Hierarchical object detection with deep reinforcement learning

We present a method for performing hierarchical object detection in images guided by a deep reinforcement learning agent. The key idea is to focus on those parts of the image that contain richer information and zoom on them. We train an intelligent agent that, given an image window, is capable of deciding where to focus the attention among five different predefined region candidates (smaller windows). This procedure is iterated providing a hierarchical image analysis. We compare two different candidate proposal strategies to guide the object search: with and without overlap. Moreover, our work compares two different strategies to extract features from a convolutional neural network for each region proposal: a first one that computes new feature maps for each region proposal, and a second one that computes the feature maps for the whole image to later generate crops for each region proposal. Experiments indicate better results for the overlapping candidate proposal strategy and a loss of performance for the cropped image features due to the loss of spatial resolution. We argue that, while this loss seems unavoidable when working with large amounts of object candidates, the much more reduced amount of region proposals generated by our reinforcement learning agent allows considering to extract features for each location without sharing convolutional computation among regions.Postprint (published version

Data Management and Mining in Astrophysical Databases

We analyse the issues involved in the management and mining of astrophysical data. The traditional approach to data management in the astrophysical field is not able to keep up with the increasing size of the data gathered by modern detectors. An essential role in the astrophysical research will be assumed by automatic tools for information extraction from large datasets, i.e. data mining techniques, such as clustering and classification algorithms. This asks for an approach to data management based on data warehousing, emphasizing the efficiency and simplicity of data access; efficiency is obtained using multidimensional access methods and simplicity is achieved by properly handling metadata. Clustering and classification techniques, on large datasets, pose additional requirements: computational and memory scalability with respect to the data size, interpretability and objectivity of clustering or classification results. In this study we address some possible solutions.Comment: 10 pages, Late

CMB Power Spectrum Estimation via Hierarchical Decomposition

We have developed a fast, accurate and generally applicable method for inferring the power spectrum and its uncertainties from maps of the cosmic microwave background (CMB) in the presence of inhomogeneous and correlated noise. For maps with 10 to 100 thousand pixels, we apply an exact power spectrum estimation algorithm to submaps of the data at various resolutions, and then combine the results in an optimal manner. To analyze larger maps efficiently one must resort to sub-optimal combinations in which cross-map power spectrum error correlations are only calculated approximately. We expect such approximations to work well in general, and in particular for the megapixel maps to come from the next generation of satellite missions.Comment: 10 pages, 5 figures, to be submitted to Phys. Rev.

Identifying Overlapping and Hierarchical Thematic Structures in Networks of Scholarly Papers: A Comparison of Three Approaches

We implemented three recently proposed approaches to the identification of overlapping and hierarchical substructures in graphs and applied the corresponding algorithms to a network of 492 information-science papers coupled via their cited sources. The thematic substructures obtained and overlaps produced by the three hierarchical cluster algorithms were compared to a content-based categorisation, which we based on the interpretation of titles and keywords. We defined sets of papers dealing with three topics located on different levels of aggregation: h-index, webometrics, and bibliometrics. We identified these topics with branches in the dendrograms produced by the three cluster algorithms and compared the overlapping topics they detected with one another and with the three pre-defined paper sets. We discuss the advantages and drawbacks of applying the three approaches to paper networks in research fields.Comment: 18 pages, 9 figure