an investigation of chinese only children’s integration experience in foreign environment

With the rising presence and importance of Chinese people in international institutions, the paper set out to investigate “Chinese struggle to mingle abroad” phenomenon. Considering Chinese “onechild- policy”, we seek to understand if being only children affects integration experience, and how Chinese only-borns interpret three influential factors: investment, brotherhood, and motivations. Results show that Chinese integration difficulties were not explained by single children, but other factors that prevent best integration were spotted. Additionally, a three-dimensional integration model was proposed specifically for Chinese population abroad, and suggestions were provided for foreign institutions in order to foster a smoother acculturation and integration

Rethinking Spatiotemporal Feature Learning: Speed-Accuracy Trade-offs in Video Classification

Despite the steady progress in video analysis led by the adoption of convolutional neural networks (CNNs), the relative improvement has been less drastic as that in 2D static image classification. Three main challenges exist including spatial (image) feature representation, temporal information representation, and model/computation complexity. It was recently shown by Carreira and Zisserman that 3D CNNs, inflated from 2D networks and pretrained on ImageNet, could be a promising way for spatial and temporal representation learning. However, as for model/computation complexity, 3D CNNs are much more expensive than 2D CNNs and prone to overfit. We seek a balance between speed and accuracy by building an effective and efficient video classification system through systematic exploration of critical network design choices. In particular, we show that it is possible to replace many of the 3D convolutions by low-cost 2D convolutions. Rather surprisingly, best result (in both speed and accuracy) is achieved when replacing the 3D convolutions at the bottom of the network, suggesting that temporal representation learning on high-level semantic features is more useful. Our conclusion generalizes to datasets with very different properties. When combined with several other cost-effective designs including separable spatial/temporal convolution and feature gating, our system results in an effective video classification system that that produces very competitive results on several action classification benchmarks (Kinetics, Something-something, UCF101 and HMDB), as well as two action detection (localization) benchmarks (JHMDB and UCF101-24).Comment: ECCV 2018 camera read

Little emperors abroad: an investigation of chinese only children’s integration experience in foreign environment

With the rising presence and importance of Chinese people in international institutions, the paper set out to investigate “Chinese struggle to mingle abroad” phenomenon. Considering Chinese “onechild- policy”, we seek to understand if being only children affects integration experience, and how Chinese only-borns interpret three influential factors: investment, brotherhood, and motivations. Results show that Chinese integration difficulties were not explained by single children, but other factors that prevent best integration were spotted. Additionally, a three-dimensional integration model was proposed specifically for Chinese population abroad, and suggestions were provided for foreign institutions in order to foster a smoother acculturation and integration

Single-Stage Diffusion NeRF: A Unified Approach to 3D Generation and Reconstruction

3D-aware image synthesis encompasses a variety of tasks, such as scene generation and novel view synthesis from images. Despite numerous task-specific methods, developing a comprehensive model remains challenging. In this paper, we present SSDNeRF, a unified approach that employs an expressive diffusion model to learn a generalizable prior of neural radiance fields (NeRF) from multi-view images of diverse objects. Previous studies have used two-stage approaches that rely on pretrained NeRFs as real data to train diffusion models. In contrast, we propose a new single-stage training paradigm with an end-to-end objective that jointly optimizes a NeRF auto-decoder and a latent diffusion model, enabling simultaneous 3D reconstruction and prior learning, even from sparsely available views. At test time, we can directly sample the diffusion prior for unconditional generation, or combine it with arbitrary observations of unseen objects for NeRF reconstruction. SSDNeRF demonstrates robust results comparable to or better than leading task-specific methods in unconditional generation and single/sparse-view 3D reconstruction.Comment: Project page: https://lakonik.github.io/ssdner

Dolfin: Diffusion Layout Transformers without Autoencoder

In this paper, we introduce a novel generative model, Diffusion Layout Transformers without Autoencoder (Dolfin), which significantly improves the modeling capability with reduced complexity compared to existing methods. Dolfin employs a Transformer-based diffusion process to model layout generation. In addition to an efficient bi-directional (non-causal joint) sequence representation, we further propose an autoregressive diffusion model (Dolfin-AR) that is especially adept at capturing rich semantic correlations for the neighboring objects, such as alignment, size, and overlap. When evaluated against standard generative layout benchmarks, Dolfin notably improves performance across various metrics (fid, alignment, overlap, MaxIoU and DocSim scores), enhancing transparency and interoperability in the process. Moreover, Dolfin's applications extend beyond layout generation, making it suitable for modeling geometric structures, such as line segments. Our experiments present both qualitative and quantitative results to demonstrate the advantages of Dolfin

A perturbative approach for the dynamics of the quantum Zeno subspaces

In this paper we investigate the dynamics of the quantum Zeno subspaces which are the eigenspaces of the interaction Hamiltonian, belonging to different eigenvalues. Using the perturbation theory and the adiabatic approximation, we get a general expression of the jump probability between different Zeno subspaces. We applied this result in some examples. In these examples, as the coupling constant of the interactions increases, the measurement keeps the system remaining in its initial subspace and the quantum Zeno effect takes place.Comment: 14 pages, 3 figure

Evaluate how steaming and sulfur fumigation change the microstructure, physicochemical properties and in vitro digestibility of Gastrodia elata Bl. starch

The sulfur dioxide gas (SO2) generated by sulfur burning can improve the appearance quality of food and enhance the storage time. However, excessive sulfur dioxide will pollute the environment and cause deterioration of food quality, and even the high residual levels can increase the risk of cancer. As Gastrodia elata Blume is prone to corruption during processing, sulfur fumigation is often used for preservation. In this study, spectral analysis and Texture Profile Analysis (TPA) were used to investigate the effects of traditional sulfur fumigation processing on the morphology quality, edible quality and structural characteristics of G. elata. The results showed that compared with direct drying, the pH decreased by 0.399 of the sulfur fumigated after steamed treatment G. elata, and the morphology quality, pasting ability and gel edible quality of the starch were significantly improved. In addition, it was suggested that sulfur fumigation after steaming could promote the release of molecular chains from starch granules and thus enhance the cross-linking between molecules, which explained the reason for the improve of starch edible quality. This study can provide technical and theoretical support for improving the quality of starch rich foods, replacing sulfur fumigation and reducing potential environmental hazards