Wind tunnel testing on underwater axisymmetric bodies at angle of attack Part III - Experimental investigation on an axisymmetric Body with Blunt Nose

This report, describes the details of the surface pressure measurements made on an axisymmetric body, of finness ratio 15, having blunt nose. The model was tested in the 0.9m dia Low Speed Tunnel at various incidences. The results are presented in this report in the forms of tables and figures

Behind the Kitchen Door

[Excerpt] How do restaurant workers live on some of the lowest wages in America? And how do poor working conditions - discriminatory labor practices, exploitation, and unsanitary kitchens - affect the meals that arrive at our restaurant tables? Saru Jayaraman, who launched the national restaurant workers\u27 organization Restaurant Opportunities Centers United, sets out to answer these questions by following the lives of restaurant workers in New York City, Washington, D.C., Philadelphia, Houston, Los Angeles, Miami, Detroit, and New Orleans

Critical Speed Analysis of a Turbine Rotor

Critical Speed Analysis was carried out for a given Turbine Rotor configuration. A computer program based on transfer matrix method has been used for the analysis. Only the first critical was found to occur in the speed range of interest. This critical was well below the rated operating speed with rigid body mode for the probable range of support stiffness

The impact of school lunches on school enrolment: Evidence from an exogenous policy change in India

Education is thought to be central to economic development. Yet, relatively little is known about how developing countries might advance school participation. In November, 2001 the Indian Supreme Court issued a remarkable interim order directing errant Indian states to other children in government primary schools a warm school lunch. This paper uses this exogenous policy change to evaluate the impact of school lunches on early primary school enrolment. It finds that the introduction of a school lunch is associated with a 25 per cent increase in class 1 enrolment. There is, however, no evidence to suggest that school lunches bridge the overall gender or caste gaps in enrolment. --education,school lunches,quasi-natural experiment

Learning Robust Representations for Computer Vision

Unsupervised learning techniques in computer vision often require learning latent representations, such as low-dimensional linear and non-linear subspaces. Noise and outliers in the data can frustrate these approaches by obscuring the latent spaces. Our main goal is deeper understanding and new development of robust approaches for representation learning. We provide a new interpretation for existing robust approaches and present two specific contributions: a new robust PCA approach, which can separate foreground features from dynamic background, and a novel robust spectral clustering method, that can cluster facial images with high accuracy. Both contributions show superior performance to standard methods on real-world test sets.Comment: 8 pages, 7 page

Learning to Look Around: Intelligently Exploring Unseen Environments for Unknown Tasks

It is common to implicitly assume access to intelligently captured inputs (e.g., photos from a human photographer), yet autonomously capturing good observations is itself a major challenge. We address the problem of learning to look around: if a visual agent has the ability to voluntarily acquire new views to observe its environment, how can it learn efficient exploratory behaviors to acquire informative observations? We propose a reinforcement learning solution, where the agent is rewarded for actions that reduce its uncertainty about the unobserved portions of its environment. Based on this principle, we develop a recurrent neural network-based approach to perform active completion of panoramic natural scenes and 3D object shapes. Crucially, the learned policies are not tied to any recognition task nor to the particular semantic content seen during training. As a result, 1) the learned "look around" behavior is relevant even for new tasks in unseen environments, and 2) training data acquisition involves no manual labeling. Through tests in diverse settings, we demonstrate that our approach learns useful generic policies that transfer to new unseen tasks and environments. Completion episodes are shown at https://goo.gl/BgWX3W

On the Landau-Ginzburg description of Boundary CFTs and special Lagrangian submanifolds

We consider Landau-Ginzburg (LG) models with boundary conditions preserving A-type N=2 supersymmetry. We show the equivalence of a linear class of boundary conditions in the LG model to a particular class of boundary states in the corresponding CFT by an explicit computation of the open-string Witten index in the LG model. We extend the linear class of boundary conditions to general non-linear boundary conditions and determine their consistency with A-type N=2 supersymmetry. This enables us to provide a microscopic description of special Lagrangian submanifolds in C^n due to Harvey and Lawson. We generalise this construction to the case of hypersurfaces in P^n. We find that the boundary conditions must necessarily have vanishing Poisson bracket with the combination (W(\phi)-\bar{W}(\bar{\phi})), where W(\phi) is the appropriate superpotential for the hypersurface. An interesting application considered is the T^3 supersymmetric cycle of the quintic in the large complex structure limit.Comment: 28+1 pages; no figures; requires JHEP.cls, amssymb; (v2) typo corrected; (v3) references adde

Zero Shot Recognition with Unreliable Attributes

In principle, zero-shot learning makes it possible to train a recognition model simply by specifying the category's attributes. For example, with classifiers for generic attributes like \emph{striped} and \emph{four-legged}, one can construct a classifier for the zebra category by enumerating which properties it possesses---even without providing zebra training images. In practice, however, the standard zero-shot paradigm suffers because attribute predictions in novel images are hard to get right. We propose a novel random forest approach to train zero-shot models that explicitly accounts for the unreliability of attribute predictions. By leveraging statistics about each attribute's error tendencies, our method obtains more robust discriminative models for the unseen classes. We further devise extensions to handle the few-shot scenario and unreliable attribute descriptions. On three datasets, we demonstrate the benefit for visual category learning with zero or few training examples, a critical domain for rare categories or categories defined on the fly.Comment: NIPS 201