DiffFashion: Reference-based Fashion Design with Structure-aware Transfer by Diffusion Models

Image-based fashion design with AI techniques has attracted increasing attention in recent years. We focus on a new fashion design task, where we aim to transfer a reference appearance image onto a clothing image while preserving the structure of the clothing image. It is a challenging task since there are no reference images available for the newly designed output fashion images. Although diffusion-based image translation or neural style transfer (NST) has enabled flexible style transfer, it is often difficult to maintain the original structure of the image realistically during the reverse diffusion, especially when the referenced appearance image greatly differs from the common clothing appearance. To tackle this issue, we present a novel diffusion model-based unsupervised structure-aware transfer method to semantically generate new clothes from a given clothing image and a reference appearance image. In specific, we decouple the foreground clothing with automatically generated semantic masks by conditioned labels. And the mask is further used as guidance in the denoising process to preserve the structure information. Moreover, we use the pre-trained vision Transformer (ViT) for both appearance and structure guidance. Our experimental results show that the proposed method outperforms state-of-the-art baseline models, generating more realistic images in the fashion design task. Code and demo can be found at https://github.com/Rem105-210/DiffFashion

GFlowCausal: Generative Flow Networks for Causal Discovery

Causal discovery aims to uncover causal structure among a set of variables. Score-based approaches mainly focus on searching for the best Directed Acyclic Graph (DAG) based on a predefined score function. However, most of them are not applicable on a large scale due to the limited searchability. Inspired by the active learning in generative flow networks, we propose a novel approach to learning a DAG from observational data called GFlowCausal. It converts the graph search problem to a generation problem, in which direct edges are added gradually. GFlowCausal aims to learn the best policy to generate high-reward DAGs by sequential actions with probabilities proportional to predefined rewards. We propose a plug-and-play module based on transitive closure to ensure efficient sampling. Theoretical analysis shows that this module could guarantee acyclicity properties effectively and the consistency between final states and fully-connected graphs. We conduct extensive experiments on both synthetic and real datasets, and results show the proposed approach to be superior and also performs well in a large-scale setting

A Survey of Deep Learning in Sports Applications: Perception, Comprehension, and Decision

Deep learning has the potential to revolutionize sports performance, with applications ranging from perception and comprehension to decision. This paper presents a comprehensive survey of deep learning in sports performance, focusing on three main aspects: algorithms, datasets and virtual environments, and challenges. Firstly, we discuss the hierarchical structure of deep learning algorithms in sports performance which includes perception, comprehension and decision while comparing their strengths and weaknesses. Secondly, we list widely used existing datasets in sports and highlight their characteristics and limitations. Finally, we summarize current challenges and point out future trends of deep learning in sports. Our survey provides valuable reference material for researchers interested in deep learning in sports applications

Genome-wide analysis for the melatonin trait associated genes and SNPs in dairy goat (Capra hircus) as the molecular breeding markers

Previous studies have reported that the endogenous melatonin level is positively associated with the quality and yield of milk of cows. In the current study, a total of 34,921 SNPs involving 1,177 genes were identified in dairy goats by using the whole genome resequencing bulked segregant analysis (BSA) analysis. These SNPs have been used to match the melatonin levels of the dairy goats. Among them, 3 SNPs has been identified to significantly correlate with melatonin levels. These 3 SNPs include CC genotype 147316, GG genotype 147379 and CC genotype 1389193 which all locate in the exon regions of ASMT and MT2 genes. Dairy goats with these SNPs have approximately 5-fold-higher melatonin levels in milk and serum than the average melatonin level detected in the current goat population. If the melatonin level impacts the milk production in goats as in cows, the results strongly suggest that these 3 SNPs can serve as the molecular markers to select the goats having the improved milk quality and yield. This is a goal of our future study

The Mitochondrial Deoxyguanosine Kinase is Required for Cancer Cell Stemness in Lung Adenocarcinoma

