Pair-Linking for Collective Entity Disambiguation: Two Could Be Better Than All

Collective entity disambiguation aims to jointly resolve multiple mentions by linking them to their associated entities in a knowledge base. Previous works are primarily based on the underlying assumption that entities within the same document are highly related. However, the extend to which these mentioned entities are actually connected in reality is rarely studied and therefore raises interesting research questions. For the first time, we show that the semantic relationships between the mentioned entities are in fact less dense than expected. This could be attributed to several reasons such as noise, data sparsity and knowledge base incompleteness. As a remedy, we introduce MINTREE, a new tree-based objective for the entity disambiguation problem. The key intuition behind MINTREE is the concept of coherence relaxation which utilizes the weight of a minimum spanning tree to measure the coherence between entities. Based on this new objective, we design a novel entity disambiguation algorithms which we call Pair-Linking. Instead of considering all the given mentions, Pair-Linking iteratively selects a pair with the highest confidence at each step for decision making. Via extensive experiments, we show that our approach is not only more accurate but also surprisingly faster than many state-of-the-art collective linking algorithms

Savings, Asset Holding and Debt: New Evidence from Chinese Household Data

This thesis consists of three empirical studies that investigate contemporary topics related to household finance. Specifically, this thesis aims to contribute to the literature relating to household savings, household risky assets and household debt by examining three distinct but related topics in the context of China. The first empirical study (Chapter 2) examines the relationship between planning for overseas education and household saving behaviour in China by using a household-level dataset, the China Household Finance Survey (CHFS) covering 2011, 2013, 2015 and 2017. This chapter also examines the role of planning for overseas education on wider measures of household assets such as household financial assets and household net wealth. The results indicate that households where parents plan to send their children to study abroad hold more household savings, household financial assets and household net wealth than those who do not plan to do so for their children. Furthermore, such a positive effect of planning for overseas education on household savings is revealed after dealing with potential endogeneity issues. In addition, different effects of planning to send children to study abroad on household savings are found across the whole savings distribution. The second empirical study (Chapter 3) examines the relationship between financial literacy and risky asset holding in China using a panel dataset from the CHFS covering 2013, 2015 and 2017 in order to control for unobserved heterogeneity. Risky asset holding is captured in three ways: the probability of holding risky assets; the log level of risky assets; and the share of risky assets in total household financial assets. Then, household risky assets are split into high-risk assets and low-risk assets. In addition, this chapter explores the relationship between financial illiteracy and household risky asset holding. The findings indicate that financial literacy is positively associated with risky asset holding. The importance of the role of financial literacy on household risky asset holding remains once time-invariant effects have been accounted for. Furthermore, financial literacy has been found to be positively associated with high-risk asset holding and low-risk asset holding but the size of the positive effect of financial literacy differs across high-risk assets and low-risk assets. Finally, these findings have been found to be robust after dealing with the potential endogeneity of financial literacy and the results have also revealed a negative relationship between financial illiteracy and household risky asset holding. The third empirical study (Chapter 4) examines the association between risk attitudes and household debt using a household-level dataset from the CHFS (2011, 2013, 2015 and 2017). Household debt is captured by the probability of holding household debt and the amount of total household debt held. Then, household debt is split into housing debt and non-housing debt to explore how risk attitudes affect the two types of debt. In addition, households are split into urban and rural households in order to explore whether the effect of risk attitudes on household debt differs across urban and rural households. Finally, this chapter investigates the two-part process related to holding total household debt: (1) the decision to hold debt; and (2) the decision over the amount of debt held. The results indicate that risk tolerance is positively associated with household debt. The findings also indicate a positive relationship between risk tolerance and non-housing debt. In addition, we have found differences in the effect of risk tolerance across total household debt, housing debt and non-housing debt by rural and urban households. For example, the magnitude of the marginal effect of risk attitudes on the probability of holding total household debt is larger for rural households than for their urban counterparts. Finally, the findings are robust to using the double hurdle approach thereby providing further evidence that the risk tolerance of the head of household is positively associated with household debt

Few-shot Class-incremental Audio Classification Using Stochastic Classifier

It is generally assumed that number of classes is fixed in current audio classification methods, and the model can recognize pregiven classes only. When new classes emerge, the model needs to be retrained with adequate samples of all classes. If new classes continually emerge, these methods will not work well and even infeasible. In this study, we propose a method for fewshot class-incremental audio classification, which continually recognizes new classes and remember old ones. The proposed model consists of an embedding extractor and a stochastic classifier. The former is trained in base session and frozen in incremental sessions, while the latter is incrementally expanded in all sessions. Two datasets (NS-100 and LS-100) are built by choosing samples from audio corpora of NSynth and LibriSpeech, respectively. Results show that our method exceeds four baseline ones in average accuracy and performance dropping rate. Code is at https://github.com/vinceasvp/meta-sc.Comment: 5 pages, 3 figures, 4 tables. Accepted for publication in INTERSPEECH 202

Research on a safety evaluation system for railway-tunnel structures by fuzzy comprehensive evaluation theory

