Nature of W51e2: Massive Cores at Different Phases of Star Formation

We present high-resolution continuum images of the W51e2 complex processed from archival data of the Submillimeter Array (SMA) at 0.85 and 1.3 mm and the Very Large Array (VLA) at 7 and 13 mm. We also made line images and profiles of W51e2 for three hydrogen radio recombination lines (H26\alpha, H53\alpha, and H66\alpha) and absorption of two molecular lines of HCN(4-3) and CO(2-1). At least four distinct continuum components have been detected in the 3" region of W51e2 from the SMA continuum images at 0.85 and 1.3 mm with resolutions of 0.3"x0.2" and 1.4"x0.7", respectively. The west component, W51e2-W, coincides with the UC HII region reported from previous radio observations. The H26\alpha line observation reveals an unresolved hyper-compact ionized core (<0.06" or <310 AU) with a high electron temperature of 1.2x10^4 K, with corresponding emission measure EM>7x10^{10} pc cm^{-6} and electron density N_e>7x10^6 cm^{-3}. The inferred Lyman continuum flux implies that the HII region W51e2-W requires a newly formed massive star, an O8 star or a cluster of B-type stars, to maintain the ionization. The east component, W51e2-E, has a total mass of ~140 M_{\sun} according to our SED analysis and a large infall rate of > 1.3x10^{-3} M_{\sun}yr^{-1} inferred from the absorption of HCN. W51e2-E appears to be the accretion center in W51e2 and to host one or more growing massive proto-stars. Located 2" northwest from W51e2-E, W51e2-NW is not detected in the continuum emission at \lambda>=7 mm. Along with the maser activities previously observed, our analysis suggests that W51e2-NW is at an earlier phase of star formation. W51e2-N is located 2" north of W51e2-E and has only been detected at 1.3 mm with a lower angular resolution (~1"), suggesting that it is a primordial, massive gas clump in the W51e2 complex.Comment: 10 pages, 5 figures, 3 table, accepted for publication in Ap

Unsupervised Generative Modeling Using Matrix Product States

Generative modeling, which learns joint probability distribution from data and generates samples according to it, is an important task in machine learning and artificial intelligence. Inspired by probabilistic interpretation of quantum physics, we propose a generative model using matrix product states, which is a tensor network originally proposed for describing (particularly one-dimensional) entangled quantum states. Our model enjoys efficient learning analogous to the density matrix renormalization group method, which allows dynamically adjusting dimensions of the tensors and offers an efficient direct sampling approach for generative tasks. We apply our method to generative modeling of several standard datasets including the Bars and Stripes, random binary patterns and the MNIST handwritten digits to illustrate the abilities, features and drawbacks of our model over popular generative models such as Hopfield model, Boltzmann machines and generative adversarial networks. Our work sheds light on many interesting directions of future exploration on the development of quantum-inspired algorithms for unsupervised machine learning, which are promisingly possible to be realized on quantum devices.Comment: 11 pages, 12 figures (not including the TNs) GitHub Page: https://congzlwag.github.io/UnsupGenModbyMPS

Effects of Rashba spin-orbit coupling and a magnetic field on a polygonal quantum ring

Using standard quantum network method, we analytically investigate the effect of Rashba spin-orbit coupling (RSOC) and a magnetic field on the spin transport properties of a polygonal quantum ring. Using Landauer-Buttiker formula, we have found that the polarization direction and phase of transmitted electrons can be controlled by both the magnetic field and RSOC. A device to generate a spin-polarized conductance in a polygon with an arbitrary number of sides is discussed. This device would permit precise control of spin and selectively provide spin filtering for either spin up or spin down simply by interchanging the source and drain

Facial Landmark Predictions with Applications to Metaverse

This research aims to make metaverse characters more realistic by adding lip animations learnt from videos in the wild. To achieve this, our approach is to extend Tacotron 2 text-to-speech synthesizer to generate lip movements together with mel spectrogram in one pass. The encoder and gate layer weights are pre-trained on LJ Speech 1.1 data set while the decoder is retrained on 93 clips of TED talk videos extracted from LRS 3 data set. Our novel decoder predicts displacement in 20 lip landmark positions across time, using labels automatically extracted by OpenFace 2.0 landmark predictor. Training converged in 7 hours using less than 5 minutes of video. We conducted ablation study for Pre/Post-Net and pre-trained encoder weights to demonstrate the effectiveness of transfer learning between audio and visual speech data