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

An Optimization Sizing Model for Solar Photovoltaic Power Generation System with Pumped Storage

AbstractIn this study, a novel sizing model for the solar photovoltaic system with pumped storage is proposed, to optimize the capacity of the PV generator and pumped storage system for power supply in remote areas. The genetic algorithm is then employed to optimize sizing system with respect to the system total cost. The variables considered in the optimization process include PV module number, upper reservoir size and water pump size. With the developed model, a technically and economically feasible power supply solution can be achieved easily. The proposed model is finally applied to a case study on renewable energy power generation system for an island, and the optimization performance has been demonstrated

Allocating Limited Resources to Protect a Massive Number of Targets using a Game Theoretic Model

Resource allocation is the process of optimizing the rare resources. In the area of security, how to allocate limited resources to protect a massive number of targets is especially challenging. This paper addresses this resource allocation issue by constructing a game theoretic model. A defender and an attacker are players and the interaction is formulated as a trade-off between protecting targets and consuming resources. The action cost which is a necessary role of consuming resource, is considered in the proposed model. Additionally, a bounded rational behavior model (Quantal Response, QR), which simulates a human attacker of the adversarial nature, is introduced to improve the proposed model. To validate the proposed model, we compare the different utility functions and resource allocation strategies. The comparison results suggest that the proposed resource allocation strategy performs better than others in the perspective of utility and resource effectiveness.Comment: 14 pages, 12 figures, 41 reference

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

Attention-Based Deep Learning Model for Predicting Collaborations Between Different Research Affiliations

It is challenging but important to predict the collaborations between different entities which in academia, for example, would enable finding evaluating trends of scientific research collaboration and the provision of decision support for policy formulation and incentive measures. In this paper, we propose an attention-based Long Short-Term Memory Convolutional Neural Network (LSTM-CNN) model to predict the collaborations between different research affiliations, which takes both the influence of research articles and time (year) relationships into consideration. The experimental results show that the proposed model outperforms the competitive Support Vector Machine (SVM), CNN and LSTM methods. It significantly improves the prediction precision by a minimum of 3.23 percent points and up to 10.80 percent points when compared with the mentioned competitive methods, while in terms of the F1-score, the performance is improved by 13.48, 4.85 and 4.24 percent points, respectively.This work was supported in part by the Humanities and Social Science Research Project of the Ministry of Education in China under Grant 17YJCZH262 and Grant 18YJAZH136, in part by the National Natural Science Foundation of China under Grant 61303167, Grant 61702306, Grant 61433012, Grant U1435215, and Grant 71772107, in part by the Natural Science Foundation of Shandong Province under Grant ZR2018BF013 and Grant ZR2017BF015, in part by the Innovative Research Foundation of Qingdao under Grant 18-2-2-41-jch, in part by the Key Project of Industrial Transformation and Upgrading in China under Grant TC170A5SW, and in part by the Scientific Research Foundation of SDUST for Innovative Team under Grant 2015TDJH102

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

Novel biosensor fabrication methodology based on processable conducting polyaniline nanoparticles

This work investigates polyaniline (PANI) nanoparticles, (synthesised using dodecylbenzenesulphonic acid (DBSA) as a dopant), as a novel, highly processable, non-diffusional mediating species in an enzyme biosensing application. These nanoparticles are readily dispersed in aqueous media which helps overcome some of the processability issues traditionally associated with polyaniline. Modification of screen-printed electrodes was readily achieved with these aqueous nanoparticle dispersions, where the nanoparticles were simply cast by a drop-coating method onto the surface. After suitable pH adjustment, it was shown that horseradish peroxidase (HRP) enzyme could be added to the dispersion, and cast simultaneously with the conducting polyaniline. This effective fabrication method involves no electrochemical steps, and as such is easily amenable to mass production. The feasibility of casting enzyme with polyaniline nanoparticles is demonstrated in this short communication. More accurate deposition of protein-containing inks onto screen-printed carbon working electrodes could in the future transfer the drop-coating protocol from manual deposition to largescale production by mechanical methods such as ink-jet printing

Effects of different fertilization conditions and different geographical locations on the diversity and composition of the rhizosphere microbiota of Qingke (Hordeum vulgare L.) plants in different growth stages

IntroductionThe excessive use of chemical fertilizer causes increasing environmental and food security crisis. Organic fertilizer improves physical and biological activities of soil. Rhizosphere microbiota, which consist of highly diverse microorganisms, play an important role in soil quality. However, there is limited information about the effects of different fertilization conditions on the growth of Qingke plants and composition of the rhizosphere microbiota of the plants.MethodsIn this study, we characterized the rhizosphere microbiota of Qingke plants grown in three main Qingke-producing areas (Tibet, Qinghai, and Gansu). In each of the three areas, seven different fertilization conditions (m1–m7, m1: Unfertilized; m2: Farmer Practice; m3: 75% Farmer Practice; m4: 75% Farmer Practice +25% Organic manure; m5: 50% Farmer Practice; m6: 50% Farmer Practice +50% Organic manure; m7: 100% Organic manure) were applied. The growth and yields of the Qingke plants were also compared under the seven fertilization conditions.ResultsThere were significant differences in alpha diversity indices among the three areas. In each area, differences in fertilization conditions and differences in the growth stages of Qingke plants resulted in differences in the beta diversity of the rhizosphere microbiota. Meanwhile, in each area, fertilization conditions, soil depths, and the growth stages of Qingke plants significantly affected the relative abundance of the top 10 phyla and the top 20 bacterial genera. For most of microbial pairs established through network analysis, the significance of their correlations in each of the microbial co-occurrence networks of the three experimental sites was different. Moreover, in each of the three networks, there were significant differences in relative abundance and genera among most nodes (i.e., the genera Pseudonocardia, Skermanella, Pseudonocardia, Skermanella, Aridibacter, and Illumatobacter). The soil chemical properties (i.e., TN, TP, SOM, AN, AK, CEC, Ca, and K) were positively or negatively correlated with the relative abundance of the top 30 genera derived from the three main Qingke-producing areas (p < 0.05). Fertilization conditions markedly influenced the height of a Qingke plant, the number of spikes in a Qingke plant, the number of kernels in a spike, and the fresh weight of a Qingke plant. Considering the yield, the most effective fertilization conditions for Qingke is combining application 50% chemical fertilizer and 50% organic manure.ConclusionThe results of the present study can provide theoretical basis for practice of reducing the use of chemical fertilizer in agriculture