Incremental Stochastic Subgradient Algorithms for Convex Optimization

In this paper we study the effect of stochastic errors on two constrained incremental sub-gradient algorithms. We view the incremental sub-gradient algorithms as decentralized network optimization algorithms as applied to minimize a sum of functions, when each component function is known only to a particular agent of a distributed network. We first study the standard cyclic incremental sub-gradient algorithm in which the agents form a ring structure and pass the iterate in a cycle. We consider the method with stochastic errors in the sub-gradient evaluations and provide sufficient conditions on the moments of the stochastic errors that guarantee almost sure convergence when a diminishing step-size is used. We also obtain almost sure bounds on the algorithm's performance when a constant step-size is used. We then consider \ram{the} Markov randomized incremental subgradient method, which is a non-cyclic version of the incremental algorithm where the sequence of computing agents is modeled as a time non-homogeneous Markov chain. Such a model is appropriate for mobile networks, as the network topology changes across time in these networks. We establish the convergence results and error bounds for the Markov randomized method in the presence of stochastic errors for diminishing and constant step-sizes, respectively

A Critique of Supernova Data Analysis in Cosmology

Observational astronomy has shown significant growth over the last decade and has made important contributions to cosmology. A major paradigm shift in cosmology was brought about by observations of Type Ia supernovae. The notion that the universe is accelerating has led to several theoretical challenges. Unfortunately, although high quality supernovae data-sets are being produced, their statistical analysis leaves much to be desired. Instead of using the data to directly test the model, several studies seem to concentrate on assuming the model to be correct and limiting themselves to estimating model parameters and internal errors. As shown here, the important purpose of testing a cosmological theory is thereby vitiated.Comment: v2: Revised, comments and references added; Published version [vailable at http://www.raa-journal.org/raa/index.php/raa/article/view/539

DeepFuse: A Deep Unsupervised Approach for Exposure Fusion with Extreme Exposure Image Pairs

We present a novel deep learning architecture for fusing static multi-exposure images. Current multi-exposure fusion (MEF) approaches use hand-crafted features to fuse input sequence. However, the weak hand-crafted representations are not robust to varying input conditions. Moreover, they perform poorly for extreme exposure image pairs. Thus, it is highly desirable to have a method that is robust to varying input conditions and capable of handling extreme exposure without artifacts. Deep representations have known to be robust to input conditions and have shown phenomenal performance in a supervised setting. However, the stumbling block in using deep learning for MEF was the lack of sufficient training data and an oracle to provide the ground-truth for supervision. To address the above issues, we have gathered a large dataset of multi-exposure image stacks for training and to circumvent the need for ground truth images, we propose an unsupervised deep learning framework for MEF utilizing a no-reference quality metric as loss function. The proposed approach uses a novel CNN architecture trained to learn the fusion operation without reference ground truth image. The model fuses a set of common low level features extracted from each image to generate artifact-free perceptually pleasing results. We perform extensive quantitative and qualitative evaluation and show that the proposed technique outperforms existing state-of-the-art approaches for a variety of natural images.Comment: ICCV 201

A case-based reasoning approach to intelligent retrieval in reusable software libraries

Software Reuse is the technique of reusing software components previously developed in order to reduce the effort required to develop new software. It is generally accepted that software reuse can improve the rate of software development, reduce the costs and increase reliability, However, software reuse is only effective if it is easier to locate and adapt a reusable software component than to write it from scratch. There are many issues and problems that need to be resolved before the benefits of software reuse become widespread. These involve philosophical issues such as the encapsulation of experience and the formation of organizational structures to support it, and also technical issues such as the identification, classification, retrieval and adaptation of reusable components. This paper is concerned with the automated retrieval of software components from a reusable software library using case-based reasoning. Current automated retrieval is generally adapted from conventional text retrieval methods which are based on matching lexical and semantic attributes of software components. While these methods are easily implemented, they have serious limitations that arise out of the fact that the words and phrases used to describe software components and their functions are usually obscure, ambiguous and imprecise. Case-based reasoning is an artificial intelligence technique that makes use of a stored set of previously solved problems (cases) in order to solve new ones. It is an effective method of applying the experience and problem solving knowledge gained in the past to bear on current problems. Case-based systems find solutions to problems by examining an input situation or problem and searching a case base to find a case or situation that matches its characteristic features. If the match is identical, the problem is solved. Case-based reasoning shells are inexpensive and their retrieval mechanisms are complex. This together with the fact that the attribute based classification of cases can be used to classify reusable components, affords the opportunity of efficient intelligent retrieval of reusable components. This paper shows how both attribute and faceted based classification schemes can be accommodated by a case-based reasoning shell and examines various methods of retrieval

The Proposal for an Indo-Pacific Treaty of Friendship and Cooperation: a Critical Reassessment

The emergence of the Indo- Pacific construct brings about interesting avenues for cooperation among states in the region. Characterised by the intertwining geographies of the Indian and the Pacific Oceans, the Indo- Pacific region is home to some of the most diverse peoples and economies in the world. In a speech delivered at the CSIS, Washington in 2013, the former Indonesian Foreign Minister Marty Natalegwa outlined the need for an “Indo- Pacific Treaty of Friendship and Cooperation”. In efforts to continue to address the prospects and challenges for a treaty among the major powers in the Indo- Pacific region, the article argues that a treaty would be necessary step and but should be concluded when sufficient groundwork for it is concluded. The article also argues that, the Indo – pacific concept would be best addressed if there is increased institutionalization of the concept and increased cooperation among middle powers such as India, Indonesia and Australia

Fermi Surface Studies of Co-Based Heusler Alloys: Ab-Initio Study

The electronic, Fermi surface (FS) and magnetic properties of ferromagnetic Heusler alloys Co2XY (X = Cr, Mn, Fe; Y=Al, Ga) have been investigated by means of first principles calculation. Out of these compounds, Co2CrAl is found to be perfectly half-metallic (HM) at ambient. Under pressure HM to nearly HM (NHM) transition is observed around 75 GPa for Co 2CrAl and NHM to HM transition is observed around 40 GPa and 18 GPa for Co2CrGa and Co2MnAl, respectively, while no transition is observed for other compounds under study and is also analyzed from the FS studies. The states at the Fermi level in the majority spin are strongly hybridized Co-d and X-d like states. The majority band FS topology change is observed under pressure for the compounds where we observe a transition, while the minority band FS remain unaltered under pressure for all compounds except in Co2FeGa, where we observed an electron sheet at X point instead of hole pocket at Γ poin