LAGOS-AND: A Large Gold Standard Dataset for Scholarly Author Name Disambiguation

In this paper, we present a method to automatically build large labeled datasets for the author ambiguity problem in the academic world by leveraging the authoritative academic resources, ORCID and DOI. Using the method, we built LAGOS-AND, two large, gold-standard datasets for author name disambiguation (AND), of which LAGOS-AND-BLOCK is created for clustering-based AND research and LAGOS-AND-PAIRWISE is created for classification-based AND research. Our LAGOS-AND datasets are substantially different from the existing ones. The initial versions of the datasets (v1.0, released in February 2021) include 7.5M citations authored by 798K unique authors (LAGOS-AND-BLOCK) and close to 1M instances (LAGOS-AND-PAIRWISE). And both datasets show close similarities to the whole Microsoft Academic Graph (MAG) across validations of six facets. In building the datasets, we reveal the variation degrees of last names in three literature databases, PubMed, MAG, and Semantic Scholar, by comparing author names hosted to the authors' official last names shown on the ORCID pages. Furthermore, we evaluate several baseline disambiguation methods as well as the MAG's author IDs system on our datasets, and the evaluation helps identify several interesting findings. We hope the datasets and findings will bring new insights for future studies. The code and datasets are publicly available.Comment: 33 pages, 7 tables, 7 figure

SA-BEV: Generating Semantic-Aware Bird's-Eye-View Feature for Multi-view 3D Object Detection

Recently, the pure camera-based Bird's-Eye-View (BEV) perception provides a feasible solution for economical autonomous driving. However, the existing BEV-based multi-view 3D detectors generally transform all image features into BEV features, without considering the problem that the large proportion of background information may submerge the object information. In this paper, we propose Semantic-Aware BEV Pooling (SA-BEVPool), which can filter out background information according to the semantic segmentation of image features and transform image features into semantic-aware BEV features. Accordingly, we propose BEV-Paste, an effective data augmentation strategy that closely matches with semantic-aware BEV feature. In addition, we design a Multi-Scale Cross-Task (MSCT) head, which combines task-specific and cross-task information to predict depth distribution and semantic segmentation more accurately, further improving the quality of semantic-aware BEV feature. Finally, we integrate the above modules into a novel multi-view 3D object detection framework, namely SA-BEV. Experiments on nuScenes show that SA-BEV achieves state-of-the-art performance. Code has been available at https://github.com/mengtan00/SA-BEV.git

Effects of Gene Methylation Reprogramming in Cloned Calves Derived from In Vitro-Transfected Somatic Cells

AbstractIn vitro transfection of cultured cells combined with nuclear transfer currently is the most effective procedure to produce transgenic livestock. In the present study, bovine primary fetal fibroblasts were transfected with a green fluorescent protein (GFP) reporter transgene and used as nuclear donor cells in oocyte reconstructions. To examine the role of host cytoplasm on transgene expression and developmental outcome, GFP-expressing fibroblasts were fused to oocytes reconstructed either metaphase or telophase activation, and PCR technology was also employed. The results showed that GFP became detectable at the 8- to 16-cell stage, approximately 80h after reconstruction, and remained positive at all later stages. Embryonic development to the blastocyst stage was not significantly different among metaphase and telophase groups. Therefore, GFP transgene technology can be used to select embryoes derived from transgenic animals

Seismic and Power Generation Performance of U-Shaped Steel Connected PV-Shear Wall under Lateral Cyclic Loading

BIPV is now widely used in office and residential buildings, but its seismic performance still remained vague especially when the photovoltaic (PV) modules are installed on high-rise building facades. A new form of reinforced concrete shear wall integrated with photovoltaic module is proposed in this paper, aiming to apply PV module to the facades of high-rise buildings. In this new form, the PV module is integrated with the reinforced concrete wall by U-shaped steel connectors through embedded steel plates. The lateral cyclic loading test is executed to investigate the seismic behavior and the electric and thermal performance with different drift angles. The seismic behavior, including failure pattern, lateral force-top displacement relationship, and deformation capacity, was investigated. The power generation and temperature variation on the back of the PV module and both sides of the shear wall were also tested. Two main results are demonstrated through the experiment: (1) the U-shaped steel connectors provide enough deformation capacity for the compatibility of the PV module to the shear wall during the whole cyclic test; (2) the electricity generation capacity is effective and stable during this seismic simulation test

