Weakly-Supervised Neural Text Classification

Deep neural networks are gaining increasing popularity for the classic text classification task, due to their strong expressive power and less requirement for feature engineering. Despite such attractiveness, neural text classification models suffer from the lack of training data in many real-world applications. Although many semi-supervised and weakly-supervised text classification models exist, they cannot be easily applied to deep neural models and meanwhile support limited supervision types. In this paper, we propose a weakly-supervised method that addresses the lack of training data in neural text classification. Our method consists of two modules: (1) a pseudo-document generator that leverages seed information to generate pseudo-labeled documents for model pre-training, and (2) a self-training module that bootstraps on real unlabeled data for model refinement. Our method has the flexibility to handle different types of weak supervision and can be easily integrated into existing deep neural models for text classification. We have performed extensive experiments on three real-world datasets from different domains. The results demonstrate that our proposed method achieves inspiring performance without requiring excessive training data and outperforms baseline methods significantly.Comment: CIKM 2018 Full Pape

(3-Pyrid­yl)methanaminium 4-nitro­phenolate 4-nitro­phenol solvate

In the crystal structure of the title compound, C6H9N2 +·C6H4NO3 −·C6H5NO3, ions and mol­ecules are connected via inter­molecular N—H⋯O, N—H⋯N, O—H⋯O and C—H⋯O hydrogen bonds into a three-dimensional network

Performance Limits and Geometric Properties of Array Localization

Location-aware networks are of great importance and interest in both civil and military applications. This paper determines the localization accuracy of an agent, which is equipped with an antenna array and localizes itself using wireless measurements with anchor nodes, in a far-field environment. In view of the Cram\'er-Rao bound, we first derive the localization information for static scenarios and demonstrate that such information is a weighed sum of Fisher information matrices from each anchor-antenna measurement pair. Each matrix can be further decomposed into two parts: a distance part with intensity proportional to the squared baseband effective bandwidth of the transmitted signal and a direction part with intensity associated with the normalized anchor-antenna visual angle. Moreover, in dynamic scenarios, we show that the Doppler shift contributes additional direction information, with intensity determined by the agent velocity and the root mean squared time duration of the transmitted signal. In addition, two measures are proposed to evaluate the localization performance of wireless networks with different anchor-agent and array-antenna geometries, and both formulae and simulations are provided for typical anchor deployments and antenna arrays.Comment: to appear in IEEE Transactions on Information Theor

An Attention-based Collaboration Framework for Multi-View Network Representation Learning

Learning distributed node representations in networks has been attracting increasing attention recently due to its effectiveness in a variety of applications. Existing approaches usually study networks with a single type of proximity between nodes, which defines a single view of a network. However, in reality there usually exists multiple types of proximities between nodes, yielding networks with multiple views. This paper studies learning node representations for networks with multiple views, which aims to infer robust node representations across different views. We propose a multi-view representation learning approach, which promotes the collaboration of different views and lets them vote for the robust representations. During the voting process, an attention mechanism is introduced, which enables each node to focus on the most informative views. Experimental results on real-world networks show that the proposed approach outperforms existing state-of-the-art approaches for network representation learning with a single view and other competitive approaches with multiple views.Comment: CIKM 201

Type-II Dirac points and Dirac nodal loops on the magnons of square-hexagon-octagon lattice

We study topological magnons on an anisotropic square-hexagon-octagon (SHO) lattice which has been found by a two-dimensional Biphenylene network (BPN). We propose the concepts of type-II Dirac magnonic states where new schemes to achieve topological magnons are unfolded without requiring the Dzyaloshinsky-Moriya interactions (DMIs). In the ferromagnetic states, the topological distinctions at the type-II Dirac points along with one-dimensional (1D) closed lines of Dirac magnon nodes are characterized by the $\mathbb{Z}_2$ invariant. We find pair annihilation of the Dirac magnons and use the Wilson loop method to depict the topological protection of the band-degeneracy. The Green's function approach is used to calculte chiral edge modes and magnon density of states (DOS). We introduce the DMIs to gap the type-II Dirac magnon points and demonstrate the Dirac nodal loops (DNLs) are robust against the DMIs within a certain parameter range. The topological phase diagram of magnon bands is given via calculating the Berry curvature and Chern number. We find that the anomalous thermal Hall conductivity gives connection to the magnon edge current. Furthermore, we derive the differential gyromagnetic ratio to exhibit the Einstein-de Haas effect (EdH) of magnons with topological features.Comment: arXiv admin note: text overlap with arXiv:2207.0288