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Machine Learning for Signal Processing

A Fully Convolutional Tri-branch Network (FCTN) For Domain Adaptation


A domain adaptation method for urban scene segmentation is proposed in this work. We develop a fully convolutional tri-branch network, where two branches assign pseudo labels to images in the unlabeled target domain while the third branch is trained with supervision based on images in the pseudo-labeled target domain. The re-labeling and re-training processes alternate. With this design, the tri-branch network learns target-specific discriminative representations progressively and, as a result, the cross-domain capability of the segmenter improves.

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Authors:
Junting Zhang, Chen Liang, C.-C. Jay Kuo
Submitted On:
12 April 2018 - 5:32pm
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A Fully Convolutional Tri-branch Network (FCTN) For Domain Adaptation-Poster

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[1] Junting Zhang, Chen Liang, C.-C. Jay Kuo, "A Fully Convolutional Tri-branch Network (FCTN) For Domain Adaptation", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2503. Accessed: Dec. 10, 2018.
@article{2503-18,
url = {http://sigport.org/2503},
author = {Junting Zhang; Chen Liang; C.-C. Jay Kuo },
publisher = {IEEE SigPort},
title = {A Fully Convolutional Tri-branch Network (FCTN) For Domain Adaptation},
year = {2018} }
TY - EJOUR
T1 - A Fully Convolutional Tri-branch Network (FCTN) For Domain Adaptation
AU - Junting Zhang; Chen Liang; C.-C. Jay Kuo
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2503
ER -
Junting Zhang, Chen Liang, C.-C. Jay Kuo. (2018). A Fully Convolutional Tri-branch Network (FCTN) For Domain Adaptation. IEEE SigPort. http://sigport.org/2503
Junting Zhang, Chen Liang, C.-C. Jay Kuo, 2018. A Fully Convolutional Tri-branch Network (FCTN) For Domain Adaptation. Available at: http://sigport.org/2503.
Junting Zhang, Chen Liang, C.-C. Jay Kuo. (2018). "A Fully Convolutional Tri-branch Network (FCTN) For Domain Adaptation." Web.
1. Junting Zhang, Chen Liang, C.-C. Jay Kuo. A Fully Convolutional Tri-branch Network (FCTN) For Domain Adaptation [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2503

Pattern Localization in Time Series through Signal-To-Model Alignment in Latent Space


In this paper, we study the problem of locating a predefined sequence of patterns in a time series. In particular, the studied scenario assumes a theoretical model is available that contains the expected locations of the patterns. This problem is found in several contexts, and it is commonly solved by first synthesizing a time series from the model, and then aligning it to the true time series through dynamic time warping. We propose a technique that increases the similarity of both time series before aligning them, by mapping them into a latent correlation space.

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Authors:
Steven Van Vaerenbergh, Ignacio Santamaría, Víctor Elvira, Matteo Salvatori
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12 April 2018 - 11:50am
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Pattern Localization in Time Series through Signal-To-Model Alignment in Latent Space

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[1] Steven Van Vaerenbergh, Ignacio Santamaría, Víctor Elvira, Matteo Salvatori, "Pattern Localization in Time Series through Signal-To-Model Alignment in Latent Space", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2412. Accessed: Dec. 10, 2018.
@article{2412-18,
url = {http://sigport.org/2412},
author = {Steven Van Vaerenbergh; Ignacio Santamaría; Víctor Elvira; Matteo Salvatori },
publisher = {IEEE SigPort},
title = {Pattern Localization in Time Series through Signal-To-Model Alignment in Latent Space},
year = {2018} }
TY - EJOUR
T1 - Pattern Localization in Time Series through Signal-To-Model Alignment in Latent Space
AU - Steven Van Vaerenbergh; Ignacio Santamaría; Víctor Elvira; Matteo Salvatori
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2412
ER -
Steven Van Vaerenbergh, Ignacio Santamaría, Víctor Elvira, Matteo Salvatori. (2018). Pattern Localization in Time Series through Signal-To-Model Alignment in Latent Space. IEEE SigPort. http://sigport.org/2412
Steven Van Vaerenbergh, Ignacio Santamaría, Víctor Elvira, Matteo Salvatori, 2018. Pattern Localization in Time Series through Signal-To-Model Alignment in Latent Space. Available at: http://sigport.org/2412.
Steven Van Vaerenbergh, Ignacio Santamaría, Víctor Elvira, Matteo Salvatori. (2018). "Pattern Localization in Time Series through Signal-To-Model Alignment in Latent Space." Web.
1. Steven Van Vaerenbergh, Ignacio Santamaría, Víctor Elvira, Matteo Salvatori. Pattern Localization in Time Series through Signal-To-Model Alignment in Latent Space [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2412

