FIRST: A Million-Entry Dataset for Text-Driven Fashion Synthesis and Design

Text-driven fashion synthesis and design is an extremely valuable part of artificial intelligence generative content(AIGC), which has the potential to propel a tremendous revolution in the traditional fashion industry. To advance the research on text-driven fashion synthesis and design, we introduce a new dataset comprising a million high-resolution fashion images with rich structured textual(FIRST) descriptions. In the FIRST, there is a wide range of attire categories and each image-paired textual description is organized at multiple hierarchical levels. Experiments on prevalent generative models trained over FISRT show the necessity of FIRST. We invite the community to further develop more intelligent fashion synthesis and design systems that make fashion design more creative and imaginative based on our dataset. The dataset will be released soon.Comment: 11 pages, 8 figure

Selective Cleavage of the Aryl Ether Bonds in Lignin for Depolymerization by Acidic Lithium Bromide Molten Salt Hydrate under Mild Conditions

The present study demonstrates that the concentrated lithium bromide (LiBr) solution with acid as catalyst was able to selectively cleave the β-<i>O</i>-4 aryl ether bond and lead to lignin depolymerization under mild conditions (e.g., in 60% LiBr with 0.3 M HCl at 110 °C for 2 h). Four industrial lignins from different pulping and biorefining processes, including softwood kraft lignin (SKL), hardwood kraft lignin (HKL), softwood ethanol organosolv lignin (EOL), and acid corncob lignin (ACL), were treated in the LiBr solution. The molecular weight, functional group, and interunit linkages of the lignins were characterized using GPC, FTIR, and NMR. The results indicated that the β-<i>O</i>-4 aryl ether bonds of the lignins were selectively cleaved, and both LiBr and HCl played crucial roles in catalyzing the cleavage of the ether bonds

Making JP-10 Superfuel Affordable with a Lignocellulosic Platform Compound

The synthesis of renewable jet fuel from lignocellulosic platform compounds has drawn a lot of attention in recent years. So far, most work has concentrated on the production of conventional jet fuels. JP-10 is an advanced jet fuel currently obtained from fossil energy. Due to its excellent properties, JP-10 has been widely used in military aircraft. However, the high price and low availability limit its application in civil aviation. Here, we report a new strategy for the synthesis of bio-JP-10 fuel from furfuryl alcohol that is produced on an industrial scale from agricultural and forestry residues. Under the optimized conditions, bio-JP-10 fuel was produced with high overall carbon yields (approximate to 65 %). A preliminary economic analysis indicates that the price of bio-JP-10 fuel can be greatly decreased from approximate to 7091 US$/ton (by fossil route) to less than 5600 US$/ton using our new strategy. This work makes the practical application of bio-JP-10 fuel forseeable

An Attention Mechanism-Improved YOLOv7 Object Detection Algorithm for Hemp Duck Count Estimation

Stocking density presents a key factor affecting livestock and poultry production on a large scale as well as animal welfare. However, the current manual counting method used in the hemp duck breeding industry is inefficient, costly in labor, less accurate, and prone to double counting and omission. In this regard, this paper uses deep learning algorithms to achieve real-time monitoring of the number of dense hemp duck flocks and to promote the development of the intelligent farming industry. We constructed a new large-scale hemp duck object detection image dataset, which contains 1500 hemp duck object detection full-body frame labeling and head-only frame labeling. In addition, this paper proposes an improved attention mechanism YOLOv7 algorithm, CBAM-YOLOv7, adding three CBAM modules to the backbone network of YOLOv7 to improve the network&rsquo;s ability to extract features and introducing SE-YOLOv7 and ECA-YOLOv7 for comparison experiments. The experimental results show that CBAM-YOLOv7 had higher precision, and the recall, [email protected], and [email protected]:0.95 were slightly improved. The evaluation index value of CBAM-YOLOv7 improved more than those of SE-YOLOv7 and ECA-YOLOv7. In addition, we also conducted a comparison test between the two labeling methods and found that the head-only labeling method led to the loss of a high volume of feature information, and the full-body frame labeling method demonstrated a better detection effect. The results of the algorithm performance evaluation show that the intelligent hemp duck counting method proposed in this paper is feasible and can promote the development of smart reliable automated duck counting