An Attention-based Collaboration Framework for Multi-View Network Representation Learning

Learning distributed node representations in networks has been attracting increasing attention recently due to its effectiveness in a variety of applications. Existing approaches usually study networks with a single type of proximity between nodes, which defines a single view of a network. However, in reality there usually exists multiple types of proximities between nodes, yielding networks with multiple views. This paper studies learning node representations for networks with multiple views, which aims to infer robust node representations across different views. We propose a multi-view representation learning approach, which promotes the collaboration of different views and lets them vote for the robust representations. During the voting process, an attention mechanism is introduced, which enables each node to focus on the most informative views. Experimental results on real-world networks show that the proposed approach outperforms existing state-of-the-art approaches for network representation learning with a single view and other competitive approaches with multiple views.Comment: CIKM 201

1,4-Bis(imidazol-1-yl)benzene–terephthalic acid (1/1)

In the title compound, C12H10N4·C8H6O4, 1,4-bis­(imidazol-1-yl)benzene and terephthalic acid mol­ecules are joined via strong O—H⋯N hydrogen bonds to form infinite zigzag chains. Both mol­ecules are located on crystallographic inversion centers. The O—H⋯N hydrogen-bonded chains are assembled into two-dimensional layers through weak C—H⋯O and strong π–π stacking inter­actions [centroid–centroid distance = 3.818 (2) Å], leading to the formation of a three-dimensional supra­molecular structure

Synthesizing Physically Plausible Human Motions in 3D Scenes

Synthesizing physically plausible human motions in 3D scenes is a challenging problem. Kinematics-based methods cannot avoid inherent artifacts (e.g., penetration and foot skating) due to the lack of physical constraints. Meanwhile, existing physics-based methods cannot generalize to multi-object scenarios since the policy trained with reinforcement learning has limited modeling capacity. In this work, we present a framework that enables physically simulated characters to perform long-term interaction tasks in diverse, cluttered, and unseen scenes. The key idea is to decompose human-scene interactions into two fundamental processes, Interacting and Navigating, which motivates us to construct two reusable Controller, i.e., InterCon and NavCon. Specifically, InterCon contains two complementary policies that enable characters to enter and leave the interacting state (e.g., sitting on a chair and getting up). To generate interaction with objects at different places, we further design NavCon, a trajectory following policy, to keep characters' locomotion in the free space of 3D scenes. Benefiting from the divide and conquer strategy, we can train the policies in simple environments and generalize to complex multi-object scenes. Experimental results demonstrate that our framework can synthesize physically plausible long-term human motions in complex 3D scenes. Code will be publicly released at https://github.com/liangpan99/InterScene

Point Scene Understanding via Disentangled Instance Mesh Reconstruction

Semantic scene reconstruction from point cloud is an essential and challenging task for 3D scene understanding. This task requires not only to recognize each instance in the scene, but also to recover their geometries based on the partial observed point cloud. Existing methods usually attempt to directly predict occupancy values of the complete object based on incomplete point cloud proposals from a detection-based backbone. However, this framework always fails to reconstruct high fidelity mesh due to the obstruction of various detected false positive object proposals and the ambiguity of incomplete point observations for learning occupancy values of complete objects. To circumvent the hurdle, we propose a Disentangled Instance Mesh Reconstruction (DIMR) framework for effective point scene understanding. A segmentation-based backbone is applied to reduce false positive object proposals, which further benefits our exploration on the relationship between recognition and reconstruction. Based on the accurate proposals, we leverage a mesh-aware latent code space to disentangle the processes of shape completion and mesh generation, relieving the ambiguity caused by the incomplete point observations. Furthermore, with access to the CAD model pool at test time, our model can also be used to improve the reconstruction quality by performing mesh retrieval without extra training. We thoroughly evaluate the reconstructed mesh quality with multiple metrics, and demonstrate the superiority of our method on the challenging ScanNet dataset

Intelligent Exploration for User Interface Modules of Mobile App with Collective Learning

A mobile app interface usually consists of a set of user interface modules. How to properly design these user interface modules is vital to achieving user satisfaction for a mobile app. However, there are few methods to determine design variables for user interface modules except for relying on the judgment of designers. Usually, a laborious post-processing step is necessary to verify the key change of each design variable. Therefore, there is a only very limited amount of design solutions that can be tested. It is timeconsuming and almost impossible to figure out the best design solutions as there are many modules. To this end, we introduce FEELER, a framework to fast and intelligently explore design solutions of user interface modules with a collective machine learning approach. FEELER can help designers quantitatively measure the preference score of different design solutions, aiming to facilitate the designers to conveniently and quickly adjust user interface module. We conducted extensive experimental evaluations on two real-life datasets to demonstrate its applicability in real-life cases of user interface module design in the Baidu App, which is one of the most popular mobile apps in China.Comment: 10 pages, accepted as a full paper in KDD 202

