Similarity Learning via Kernel Preserving Embedding

Data similarity is a key concept in many data-driven applications. Many algorithms are sensitive to similarity measures. To tackle this fundamental problem, automatically learning of similarity information from data via self-expression has been developed and successfully applied in various models, such as low-rank representation, sparse subspace learning, semi-supervised learning. However, it just tries to reconstruct the original data and some valuable information, e.g., the manifold structure, is largely ignored. In this paper, we argue that it is beneficial to preserve the overall relations when we extract similarity information. Specifically, we propose a novel similarity learning framework by minimizing the reconstruction error of kernel matrices, rather than the reconstruction error of original data adopted by existing work. Taking the clustering task as an example to evaluate our method, we observe considerable improvements compared to other state-of-the-art methods. More importantly, our proposed framework is very general and provides a novel and fundamental building block for many other similarity-based tasks. Besides, our proposed kernel preserving opens up a large number of possibilities to embed high-dimensional data into low-dimensional space.Comment: Published in AAAI 201

Heparin-Mimicking Polymer Modified Polyethersulfone Membranes - A Mini Review

Recent studies on the modification of polyethersulfone (PES) membranes using heparin-mimicking polymers are reviewed. The general conception of heparin-mimicking polymersis defined as the syntheticpolymers (including the biopolymer derivates and synthetic sulfated artificial polymers) with similar biologically functionalities as heparin, such as the anticoagulant, growth factor binding, and also disease mediation. In the review, heparin-mimicking polymers is briefly reviewed; then heparin-mimicking polymer modified PES membranes, including blended, coated, and grafted membranes are discussed respectively

Near-Field Positioning and Attitude Sensing Based on Electromagnetic Propagation Modeling

Positioning and sensing over wireless networks are imperative for many emerging applications. However, traditional wireless channel models cannot be used for sensing the attitude of the user equipment (UE), since they over-simplify the UE as a point target. In this paper, a comprehensive electromagnetic propagation modeling (EPM) based on electromagnetic theory is developed to precisely model the near-field channel. For the noise-free case, the EPM model establishes the non-linear functional dependence of observed signals on both the position and attitude of the UE. To address the difficulty in the non-linear coupling, we first propose to divide the distance domain into three regions, separated by the defined Phase ambiguity distance and Spacing constraint distance. Then, for each region, we obtain the closed-form solutions for joint position and attitude estimation with low complexity. Next, to investigate the impact of random noise on the joint estimation performance, the Ziv-Zakai bound (ZZB) is derived to yield useful insights. The expected Cram\'er-Rao bound (ECRB) is further provided to obtain the simplified closed-form expressions for the performance lower bounds. Our numerical results demonstrate that the derived ZZB can provide accurate predictions of the performance of estimators in all signal-to-noise ratio (SNR) regimes. More importantly, we achieve the millimeter-level accuracy in position estimation and attain the 0.1-level accuracy in attitude estimation.Comment: 16 pages, 9 figures. Submitted to JSAC - Special Issue on Positioning and Sensing Over Wireless Network

Research of concrete cracking propagation based on information entropy evolution

The distribution state evolution of concrete cracking evolution energy has been discussed in which with dissipative system characteristics is considered, and combined the theory of information entropy with energy method. The function of entropy evolution change for in different stage of crack stable and unstable propagations evolution is established. The element damage extent formula is deduced, which can be applied to judge the stage of crack. Finally, the cracking process of double span continuous beam is simulated by Midas/FEA to compare with other literature. The result shows that the strain energy entropy function proposed can is be capable of well describing the evolution law of concrete cracking evolution

Fusion of 3D B-Spline Surface Patches Reconstructed from Image Sequences

International audienceThis paper considers the problem of merging a set of distinct three dimensional B-spline surface patches, which are reconstructed from observations of the motion of occluding contours in image sequences. We propose an original method of fusing these partially overlapping patches in order to obtain a whole surface. This approach is based on a triangular mesh and surface interpolation through regularized uniform bicubic B-spline surface patches. Experimental results are presented for both synthetic and real data

Proteomics analysis reveals differentially activated pathways that operate in peanut gynophores at different developmental stages

Specific proteins identified in S3 gynophores. (XLS 91 kb