Hybrid-SORT: Weak Cues Matter for Online Multi-Object Tracking

Multi-Object Tracking (MOT) aims to detect and associate all desired objects across frames. Most methods accomplish the task by explicitly or implicitly leveraging strong cues (i.e., spatial and appearance information), which exhibit powerful instance-level discrimination. However, when object occlusion and clustering occur, both spatial and appearance information will become ambiguous simultaneously due to the high overlap between objects. In this paper, we demonstrate that this long-standing challenge in MOT can be efficiently and effectively resolved by incorporating weak cues to compensate for strong cues. Along with velocity direction, we introduce the confidence state and height state as potential weak cues. With superior performance, our method still maintains Simple, Online and Real-Time (SORT) characteristics. Furthermore, our method shows strong generalization for diverse trackers and scenarios in a plug-and-play and training-free manner. Significant and consistent improvements are observed when applying our method to 5 different representative trackers. Further, by leveraging both strong and weak cues, our method Hybrid-SORT achieves superior performance on diverse benchmarks, including MOT17, MOT20, and especially DanceTrack where interaction and occlusion are frequent and severe. The code and models are available at https://github.com/ymzis69/HybirdSORT

Design and control of a novel variable stiffness soft arm

Soft robot arms possess such characteristics as light weight, simple structure and good adaptability to the environment, among others. On the other hand, robust control of soft robot arms presents many difficulties. Based on these reasons, this paper presents a novel design and modelling of a fuzzy active disturbance rejection control (FADRC) controller for a soft PAM arm. The soft arm comprises three contractile and one extensor PAMs, which can vary its stiffness independently of its position in space. Force analysis for the soft arm is conducted, and stiffness model of the arm is established based on the relational model of contractile and extensor PAM. The accuracy of stiffness model for the soft arm was verified through experiments. Associated to this, a controller based on the fuzzy adaptive theory and ADRC, FADRC, has been designed to control the arm. The fuzzy adaptive theory is used to adjust the parameters of the ADRC, the control algorithm has the ability to control stiffness and position of the soft arm. In this paper, FADRC was further verified through comparative experiments on the soft arm. This paper reinforces the hypothesis that FADRC control, as an algorithm, indeed possesses good robustness and adaptive abilities. Key words: soft robot, variable stiffness, PAM, stiffness modelling, FADR

Perinatal Blockade of B7-1 and B7-2 Inhibits Clonal Deletion of Highly Pathogenic Autoreactive T Cells

A number of in vitro studies have suggested that costimulatory molecules B7-1 and B7-2 and their receptor CD28 can promote clonal deletion, and limited in vivo studies have indicated that CD28 is involved in the clonal deletion of some T cells. However, the significance of B7-mediated clonal deletion in preventing autoimmune diseases has not been studied systematically. Here we report that the perinatal blockade of B7-1 and B7-2 substantially inhibits the clonal deletion of T cells in the thymus and leads to an accumulation of T cells capable of inducing fatal multiorgan inflammation. These results reveal a critical role for costimulatory molecules B7-1 and B7-2 in deleting pathogenic autoreactive T cells in the thymus. The critical role of B7-1 and B7-2 in T cell clonal deletion may explain, at least in part, the paradoxical increase of autoimmune disease in mice deficient for this family of costimulatory molecules, such as cytotoxic T lymphocyte associated molecule 4, CD28, and B7-2. The strong pathogenicity of the self-reactive T cells supports a central hypothesis in immunology, which is that clonal deletion plays an important role in preventing autoimmune diseases

Population structure analysis and genome-wide association study of a hexaploid oat landrace and cultivar collection

IntroductionOat (Avena sativa L.) is an important cereal crop grown worldwide for grain and forage, owing to its high adaptability to diverse environments. However, the genetic and genomics research of oat is lagging behind that of other staple cereal crops. MethodsIn this study, a collection of 288 oat lines originating worldwide was evaluated using 2,213 single nucleotide polymorphism (SNP) markers obtained from an oat iSelect 6K-beadchip array to study its genetic diversity, population structure, and linkage disequilibrium (LD) as well as the genotype–phenotype association for hullessness and lemma color.ResultsThe average gene diversity and polymorphic information content (PIC) were 0.324 and 0.262, respectively. The first three principal components (PCs) accounted for 30.33% of the genetic variation, indicating that the population structure of this panel of oat lines was stronger than that reported in most previous studies. In addition, accessions could be classified into two subpopulations using a Bayesian clustering approach, and the clustering pattern of accessions was closely associated with their region of origin. Additionally, evaluation of LD decay using 2,143 mapped markers revealed that the intrachromosomal whole-genome LD decayed rapidly to a critical r2 value of 0.156 for marker pairs separated by a genetic distance of 1.41 cM. Genome-wide association study (GWAS) detected six significant associations with the hullessness trait. Four of these six markers were located on the Mrg21 linkage group between 194.0 and 205.7 cM, while the other two significant markers mapped to Mrg05 and Mrg09. Three significant SNPs, showing strong association with lemma color, were located on linkage groups Mrg17, Mrg18, and Mrg20.DiscussionOur results discerned relevant patterns of genetic diversity, population structure, and LD among members of a worldwide collection of oat landraces and cultivars proposed to be ‘typical’ of the Qinghai-Tibetan Plateau. These results have important implications for further studies on association mapping and practical breeding in high-altitude oat