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Emerging: Big Data

Performance analysis of distributed radio interferometric calibration


Distributed calibration based on consensus optimization is a computationally efficient method to calibrate large radio interferometers such as LOFAR and SKA. Calibrating along multiple directions in the sky and removing the bright foreground signal is a crucial step in many science cases in radio interferometry. The residual data contain weak signals of huge scientific interest and of particular concern is the effect of incomplete sky models used in calibration on the residual. In order to study this, we consider the mapping between the input uncalibrated data and the output residual data.

lofar75.pdf

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17 July 2018 - 6:22am
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[1] , "Performance analysis of distributed radio interferometric calibration", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3358. Accessed: Sep. 21, 2018.
@article{3358-18,
url = {http://sigport.org/3358},
author = { },
publisher = {IEEE SigPort},
title = {Performance analysis of distributed radio interferometric calibration},
year = {2018} }
TY - EJOUR
T1 - Performance analysis of distributed radio interferometric calibration
AU -
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3358
ER -
. (2018). Performance analysis of distributed radio interferometric calibration. IEEE SigPort. http://sigport.org/3358
, 2018. Performance analysis of distributed radio interferometric calibration. Available at: http://sigport.org/3358.
. (2018). "Performance analysis of distributed radio interferometric calibration." Web.
1. . Performance analysis of distributed radio interferometric calibration [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3358

Multi-scale algorithms for optimal transport


Optimal transport is a geometrically intuitive and robust way to quantify differences between probability measures.
It is becoming increasingly popular as numerical tool in image processing, computer vision and machine learning.
A key challenge is its efficient computation, in particular on large problems. Various algorithms exist, tailored to different special cases.
Multi-scale methods can be applied to classical discrete algorithms, as well as entropy regularization techniques. They provide a good compromise between efficiency and flexibility.

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2 June 2018 - 2:58am
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schmitzer_2018-06_Lausanne.pdf

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[1] , "Multi-scale algorithms for optimal transport", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3230. Accessed: Sep. 21, 2018.
@article{3230-18,
url = {http://sigport.org/3230},
author = { },
publisher = {IEEE SigPort},
title = {Multi-scale algorithms for optimal transport},
year = {2018} }
TY - EJOUR
T1 - Multi-scale algorithms for optimal transport
AU -
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3230
ER -
. (2018). Multi-scale algorithms for optimal transport. IEEE SigPort. http://sigport.org/3230
, 2018. Multi-scale algorithms for optimal transport. Available at: http://sigport.org/3230.
. (2018). "Multi-scale algorithms for optimal transport." Web.
1. . Multi-scale algorithms for optimal transport [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3230

Vector compression for similarity search using Multi-layer Sparse Ternary Codes


It was shown recently that Sparse Ternary Codes (STC) posses superior ``coding gain'' compared to the classical binary hashing framework and can successfully be used for large-scale search applications. This work extends the STC for compression and proposes a rate-distortion efficient design. We first study a single-layer setup where we show that binary encoding intrinsically suffers from poor compression quality while STC, thanks to the flexibility in design, can have near-optimal rate allocation. We further show that single-layer codes should be limited to very low rates.

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Authors:
Sohrab Ferdowsi, Slava Voloshynovskiy, Dimche Kostadinov
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1 June 2018 - 12:45pm
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DSW2018_poster.pdf

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[1] Sohrab Ferdowsi, Slava Voloshynovskiy, Dimche Kostadinov, "Vector compression for similarity search using Multi-layer Sparse Ternary Codes", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3229. Accessed: Sep. 21, 2018.
@article{3229-18,
url = {http://sigport.org/3229},
author = {Sohrab Ferdowsi; Slava Voloshynovskiy; Dimche Kostadinov },
publisher = {IEEE SigPort},
title = {Vector compression for similarity search using Multi-layer Sparse Ternary Codes},
year = {2018} }
TY - EJOUR
T1 - Vector compression for similarity search using Multi-layer Sparse Ternary Codes
AU - Sohrab Ferdowsi; Slava Voloshynovskiy; Dimche Kostadinov
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3229
ER -
Sohrab Ferdowsi, Slava Voloshynovskiy, Dimche Kostadinov. (2018). Vector compression for similarity search using Multi-layer Sparse Ternary Codes. IEEE SigPort. http://sigport.org/3229
Sohrab Ferdowsi, Slava Voloshynovskiy, Dimche Kostadinov, 2018. Vector compression for similarity search using Multi-layer Sparse Ternary Codes. Available at: http://sigport.org/3229.
Sohrab Ferdowsi, Slava Voloshynovskiy, Dimche Kostadinov. (2018). "Vector compression for similarity search using Multi-layer Sparse Ternary Codes." Web.
1. Sohrab Ferdowsi, Slava Voloshynovskiy, Dimche Kostadinov. Vector compression for similarity search using Multi-layer Sparse Ternary Codes [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3229

Sparse Subspace Clustering with Missing and Corrupted Data


In many settings, we can accurately model high-dimensional data as lying in a union of subspaces. Subspace clustering is the process of inferring the subspaces and determining which point belongs to each subspace. In this paper we study a ro- bust variant of sparse subspace clustering (SSC). While SSC is well-understood when there is little or no noise, less is known about SSC under significant noise or missing en- tries. We establish clustering guarantees in the presence of corrupted or missing entries.

