WorldDreamer: Towards General World Models for Video Generation via Predicting Masked Tokens

World models play a crucial role in understanding and predicting the dynamics of the world, which is essential for video generation. However, existing world models are confined to specific scenarios such as gaming or driving, limiting their ability to capture the complexity of general world dynamic environments. Therefore, we introduce WorldDreamer, a pioneering world model to foster a comprehensive comprehension of general world physics and motions, which significantly enhances the capabilities of video generation. Drawing inspiration from the success of large language models, WorldDreamer frames world modeling as an unsupervised visual sequence modeling challenge. This is achieved by mapping visual inputs to discrete tokens and predicting the masked ones. During this process, we incorporate multi-modal prompts to facilitate interaction within the world model. Our experiments show that WorldDreamer excels in generating videos across different scenarios, including natural scenes and driving environments. WorldDreamer showcases versatility in executing tasks such as text-to-video conversion, image-tovideo synthesis, and video editing. These results underscore WorldDreamer's effectiveness in capturing dynamic elements within diverse general world environments.Comment: project page: https://world-dreamer.github.io

DriveDreamer: Towards Real-world-driven World Models for Autonomous Driving

World models, especially in autonomous driving, are trending and drawing extensive attention due to their capacity for comprehending driving environments. The established world model holds immense potential for the generation of high-quality driving videos, and driving policies for safe maneuvering. However, a critical limitation in relevant research lies in its predominant focus on gaming environments or simulated settings, thereby lacking the representation of real-world driving scenarios. Therefore, we introduce DriveDreamer, a pioneering world model entirely derived from real-world driving scenarios. Regarding that modeling the world in intricate driving scenes entails an overwhelming search space, we propose harnessing the powerful diffusion model to construct a comprehensive representation of the complex environment. Furthermore, we introduce a two-stage training pipeline. In the initial phase, DriveDreamer acquires a deep understanding of structured traffic constraints, while the subsequent stage equips it with the ability to anticipate future states. The proposed DriveDreamer is the first world model established from real-world driving scenarios. We instantiate DriveDreamer on the challenging nuScenes benchmark, and extensive experiments verify that DriveDreamer empowers precise, controllable video generation that faithfully captures the structural constraints of real-world traffic scenarios. Additionally, DriveDreamer enables the generation of realistic and reasonable driving policies, opening avenues for interaction and practical applications.Comment: Project Page: https://drivedreamer.github.i

Development of a Novel Restrictive Medium for Monascus Enrichment From Hongqu Based on the Synergistic Stress of Lactic Acid and Ethanol

Hongqu is a famous fermented food produced by Monascus and has been used as food coloring, wine starters and food additives for thousands of years in China. Excellent Monascus strain is an important prerequisite for producing high-quality Hongqu. However, the isolation of Monascus pure culture from Hongqu samples is time-consuming and laborious because it is easily interfered by other microorganisms (especially filamentous fungi). Therefore, the development of restrictive medium for Monascus enrichment from Hongqu is of great significance for the preparation and screening of excellent Monascus strains. Results of this study showed that Monascus has good tolerance to lactic acid and ethanol. Under the conditions of tolerance limits [7.5% lactic acid (v/v) and 12.0% ethanol (v/v)], Monascus could not grow but it still retained the vitality of spore germination, and the spore activity gradually decreased with the increasing concentrations of lactic acid and ethanol. More interestingly, the addition of lactic acid and ethanol significantly changed the microbial community structure in rice milk inoculated with Hongqu. After response surface optimization, Monascus could be successfully enriched without the interference of other microorganisms when 3.98% (v/v) lactic acid and 6.24% (v/v) ethanol were added to rice milk simultaneously. The optimal enrichment duration of Monascus by the restrictive medium based on the synergistic stress of lactic acid and ethanol is 8∼24 h. The synergistic stress of lactic acid and ethanol had no obvious effects on the accumulation of major metabolites in the progeny of Monascus, and was suitable for the enrichment of Monascus from different types of Hongqu. Finally, the possible mechanisms on the tolerance of Monascus to the synergistic stress of lactic acid and ethanol were preliminarily studied. Under the synergistic stress of lactic acid and ethanol, the cell membrane of Monascus defends against lactic acid and ethanol into cells to some extent, and the superoxide dismutase (SOD), catalase (CAT) and glutathione peroxidase (GSH-Px) activities of Monascus were higher than those of other fungi, which significantly reduced the degree of lipid peroxidation of cell membrane, while secreting more amylase to make reducing sugars to provide the cells with enough energy to resist environmental stress. This work has great application value for the construction of Monascus strain library and the better development of its germplasm resources

Take a Break in the Middle: Investigating Subgoals towards Hierarchical Script Generation

