Massive Quiescent Cores in Orion: VI. The Internal Structures and a Candidate of Transiting Core in NGC 2024 Filament

We present a multi-wavelength observational study of the NGC 2024 filament using infrared to sub-millimeter continuum and the NH$_3$ $(1,1)$ and $(2,2)$ inversion transitions centered on FIR-3, the most massive core therein. FIR-3 is found to have no significant infrared point sources in the Spitzer/IRAC bands. But the NH$_3$ kinetic temperature map shows a peak value at the core center with $T_{\rm k}=25$ K which is significantly higher than the surrounding level ($T_{\rm k}=15-19$ K). Such internal heating signature without an infrared source suggests an ongoing core collapse possibly at a transition stage from first hydrostatic core (FHSC) to protostar. The eight dense cores in the filament have dust temperatures between 17.5 and 22 K. They are much cooler than the hot ridge ($T_{\rm d}=55$ K) around the central heating star IRS-2b. Comparison with a dust heating model suggests that the filament should have a distance of $3-5$ pc from IRS-2b. This value is much larger than the spatial extent of the hot ridge, suggesting that the filament is spatially separated from the hot region along the line of sight.Comment: 20 pages, 7 figures, 6 tables. Accepted to Ap

A CO observation of the galactic methanol masers

Context: We investigated the molecular gas associated with 6.7 GHz methanol masers throughout the Galaxy using a J=1-0 transition of the CO isotopologues. Methods:Using the 13.7-meter telescope at the Purple Mountain Observatory (PMO), we have obtained ^{12}CO and ^{13}CO (1-0) lines for 160 methanol masers sources from the first to the third Galactic quadrants. We made efforts to resolve the distance ambiguity by careful comparison with the radio continuum and HI 21 cm observations. Results: First, the maser sources show increased ^{13}CO line widths toward the Galactic center, suggesting that the molecular gas are more turbulent toward the Galactic center. This trend can be noticeably traced by the ^{13}CO line width. Second, the ^{12}CO excitation temperature shows a noticeable correlation with the H_2 column density. A possible explanation consistent with the collapse model is that the higher surface-density gas is more efficient to the stellar heating and/or has a higher formation rate of high-mass stars. Third, comparing the IRDCs, the maser sources on average have significantly lower H_2 column densities, moderately higher temperatures, and similar line widths. Fourth, in the mapped regions, 51 ^{13}CO cores have been revealed. Only 17 coincide with the radio continuum emission (F_{cm}>6 mJy), while a larger fraction (30 cores) coincide with the infrared emissions. The IR-bright and radio-bright sources exhibit significantly higher $^{12}$CO excitation temperatures than the IR-faint and radio-faint sources, respectively. Conclusions: The 6.7 GHz masers show a moderately low ionization rate but have a common-existing stellar heating that generates the IR emissions. The relevant properties can be characterized by the ^{12}CO and ^{13}CO (1-0) emissions in several aspects as described above.Comment: 38 pages, 13 figures, 4 tables, accepted to Astronomy and Astrophysic

Broadband second harmonic generation in one-dimensional randomized nonlinear photonic crystal

We study experimentally second harmonic generation in a one-dimensional nonlinear photonic crystal with randomized inverted-domain structure. We show that the randomness enables one to realize an efficient broadband emission of high-quality second harmonic beam.The authors acknowledge financial support from the Australian Research Council and Australian Academy of Science

Bond-based nonlocal models by nonlocal operator method in symmetric support domain

This paper is concerned with the energy decomposition of various nonlocal models, including elasticity, thin plates, and gradient elasticity, to arrive at bond-based nonlocal models in which the bond force depends only on the deformation of a single bond. By assuming an appropriate form of bond force and using energy equivalence between local and nonlocal models, several very concise bond-based models are derived. We also revisit the nonlocal operator methods and study the simplified form of second-order NOM in the symmetric support domain. A bent-bond consisting of three points is proposed to describe the curvature and moment. To model the damage, a rule based on Griffith theory for the critical normal strain of the bond is proposed in analogy to the phase field model, which can be applied individually to each bond and provides strain localization. With this rule, the crack direction can be automatically predicted by simply cutting the bond, giving comparable results to the phase field method. At the same time, a damage rule for critical shear strains in shear fractures is proposed. Furthermore, an incremental form of the plasticity model for bond reaction force is derived. Several numerical examples are presented to further validate the nonlocal bond-based models

Exploring the Vulnerability of Deep Neural Networks: A Study of Parameter Corruption

We argue that the vulnerability of model parameters is of crucial value to the study of model robustness and generalization but little research has been devoted to understanding this matter. In this work, we propose an indicator to measure the robustness of neural network parameters by exploiting their vulnerability via parameter corruption. The proposed indicator describes the maximum loss variation in the non-trivial worst-case scenario under parameter corruption. For practical purposes, we give a gradient-based estimation, which is far more effective than random corruption trials that can hardly induce the worst accuracy degradation. Equipped with theoretical support and empirical validation, we are able to systematically investigate the robustness of different model parameters and reveal vulnerability of deep neural networks that has been rarely paid attention to before. Moreover, we can enhance the models accordingly with the proposed adversarial corruption-resistant training, which not only improves the parameter robustness but also translates into accuracy elevation.Comment: Accepted by AAAI 202

Mahalanobis Distance Map Approach for Anomaly Detection

Web servers and web-based applications are commonly used as attack targets. The main issues are how to prevent unauthorised access and to protect web servers from the attack. Intrusion Detection Systems (IDSs) are widely used security tools to detect cyber-attacks and malicious activities in computer systems and networks. In this paper, we focus on the detection of various web-based attacks using Geometrical Structure Anomaly Detection (GSAD) model and we also propose a novel algorithm for the selection of most discriminating features to improve the computational complexity of payload-based GSAD model. Linear Discriminant method (LDA) is used for the feature reduction and classification of the incoming network traffic. GSAD model is based on a pattern recognition technique used in image processing. It analyses the correlations between various payload features and uses Mahalanobis Distance Map (MDM) to calculate the difference between normal and abnormal network traffic. We focus on the detection of generic attacks, shell code attacks, polymorphic attacks and polymorphic blending attacks. We evaluate accuracy of GSAD model experimentally on the real-world attacks dataset created at Georgia Institute of Technology. We conducted preliminary experiments on the DARPA 99 dataset to evaluate the accuracy of feature reduction

PKUSEG: A Toolkit for Multi-Domain Chinese Word Segmentation

Chinese word segmentation (CWS) is a fundamental step of Chinese natural language processing. In this paper, we build a new toolkit, named PKUSEG, for multi-domain word segmentation. Unlike existing single-model toolkits, PKUSEG targets multi-domain word segmentation and provides separate models for different domains, such as web, medicine, and tourism. Besides, due to the lack of labeled data in many domains, we propose a domain adaptation paradigm to introduce cross-domain semantic knowledge via a translation system. Through this method, we generate synthetic data using a large amount of unlabeled data in the target domain and then obtain a word segmentation model for the target domain. We also further refine the performance of the default model with the help of synthetic data. Experiments show that PKUSEG achieves high performance on multiple domains. The new toolkit also supports POS tagging and model training to adapt to various application scenarios. The toolkit is now freely and publicly available for the usage of research and industry