Measuring and Predicting Importance of Objects in Our Visual World

Associating keywords with images automatically is an approachable and useful goal for visual recognition researchers. Keywords are distinctive and informative objects. We argue that keywords need to be sorted by 'importance', which we define as the probability of being mentioned first by an observer. We propose a method for measuring the `importance' of words using the object labels that multiple human observers give an everyday scene photograph. We model object naming as drawing balls from an urn, and fit this model to estimate `importance'; this combines order and frequency, enabling precise prediction under limited human labeling. We explore the relationship between the importance of an object in a particular image and the area, centrality, and saliency of the corresponding image patches. Furthermore, our data shows that many words are associated with even simple environments, and that few frequently appearing objects are shared across environments

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Experiments with phase transitions at very high pressure

Diamond cells were constructed for use to 1 Mbar. A refrigerator for cooling diamond cells was adapted for studies between 15 and 300 K. A cryostat for superconductivity studies between 1.5 to 300 K was constructed. Optical equipment was constructed for fluorescence, transmission, and reflectance studies. X-ray equipment was adapted for use with diamond cells. Experimental techniques were developed for X-ray diffraction studies using synchrotron radiation. AC susceptibility techniques were developed for detecting superconducting transitions. The following materials were studied: compressed solidified gases (Xe, Ar), semiconductors (Ge, Si, GaAs), superconductors (Nb3Ge, Nb3Si, Nb3As, CuCl), molecular crystals (I)

Application of a system modification technique to dynamic tuning of a spinning rotor blade

An important consideration in the development of modern helicopters is the vibratory response of the main rotor blade. One way to minimize vibration levels is to ensure that natural frequencies of the spinning main rotor blade are well removed from integer multiples of the rotor speed. A technique for dynamically tuning a finite-element model of a rotor blade to accomplish that end is demonstrated. A brief overview is given of the general purpose finite element system known as Engineering Analysis Language (EAL) which was used in this work. A description of the EAL System Modification (SM) processor is then given along with an explanation of special algorithms developed to be used in conjunction with SM. Finally, this technique is demonstrated by dynamically tuning a model of an advanced composite rotor blade

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Alternatives to Fiscal Austerity in Spain

This paper looks at the planned austerity measures in Spain, the rationale for the spending cuts and tax increases, likely outcomes for future debt-to-GDP ratios, and the probable results of alternative policies.IMF, Spain, EU, European Central Bank, deficit spending, budget deficits, fiscal policy, monetary policy

A multi-modal event detection system for river and coastal marine monitoring applications

Abstract—This work is investigating the use of a multi-modal sensor network where visual sensors such as cameras and satellite imagers, along with context information can be used to complement and enhance the usefulness of a traditional in-situ sensor network in measuring and tracking some feature of a river or coastal location. This paper focuses on our work in relation to the use of an off the shelf camera as part of a multi-modal sensor network for monitoring a river environment. It outlines our results in relation to the estimation of water level using a visual sensor. It also outlines the benefits of a multi-modal sensor network for marine environmental monitoring and how this can lead to a smarter, more efficient sensing network

Objects predict fixations better than early saliency

Humans move their eyes while looking at scenes and pictures. Eye movements correlate with shifts in attention and are thought to be a consequence of optimal resource allocation for high-level tasks such as visual recognition. Models of attention, such as “saliency maps,” are often built on the assumption that “early” features (color, contrast, orientation, motion, and so forth) drive attention directly. We explore an alternative hypothesis: Observers attend to “interesting” objects. To test this hypothesis, we measure the eye position of human observers while they inspect photographs of common natural scenes. Our observers perform different tasks: artistic evaluation, analysis of content, and search. Immediately after each presentation, our observers are asked to name objects they saw. Weighted with recall frequency, these objects predict fixations in individual images better than early saliency, irrespective of task. Also, saliency combined with object positions predicts which objects are frequently named. This suggests that early saliency has only an indirect effect on attention, acting through recognized objects. Consequently, rather than treating attention as mere preprocessing step for object recognition, models of both need to be integrated