End-to-End Multi-View Networks for Text Classification

We propose a multi-view network for text classification. Our method automatically creates various views of its input text, each taking the form of soft attention weights that distribute the classifier's focus among a set of base features. For a bag-of-words representation, each view focuses on a different subset of the text's words. Aggregating many such views results in a more discriminative and robust representation. Through a novel architecture that both stacks and concatenates views, we produce a network that emphasizes both depth and width, allowing training to converge quickly. Using our multi-view architecture, we establish new state-of-the-art accuracies on two benchmark tasks.Comment: 6 page

Effect of Soft Ground Tunneling-Induced Displacements on Railway Embankments

With the rapid development of urban infrastructure, certain transportation lines, utilities and pipelines are needed to be excavated under operating railway lines. When a tunnel under a railway is excavated, it will inevitably cause disturbance to the track structures, and the disturbance could influence the safety of railway operations. Consequently, the alleviation of ground surface displacement is of great significance to ensure the safety of both railway operations and tunnel construction. This thesis is a fundamental study of the surface displacements due to the construction of both shallow (near-surface) and deep (away from the surface) tunnels. The analysis of displacement along the surface of railway embankments is performed via two-dimensional finite element modeling. The freight train operating speed, tunnel diameter and tunnel depth are the three key factors that affect the surface displacement. The results illustrate that a 3 m diameter tunnel at depths greater than 3 m or a 4 m diameter tunnel at any depth greater than 16 m can be constructed beneath an existing railway without significantly affecting the safety of railway operations by considering subsidence control standards. Thus, this thesis contributes to determination of the maximum displacement of railway embankments induced by tunnel excavation as a function of various factors considered. Also, the findings of this thesis can help to guide future tunnel design and displacement control measures for excavations under operating freight railway lines

Multilabel Classification through Structured Output Learning - Methods and Applications

Multilabel classification is an important topic in machine learning that arises naturally from many real world applications. For example, in document classification, a research article can be categorized as “science”, “drug discovery” and “genomics” at the same time. The goal of multilabel classification is to reliably predict multiple outputs for a given input. As multiple interdependent labels can be “on” and “off” simultaneously, the central problem in multilabel classification is how to best exploit the correlation between labels to make accurate predictions. Compared to the previous flat multilabel classification approaches which treat multiple labels as a flat vector, structured output learning relies on an output graph connecting multiple labels to model the correlation between labels in a comprehensive manner. The main question studied in this thesis is how to tackle multilabel classification through structured output learning. This thesis starts with an extensive review on the topic of classification learning including both single-label and multilabel classification. The first problem we address is how to solve the multilabel classification problem when the output graph is observed apriori. We discuss several well-established structured output learning algorithms and study the network response prediction problem within the context of social network analysis. As the current structured output learning algorithms rely on the output graph to exploit the dependency between labels, the second problem we address is how to use structured output learning when the output graph is not known. Specifically, we examine the potential of learning on a set of random output graphs when the “real” one is hidden. This problem is relevant as in most multilabel classification problems there does not exist any output graph that reveals the dependency between labels. The third problem we address is how to analyze the proposed learning algorithms in a theoretical manner. Specifically, we want to explain the intuition behind the proposed models and to study the generalization error. The main contributions of this thesis are several new learning algorithms that widen the applicability of structured output learning. For the problem with an observed output graph, the proposed algorithm “SPIN” is able to predict an optimal directed acyclic graph from an observed underlying network that best responses to an input. For general multilabel classification problems without any known output graph, we proposed several learning algorithms that combine a set of structured output learners built on random output graphs. In addition, we develop a joint learning and inference framework which is based on max-margin learning over a random sample of spanning trees. The theoretic analysis also guarantees the generalization error of the proposed methods

Transferable E(3) equivariant parameterization for Hamiltonian of molecules and solids

Using the message-passing mechanism in machine learning (ML) instead of self-consistent iterations to directly build the mapping from structures to electronic Hamiltonian matrices will greatly improve the efficiency of density functional theory (DFT) calculations. In this work, we proposed a general analytic Hamiltonian representation in an E(3) equivariant framework, which can fit the ab initio Hamiltonian of molecules and solids by a complete data-driven method and are equivariant under rotation, space inversion, and time reversal operations. Our model reached state-of-the-art precision in the benchmark test and accurately predicted the electronic Hamiltonian matrices and related properties of various periodic and aperiodic systems, showing high transferability and generalization ability. This framework provides a general transferable model that can be used to accelerate the electronic structure calculations on different large systems with the same network weights trained on small structures.Comment: 33 pages, 6 figure

Theory and technique of permeability enhancement and coal mine gas extraction by fracture network stimulation of surrounding beds and coal beds

AbstractThe existing reservoir stimulating technologies are only applicable to hard coal but helpless for soft coal, which is one of the main factors hindering the CBM industrialization in China. Therefore, it is urgent to develop a universal stimulating technology which can increase the permeability in various coal reservoirs. Theoretical analysis and field tests were used to systematically analyze the mechanical mechanisms causing the formation of various levels and types of fractures, such as radial tensile fractures, peripheral tensile fractures, and shear fractures in hydraulic fracturing, and reveal the mechanism of permeability enhancement by fracture network stimulating in surrounding beds and coal reservoirs. The results show that multi-staged perforation fracturing of horizontal wells, hydraulic-jet staged fracturing, four-variation hydraulic fracturing and some auxiliary measures are effective technical approaches to fracture network stimulation, especially the four-variation hydraulic fracturing can stimulate the fracture network in vertical and cluster wells. It is concluded that the fracture network stimulating technology for surrounding beds has significant advantages, such as safe drilling operation, strong stimulation effect, strong adaptability to stress-sensitive and velocity-sensitive beds, and is suitable for coal reservoirs of any structure. Except for the limitation in extremely water-sensitive and high water-yield surrounding beds, the technology can be universally used in all other beds. The successful industrial tests in surface coal bed methane and underground coal mines gas extraction prove that the theory and technical system of fracture network stimulating in surrounding beds and coal reservoirs, as a universally applicable measure, will play a role in the CBM development in China