559 research outputs found
The core starbursts of the galaxy NGC 3628: Radio very long baseline interferometry and X-ray studies
We present radio very long baseline interferometry (VLBI) and X-ray studies
of the starburst galaxy NGC 3628. The VLBI observation at 1.5 GHz reveals seven
compact (0.77 parsec) radio sources in the central 250 parsec region
of NGC 3628. Based on their morphology, high radio brightness temperatures
( K), and steep radio spectra, none of these seven sources can be
associated with active galactic nuclei (AGNs); instead, they can be identified
as supernova remnants (SNRs), with three of them appearing consistent with
partial shells. Notably, one of them (W2) is likely a nascent radio supernova
and appears to be consistent with the star formation rate of NGC 3628 when
assuming a canonical initial mass function. The VLBI observation provides the
first precise measurement of the diameter of the radio sources in NGC 3628,
which allow us to fit a well-constrained radio surface brightness - diameter
() correlation by including the detected SNRs. Furthermore, future
VLBI observations can be conducted to measure the expansion velocity of the
detected SNRs. In addition to our radio VLBI study, we analyze Chandra and
XMM-Newton spectra of NGC 3628. The spectral fitting indicates that the SNR
activities could well account for the observed X-ray emissions. Along with the
Chandra X-ray image, it further reveals that the X-ray emission is likely
maintained by the galactic-scale outflow triggered by SN activities. These
results provide strong evidence that SN-triggered activities play a critical
role in generating both radio and X-ray emissions in NGC 3628 and further
suggest that the galaxy NGC 3628 is in an early stage of starbursts.Comment: 15 pages, 4 Tables and 6 Figures, accepted for publication in Ap
Research on China’s fiscal and taxation policy of new energy vehicle industry technological innovation
Technological innovation in the new energy vehicle industry is conducive
to the achievement of China’s major strategic goal of ‘carbon
peak and carbon neutrality’. This research involved an empirical
study on the relevant data of 14 listed new energy vehicle companies
from 2012 to 2019. It used the entropy weight method to
obtain the technological innovation index through the four indicators
of research and development (R&D) investment, fixed asset
investment, intangible assets, and patent application volume.
Taking fiscal subsidies and tax burdens as independent variables, a
fixed effect model was used to analyze the impact of fiscal and taxation
policies on technological innovation in the new energy vehicle
industry. The research results show that financial subsidies will
encourage new energy vehicle companies to carry out technological
innovation, the tax burden has no significant impact on the
technological innovation of new energy vehicle enterprises, the
scale and age of enterprises, as well as the proportion of R&D personnel
to the total number of employees, will all encourage new
energy vehicle companies to carry out technological innovation.
Based on this, we put forward specific suggestions on further
improving the fiscal subsidy and tax incentive policies
Crossover effects of servant leadership and job social support on employee spouses:the mediating role of employee organization-based self-esteem
The present study investigated the crossover effects of employee perceptions of servant leadership and job social support on the family satisfaction and quality of family life experienced by the employees’ spouses. These effects were explored through a focus on the mediating role of employee organization-based self-esteem (OBSE). Results from a three-wave field survey of 199 employee–spouse dyads in the People’s Republic of China support our hypotheses, indicating that OBSE fully mediates the positive effects of servant leadership and job social support on family satisfaction and quality of family life. These findings provide new theoretical directions for work–family research
Rerender A Video: Zero-Shot Text-Guided Video-to-Video Translation
Large text-to-image diffusion models have exhibited impressive proficiency in
generating high-quality images. However, when applying these models to video
domain, ensuring temporal consistency across video frames remains a formidable
challenge. This paper proposes a novel zero-shot text-guided video-to-video
translation framework to adapt image models to videos. The framework includes
two parts: key frame translation and full video translation. The first part
uses an adapted diffusion model to generate key frames, with hierarchical
cross-frame constraints applied to enforce coherence in shapes, textures and
colors. The second part propagates the key frames to other frames with
temporal-aware patch matching and frame blending. Our framework achieves global
style and local texture temporal consistency at a low cost (without re-training
or optimization). The adaptation is compatible with existing image diffusion
techniques, allowing our framework to take advantage of them, such as
customizing a specific subject with LoRA, and introducing extra spatial
guidance with ControlNet. Extensive experimental results demonstrate the
effectiveness of our proposed framework over existing methods in rendering
high-quality and temporally-coherent videos.Comment: Accepted to SIGGRAPH Asia 2023. Project page:
https://www.mmlab-ntu.com/project/rerender
StyleGANEX: StyleGAN-Based Manipulation Beyond Cropped Aligned Faces
Recent advances in face manipulation using StyleGAN have produced impressive
results. However, StyleGAN is inherently limited to cropped aligned faces at a
fixed image resolution it is pre-trained on. In this paper, we propose a simple
and effective solution to this limitation by using dilated convolutions to
rescale the receptive fields of shallow layers in StyleGAN, without altering
any model parameters. This allows fixed-size small features at shallow layers
to be extended into larger ones that can accommodate variable resolutions,
making them more robust in characterizing unaligned faces. To enable real face
inversion and manipulation, we introduce a corresponding encoder that provides
the first-layer feature of the extended StyleGAN in addition to the latent
style code. We validate the effectiveness of our method using unaligned face
inputs of various resolutions in a diverse set of face manipulation tasks,
including facial attribute editing, super-resolution, sketch/mask-to-face
translation, and face toonification.Comment: ICCV 2023. Code: https://github.com/williamyang1991/StyleGANEX
Project page: https://www.mmlab-ntu.com/project/styleganex
VToonify: Controllable High-Resolution Portrait Video Style Transfer
Generating high-quality artistic portrait videos is an important and
desirable task in computer graphics and vision. Although a series of successful
portrait image toonification models built upon the powerful StyleGAN have been
proposed, these image-oriented methods have obvious limitations when applied to
videos, such as the fixed frame size, the requirement of face alignment,
missing non-facial details and temporal inconsistency. In this work, we
investigate the challenging controllable high-resolution portrait video style
transfer by introducing a novel VToonify framework. Specifically, VToonify
leverages the mid- and high-resolution layers of StyleGAN to render
high-quality artistic portraits based on the multi-scale content features
extracted by an encoder to better preserve the frame details. The resulting
fully convolutional architecture accepts non-aligned faces in videos of
variable size as input, contributing to complete face regions with natural
motions in the output. Our framework is compatible with existing StyleGAN-based
image toonification models to extend them to video toonification, and inherits
appealing features of these models for flexible style control on color and
intensity. This work presents two instantiations of VToonify built upon Toonify
and DualStyleGAN for collection-based and exemplar-based portrait video style
transfer, respectively. Extensive experimental results demonstrate the
effectiveness of our proposed VToonify framework over existing methods in
generating high-quality and temporally-coherent artistic portrait videos with
flexible style controls.Comment: ACM Transactions on Graphics (SIGGRAPH Asia 2022). Code:
https://github.com/williamyang1991/VToonify Project page:
https://www.mmlab-ntu.com/project/vtoonify
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