EXAFS Study on Local Structure of Iron Crystal by the Use of Asymmetrical Monochromator and PSPC

The EXAFS spectroscopy equipment constructed from an asymmetrical cut flat monochromator and PSPC is applied to the structural determination of pure α-iron which has small difference (0.038nm) in the first and second nearest neighbour distance. The efficiency of the curve fitting method for the two shell model of known structure material (α-iron) is discussed, in addition to describing the details of the experimental procedure of our new type of spectrometer and of the EXAFS data analysis

The Evolution of Neural Network-Based Chart Patterns: A Preliminary Study

A neural network-based chart pattern represents adaptive parametric features, including non-linear transformations, and a template that can be applied in the feature space. The search of neural network-based chart patterns has been unexplored despite its potential expressiveness. In this paper, we formulate a general chart pattern search problem to enable cross-representational quantitative comparison of various search schemes. We suggest a HyperNEAT framework applying state-of-the-art deep neural network techniques to find attractive neural network-based chart patterns; These techniques enable a fast evaluation and search of robust patterns, as well as bringing a performance gain. The proposed framework successfully found attractive patterns on the Korean stock market. We compared newly found patterns with those found by different search schemes, showing the proposed approach has potential.Comment: 8 pages, In proceedings of Genetic and Evolutionary Computation Conference (GECCO 2017), Berlin, German

Estimating Autonomous Vehicle Localization Error Using 2D Geographic Information

Accurately and precisely knowing the location of the vehicle is a critical requirement for safe and successful autonomous driving. Recent studies suggest that error for map-based localization methods are tightly coupled with the surrounding environment. Considering this relationship, it is therefore possible to estimate localization error by quantifying the representation and layout of real-world phenomena. To date, existing work on estimating localization error have been limited to using self-collected 3D point cloud maps. This paper investigates the use of pre-existing 2D geographic information datasets as a proxy to estimate autonomous vehicle localization error. Seven map evaluation factors were defined for 2D geographic information in a vector format, and random forest regression was used to estimate localization error for five experiment paths in Shinjuku, Tokyo. In the best model, the results show that it is possible to estimate autonomous vehicle localization error with 69.8% of predictions within 2.5 cm and 87.4% within 5 cm

Evaluating the Capability of OpenStreetMap for Estimating Vehicle Localization Error

Accurate localization is an important part of successful autonomous driving. Recent studies suggest that when using map-based localization methods, the representation and layout of real-world phenomena within the prebuilt map is a source of error. To date, the investigations have been limited to 3D point clouds and normal distribution (ND) maps. This paper explores the potential of using OpenStreetMap (OSM) as a proxy to estimate vehicle localization error. Specifically, the experiment uses random forest regression to estimate mean 3D localization error from map matching using LiDAR scans and ND maps. Six map evaluation factors were defined for 2D geographic information in a vector format. Initial results for a 1.2 km path in Shinjuku, Tokyo, show that vehicle localization error can be estimated with 56.3% model prediction accuracy with two existing OSM data layers only. When OSM data quality issues (inconsistency and completeness) were addressed, the model prediction accuracy was improved to 73.1%

RANKL directly induces bone morphogenetic protein-2 expression in RANK-expressing POS-1 osteosarcoma cells

International audienceThe POS-1 murine model of osteolytic osteosarcoma was used to elucidate the molecular and cellular mechanisms involved in the development of primary bone tumors and associated lung metastasis. The POS-1 cell line is derived from an osteosarcoma tumor which develops spontaneously in C3H mice. The POS-1 cell line was characterized in vitro by mineralization capacity and expression of bone markers by semi-quantitative RT-PCR, compared to primary osteoblasts and bone marrow cells. POS-1 cells showed no mineralization capacity and exhibited an undifferentiated phenotype, expressing both osteoblastic and unexpected osteoclastic markers (TRAP, cathepsin K and RANK). Thereby, experiments were performed to determine whether RANK was functional, by studying the biological activity of murine RANKL through the receptor RANK expressed on POS-1 cells. Results revealed a RANKL-induced increase in ERK phosphorylation, as well as BMP-2 induction at the mRNA and protein levels, and a decrease of POS-1 cell proliferation in the presence of 10 ng/ml RANKL. BMP-2 induction is dependent on the ERK 1/2 signal transduction pathway, as its expression is abolished in the presence of UO126, a specific synthetic inhibitor of the ERK 1/2 pathway. Moreover, a 2-fold molar excess of soluble RANK blocks the RANKL-induced BMP-2 expression, demonstrating that the biological effects of RANKL observed in POS-1 cells are mediated by RANK. This is the first report describing a functional RANK expressed on osteosarcoma cells, as shown by its ability to induce signal transduction pathways and biological activity when stimulated by RANKL