Learning And Optimization Of The Kernel Functions From Insufficiently Labeled Data

Amongst all the machine learning techniques, kernel methods are increasingly becoming popular due to their efficiency, accuracy and ability to handle high-dimensional data. The fundamental problem related to these learning techniques is the selection of the kernel function. Therefore, learning the kernel as a procedure in which the kernel function is selected for a particular dataset is highly important. In this thesis, two approaches to learn the kernel function are proposed: transferred learning of the kernel and an unsupervised approach to learn the kernel. The first approach uses transferred knowledge from unlabeled data to cope with situations where training examples are scarce. Unlabeled data is used in conjunction with labeled data to construct an optimized kernel using Fisher discriminant analysis and maximum mean discrepancy. The accuracy of classification which indicates the number of correctly predicted test examples from the base kernels and the optimized kernel are compared in two datasets involving satellite images and synthetic data where proposed approach produces better results. The second approach is an unsupervised method to learn a linear combination of kernel functions

Adversarial Variational Embedding for Robust Semi-supervised Learning

Semi-supervised learning is sought for leveraging the unlabelled data when labelled data is difficult or expensive to acquire. Deep generative models (e.g., Variational Autoencoder (VAE)) and semisupervised Generative Adversarial Networks (GANs) have recently shown promising performance in semi-supervised classification for the excellent discriminative representing ability. However, the latent code learned by the traditional VAE is not exclusive (repeatable) for a specific input sample, which prevents it from excellent classification performance. In particular, the learned latent representation depends on a non-exclusive component which is stochastically sampled from the prior distribution. Moreover, the semi-supervised GAN models generate data from pre-defined distribution (e.g., Gaussian noises) which is independent of the input data distribution and may obstruct the convergence and is difficult to control the distribution of the generated data. To address the aforementioned issues, we propose a novel Adversarial Variational Embedding (AVAE) framework for robust and effective semi-supervised learning to leverage both the advantage of GAN as a high quality generative model and VAE as a posterior distribution learner. The proposed approach first produces an exclusive latent code by the model which we call VAE++, and meanwhile, provides a meaningful prior distribution for the generator of GAN. The proposed approach is evaluated over four different real-world applications and we show that our method outperforms the state-of-the-art models, which confirms that the combination of VAE++ and GAN can provide significant improvements in semisupervised classification.Comment: 9 pages, Accepted by Research Track in KDD 201

Reliability of B-mode ultrasonography for abdominal muscles in asymptomatic and patients with acute low back pain

The purpose of this methological study was to develop a reliable method for measuring transversus abdominis, rectus abdominis, external oblique and internal oblique muscles in asymptomatic human subjects and patients with acute low back pain (ALBP). This was a single operator reliability design study using ultrasound imaging to measure muscle thickness in 27 subjects on three separate occasions. Intra-class correlation coefficients (ICC) and standard error of measurement (SEM) were used to analyze muscle thickness. The mean, SD, ICC and SEM for external oblique, internal oblique, transversus abdominis and rectus abdominis muscles in asymptomatic subjects were (5.38, 1.64, 0.96, 0.33), (9.35, 3.42, 0.97, 0.073), (4.36, 1.93, 0.81, 0.45), (10.8, 2.18, 0.85, 0.84), respectively. The mean, SD, ICC, SEM for external oblique, internal oblique and transversus abdominis muscles in patients with ALBP were (5.58, 0.97, 0.87, 0.35), (9.72, 1.92, 0.87, 0.31), (4.36, 1, 0.91, 0.3), respectively. Earlier study on ultrasonographic measurement for neck multifidus muscles has suggested that the reliability of muscle thickness is higher in asymptomatic subjects compared with those in the symptomatic subjects. However, the present study showed high reliability for both symptomatic and asymptomatic subjects. This difference may be related to non-atrophic changes in abdominal muscles in acute low back patients. The results of this study indicate that the measurement of abdominal muscle thickness with B-mode ultrasonography can be performed reliably even in patients with ALBP. © 2005 Elsevier Ltd. All rights reserved

The effect of the eye movement desensitization and reprocessing intervention on anxiety and depression among patients undergoing hemodialysis: A randomized controlled trial

Author's accepted version (postprint).This is an Accepted Manuscript of an article published by Wiley in Perspectives in psychiatric care on 29/04/2019.Available online: https://onlinelibrary.wiley.com/doi/epdf/10.1111/ppc.12389acceptedVersio

Workspace Analysis of a 4 Cable-Driven Spatial Parallel Robot

International audienceThis paper presents the static equilibrium workspace of an under-constrained cable-driven robot with four cables taking into account the forces and the moments due to the forces acting on the moving platform. The problem is formulated as a non-linear optimization problem with maintaining static equilibrium as the objective function. The simulations are done using MATLAB. The maximum force on the cables and tilting angle of the platform are used to define the feasible static equilibrium workspace and the results obtained are used to finalize the design of the collaborative cable-driven robot to be installed in existing production lines for the agile handling of parts in a manufacturing industry

Sleep paralysis in medieval Persia – the Hidayat of Akhawayni (?–983 AD)

Among the first three manuscripts written in Persian, Akhawayni’s Hidayat al-muta’allemin fi al-tibb was the most significant work compiled in the 10th century. Along with the hundreds of chapters on hygiene, anatomy, physiology, symptoms and treatments of the diseases of various organs, there is a chapter on sleep paralysis (night-mare) prior to description and treatment of epilepsy. The present article is a review of the Akhawayni’s teachings on sleep paralysis and of descriptions and treatments of sleep paralysis by the Greek, medieval, and Renaissance scholars. Akhawayni’s descriptions along with other early writings provide insight into sleep paralysis during the Middle Ages in general and in Persia in particular

Real solutions of the direct geometrico-static problem of under-constrained cable-driven parallel robots with 3 cables: a numerical investigation

This paper addresses the direct geometrico-static problem of under-constrained cable-driven parallel robots with 3 cables. The task at hand consists in finding all equilibrium configurations of the end-effector when the cable lengths are assigned. This problem is known to admit 156 solutions in the complex field, but the upper bound on the number of real solutions is as yet an open issue. Finding this bound is the objective of the paper. For this purpose, three numerical approaches are developed, namely a continuation procedure adapted from an algorithm originally proposed by Dietmaier and two evolutionary techniques based on a genetic algorithm and particle swarm optimization. In all cases, a number of sets of robot parameters for which the direct geometrico-static problem provides at the most 54 real configurations is found. The coherence of the obtained results leads to conjecture that the achieved bound is tight. However, formal proof is yet to be discovered

Real Solutions of the Direct Geometrico-Static Analysis of Under-Constrained Cable-Driven Parallel Robots with Three Cables

This paper addresses the direct geometrico-static analysis of under-constrained cable-driven parallel robots with 3 cables. The task at hand consists in finding all equilibrium configurations of the end-effector when the cable lengths are assigned. This problem is known to admit 156 solutions in the complex field, but the upper bound on the number of real solutions is as yet an open issue. Finding this bound is the objective of the paper. For this purpose, three numerical iterative approaches are developed, namely a continuation procedure adapted from an algorithm originally proposed by Dietmaier and two evolutionary techniques based on a genetic algorithm and particle swarm optimization. In all cases, a number of sets of robot parameters for which the direct geometrico-static problem provides at the most 54 real configurations is found. The coherence of the obtained results leads to conjecture that the achieved bound is tight. However, formal proof is yet to be discovered