Direct Inversion: Boosting Diffusion-based Editing with 3 Lines of Code

Text-guided diffusion models have revolutionized image generation and editing, offering exceptional realism and diversity. Specifically, in the context of diffusion-based editing, where a source image is edited according to a target prompt, the process commences by acquiring a noisy latent vector corresponding to the source image via the diffusion model. This vector is subsequently fed into separate source and target diffusion branches for editing. The accuracy of this inversion process significantly impacts the final editing outcome, influencing both essential content preservation of the source image and edit fidelity according to the target prompt. Prior inversion techniques aimed at finding a unified solution in both the source and target diffusion branches. However, our theoretical and empirical analyses reveal that disentangling these branches leads to a distinct separation of responsibilities for preserving essential content and ensuring edit fidelity. Building on this insight, we introduce "Direct Inversion," a novel technique achieving optimal performance of both branches with just three lines of code. To assess image editing performance, we present PIE-Bench, an editing benchmark with 700 images showcasing diverse scenes and editing types, accompanied by versatile annotations and comprehensive evaluation metrics. Compared to state-of-the-art optimization-based inversion techniques, our solution not only yields superior performance across 8 editing methods but also achieves nearly an order of speed-up

Multi-Patch Prediction: Adapting LLMs for Time Series Representation Learning

In this study, we present aLLM4TS, an innovative framework that adapts Large Language Models (LLMs) for time-series representation learning. Central to our approach is that we reconceive time-series forecasting as a self-supervised, multi-patch prediction task, which, compared to traditional contrastive learning or mask-and-reconstruction methods, captures temporal dynamics in patch representations more effectively. Our strategy encompasses two-stage training: (i). a causal continual pre-training phase on various time-series datasets, anchored on next patch prediction, effectively syncing LLM capabilities with the intricacies of time-series data; (ii). fine-tuning for multi-patch prediction in the targeted time-series context. A distinctive element of our framework is the patch-wise decoding layer, which departs from previous methods reliant on sequence-level decoding. Such a design directly transposes individual patches into temporal sequences, thereby significantly bolstering the model's proficiency in mastering temporal patch-based representations. aLLM4TS demonstrates superior performance in several downstream tasks, proving its effectiveness in deriving temporal representations with enhanced transferability and marking a pivotal advancement in the adaptation of LLMs for time-series analysis

BrushNet: A Plug-and-Play Image Inpainting Model with Decomposed Dual-Branch Diffusion

Image inpainting, the process of restoring corrupted images, has seen significant advancements with the advent of diffusion models (DMs). Despite these advancements, current DM adaptations for inpainting, which involve modifications to the sampling strategy or the development of inpainting-specific DMs, frequently suffer from semantic inconsistencies and reduced image quality. Addressing these challenges, our work introduces a novel paradigm: the division of masked image features and noisy latent into separate branches. This division dramatically diminishes the model's learning load, facilitating a nuanced incorporation of essential masked image information in a hierarchical fashion. Herein, we present BrushNet, a novel plug-and-play dual-branch model engineered to embed pixel-level masked image features into any pre-trained DM, guaranteeing coherent and enhanced image inpainting outcomes. Additionally, we introduce BrushData and BrushBench to facilitate segmentation-based inpainting training and performance assessment. Our extensive experimental analysis demonstrates BrushNet's superior performance over existing models across seven key metrics, including image quality, mask region preservation, and textual coherence

CFGPT: Chinese Financial Assistant with Large Language Model

Large language models (LLMs) have demonstrated great potential in natural language processing tasks within the financial domain. In this work, we present a Chinese Financial Generative Pre-trained Transformer framework, named CFGPT, which includes a dataset~(CFData) for pre-training and supervised fine-tuning, a financial LLM~(CFLLM) to adeptly manage financial texts, and a deployment framework~(CFAPP) designed to navigate real-world financial applications. The CFData comprising both a pre-training dataset and a supervised fine-tuning dataset, where the pre-training dataset collates Chinese financial data and analytics, alongside a smaller subset of general-purpose text with 584M documents and 141B tokens in total, and the supervised fine-tuning dataset is tailored for six distinct financial tasks, embodying various facets of financial analysis and decision-making with 1.5M instruction pairs and 1.5B tokens in total. The CFLLM, which is based on InternLM-7B to balance the model capability and size, is trained on CFData in two stage, continued pre-training and supervised fine-tuning. The CFAPP is centered on large language models (LLMs) and augmented with additional modules to ensure multifaceted functionality in real-world application. Our codes are released at https://github.com/TongjiFinLab/CFGPT.Comment: 12 pages, 5 figure

Mutual promotion between aerosol particle liquid water and particulate nitrate enhancement leads to severe nitrate-dominated particulate matter pollution and low visibility

