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Audio and Acoustic Signal Processing

Attentive Adversarial Learning for Domain-Invariant Training


Adversarial domain-invariant training (ADIT) proves to be effective in suppressing the effects of domain variability in acoustic modeling and has led to improved performance in automatic speech recognition (ASR). In ADIT, an auxiliary domain classifier takes in equally-weighted deep features from a deep neural network (DNN) acoustic model and is trained to improve their domain-invariance by optimizing an adversarial loss function.

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Authors:
Zhong Meng, Jinyu Li, Yifan Gong
Submitted On:
12 May 2019 - 9:03pm
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aadit_poster.pptx

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[1] Zhong Meng, Jinyu Li, Yifan Gong, "Attentive Adversarial Learning for Domain-Invariant Training", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4474. Accessed: Oct. 17, 2019.
@article{4474-19,
url = {http://sigport.org/4474},
author = {Zhong Meng; Jinyu Li; Yifan Gong },
publisher = {IEEE SigPort},
title = {Attentive Adversarial Learning for Domain-Invariant Training},
year = {2019} }
TY - EJOUR
T1 - Attentive Adversarial Learning for Domain-Invariant Training
AU - Zhong Meng; Jinyu Li; Yifan Gong
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4474
ER -
Zhong Meng, Jinyu Li, Yifan Gong. (2019). Attentive Adversarial Learning for Domain-Invariant Training. IEEE SigPort. http://sigport.org/4474
Zhong Meng, Jinyu Li, Yifan Gong, 2019. Attentive Adversarial Learning for Domain-Invariant Training. Available at: http://sigport.org/4474.
Zhong Meng, Jinyu Li, Yifan Gong. (2019). "Attentive Adversarial Learning for Domain-Invariant Training." Web.
1. Zhong Meng, Jinyu Li, Yifan Gong. Attentive Adversarial Learning for Domain-Invariant Training [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4474

Adversarial Speaker Verification


The use of deep networks to extract embeddings for speaker recognition has proven successfully. However, such embeddings are susceptible to performance degradation due to the mismatches among the training, enrollment, and test conditions. In this work, we propose an adversarial speaker verification (ASV) scheme to learn the condition-invariant deep embedding via adversarial multi-task training. In ASV, a speaker classification network and a condition identification network are jointly optimized to minimize the speaker classification loss and simultaneously mini-maximize the condition loss.

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Authors:
Zhong Meng, Yong Zhao, Jinyu Li, Yifan Gong
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12 May 2019 - 9:24pm
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asv_poster_v3.pptx

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[1] Zhong Meng, Yong Zhao, Jinyu Li, Yifan Gong, "Adversarial Speaker Verification", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4473. Accessed: Oct. 17, 2019.
@article{4473-19,
url = {http://sigport.org/4473},
author = {Zhong Meng; Yong Zhao; Jinyu Li; Yifan Gong },
publisher = {IEEE SigPort},
title = {Adversarial Speaker Verification},
year = {2019} }
TY - EJOUR
T1 - Adversarial Speaker Verification
AU - Zhong Meng; Yong Zhao; Jinyu Li; Yifan Gong
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4473
ER -
Zhong Meng, Yong Zhao, Jinyu Li, Yifan Gong. (2019). Adversarial Speaker Verification. IEEE SigPort. http://sigport.org/4473
Zhong Meng, Yong Zhao, Jinyu Li, Yifan Gong, 2019. Adversarial Speaker Verification. Available at: http://sigport.org/4473.
Zhong Meng, Yong Zhao, Jinyu Li, Yifan Gong. (2019). "Adversarial Speaker Verification." Web.
1. Zhong Meng, Yong Zhao, Jinyu Li, Yifan Gong. Adversarial Speaker Verification [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4473

Conditional Teacher-Student Learning


The teacher-student (T/S) learning has been shown to be effective for a variety of problems such as domain adaptation and model compression. One shortcoming of the T/S learning is that a teacher model, not always perfect, sporadically produces wrong guidance in form of posterior probabilities that misleads the student model towards a suboptimal performance.

