Adaptive image retrieval using a graph model for semantic feature integration

The variety of features available to represent multimedia data constitutes a rich pool of information. However, the plethora of data poses a challenge in terms of feature selection and integration for effective retrieval. Moreover, to further improve effectiveness, the retrieval model should ideally incorporate context-dependent feature representations to allow for retrieval on a higher semantic level. In this paper we present a retrieval model and learning framework for the purpose of interactive information retrieval. We describe how semantic relations between multimedia objects based on user interaction can be learnt and then integrated with visual and textual features into a unified framework. The framework models both feature similarities and semantic relations in a single graph. Querying in this model is implemented using the theory of random walks. In addition, we present ideas to implement short-term learning from relevance feedback. Systematic experimental results validate the effectiveness of the proposed approach for image retrieval. However, the model is not restricted to the image domain and could easily be employed for retrieving multimedia data (and even a combination of different domains, eg images, audio and text documents)

EGO: a personalised multimedia management tool

The problems of Content-Based Image Retrieval (CBIR) sys- tems can be attributed to the semantic gap between the low-level data representation and the high-level concepts the user associates with images, on the one hand, and the time-varying and often vague nature of the underlying information need, on the other. These problems can be addressed by improving the interaction between the user and the system. In this paper, we sketch the development of CBIR interfaces, and introduce our view on how to solve some of the problems of the studied interfaces. To address the semantic gap and long-term multifaceted information needs, we propose a "retrieval in context" system. EGO is a tool for the management of image collections, supporting the user through personalisation and adaptation. We will describe how it learns from the user's personal organisation, allowing it to recommend relevant images to the user. The recommendation algorithm is detailed, which is based on relevance feedback techniques

Evidence combination for multi-point query learning in content-based image retrieval

In multipoint query learning a number of query representatives are selected based on the positive feedback samples. The similarity score to a multipoint query is obtained from merging the individual scores. In this paper, we investigate three different combination strategies and present a comparative evaluation of their performance. Results show that the performance of multipoint queries relies heavily on the right choice of settings for the fusion. Unlike previous results, suggesting that multipoint queries generally perform better than a single query representation, our evaluation results do not allow such an overall conclusion. Instead our study points to the type of queries for which query expansion is better suited than a single query, and vice versa

International portfolio diversification and labor/leisure choice

When marginal utility of consumption depends on leisure, investors will take this into account when allocating their wealth among different assets. This paper presents a multi-country general equilibrium model driven by productivity shocks, where labor-leisure and consumption are chosen endogenously. We use this framework to study the effect of leisure for optimal international diversification. We find that in the symmetric case the model's ability to help explain home-bias depends crucially on the level of substitutability between consumption and leisure.Saving and investment ; Econometric models

Automated Reasoning and Presentation Support for Formalizing Mathematics in Mizar

This paper presents a combination of several automated reasoning and proof presentation tools with the Mizar system for formalization of mathematics. The combination forms an online service called MizAR, similar to the SystemOnTPTP service for first-order automated reasoning. The main differences to SystemOnTPTP are the use of the Mizar language that is oriented towards human mathematicians (rather than the pure first-order logic used in SystemOnTPTP), and setting the service in the context of the large Mizar Mathematical Library of previous theorems,definitions, and proofs (rather than the isolated problems that are solved in SystemOnTPTP). These differences poses new challenges and new opportunities for automated reasoning and for proof presentation tools. This paper describes the overall structure of MizAR, and presents the automated reasoning systems and proof presentation tools that are combined to make MizAR a useful mathematical service.Comment: To appear in 10th International Conference on. Artificial Intelligence and Symbolic Computation AISC 201

Quantitative asset pricing implications of endogenous solvency constraints

The authors study the asset pricing implications of an economy where solvency constraints are determined to efficiently deter agents from defaulting. The authors present a simple example for which efficient allocations and all equilibrium elements are characterized analytically. The main model produces large equity premia and risk premia for long-term bonds with low risk aversion and a plausibly calibrated income process. The authors characterize the deviations from independence of aggregate and individual income uncertainty that produce equity and term premia.Asset pricing

Fragmentation And Evolution Of Molecular Clouds. III. The Effect Of Dust And Gas Energetics

Dust and gas energetics are incorporated into a cluster-scale simulation of star formation in order to study the effect of heating and cooling on the star formation process. We build on our previous work by calculating separately the dust and gas temperatures. The dust temperature is set by radiative equilibrium between heating by embedded stars and radiation from dust. The gas temperature is determined using an energy-rate balance algorithm which includes molecular cooling, dust-gas collisional energy transfer, and cosmic-ray ionization. The fragmentation proceeds roughly similarly to simulations in which the gas temperature is set to the dust temperature, but there are differences. The structure of regions around sink particles has properties similar to those of Class 0 objects, but the infall speeds and mass accretion rates are, on average, higher than those seen for regions forming only low-mass stars. The gas and dust temperature have complex distributions not well modeled by approximations that ignore the detailed thermal physics. There is no simple relationship between density and kinetic temperature. In particular, high-density regions have a large range of temperatures, determined by their location relative to heating sources. The total luminosity underestimates the star formation rate at these early stages, before ionizing sources are included, by an order of magnitude. As predicted in our previous work, a larger number of intermediate-mass objects form when improved thermal physics is included, but the resulting initial mass function (IMF) still has too few low-mass stars. However, if we consider recent evidence on core-to-star efficiencies, the match to the IMF is improved.NASA NAG5-10826, NAG5-13271Canada Research Chair programNSERCNSF AST-0607793, AST-1109116NASA GSRP Fellowship ProgramAstronom

Using Asset Prices to Measure the Cost of Business Cycles

We propose a method to measure the welfare cost of economic fluctuations that does not require full specification of consumer preferences and instead uses asset prices. The method is based on the marginal cost of consumption fluctuations, the per unit benefit of a marginal reduction in consumption fluctuations expressed as a percentage of consumption. We show that this measure is an upper bound for the benefit of reducing all consumption fluctuations. We also clarify the link between the cost of consumption uncertainty, the equity premium, and the slope of the real term structure. To measure the marginal cost of fluctuations, we fit a variety of pricing kernels that reproduce key asset pricing statistics. We find that consumers would be willing to pay a very high price for a reduction in overall consumption uncertainty. However, for consumption fluctuations corresponding to business cycle frequencies, we estimate the marginal cost to be about 0.55% of lifetime consumption based on the period 1889-1997 and about 0.30% based on 1954-97.