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Authors:
Amin Jalali, Rebecca Willett
Submitted On:
31 May 2018 - 6:30pm
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sparse_subpsace_clustering_with_missing_and_corrupted_data.pdf

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[1] Amin Jalali, Rebecca Willett, "Sparse Subspace Clustering with Missing and Corrupted Data", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3227. Accessed: Sep. 21, 2018.
@article{3227-18,
url = {http://sigport.org/3227},
author = {Amin Jalali; Rebecca Willett },
publisher = {IEEE SigPort},
title = {Sparse Subspace Clustering with Missing and Corrupted Data},
year = {2018} }
TY - EJOUR
T1 - Sparse Subspace Clustering with Missing and Corrupted Data
AU - Amin Jalali; Rebecca Willett
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3227
ER -
Amin Jalali, Rebecca Willett. (2018). Sparse Subspace Clustering with Missing and Corrupted Data. IEEE SigPort. http://sigport.org/3227
Amin Jalali, Rebecca Willett, 2018. Sparse Subspace Clustering with Missing and Corrupted Data. Available at: http://sigport.org/3227.
Amin Jalali, Rebecca Willett. (2018). "Sparse Subspace Clustering with Missing and Corrupted Data." Web.
1. Amin Jalali, Rebecca Willett. Sparse Subspace Clustering with Missing and Corrupted Data [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3227

SUBSAMPLING LEAST SQUARES AND ELEMENTAL ESTIMATION

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30 May 2018 - 9:30am
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poster2018.pdf

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[1] , "SUBSAMPLING LEAST SQUARES AND ELEMENTAL ESTIMATION", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3219. Accessed: Sep. 21, 2018.
@article{3219-18,
url = {http://sigport.org/3219},
author = { },
publisher = {IEEE SigPort},
title = {SUBSAMPLING LEAST SQUARES AND ELEMENTAL ESTIMATION},
year = {2018} }
TY - EJOUR
T1 - SUBSAMPLING LEAST SQUARES AND ELEMENTAL ESTIMATION
AU -
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3219
ER -
. (2018). SUBSAMPLING LEAST SQUARES AND ELEMENTAL ESTIMATION. IEEE SigPort. http://sigport.org/3219
, 2018. SUBSAMPLING LEAST SQUARES AND ELEMENTAL ESTIMATION. Available at: http://sigport.org/3219.
. (2018). "SUBSAMPLING LEAST SQUARES AND ELEMENTAL ESTIMATION." Web.
1. . SUBSAMPLING LEAST SQUARES AND ELEMENTAL ESTIMATION [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3219

THE ASYNCHRONOUS POWER ITERATION: A GRAPH SIGNAL PERSPECTIVE

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Oguzhan Teke, P. P. Vaidyanathan
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22 April 2018 - 12:23am
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async_updates_icassp_presentation.pdf

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[1] Oguzhan Teke, P. P. Vaidyanathan, "THE ASYNCHRONOUS POWER ITERATION: A GRAPH SIGNAL PERSPECTIVE", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3126. Accessed: Sep. 21, 2018.
@article{3126-18,
url = {http://sigport.org/3126},
author = {Oguzhan Teke; P. P. Vaidyanathan },
publisher = {IEEE SigPort},
title = {THE ASYNCHRONOUS POWER ITERATION: A GRAPH SIGNAL PERSPECTIVE},
year = {2018} }
TY - EJOUR
T1 - THE ASYNCHRONOUS POWER ITERATION: A GRAPH SIGNAL PERSPECTIVE
AU - Oguzhan Teke; P. P. Vaidyanathan
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3126
ER -
Oguzhan Teke, P. P. Vaidyanathan. (2018). THE ASYNCHRONOUS POWER ITERATION: A GRAPH SIGNAL PERSPECTIVE. IEEE SigPort. http://sigport.org/3126
Oguzhan Teke, P. P. Vaidyanathan, 2018. THE ASYNCHRONOUS POWER ITERATION: A GRAPH SIGNAL PERSPECTIVE. Available at: http://sigport.org/3126.
Oguzhan Teke, P. P. Vaidyanathan. (2018). "THE ASYNCHRONOUS POWER ITERATION: A GRAPH SIGNAL PERSPECTIVE." Web.
1. Oguzhan Teke, P. P. Vaidyanathan. THE ASYNCHRONOUS POWER ITERATION: A GRAPH SIGNAL PERSPECTIVE [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3126

EXTENDABLE NEURAL MATRIX COMPLETION

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20 April 2018 - 4:21am
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ICASSP-MC-poster.pdf

