BLT: Bidirectional Layout Transformer for Controllable Layout Generation

Creating visual layouts is a critical step in graphic design. Automatic generation of such layouts is essential for scalable and diverse visual designs. To advance conditional layout generation, we introduce BLT, a bidirectional layout transformer. BLT differs from previous work on transformers in adopting non-autoregressive transformers. In training, BLT learns to predict the masked attributes by attending to surrounding attributes in two directions. During inference, BLT first generates a draft layout from the input and then iteratively refines it into a high-quality layout by masking out low-confident attributes. The masks generated in both training and inference are controlled by a new hierarchical sampling policy. We verify the proposed model on six benchmarks of diverse design tasks. Experimental results demonstrate two benefits compared to the state-of-the-art layout transformer models. First, our model empowers layout transformers to fulfill controllable layout generation. Second, it achieves up to 10x speedup in generating a layout at inference time than the layout transformer baseline. Code is released at https://shawnkx.github.io/blt.Comment: ECCV 202

Learning Disentangled Prompts for Compositional Image Synthesis

We study domain-adaptive image synthesis, the problem of teaching pretrained image generative models a new style or concept from as few as one image to synthesize novel images, to better understand the compositional image synthesis. We present a framework that leverages a pretrained class-conditional generation model and visual prompt tuning. Specifically, we propose a novel source class distilled visual prompt that learns disentangled prompts of semantic (e.g., class) and domain (e.g., style) from a few images. Learned domain prompt is then used to synthesize images of any classes in the style of target domain. We conduct studies on various target domains with the number of images ranging from one to a few to many, and show qualitative results which show the compositional generalization of our method. Moreover, we show that our method can help improve zero-shot domain adaptation classification accuracy.Comment: tech repor

MAGVIT: Masked Generative Video Transformer

We introduce the MAsked Generative VIdeo Transformer, MAGVIT, to tackle various video synthesis tasks with a single model. We introduce a 3D tokenizer to quantize a video into spatial-temporal visual tokens and propose an embedding method for masked video token modeling to facilitate multi-task learning. We conduct extensive experiments to demonstrate the quality, efficiency, and flexibility of MAGVIT. Our experiments show that (i) MAGVIT performs favorably against state-of-the-art approaches and establishes the best-published FVD on three video generation benchmarks, including the challenging Kinetics-600. (ii) MAGVIT outperforms existing methods in inference time by two orders of magnitude against diffusion models and by 60x against autoregressive models. (iii) A single MAGVIT model supports ten diverse generation tasks and generalizes across videos from different visual domains. The source code and trained models will be released to the public at https://magvit.cs.cmu.edu

Association between IL-6 polymorphisms and Atopic Dermatitis in Chinese Han children

IntroductionAtopic Dermatitis (AD) is a chronic inflammatory skin disease that affects almost 20% of children and 2 -10% of adults worldwide. Previous studies revealed that Interleukin-6 (IL-6) plays an essential role in autoimmune and chronic inflammatory diseases. This study aims to investigate the associations between IL-6 polymorphisms and AD.MethodsBlood samples were collected from 132 AD patients and 100 controls, and single nucleotide polymorphisms (SNPs) in IL-6 (rs2069840 (C/G), rs2066992 (G/T), rs2069837 (A/G) and rs1800796 (G/C)) were analyzed using Multiplex PCR-Based Next Generation Sequencing (NGS).ResultsResults showed that the A/G genotype of IL-6/rs2069837 was significantly associated with a 1.933-fold increased risk of AD compared to those patients with A/A genotype (OR 1.933; 95%CI 1.086-3.438; p=0.024). The combined A/G-G/G genotype raised AD risk by 1.856 times compared to patients with the A/A genotype in dominant model (OR: 1.856; 95% CI: 1.056-3.261; p=0.030). No association was observed for 3 other SNPs and 4 haplotypes.DiscussionThese findings suggested that the A/G genotype of IL-6/rs2069837 was more susceptible to AD than A/A genotype in Chinese Han children, indicating the risk role of IL-6/rs2069837 in the occurrence of AD

Interacting multi-channel topological boundary modes in a quantum Hall valley system

Symmetry and topology play key roles in the identification of phases of matter and their properties. Both concepts are central to understanding quantum Hall ferromagnets (QHFMs), two-dimensional electronic phases with spontaneously broken spin or pseudospin symmetry whose wavefunctions also have topological properties. Domain walls between distinct broken symmetry QHFM phases are predicted to host gapless one-dimensional (1D) modes that emerge due to a topological change of the underlying electronic wavefunctions at such interfaces. Although a variety of QHFMs have been identified in different materials, probing interacting electronic modes at these domain walls has not yet been accomplished. Here we use a scanning tunneling microscope (STM) to directly visualize the spontaneous formation of boundary modes, within a sign-changing topological gap, at domain walls between different valley-polarized quantum Hall phases on the surface of bismuth. By changing the valley occupation and the corresponding number of modes at the domain wall, we can realize different regimes where the valley-polarized channels are either metallic or develop a spectroscopic gap. This behavior is a consequence of Coulomb interactions constrained by the symmetry-breaking valley flavor, which determines whether electrons in the topological modes can backscatter, making these channels a unique class of interacting Luttinger liquids