Outlier-Robust Matrix Completion via lp-Minimization


Matrix completion refers to the recovery of a low‐rank matrix from only a subset of its possibly noisy entries, and has a variety of important applications such as collaborative filtering, image inpainting and restoration, system identification, node localization and genotype imputation. It is because many real-world signals can be approximated by a matrix whose rank is much smaller than the row and column numbers. Most techniques for matrix completion in the literature assume Gaussian noise and thus they are not robust to outliers.

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Authors:
Wen-Jun Zeng, Hing Cheung So
Submitted On:
2 March 2018 - 1:57am
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rmp.pdf

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[1] Wen-Jun Zeng, Hing Cheung So, "Outlier-Robust Matrix Completion via lp-Minimization", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2373. Accessed: Dec. 10, 2018.
@article{2373-18,
url = {http://sigport.org/2373},
author = {Wen-Jun Zeng; Hing Cheung So },
publisher = {IEEE SigPort},
title = {Outlier-Robust Matrix Completion via lp-Minimization},
year = {2018} }
TY - EJOUR
T1 - Outlier-Robust Matrix Completion via lp-Minimization
AU - Wen-Jun Zeng; Hing Cheung So
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2373
ER -
Wen-Jun Zeng, Hing Cheung So. (2018). Outlier-Robust Matrix Completion via lp-Minimization. IEEE SigPort. http://sigport.org/2373
Wen-Jun Zeng, Hing Cheung So, 2018. Outlier-Robust Matrix Completion via lp-Minimization. Available at: http://sigport.org/2373.
Wen-Jun Zeng, Hing Cheung So. (2018). "Outlier-Robust Matrix Completion via lp-Minimization." Web.
1. Wen-Jun Zeng, Hing Cheung So. Outlier-Robust Matrix Completion via lp-Minimization [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2373

ASSESSING THE PROGNOSTIC IMPACT OF 3D CT IMAGE TUMOUR RIND TEXTURE FEATURES ON LUNG CANCER SURVIVAL MODELLING


In this paper we examine a technique for developing prognostic image characteristics, termed radiomics, for non-small cell lung cancer based on a tumour edge region-based analysis. Texture features were extracted from the rind of the tumour in a publicly available 3D CT data set to predict two-year survival. The derived models were compared against the previous methods of training radiomic signatures that are descriptive of the whole tumour volume. Radiomic features derived solely from regions external, but neighbouring, the tumour were shown to also have prognostic value.

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Authors:
Alanna Vial, David Stirling, Matthew Field, Montserrat Ros, Christian Ritz, Martin Carolan, Lois Hollowayn, Alexis A. Miller
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11 December 2017 - 5:01pm
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GlobalSIP - Conference Presentation v2.pdf