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Authors:
Zhong Meng, Jinyu Li, Yong Zhao, Yifan Gong
Submitted On:
12 May 2019 - 9:23pm
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[1] Zhong Meng, Jinyu Li, Yong Zhao, Yifan Gong, "Conditional Teacher-Student Learning", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4472. Accessed: Oct. 17, 2019.
@article{4472-19,
url = {http://sigport.org/4472},
author = {Zhong Meng; Jinyu Li; Yong Zhao; Yifan Gong },
publisher = {IEEE SigPort},
title = {Conditional Teacher-Student Learning},
year = {2019} }
TY - EJOUR
T1 - Conditional Teacher-Student Learning
AU - Zhong Meng; Jinyu Li; Yong Zhao; Yifan Gong
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4472
ER -
Zhong Meng, Jinyu Li, Yong Zhao, Yifan Gong. (2019). Conditional Teacher-Student Learning. IEEE SigPort. http://sigport.org/4472
Zhong Meng, Jinyu Li, Yong Zhao, Yifan Gong, 2019. Conditional Teacher-Student Learning. Available at: http://sigport.org/4472.
Zhong Meng, Jinyu Li, Yong Zhao, Yifan Gong. (2019). "Conditional Teacher-Student Learning." Web.
1. Zhong Meng, Jinyu Li, Yong Zhao, Yifan Gong. Conditional Teacher-Student Learning [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4472

Universal Acoustic Using Neural Mixture Models

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Authors:
Amit Das, Jinyu Li, Yifan Gong, Changliang Lu
Submitted On:
12 May 2019 - 2:42am
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[1] Amit Das, Jinyu Li, Yifan Gong, Changliang Lu, "Universal Acoustic Using Neural Mixture Models", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4458. Accessed: Oct. 17, 2019.
@article{4458-19,
url = {http://sigport.org/4458},
author = {Amit Das; Jinyu Li; Yifan Gong; Changliang Lu },
publisher = {IEEE SigPort},
title = {Universal Acoustic Using Neural Mixture Models},
year = {2019} }
TY - EJOUR
T1 - Universal Acoustic Using Neural Mixture Models
AU - Amit Das; Jinyu Li; Yifan Gong; Changliang Lu
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4458
ER -
Amit Das, Jinyu Li, Yifan Gong, Changliang Lu. (2019). Universal Acoustic Using Neural Mixture Models. IEEE SigPort. http://sigport.org/4458
Amit Das, Jinyu Li, Yifan Gong, Changliang Lu, 2019. Universal Acoustic Using Neural Mixture Models. Available at: http://sigport.org/4458.
Amit Das, Jinyu Li, Yifan Gong, Changliang Lu. (2019). "Universal Acoustic Using Neural Mixture Models." Web.
1. Amit Das, Jinyu Li, Yifan Gong, Changliang Lu. Universal Acoustic Using Neural Mixture Models [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4458

Geometric Invariants for Sparse Unknown View Tomography


In this paper, we study a 2D tomography problem for point source models with random unknown view angles. Rather than recovering the projection angles, we reconstruct the model through a set of rotation-invariant features that are estimated from the projection data. For a point source model, we show that these features reveal geometric information about the model such as the radial and pairwise distances. This establishes a connection between unknown view tomography and unassigned distance geometry problem (uDGP).

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Authors:
Mona Zehni, Shuai Huang, Ivan Dokmanic, Zhizhen Zhao
Submitted On:
11 May 2019 - 7:56pm
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CryoPDF_poster.pdf

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[1] Mona Zehni, Shuai Huang, Ivan Dokmanic, Zhizhen Zhao, "Geometric Invariants for Sparse Unknown View Tomography", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4456. Accessed: Oct. 17, 2019.
@article{4456-19,
url = {http://sigport.org/4456},
author = {Mona Zehni; Shuai Huang; Ivan Dokmanic; Zhizhen Zhao },
publisher = {IEEE SigPort},
title = {Geometric Invariants for Sparse Unknown View Tomography},
year = {2019} }
TY - EJOUR
T1 - Geometric Invariants for Sparse Unknown View Tomography
AU - Mona Zehni; Shuai Huang; Ivan Dokmanic; Zhizhen Zhao
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4456
ER -
Mona Zehni, Shuai Huang, Ivan Dokmanic, Zhizhen Zhao. (2019). Geometric Invariants for Sparse Unknown View Tomography. IEEE SigPort. http://sigport.org/4456
Mona Zehni, Shuai Huang, Ivan Dokmanic, Zhizhen Zhao, 2019. Geometric Invariants for Sparse Unknown View Tomography. Available at: http://sigport.org/4456.
Mona Zehni, Shuai Huang, Ivan Dokmanic, Zhizhen Zhao. (2019). "Geometric Invariants for Sparse Unknown View Tomography." Web.
1. Mona Zehni, Shuai Huang, Ivan Dokmanic, Zhizhen Zhao. Geometric Invariants for Sparse Unknown View Tomography [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4456