The mitochondrial deoxynucleotide triphosphate (dNTP) is maintained by the mitochondrial deoxynucleoside salvage pathway and dedicated for the mtDNA homeostasis, and the mitochondrial deoxyguanosine kinase (DGUOK) is a rate-limiting enzyme in this pathway. Here, we investigated the role of the DGUOK in the self-renewal of lung cancer stem-like cells (CSC). Our data support that DGUOK overexpression strongly correlates with cancer progression and patient survival. The depletion of DGUOK robustly inhibited lung adenocarcinoma tumor growth, metastasis, and CSC self-renewal. Mechanistically, DGUOK is required for the biogenesis of respiratory complex I and mitochondrial OXPHOS, which in turn regulates CSC self-renewal through AMPK-YAP1 signaling. The restoration of mitochondrial OXPHOS in DGUOK KO lung cancer cells using NDI1 was able to prevent AMPK-mediated phosphorylation of YAP and to rescue CSC stemness. Genetic targeting of DGUOK using doxycycline-inducible CRISPR/Cas9 was able to markedly induce tumor regression. Our findings reveal a novel role for mitochondrial dNTP metabolism in lung cancer tumor growth and progression, and implicate that the mitochondrial deoxynucleotide salvage pathway could be potentially targeted to prevent CSC-mediated therapy resistance and metastatic recurrence

Galangin Protects against Symptoms of Dextran Sodium Sulfate-Induced Acute Colitis by Activating Autophagy and Modulating the Gut Microbiota

Galangin is a natural flavonoid that has been reported to provide substantial health benefits. Nevertheless, little is known about the potential effects of galangin against inflammatory bowel diseases. Here, an in vivo study was performed to investigate the preventive effects of galangin against dextran sulphate sodium (DSS)-induced acute murine colitis, which mimics the symptoms of human ulcerative colitis (UC). Pre-treatment with galangin (15 mg/kg, p.o.) resulted in a significant decreased in the macroscopic signs of DSS-induced colitic symptoms, including a decreased disease activity index, prevention of the colon length shortening, and alleviation of the pathological changes occurring in the colon. Colonic pro-inflammatory mediators, including tumor necrosis factor-alpha, interleukin (IL)-1 beta, and IL-6, as well as myeloperoxidase activities were decreased following galangin pre-treatment when compared with the DSS control group. Moreover, galangin pre-treatment significantly increased the expressions of autophagy-related proteins and promoted the formation of autophagosome in the colon. Galangin pre-treatment increased the diversity of the gut microbiota, and this was accompanied by increased levels of short-chain fatty acids. These observed changes could involve the modulating effects conferred by galangin in relation to some specific bacteria populations, including the recovery of Lactobacillus spp., and increased Butyricimonas spp. Overall, these results support the use of galangin in the prevention of UC

Rock-Breaking Characteristics of High-Pressure, Dual-Stranded Water Jets

Because of the unclear understanding of the characteristics associated with coupled rock breaking using multiple water jets, a numerical model combining smoothed particle hydrodynamics (SPH) and the finite element method (FEM) was established to investigate the rock-breaking capacity of a high-pressure, double-stranded water jet structure. The effectiveness of this model was verified through field experiments. The study further examined the specific energy required for rock breaking using the high-pressure double water jets and analyzed the effects of jet pressure, nozzle diameter, jet impact angle, and impact point spacing on rock-breaking volume. The results demonstrate that the rock-breaking ability of a high-pressure double water jets is better than that of a single water jet. When the impact angle of the high-pressure double water jets was 15° and the distance between impact points was 2.0 d, the rock damage effect was the best. By comparing the specific energies for rock breaking of a single water jet and a double water jet, it was concluded that the best rock-breaking nozzle diameter is 1.6 mm. Furthermore, an orthogonal testing approach was employed to determine the main and secondary factors influencing the rock-breaking energy of the high-pressure double water jet. The order of significance was found to be jet pressure > impact angle > impact point spacing > nozzle diameter. These findings provide valuable guidance and reference for application in the coal mining industry