Study on the key issues of beam string structures

p. 2814-2825Parameter analysis is made on the beam string structure in six aspects which are respectively vector height, sag, prestress, number of strut, stiffness of upper chord and the arrangement of struts in the paper. The influence mode and effect on the mechanical properties of beam string structures due to these parameters are studied and the suggested values in practical engineering are presented then, which can provide the reference for design and type selection.Tian, W.; Yang, L.; Cui, J.; Li, C. (2009). Study on the key issues of beam string structures. Editorial Universitat Politècnica de València. http://hdl.handle.net/10251/664

High Diversity of Cytospora Associated With Canker and Dieback of Rosaceae in China, With 10 New Species Described

Cytospora canker is a destructive disease of numerous hosts and causes serious economic losses with a worldwide distribution. Identification of Cytospora species is difficult due to insufficient phylogenetic understanding and overlapped morphological characteristics. In this study, we provide an assessment of 23 Cytospora spp., which covered nine genera of Rosaceae, and focus on 13 species associated with symptomatic branch or twig canker and dieback disease in China. Through morphological observation and multilocus phylogeny of internal transcribed spacer (ITS), large nuclear ribosomal RNA subunit (LSU), actin (act), RNA polymerase II subunit (rpb2), translation elongation factor 1-α (tef1-α), and beta-tubulin (tub2) gene regions, the results indicate 13 distinct lineages with high branch support. These include 10 new Cytospora species, i.e., C. cinnamomea, C. cotoneastricola, C. mali-spectabilis, C. ochracea, C. olivacea, C. pruni-mume, C. rosicola, C. sorbina, C. tibetensis, and C. xinjiangensis and three known taxa including Cytospora erumpens, C. leucostoma, and C. parasitica. This study provides an initial understanding of the taxonomy of Cytospora associated with canker and dieback disease of Rosaceae in China

CEAZ: Accelerating Parallel I/O via Hardware-Algorithm Co-Design of Efficient and Adaptive Lossy Compression

As supercomputers continue to grow to exascale, the amount of data that needs to be saved or transmitted is exploding. To this end, many previous works have studied using error-bounded lossy compressors to reduce the data size and improve the I/O performance. However, little work has been done for effectively offloading lossy compression onto FPGA-based SmartNICs to reduce the compression overhead. In this paper, we propose a hardware-algorithm co-design of efficient and adaptive lossy compressor for scientific data on FPGAs (called CEAZ) to accelerate parallel I/O. Our contribution is fourfold: (1) We propose an efficient Huffman coding approach that can adaptively update Huffman codewords online based on codewords generated offline (from a variety of representative scientific datasets). (2) We derive a theoretical analysis to support a precise control of compression ratio under an error-bounded compression mode, enabling accurate offline Huffman codewords generation. This also helps us create a fixed-ratio compression mode for consistent throughput. (3) We develop an efficient compression pipeline by adopting cuSZ's dual-quantization algorithm to our hardware use case. (4) We evaluate CEAZ on five real-world datasets with both a single FPGA board and 128 nodes from Bridges-2 supercomputer. Experiments show that CEAZ outperforms the second-best FPGA-based lossy compressor by 2X of throughput and 9.6X of compression ratio. It also improves MPI_File_write and MPI_Gather throughputs by up to 25.8X and 24.8X, respectively.Comment: 14 pages, 17 figures, 8 table

An operator approach to the rational solutions of the classical Yang-Baxter equation

Motivated by the study of the operator forms of the constant classical Yang-Baxter equation given by Semonov-Tian-Shansky, Kupershmidt and the others, we try to construct the rational solutions of the classical Yang-Baxter equation with parameters by certain linear operators. The fact that the rational solutions of the CYBE for the simple complex Lie algebras can be interpreted in term of certain linear operators motivates us to give the notion of $\mathcal O$-operators such that these linear operators are the $\mathcal O$-operators associated to the adjoint representations. Such a study can be generalized to the Lie algebras with nondegenerate symmetric invariant bilinear forms. Furthermore we give a construction of a rational solution of the CYBE from an $\mathcal O$-operator associated to the coadjoint representation and an arbitrary representation with a trivial product in the representation space respectively.Comment: 23page

Some results on L-dendriform algebras

We introduce a notion of L-dendriform algebra due to several different motivations. L-dendriform algebras are regarded as the underlying algebraic structures of pseudo-Hessian structures on Lie groups and the algebraic structures behind the $\mathcal O$-operators of pre-Lie algebras and the related $S$-equation. As a direct consequence, they provide some explicit solutions of $S$-equation in certain pre-Lie algebras constructed from L-dendriform algebras. They also fit into a bigger framework as Lie algebraic analogues of dendriform algebras. Moreover, we introduce a notion of $\mathcal O$-operator of an L-dendriform algebra which gives an algebraic equation regarded as an analogue of the classical Yang-Baxter equation in a Lie algebra.Comment: 15 page

Cytospora elaeagnicola sp. nov. Associated with Narrow-leaved Oleaster Canker Disease in China

Cytospora is a genus including important phytopathogens causing severe dieback and canker diseases distributed worldwide with a wide host range. However, identification of Cytospora species is difficult since the currently available DNA sequence data are insufficient. Aside the limited availability of ex-type sequence data, most of the genetic work is only based on the ITS region DNA marker which lacks the resolution to delineate to the species level in Cytospora. In this study, three fresh strains were isolated from the symptomatic branches of Elaeagnus angustifolia in Xinjiang Uygur Autonomous Region, China. Morphological observation and multi-locus phylogenetic analyses (ITS, LSU, ACT and RPB2) support these specimens are best accommodated as a distinct novel species of Cytospora. Cytospora elaeagnicola sp. nov. is introduced, having discoid, nearly flat, pycnidial conidiomata with hyaline, allantoid conidia, and differs from its relatives genetically and by host association

SOLAR: A Highly Optimized Data Loading Framework for Distributed Training of CNN-based Scientific Surrogates

CNN-based surrogates have become prevalent in scientific applications to replace conventional time-consuming physical approaches. Although these surrogates can yield satisfactory results with significantly lower computation costs over small training datasets, our benchmarking results show that data-loading overhead becomes the major performance bottleneck when training surrogates with large datasets. In practice, surrogates are usually trained with high-resolution scientific data, which can easily reach the terabyte scale. Several state-of-the-art data loaders are proposed to improve the loading throughput in general CNN training; however, they are sub-optimal when applied to the surrogate training. In this work, we propose SOLAR, a surrogate data loader, that can ultimately increase loading throughput during the training. It leverages our three key observations during the benchmarking and contains three novel designs. Specifically, SOLAR first generates a pre-determined shuffled index list and accordingly optimizes the global access order and the buffer eviction scheme to maximize the data reuse and the buffer hit rate. It then proposes a tradeoff between lightweight computational imbalance and heavyweight loading workload imbalance to speed up the overall training. It finally optimizes its data access pattern with HDF5 to achieve a better parallel I/O throughput. Our evaluation with three scientific surrogates and 32 GPUs illustrates that SOLAR can achieve up to 24.4X speedup over PyTorch Data Loader and 3.52X speedup over state-of-the-art data loaders.Comment: 14 pages, 15 figures, 5 tables, submitted to VLDB '2