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[1] Alanna Vial, David Stirling, Matthew Field, Montserrat Ros, Christian Ritz, Martin Carolan, Lois Hollowayn, Alexis A. Miller, "ASSESSING THE PROGNOSTIC IMPACT OF 3D CT IMAGE TUMOUR RIND TEXTURE FEATURES ON LUNG CANCER SURVIVAL MODELLING", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/2371. Accessed: Dec. 10, 2018.
@article{2371-17,
url = {http://sigport.org/2371},
author = {Alanna Vial; David Stirling; Matthew Field; Montserrat Ros; Christian Ritz; Martin Carolan; Lois Hollowayn; Alexis A. Miller },
publisher = {IEEE SigPort},
title = {ASSESSING THE PROGNOSTIC IMPACT OF 3D CT IMAGE TUMOUR RIND TEXTURE FEATURES ON LUNG CANCER SURVIVAL MODELLING},
year = {2017} }
TY - EJOUR
T1 - ASSESSING THE PROGNOSTIC IMPACT OF 3D CT IMAGE TUMOUR RIND TEXTURE FEATURES ON LUNG CANCER SURVIVAL MODELLING
AU - Alanna Vial; David Stirling; Matthew Field; Montserrat Ros; Christian Ritz; Martin Carolan; Lois Hollowayn; Alexis A. Miller
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/2371
ER -
Alanna Vial, David Stirling, Matthew Field, Montserrat Ros, Christian Ritz, Martin Carolan, Lois Hollowayn, Alexis A. Miller. (2017). ASSESSING THE PROGNOSTIC IMPACT OF 3D CT IMAGE TUMOUR RIND TEXTURE FEATURES ON LUNG CANCER SURVIVAL MODELLING. IEEE SigPort. http://sigport.org/2371
Alanna Vial, David Stirling, Matthew Field, Montserrat Ros, Christian Ritz, Martin Carolan, Lois Hollowayn, Alexis A. Miller, 2017. ASSESSING THE PROGNOSTIC IMPACT OF 3D CT IMAGE TUMOUR RIND TEXTURE FEATURES ON LUNG CANCER SURVIVAL MODELLING. Available at: http://sigport.org/2371.
Alanna Vial, David Stirling, Matthew Field, Montserrat Ros, Christian Ritz, Martin Carolan, Lois Hollowayn, Alexis A. Miller. (2017). "ASSESSING THE PROGNOSTIC IMPACT OF 3D CT IMAGE TUMOUR RIND TEXTURE FEATURES ON LUNG CANCER SURVIVAL MODELLING." Web.
1. Alanna Vial, David Stirling, Matthew Field, Montserrat Ros, Christian Ritz, Martin Carolan, Lois Hollowayn, Alexis A. Miller. ASSESSING THE PROGNOSTIC IMPACT OF 3D CT IMAGE TUMOUR RIND TEXTURE FEATURES ON LUNG CANCER SURVIVAL MODELLING [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/2371

Adaptive Basis Selection for Compressed Sensing in Robotic Tactile Skins

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Authors:
Brayden Hollis, Stacy Patterson, Jeff Trinkle
Submitted On:
14 November 2017 - 12:55pm
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GlobalSIP_Poster.pdf

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[1] Brayden Hollis, Stacy Patterson, Jeff Trinkle, "Adaptive Basis Selection for Compressed Sensing in Robotic Tactile Skins", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/2351. Accessed: Dec. 10, 2018.
@article{2351-17,
url = {http://sigport.org/2351},
author = {Brayden Hollis; Stacy Patterson; Jeff Trinkle },
publisher = {IEEE SigPort},
title = {Adaptive Basis Selection for Compressed Sensing in Robotic Tactile Skins},
year = {2017} }
TY - EJOUR
T1 - Adaptive Basis Selection for Compressed Sensing in Robotic Tactile Skins
AU - Brayden Hollis; Stacy Patterson; Jeff Trinkle
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/2351
ER -
Brayden Hollis, Stacy Patterson, Jeff Trinkle. (2017). Adaptive Basis Selection for Compressed Sensing in Robotic Tactile Skins. IEEE SigPort. http://sigport.org/2351
Brayden Hollis, Stacy Patterson, Jeff Trinkle, 2017. Adaptive Basis Selection for Compressed Sensing in Robotic Tactile Skins. Available at: http://sigport.org/2351.
Brayden Hollis, Stacy Patterson, Jeff Trinkle. (2017). "Adaptive Basis Selection for Compressed Sensing in Robotic Tactile Skins." Web.
1. Brayden Hollis, Stacy Patterson, Jeff Trinkle. Adaptive Basis Selection for Compressed Sensing in Robotic Tactile Skins [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/2351

Cepstrum Coefficients Based Sleep Stage Classification

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Authors:
Emin Argun Oral, Muhammet Mustafa Codur, Ibrahim Yucel Ozbek
Submitted On:
14 November 2017 - 10:10am
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Cepstrum Coefficients Sleep Classification