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[1] , "EXTENDABLE NEURAL MATRIX COMPLETION", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3094. Accessed: Sep. 21, 2018.
@article{3094-18,
url = {http://sigport.org/3094},
author = { },
publisher = {IEEE SigPort},
title = {EXTENDABLE NEURAL MATRIX COMPLETION},
year = {2018} }
TY - EJOUR
T1 - EXTENDABLE NEURAL MATRIX COMPLETION
AU -
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3094
ER -
. (2018). EXTENDABLE NEURAL MATRIX COMPLETION. IEEE SigPort. http://sigport.org/3094
, 2018. EXTENDABLE NEURAL MATRIX COMPLETION. Available at: http://sigport.org/3094.
. (2018). "EXTENDABLE NEURAL MATRIX COMPLETION." Web.
1. . EXTENDABLE NEURAL MATRIX COMPLETION [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3094

Low-Rank Optimization for Data Shuffling in Wireless Distributed Computing


Wireless distributed computing presents new opportunities to execute intelligent tasks on mobile devices for low-latency applications, by wirelessly aggregating the computation and storage resources among mobile devices. However, for low-latency applications, the key bottleneck lies in the exchange of intermediate results among mobile devices for data shuffling. To improve communication efficiency therein, we establish a novel interference alignment condition by exploiting the locally computed intermediate values as side information.

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Authors:
Yuanming Shi, Zhi Ding
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20 April 2018 - 1:48am
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ICASSP_poster.pdf

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[1] Yuanming Shi, Zhi Ding, "Low-Rank Optimization for Data Shuffling in Wireless Distributed Computing", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3080. Accessed: Sep. 21, 2018.
@article{3080-18,
url = {http://sigport.org/3080},
author = {Yuanming Shi; Zhi Ding },
publisher = {IEEE SigPort},
title = {Low-Rank Optimization for Data Shuffling in Wireless Distributed Computing},
year = {2018} }
TY - EJOUR
T1 - Low-Rank Optimization for Data Shuffling in Wireless Distributed Computing
AU - Yuanming Shi; Zhi Ding
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3080
ER -
Yuanming Shi, Zhi Ding. (2018). Low-Rank Optimization for Data Shuffling in Wireless Distributed Computing. IEEE SigPort. http://sigport.org/3080
Yuanming Shi, Zhi Ding, 2018. Low-Rank Optimization for Data Shuffling in Wireless Distributed Computing. Available at: http://sigport.org/3080.
Yuanming Shi, Zhi Ding. (2018). "Low-Rank Optimization for Data Shuffling in Wireless Distributed Computing." Web.
1. Yuanming Shi, Zhi Ding. Low-Rank Optimization for Data Shuffling in Wireless Distributed Computing [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3080

Twitter User Geolocation using Multivew Deep Learning


Predicting the geographical location of users on social networks like Twitter is an active research topic with plenty of methods proposed so far. Most of the existing work follows either a content-based or a network-based approach. The former is based on user-generated content while the latter exploits the structure of the network of users. In this paper, we propose a more generic approach, which incorporates not only both content-based and network-based features, but also other available information into a unified model.

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20 April 2018 - 4:22am
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icassp_2018_twitter.pdf

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icassp_2018_twitter.pdf

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[1] , "Twitter User Geolocation using Multivew Deep Learning", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3033. Accessed: Sep. 21, 2018.
@article{3033-18,
url = {http://sigport.org/3033},
author = { },
publisher = {IEEE SigPort},
title = {Twitter User Geolocation using Multivew Deep Learning},
year = {2018} }
TY - EJOUR
T1 - Twitter User Geolocation using Multivew Deep Learning
AU -
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3033
ER -
. (2018). Twitter User Geolocation using Multivew Deep Learning. IEEE SigPort. http://sigport.org/3033
, 2018. Twitter User Geolocation using Multivew Deep Learning. Available at: http://sigport.org/3033.
. (2018). "Twitter User Geolocation using Multivew Deep Learning." Web.
1. . Twitter User Geolocation using Multivew Deep Learning [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3033

Distributed coupled learning over adaptive networks


This work develops an effective distributed algorithm for the solution of stochastic optimization problems that involve partial coupling among both local constraints and local cost functions. While the collection of networked agents is interested in discovering a global model, the individual agents are sensing data that is only dependent on parts of the model. Moreover, different agents may be dependent on different subsets of the model. In this way, cooperation is justified and also necessary to enable recovery of the global information.

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Authors:
Ali H. Sayed
Submitted On:
19 April 2018 - 4:35pm
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Poster_ICASSP.pdf

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[1] Ali H. Sayed, "Distributed coupled learning over adaptive networks", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3021. Accessed: Sep. 21, 2018.
@article{3021-18,
url = {http://sigport.org/3021},
author = {Ali H. Sayed },
publisher = {IEEE SigPort},
title = {Distributed coupled learning over adaptive networks},
year = {2018} }
TY - EJOUR
T1 - Distributed coupled learning over adaptive networks
AU - Ali H. Sayed
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3021
ER -
Ali H. Sayed. (2018). Distributed coupled learning over adaptive networks. IEEE SigPort. http://sigport.org/3021
Ali H. Sayed, 2018. Distributed coupled learning over adaptive networks. Available at: http://sigport.org/3021.
Ali H. Sayed. (2018). "Distributed coupled learning over adaptive networks." Web.
1. Ali H. Sayed. Distributed coupled learning over adaptive networks [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3021

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