Point2Sequence: Learning the Shape Representation of 3D Point Clouds with an Attention-based Sequence to Sequence Network

Exploring contextual information in the local region is important for shape understanding and analysis. Existing studies often employ hand-crafted or explicit ways to encode contextual information of local regions. However, it is hard to capture fine-grained contextual information in hand-crafted or explicit manners, such as the correlation between different areas in a local region, which limits the discriminative ability of learned features. To resolve this issue, we propose a novel deep learning model for 3D point clouds, named Point2Sequence, to learn 3D shape features by capturing fine-grained contextual information in a novel implicit way. Point2Sequence employs a novel sequence learning model for point clouds to capture the correlations by aggregating multi-scale areas of each local region with attention. Specifically, Point2Sequence first learns the feature of each area scale in a local region. Then, it captures the correlation between area scales in the process of aggregating all area scales using a recurrent neural network (RNN) based encoder-decoder structure, where an attention mechanism is proposed to highlight the importance of different area scales. Experimental results show that Point2Sequence achieves state-of-the-art performance in shape classification and segmentation tasks.Comment: To be published in AAAI 201

SPU-Net: Self-Supervised Point Cloud Upsampling by Coarse-to-Fine Reconstruction with Self-Projection Optimization

The task of point cloud upsampling aims to acquire dense and uniform point sets from sparse and irregular point sets. Although significant progress has been made with deep learning models, they require ground-truth dense point sets as the supervision information, which can only trained on synthetic paired training data and are not suitable for training under real-scanned sparse data. However, it is expensive and tedious to obtain large scale paired sparse-dense point sets for training from real scanned sparse data. To address this problem, we propose a self-supervised point cloud upsampling network, named SPU-Net, to capture the inherent upsampling patterns of points lying on the underlying object surface. Specifically, we propose a coarse-to-fine reconstruction framework, which contains two main components: point feature extraction and point feature expansion, respectively. In the point feature extraction, we integrate self-attention module with graph convolution network (GCN) to simultaneously capture context information inside and among local regions. In the point feature expansion, we introduce a hierarchically learnable folding strategy to generate the upsampled point sets with learnable 2D grids. Moreover, to further optimize the noisy points in the generated point sets, we propose a novel self-projection optimization associated with uniform and reconstruction terms, as a joint loss, to facilitate the self-supervised point cloud upsampling. We conduct various experiments on both synthetic and real-scanned datasets, and the results demonstrate that we achieve comparable performance to the state-of-the-art supervised methods

Experiments and transient simulation on spring-loaded pressure relief valve under high temperature and high pressure steam conditions

Reliable performances of high temperature and high pressure operating steam pressure relief valves (HTHP PRVs) are extremely important for the safety of nuclear power plants. It is still a challenge to accurately describe the dynamic performance of HTHP PRVs. In this study, the accuracy of computational fluid dynamics (CFD) based modelling of the transient processes is examined. For one of the HTHP PRVs named DWPRV, the effects of different parameters on the dynamic performance were investigated by combining CFD simulation and experiments. In the simulation, the domain decomposition method (DDM) and the Grid Pre-deformation Method (GPM) were adopted to handle the moving disk geometry and the large mesh deformation. The effect of damping was also studied. It is confirmed that the use of CFD simulation can improve the design and settings of a HTHP PRV in a highly energetic service that is difficult to test due to safety reasons. For the DWPRV, it was found that the maximum flow rate occurs when the curtain area is 1.18 times the throat area. The degree of superheat ranging from 0 C to 100 C has a negligible effect on the performance of DWPRV regardless of the changes in the material mechanical properties with operating temperatures. The reseating pressure increases linearly with the rise in the distance between the upper adjusting ring and the sealing face. The lower adjusting ring exhibits a weak effect on the reseating pressure. For the ratios of rated lift to throat diameter equalling to 0.3 and 0.35, the DWPRV exhibits the higher blowdown for the ratio of 0.3

L2G Auto-encoder: Understanding Point Clouds by Local-to-Global Reconstruction with Hierarchical Self-Attention

Auto-encoder is an important architecture to understand point clouds in an encoding and decoding procedure of self reconstruction. Current auto-encoder mainly focuses on the learning of global structure by global shape reconstruction, while ignoring the learning of local structures. To resolve this issue, we propose Local-to-Global auto-encoder (L2G-AE) to simultaneously learn the local and global structure of point clouds by local to global reconstruction. Specifically, L2G-AE employs an encoder to encode the geometry information of multiple scales in a local region at the same time. In addition, we introduce a novel hierarchical self-attention mechanism to highlight the important points, scales and regions at different levels in the information aggregation of the encoder. Simultaneously, L2G-AE employs a recurrent neural network (RNN) as decoder to reconstruct a sequence of scales in a local region, based on which the global point cloud is incrementally reconstructed. Our outperforming results in shape classification, retrieval and upsampling show that L2G-AE can understand point clouds better than state-of-the-art methods

