HIP Network: Historical Information Passing Network for Extrapolation Reasoning on Temporal Knowledge Graph

In recent years, temporal knowledge graph (TKG) reasoning has received significant attention. Most existing methods assume that all timestamps and corresponding graphs are available during training, which makes it difficult to predict future events. To address this issue, recent works learn to infer future events based on historical information. However, these methods do not comprehensively consider the latent patterns behind temporal changes, to pass historical information selectively, update representations appropriately and predict events accurately. In this paper, we propose the Historical Information Passing (HIP) network to predict future events. HIP network passes information from temporal, structural and repetitive perspectives, which are used to model the temporal evolution of events, the interactions of events at the same time step, and the known events respectively. In particular, our method considers the updating of relation representations and adopts three scoring functions corresponding to the above dimensions. Experimental results on five benchmark datasets show the superiority of HIP network, and the significant improvements on Hits@1 prove that our method can more accurately predict what is going to happen.Comment: 7 pages, 3 figure

Learning to Correct Noisy Labels for Fine-Grained Entity Typing via Co-Prediction Prompt Tuning

Fine-grained entity typing (FET) is an essential task in natural language processing that aims to assign semantic types to entities in text. However, FET poses a major challenge known as the noise labeling problem, whereby current methods rely on estimating noise distribution to identify noisy labels but are confused by diverse noise distribution deviation. To address this limitation, we introduce Co-Prediction Prompt Tuning for noise correction in FET, which leverages multiple prediction results to identify and correct noisy labels. Specifically, we integrate prediction results to recall labeled labels and utilize a differentiated margin to identify inaccurate labels. Moreover, we design an optimization objective concerning divergent co-predictions during fine-tuning, ensuring that the model captures sufficient information and maintains robustness in noise identification. Experimental results on three widely-used FET datasets demonstrate that our noise correction approach significantly enhances the quality of various types of training samples, including those annotated using distant supervision, ChatGPT, and crowdsourcing.Comment: Accepted by Findings of EMNLP 2023, 11 page

A Boundary Offset Prediction Network for Named Entity Recognition

Named entity recognition (NER) is a fundamental task in natural language processing that aims to identify and classify named entities in text. However, span-based methods for NER typically assign entity types to text spans, resulting in an imbalanced sample space and neglecting the connections between non-entity and entity spans. To address these issues, we propose a novel approach for NER, named the Boundary Offset Prediction Network (BOPN), which predicts the boundary offsets between candidate spans and their nearest entity spans. By leveraging the guiding semantics of boundary offsets, BOPN establishes connections between non-entity and entity spans, enabling non-entity spans to function as additional positive samples for entity detection. Furthermore, our method integrates entity type and span representations to generate type-aware boundary offsets instead of using entity types as detection targets. We conduct experiments on eight widely-used NER datasets, and the results demonstrate that our proposed BOPN outperforms previous state-of-the-art methods.Comment: Accepted by Findings of EMNLP 2023, 13 page

Iteratively Learning Representations for Unseen Entities with Inter-Rule Correlations

Recent work on knowledge graph completion (KGC) focused on learning embeddings of entities and relations in knowledge graphs. These embedding methods require that all test entities are observed at training time, resulting in a time-consuming retraining process for out-of-knowledge-graph (OOKG) entities. To address this issue, current inductive knowledge embedding methods employ graph neural networks (GNNs) to represent unseen entities by aggregating information of known neighbors. They face three important challenges: (i) data sparsity, (ii) the presence of complex patterns in knowledge graphs (e.g., inter-rule correlations), and (iii) the presence of interactions among rule mining, rule inference, and embedding. In this paper, we propose a virtual neighbor network with inter-rule correlations (VNC) that consists of three stages: (i) rule mining, (ii) rule inference, and (iii) embedding. In the rule mining process, to identify complex patterns in knowledge graphs, both logic rules and inter-rule correlations are extracted from knowledge graphs based on operations over relation embeddings. To reduce data sparsity, virtual neighbors for OOKG entities are predicted and assigned soft labels by optimizing a rule-constrained problem. We also devise an iterative framework to capture the underlying relations between rule learning and embedding learning. In our experiments, results on both link prediction and triple classification tasks show that the proposed VNC framework achieves state-of-the-art performance on four widely-used knowledge graphs. Further analysis reveals that VNC is robust to the proportion of unseen entities and effectively mitigates data sparsity.Comment: Accepted at CIKM 202

Investigation of Stellar Kinematics and Ionized gas Outflows in Local [U]LIRGs

We explore properties of stellar kinematics and ionized gas in a sample of 1106 local [U]LIRGs from the AKARI telescope. We combine data from $Wide-field\ Infrared\ Survey\ Explorer$(WISE) and Sloan Digital Sky Survey (SDSS) Data Release 13 (DR13) to fit the spectral energy distribution (SED) of each source to constrain the contribution of AGN to the total IR luminosity and estimate physical parameters such as stellar mass and star-formation rate (SFR). We split our sample into AGNs and weak/non-AGNs. We find that our sample is considerably above the main sequence. The highest SFRs and stellar masses are associated with ULIRGs. We also fit the H$\beta$and H$\alpha$regions to characterize the outflows. We find that the incidence of ionized gas outflows in AGN [U]LIRGs ($\sim$72\%) is much higher than that in weak/non-AGN ones ($\sim$39\%). The AGN ULIRGs have extreme outflow velocities (up to$\sim$2300 km s$^{-1}$) and high mass outflow rates (up to$\sim 60 \solarm~yr^{-1}$). Our results suggest that starbursts are insufficient to produce such powerful outflows. We explore the correlations of SFR and specific SFR (sSFR) with ionized gas outflows. We find that AGN hosts with the highest SFRs exhibit a negative correlation between outflow velocity and sSFR. Therefore, in AGNs containing large amounts of gas, the negative feedback scenario might be suggested.Comment: 20 pages, 14 figures, accepted for publication in Ap

Automata-Based Analysis of Stage Suspended Boom Systems

A stage suspended boom system is an automatic steeve system orchestrated by the PLC (programmable logic controller). Security and fault-recovering are two important properties. In this paper, we analyze and verify the boom system formally. We adopt the hybrid automaton to model the boom system. The forward reachability is used to verify the properties with the reachable states. We also present a case study to illustrate the feasibility of the proposed verification

Overexpression of Wnt7b antagonizes the inhibitory effect of dexamethasone on osteoblastogenesis of ST2 cells

Introduction: It is well established that glucocorticoid-induced osteoporosis is highly associated with preosteoblast differentiation and function. This study is based on the premise that Wnt7b can promote bone formation through Wnt signalling pathway because it can stimulate preosteoblast differentiation and increase its activity. However, it is unknown whether Wnt7b can rescue the inhibited osteoblast differentiation and function caused by exogenous glucocorticoid. Material and methods: In this study we used Wnt7b overexpression ST2 cells to explore whether Wnt7bcan rescue the inhibited osteoblast differentiation and function, which can provide strong proof to investigate a new drug for curing the glucocorticoid induced osteoporosis. Results/Conclusion: We found that Wnt7b can rescue the suppressed osteoblast differentiation and function without cell viability caused by dexamethasone