Pulse-duration dependence of high-order harmonic generation with coherent superposition state

We make a systematic study of high-order harmonic generation (HHG) in a He$^+$-like model ion when the initial states are prepared as a coherent superposition of the ground state and an excited state. It is found that, according to the degree of the ionization of the excited state, the laser intensity can be divided into three regimes in which HHG spectra exhibit different characteristics. The pulse-duration dependence of the HHG spectra in these regimes is studied. We also demonstrate evident advantages of using coherent superposition state to obtain high conversion efficiency. The conversion efficiency can be increased further if ultrashort laser pulses are employed

An Analysis of Dai Brocade Pattern and Application Innovation in Denim Garment

This research is financed by “2019 Graduate research innovation program project of Jiangnan university”（2019 JNKY19-035; “Major Projects of Philosophy and Social Sciences in Jiangsu Universities” (2019STZDA02) Abstract The Dai brocade has a long history, unique craftsmanship, simple and unique style. It has bright colors, strong contrasts, and exquisite patterns, with unique national art and cultural colors. This article expounds the artistic form of the brocade pattern from the subject matter of the pattern, analyzes its artistic aesthetics, cultural connotation, and visual symbols. Attempting to apply the Dai brocade pattern to the design of denim women's clothing. The results show that the combination of traditional Dai brocade patterns and modern denim elements can leading denim clothing a more diversified and international charm Keywords: The Dai ethnic Brocade Pattern; denim; fashion design; art; national culture DOI: 10.7176/ADS/80-06 Publication date: February 29th 202

DYNAMIC CONSTRUCTION CONTROL METHOD FOR A DEEP FOUNDATION PIT WITH SAND-PEBBLE GEOLOGY

Taking the water-rich sand and pebble geology deep foundation pit of Jinfu Station of Chengdu Metro Line 6 as the research object, combined with the ladder excavation method of slotting, utilizing finite difference software FLAC 3D as well as on-site monitoring result, the deformation law of the diaphragm wall during the dynamic excavation of the foundation pit is analysed, and the influence of the relative stiffness between the vertical and horizontal walls of the foundation pit on the lateral deformation of the retaining structure is discussed. The results show that while using the ladder excavation method of slotting, the maximum lateral displacement of the underground diaphragm walls decreases gradually with the excavation depth of the foundation pit, which occurs at the intersection of the middle point of the oblique excavation line and the step distance section of the transverse excavation. Additionally, the lateral displacement increases closer to the excavation section. The lateral displacement of the envelope enclosure mainly depends on the relative constraint stiffness of the vertical and horizontal underground diaphragm wall of the foundation pit. The use of the ladder layered excavation method of slotting can effectively reduce the lateral displacement of the underground diaphragm wall. The simulated result and on-site monitoring result are nearly the same. These results can provide a corresponding theory and engineering basis for the selection of excavation methods for the same type of sand and pebble stratum foundation pit

S3CNet: A Sparse Semantic Scene Completion Network for LiDAR Point Clouds

With the increasing reliance of self-driving and similar robotic systems on robust 3D vision, the processing of LiDAR scans with deep convolutional neural networks has become a trend in academia and industry alike. Prior attempts on the challenging Semantic Scene Completion task - which entails the inference of dense 3D structure and associated semantic labels from "sparse" representations - have been, to a degree, successful in small indoor scenes when provided with dense point clouds or dense depth maps often fused with semantic segmentation maps from RGB images. However, the performance of these systems drop drastically when applied to large outdoor scenes characterized by dynamic and exponentially sparser conditions. Likewise, processing of the entire sparse volume becomes infeasible due to memory limitations and workarounds introduce computational inefficiency as practitioners are forced to divide the overall volume into multiple equal segments and infer on each individually, rendering real-time performance impossible. In this work, we formulate a method that subsumes the sparsity of large-scale environments and present S3CNet, a sparse convolution based neural network that predicts the semantically completed scene from a single, unified LiDAR point cloud. We show that our proposed method outperforms all counterparts on the 3D task, achieving state-of-the art results on the SemanticKITTI benchmark. Furthermore, we propose a 2D variant of S3CNet with a multi-view fusion strategy to complement our 3D network, providing robustness to occlusions and extreme sparsity in distant regions. We conduct experiments for the 2D semantic scene completion task and compare the results of our sparse 2D network against several leading LiDAR segmentation models adapted for bird's eye view segmentation on two open-source datasets.Comment: 14 page

Predicting the Silent Majority on Graphs: Knowledge Transferable Graph Neural Network

Graphs consisting of vocal nodes ("the vocal minority") and silent nodes ("the silent majority"), namely VS-Graph, are ubiquitous in the real world. The vocal nodes tend to have abundant features and labels. In contrast, silent nodes only have incomplete features and rare labels, e.g., the description and political tendency of politicians (vocal) are abundant while not for ordinary people (silent) on the twitter's social network. Predicting the silent majority remains a crucial yet challenging problem. However, most existing message-passing based GNNs assume that all nodes belong to the same domain, without considering the missing features and distribution-shift between domains, leading to poor ability to deal with VS-Graph. To combat the above challenges, we propose Knowledge Transferable Graph Neural Network (KT-GNN), which models distribution shifts during message passing and representation learning by transferring knowledge from vocal nodes to silent nodes. Specifically, we design the domain-adapted "feature completion and message passing mechanism" for node representation learning while preserving domain difference. And a knowledge transferable classifier based on KL-divergence is followed. Comprehensive experiments on real-world scenarios (i.e., company financial risk assessment and political elections) demonstrate the superior performance of our method. Our source code has been open sourced.Comment: Paper was accepted by WWW202

Towards Generalizable Graph Contrastive Learning: An Information Theory Perspective

Graph contrastive learning (GCL) emerges as the most representative approach for graph representation learning, which leverages the principle of maximizing mutual information (InfoMax) to learn node representations applied in downstream tasks. To explore better generalization from GCL to downstream tasks, previous methods heuristically define data augmentation or pretext tasks. However, the generalization ability of GCL and its theoretical principle are still less reported. In this paper, we first propose a metric named GCL-GE for GCL generalization ability. Considering the intractability of the metric due to the agnostic downstream task, we theoretically prove a mutual information upper bound for it from an information-theoretic perspective. Guided by the bound, we design a GCL framework named InfoAdv with enhanced generalization ability, which jointly optimizes the generalization metric and InfoMax to strike the right balance between pretext task fitting and the generalization ability on downstream tasks. We empirically validate our theoretical findings on a number of representative benchmarks, and experimental results demonstrate that our model achieves state-of-the-art performance.Comment: 25 pages, 7 figures, 6 table