As has been the case in North America and western Europe, the SO2 emissions have substantially reduced in the North China Plain (NCP) in recent years. Differential rates of reduction in SO2 and NOx concentrations result in the frequent occurrence of particulate matter pollution dominated by nitrate (pNO−3) over the NCP. In this study, we observed a polluted episode with the particulate nitrate mass fraction in nonrefractory PM1 (NR-PM1) being up to 44 % during wintertime in Beijing. Based on this typical pNO−3-dominated haze event, the linkage between aerosol water uptake and pNO−3 enhancement, further impacting on visibility degradation, has been investigated based on field observations and theoretical calculations. During haze development, as ambient relative humidity (RH) increased from ∼10 % to 70 %, the aerosol particle liquid water increased from ∼1 µg m−3 at the beginning to ∼75 µg m−3 in the fully developed haze period. The aerosol liquid water further increased the aerosol surface area and volume, enhancing the condensational loss of N2O5 over particles. From the beginning to the fully developed haze, the condensational loss of N2O5 increased by a factor of 20 when only considering aerosol surface area and volume of dry particles, while increasing by a factor of 25 when considering extra surface area and volume due to water uptake. Furthermore, aerosol liquid water favored the thermodynamic equilibrium of HNO3 in the particle phase under the supersaturated HNO3 and NH3 in the atmosphere. All the above results demonstrated that pNO−3 is enhanced by aerosol water uptake with elevated ambient RH during haze development, in turn facilitating the aerosol take-up of water due to the hygroscopicity of particulate nitrate salt. Such mutual promotion between aerosol particle liquid water and particulate nitrate enhancement can rapidly degrade air quality and halve visibility within 1 d. Reduction of nitrogen-containing gaseous precursors, e.g., by control of traffic emissions, is essential in mitigating severe haze events in the NCP

Robust estimation of bacterial cell count from optical density

Optical density (OD) is widely used to estimate the density of cells in liquid culture, but cannot be compared between instruments without a standardized calibration protocol and is challenging to relate to actual cell count. We address this with an interlaboratory study comparing three simple, low-cost, and highly accessible OD calibration protocols across 244 laboratories, applied to eight strains of constitutive GFP-expressing E. coli. Based on our results, we recommend calibrating OD to estimated cell count using serial dilution of silica microspheres, which produces highly precise calibration (95.5% of residuals <1.2-fold), is easily assessed for quality control, also assesses instrument effective linear range, and can be combined with fluorescence calibration to obtain units of Molecules of Equivalent Fluorescein (MEFL) per cell, allowing direct comparison and data fusion with flow cytometry measurements: in our study, fluorescence per cell measurements showed only a 1.07-fold mean difference between plate reader and flow cytometry data

A residual-based deep learning approach for ghost imaging.

Ghost imaging using deep learning (GIDL) is a kind of computational quantum imaging method devised to improve the imaging efficiency. However, among most proposals of GIDL so far, the same set of random patterns were used in both the training and test set, leading to a decrease of the generalization ability of networks. Thus, the GIDL technique can only reconstruct the profile of the image of the object, losing most of the details. Here we optimize the simulation algorithm of ghost imaging (GI) by introducing the concept of "batch" into the pre-processing stage. It can significantly reduce the data acquisition time and create reliable simulation data. The generalization ability of GIDL has been appreciably enhanced. Furthermore, we develop a residual-based framework for the GI system, namely the double residual U-Net (DRU-Net). The imaging quality of GI has been tripled in the evaluation of the structural similarity index by our proposed DRU-Net

Pore Structure Fractal Characterization and Permeability Simulation of Natural Gas Hydrate Reservoir Based on CT Images

The gas-water two-phase seepage process is complex during the depressurization process of natural gas hydrate in a clayey silt reservoir in the South China Sea, the transport mechanism of which has not been clarified as it is affected by the pore structure. In this study, we select six clayey silt samples formed after the dissociation of natural gas hydrate in the South China Sea, employing CT scanning technology to observe the pore structure of clayey silt porous media directly. The original CT scanning images are further processed to get the binarized images of the samples, which can be used for simulation of the porosity and absolute permeability. Based on the fractal geometry theory, pore structures of the samples are quantitatively characterized from the aspect of pore distribution, heterogeneity, and anisotropy (represented by three main fractal geometric parameters: fractal dimension, lacunarity, and succolarity, respectively). As a comparison, the binarized CT images of two conventional sandstone cores are simulated with the same parameters. The results show that the correlation between porosity and permeability of the hydrate samples is poor, while there is a strong correlation among the succolarity and the permeability. Fractal dimension (represents complexity) of clayey silt samples is higher compared with conventional sandstone cores. Lacunarity explains the difference in permeability among samples from the perspective of pore throat diameter and connectivity. Succolarity indicates the extent to which the fluid in the pore is permeable, which can be used to characterize the anisotropy of pore structures. Therefore, these three fractal parameters clarify the relationship between the microstructure and macroscopic physical properties of clayey silt porous media