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[1] Emin Argun Oral, Muhammet Mustafa Codur, Ibrahim Yucel Ozbek, "Cepstrum Coefficients Based Sleep Stage Classification", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/2344. Accessed: Dec. 10, 2018.
@article{2344-17,
url = {http://sigport.org/2344},
author = {Emin Argun Oral; Muhammet Mustafa Codur; Ibrahim Yucel Ozbek },
publisher = {IEEE SigPort},
title = {Cepstrum Coefficients Based Sleep Stage Classification},
year = {2017} }
TY - EJOUR
T1 - Cepstrum Coefficients Based Sleep Stage Classification
AU - Emin Argun Oral; Muhammet Mustafa Codur; Ibrahim Yucel Ozbek
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/2344
ER -
Emin Argun Oral, Muhammet Mustafa Codur, Ibrahim Yucel Ozbek. (2017). Cepstrum Coefficients Based Sleep Stage Classification. IEEE SigPort. http://sigport.org/2344
Emin Argun Oral, Muhammet Mustafa Codur, Ibrahim Yucel Ozbek, 2017. Cepstrum Coefficients Based Sleep Stage Classification. Available at: http://sigport.org/2344.
Emin Argun Oral, Muhammet Mustafa Codur, Ibrahim Yucel Ozbek. (2017). "Cepstrum Coefficients Based Sleep Stage Classification." Web.
1. Emin Argun Oral, Muhammet Mustafa Codur, Ibrahim Yucel Ozbek. Cepstrum Coefficients Based Sleep Stage Classification [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/2344

COMBINATORIAL MULTI-ARMED BANDIT PROBLEM WITH PROBABILISTICALLY TRIGGERED ARMS: A CASE WITH BOUNDED REGRET

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Authors:
A. Omer Saritac, C. Tekin
Submitted On:
14 November 2017 - 7:11am
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presentation_1.pdf

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[1] A. Omer Saritac, C. Tekin, "COMBINATORIAL MULTI-ARMED BANDIT PROBLEM WITH PROBABILISTICALLY TRIGGERED ARMS: A CASE WITH BOUNDED REGRET", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/2342. Accessed: Dec. 10, 2018.
@article{2342-17,
url = {http://sigport.org/2342},
author = {A. Omer Saritac; C. Tekin },
publisher = {IEEE SigPort},
title = {COMBINATORIAL MULTI-ARMED BANDIT PROBLEM WITH PROBABILISTICALLY TRIGGERED ARMS: A CASE WITH BOUNDED REGRET},
year = {2017} }
TY - EJOUR
T1 - COMBINATORIAL MULTI-ARMED BANDIT PROBLEM WITH PROBABILISTICALLY TRIGGERED ARMS: A CASE WITH BOUNDED REGRET
AU - A. Omer Saritac; C. Tekin
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/2342
ER -
A. Omer Saritac, C. Tekin. (2017). COMBINATORIAL MULTI-ARMED BANDIT PROBLEM WITH PROBABILISTICALLY TRIGGERED ARMS: A CASE WITH BOUNDED REGRET. IEEE SigPort. http://sigport.org/2342
A. Omer Saritac, C. Tekin, 2017. COMBINATORIAL MULTI-ARMED BANDIT PROBLEM WITH PROBABILISTICALLY TRIGGERED ARMS: A CASE WITH BOUNDED REGRET. Available at: http://sigport.org/2342.
A. Omer Saritac, C. Tekin. (2017). "COMBINATORIAL MULTI-ARMED BANDIT PROBLEM WITH PROBABILISTICALLY TRIGGERED ARMS: A CASE WITH BOUNDED REGRET." Web.
1. A. Omer Saritac, C. Tekin. COMBINATORIAL MULTI-ARMED BANDIT PROBLEM WITH PROBABILISTICALLY TRIGGERED ARMS: A CASE WITH BOUNDED REGRET [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/2342

Generating Forbidden Region Virtual Fixtures By Classification of Movement Intention Based on Event-Related Desynchronization


The development of children’s cognitive and perceptual skills depends heavily on object exploration and manipulative experiences. New types of robotic assistive technologies that enable children with disabilities to interact with their environment, which prove to be beneficial for their cognitive and perceptual skills development, have emerged in recent years. In this study, a human-robot interface that uses Event-Related Desynchronization (ERD) brain response during movement was developed.

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12 November 2017 - 10:57pm
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2017 GlobalSIP slide.pdf

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[1] , "Generating Forbidden Region Virtual Fixtures By Classification of Movement Intention Based on Event-Related Desynchronization", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/2317. Accessed: Dec. 10, 2018.
@article{2317-17,
url = {http://sigport.org/2317},
author = { },
publisher = {IEEE SigPort},
title = {Generating Forbidden Region Virtual Fixtures By Classification of Movement Intention Based on Event-Related Desynchronization},
year = {2017} }
TY - EJOUR
T1 - Generating Forbidden Region Virtual Fixtures By Classification of Movement Intention Based on Event-Related Desynchronization
AU -
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/2317
ER -
. (2017). Generating Forbidden Region Virtual Fixtures By Classification of Movement Intention Based on Event-Related Desynchronization. IEEE SigPort. http://sigport.org/2317
, 2017. Generating Forbidden Region Virtual Fixtures By Classification of Movement Intention Based on Event-Related Desynchronization. Available at: http://sigport.org/2317.
. (2017). "Generating Forbidden Region Virtual Fixtures By Classification of Movement Intention Based on Event-Related Desynchronization." Web.
1. . Generating Forbidden Region Virtual Fixtures By Classification of Movement Intention Based on Event-Related Desynchronization [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/2317

