6,769 research outputs found
Spinal cord gray matter segmentation using deep dilated convolutions
Gray matter (GM) tissue changes have been associated with a wide range of
neurological disorders and was also recently found relevant as a biomarker for
disability in amyotrophic lateral sclerosis. The ability to automatically
segment the GM is, therefore, an important task for modern studies of the
spinal cord. In this work, we devise a modern, simple and end-to-end fully
automated human spinal cord gray matter segmentation method using Deep
Learning, that works both on in vivo and ex vivo MRI acquisitions. We evaluate
our method against six independently developed methods on a GM segmentation
challenge and report state-of-the-art results in 8 out of 10 different
evaluation metrics as well as major network parameter reduction when compared
to the traditional medical imaging architectures such as U-Nets.Comment: 13 pages, 8 figure
Online dictionary learning from big data using accelerated stochastic approximation algorithms
Applications involving large-scale dictionary learning tasks motivate well online optimization algorithms for generally non-convex and non-smooth problems. In this big data context, the present paper develops an online learning framework by jointly leveraging the stochastic approximation paradigm with first-order acceleration schemes. The generally non-convex objective evaluated online at the resultant iterates enjoys quadratic rate of convergence. The generality
of the novel approach is demonstrated in two online learning applications: (i) Online linear regression using the total least-squares approach; and, (ii) a semi-supervised dictionary learning approach
to network-wide link load tracking and imputation of real data with missing entries. In both cases, numerical tests highlight the potential of the proposed online framework for big data network analytics
Deliverable 2.2.1: Online Dictionary Learning from Big Data Using Accelerated Stochastic Approximation Algorithms
Applications involving large-scale dictionary learning tasks motivate well online optimization algorithms for generally non-convex and non-smooth problems. In this big data context, the present paper develops an online learning framework by jointly leveraging the stochastic approximation paradigm with ïŹrst-order acceleration schemes. The generally non-convex objective evaluated online at the resultant iterates enjoys quadratic rate of convergence. The generality of the novel approach is demonstrated in two online learning applications: (i) Online linear regression using the total least-squares approach; and, (ii) a semi-supervised dictionary learning approach to network-wide link load tracking and imputation of real data with missing entries. In both cases, numerical tests highlight the potential of the proposed online framework for big data network analytics
A Very Brief Introduction to Machine Learning With Applications to Communication Systems
Given the unprecedented availability of data and computing resources, there
is widespread renewed interest in applying data-driven machine learning methods
to problems for which the development of conventional engineering solutions is
challenged by modelling or algorithmic deficiencies. This tutorial-style paper
starts by addressing the questions of why and when such techniques can be
useful. It then provides a high-level introduction to the basics of supervised
and unsupervised learning. For both supervised and unsupervised learning,
exemplifying applications to communication networks are discussed by
distinguishing tasks carried out at the edge and at the cloud segments of the
network at different layers of the protocol stack
DEFINE: friendship detection based on node enhancement
Network representation learning (NRL) is a matter of importance to a variety of tasks such as link prediction. Learning low-dimensional vector representations for node enhancement based on nodes attributes and network structures can improve link prediction performance. Node attributes are important factors in forming networks, like psychological factors and appearance features affecting friendship networks. However, little to no work has detected friendship using the NRL technique, which combines studentsâ psychological features and perceived traits based on facial appearance. In this paper, we propose a framework named DEFINE (No enhancement based r e dship D tection) to detect studentsâ friend relationships, which combines with studentsâ psychological factors and facial perception information. To detect friend relationships accurately, DEFINE uses the NRL technique, which considers network structure and the additional attributes information for nodes. DEFINE transforms them into low-dimensional vector spaces while preserving the inherent properties of the friendship network. Experimental results on real-world friendship network datasets illustrate that DEFINE outperforms other state-of-art methods. © 2020, Springer Nature Switzerland AG.E
Exploiting and Ranking Dominating Product Features through Communal Sentiments
The rapidly expanding e-commerce has facilitated consumers to purchase products online. Various brands and millions of products have been offered online. Varieties of customersâ reviews are available now days in internet. These reviews are important for the consumers as well as the merchants. Most of the reviews are disorganized so it generates difficulty for usefulness of information. In this paper we are proposing a product feature ranking framework, which will identify important features of products from online customer opinions, and aim to improve the usability of the different reviews. The important product features are recognized using two observations 1) the important features are mostly commented on by a large number of users 2) users reviews on the important features are greatly influence on the overall reviews on the product. We first identify product features by shallow dependency parser and determine customerâs reviews on these features via a sentiment classifier. Then we adopt develop a probabilistic feature ranking algorithm to conclude the importance of features by considering frequency and the influence of the influence of the users reviews given to each feature over their overall reviews.
DOI: 10.17762/ijritcc2321-8169.15068
Graph Signal Processing: Overview, Challenges and Applications
Research in Graph Signal Processing (GSP) aims to develop tools for
processing data defined on irregular graph domains. In this paper we first
provide an overview of core ideas in GSP and their connection to conventional
digital signal processing. We then summarize recent developments in developing
basic GSP tools, including methods for sampling, filtering or graph learning.
Next, we review progress in several application areas using GSP, including
processing and analysis of sensor network data, biological data, and
applications to image processing and machine learning. We finish by providing a
brief historical perspective to highlight how concepts recently developed in
GSP build on top of prior research in other areas.Comment: To appear, Proceedings of the IEE
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