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Due to a lack of image-based "part controllers", shape manipulation of man-made shape images, such as resizing the backrest of a chair or replacing a cup handle is not intuitive. To tackle this problem, we present StylePart, a framework that enables direct shape manipulation of an image by leveraging generative models of both images and 3D shapes. Our key contribution is a shape-consistent latent mapping function that connects the image generative latent space and the 3D man-made shape attribute latent space. Our method "forwardly maps" the image content to its corresponding 3D shape attributes, where the shape part can be easily manipulated. The attribute codes of the manipulated 3D shape are then "backwardly mapped" to the image latent code to obtain the final manipulated image. We demonstrate our approach through various manipulation tasks, including part replacement, part resizing, and viewpoint manipulation, and evaluate its effectiveness through extensive ablation studies., Comment: 10 pages, Project page: https://jdily.github.io/proj_site/stylepart_proj.html

We have investigated the feasibilities and accuracies of the identifications of RR Lyrae stars and quasars from the simulated data of the Multi-channel Photometric Survey Telescope (Mephisto) W Survey. Based on the variable sources light curve libraries from the Sloan Digital Sky Survey (SDSS) Stripe 82 data and the observation history simulation from the Mephisto-W Survey Scheduler, we have simulated the $uvgriz$ multi-band light curves of RR Lyrae stars, quasars and other variable sources for the first year observation of Mephisto-W Survey. We have applied the ensemble machine learning algorithm Random Forest Classifier (RFC) to identify RR Lyrae stars and quasars, respectively. We build training and test samples and extract ~ 150 features from the simulated light curves and train two RFCs respectively for the RR Lyrae star and quasar classification. We find that, our RFCs are able to select the RR Lyrae stars and quasars with remarkably high precision and completeness, with $purity$ = 95.4 per cent and $completeness$ = 96.9 per cent for the RR Lyrae RFC and $purity$ = 91.4 per cent and $completeness$ = 90.2 per cent for the quasar RFC. We have also derived relative importances of the extracted features utilized to classify RR Lyrae stars and quasars., Comment: 12 pages, 5 figures. Accepted to be publication in Research in Astronomy and Astrophysics (RAA)

Future Internet involves several emerging technologies such as 5G and beyond 5G networks, vehicular networks, unmanned aerial vehicle (UAV) networks, and Internet of Things (IoTs). Moreover, future Internet becomes heterogeneous and decentralized with a large number of involved network entities. Each entity may need to make its local decision to improve the network performance under dynamic and uncertain network environments. Standard learning algorithms such as single-agent Reinforcement Learning (RL) or Deep Reinforcement Learning (DRL) have been recently used to enable each network entity as an agent to learn an optimal decision-making policy adaptively through interacting with the unknown environments. However, such an algorithm fails to model the cooperations or competitions among network entities, and simply treats other entities as a part of the environment that may result in the non-stationarity issue. Multi-agent Reinforcement Learning (MARL) allows each network entity to learn its optimal policy by observing not only the environments, but also other entities' policies. As a result, MARL can significantly improve the learning efficiency of the network entities, and it has been recently used to solve various issues in the emerging networks. In this paper, we thus review the applications of MARL in the emerging networks. In particular, we provide a tutorial of MARL and a comprehensive survey of applications of MARL in next generation Internet. In particular, we first introduce single-agent RL and MARL. Then, we review a number of applications of MARL to solve emerging issues in future Internet. The issues consist of network access, transmit power control, computation offloading, content caching, packet routing, trajectory design for UAV-aided networks, and network security issues.

The evolution of the molecular interstellar medium is controlled by processes such as turbulence, gravity, stellar feedback, and Galactic shear. AL a part of the ISM-6D https://gxli.github.io/ISM-6D/ project, using Gaia astrometric measurements towards a sample of young stellar objects (YSOs), we study morphology and kinematic structure of the associated molecular gas. We identify 150 YSO associations with distance $d \lesssim 3 \;\rm kpc$. The YSO associations are elongated, with a median aspect ratio of 1.97, and are oriented parallel to the disk midplane, with a median angle of 30$^{\circ}$. The turbulence in the molecular clouds as probed by the YSOs is isotropic, and the velocity dispersions are related to the sizes by $\sigma_{v,{\rm 2D}} = 0.74\;(r/{\rm pc})^{0.67} \;({\rm km/s})\;$. The slope is on the steeper side, yet consistent with previous measurements. The energy dissipation rate of turbulence $\dot{\epsilon} = \sigma_{v,{\rm 3D}}^3 /L$ decreases with the Galactocentric distance, with a gradient of 0.2 $\rm dex \; kpc^{-1}$, which can be explained if turbulence is driven by cloud collisions. In this scenario, the clouds located in the inner Galaxy have higher chances to accrete smaller clouds and are more turbulent. Although the density structures of the complexes are anisotropic, the turbulence is consistent with being isotropic. If the alignment between density structures and the Galactic-disk mid-plane is due to shear, we expect $t_{\rm cloud} \gtrsim t_{\rm shear}\approx 30\; \rm Myr$. This cloud lifetime is longer than the turbulence crossing time, and a continuous energy injection is required to maintain the turbulence., Comment: Accepted by MNRAS, https://gxli.github.io/ISM-6D/

Single-pixel imaging, with the advantages of a wide spectrum, beyond-visual-field imaging, and robustness to light scattering, has attracted increasing attention in recent years. Fourier single-pixel imaging (FSI) can reconstruct sharp images under sub-Nyquist sampling. However, the conventional FSI has difficulty with balancing the imaging quality and efficiency. To overcome this issue, we proposed a novel approach called complementary Fourier single-pixel imaging (CFSI) to reduce measurements while retaining its robustness. The complementary nature of Fourier patterns based on a four-step phase-shift algorithm is combined with the complementary nature of a digital micromirror device. CFSI only requires two phase-shifted patterns to obtain one Fourier spectral value. Four light intensity values are obtained by load the two patterns, and the spectral value is calculated through differential measurement, which has good robustness to noise. The proposed method is verified by simulations and experiments compared with FSI based on two-, three-, and four-step phase shift algorithms. CFSI performed better than the other methods under the condition that the best imaging quality of CFSI is not reached. The reported technique provides an alternative approach to realize real-time and high-quality imaging.