A Sialidase‐Deficient Porphyromonas gingivalis Mutant Strain Induces Less Interleukin‐1β and Tumor Necrosis Factor‐α in Epi4 Cells Than W83 Strain Through Regulation of c‐Jun N‐Terminal Kinase Pathway

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/142178/1/jpere129.pd

Aggregation-Induced Emission Luminogens for Direct Exfoliation of 2D Layered Materials in Ethanol

© 2020 Wiley-VCH GmbH Aggregation-induced emission (AIE) luminogens are an important type of advanced functional materials with fantastic optical properties and have found potential applications in organic electronics, biochemistry, and molecular imaging. Herein, this article presents a novel application of AIE luminogens (AIEgens) for efficient exfoliation of layered transition metal dichalcogenides (TMDs, such as MoS2 and WSe2). From the 1H NMR spectroscopic analysis, the designed AIEgens can insert into the space between layers of MoS2 in ethanol solution and the dynamic molecular rotation against the weak interactions affords large-scale few-layer MoS2 nanosheets (7–8 layers) with enhanced smoothness. The 3D AIEgens play a significant role in preserving the crystal lattice of MoS2 even at high pressure (>15 GPa). More importantly, the new approach can also be used for exfoliation of WSe2 to achieve large-scale few-layer nanosheets. The present work thus provides a facile and high yielding synthetic method for accessing on a large scale 2D layered materials with enhanced properties for high-technology applications

Transcriptional profiles of bovine in vivo pre-implantation development

© 2014 Jiang et al.; licensee BioMed Central Ltd. Background: During mammalian pre-implantation embryonic development dramatic and orchestrated changes occur in gene transcription. The identification of the complete changes has not been possible until the development of the Next Generation Sequencing Technology.Results: Here we report comprehensive transcriptome dynamics of single matured bovine oocytes and pre-implantation embryos developed in vivo. Surprisingly, more than half of the estimated 22,000 bovine genes, 11,488 to 12,729 involved in more than 100 pathways, is expressed in oocytes and early embryos. Despite the similarity in the total numbers of genes expressed across stages, the nature of the expressed genes is dramatically different. A total of 2,845 genes were differentially expressed among different stages, of which the largest change was observed between the 4- and 8-cell stages, demonstrating that the bovine embryonic genome is activated at this transition. Additionally, 774 genes were identified as only expressed/highly enriched in particular stages of development, suggesting their stage-specific roles in embryogenesis. Using weighted gene co-expression network analysis, we found 12 stage-specific modules of co-expressed genes that can be used to represent the corresponding stage of development. Furthermore, we identified conserved key members (or hub genes) of the bovine expressed gene networks. Their vast association with other embryonic genes suggests that they may have important regulatory roles in embryo development; yet, the majority of the hub genes are relatively unknown/under-studied in embryos. We also conducted the first comparison of embryonic expression profiles across three mammalian species, human, mouse and bovine, for which RNA-seq data are available. We found that the three species share more maternally deposited genes than embryonic genome activated genes. More importantly, there are more similarities in embryonic transcriptomes between bovine and humans than between humans and mice, demonstrating that bovine embryos are better models for human embryonic development.Conclusions: This study provides a comprehensive examination of gene activities in bovine embryos and identified little-known potential master regulators of pre-implantation development

Halophyte Nitraria billardieri CIPK25 promotes photosynthesis in Arabidopsis under salt stress

The calcineurin B-like (CBL)-interacting protein kinases (CIPKs), a type of plant-specific genes in the calcium signaling pathway, function in response to adverse environments. However, few halophyte derived CIPKs have been studied for their role in plant physiological and developmental adaptation during abiotic stresses, which inhibits the potential application of these genes to improve environmental adaptability of glycophytes. In this study, we constructed Nitraria billardieri CIPK25 overexpressing Arabidopsis and analyzed the seedling development under salt treatment. Our results show that Arabidopsis with NbCIPK25 expression exhibits more vigorous growth than wild type plants under salt condition. To gain insight into the molecular mechanisms underlying salt tolerance, we profiled the transcriptome of WT and transgenic plants via RNA-seq. GO and KEGG analyses revealed that upregulated genes in NbCIPK25 overexpressing seedlings under salt stress are enriched in photosynthesis related terms; Calvin-cycle genes including glyceraldehyde-3-phosphate dehydrogenases (GAPDHs) are significantly upregulated in transgenic plants, which is consistent with a decreased level of NADPH (GAPDH substrate) and increased level of NADP+. Accordingly, NbCIPK25 overexpressing plants exhibited more efficient photosynthesis; soluble sugar and proteins, as photosynthesis products, showed a higher accumulation in transgenic plants. These results provide molecular insight into how NbCIPK25 promotes the expression of genes involved in photosynthesis, thereby maintaining plant growth under salt stress. Our finding supports the potential application of halophyte-derived NbCIPK25 in genetic modification for better salt adaptation