structures with tessellated scattering wavelet networks. IEEE SigPort. http://sigport.org/2704
Yash BHALGAT, Jean CHARLETY, 2018. CATSEYES: Categorizing Seismic structures with tessellated scattering wavelet networks. Available at: http://sigport.org/2704.
Yash BHALGAT, Jean CHARLETY. (2018). "CATSEYES: Categorizing Seismic structures with tessellated scattering wavelet networks." Web.
1. Yash BHALGAT, Jean CHARLETY. CATSEYES: Categorizing Seismic structures with tessellated scattering wavelet networks [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2704

A Large-Scale Study Of Language Models for Chord Prediction

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Submitted On:
13 April 2018 - 6:36am
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icassp2018.pdf

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[1] , "A Large-Scale Study Of Language Models for Chord Prediction", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2685. Accessed: Jul. 23, 2019.
@article{2685-18,
url = {http://sigport.org/2685},
author = { },
publisher = {IEEE SigPort},
title = {A Large-Scale Study Of Language Models for Chord Prediction},
year = {2018} }
TY - EJOUR
T1 - A Large-Scale Study Of Language Models for Chord Prediction
AU -
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2685
ER -
. (2018). A Large-Scale Study Of Language Models for Chord Prediction. IEEE SigPort. http://sigport.org/2685
, 2018. A Large-Scale Study Of Language Models for Chord Prediction. Available at: http://sigport.org/2685.
. (2018). "A Large-Scale Study Of Language Models for Chord Prediction." Web.
1. . A Large-Scale Study Of Language Models for Chord Prediction [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2685

Cofnet: Predict with Confidence

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Authors:
Tung-yu Wu, Wing H. Wong, Chen-Yi Lee
Submitted On:
13 April 2018 - 5:23am
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POSTER.pdf

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[1] Tung-yu Wu, Wing H. Wong, Chen-Yi Lee, "Cofnet: Predict with Confidence", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2667. Accessed: Jul. 23, 2019.
@article{2667-18,
url = {http://sigport.org/2667},
author = {Tung-yu Wu; Wing H. Wong; Chen-Yi Lee },
publisher = {IEEE SigPort},
title = {Cofnet: Predict with Confidence},
year = {2018} }
TY - EJOUR
T1 - Cofnet: Predict with Confidence
AU - Tung-yu Wu; Wing H. Wong; Chen-Yi Lee
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2667
ER -
Tung-yu Wu, Wing H. Wong, Chen-Yi Lee. (2018). Cofnet: Predict with Confidence. IEEE SigPort. http://sigport.org/2667
Tung-yu Wu, Wing H. Wong, Chen-Yi Lee, 2018. Cofnet: Predict with Confidence. Available at: http://sigport.org/2667.
Tung-yu Wu, Wing H. Wong, Chen-Yi Lee. (2018). "Cofnet: Predict with Confidence." Web.
1. Tung-yu Wu, Wing H. Wong, Chen-Yi Lee. Cofnet: Predict with Confidence [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2667

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