End-To-End Chinese Text Recognition

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Authors:
Jie Hu, Tszhang Guo, Ji Cao, Changshui Zhang
Submitted On:
11 November 2017 - 12:19am
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presentation at GlobalSIP

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[1] Jie Hu, Tszhang Guo, Ji Cao, Changshui Zhang, "End-To-End Chinese Text Recognition", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/2304. Accessed: Dec. 10, 2018.
@article{2304-17,
url = {http://sigport.org/2304},
author = {Jie Hu; Tszhang Guo; Ji Cao; Changshui Zhang },
publisher = {IEEE SigPort},
title = {End-To-End Chinese Text Recognition},
year = {2017} }
TY - EJOUR
T1 - End-To-End Chinese Text Recognition
AU - Jie Hu; Tszhang Guo; Ji Cao; Changshui Zhang
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/2304
ER -
Jie Hu, Tszhang Guo, Ji Cao, Changshui Zhang. (2017). End-To-End Chinese Text Recognition. IEEE SigPort. http://sigport.org/2304
Jie Hu, Tszhang Guo, Ji Cao, Changshui Zhang, 2017. End-To-End Chinese Text Recognition. Available at: http://sigport.org/2304.
Jie Hu, Tszhang Guo, Ji Cao, Changshui Zhang. (2017). "End-To-End Chinese Text Recognition." Web.
1. Jie Hu, Tszhang Guo, Ji Cao, Changshui Zhang. End-To-End Chinese Text Recognition [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/2304

Poster for GlobalSIP 2017 Paper #1180: The Impact of Sports Sentiment on Stock Returns: A Case Study from Professional Sports Leagues

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Authors:
QINGLIANG FAN; WENWEN LEI; XIAP-PING ZHANG
Submitted On:
9 November 2017 - 10:15pm
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poster.pdf

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[1] QINGLIANG FAN; WENWEN LEI; XIAP-PING ZHANG, "Poster for GlobalSIP 2017 Paper #1180: The Impact of Sports Sentiment on Stock Returns: A Case Study from Professional Sports Leagues", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/2280. Accessed: Dec. 10, 2018.
@article{2280-17,
url = {http://sigport.org/2280},
author = {QINGLIANG FAN; WENWEN LEI; XIAP-PING ZHANG },
publisher = {IEEE SigPort},
title = {Poster for GlobalSIP 2017 Paper #1180: The Impact of Sports Sentiment on Stock Returns: A Case Study from Professional Sports Leagues},
year = {2017} }
TY - EJOUR
T1 - Poster for GlobalSIP 2017 Paper #1180: The Impact of Sports Sentiment on Stock Returns: A Case Study from Professional Sports Leagues
AU - QINGLIANG FAN; WENWEN LEI; XIAP-PING ZHANG
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/2280
ER -
QINGLIANG FAN; WENWEN LEI; XIAP-PING ZHANG. (2017). Poster for GlobalSIP 2017 Paper #1180: The Impact of Sports Sentiment on Stock Returns: A Case Study from Professional Sports Leagues. IEEE SigPort. http://sigport.org/2280
QINGLIANG FAN; WENWEN LEI; XIAP-PING ZHANG, 2017. Poster for GlobalSIP 2017 Paper #1180: The Impact of Sports Sentiment on Stock Returns: A Case Study from Professional Sports Leagues. Available at: http://sigport.org/2280.
QINGLIANG FAN; WENWEN LEI; XIAP-PING ZHANG. (2017). "Poster for GlobalSIP 2017 Paper #1180: The Impact of Sports Sentiment on Stock Returns: A Case Study from Professional Sports Leagues." Web.
1. QINGLIANG FAN; WENWEN LEI; XIAP-PING ZHANG. Poster for GlobalSIP 2017 Paper #1180: The Impact of Sports Sentiment on Stock Returns: A Case Study from Professional Sports Leagues [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/2280

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