Long-term health detection of railway-tunnel is the development direction and trend of future railway tunnel research. Based on the actual engineering of a railway tunnel, this study developed a safety evaluation model for railway tunnel structures using a fuzzy comprehensive evaluation method and examined a health state evaluation method suitable for most railway tunnel structures. The results showed that the evaluation method comprehensively reflected the impact of various factors, which had strong practicality. The evaluation results were clear, accurate, and consistent with engineering practice. When using the safety factor index to study the stress of a railway tunnel structure, Midas/civil analysis showed that different levels of the surrounding rock structural vault in railway tunnels were in a tensile, control-bearing capacity state. When calculating safety factors, the range of a 60° central angle of a railway tunnel vault was calculated according to the tensile control-bearing capacity. Theoretical formulas of the range of the center angle φ0 of the vault tension zone were derived and then verified by experiments and numerical analysis

Controlled synthesis of mussel-inspired Ag nanoparticle coatings with demonstrated in vitro and in vivo antibacterial properties

The in-situ formation of silver nanoparticles (AgNPs) via dopamine-reduction of Ag+ has been widely utilized for titanium implants to introduce antibacterial properties. In previous studies, the preparation of AgNPs has focused on controlling the feeding concentrations, while the pH of the reaction solution was ignored. Herein, we systematically determined the influence of various pH (4, 7, 10) and Ag+ concentrations (0.01, 0.1 mg/mL) on the AgNPs formation, followed by the evaluation of the antibacterial properties in vitro and in vivo. The results revealed that an alkaline environment was favourable for AgNP formation and resulted in more particles. Although the AgNPs bearing Ti had lower biocompatibilities, it was significantly improved after 7 days of mineralization in simulated body fluid. The outstanding antibacterial property of the AgNPs was well maintained after one day and seven days of implantation. Moreover, 3D micro-CT modelling showed that the pH 10/0.1 group exhibited remarkable osteogenesis, which may be due to their strong antibacterial properties and ability to promote mineralization. Therefore, we have demonstrated that the solution pH was as important as the feeding Ag+ concentration in determining AgNP formation, and it has paved the way for developing various AgNP-loaded surfaces that could meet different antibacterial needs

Graph Anomaly Detection at Group Level: A Topology Pattern Enhanced Unsupervised Approach

Graph anomaly detection (GAD) has achieved success and has been widely applied in various domains, such as fraud detection, cybersecurity, finance security, and biochemistry. However, existing graph anomaly detection algorithms focus on distinguishing individual entities (nodes or graphs) and overlook the possibility of anomalous groups within the graph. To address this limitation, this paper introduces a novel unsupervised framework for a new task called Group-level Graph Anomaly Detection (Gr-GAD). The proposed framework first employs a variant of Graph AutoEncoder (GAE) to locate anchor nodes that belong to potential anomaly groups by capturing long-range inconsistencies. Subsequently, group sampling is employed to sample candidate groups, which are then fed into the proposed Topology Pattern-based Graph Contrastive Learning (TPGCL) method. TPGCL utilizes the topology patterns of groups as clues to generate embeddings for each candidate group and thus distinct anomaly groups. The experimental results on both real-world and synthetic datasets demonstrate that the proposed framework shows superior performance in identifying and localizing anomaly groups, highlighting it as a promising solution for Gr-GAD. Datasets and codes of the proposed framework are at the github repository https://anonymous.4open.science/r/Topology-Pattern-Enhanced-Unsupervised-Group-level-Graph-Anomaly-Detection

Ultrafast-and-Ultralight ConvNet-Based Intelligent Monitoring System for Diagnosing Early-Stage Mpox Anytime and Anywhere

Due to the lack of more efficient diagnostic tools for monkeypox, its spread remains unchecked, presenting a formidable challenge to global health. While the high efficacy of deep learning models for monkeypox diagnosis has been demonstrated in related studies, the overlook of inference speed, the parameter size and diagnosis performance for early-stage monkeypox renders the models inapplicable in real-world settings. To address these challenges, we proposed an ultrafast and ultralight network named Fast-MpoxNet. Fast-MpoxNet possesses only 0.27M parameters and can process input images at 68 frames per second (FPS) on the CPU. To counteract the diagnostic performance limitation brought about by the small model capacity, it integrates the attention-based feature fusion module and the multiple auxiliary losses enhancement strategy for better detecting subtle image changes and optimizing weights. Using transfer learning and five-fold cross-validation, Fast-MpoxNet achieves 94.26% Accuracy on the Mpox dataset. Notably, its recall for early-stage monkeypox achieves 93.65%. By adopting data augmentation, our model's Accuracy rises to 98.40% and attains a Practicality Score (A new metric for measuring model practicality in real-time diagnosis application) of 0.80. We also developed an application system named Mpox-AISM V2 for both personal computers and mobile phones. Mpox-AISM V2 features ultrafast responses, offline functionality, and easy deployment, enabling accurate and real-time diagnosis for both the public and individuals in various real-world settings, especially in populous settings during the outbreak. Our work could potentially mitigate future monkeypox outbreak and illuminate a fresh paradigm for developing real-time diagnostic tools in the healthcare field