One Point is All You Need: Directional Attention Point for Feature Learning

We present a novel attention-based mechanism for learning enhanced point features for tasks such as point cloud classification and segmentation. Our key message is that if the right attention point is selected, then "one point is all you need" -- not a sequence as in a recurrent model and not a pre-selected set as in all prior works. Also, where the attention point is should be learned, from data and specific to the task at hand. Our mechanism is characterized by a new and simple convolution, which combines the feature at an input point with the feature at its associated attention point. We call such a point a directional attention point (DAP), since it is found by adding to the original point an offset vector that is learned by maximizing the task performance in training. We show that our attention mechanism can be easily incorporated into state-of-the-art point cloud classification and segmentation networks. Extensive experiments on common benchmarks such as ModelNet40, ShapeNetPart, and S3DIS demonstrate that our DAP-enabled networks consistently outperform the respective original networks, as well as all other competitive alternatives, including those employing pre-selected sets of attention points

Integrating SPC and EPC for Multivariate Autocorrelated Process

Statistical process control (SPC) is a widely employed quality control method in industry. SPC is mainly designed for monitoring single quality characteristic. However, as the design of a product/process becomes complex, a process usually has multiple quality characteristics related to it. These characteristics must be monitored by multivariate SPC. When the autocorrelation is present in the process data, the traditional SPC may mislead the results. Hence, the autocorrelated data must be treated to eliminate the autocorrelation effect before employing SPC to detect the assignable causes. Besides, chance causes also have impact on the processes. When the process is out of control but no assignable cause is found, it can be adjusted by employing engineering process control (EPC). However, only using EPC to adjust the process may make inappropriate adjustments due to external disturbances or assignable causes. This study presents an integrated SPC and EPC procedure for multivariate autocorrelated process. The SPC procedure constructs a predicting model using group method of data handling (GMDH), which can transfer the autocorrelated data into uncorrelated data. Then, the Hotelling’s T2 and multivariate cumulative sum control charts are constructed to monitor the process. The EPC procedure constructs a controller utilizing data mining technique to adjust the multiple quality characteristics to their target values. Industry can employ this procedure to monitor and adjust the multivariate autocorrelated process

Ontology-based Fuzzy Markup Language Agent for Student and Robot Co-Learning

An intelligent robot agent based on domain ontology, machine learning mechanism, and Fuzzy Markup Language (FML) for students and robot co-learning is presented in this paper. The machine-human co-learning model is established to help various students learn the mathematical concepts based on their learning ability and performance. Meanwhile, the robot acts as a teacher's assistant to co-learn with children in the class. The FML-based knowledge base and rule base are embedded in the robot so that the teachers can get feedback from the robot on whether students make progress or not. Next, we inferred students' learning performance based on learning content's difficulty and students' ability, concentration level, as well as teamwork sprit in the class. Experimental results show that learning with the robot is helpful for disadvantaged and below-basic children. Moreover, the accuracy of the intelligent FML-based agent for student learning is increased after machine learning mechanism.Comment: This paper is submitted to IEEE WCCI 2018 Conference for revie

USING A LEAST SQUARES SUPPORT VECTOR MACHINE TO ESTIMATE A LOCAL GEOMETRIC GEOID MODEL

In this study, test-region global positioning system (GPS) control points exhibitingknown first-order orthometric heights were employed to obtain the points of planecoordinates and ellipsoidal heights by using the real-time GPS kinematicmeasurement method. Plane-fitting, second-order curve-surface fitting, back-propagation (BP) neural networks, and least-squares support vector machine (LS-SVM) calculation methods were employed. The study includes a discussion on dataintegrity and localization, changing reference-point quantities and distributions toobtain an optimal solution. Furthermore, the LS-SVM was combined with localgeoidal-undulation models that were established by researching and analyzing3kernel functions. The results indicated that the overall precision of the localgeometric geoidal-undulation values calculated using the radial basis function(RBF) and third-order polynomial kernel function was optimal and the root meansquare error (RMSE) was approximately ± 1.5 cm. These findings demonstrated thatthe LS-SVM provides a rapid and practical method for determining orthometricheights and should serve as a valuable academic reference regarding local geoidmodels

Poly[(μ3-quinoline-6-carboxyl­ato-κ3 N:O:O′)silver(I)]

In the title coordination polymer, [Ag(C10H6NO2)]n, the AgI cation is coordinated by two O atoms and one N atom from three 6-quinoline­carboxyl­ate anions in a distorted T-shaped AgNO2 geometry, in which the O—Ag—O angle is 160.44 (9)°. The 6-quinoline­carboxyl­ate anion bridges three Ag+ cations, forming a nearly planar polymeric sheet parallel to (101). The distance between Ag+ cations bridged by the carboxyl group is 2.9200 (5) Å. In the crystal, π–π stacking is observed between parallel quinoline ring systems, the centroid–centroid distance being 3.7735 (16) Å

Downstream Impact Investigation of Released Sediment from Reservoir Desilting Operation

Source: ICHE Conference Archive - https://mdi-de.baw.de/icheArchive

Ellagic Acid, the Active Compound of Phyllanthus urinaria, Exerts In Vivo Anti-Angiogenic Effect and Inhibits MMP-2 Activity

This study aimed to assess the potential anti-angiogenic mechanism of Phyllanthus urinaria (P. urinaria) and characterize the major compound in P. urinaria that exerts anti-angiogenic effect. The water extract of P. urinaria and Ellagic Acid were used to evaluate the anti-angiogenic effect in chorioallantoic membrane (CAM) in chicken embryo and human vascular endothelial cells (HUVECs). The matrix metalloproteinase-2 (MMP-2) activity was determined by gelatin zymography. The mRNA expressions of MMP-2, MMP-14 and tissue inhibitor of metalloproteinase-2 (TIMP-2) were analyzed by reverse transcription polymerase chain reaction (RT-PCR). Level of MMP-2 proteins in conditioned medium or cytosol was determined by western blot analysis. We confirmed that P. urinaria's in vivo anti-angiogenic effect was associated with a reduction in MMP-2 activity. Ellagic acid, one of the major polyphenolic components as identified in P. urinaria by high performance liquid chromatography mass spectrometry (HPLC/MS), exhibited the same anti-angiogenic effect in vivo. Both P. urinaria and Ellagic Acid inhibited MMP-2 activity in HUVECs with unchanged mRNA level. The mRNA expression levels of MMP-14 and TIMP-2 were not altered either. Results from comparing the change of MMP-2 protein levels in conditioned medium and cytosol of HUVECs after the P. urinaria or Ellagic Acid treatment revealed an inhibitory effect on the secretion of MMP-2 protein. This study concluded that Ellagic Acid is the active compound in P. urinaria to exhibit anti-angiogenic activity and to inhibit the secretion of MMP-2 protein from HUVECs