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

A survey of the state of the art in learning the kernels

In recent years, the machine learning community has witnessed a tremendous growth in the development of kernel-based learning algorithms. However, the performance of this class of algorithms greatly depends on the choice of the kernel function. Kernel function implicitly represents the inner product between a pair of points of a dataset in a higher dimensional space. This inner product amounts to the similarity between points and provides a solid foundation for nonlinear analysis in kernel-based learning algorithms. The most important challenge in kernel-based learning is the selection of an appropriate kernel for a given dataset. To remedy this problem, algorithms to learn the kernel have recently been proposed. These methods formulate a learning algorithm that finds an optimal kernel for a given dataset. In this paper, we present an overview of these algorithms and provide a comparison of various approaches to find an optimal kernel. Furthermore, a list of pivotal issues that lead to efficient design of such algorithms will be presented.M. Ehsan Abbasnejad, Dhanesh Ramachandram, Rajeswari Mandav

Structural, electronic, and dynamical properties of Pca21-TiO

First-principles calculations of the structural, electronic, and mechanical properties of the modified fluorite structure of TiO2 with Pca21 symmetry are obtained using the plane-wave pseudopotential density functional theory. The results indicate that Pca21-TiO2 is a semiconductor with an indirect band gap. The calculated static dielectric constants are larger than those of anatase and brookite, but they are much smaller than those of rutile. The calculated bulk modulus using the equation of state is in good agreement with that calculated from elastic constants. The calculated bulk modulus is in agreement with a recent theoretical and experimental report, which confirms that the experimentally claimed structure (cubic fluorite phase) can be Pca21-TiO2

Incremental real-time multibody VSLAM with trajectory optimization using stereo camera

Real time outdoor navigation in highly dynamic environments is an crucial problem. The recent literature on real time static SLAM don't scale up to dynamic outdoor environments. Most of these methods assume moving objects as outliers or discard the information provided by them. We propose an algorithm to jointly infer the camera trajectory and the moving object trajectory simultaneously. In this paper, we perform a sparse scene flow based motion segmentation using a stereo camera. The segmented objects motion models are used for accurate localization of the camera trajectory as well as the moving objects. We exploit the relationship between moving objects for improving the accuracy of the poses. We formulate the poses as a factor graph incorporating all the constraints. We achieve exact incremental solution by solving a full nonlinear optimization problem in real time. The evaluation is performed on the challenging KITTI dataset with multiple moving cars.Our method outperforms the previous baselines in outdoor navigation.Comment: Available on IRO

The involvement of orexin 1 and cannabinoid 1 receptors within the ventrolateral periaqueductal gray matter in the modulation of migraine-induced anxiety and social behavior deficits of rats

© 2021 Elsevier Inc.Orexin 1 receptors (Orx1R) and cannabinoid 1 receptors (CB1R) are implicated in migraine pathophysiology. This study evaluated the potential involvement of Orx1R and CB1R within the ventrolateral periaqueductal gray matter (vlPAG) in the modulation of anxiety-like behavior and social interaction of migraineurs rats. A rat model of migraine induced by recurrent administration of nitroglycerin (NTG) (5 mg/kg/i.p.). The groups of rats (n = 6) were then subjected to intra-vlPAG microinjection of orexin-A (25, 50 pM), and Orx1R antagonist SB334867 (20, 40 nM) or AM 251 (2, 4 μg) as a CB1R antagonist. Behavioral responses were evaluated in elevated plus maze (EPM), open field (OF) and three-chambered social test apparatus. NTG produced a marked anxiety like behaviors, in both EPM and OF tasks. It did also decrease social performance. NTG–related anxiety and social conflicts were attenuated by orexin-A (25, 50 pM). However, NTG effects were exacerbated by SB334867 (40 nM) and AM251 (2, 4 μg). The orexin-A-mediated suppression of NTG-induced anxiety and social conflicts were prevented by either SB334867 (20 nM) or AM251 (2 μg). The findings suggest roles for Orx1R and CB1R signaling within vlPAG in the modulation of migraine-induced anxiety-like behavior and social dysfunction in rats