High-performance Kernel Machines with Implicit Distributed Optimization and Randomization

In order to fully utilize "big data", it is often required to use "big models". Such models tend to grow with the complexity and size of the training data, and do not make strong parametric assumptions upfront on the nature of the underlying statistical dependencies. Kernel methods fit this need well, as they constitute a versatile and principled statistical methodology for solving a wide range of non-parametric modelling problems. However, their high computational costs (in storage and time) pose a significant barrier to their widespread adoption in big data applications. We propose an algorithmic framework and high-performance implementation for massive-scale training of kernel-based statistical models, based on combining two key technical ingredients: (i) distributed general purpose convex optimization, and (ii) the use of randomization to improve the scalability of kernel methods. Our approach is based on a block-splitting variant of the Alternating Directions Method of Multipliers, carefully reconfigured to handle very large random feature matrices, while exploiting hybrid parallelism typically found in modern clusters of multicore machines. Our implementation supports a variety of statistical learning tasks by enabling several loss functions, regularization schemes, kernels, and layers of randomized approximations for both dense and sparse datasets, in a highly extensible framework. We evaluate the ability of our framework to learn models on data from applications, and provide a comparison against existing sequential and parallel libraries.Comment: Work presented at MMDS 2014 (June 2014) and JSM 201

Recycling Randomness with Structure for Sublinear time Kernel Expansions

We propose a scheme for recycling Gaussian random vectors into structured matrices to approximate various kernel functions in sublinear time via random embeddings. Our framework includes the Fastfood construction as a special case, but also extends to Circulant, Toeplitz and Hankel matrices, and the broader family of structured matrices that are characterized by the concept of low-displacement rank. We introduce notions of coherence and graph-theoretic structural constants that control the approximation quality, and prove unbiasedness and low-variance properties of random feature maps that arise within our framework. For the case of low-displacement matrices, we show how the degree of structure and randomness can be controlled to reduce statistical variance at the cost of increased computation and storage requirements. Empirical results strongly support our theory and justify the use of a broader family of structured matrices for scaling up kernel methods using random features

Fast Conical Hull Algorithms for Near-separable Non-negative Matrix Factorization

The separability assumption (Donoho & Stodden, 2003; Arora et al., 2012) turns non-negative matrix factorization (NMF) into a tractable problem. Recently, a new class of provably-correct NMF algorithms have emerged under this assumption. In this paper, we reformulate the separable NMF problem as that of finding the extreme rays of the conical hull of a finite set of vectors. From this geometric perspective, we derive new separable NMF algorithms that are highly scalable and empirically noise robust, and have several other favorable properties in relation to existing methods. A parallel implementation of our algorithm demonstrates high scalability on shared- and distributed-memory machines.Comment: 15 pages, 6 figure

A REVIEW ON THE NEURAL CIRCUITS IN ANXIETY DISORDERS

ABSTRACTAnxiety disorders are among the most common mental, emotional, and behavioral problems. These affect one-eighth of the total population worldwide.Anxiety disorders are a group of mental disorders characterized by irritability, fear, insomnia, nervousness, tachycardia, inability to concentrate,poor coping skills, palpitation, sweating, agoraphobia, and social withdrawal. Brain regions and networks involved in anxiety symptomatology isan effort to better understand the mechanism involved and to develop more effective treatments for the anxiety disorders. Thus, neuroanatomicaland neuroimaging research in anxiety disorders has centered on the role of the amygdala, reciprocal connections between the amygdala and theprefrontal cortex, and, most recently, alterations in interoceptive processing by the anterior insula. Anxiety disorders are characterized by alterationsin a diverse range of neurochemical systems, suggesting ample novel targets for drug therapies. The neurotransmitter like corticotropin-releasingfactor, neuropeptides (substance P, neuropeptide Y, oxytocin, orexin, and galanin) are implicated in anxiety pathways. Each of these active areas ofresearch holds promise for expanding and improving evidence-based treatment options for individuals suffering with clinical anxiety. Therefore,this article gives the information on the neurocognitive mechanisms, causes, neurotransmitter involved in anxiety disorders and emphasize on thetherapeutic targets for anxiety disorders.Keywords: Anxiety, Stress, Amygdala, Corticotropin releasing factor, Insula, Thalamus

NEUROTRANSMITTER AND BRAIN PARTS INVOLVED IN SCHIZOPHRENIA

Schizophrenia (SCZ) is a major debilitating, complex, and costly illness that strikes 1% of the world's population. It is characterized by three general types of symptoms: Atypical symptoms (aggressiveness, agitation, delusions, hallucinations), depressive symptoms (alogia, avolition, anhedonia, apathy), and cognitive symptoms (impaired attention, learning, memory). The etiology of SCZ has still not been fully understood. Alteration in various neurochemical systems such as dopamine, serotonin, norepinephrine, gamma-aminobutyric acid, and glutamate are involved in the pathophysiology of SCZ. The lack of understanding regarding the exact pathogenic process may be the likely a reason for the non-availability of effective treatment, which can prevent onset and progression of the SCZ. The tools of modern neuroscience, drawing from neuroanatomy, neurophysiology, brain imaging, and psychopharmacology, promise to provide a host of new insights into the etiology and treatment of SCZ. In this review, we will discuss the role of the various neurotransmitter concerned and brain parts exaggerated in the SCZ