Goal-oriented Script Generation is a new task of generating a list of steps that can fulfill the given goal. In this paper, we propose to extend the task from the perspective of cognitive theory. Instead of a simple flat structure, the steps are typically organized hierarchically - Human often decompose a complex task into subgoals, where each subgoal can be further decomposed into steps. To establish the benchmark, we contribute a new dataset, propose several baseline methods, and set up evaluation metrics. Both automatic and human evaluation verify the high-quality of dataset, as well as the effectiveness of incorporating subgoals into hierarchical script generation. Furthermore, We also design and evaluate the model to discover subgoal, and find that it is a bit more difficult to decompose the goals than summarizing from segmented steps.Comment: Accepted by ACL 2023 Finding

Improved dynamic state estimation of power system using unscented Kalman filter with more accurate prediction model

Power system dynamic state estimation plays an important role. However, rapid changes in states cause state estimation to become very hard. To reduce the residual between pseudo and real measurement, prediction models are adopted, which are strongly associated with the convergence rates and accuracies of estimation methods. In this paper, to improve the estimation accuracy, a prediction model that consists of the convolutional neural network and long short-term memory (CNN-LSTM) is employed and then integrated into the unscented Kalman filter (UKF). In the proposed UKF with CNN-LSTM, state vectors are considered as time-series data, so CNN performs feature extraction for data pre-processing first, and then the features go through LSTM to improve its forecast accuracy in real-time. Next, online training and error normalization are introduced to UKF, which increases the estimation accuracy effectively. Finally, simulations are carried out on the IEEE 33-bus system. Simulation results show that the accuracies of the CNN-LSTM prediction model are much higher than those of conventional methods. Furthermore, compared to widely used state estimation methods, our method decreases RMSE and MAPE by about 2 multiples

Mechanical and environmental properties of geopolymer-stabilized domestic waste incineration slag in an asphalt pavement base

Domestic waste incineration slag (WIS) includes fly ash and slag. Fly ash is classified as hazardous waste because it contains heavy metals. Most of slag are directly stacked or landfilled due to problems such as large output and low utilization rate. Harmless treatment is imminent. If WIS is used effectively in the road engineering, which can realize the high-quality and high-efficiency recycling of WIS, and it is of great significance to save resources and protect the environment. This study applies a geopolymer prepared from WIS fly ash as a stabilizing agent in WIS blending macadam for use as a pavement base mixture, and reports the mechanical properties (unconfined compressive strength, splitting strength, and resilience modulus) of the geopolymer-stabilized WIS blending macadam (GeoWIS). The leaching concentrations of harmful heavy metals of GeoWIS soaked in water were also investigated. Finally, the strength formation and heavy metal stability mechanisms were explored. The unconfined compressive strength, splitting strength, and compressive resilient modulus of GeoWIS all increased with increasing geopolymer content and decreasing WIS content. The strength of GeoWIS was derived from its geopolymerization and hydration products (C-S-H gel, N-A-S-H gel, and AFt). When the geopolymer content reached 12%–14%, the GeoWIS without natural macadam met the strength criterion of the asphalt pavement base. Through physical adsorption and chemical bonding, the geopolymer significantly reduced the leaching of harmful heavy metals. In GeoWIS with 50% WIS and stabilized with 10% geopolymer, the Cr, Ni, Cd, and Pb concentrations met the grade Ⅲ groundwater standard. Concentrations of heavy metals leached from GeoWIS are low and exert little impact on environment

Rapid detection of adulteration of dehydroepiandrosterone in slimming products by competitive indirect enzyme-linked immunosorbent assay and lateral flow immunochromatography

In this study, dehydroepiandrosterone (DHEA) hapten and high sensitivity polyclonal antibody against DHEA were prepared, a competitive indirect enzyme-linked immunosorbent assay (ciELISA) and lateral flow immunochromatography (LFA) for the determination of DHEA in slimming products were developed for the first time. The sample pretreatment was very simple and fast, the limit of detection (LOD) of the ciELISA for DHEA in slimming products was 9.6 μg/kg. The average recoveries of slimming tea, slimming tablets and slimming capsules ranged from 93.2% to 99.4%, 86.8% to 100.0%, and 80.5% to 115.4%, respectively, with the coefficient of variations (CVs) between 7.7% and 14.1%, 2.7% and 11.0%, and 2.2% and 8.6%, respectively. The pAb had negligible cross-reactivity to the structural and functional analogs of DHEA. Simultaneously, the LFA showed a cut-off value of 500 μg/kg in slimming products, and the results could be attained within 3 min. A parallel HPLC analysis in 30 commercial slimming products validated a good agreement with the proposed ciELISA and LFA