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

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

Learning Gradient Fields for Scalable and Generalizable Irregular Packing

The packing problem, also known as cutting or nesting, has diverse applications in logistics, manufacturing, layout design, and atlas generation. It involves arranging irregularly shaped pieces to minimize waste while avoiding overlap. Recent advances in machine learning, particularly reinforcement learning, have shown promise in addressing the packing problem. In this work, we delve deeper into a novel machine learning-based approach that formulates the packing problem as conditional generative modeling. To tackle the challenges of irregular packing, including object validity constraints and collision avoidance, our method employs the score-based diffusion model to learn a series of gradient fields. These gradient fields encode the correlations between constraint satisfaction and the spatial relationships of polygons, learned from teacher examples. During the testing phase, packing solutions are generated using a coarse-to-fine refinement mechanism guided by the learned gradient fields. To enhance packing feasibility and optimality, we introduce two key architectural designs: multi-scale feature extraction and coarse-to-fine relation extraction. We conduct experiments on two typical industrial packing domains, considering translations only. Empirically, our approach demonstrates spatial utilization rates comparable to, or even surpassing, those achieved by the teacher algorithm responsible for training data generation. Additionally, it exhibits some level of generalization to shape variations. We are hopeful that this method could pave the way for new possibilities in solving the packing problem