BeautifulPrompt: Towards Automatic Prompt Engineering for Text-to-Image Synthesis

Recently, diffusion-based deep generative models (e.g., Stable Diffusion) have shown impressive results in text-to-image synthesis. However, current text-to-image models often require multiple passes of prompt engineering by humans in order to produce satisfactory results for real-world applications. We propose BeautifulPrompt, a deep generative model to produce high-quality prompts from very simple raw descriptions, which enables diffusion-based models to generate more beautiful images. In our work, we first fine-tuned the BeautifulPrompt model over low-quality and high-quality collecting prompt pairs. Then, to ensure that our generated prompts can generate more beautiful images, we further propose a Reinforcement Learning with Visual AI Feedback technique to fine-tune our model to maximize the reward values of the generated prompts, where the reward values are calculated based on the PickScore and the Aesthetic Scores. Our results demonstrate that learning from visual AI feedback promises the potential to improve the quality of generated prompts and images significantly. We further showcase the integration of BeautifulPrompt to a cloud-native AI platform to provide better text-to-image generation service in the cloud.Comment: emnlp 202

Opposition-Based Improved PSO for Optimal Reactive Power Dispatch and Voltage Control

An opposition-based improved particle swarm optimization algorithm (OIPSO) is presented for solving multiobjective reactive power optimization problem. OIPSO uses the opposition learning to improve search efficiency, adopts inertia weight factors to balance global and local exploration, and takes crossover and mutation and neighborhood model strategy to enhance population diversity. Then, a new multiobjective model is built, which includes system network loss, voltage dissatisfaction, and switching operation. Based on the market cost prices, objective functions are converted to least-cost model. In modeling process, switching operation cost is described according to the life cycle cost of transformer, and voltage dissatisfaction penalty is developed considering different voltage quality requirements of customers. The experiment is done on the new mathematical model. Through the simulation of IEEE 30-, 118-bus power systems, the results prove that OIPSO is more efficient to solve reactive power optimization problems and the model is more accurate to reflect the real power system operation

Structure Optimal Design of Electromagnetic Levitation Load Reduction Device for Hydroturbine Generator Set

Thrust bearing is one part with the highest failure rate in hydroturbine generator set, which is primarily due to heavy axial load. Such heavy load often makes oil film destruction, bearing friction, and even burning. It is necessary to study the load and the reduction method. The dynamic thrust is an important factor to influence the axial load and reduction design of electromagnetic device. Therefore, in the paper, combined with the structure features of vertical turbine, the hydraulic thrust is analyzed accurately. Then, take the turbine model HL-220-LT-550, for instance; the electromagnetic levitation load reduction device is designed, and its mathematical model is built, whose purpose is to minimize excitation loss and total quality under the constraints of installation space, connection layout, and heat dissipation. Particle swarm optimization (PSO) is employed to search for the optimum solution; finally, the result is verified by finite element method (FEM), which demonstrates that the optimized structure is more effective

Structure Optimal Design of Electromagnetic Levitation Load Reduction Device for Hydroturbine Generator Set

Thrust bearing is one part with the highest failure rate in hydroturbine generator set, which is primarily due to heavy axial load. Such heavy load often makes oil film destruction, bearing friction, and even burning. It is necessary to study the load and the reduction method. The dynamic thrust is an important factor to influence the axial load and reduction design of electromagnetic device. Therefore, in the paper, combined with the structure features of vertical turbine, the hydraulic thrust is analyzed accurately. Then, take the turbine model HL-220-LT-550, for instance; the electromagnetic levitation load reduction device is designed, and its mathematical model is built, whose purpose is to minimize excitation loss and total quality under the constraints of installation space, connection layout, and heat dissipation. Particle swarm optimization (PSO) is employed to search for the optimum solution; finally, the result is verified by finite element method (FEM), which demonstrates that the optimized structure is more effective

A Method for Classifying Residential Prices in Apartment Complex Using Computer Simulation Analysis: A Case Study

The price fluctuation of the real estate market has become an important factor affecting the stability of the national macro-economy. In the history of the worldwide financial crisis, there have been many times related to the real estate market. China's real estate industry occupies a pivotal position in the national economy, and entered a rapid development period in 2000, gradually becoming a pillar industry of the national economy. It ushered in the process of rapid expansion, with investment scale, construction scale, transaction scale and transaction price rising. From the perspective of noise and landscape, this paper discusses the possibility of applying landscape analysis to the price and pricing of apartment complexes. The noise, landscape and sunlight are analyzed based on the landscape analysis (including the point and line through the view analysis, and the line of sight analysis) to provide a basis for the unit price classification of the area

Use of magnoflorine-phospholipid complex to permeate blood-brain barrier and treat depression in the CUMS animal model

To improve the liposolubility and blood-brain barrier permeability of magnoflorine, a new formulation of magnoflorine-phospholipid complex was prepared, characterized, and pharmacologically evaluated in the chronic unpredictable mild stress animal model. In this paper, the magnoflorine-phospholipid complex was synthesized and its characterization was determined. The antidepressant-like and antioxidant activity of magnoflorine-phospholipid complex was investigated by behavioral tests and western blotting analysis. As a result, the magnoflorine-phospholipid complex displayed high encapsulation efficiency and significantly improved the oil-water participate coefficient. In vivo blood-brain distribution study, the magnoflorine-phospholipid complex extended the duration of magnoflorine in blood and help magnoflorine to permeate the blood-brain barrier into brain. In behavioral tests, the magnoflorine-phospholipid complex significantly decreased immobility time compared to model control group in both FST and TST. Furthermore, the magnoflorine-phospholipid complex increased the expression of antioxidative stress-related proteins by the western blotting analysis. These findings strongly suggest that the phospholipid complex could significantly improve liposolubility, drug properties of magnoflorine and help magnoflorine permeate blood-brain barrier and exert the antidepressant effect