Improving Local Search for Structured SAT Formulas via Unit Propagation Based Construct and Cut Initialization (Short Paper)

This work is dedicated to improving local search solvers for the Boolean satisfiability (SAT) problem on structured instances. We propose a construct-and-cut (CnC) algorithm based on unit propagation, which is used to produce initial assignments for local search. We integrate our CnC initialization procedure within several state-of-the-art local search SAT solvers, and obtain the improved solvers. Experiments are carried out with a benchmark encoded from a spectrum repacking project as well as benchmarks encoded from two important mathematical problems namely Boolean Pythagorean Triple and Schur Number Five. The experiments show that the CnC initialization improves the local search solvers, leading to better performance than state-of-the-art SAT solvers based on Conflict Driven Clause Learning (CDCL) solvers

Time Series Prediction with Recurrent Neural Networks using a Hybrid PSO-EA Algorithm

To predict the 100 missing values from the time series consisting of 5000 data given for the IJCNN 2004 time series prediction competition, we applied an architecture which automates the design of recurrent neural networks using a new evolutionary learning algorithm. This new evolutionary learning algorithm is based on a hybrid of particle swarm optimization (PSO) and evolutionary algorithm (EA). By combining the searching abilities of these two global optimization methods, the evolution of individuals is no longer restricted to be in the same generation, and better performed individuals may produce offspring to replace those with poor performance. The novel algorithm is then applied to the recurrent neural network for the time series prediction. The experimental results show that our approach gives good performance in predicting the missing values from the time series

RenderIH: A Large-scale Synthetic Dataset for 3D Interacting Hand Pose Estimation

The current interacting hand (IH) datasets are relatively simplistic in terms of background and texture, with hand joints being annotated by a machine annotator, which may result in inaccuracies, and the diversity of pose distribution is limited. However, the variability of background, pose distribution, and texture can greatly influence the generalization ability. Therefore, we present a large-scale synthetic dataset RenderIH for interacting hands with accurate and diverse pose annotations. The dataset contains 1M photo-realistic images with varied backgrounds, perspectives, and hand textures. To generate natural and diverse interacting poses, we propose a new pose optimization algorithm. Additionally, for better pose estimation accuracy, we introduce a transformer-based pose estimation network, TransHand, to leverage the correlation between interacting hands and verify the effectiveness of RenderIH in improving results. Our dataset is model-agnostic and can improve more accuracy of any hand pose estimation method in comparison to other real or synthetic datasets. Experiments have shown that pretraining on our synthetic data can significantly decrease the error from 6.76mm to 5.79mm, and our Transhand surpasses contemporary methods. Our dataset and code are available at https://github.com/adwardlee/RenderIH.Comment: Accepted by ICCV 202

Cloth2Tex: A Customized Cloth Texture Generation Pipeline for 3D Virtual Try-On

Fabricating and designing 3D garments has become extremely demanding with the increasing need for synthesizing realistic dressed persons for a variety of applications, e.g. 3D virtual try-on, digitalization of 2D clothes into 3D apparel, and cloth animation. It thus necessitates a simple and straightforward pipeline to obtain high-quality texture from simple input, such as 2D reference images. Since traditional warping-based texture generation methods require a significant number of control points to be manually selected for each type of garment, which can be a time-consuming and tedious process. We propose a novel method, called Cloth2Tex, which eliminates the human burden in this process. Cloth2Tex is a self-supervised method that generates texture maps with reasonable layout and structural consistency. Another key feature of Cloth2Tex is that it can be used to support high-fidelity texture inpainting. This is done by combining Cloth2Tex with a prevailing latent diffusion model. We evaluate our approach both qualitatively and quantitatively and demonstrate that Cloth2Tex can generate high-quality texture maps and achieve the best visual effects in comparison to other methods. Project page: tomguluson92.github.io/projects/cloth2tex/Comment: 15 pages, 15 figure

Effect of Metformin on Lactate Metabolism in Normal Hepatocytes under High Glucose Stress in Vitro

Objective: To study the effect of metformin on lactate metabolism in hepatocytes in vitro under high glucose stress. Method: LO2 hepatocytes was cultured in vitro, hepatocytes were randomly divided into blank control group, 25 mmol/L glucose solution, 27 mmol/L glucose solution, 29 mmol/L glucose solution, 31 mmol/L glucose solution, 33 mmol/L glucose solution, 35 mmol/L glucose solution treatment group, after determining the optimal concentration as 31 mmol/L, use 30 mmol/L metformin solution, and then divided into blank control group, normal hepatocytes + the optimal concentration of glucose solution, normal hepatocytes + metformin solution , normal hepatocytes+. The optimal concentration of glucose solution normal hepatocytes + metformin solution, calculate the number of hepatocytes on cell count plate respectively in the 12 h, 24 h, 48 h, and use the lactic acid kit to determine the lactic acid value of the cell culture medium of normal liver cells + optimal concentration glucose solution and normal liver cells + optimal concentration glucose solution + metformin solution at 12 h, 24 h, and 48 h, respectively. Results: There was no significant change in the lactic acid concentration but significant increase in the number of surviving hepatocytes in the high-glycemic control group compared with that in the high-glycemic control group without metformin. Conclusions: Metformin has no significant effect on lactic acid metabolism of hepatocytes under high glucose stress in vitro, and has a protective effect on hepatocytes under high glucose stress. Based on this, it is preliminarily believed that metformin is not the direct factor leading to diabetic lactic acidosis

Source attribution of poly- and perfluoroalkyl substances (PFASs) in surface waters from Rhode Island and the New York Metropolitan Area

Exposure to poly- and perfluoroalkyl substances (PFASs) has been associated with adverse health effects in humans and wildlife. Understanding pollution sources is essential for environmental regulation, but source attribution for PFASs has been confounded by limited information about industrial releases and rapid changes in chemical production. Here we use principal component analysis (PCA), hierarchical clustering, and geospatial analysis to understand source contributions to 14 PFASs measured across 37 sites in the northeastern United States in 2014. PFASs are significantly elevated in urban areas compared to rural sites except for perfluorobutanesulfonate, N-methyl perfluorooctanesulfonamidoacetic acid, perfluoroundecanate, and perfluorododecanate. The highest PFAS concentrations across sites were those of perfluorooctanate (PFOA, 56 ng L−1) and perfluorohexanesulfonate (PFHxS, 43 ng L−1), and perfluorooctanesulfonate (PFOS) levels are lower than earlier measurements of U.S. surface waters. PCA and cluster analysis indicate three main statistical groupings of PFASs. Geospatial analysis of watersheds reveals the first component/cluster originates from a mixture of contemporary point sources such as airports and textile mills. Atmospheric sources from the waste sector are consistent with the second component, and the metal smelting industry plausibly explains the third component. We find this source-attribution technique is effective for better understanding PFAS sources in urban areas