Variable O VI and N V emission from the X-ray binary LMC X-3 : heating of the black hole companion

Based on high-resolution ultraviolet spectroscopy obtained with the Far Ultraviolet Spectroscopic Explorer (FUSE) and the Cosmic Origins Spectrograph, we present new detections of Ovi and Nv emission from the black hole X-ray binary (XRB) system LMCX-3. We also update the ephemeris of the XRB using recent radial velocity measurements obtained with the echelle spectrograph on the Magellan-Clay telescope. We observe significant velocity variability of the UV emission, and we find that the Ovi and Nv emission velocities follow the optical velocity curve of the XRB. Moreover, the Ovi and Nv intensities regularly decrease between binary phase=0.5 and 1.0, which suggests that the source of the UV emission is increasingly occulted as the B star in the XRB moves from superior to inferior conjunction. These trends suggest that illumination of the B star atmosphere by the intense X-ray emission from the accreting black hole creates a hot spot on one side of the B star, and this hot spot is the origin of the Ovi and Nv emission. However, the velocity semiamplitude of the ultraviolet emission, K-UV approximate to 180 km s(-1), is lower than the optical semiamplitude; this difference could be due to rotation of the B star. Comparison of the FUSE observations taken in 2001 November and 2004 April shows a significant change in the Ovi emission characteristics: in the 2001 data, the Ovi region shows both broad and narrow emission features, while in 2004 only the narrow Ovi emission is clearly present. Rossi X-ray Timing Explorer data show that the XRB was in a high/soft state in the 2001 November epoch but was in a transitional state in 2004 April, so the shape of the X-ray spectrum might change the properties of the region illuminated on the B star and thus change the broad versus narrow characteristics of the UV emission. If our hypothesis about the origin of the highly ionized emission is correct, then careful analysis of the emission occultation could, in principle, constrain the inclination of the XRB and the mass of the black hole

City branding in China's Northeastern region

The past decade has seen a surge in the use of city branding, which is used to attract specific target groups of investors, high-tech green firms and talented workforce and reflects a desired shift from old, polluting manufacturing industries to new, clean service industries. Previous studies in the Chinese mega-city regions Pearl River Delta, Yangtze River Delta a

Newton Like Iterative Method without Derivative for Solving Nonlinear Equations Based on Dynamical Systems

The iterative problem of solving nonlinear equations is studied. A new Newton like iterative method with adjustable parameters is designed based on the dynamic system theory. In order to avoid the derivative function in the iterative scheme, the difference quotient is used instead of the derivative. Different from the existing methods, the difference quotient scheme in this paper has higher accuracy. Thus, the new iterative method is suitable for a wider range of initial values. Finally, several numerical examples are given to verify the practicability and superiority of the method.Comment: 7pages,under revie

The Life Cycle Reliability Evaluation of Optical Cable

Along with the optical fiber communication technology is widely used in power system, optical cable fault which will lead to failure has become an important factor influencing power grid reliability, but the existing optical cable evaluation index is single, only statistical cable fault conditions operation index, can not fully reflect the actual reliability of cable. This paper tries to construct a whole life cycle based on optical cable statistical reliability evaluation index system, uses the entropy method to evaluate the reliability of the optical cable is more objective, true, to carry out cable reliability research has certain reference significance. DOI: http://dx.doi.org/10.11591/telkomnika.v11i4.237

MixFormerV2: Efficient Fully Transformer Tracking

Transformer-based trackers have achieved strong accuracy on the standard benchmarks. However, their efficiency remains an obstacle to practical deployment on both GPU and CPU platforms. In this paper, to overcome this issue, we propose a fully transformer tracking framework, coined as \emph{MixFormerV2}, without any dense convolutional operation and complex score prediction module. Our key design is to introduce four special prediction tokens and concatenate them with the tokens from target template and search areas. Then, we apply the unified transformer backbone on these mixed token sequence. These prediction tokens are able to capture the complex correlation between target template and search area via mixed attentions. Based on them, we can easily predict the tracking box and estimate its confidence score through simple MLP heads. To further improve the efficiency of MixFormerV2, we present a new distillation-based model reduction paradigm, including dense-to-sparse distillation and deep-to-shallow distillation. The former one aims to transfer knowledge from the dense-head based MixViT to our fully transformer tracker, while the latter one is used to prune some layers of the backbone. We instantiate two types of MixForemrV2, where the MixFormerV2-B achieves an AUC of 70.6\% on LaSOT and an AUC of 57.4\% on TNL2k with a high GPU speed of 165 FPS, and the MixFormerV2-S surpasses FEAR-L by 2.7\% AUC on LaSOT with a real-time CPU speed.Comment: NIPS202

SportsMOT: A Large Multi-Object Tracking Dataset in Multiple Sports Scenes

Multi-object tracking in sports scenes plays a critical role in gathering players statistics, supporting further analysis, such as automatic tactical analysis. Yet existing MOT benchmarks cast little attention on the domain, limiting its development. In this work, we present a new large-scale multi-object tracking dataset in diverse sports scenes, coined as \emph{SportsMOT}, where all players on the court are supposed to be tracked. It consists of 240 video sequences, over 150K frames (almost 15\times MOT17) and over 1.6M bounding boxes (3\times MOT17) collected from 3 sports categories, including basketball, volleyball and football. Our dataset is characterized with two key properties: 1) fast and variable-speed motion and 2) similar yet distinguishable appearance. We expect SportsMOT to encourage the MOT trackers to promote in both motion-based association and appearance-based association. We benchmark several state-of-the-art trackers and reveal the key challenge of SportsMOT lies in object association. To alleviate the issue, we further propose a new multi-object tracking framework, termed as \emph{MixSort}, introducing a MixFormer-like structure as an auxiliary association model to prevailing tracking-by-detection trackers. By integrating the customized appearance-based association with the original motion-based association, MixSort achieves state-of-the-art performance on SportsMOT and MOT17. Based on MixSort, we give an in-depth analysis and provide some profound insights into SportsMOT. The dataset and code will be available at https://deeperaction.github.io/datasets/sportsmot.html

StableDrag: Stable Dragging for Point-based Image Editing

Point-based image editing has attracted remarkable attention since the emergence of DragGAN. Recently, DragDiffusion further pushes forward the generative quality via adapting this dragging technique to diffusion models. Despite these great success, this dragging scheme exhibits two major drawbacks, namely inaccurate point tracking and incomplete motion supervision, which may result in unsatisfactory dragging outcomes. To tackle these issues, we build a stable and precise drag-based editing framework, coined as StableDrag, by designing a discirminative point tracking method and a confidence-based latent enhancement strategy for motion supervision. The former allows us to precisely locate the updated handle points, thereby boosting the stability of long-range manipulation, while the latter is responsible for guaranteeing the optimized latent as high-quality as possible across all the manipulation steps. Thanks to these unique designs, we instantiate two types of image editing models including StableDrag-GAN and StableDrag-Diff, which attains more stable dragging performance, through extensive qualitative experiments and quantitative assessment on DragBench