SSD-GAN: Measuring the Realness in the Spatial and Spectral Domains

This paper observes that there is an issue of high frequencies missing in the discriminator of standard GAN, and we reveal it stems from downsampling layers employed in the network architecture. This issue makes the generator lack the incentive from the discriminator to learn high-frequency content of data, resulting in a significant spectrum discrepancy between generated images and real images. Since the Fourier transform is a bijective mapping, we argue that reducing this spectrum discrepancy would boost the performance of GANs. To this end, we introduce SSD-GAN, an enhancement of GANs to alleviate the spectral information loss in the discriminator. Specifically, we propose to embed a frequency-aware classifier into the discriminator to measure the realness of the input in both the spatial and spectral domains. With the enhanced discriminator, the generator of SSD-GAN is encouraged to learn high-frequency content of real data and generate exact details. The proposed method is general and can be easily integrated into most existing GANs framework without excessive cost. The effectiveness of SSD-GAN is validated on various network architectures, objective functions, and datasets. Code will be available at https://github.com/cyq373/SSD-GAN.Comment: Accepted to AAAI 2021. Code: https://github.com/cyq373/SSD-GA

On Separate Normalization in Self-supervised Transformers

Self-supervised training methods for transformers have demonstrated remarkable performance across various domains. Previous transformer-based models, such as masked autoencoders (MAE), typically utilize a single normalization layer for both the [CLS] symbol and the tokens. We propose in this paper a simple modification that employs separate normalization layers for the tokens and the [CLS] symbol to better capture their distinct characteristics and enhance downstream task performance. Our method aims to alleviate the potential negative effects of using the same normalization statistics for both token types, which may not be optimally aligned with their individual roles. We empirically show that by utilizing a separate normalization layer, the [CLS] embeddings can better encode the global contextual information and are distributed more uniformly in its anisotropic space. When replacing the conventional normalization layer with the two separate layers, we observe an average 2.7% performance improvement over the image, natural language, and graph domains.Comment: NIPS 202

Deep3DSketch+: Obtaining Customized 3D Model by Single Free-Hand Sketch through Deep Learning

As 3D models become critical in today's manufacturing and product design, conventional 3D modeling approaches based on Computer-Aided Design (CAD) are labor-intensive, time-consuming, and have high demands on the creators. This work aims to introduce an alternative approach to 3D modeling by utilizing free-hand sketches to obtain desired 3D models. We introduce Deep3DSketch+, which is a deep-learning algorithm that takes the input of a single free-hand sketch and produces a complete and high-fidelity model that matches the sketch input. The neural network has view- and structural-awareness enabled by a Shape Discriminator (SD) and a Stroke Enhancement Module (SEM), which overcomes the limitations of sparsity and ambiguity of the sketches. The network design also brings high robustness to partial sketch input in industrial applications.Our approach has undergone extensive experiments, demonstrating its state-of-the-art (SOTA) performance on both synthetic and real-world datasets. These results validate the effectiveness and superiority of our method compared to existing techniques. We have demonstrated the conversion of free-hand sketches into physical 3D objects using additive manufacturing. We believe that our approach has the potential to accelerate product design and democratize customized manufacturing

Improving Molecular Pretraining with Complementary Featurizations

Molecular pretraining, which learns molecular representations over massive unlabeled data, has become a prominent paradigm to solve a variety of tasks in computational chemistry and drug discovery. Recently, prosperous progress has been made in molecular pretraining with different molecular featurizations, including 1D SMILES strings, 2D graphs, and 3D geometries. However, the role of molecular featurizations with their corresponding neural architectures in molecular pretraining remains largely unexamined. In this paper, through two case studies -- chirality classification and aromatic ring counting -- we first demonstrate that different featurization techniques convey chemical information differently. In light of this observation, we propose a simple and effective MOlecular pretraining framework with COmplementary featurizations (MOCO). MOCO comprehensively leverages multiple featurizations that complement each other and outperforms existing state-of-the-art models that solely relies on one or two featurizations on a wide range of molecular property prediction tasks.Comment: 24 pages, work in progres