ONLINE ESTIMATION AND SMOOTHING OF A TARGET TRAJECTORY IN MIXED STATIONARY/MOVING CONDITIONS


A novel maximum likelihood trajectory estimation algorithm for targets in mixed stationary/moving conditions is presented. The proposed approach is able to estimate position and velocity of the target over arbitrary complex trajectories, while explicitly taking into account the possibility of stop&go motion. Moreover, a novel trajectory reconstruction method based on the theory of Bezier curve is developed for online smoothing of the trajectory, which keeps the advantages of Bayesian smoothing while introducing only a fixed lag in the estimation process.

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Authors:
Angelo Coluccia, Alessio Fascista, Giuseppe Ricci
Submitted On:
11 May 2019 - 12:32pm
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Poster ICASSP 2019

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[1] Angelo Coluccia, Alessio Fascista, Giuseppe Ricci, "ONLINE ESTIMATION AND SMOOTHING OF A TARGET TRAJECTORY IN MIXED STATIONARY/MOVING CONDITIONS", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4448. Accessed: Oct. 17, 2019.
@article{4448-19,
url = {http://sigport.org/4448},
author = {Angelo Coluccia; Alessio Fascista; Giuseppe Ricci },
publisher = {IEEE SigPort},
title = {ONLINE ESTIMATION AND SMOOTHING OF A TARGET TRAJECTORY IN MIXED STATIONARY/MOVING CONDITIONS},
year = {2019} }
TY - EJOUR
T1 - ONLINE ESTIMATION AND SMOOTHING OF A TARGET TRAJECTORY IN MIXED STATIONARY/MOVING CONDITIONS
AU - Angelo Coluccia; Alessio Fascista; Giuseppe Ricci
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4448
ER -
Angelo Coluccia, Alessio Fascista, Giuseppe Ricci. (2019). ONLINE ESTIMATION AND SMOOTHING OF A TARGET TRAJECTORY IN MIXED STATIONARY/MOVING CONDITIONS. IEEE SigPort. http://sigport.org/4448
Angelo Coluccia, Alessio Fascista, Giuseppe Ricci, 2019. ONLINE ESTIMATION AND SMOOTHING OF A TARGET TRAJECTORY IN MIXED STATIONARY/MOVING CONDITIONS. Available at: http://sigport.org/4448.
Angelo Coluccia, Alessio Fascista, Giuseppe Ricci. (2019). "ONLINE ESTIMATION AND SMOOTHING OF A TARGET TRAJECTORY IN MIXED STATIONARY/MOVING CONDITIONS." Web.
1. Angelo Coluccia, Alessio Fascista, Giuseppe Ricci. ONLINE ESTIMATION AND SMOOTHING OF A TARGET TRAJECTORY IN MIXED STATIONARY/MOVING CONDITIONS [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4448

Deep Learning for Classroom Activity Detection from Audio


Increasingly, post-secondary instructors are incorporating innovative teaching practices into their classrooms to improve student learning outcomes. In order to assess the effect of these techniques, it is helpful to quantify the types of activity being conducted in the classroom. Unfortunately, self-reporting is unreliable and manual annotation is tedious and scales poorly.

