Learning Profitable NFT Image Diffusions via Multiple Visual-Policy Guided Reinforcement Learning

We study the task of generating profitable Non-Fungible Token (NFT) images from user-input texts. Recent advances in diffusion models have shown great potential for image generation. However, existing works can fall short in generating visually-pleasing and highly-profitable NFT images, mainly due to the lack of 1) plentiful and fine-grained visual attribute prompts for an NFT image, and 2) effective optimization metrics for generating high-quality NFT images. To solve these challenges, we propose a Diffusion-based generation framework with Multiple Visual-Policies as rewards (i.e., Diffusion-MVP) for NFT images. The proposed framework consists of a large language model (LLM), a diffusion-based image generator, and a series of visual rewards by design. First, the LLM enhances a basic human input (such as "panda") by generating more comprehensive NFT-style prompts that include specific visual attributes, such as "panda with Ninja style and green background." Second, the diffusion-based image generator is fine-tuned using a large-scale NFT dataset to capture fine-grained image styles and accessory compositions of popular NFT elements. Third, we further propose to utilize multiple visual-policies as optimization goals, including visual rarity levels, visual aesthetic scores, and CLIP-based text-image relevances. This design ensures that our proposed Diffusion-MVP is capable of minting NFT images with high visual quality and market value. To facilitate this research, we have collected the largest publicly available NFT image dataset to date, consisting of 1.5 million high-quality images with corresponding texts and market values. Extensive experiments including objective evaluations and user studies demonstrate that our framework can generate NFT images showing more visually engaging elements and higher market value, compared with SOTA approaches

MM-Diffusion: Learning Multi-Modal Diffusion Models for Joint Audio and Video Generation

We propose the first joint audio-video generation framework that brings engaging watching and listening experiences simultaneously, towards high-quality realistic videos. To generate joint audio-video pairs, we propose a novel Multi-Modal Diffusion model (i.e., MM-Diffusion), with two-coupled denoising autoencoders. In contrast to existing single-modal diffusion models, MM-Diffusion consists of a sequential multi-modal U-Net for a joint denoising process by design. Two subnets for audio and video learn to gradually generate aligned audio-video pairs from Gaussian noises. To ensure semantic consistency across modalities, we propose a novel random-shift based attention block bridging over the two subnets, which enables efficient cross-modal alignment, and thus reinforces the audio-video fidelity for each other. Extensive experiments show superior results in unconditional audio-video generation, and zero-shot conditional tasks (e.g., video-to-audio). In particular, we achieve the best FVD and FAD on Landscape and AIST++ dancing datasets. Turing tests of 10k votes further demonstrate dominant preferences for our model. The code and pre-trained models can be downloaded at https://github.com/researchmm/MM-Diffusion.Comment: Accepted by CVPR 202

High Performance Acoustic Wave Nitrogen Dioxide Sensor with Ultraviolet Activated 3D Porous Architecture of Ag-Decorated Reduced Graphene Oxide and Polypyrrole Aerogel

Surface acoustic wave (SAW) devices have been widely explored for real-time monitoring of toxic and irritant chemical gases such as nitrogen oxide (NO2), but they often have issues such as a complicated process of the sensing layer, low sensitivity, long response time, irreversibility, and/or requirement of high temperatures to enhance sensitivity. Herein, we report a sensing material design for room-temperature NO2 detection based on a 3D porous architecture of Ag-decorated reduced graphene oxide-polypyrrole hybrid aerogels (rGO-PPy/Ag) and apply UV activation as an effective strategy to further enhance the NO2 sensing performance. The rGO-PPy/Ag-based SAW sensor with the UV activation exhibits high sensitivity (127.68 Hz/ppm), fast response/recovery time (36.7 s/58.5 s), excellent reproducibility and selectivity, and fast recoverability. Its enhancement mechanisms for highly sensitive and selective detection of NO2 are based on a 3D porous architecture, Ag-decorated rGO-PPy, p-p heterojunction in rGO-PPy/Ag, and UV photogenerated carriers generated in the sensing layer. The scientific findings of this work will provide the guidance for future exploration of next-generation acoustic-wave-based gas sensors

Organic Fluorescent Probes for Monitoring Micro-Environments in Living Cells and Tissues

As a vital parameter in living cells and tissues, the micro-environment is crucial for the living organisms. Significantly, organelles require proper micro-environment to achieve normal physiological processes, and the micro-environment in organelles can reflect the state of organelles in living cells. Moreover, some abnormal micro-environments in organelles are closely related to organelle dysfunction and disease development. So, visualizing and monitoring the variation of micro-environments in organelles is helpful for physiologists and pathologists to study the mechanisms of the relative diseases. Recently, a large variety of fluorescent probes was developed to study the micro-environments in living cells and tissues. However, the systematic and comprehensive reviews on the organelle micro-environment in living cells and tissues have rarely been published, which may hinder the research progress in the field of organic fluorescent probes. In this review, we will summarize the organic fluorescent probes for monitoring the microenvironment, such as viscosity, pH values, polarity, and temperature. Further, diverse organelles (mitochondria, lysosome, endoplasmic reticulum, cell membrane) about microenvironments will be displayed. In this process, the fluorescent probes about the “off-on” and ratiometric category (the diverse fluorescence emission) will be discussed. Moreover, the molecular designing, chemical synthesis, fluorescent mechanism, and the bio-applications of these organic fluorescent probes in cells and tissues will also be discussed. Significantly, the merits and defects of current microenvironment-sensitive probes are outlined and discussed, and the development tendency and challenges for this kind of probe are presented. In brief, this review mainly summarizes some typical examples and highlights the progress of organic fluorescent probes for monitoring micro-environments in living cells and tissues in recent research. We anticipate that this review will deepen the understanding of microenvironment in cells and tissues and facilitate the studies and development of physiology and pathology