Improving Neural Relation Extraction with Implicit Mutual Relations

Relation extraction (RE) aims at extracting the relation between two entities from the text corpora. It is a crucial task for Knowledge Graph (KG) construction. Most existing methods predict the relation between an entity pair by learning the relation from the training sentences, which contain the targeted entity pair. In contrast to existing distant supervision approaches that suffer from insufficient training corpora to extract relations, our proposal of mining implicit mutual relation from the massive unlabeled corpora transfers the semantic information of entity pairs into the RE model, which is more expressive and semantically plausible. After constructing an entity proximity graph based on the implicit mutual relations, we preserve the semantic relations of entity pairs via embedding each vertex of the graph into a low-dimensional space. As a result, we can easily and flexibly integrate the implicit mutual relations and other entity information, such as entity types, into the existing RE methods. Our experimental results on a New York Times and another Google Distant Supervision datasets suggest that our proposed neural RE framework provides a promising improvement for the RE task, and significantly outperforms the state-of-the-art methods. Moreover, the component for mining implicit mutual relations is so flexible that can help to improve the performance of both CNN-based and RNN-based RE models significant.Comment: 12 page

Learning Relation Prototype from Unlabeled Texts for Long-tail Relation Extraction

Relation Extraction (RE) is a vital step to complete Knowledge Graph (KG) by extracting entity relations from texts.However, it usually suffers from the long-tail issue. The training data mainly concentrates on a few types of relations, leading to the lackof sufficient annotations for the remaining types of relations. In this paper, we propose a general approach to learn relation prototypesfrom unlabeled texts, to facilitate the long-tail relation extraction by transferring knowledge from the relation types with sufficient trainingdata. We learn relation prototypes as an implicit factor between entities, which reflects the meanings of relations as well as theirproximities for transfer learning. Specifically, we construct a co-occurrence graph from texts, and capture both first-order andsecond-order entity proximities for embedding learning. Based on this, we further optimize the distance from entity pairs tocorresponding prototypes, which can be easily adapted to almost arbitrary RE frameworks. Thus, the learning of infrequent or evenunseen relation types will benefit from semantically proximate relations through pairs of entities and large-scale textual information.We have conducted extensive experiments on two publicly available datasets: New York Times and Google Distant Supervision.Compared with eight state-of-the-art baselines, our proposed model achieves significant improvements (4.1% F1 on average). Furtherresults on long-tail relations demonstrate the effectiveness of the learned relation prototypes. We further conduct an ablation study toinvestigate the impacts of varying components, and apply it to four basic relation extraction models to verify the generalization ability.Finally, we analyze several example cases to give intuitive impressions as qualitative analysis. Our codes will be released later

Field Measurement of Wind Speeds and Wind-Induced Responses atop the Shanghai World Financial Center under Normal Climate Conditions

Field measurement data on wind velocities and wind-induced acceleration responses at the top of the 492 m high Shanghai World Financial Center (SWFC) under normal climate conditions are studied. Characteristics of the mean wind speeds and turbulence intensities, gust factors, power spectral densities, and turbulence integral scales of the fluctuating wind speed are analyzed in different observation time intervals. Power spectral densities of wind-induced acceleration are also investigated. The basic natural frequencies and structural damping ratios of the building are identified based on Hilbert-Huang transform method and random decrement method. The field measurement results of wind-induced responses of the SWFC are finally compared with those from the corresponding high-frequency force balance wind tunnel test study

Coordination Dynamics and Coordination Mechanism of a New Type of Anticoagulant Diethyl Citrate with Ca 2+

Diethyl citrate (Et2Cit) is a new potential anticoagulant. The coordination dynamics and coordination mechanism of Et2Cit with Ca2+ ions and the effect of pH on the complex were examined. The result was compared with that for the conventional anticoagulant sodium citrate (Na3Cit). The reaction order (n) of Et2Cit and Na3Cit with Ca2+ was 2.46 and 2.44, respectively. The reaction rate constant (k) was 120 and 289 L·mol−1·s−1. The reverse reaction rate constant (kre) was 0.52 and 0.15 L·mol−1·s−1, respectively. It is indicated that the coordination ability of Et2Cit with Ca2+ was weaker than that of Na3Cit. However, the dissociation rate of the calcium complex of Et2Cit was faster than that of Na3Cit. Increased pH accelerated the dissociation rate of the complex and improved its anticoagulant effect. The Et2Cit complex with calcium was synthesized and characterized by elemental analysis, XRD, FT-IR, 1H NMR, and ICP. These characteristics indicated that O in –COOH and C–O–C of Et2Cit was coordinated with Ca2+ in a bidentate manner with 1 : 1 coordination proportion; that is, complex CaEt2Cit was formed. Given that CaEt2Cit released Ca2+ more easily than Na3Cit, a calcium solution was not needed in intravenous infusions using Et2Cit as anticoagulant unlike using Na3Cit. Consequently, hypocalcemia and hypercalcemia were avoided

Meta-Learning Triplet Network with Adaptive Margins for Few-Shot Named Entity Recognition