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Authors:
Robin Cosbey, Allison Wusterbarth, Brian Hutchinson
Submitted On:
10 May 2019 - 4:33pm
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[POSTER] Deep Learning for Classroom Activity Detection from Audio

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[1] Robin Cosbey, Allison Wusterbarth, Brian Hutchinson, "Deep Learning for Classroom Activity Detection from Audio", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4404. Accessed: Oct. 17, 2019.
@article{4404-19,
url = {http://sigport.org/4404},
author = {Robin Cosbey; Allison Wusterbarth; Brian Hutchinson },
publisher = {IEEE SigPort},
title = {Deep Learning for Classroom Activity Detection from Audio},
year = {2019} }
TY - EJOUR
T1 - Deep Learning for Classroom Activity Detection from Audio
AU - Robin Cosbey; Allison Wusterbarth; Brian Hutchinson
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4404
ER -
Robin Cosbey, Allison Wusterbarth, Brian Hutchinson. (2019). Deep Learning for Classroom Activity Detection from Audio. IEEE SigPort. http://sigport.org/4404
Robin Cosbey, Allison Wusterbarth, Brian Hutchinson, 2019. Deep Learning for Classroom Activity Detection from Audio. Available at: http://sigport.org/4404.
Robin Cosbey, Allison Wusterbarth, Brian Hutchinson. (2019). "Deep Learning for Classroom Activity Detection from Audio." Web.
1. Robin Cosbey, Allison Wusterbarth, Brian Hutchinson. Deep Learning for Classroom Activity Detection from Audio [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4404

Methodical Design and Trimming of Deep Learning Networks: Enhancing External BP learning with Internal Omnipresent-Supervision Training Paradigm


Back-propagation (BP) is now a classic learning paradigm
whose source of supervision is exclusively from the external
(input/output) nodes. Consequently, BP is easily vulnerable
to curse-of-depth in (very) Deep Learning Networks
(DLNs). This prompts us to advocate Internal Neuron’s
Learnablility (INL) with (1)internal teacher labels (ITL); and
(2)internal optimization metrics (IOM) for evaluating hidden
layers/nodes. Conceptually, INL is a step beyond the notion
of Internal Neuron’s Explainablility (INE), championed by

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Authors:
S. Y. Kung, Zejiang Hou, Yuchen Liu
Submitted On:
10 May 2019 - 2:03pm
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[1] S. Y. Kung, Zejiang Hou, Yuchen Liu, "Methodical Design and Trimming of Deep Learning Networks: Enhancing External BP learning with Internal Omnipresent-Supervision Training Paradigm", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4384. Accessed: Oct. 17, 2019.
@article{4384-19,
url = {http://sigport.org/4384},
author = {S. Y. Kung; Zejiang Hou; Yuchen Liu },
publisher = {IEEE SigPort},
title = {Methodical Design and Trimming of Deep Learning Networks: Enhancing External BP learning with Internal Omnipresent-Supervision Training Paradigm},
year = {2019} }
TY - EJOUR
T1 - Methodical Design and Trimming of Deep Learning Networks: Enhancing External BP learning with Internal Omnipresent-Supervision Training Paradigm
AU - S. Y. Kung; Zejiang Hou; Yuchen Liu
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4384
ER -
S. Y. Kung, Zejiang Hou, Yuchen Liu. (2019). Methodical Design and Trimming of Deep Learning Networks: Enhancing External BP learning with Internal Omnipresent-Supervision Training Paradigm. IEEE SigPort. http://sigport.org/4384
S. Y. Kung, Zejiang Hou, Yuchen Liu, 2019. Methodical Design and Trimming of Deep Learning Networks: Enhancing External BP learning with Internal Omnipresent-Supervision Training Paradigm. Available at: http://sigport.org/4384.
S. Y. Kung, Zejiang Hou, Yuchen Liu. (2019). "Methodical Design and Trimming of Deep Learning Networks: Enhancing External BP learning with Internal Omnipresent-Supervision Training Paradigm." Web.
1. S. Y. Kung, Zejiang Hou, Yuchen Liu. Methodical Design and Trimming of Deep Learning Networks: Enhancing External BP learning with Internal Omnipresent-Supervision Training Paradigm [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4384

Frequency-selective hybrid precoding and combining for mmWave MIMO systems with per-antenna power constraints


Configuring hybrid precoders and combiners is the main challenge to be solved to operate at millimeter wave (mmWave) frequencies. The use of hybrid architectures imposse hardware constraints on the analog precoder that need to be carefully dealt with. In this paper, we develop hybrid precoders and combiners aiming at minimizing the Euclidean distance with respect to the approximate all-digital precoders and combiners maximizing the spectral efficiency under per-antenna power constraints.

