45 research outputs found
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