Multi-epoch, multi-frequency VLBI study of the parsec-scale jet in the blazar 3C 66A

We present the observational results of the Gamma-ray blazar, 3C 66A, at 2.3, 8.4, and 22 GHz at 4 epochs during 2004-05 with the VLBA. The resulting images show an overall core-jet structure extending roughly to the south with two intermediate breaks occurring in the region near the core. By model-fitting to the visibility data, the northmost component, which is also the brightest, is identified as the core according to its relatively flat spectrum and its compactness. As combined with some previous results to investigate the proper motions of the jet components, it is found the kinematics of 3C 66A is quite complicated with components of inward and outward, subluminal and superluminal motions all detected in the radio structure. The superluminal motions indicate strong Doppler boosting exists in the jet. The apparent inward motions of the innermost components last for at least 10 years and could not be caused by new-born components. The possible reason could be non-stationarity of the core due to opacity change.Comment: 24 pages, 4 figure

An hourglass model for the flare of HST-1 in M87

To explain the multi-wavelength light curves (from radio to X-ray) of HST-1 in the M87 jet, we propose an hourglass model that is a modified two-zone system of Tavecchio & Ghisellini (hereafter TG08): a slow hourglass-shaped or Laval nozzle-shaped layer connected by two revolving exponential surfaces surrounding a fast spine, through which plasma blobs flow. Based on the conservation of magnetic flux, the magnetic field changes along the axis of the hourglass. We adopt the result of TG08---the high-energy emission from GeV to TeV can be produced through inverse Compton by the two-zone system, and the photons from radio to X-ray are mainly radiated by the fast inner zone system. Here, we only discuss the light curves of the fast inner blob from radio to X-ray. When a compressible blob travels down the axis of the first bulb in the hourglass, because of magnetic flux conservation, its cross section experiences an adiabatic compression process, which results in particle acceleration and the brightening of HST-1. When the blob moves into the second bulb of the hourglass, because of magnetic flux conservation, the dimming of the knot occurs along with an adiabatic expansion of its cross section. A similar broken exponential function could fit the TeV peaks in M87, which may imply a correlation between the TeV flares of M87 and the light curves from radio to X-ray in HST-1. The Very Large Array (VLA) 22 GHz radio light curve of HST-1 verifies our prediction based on the model fit to the main peak of the VLA 15 GHz radio light curve.Comment: 14 pages, 2 figures, accepted for publication in A

Lightweight Vision Transformer with Cross Feature Attention

Recent advances in vision transformers (ViTs) have achieved great performance in visual recognition tasks. Convolutional neural networks (CNNs) exploit spatial inductive bias to learn visual representations, but these networks are spatially local. ViTs can learn global representations with their self-attention mechanism, but they are usually heavy-weight and unsuitable for mobile devices. In this paper, we propose cross feature attention (XFA) to bring down computation cost for transformers, and combine efficient mobile CNNs to form a novel efficient light-weight CNN-ViT hybrid model, XFormer, which can serve as a general-purpose backbone to learn both global and local representation. Experimental results show that XFormer outperforms numerous CNN and ViT-based models across different tasks and datasets. On ImageNet1K dataset, XFormer achieves top-1 accuracy of 78.5% with 5.5 million parameters, which is 2.2% and 6.3% more accurate than EfficientNet-B0 (CNN-based) and DeiT (ViT-based) for similar number of parameters. Our model also performs well when transferring to object detection and semantic segmentation tasks. On MS COCO dataset, XFormer exceeds MobileNetV2 by 10.5 AP (22.7 -> 33.2 AP) in YOLOv3 framework with only 6.3M parameters and 3.8G FLOPs. On Cityscapes dataset, with only a simple all-MLP decoder, XFormer achieves mIoU of 78.5 and FPS of 15.3, surpassing state-of-the-art lightweight segmentation networks.Comment: Technical Repor

(3R*)-Methyl 3-[(2S*)-4,6-dimethoxy-2-(4-methoxyphenyl)-3-oxo-2,3-dihydro-1-benzofuran-2-yl]-2-methoxycarbonyl-3-phenylpropionate

The title compound, C29H28O9, was isolated from the reaction of 4,6-dimeth­oxy-2-(4-methoxy­phen­yl)-3-benzofuran and α-methoxy­carbonyl­cinnaminate. The two aromatic rings form a dihedral angle of 22.7 (1)°. One methoxy­carbonyl group is disordered between two orientations in a 0.612 (4):0.388 (4) ratio. The crystal structure exhibits no significantly short inter­molecular contacts