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Authors:
Javier Rodriguez-Fernandez, Roberto Lopez-Valcarce, Nuria Gonzalez-Prelcic
Submitted On:
10 May 2019 - 1:42pm
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[1] Javier Rodriguez-Fernandez, Roberto Lopez-Valcarce, Nuria Gonzalez-Prelcic, "Frequency-selective hybrid precoding and combining for mmWave MIMO systems with per-antenna power constraints", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4380. Accessed: Oct. 17, 2019.
@article{4380-19,
url = {http://sigport.org/4380},
author = {Javier Rodriguez-Fernandez; Roberto Lopez-Valcarce; Nuria Gonzalez-Prelcic },
publisher = {IEEE SigPort},
title = {Frequency-selective hybrid precoding and combining for mmWave MIMO systems with per-antenna power constraints},
year = {2019} }
TY - EJOUR
T1 - Frequency-selective hybrid precoding and combining for mmWave MIMO systems with per-antenna power constraints
AU - Javier Rodriguez-Fernandez; Roberto Lopez-Valcarce; Nuria Gonzalez-Prelcic
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4380
ER -
Javier Rodriguez-Fernandez, Roberto Lopez-Valcarce, Nuria Gonzalez-Prelcic. (2019). Frequency-selective hybrid precoding and combining for mmWave MIMO systems with per-antenna power constraints. IEEE SigPort. http://sigport.org/4380
Javier Rodriguez-Fernandez, Roberto Lopez-Valcarce, Nuria Gonzalez-Prelcic, 2019. Frequency-selective hybrid precoding and combining for mmWave MIMO systems with per-antenna power constraints. Available at: http://sigport.org/4380.
Javier Rodriguez-Fernandez, Roberto Lopez-Valcarce, Nuria Gonzalez-Prelcic. (2019). "Frequency-selective hybrid precoding and combining for mmWave MIMO systems with per-antenna power constraints." Web.
1. Javier Rodriguez-Fernandez, Roberto Lopez-Valcarce, Nuria Gonzalez-Prelcic. Frequency-selective hybrid precoding and combining for mmWave MIMO systems with per-antenna power constraints [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4380

MULTI-VIEW NETWORKS FOR MULTI-CHANNEL AUDIO CLASSIFICATION

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Authors:
Jonah Casebeer, Zhepei Wang, Paris Smaragdis
Submitted On:
10 May 2019 - 1:41pm
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[1] Jonah Casebeer, Zhepei Wang, Paris Smaragdis, "MULTI-VIEW NETWORKS FOR MULTI-CHANNEL AUDIO CLASSIFICATION", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4379. Accessed: Oct. 17, 2019.
@article{4379-19,
url = {http://sigport.org/4379},
author = {Jonah Casebeer; Zhepei Wang; Paris Smaragdis },
publisher = {IEEE SigPort},
title = {MULTI-VIEW NETWORKS FOR MULTI-CHANNEL AUDIO CLASSIFICATION},
year = {2019} }
TY - EJOUR
T1 - MULTI-VIEW NETWORKS FOR MULTI-CHANNEL AUDIO CLASSIFICATION
AU - Jonah Casebeer; Zhepei Wang; Paris Smaragdis
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4379
ER -
Jonah Casebeer, Zhepei Wang, Paris Smaragdis. (2019). MULTI-VIEW NETWORKS FOR MULTI-CHANNEL AUDIO CLASSIFICATION. IEEE SigPort. http://sigport.org/4379
Jonah Casebeer, Zhepei Wang, Paris Smaragdis, 2019. MULTI-VIEW NETWORKS FOR MULTI-CHANNEL AUDIO CLASSIFICATION. Available at: http://sigport.org/4379.
Jonah Casebeer, Zhepei Wang, Paris Smaragdis. (2019). "MULTI-VIEW NETWORKS FOR MULTI-CHANNEL AUDIO CLASSIFICATION." Web.
1. Jonah Casebeer, Zhepei Wang, Paris Smaragdis. MULTI-VIEW NETWORKS FOR MULTI-CHANNEL AUDIO CLASSIFICATION [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4379

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