A 2D based Partition Strategy for Solving Ranking under Team Context (RTP)

In this paper, we propose a 2D based partition method for solving the problem of Ranking under Team Context(RTC) on datasets without a priori. We first map the data into 2D space using its minimum and maximum value among all dimensions. Then we construct window queries with consideration of current team context. Besides, during the query mapping procedure, we can pre-prune some tuples which are not top ranked ones. This pre-classified step will defer processing those tuples and can save cost while providing solutions for the problem. Experiments show that our algorithm performs well especially on large datasets with correctness

A REACTIVE INDOOR AIR PURIFICATION DEVICE AND ITS ESCHERICHIA COLI INACTIVATION EFFECT

E. coli bioaerosol was disinfected using an indoor air purification device that is composed of an annular photocatalytic reactor with a baffle system inside and a gas buffer tank. Activated carbon supported photocatalyst 10%Ti02-10%Si02/AC prepared by dip coating method, using 10 wt% colloidal silica and 10 %(w/v) TiCb, has the highest photocatalytic oxidation activity and was used for the E. coli bioaerosol disinfection tests in terms of air flow rate, UV intensity, TiC\u3e2 loading, and photocatalyst preparation methods. The results showed that it took longer time to completely disinfect E. coli if the flow rate is higher. Higher UV intensity significantly reduced the E. coli disinfection time. E. coli disinfection rate increased when the TiC\u3e2 loading going up from 1 to 10 %(w/v). E. coli disinfection efficiency can also be improved by adding colloidal silica into the impregnation suspension when the activated carbon supported photocatalysts were prepared

BLIP-Diffusion: Pre-trained Subject Representation for Controllable Text-to-Image Generation and Editing

Subject-driven text-to-image generation models create novel renditions of an input subject based on text prompts. Existing models suffer from lengthy fine-tuning and difficulties preserving the subject fidelity. To overcome these limitations, we introduce BLIP-Diffusion, a new subject-driven image generation model that supports multimodal control which consumes inputs of subject images and text prompts. Unlike other subject-driven generation models, BLIP-Diffusion introduces a new multimodal encoder which is pre-trained to provide subject representation. We first pre-train the multimodal encoder following BLIP-2 to produce visual representation aligned with the text. Then we design a subject representation learning task which enables a diffusion model to leverage such visual representation and generates new subject renditions. Compared with previous methods such as DreamBooth, our model enables zero-shot subject-driven generation, and efficient fine-tuning for customized subject with up to 20x speedup. We also demonstrate that BLIP-Diffusion can be flexibly combined with existing techniques such as ControlNet and prompt-to-prompt to enable novel subject-driven generation and editing applications. Code and models will be released at https://github.com/salesforce/LAVIS/tree/main/projects/blip-diffusion. Project page at https://dxli94.github.io/BLIP-Diffusion-website/