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 0◦ 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