Search for serendipitous TNO occultation in X-rays

To study the population properties of small, remote objects beyond Neptune's orbit in the outer solar system, of kilometer size or smaller, serendipitous occultation search is so far the only way. For hectometer-sized Trans-Neptunian Objects (TNOs), optical shadows actually disappear because of diffraction. Observations at shorter wave lengths are needed. Here we report the effort of TNO occultation search in X-rays using RXTE/PCA data of Sco X-1 taken from June 2007 to October 2011. No definite TNO occultation events were found in the 334 ks data. We investigate the detection efficiency dependence on the TNO size to better define the sensible size range of our approach and suggest upper limits to the TNO size distribution in the size range from 30 m to 300 m. A list of X-ray sources suitable for future larger facilities to observe is proposed.Comment: Accepted to publish in MNRA

Order-Free RNN with Visual Attention for Multi-Label Classification

In this paper, we propose the joint learning attention and recurrent neural network (RNN) models for multi-label classification. While approaches based on the use of either model exist (e.g., for the task of image captioning), training such existing network architectures typically require pre-defined label sequences. For multi-label classification, it would be desirable to have a robust inference process, so that the prediction error would not propagate and thus affect the performance. Our proposed model uniquely integrates attention and Long Short Term Memory (LSTM) models, which not only addresses the above problem but also allows one to identify visual objects of interests with varying sizes without the prior knowledge of particular label ordering. More importantly, label co-occurrence information can be jointly exploited by our LSTM model. Finally, by advancing the technique of beam search, prediction of multiple labels can be efficiently achieved by our proposed network model.Comment: Accepted at 32nd AAAI Conference on Artificial Intelligence (AAAI-18

An image retrieval system based on the feature of color differences among the edges of objects

This paper focuses on color differences among the edges of objects in an image. The variations of colors among the objects in the image can depict the directions and simple geometric shapes of most objects in the image regardless of the influence from the shift variant of objects and scale variant in the image. Based on the feature of color differences among the edges of objects, this paper constructs an image retrieval system. Experimental results show that this system can effectively and quickly deliver the desired database images to the usersFacultad de Informátic

An image retrieval system based on the feature of color differences among the edges of objects

This paper focuses on color differences among the edges of objects in an image. The variations of colors among the objects in the image can depict the directions and simple geometric shapes of most objects in the image regardless of the influence from the shift variant of objects and scale variant in the image. Based on the feature of color differences among the edges of objects, this paper constructs an image retrieval system. Experimental results show that this system can effectively and quickly deliver the desired database images to the usersFacultad de Informátic

Alternative Ingredient Recommendation: A Co-occurrence and Ingredient Category Importance Based Approach

As many people will refer to a recipe when cooking, there are several recipe-sharing websites that include lots of recipes and make recipes easier to access than before. However, there is often the case that we could not get all the ingredients listed on the recipe. Prior research on alternative ingredient substitution has built a recommendation system considering the suitability of a recommended ingredient with the remained ingredients. In this paper, in addition to suitability, we also take the diversity of the ingredient categories and the novelty of new combination of ingredients into account. Besides, we combine suitability with novelty as an index, to see whether our method could help find out a new combination of ingredients that is possibly to be a new dish. Our evaluation results show that our proposed method attains a comparable or even better performance on each perspective

An Improved Tax Scheme for Selfish Routing

We study the problem of routing traffic for independent selfish users in a congested network to minimize the total latency. The inefficiency of selfish routing motivates regulating the flow of the system to lower the total latency of the Nash Equilibrium by economic incentives or penalties. When applying tax to the routes, we follow the definition of [Christodoulou et al, Algorithmica, 2014] to define ePoA as the Nash total cost including tax in the taxed network over the optimal cost in the original network. We propose a simple tax scheme consisting of step functions imposed on the links. The tax scheme can be applied to routing games with parallel links, affine cost functions and single-commodity networks to lower the ePoA to at most 4/3 - epsilon, where epsilon only depends on the discrepancy between the links. We show that there exists a tax scheme in the two link case with an ePoA upperbound less than 1.192 which is almost tight. Moreover, we design another tax scheme that lowers ePoA down to 1.281 for routing games with groups of links such that links in the same group are similar to each other and groups are sufficiently different