Discovery of 21 New Changing-look AGNs in Northern Sky

The rare case of changing-look (CL) AGNs, with the appearance or disappearance of broad Balmer emission lines within a few years, challenges our understanding of the AGN unified model. We present a sample of 21 new CL AGNs at $0.08<z<0.58$, which doubles the number of such objects known to date. These new CL AGNs were discovered by several ways, from (1) repeat spectra in the SDSS, (2) repeat spectra in the Large Sky Area Multi-Object Fiber Spectroscopic Telescope (LAMOST) and SDSS, and (3) photometric variability and new spectroscopic observations. We use the photometric data from surveys, including the SDSS imaging survey, the Pan-STARRS1, the DESI Legacy imaging survey, the Wide-field Infrared Survey Explorer (WISE), the Catalina Real-time Transient Survey, and the Palomar Transient Factory. The estimated upper limits of transition timescale of the CL AGNs in this sample spans from 0.9 to 13 years in the rest frame. The continuum flux in the optical and mid-infrared becomes brighter when the CL AGNs turn on, or vice versa. Variations of more than 0.2 mag in $W1$ band were detected in 15 CL AGNs during the transition. The optical and mid-infrared variability is not consistent with the scenario of variable obscuration in 10 CL AGNs at more than $3\sigma$ confidence level. We confirm a bluer-when-brighter trend in the optical. However, the mid-infrared WISE colors $W1-W2$ become redder when the objects become brighter in the $W1$ band, possibly due to a stronger hot dust contribution in the $W2$ band when the AGN activity becomes stronger. The physical mechanism of type transition is important for understanding the evolution of AGNs.Comment: Accepted for publication in Ap

The latest research progress on P53 and tumor metabolism

P53 is a key factor encoded by the TP53, and it prevents cells from becoming cancerous and has a wide range of powerful functions. p53 is found to play an important role in inducing DNA repair, apoptosis, cell cycle arrest and senescence, and the loss of these functions does not abrogate P53’s tumor suppressive activity. Metabolism is the basis of life, and metabolic abnormalities can lead to a variety of diseases, including tumors, and is one of the main drivers of cancer progression. It has recently been discovered that P53 plays a key role in regulating metabolism. P53-mediated regulation of cell metabolism is a fundamental mechanism controlling cancer occurrence and development and contributes to its tumor suppressive activity. Here, this article reviewed the relationship between P53 and glucose, fatty acid, amino acid and nucleotide metabolism, and discussed the complex mechanism and the latest research progress of P53 in the metabolic regulation in tumor development

Can patient gratitude expression boost innovative performance? The role of work meaningfulness and supervisory support

Based on emotions as social information (EASI) theory, the current study proposed how and when patient gratitude expression could promote nurses’ innovative performance. Using a time-lagged data of 649 nurses from three class A tertiary hospitals in China, the results showed that patient gratitude expression was positively related to nurses’ innovative performance, and nurses’ work meaningfulness mediated such effect. Furthermore, supervisory support moderated the relationship of work meaningfulness with nurses’ innovative performance, as well as the indirect relationship between patient gratitude expression and innovative performance through work meaningfulness, such that the indirect relationship was stronger when supervisory support is higher. Our research helps to expand our understanding of how patient gratitude expression as an organizational external factor influences nurses’ innovation in healthcare, and meanwhile, provides management insights for hospital managers to focus on patient gratitude expression and enhance nurse innovation

VLBA reveals the absence of a compact radio core in the radio intermediate quasar J2242+0334 at z =5.9

High-resolution imaging is crucial for exploring the origin and mechanism of radio emission in quasars, especially at high redshifts. We present 1.5 GHz Very Long Baseline Array (VLBA) images of the radio continuum emission from the radio-intermediate quasar (RIQ) J2242+0334 at $z = 5.9$. This object was previously detected at both 1.5 GHz and 3 GHz with the Karl G. Jansky Very Large Array (VLA) as a point source. However, there is no clear detection in the VLBA images at both the full resolution of 10.7 milliarcsecond (mas) $\times$ 4.5 mas (61.7 pc $\times$ 26.0 pc) and a tapered resolution of 26 mas $\times$ 21 mas (150 pc $\times$ 121 pc). This suggests that the radio emission from the quasar is diffuse on mas scales with surface brightness fainter than the $3\sigma$ detection limit of 40.5 $\mu \rm Jy \ beam^{-1}$ in the full resolution image. The radio emission in the RIQ J2242+0334 is likely to be wind-like (i.e., diffuse) rather than in the form of collimated jets. This is different from the previous radio detections of the most luminous quasars at $z \sim$6 which are usually dominated by compact, high brightness temperature radio sources. Meanwhile, compared with RIQs at low redshifts, the case of J2242+0334 suggests that not all RIQs are beamed radio-quiet quasars. This optically faint RIQ provides an important and unique example to investigate the radio activity in the less powerful active galactic nuclei at the earliest cosmic epoch.Comment: 6 pages, 1 figure, accepted for publication in ApJ