NuSTAR observations of the young, energetic radio pulsar PSR B1509-58

We report on Nuclear Spectroscopic Telescope Array (NuSTAR) hard X-ray observations of the young rotation-powered radio pulsar PSR B1509$-$58 in the supernova remnant MSH 15$-$52. We confirm the previously reported curvature in the hard X-ray spectrum, showing that a log parabolic model provides a statistically superior fit to the spectrum compared with the standard power law. The log parabolic model describes the NuSTAR data, as well as previously published gamma-ray data obtained with COMPTEL and AGILE, all together spanning 3 keV through 500 MeV. Our spectral modelling allows us to constrain the peak of the broadband high energy spectrum to be at 2.6$\pm$0.8 MeV, an improvement of nearly an order of magnitude in precision over previous measurements. In addition, we calculate NuSTAR spectra in 26 pulse phase bins and confirm previously reported variations of photon indices with phase. Finally, we measure the pulsed fraction of PSR B1509$-$58 in the hard X-ray energy band for the first time. Using the energy resolved pulsed fraction results, we estimate that the pulsar's off-pulse emission has a photon index value between 1.26 and 1.96. Our results support a model in which the pulsar's lack of GeV emission is due to viewing geometry, with the X-rays originating from synchrotron emission from secondary pairs in the magnetosphere.Comment: 10 pages, 8 figures, 6 tables, ApJ accepte

Navigating Beyond Dropout: An Intriguing Solution Towards Generalizable Image Super Resolution

Deep learning has led to a dramatic leap on Single Image Super-Resolution (SISR) performances in recent years. %Despite the substantial advancement% While most existing work assumes a simple and fixed degradation model (e.g., bicubic downsampling), the research of Blind SR seeks to improve model generalization ability with unknown degradation. Recently, Kong et al pioneer the investigation of a more suitable training strategy for Blind SR using Dropout. Although such method indeed brings substantial generalization improvements via mitigating overfitting, we argue that Dropout simultaneously introduces undesirable side-effect that compromises model's capacity to faithfully reconstruct fine details. We show both the theoretical and experimental analyses in our paper, and furthermore, we present another easy yet effective training strategy that enhances the generalization ability of the model by simply modulating its first and second-order features statistics. Experimental results have shown that our method could serve as a model-agnostic regularization and outperforms Dropout on seven benchmark datasets including both synthetic and real-world scenarios

Joint Extraction of Entities and Relations Using Reinforcement Learning and Deep Learning

We use both reinforcement learning and deep learning to simultaneously extract entities and relations from unstructured texts. For reinforcement learning, we model the task as a two-step decision process. Deep learning is used to automatically capture the most important information from unstructured texts, which represent the state in the decision process. By designing the reward function per step, our proposed method can pass the information of entity extraction to relation extraction and obtain feedback in order to extract entities and relations simultaneously. Firstly, we use bidirectional LSTM to model the context information, which realizes preliminary entity extraction. On the basis of the extraction results, attention based method can represent the sentences that include target entity pair to generate the initial state in the decision process. Then we use Tree-LSTM to represent relation mentions to generate the transition state in the decision process. Finally, we employ Q-Learning algorithm to get control policy π in the two-step decision process. Experiments on ACE2005 demonstrate that our method attains better performance than the state-of-the-art method and gets a 2.4% increase in recall-score