824,112 research outputs found

    Object Distribution Networks for World-wide Document Circulation

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    This paper presents an Object Distribution System (ODS), a distributed system inspired by the ultra-large scale distribution models used in everyday life (e.g. food or newspapers distribution chains). Beyond traditional mechanisms of approaching information to readers (e.g. caching and mirroring), this system enables the publication, classification and subscription to volumes of objects (e.g. documents, events). Authors submit their contents to publication agents. Classification authorities provide classification schemes to classify objects. Readers subscribe to topics or authors, and retrieve contents from their local delivery agent (like a kiosk or library, with local copies of objects). Object distribution is an independent process where objects circulate asynchronously among distribution agents. ODS is designed to perform specially well in an increasingly populated, widespread and complex Internet jungle, using weak consistency replication by object distribution, asynchronous replication, and local access to objects by clients. ODS is based on two independent virtual networks, one dedicated to the distribution (replication) of objects and the other to calculate optimised distribution chains to be applied by the first network

    Deconfounding Causal Inference for Zero-shot Action Recognition

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    Zero-shot action recognition (ZSAR) aims to recognize unseen action categories in the test set without corresponding training examples. Most existing zero-shot methods follow the feature generation framework to transfer knowledge from seen action categories to model the feature distribution of unseen categories. However, due to the complexity and diversity of actions, it remains challenging to generate unseen feature distribution, especially for the cross-dataset scenario when there is potentially larger domain shift. This paper proposes a De confounding Ca usa l GAN (DeCalGAN) for generating unseen action video features with the following technical contributions: 1) Our model unifies compositional ZSAR with traditional visual-semantic models to incorporate local object information with global semantic information for feature generation. 2) A GAN-based architecture is proposed for causal inference and unseen distribution discovery. 3) A deconfounding module is proposed to refine representations of local object and global semantic information confounder in the training data. Action descriptions and random object feature after causal inference are then used to discover unseen distributions of novel actions in different datasets. Our extensive experiments on C ross- D ataset Z ero- S hot A ction R ecognition (CD-ZSAR) demonstrate substantial improvement over the UCF101 and HMDB51 standard benchmarks for this problem

    The Structure of Active Merger Remnant NGC 6240 from IRAC Observations

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    NGC 6240 is a rare object in the local universe: an active merger remnant viewed at the point of merging where two active galactic nuclei are visible. We present IRAC data of this object, providing high sensitivity maps of the stellar and PAH distribution in this complicated system. We use photometry to analyze the variation in these distributions with radius and provide an SED in the four IRAC bands: 3.6 microns, 4.5 microns, 5.8 microns and 8.0 microns. We fit the radial profiles of the 3.6 micron band to r^.25 and exponential profiles to evaluate the structure of the remnant. Finally, we compare the IRAC images with multi-wavelength data and examine how outflows in the X-ray, Halpha and CO correlate with 8 micron emission. The results support the general picture of NGC 6240 as a system experiencing a major merger and transitioning from a disk galaxy to a spheroid. The sensitivity of IRAC to low surface brightness mid-infrared features provides detailed information on the extended distributions of stars and dust in this rare system.Comment: Accepted for publication in Ap

    3D Object Detection Algorithm Based on the Reconstruction of Sparse Point Clouds in the Viewing Frustum

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    In response to the problem that the detection precision of the current 3D object detection algorithm is low when the object is severely occluded, this study proposes an object detection algorithm based on the reconstruction of sparse point clouds in the viewing frustum. The algorithm obtains more local feature information of the sparse point clouds in the viewing frustum through dimensional expansion, performs the fusion of local and global feature information of the point cloud data to obtain point cloud data with more complete semantic information, and then applies the obtained data to the 3D object detection task. The experimental results show that the precision of object detection in both 3D view and BEV (Bird’s Eye View) can be improved effectively through the algorithm, especially object detection of moderate and hard levels when the object is severely occluded. In the 3D view, the average precision of the 3D detection of cars, pedestrians, and cyclists at a moderate level can be increased by 7.1p.p., 16.39p.p., and 5.42p.p., respectively; in BEV, the average precision of the 3D detection of car, pedestrians, and cyclists at hard level can be increased by 6.51p.p., 16.57p.p., and 7.18p.p., respectively, thus indicating the effectiveness of the algorithm.© 2022 Xing Xu et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.fi=vertaisarvioitu|en=peerReviewed

    The influence of search components and problem characteristics in early life cycle class modelling

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    © 2014 Elsevier Inc. All rights reserved. This paper examines the factors affecting the quality of solution found by meta-heuristic search when optimising object-oriented software class models. From the algorithmic perspective, we examine the effect of encoding, choice of components such as the global search heuristic, and various means of incorporating problem- and instance-specific information. We also consider the effect of problem characteristics on the (estimated) cost of the global optimum, and the quality and distribution of local optima. The choice of global search component appears important, and adding problem and instance-specific information is generally beneficial to an evolutionary algorithm but detrimental to ant colony optimisation. The effect of problem characteristics is more complex. Neither scale nor complexity have a significant effect on the global optimum as estimated by the best solution ever found. However, using local search to locate 100,000 local optima for each problem confirms the results from meta-heuristic search: there are patterns in the distribution of local optima that increase with scale (problem size) and complexity (number of classes) and will cause problems for many classes of meta-heuristic search
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