56 research outputs found
Plasmonic Tamm states: second enhancement of light inside the plasmonic waveguide
A type of Tamm states inside metal-insulator-metal (MIM) waveguides is
proposed. An impedance based transfer matrix method is adopted to study and
optimize it. With the participation of the plasmonic Tamm states, fields could
be enhanced twice: the ffirst is due to the coupling between a normal waveguide
and a nanoscaled plasmonic waveguide and the second is due to the strong
localization and field enhancement of Tamm states. As shown in our 2D coupling
configuration, |E|^2 is enhanced up to 1050 times when 1550 nm light is coupled
from an 300 nm Si slab waveguide into an 40 nm MIM waveguide.Comment: 3 pages, 4 figure
A nonlinear analytical model for tensile failure prediction of pseudo-ductile composite laminates
In this study, the tensile nonlinear responses of composite laminates with [±θn]
s and [±θn∕0] s layups are
investigated. An analytical model that integrates the progressive failure, shear nonlinearity, fiber rotation,
and fragmentation is established to characterize the nonlinear tensile behavior. A nonlinear factor is used
to describe the shear nonlinearity of the resin matrix, which is governed by shear stress, while progressive
damage indexes are determined by normal stresses. The degree of fiber rotation and the fragmentation between
layers are analytically formulated. Tensile results from experiments conducted in this study and from others in
the literature are used to verify the model’s prediction accuracy. The proposed model provides acceptably
good predictions of nonlinear behavior for pseudo-ductile carbon fiber reinforced composite laminates. A
sensitivity analysis shows that the dominant model parameter changes from axial modulus to shear modulus,
and eventually to transverse modulus as the off-axial angle increases from 0â—¦ to 9
SVDiff: Compact Parameter Space for Diffusion Fine-Tuning
Diffusion models have achieved remarkable success in text-to-image
generation, enabling the creation of high-quality images from text prompts or
other modalities. However, existing methods for customizing these models are
limited by handling multiple personalized subjects and the risk of overfitting.
Moreover, their large number of parameters is inefficient for model storage. In
this paper, we propose a novel approach to address these limitations in
existing text-to-image diffusion models for personalization. Our method
involves fine-tuning the singular values of the weight matrices, leading to a
compact and efficient parameter space that reduces the risk of overfitting and
language-drifting. We also propose a Cut-Mix-Unmix data-augmentation technique
to enhance the quality of multi-subject image generation and a simple
text-based image editing framework. Our proposed SVDiff method has a
significantly smaller model size (1.7MB for StableDiffusion) compared to
existing methods (vanilla DreamBooth 3.66GB, Custom Diffusion 73MB), making it
more practical for real-world applications.Comment: Revised appendix with the addition of cross-attention regularization
for single-subject generatio
MaxViT: Multi-Axis Vision Transformer
Transformers have recently gained significant attention in the computer
vision community. However, the lack of scalability of self-attention mechanisms
with respect to image size has limited their wide adoption in state-of-the-art
vision backbones. In this paper we introduce an efficient and scalable
attention model we call multi-axis attention, which consists of two aspects:
blocked local and dilated global attention. These design choices allow
global-local spatial interactions on arbitrary input resolutions with only
linear complexity. We also present a new architectural element by effectively
blending our proposed attention model with convolutions, and accordingly
propose a simple hierarchical vision backbone, dubbed MaxViT, by simply
repeating the basic building block over multiple stages. Notably, MaxViT is
able to "see" globally throughout the entire network, even in earlier,
high-resolution stages. We demonstrate the effectiveness of our model on a
broad spectrum of vision tasks. On image classification, MaxViT achieves
state-of-the-art performance under various settings: without extra data, MaxViT
attains 86.5\% ImageNet-1K top-1 accuracy; with ImageNet-21K pre-training, our
model achieves 88.7\% top-1 accuracy. For downstream tasks, MaxViT as a
backbone delivers favorable performance on object detection as well as visual
aesthetic assessment. We also show that our proposed model expresses strong
generative modeling capability on ImageNet, demonstrating the superior
potential of MaxViT blocks as a universal vision module. We will make the code
and models publicly available
DeepStore: an interaction-aware Wide&Deep model for store site recommendation with attentional spatial embeddings
International audienceStore site recommendation is one of the essential business services in smart cities for brick-and-mortar enterprises. In recent years, the proliferation of multisource data in cities has fostered unprecedented opportunities to the data-driven store site recommendation, which aims at leveraging large-scale user-generated data to analyze and mine users' preferences for identifying the optimal location for a new store. However, most works in store site recommendation pay more attention to a single data source which lacks some significant data (e.g., consumption data and user profile data). In this paper, we aim to study the store site recommendation in a fine-grained manner. Specifically, we predict the consumption level of different users at the store based on multisource data, which can not only help the store placement but also benefit analyzing customer behavior in the store at different time periods. To solve this problem, we design a novel model based on the deep neural network, named DeepStore, which learns low-and high-order feature interactions explicitly and implicitly from dense and sparse features simultaneously. In particular, DeepStore incorporates three modules: 1) the cross network; 2) the deep network; and 3) the linear component. In addition, to learn the latent feature representation from multisource data, we propose two embedding methods for different types of data: 1) the filed embedding and 2) attention-based spatial embedding. Extensive experiments are conducted on a real-world dataset including store data, user data, and point-of-interest data, the results demonstrate that DeepStore outperforms the state-of-the-art models
Persistent residential burning-related primary organic particles during wintertime hazes in North China:insights into their aging and optical changes
Chemistry of atmospheric fine particles during the COVID-19 pandemic in a megacity of eastern China
Direct observations of fine primary particles from residential coal burning:insights into their morphology, composition, and hygroscopicity
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