On Sensor Network Localization Using SDP Relaxation

A Semidefinite Programming (SDP) relaxation is an effective computational method to solve a Sensor Network Localization problem, which attempts to determine the locations of a group of sensors given the distances between some of them [11]. In this paper, we analyze and determine new sufficient conditions and formulations that guarantee that the SDP relaxation is exact, i.e., gives the correct solution. These conditions can be useful for designing sensor networks and managing connectivities in practice. Our main contribution is twofold: We present the first non-asymptotic bound on the connectivity or radio range requirement of the sensors in order to ensure the network is uniquely localizable. Determining this range is a key component in the design of sensor networks, and we provide a result that leads to a correct localization of each sensor, for any number of sensors. Second, we introduce a new class of graphs that can always be correctly localized by an SDP relaxation. Specifically, we show that adding a simple objective function to the SDP relaxation model will ensure that the solution is correct when applied to a triangulation graph. Since triangulation graphs are very sparse, this is informationally efficient, requiring an almost minimal amount of distance information. We also analyze a number objective functions for the SDP relaxation to solve the localization problem for a general graph.Comment: 20 pages, 4 figures, submitted to the Fields Institute Communications Series on Discrete Geometry and Optimizatio

NerVE: Neural Volumetric Edges for Parametric Curve Extraction from Point Cloud

Extracting parametric edge curves from point clouds is a fundamental problem in 3D vision and geometry processing. Existing approaches mainly rely on keypoint detection, a challenging procedure that tends to generate noisy output, making the subsequent edge extraction error-prone. To address this issue, we propose to directly detect structured edges to circumvent the limitations of the previous point-wise methods. We achieve this goal by presenting NerVE, a novel neural volumetric edge representation that can be easily learned through a volumetric learning framework. NerVE can be seamlessly converted to a versatile piece-wise linear (PWL) curve representation, enabling a unified strategy for learning all types of free-form curves. Furthermore, as NerVE encodes rich structural information, we show that edge extraction based on NerVE can be reduced to a simple graph search problem. After converting NerVE to the PWL representation, parametric curves can be obtained via off-the-shelf spline fitting algorithms. We evaluate our method on the challenging ABC dataset. We show that a simple network based on NerVE can already outperform the previous state-of-the-art methods by a great margin. Project page: https://dongdu3.github.io/projects/2023/NerVE/.Comment: Accepted by CVPR2023. Project page: https://dongdu3.github.io/projects/2023/NerVE

Data-driven train set crash dynamics simulation

© 2016 Informa UK Limited, trading as Taylor & Francis GroupTraditional finite element (FE) methods are arguably expensive in computation/simulation of the train crash. High computational cost limits their direct applications in investigating dynamic behaviours of an entire train set for crashworthiness design and structural optimisation. On the contrary, multi-body modelling is widely used because of its low computational cost with the trade-off in accuracy. In this study, a data-driven train crash modelling method is proposed to improve the performance of a multi-body dynamics simulation of train set crash without increasing the computational burden. This is achieved by the parallel random forest algorithm, which is a machine learning approach that extracts useful patterns of force–displacement curves and predicts a force–displacement relation in a given collision condition from a collection of offline FE simulation data on various collision conditions, namely different crash velocities in our analysis. Using the FE simulation results as a benchmark, we compared our method with traditional multi-body modelling methods and the result shows that our data-driven method improves the accuracy over traditional multi-body models in train crash simulation and runs at the same level of efficiency

(±)-Peniorthoesters A and B, Two Pairs of Novel Spiro-Orthoester en-antiomers With an Unusual 1,4,6-Trioxaspi-ro[4.5]decane-7-One Unit From Penicillium minioluteum

(±)-Peniorthoesters A and B (±1 and ±2), two pairs of unprecedented spiro-orthoester enantiomers with a 1,4,6-trioxaspiro[4. 5]decane-7-one unit, were obtained from Penicillium minioluteum. Their structures were determined by spectroscopic methods, X-ray diffraction analyses, and ECD calculations. (±)-Peniorthoesters A and B are the first examples of spiro-orthoester enantiomers, and they represent the first spiro-orthoesters originating from fungi. All compounds showed potential inhibitory activities comparable to dexamethasone against NO production with IC50 values ranging from 14.2 to 34.5 μM

Identification of the Genes Involved in Riemerella anatipestifer Biofilm Formation by Random Transposon Mutagenesis

Riemerella anatipestifer causes epizootics of infectious disease in poultry that result in serious economic losses to the duck industry. Our previous studies have shown that some strains of R. anatipestifer can form a biofilm, and this may explain the intriguing persistence of R. anatipestifer on duck farms post infection. In this study we used strain CH3, a strong producer of biofilm, to construct a library of random Tn4351 transposon mutants in order to investigate the genetic basis of biofilm formation by R. anatipestifer on abiotic surfaces. A total of 2,520 mutants were obtained and 39 of them showed a reduction in biofilm formation of 47%–98% using crystal violet staining. Genetic characterization of the mutants led to the identification of 33 genes. Of these, 29 genes are associated with information storage and processing, as well as basic cellular processes and metabolism; the function of the other four genes is currently unknown. In addition, a mutant strain BF19, in which biofilm formation was reduced by 98% following insertion of the Tn4351 transposon at the dihydrodipicolinate synthase (dhdps) gene, was complemented with a shuttle plasmid pCP-dhdps. The complemented mutant strain was restored to give 92.6% of the biofilm formation of the wild-type strain CH3, which indicates that the dhdp gene is associated with biofilm formation. It is inferred that such complementation applies also to other mutant strains. Furthermore, some biological characteristics of biofilm-defective mutants were investigated, indicating that the genes deleted in the mutant strains function in the biofilm formation of R. anatipestifer. Deletion of either gene will stall the biofilm formation at a specific stage thus preventing further biofilm development. In addition, the tested biofilm-defective mutants had different adherence capacity to Vero cells. This study will help us to understand the molecular mechanisms of biofilm development by R. anatipestifer and to study the pathogenesis of R. anatipestifer further