An Iterative Co-Saliency Framework for RGBD Images

As a newly emerging and significant topic in computer vision community, co-saliency detection aims at discovering the common salient objects in multiple related images. The existing methods often generate the co-saliency map through a direct forward pipeline which is based on the designed cues or initialization, but lack the refinement-cycle scheme. Moreover, they mainly focus on RGB image and ignore the depth information for RGBD images. In this paper, we propose an iterative RGBD co-saliency framework, which utilizes the existing single saliency maps as the initialization, and generates the final RGBD cosaliency map by using a refinement-cycle model. Three schemes are employed in the proposed RGBD co-saliency framework, which include the addition scheme, deletion scheme, and iteration scheme. The addition scheme is used to highlight the salient regions based on intra-image depth propagation and saliency propagation, while the deletion scheme filters the saliency regions and removes the non-common salient regions based on interimage constraint. The iteration scheme is proposed to obtain more homogeneous and consistent co-saliency map. Furthermore, a novel descriptor, named depth shape prior, is proposed in the addition scheme to introduce the depth information to enhance identification of co-salient objects. The proposed method can effectively exploit any existing 2D saliency model to work well in RGBD co-saliency scenarios. The experiments on two RGBD cosaliency datasets demonstrate the effectiveness of our proposed framework.Comment: 13 pages, 13 figures, Accepted by IEEE Transactions on Cybernetics 2017. Project URL: https://rmcong.github.io/proj_RGBD_cosal_tcyb.htm

Tentacular nature of the ‘column’ of the Cambrian diploblastic Xianguangia sinica

Unveiling the body architectures of Cambrian problematic fossils would provide novel insights into the radiation of metazoan body plans during the ‘Cambrian Explosion’ and the ancestral traits of major living animal clades. Xianguangia sinica, from the celebrated Chengjiang biota (518Ma), is a typical Cambrian problematicum with disputable body architecture, particularly about its ‘column’ part. The contradictory interpretations of the ‘column’ of X. sinica have led to at least three hypotheses regarding its affinity in the diploblastic clade. Here we depict the detailed anatomy of the ‘column’ based on new, exquisitely preserved material. The ‘column’ of X. sinica is formed by 18 longsword-shaped tentacle-sheath complexes that can either close or be in a flowering state. There is no partitioned cavity internally when the ‘column’ is closed, invalidating the homology with the true column of living sea anemones. Each tentacle tapers distally and includes a distal flexible portion at about one-fourth the length of the tentacle. The proximal portion is stiff, bearing a set of paired dark stains, and is enveloped by a single outer sheath. Pinnules carrying a row of large cilia are fringed on both sides along the whole length of the tentacles. The body plan of X. sinica is accordingly revised as consisting of a calyx and 18 unique tentacle-sheath complexes that radially surround the mouth. Our result corroborates previous observations that suggest a close relationship between Xianguangia, Daihua and Dinomischus, all of which are here formally assigned to the family Dinomischidae, a monophyletic clade recovered in our phylogenetic analyses. Xianguangia sinica likely employs cilia-bearing pinnate tentacles for sieving particle matter down to 21 mm, indicating that its nutrition source is suspended micro-planktonic organisms or other organic matter in the bottom water

Magnetization, crystal structure and anisotropic thermal expansion of single-crystal SrEr2O4

The magnetization, crystal structure, and thermal expansion of a nearly stoichiometric Sr$_{1.04(3)}$Er$_{2.09(6)}$O$_{4.00(1)}$ single crystal have been studied by PPMS measurements and in-house and high-resolution synchrotron X-ray powder diffraction. No evidence was detected for any structural phase transitions even up to 500 K. The average thermal expansions of lattice constants and unit-cell volume are consistent with the first-order Gr\"uneisen approximations taking into account only the phonon contributions for an insulator, displaying an anisotropic character along the crystallographic \emph{a}, \emph{b}, and \emph{c} axes. Our magnetization measurements indicate that obvious magnetic frustration appears below $\sim$15 K, and antiferromagnetic correlations may persist up to 300 K.Comment: 6 pages, 5 figure, 2 table

Local variance of atmospheric 14C concentrations around Fukushima Dai-ichi Nuclear Power Plant from 2010 to 2012

Radiocarbon (14C) has been measured in single tree ring samples collected from the southwest of the Fukushima Dai-ichi Nuclear Power Plant. Our data indicate south-westwards dispersion of radiocarbon and the highest 14C activity observed so far in the local environment during the 2011 accident. The abnormally high 14C activity in the late wood of 2011 ring may imply an unknown source of radiocarbon nearby after the accident. The influence of 14C shrank from 30 km during normal reactor operation to 14 km for the accident in the northwest of FDNPP, but remains unclear in the southwest

Graph Neural Network-Aided Exploratory Learning for Community Detection with Unknown Topology

In social networks, the discovery of community structures has received considerable attention as a fundamental problem in various network analysis tasks. However, due to privacy concerns or access restrictions, the network structure is often unknown, thereby rendering established community detection approaches ineffective without costly network topology acquisition. To tackle this challenge, we present META-CODE, a novel end-to-end solution for detecting overlapping communities in networks with unknown topology via exploratory learning aided by easy-to-collect node metadata. Specifically, META-CODE consists of three iterative steps in addition to the initial network inference step: 1) node-level community-affiliation embeddings based on graph neural networks (GNNs) trained by our new reconstruction loss, 2) network exploration via community affiliation-based node queries, and 3) network inference using an edge connectivity-based Siamese neural network model from the explored network. Through comprehensive evaluations using five real-world datasets, we demonstrate that META-CODE exhibits (a) its superiority over benchmark community detection methods, (b) empirical evaluations as well as theoretical findings to see the effectiveness of our node query, (c) the influence of each module, and (d) its computational efficiency.Comment: 15 pages, 8 figures, 5 tables; its conference version was presented at the ACM International Conference on Information and Knowledge Management (CIKM 2022