A critical review on production, modification and utilization of biochar

There has been an increased interest in the production of sustainable biochar in the past years, as biochar shows versatile physicochemical properties and, can have a wide applicability in diverse fields. Comprehensive studies have been made to characterize biochar produced from various biomass materials, using different production technologies and under different process conditions. However, research is still lacking in correlating biochar properties needed for certain applications with (i) feedstock, (ii) biochar production processes and conditions and (iii) biochar upgrading and modification strategies. To produce biochar with desired properties, there is a great need to establish and clarify such correlations, which can guide the selection of feedstock, tuning and optimization of the production process and more efficient utilization of biochar. On the other hand, further elucidation of these correlations is also important for biochar-stakeholder and end-users for predicting physiochemical properties of biochar from certain feedstock and production conditions, assessing potential effects of biochar utilization and clearly address needs towards biochar critical properties. This review summarizes a wide range of literature on the impact of feedstocks and production processes and reactions conditions on the biochar properties and the most important biochar properties required for the different potential applications. Based on collected data, recommendations are provided for mapping out biochar production for different biochar applications. Knowledge gaps and perspectives for future research have also been identified regarding the characterization and production of biochar.acceptedVersio

Prediction of blowdown of a pressure relief valve using response surface methodology and CFD techniques

In this study, parametric assessment of the main geometric design features of a pressure relief valve (PRV) with a backpressure chamber and two adjusting rings was conducted using response surface methodology. This design approach was established by using computational fluid dynamics (CFD) to model the dynamic performance of the opening and closing of a nuclear power main steam pressure relief valve (NPMS PRV). An experimental facility was established to test the NPMS PRV in accordance with the standard ASME PTC 25, and to validate the CFD model. It was found that the model can accurately simulate the dynamic performance of the NPMS PRV; the difference in blowdown between the simulation and experiment results is found to be below 0.6%. Thus, themodel can be used as part of a design analysis tool. The backpressure chamber assisted in the reseating and decreased the blowdown of the NPMS PRV from 18.13% to 5.50%. The sensitivity to valve geometry was investigated, and an explicit relationship between blowdown and valve geometry was established (with a relative error less than 1%) using the response surface methodology; this will allow designers to assess the valve settings without the need for a CFD model

Genetic characterization and linkage disequilibrium mapping of resistance to gray leaf spot in maize (Zea mays L.)

AbstractGray leaf spot (GLS), caused by Cercospora zeae-maydis, is an important foliar disease of maize (Zea mays L.) worldwide, resistance to which is controlled by multiple quantitative trait loci (QTL). To gain insights into the genetic architecture underlying the resistance to this disease, an association mapping population consisting of 161 inbred lines was evaluated for resistance to GLS in a plant pathology nursery at Shenyang in 2010 and 2011. Subsequently, a genome-wide association study, using 41,101 single-nucleotide polymorphisms (SNPs), identified 51 SNPs significantly (P<0.001) associated with GLS resistance, which could be converted into 31 QTL. In addition, three candidate genes related to plant defense were identified, including nucleotide-binding-site/leucine-rich repeat, receptor-like kinase genes similar to those involved in basal defense. Two genic SNPs, PZE-103142893 and PZE-109119001, associated with GLS resistance in chromosome bins 3.07 and 9.07, can be used for marker-assisted selection (MAS) of GLS resistance. These results provide an important resource for developing molecular markers closely linked with the target trait, enhancing breeding efficiency

E3 Ligase Activity of XIAP RING Domain Is Required for XIAP-Mediated Cancer Cell Migration, but Not for Its RhoGDI Binding Activity

Although an increased expression level of XIAP is associated with cancer cell metastasis, the underlying molecular mechanisms remain largely unexplored. To verify the specific structural basis of XIAP for regulation of cancer cell migration, we introduced different XIAP domains into XIAP−/− HCT116 cells, and found that reconstitutive expression of full length HA-XIAP and HA-XIAP ΔBIR, both of which have intact RING domain, restored β-Actin expression, actin polymerization and cancer cell motility. Whereas introduction of HA-XIAP ΔRING or H467A mutant, which abolished its E3 ligase function, did not show obvious restoration, demonstrating that E3 ligase activity of XIAP RING domain played a crucial role of XIAP in regulation of cancer cell motility. Moreover, RING domain rather than BIR domain was required for interaction with RhoGDI independent on its E3 ligase activity. To sum up, our present studies found that role of XIAP in regulating cellular motility was uncoupled from its caspase-inhibitory properties, but related to physical interaction between RhoGDI and its RING domain. Although E3 ligase activity of RING domain contributed to cell migration, it was not involved in RhoGDI binding nor its ubiquitinational modification