Dalian University of Technology

About: Dalian University of Technology is a education organization based out in Dalian, China. It is known for research contribution in the topics: Catalysis & Finite element method. The organization has 60890 authors who have published 71921 publications receiving 1188356 citations. The organization is also known as: Dàlián Lǐgōng Dàxué.

Crossings and nestings of matchings and partitions

Abstract: We present results on the enumeration of crossings and nestings for matchings and set partitions. Using a bijection between partitions and vacillating tableaux, we show that if we fix the sets of minimal block elements and maximal block elements, the crossing number and the nesting number of partitions have a symmetric joint distribution. It follows that the crossing numbers and the nesting numbers are distributed symmetrically over all partitions of [n], as well as over all matchings on [2n]. As a corollary, the number of k-noncrossing partitions is equal to the number of k-nonnesting partitions. The same is also true for matchings. An application is given to the enumeration of matchings with no k-crossing (or with no k-nesting). Mathematics Subject Classification. Primary 05A18, secondary 05E10, 05A15.

An attention-based BiLSTM-CRF approach to document-level chemical named entity recognition

Abstract: Motivation In biomedical research, chemical is an important class of entities, and chemical named entity recognition (NER) is an important task in the field of biomedical information extraction. However, most popular chemical NER methods are based on traditional machine learning and their performances are heavily dependent on the feature engineering. Moreover, these methods are sentence-level ones which have the tagging inconsistency problem. Results In this paper, we propose a neural network approach, i.e. attention-based bidirectional Long Short-Term Memory with a conditional random field layer (Att-BiLSTM-CRF), to document-level chemical NER. The approach leverages document-level global information obtained by attention mechanism to enforce tagging consistency across multiple instances of the same token in a document. It achieves better performances with little feature engineering than other state-of-the-art methods on the BioCreative IV chemical compound and drug name recognition (CHEMDNER) corpus and the BioCreative V chemical-disease relation (CDR) task corpus (the F-scores of 91.14 and 92.57%, respectively). Availability and implementation Data and code are available at https://github.com/lingluodlut/Att-ChemdNER. Contact yangzh@dlut.edu.cn or wangleibihami@gmail.com. Supplementary information Supplementary data are available at Bioinformatics online.

Robust Fault Detection for Switched Linear Systems With State Delays

Abstract: This correspondence deals with the problem of robust fault detection for discrete-time switched systems with state delays under an arbitrary switching signal. The fault detection filter is used as the residual generator, in which the filter parameters are dependent on the system mode. Attention is focused on designing the robust fault detection filter such that, for unknown inputs, control inputs, and model uncertainties, the estimation error between the residuals and faults is minimized. The problem of robust fault detection is converted into an H infin-filtering problem. By a switched Lyapunov functional approach, a sufficient condition for the solvability of this problem is established in terms of linear matrix inequalities. A numerical example is provided to demonstrate the effectiveness of the proposed method.

Self-Sensing, Ultralight, and Conductive 3D Graphene/Iron Oxide Aerogel Elastomer Deformable in a Magnetic Field

Abstract: Three-dimensional (3D) graphene aerogels (GA) show promise for applications in supercapacitors, electrode materials, gas sensors, and oil absorption due to their high porosity, mechanical strength, and electrical conductivity. However, the control, actuation, and response properties of graphene aerogels have not been well studied. In this paper, we synthesized 3D graphene aerogels decorated with Fe3O4 nanoparticles (Fe3O4/GA) by self-assembly of graphene with simultaneous decoration by Fe3O4 nanoparticles using a modified hydrothermal reduction process. The aerogels exhibit up to 52% reversible magnetic field-induced strain and strain-dependent electrical resistance that can be used to monitor the degree of compression/stretching of the material. The density of Fe3O4/GA is only about 5.8 mg cm(-3), making it an ultralight magnetic elastomer with potential applications in self-sensing soft actuators, microsensors, microswitches, and environmental remediation.