Meta-learning methods have been widely used in few-shot named entity recognition (NER), especially prototype-based methods. However, the Other(O) class is difficult to be represented by a prototype vector because there are generally a large number of samples in the class that have miscellaneous semantics. To solve the problem, we propose MeTNet, which generates prototype vectors for entity types only but not O-class. We design an improved triplet network to map samples and prototype vectors into a low-dimensional space that is easier to be classified and propose an adaptive margin for each entity type. The margin plays as a radius and controls a region with adaptive size in the low-dimensional space. Based on the regions, we propose a new inference procedure to predict the label of a query instance. We conduct extensive experiments in both in-domain and cross-domain settings to show the superiority of MeTNet over other state-of-the-art methods. In particular, we release a Chinese few-shot NER dataset FEW-COMM extracted from a well-known e-commerce platform. To the best of our knowledge, this is the first Chinese few-shot NER dataset. All the datasets and codes are provided at https://github.com/hccngu/MeTNet

The gut–liver axis in immune remodeling of hepatic cirrhosis

In healthy settings, the gut–liver axis allows host–microbiota communications and mediates immune homeostasis through bidirectional regulation. Meanwhile, in diseases, gut dysbiosis, combined with an impaired intestinal barrier, introduces pathogens and their toxic metabolites into the system, causing massive immune alternations in the liver and other extrahepatic organs. Accumulating evidence suggests that these immune changes are associated with the progression of many liver diseases, especially hepatic cirrhosis. Pathogen-associated molecular patterns that originated from gut microbes directly stimulate hepatocytes and liver immune cells through different pattern recognition receptors, a process further facilitated by damage-associated molecular patterns released from injured hepatocytes. Hepatic stellate cells, along with other immune cells, contribute to this proinflammatory and profibrogenic transformation. Moreover, cirrhosis-associated immune dysfunction, an imbalanced immune status characterized by systemic inflammation and immune deficiency, is linked to gut dysbiosis. Though the systemic inflammation hypothesis starts to link gut dysbiosis to decompensated cirrhosis from a clinical perspective, a clearer demonstration is still needed for the role of the gut–liver–immune axis in cirrhosis progression. This review discusses the different immune states of the gut–liver axis in both healthy and cirrhotic settings and, more importantly, summarizes the current evidence about how microbiota-derived immune remodeling contributes to the progression of hepatic cirrhosis via the gut–liver axis

A Blueprint for Applications in Enterprise Information Portals

Electronic Commerce (E-Commerce, EC) is thoroughly changing business models of organizations (governments, corporations, and communities) and individuals the way of living and working. However, the major success will accrue to those companies that are willing to transform their organizations and business processes, which is the scope of e-Business. An Enterprise Information Portal (EIP) provides real time information and integrated applications to knowledge workers, employees, customers, business partners and the general public as well. Effective applications of EIP facilitate high quality strategic decisions. That is, an EIP can enhance an organization’s productivity, improve the collaboration to facilitate E-Commerce and gain competitive advantages. However, the EIP solutions are usually too expensive to small businesses. With Enterprise Application Integration (EAI) approach, this paper presents an economic way to design a low-cost EIP that leverages existing systems. Moreover, a prototype is implemented to show the feasibility. For the external data access, the web mining technology is utilized to mine some relevant and valuable web contents from the Internet and put these contents into the document warehouse. By combining the textual information inside the document warehouse and the numeric data from the data warehouse, competitive advantages can be provided over those who work with just the numbers

Hippocampal Synaptic and Neural Network Deficits in Young Mice Carrying the Human APOE4 Gene

Introduction: Apolipoprotein E4 (APOE4) is a major genetic risk factor for late-onset sporadic Alzheimer disease. Emerging evidence demonstrates a hippocampus-associated learning and memory deficit in aged APOE4 human carriers and also in aged mice carrying human APOE4 gene. This suggests that either exogenous APOE4 or endogenous APOE4 alters the cognitive profile and hippocampal structure and function. However, little is known regarding how Apoe4 modulates hippocampal dendritic morphology, synaptic function, and neural network activity in young mice. Aim: In this study, we compared hippocampal dendritic and spine morphology and synaptic function of young (4 months) mice with transgenic expression of the human APOE4 and APOE3 genes. Methods: Hippocampal dendritic and spine morphology and synaptic function were assessed by neuronal imaging and electrophysiological approaches. Results: Morphology results showed that shortened dendritic length and reduced spine density occurred at hippocampal CA1 neurons in Apoe4 mice compared to Apoe3 mice. Electrophysiological results demonstrated that in the hippocampal CA3-CA1 synapses of young Apoe4 mice, basic synaptic transmission, and paired-pulse facilitation were enhanced but long-term potentiation and carbachol-induced hippocampal theta oscillations were impaired compared to young Apoe3 mice. However, both Apoe genotypes responded similarly to persistent stimulations (4, 10, and 40 Hz for 4 seconds). Conclusion: Our results suggest significant alterations in hippocampal dendritic structure and synaptic function in Apoe4 mice, even at an early age