Entity Linking for Queries by Searching Wikipedia Sentences

We present a simple yet effective approach for linking entities in queries. The key idea is to search sentences similar to a query from Wikipedia articles and directly use the human-annotated entities in the similar sentences as candidate entities for the query. Then, we employ a rich set of features, such as link-probability, context-matching, word embeddings, and relatedness among candidate entities as well as their related entities, to rank the candidates under a regression based framework. The advantages of our approach lie in two aspects, which contribute to the ranking process and final linking result. First, it can greatly reduce the number of candidate entities by filtering out irrelevant entities with the words in the query. Second, we can obtain the query sensitive prior probability in addition to the static link-probability derived from all Wikipedia articles. We conduct experiments on two benchmark datasets on entity linking for queries, namely the ERD14 dataset and the GERDAQ dataset. Experimental results show that our method outperforms state-of-the-art systems and yields 75.0% in F1 on the ERD14 dataset and 56.9% on the GERDAQ dataset

Seismic performance of concrete-filled SHS column-to-beam connections with slip-critical blind bolts

© 2020 Elsevier Ltd This paper investigates the use of slip-critical blind bolts to connect I-beams to concrete-filled steel square hollow section (SHS) columns. The strength and stiffness of the resulting joints are determined experimentally for the purpose of classifying them according to the Eurocode. Their suitability for use in special moment frames is also assessed through cyclic bending tests. Three types of beam sections are tested, being a compact welded section, a reduced beam (flange) section, and a reduced beam section with concrete slab at the top. All tested joints are full strength according to the Eurocode, allowing the connected beams to reach their respective plastic moment capacities. In addition, they are rigid for braced and unbraced frames, except for the reduced beam section specimen, which are semi-rigid only for unbraced frames according to the Eurocode. However, all specimens have sufficient ductility to be used in special moment frames, with no pinching effect in their hysteretic moment-rotation curves. Their initial rotational stiffness is dominated by the stiffness of the column flange in bending, which can be conservatively estimated using the formulation presented in this paper

Learning to Sit: Synthesizing Human-Chair Interactions via Hierarchical Control

Recent progress on physics-based character animation has shown impressive breakthroughs on human motion synthesis, through imitating motion capture data via deep reinforcement learning. However, results have mostly been demonstrated on imitating a single distinct motion pattern, and do not generalize to interactive tasks that require flexible motion patterns due to varying human-object spatial configurations. To bridge this gap, we focus on one class of interactive tasks -- sitting onto a chair. We propose a hierarchical reinforcement learning framework which relies on a collection of subtask controllers trained to imitate simple, reusable mocap motions, and a meta controller trained to execute the subtasks properly to complete the main task. We experimentally demonstrate the strength of our approach over different non-hierarchical and hierarchical baselines. We also show that our approach can be applied to motion prediction given an image input. A supplementary video can be found at https://youtu.be/3CeN0OGz2cA.Comment: Accepted to AAAI 202

Conceptual Design of Non-ideal Mixtures Separation with Light Entrainers

A method is proposed to study the separation of minimum-, maximum-boiling azeotropic, and low volatility mixtures with a light entrainer, to investigate feasible regions of the key operating parameters reboil ratio (S) and entrainer - feed flowrate ratio (FE/F) for continuous processes. The thermodynamic topological predictions are carried out for 1.0–2, 1.0–1a, and 0.0–1 Serafimov’s class diagrams. It relies upon the knowledge of residue curve maps, along with the univolatility line, and it enables the prediction of possible products at the bottom of the column and limiting values of FE/F. The profiles of the stripping, extractive, and rectifying sections are calculated by equations considering S and FE/F, and they bring information about the location of singular points and possible composition profile separatrices that could impair process feasibility. Providing specified product composition and recovery, the approximate calculations are compared with rigorous simulations of extractive distillation processes. Separating non-ideal mixtures using a light entrainer provides more opportunities for the case when it is not easy to find an appropriate heavy or intermediate entraine

Using improved support vector regression to predict the transmitted energy consumption data by distributed wireless sensor network

AbstractMassive energy consumption data of buildings was generated with the development of information technology, and the real-time energy consumption data was transmitted to energy consumption monitoring system by the distributed wireless sensor network (WSN). Accurately predicting the energy consumption is of importance for energy manager to make advisable decision and achieve the energy conservation. In recent years, considerable attention has been gained on predicting energy use of buildings in China. More and more predictive models appeared in recent years, but it is still a hard work to construct an accurate model to predict the energy consumption due to the complexity of the influencing factors. In this paper, 40 weather factors were considered into the research as input variables, and the electricity of supermarket which was acquired by the energy monitoring system was taken as the target variable. With the aim to seek the optimal subset, three feature selection (FS) algorithms were involved in the study, respectively: stepwise, least angle regression (Lars), and Boruta algorithms. In addition, three machine learning methods that include random forest (RF) regression, gradient boosting regression (GBR), and support vector regression (SVR) algorithms were utilized in this paper and combined with three feature selection (FS) algorithms, totally are nine hybrid models aimed to explore an improved model to get a higher prediction performance. The results indicate that the FS algorithm Boruta has relatively better performance because it could work well both on RF and SVR algorithms, the machine learning method SVR could get higher accuracy on small dataset compared with the RF and GBR algorithms, and the hybrid model called SVR-Boruta was chosen to be the proposed model in this paper. What is more, four evaluate indicators were selected to verify the model performance respectively are the mean absolute error (MAE), the mean squared error(MSE), the root mean squared error (RMSE), and the R-squared (R2), and the experiment results further verified the superiority of the recommended methodology

Novel Procedure for Assessment of Feasible Design Parameters of Dividing-Wall Columns: Application to Non-azeotropic Mixtures

Dividing wall columns (DWCs), as a subset of fully thermally coupled distillation systems (FTCDS), is considered as one of most appealing distillation technologies to the chemical industry, because it can bring about substantial reduction in the capital investment, as well as savings in the operating costs. This study targets on how to improve the energy efficiency of DWCs by achieving their well-designed feasible parameters. Two methods are applied to study the effect of liquid and vapor split ratios including a shortcut method and a method of systematic calculations by using differential equation profiles. In the latter approach, differential composition profiles in each column section are obtained by considering feasible key design parameters. The finding of pinch points for each section profiles allowed determining the limiting values of the operating parameters. The intersections of these profiles are used to get well-designed feasible parameters of the liquid and vapor split ratios in an attempt to obtain the desired purities of the top, bottom, and side-stream products. The obtained parameters are validated by rigorous simulations. Three types of case studies involve the separation of hydrocarbons (n-pentane, n-hexane, n-heptane), aromatics (benzene, toluene, p-xylene), and alcohols (ethanol, propanol, butanol)