Enantioselective Total Synthesis of (-)-Lansai B and (+)-Nocardioazines A and B

The concise total syntheses of the bis(pyrroloindolines) (−)-lansai B and (+)- nocardioazines A and B are reported. The key pyrroloindoline building blocks are rapidly prepared by enantioselective formal [3+2] cycloaddition reactions. The macrocycle of (+)-nocardioazine A is constructed by an unusual intramolecular diketopiperazine formation

Direct, enantioselective synthesis of pyrroloindolines and indolines from simple indole derivatives

The (R)-BINOL·SnCl_4-catalyzed formal (3+2) cycloaddition between 3-substituted indoles and benzyl 2-trifluoroacetamidoacrylate is a direct, enantioselective method to prepare pyrroloindolines from simple starting materials. However, under the originally disclosed conditions, the pyrroloindolines are formed as mixtures of diastereomers, typically in the range of 3:1 to 5:1 favoring the exo-product. The poor diastereoselectivity detracts from the synthetic utility of the reaction. We report here that use of methyl 2-trifluoroacetamidoacrylate in conjunction with (R)-3,3′-dichloro-BINOL·SnCl_4 provides the corresponding pyrroloindolines with improved diastereoselectivity (typically ≥10:1). Guided by mechanistic studies, a one-flask synthesis of enantioenriched indolines by in situ reduction of a persistent iminium ion is also described

MQENet: A Mesh Quality Evaluation Neural Network Based on Dynamic Graph Attention

With the development of computational fluid dynamics, the requirements for the fluid simulation accuracy in industrial applications have also increased. The quality of the generated mesh directly affects the simulation accuracy. However, previous mesh quality metrics and models cannot evaluate meshes comprehensively and objectively. To this end, we propose MQENet, a structured mesh quality evaluation neural network based on dynamic graph attention. MQENet treats the mesh evaluation task as a graph classification task for classifying the quality of the input structured mesh. To make graphs generated from structured meshes more informative, MQENet introduces two novel structured mesh preprocessing algorithms. These two algorithms can also improve the conversion efficiency of structured mesh data. Experimental results on the benchmark structured mesh dataset NACA-Market show the effectiveness of MQENet in the mesh quality evaluation task

SamLP: A Customized Segment Anything Model for License Plate Detection

With the emergence of foundation model, this novel paradigm of deep learning has encouraged many powerful achievements in natural language processing and computer vision. There are many advantages of foundation model, such as excellent feature extraction power, mighty generalization ability, great few-shot and zero-shot learning capacity, etc. which are beneficial to vision tasks. As the unique identity of vehicle, different countries and regions have diverse license plate (LP) styles and appearances, and even different types of vehicles have different LPs. However, recent deep learning based license plate detectors are mainly trained on specific datasets, and these limited datasets constrain the effectiveness and robustness of LP detectors. To alleviate the negative impact of limited data, an attempt to exploit the advantages of foundation model is implement in this paper. We customize a vision foundation model, i.e. Segment Anything Model (SAM), for LP detection task and propose the first LP detector based on vision foundation model, named SamLP. Specifically, we design a Low-Rank Adaptation (LoRA) fine-tuning strategy to inject extra parameters into SAM and transfer SAM into LP detection task. And then, we further propose a promptable fine-tuning step to provide SamLP with prompatable segmentation capacity. The experiments show that our proposed SamLP achieves promising detection performance compared to other LP detectors. Meanwhile, the proposed SamLP has great few-shot and zero-shot learning ability, which shows the potential of transferring vision foundation model. The code is available at https://github.com/Dinghaoxuan/SamL

Distributionally Robust Learning for Unsupervised Domain Adaptation

We propose a distributionally robust learning (DRL) method for unsupervised domain adaptation (UDA) that scales to modern computer vision benchmarks. DRL can be naturally formulated as a competitive two-player game between a predictor and an adversary that is allowed to corrupt the labels, subject to certain constraints, and reduces to incorporating a density ratio between the source and target domains (under the standard log loss). This formulation motivates the use of two neural networks that are jointly trained - a discriminative network between the source and target domains for density-ratio estimation, in addition to the standard classification network. The use of a density ratio in DRL prevents the model from being overconfident on target inputs far away from the source domain. Thus, DRL provides conservative confidence estimation in the target domain, even when the target labels are not available. This conservatism motivates the use of DRL in self-training for sample selection, and we term the approach distributionally robust self-training (DRST). In our experiments, DRST generates more calibrated probabilities and achieves state-of-the-art self-training accuracy on benchmark datasets. We demonstrate that DRST captures shape features more effectively, and reduces the extent of distributional shift during self-training

Commercial economic value of non-metallic fiber concrete

Non-metallic fiber concrete is a new type of high-performance concrete with excellent crack resistance, toughness and durability. And mostly plant fibers have strong environmental value and sustainable development performance, so it is widely used in construction, bridges, roads and other fields, and has a high commercial economic value. In this paper, the economic value of non-metallic fiber concrete is evaluated by a comprehensive analysis of its cost-effectiveness and commercial prospects. The results show that non-metallic fiber concrete has low maintenance cost and long service life, which can bring significant economic benefits to the project. Meanwhile, with the continuous development of technology and market expansion, the commercial prospect of non-metallic fiber concrete is broader