Evaluating Generalization Ability of Convolutional Neural Networks and Capsule Networks for Image Classification via Top-2 Classification

Image classification is a challenging problem which aims to identify the category of object in the image. In recent years, deep Convolutional Neural Networks (CNNs) have been applied to handle this task, and impressive improvement has been achieved. However, some research showed the output of CNNs can be easily altered by adding relatively small perturbations to the input image, such as modifying few pixels. Recently, Capsule Networks (CapsNets) are proposed, which can help eliminating this limitation. Experiments on MNIST dataset revealed that capsules can better characterize the features of object than CNNs. But it's hard to find a suitable quantitative method to compare the generalization ability of CNNs and CapsNets. In this paper, we propose a new image classification task called Top-2 classification to evaluate the generalization ability of CNNs and CapsNets. The models are trained on single label image samples same as the traditional image classification task. But in the test stage, we randomly concatenate two test image samples which contain different labels, and then use the trained models to predict the top-2 labels on the unseen newly-created two label image samples. This task can provide us precise quantitative results to compare the generalization ability of CNNs and CapsNets. Back to the CapsNet, because it uses Full Connectivity (FC) mechanism among all capsules, it requires many parameters. To reduce the number of parameters, we introduce the Parameter-Sharing (PS) mechanism between capsules. Experiments on five widely used benchmark image datasets demonstrate the method significantly reduces the number of parameters, without losing the effectiveness of extracting features. Further, on the Top-2 classification task, the proposed PS CapsNets obtain impressive higher accuracy compared to the traditional CNNs and FC CapsNets by a large margin.Comment: This paper is under consideration at Computer Vision and Image Understandin

Naive Bayes-based Context Extension for Large Language Models

Large Language Models (LLMs) have shown promising in-context learning abilities. However, conventional In-Context Learning (ICL) approaches are often impeded by length limitations of transformer architecture, which pose challenges when attempting to effectively integrate supervision from a substantial number of demonstration examples. In this paper, we introduce a novel framework, called Naive Bayes-based Context Extension (NBCE), to enable existing LLMs to perform ICL with an increased number of demonstrations by significantly expanding their context size. Importantly, this expansion does not require fine-tuning or dependence on particular model architectures, all the while preserving linear efficiency. NBCE initially splits the context into equal-sized windows fitting the target LLM's maximum length. Then, it introduces a voting mechanism to select the most relevant window, regarded as the posterior context. Finally, it employs Bayes' theorem to generate the test task. Our experimental results demonstrate that NBCE substantially enhances performance, particularly as the number of demonstration examples increases, consistently outperforming alternative methods. The NBCE code will be made publicly accessible. The code NBCE is available at: https://github.com/amurtadha/NBCE-masterComment: Accepted to main NAACL 202

Valley vortex states and degeneracy lifting via photonic higher-band excitation

We demonstrate valley-dependent vortex generation in a photonic graphene. Without breaking the inversion symmetry, excitation of two equivalent valleys leads to formation of an optical vortex upon Bragg-reflection to the third valley, with its chirality determined by the valley degree of freedom. Vortex-antivortex pairs with valley-dependent topological charge flipping are also observed and corroborated by numerical simulations. Furthermore, we develop a three-band effective Hamiltonian model to describe the dynamics of the coupled valleys, and find that the commonly used two-band model is not sufficient to explain the observed vortex degeneracy lifting. Such valley-polarized vortex states arise from high-band excitation without inversion symmetry breaking or synthetic-field-induced gap opening. Our results from a photonic setting may provide insight for the study of valley contrasting and Berry-phase mediated topological phenomena in other systems

Dissipative elastic metamaterial with a lowfrequency passband

We design and experimentally demonstrate a dissipative elastic metamaterial structure that functions as a bandpass filter with a low-frequency passband. The mechanism of dissipation in this structure is well described by a mass-spring-damper model that reveals that the imaginary part of the wavenumber is non-zero, even in the passband of dissipative metamaterials. This indicates that transmittance in this range can be low. A prototype for this viscoelastic metamaterial model is fabricated by 3D printing techniques using soft and hard acrylics as constituent materials. The transmittance of the printed metamaterial is measured and shows good agreement with theoretical predictions, demonstrating its potential in the design of compact waveguides, filters and other advanced devices for controlling mechanical waves

Dissipative elastic metamaterial with a lowfrequency passband

We design and experimentally demonstrate a dissipative elastic metamaterial structure that functions as a bandpass filter with a low-frequency passband. The mechanism of dissipation in this structure is well described by a mass-spring-damper model that reveals that the imaginary part of the wavenumber is non-zero, even in the passband of dissipative metamaterials. This indicates that transmittance in this range can be low. A prototype for this viscoelastic metamaterial model is fabricated by 3D printing techniques using soft and hard acrylics as constituent materials. The transmittance of the printed metamaterial is measured and shows good agreement with theoretical predictions, demonstrating its potential in the design of compact waveguides, filters and other advanced devices for controlling mechanical waves