Lifelong Learning CRF for Supervised Aspect Extraction

This paper makes a focused contribution to supervised aspect extraction. It shows that if the system has performed aspect extraction from many past domains and retained their results as knowledge, Conditional Random Fields (CRF) can leverage this knowledge in a lifelong learning manner to extract in a new domain markedly better than the traditional CRF without using this prior knowledge. The key innovation is that even after CRF training, the model can still improve its extraction with experiences in its applications.Comment: Accepted at ACL 2017. arXiv admin note: text overlap with arXiv:1612.0794

A bipartite separable ball and its applications

In this paper, based on a matrix norm, we first present a ball of separable unnormalized states around the identity matrix for the bipartite quantum system, which is larger than the separable ball in Frobenius norm. Then the proposed ball is used to get not only simple sufficient conditions for the separability of pseudopure states and the states with strong positive partial transposes, but also a separable ball centered at the identity matrix for the multipartite quantum system

Adaptive Multiscale Weighted Permutation Entropy for Rolling Bearing Fault Diagnosis

© 2020 The Author(s). This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/.Bearing vibration signals contain non-linear and non-stationary features due to instantaneous variations in the operation of rotating machinery. It is important to characterize and analyze the complexity change of the bearing vibration signals so that bearing health conditions can be accurately identified. Entropy measures are non-linear indicators that are applicable to the time series complexity analysis for machine fault diagnosis. In this paper, an improved entropy measure, termed Adaptive Multiscale Weighted Permutation Entropy (AMWPE), is proposed. Then, a new rolling bearing fault diagnosis method is developed based on the AMWPE and multi-class SVM. For comparison, experimental bearing data are analyzed using the AMWPE, compared with the conventional entropy measures, where a multi-class SVM is adopted for fault type classification. Moreover, the robustness of different entropy measures is further studied for the analysis of noisy signals with various Signal-to-Noise Ratios (SNRs). The experimental results have demonstrated the effectiveness of the proposed method in fault diagnosis of rolling bearing under different fault types, severity degrees, and SNR levels.Peer reviewedFinal Accepted Versio

Open-world Learning and Application to Product Classification

Classic supervised learning makes the closed-world assumption, meaning that classes seen in testing must have been seen in training. However, in the dynamic world, new or unseen class examples may appear constantly. A model working in such an environment must be able to reject unseen classes (not seen or used in training). If enough data is collected for the unseen classes, the system should incrementally learn to accept/classify them. This learning paradigm is called open-world learning (OWL). Existing OWL methods all need some form of re-training to accept or include the new classes in the overall model. In this paper, we propose a meta-learning approach to the problem. Its key novelty is that it only needs to train a meta-classifier, which can then continually accept new classes when they have enough labeled data for the meta-classifier to use, and also detect/reject future unseen classes. No re-training of the meta-classifier or a new overall classifier covering all old and new classes is needed. In testing, the method only uses the examples of the seen classes (including the newly added classes) on-the-fly for classification and rejection. Experimental results demonstrate the effectiveness of the new approach.Comment: accepted by The Web Conference (WWW 2019) Previous title: Learning to Accept New Classes without Trainin

Giant X-ray Bump in GRB 121027A: Evidence for Fall-back Disk Accretion

A particularly interesting discovery in observations of GRB 121027A is that of a giant X-ray bump detected by the Swift/X-Ray Telescope. The X-ray afterglow re-brightens sharply at about 1000 s after the trigger by more than two orders of magnitude in less than 200 s. This X-ray bump lasts for more than 10 ks. It is quite different from typical X-ray flares. In this Letter we propose a fall-back accretion model to interpret this X-ray bump within the context of the collapse of a massive star for a long-duration gamma-ray burst. The required fall-back radius of about 3.5e10 cm and mass of about 0.9-2.6 solar masses imply that a significant part of the helium envelope should survive through the mass loss during the last stage of the massive progenitor of GRB 121027A.Comment: 5 pages, 3 figures, 2013, ApJL, 767:L3