Rotational Doppler effect in left-handed materials

We explain the rotational Doppler effect associated with light beams carrying with orbital angular momentum in left-handed materials (LHMs). We demonstrate that the rotational Doppler effect in LHMs is unreversed, which is significantly different from the linear Doppler effect. The physics underlying this intriguing effect is the combined contributions of negative phase velocity and inverse screw of wave-front. In the normal dispersion region, the rotational Doppler effect induces a upstream energy flow but a downstream momentum flow. In the anomalous dispersion region, however, the rotational Doppler effect produces a downstream energy flow but a upstream momentum flow. We theoretically predict that the rotational Doppler effect can induce a transfer of angular momentum of the LHM to orbital angular momentum of the beam.Comment: 6 pages, 3 figure

RGBT Tracking via Progressive Fusion Transformer with Dynamically Guided Learning

Existing Transformer-based RGBT tracking methods either use cross-attention to fuse the two modalities, or use self-attention and cross-attention to model both modality-specific and modality-sharing information. However, the significant appearance gap between modalities limits the feature representation ability of certain modalities during the fusion process. To address this problem, we propose a novel Progressive Fusion Transformer called ProFormer, which progressively integrates single-modality information into the multimodal representation for robust RGBT tracking. In particular, ProFormer first uses a self-attention module to collaboratively extract the multimodal representation, and then uses two cross-attention modules to interact it with the features of the dual modalities respectively. In this way, the modality-specific information can well be activated in the multimodal representation. Finally, a feed-forward network is used to fuse two interacted multimodal representations for the further enhancement of the final multimodal representation. In addition, existing learning methods of RGBT trackers either fuse multimodal features into one for final classification, or exploit the relationship between unimodal branches and fused branch through a competitive learning strategy. However, they either ignore the learning of single-modality branches or result in one branch failing to be well optimized. To solve these problems, we propose a dynamically guided learning algorithm that adaptively uses well-performing branches to guide the learning of other branches, for enhancing the representation ability of each branch. Extensive experiments demonstrate that our proposed ProFormer sets a new state-of-the-art performance on RGBT210, RGBT234, LasHeR, and VTUAV datasets.Comment: 13 pages, 9 figure

Meta-DMoE: Adapting to Domain Shift by Meta-Distillation from Mixture-of-Experts

In this paper, we tackle the problem of domain shift. Most existing methods perform training on multiple source domains using a single model, and the same trained model is used on all unseen target domains. Such solutions are sub-optimal as each target domain exhibits its own speciality, which is not adapted. Furthermore, expecting the single-model training to learn extensive knowledge from the multiple source domains is counterintuitive. The model is more biased toward learning only domain-invariant features and may result in negative knowledge transfer. In this work, we propose a novel framework for unsupervised test-time adaptation, which is formulated as a knowledge distillation process to address domain shift. Specifically, we incorporate Mixture-of-Experts (MoE) as teachers, where each expert is separately trained on different source domains to maximize their speciality. Given a test-time target domain, a small set of unlabeled data is sampled to query the knowledge from MoE. As the source domains are correlated to the target domains, a transformer-based aggregator then combines the domain knowledge by examining the interconnection among them. The output is treated as a supervision signal to adapt a student prediction network toward the target domain. We further employ meta-learning to enforce the aggregator to distill positive knowledge and the student network to achieve fast adaptation. Extensive experiments demonstrate that the proposed method outperforms the state-of-the-art and validates the effectiveness of each proposed component. Our code is available at https://github.com/n3il666/Meta-DMoE.Comment: Accepted at NeurIPS202

Few-Shot Class-Incremental Learning via Entropy-Regularized Data-Free Replay

Few-shot class-incremental learning (FSCIL) has been proposed aiming to enable a deep learning system to incrementally learn new classes with limited data. Recently, a pioneer claims that the commonly used replay-based method in class-incremental learning (CIL) is ineffective and thus not preferred for FSCIL. This has, if truth, a significant influence on the fields of FSCIL. In this paper, we show through empirical results that adopting the data replay is surprisingly favorable. However, storing and replaying old data can lead to a privacy concern. To address this issue, we alternatively propose using data-free replay that can synthesize data by a generator without accessing real data. In observing the the effectiveness of uncertain data for knowledge distillation, we impose entropy regularization in the generator training to encourage more uncertain examples. Moreover, we propose to relabel the generated data with one-hot-like labels. This modification allows the network to learn by solely minimizing the cross-entropy loss, which mitigates the problem of balancing different objectives in the conventional knowledge distillation approach. Finally, we show extensive experimental results and analysis on CIFAR-100, miniImageNet and CUB-200 to demonstrate the effectiveness of our proposed one.Comment: Accepted by ECCV 202

Research on the interface properties of geogrid with different mesh sizes

Due to its special mesh structure, geogrid can be embedded in the surrounding soil so effectively that the effects of reinforcement are comparatively better the other geotechnical composite materials. Geogrid has been adopted more and more widely in steep embankment reinforcement engineering. In practical engineering, the design of a reinforced body of soil with geogrid is usually based on Finite Elemental Method (FEM) numerical methods and calculation is carried out as a two-dimensional plane strain problem. This simplifies the geogrid with mesh structure into a single strip. The plausibility of calculating the strength indexes of the interface through interface parameters without considering the influence of the mesh size of the geogrid on the features of the interface should be studied. The current research on the interface properties of geogrid with different mesh sizes does not examine this issue thoroughly. By using large-sized shear experiments and FEM numerical methods, this paper studies the influences of the mesh size of geogrid on interface properties. The influence of mesh size on the features of the interface with geogrid can be displayed directly and quantitatively. This shows that larger mesh sizes result in higher strength indexes of the interface and a clearer reinforcement effect. The corresponding requirements of the geogrid material also increase; otherwise, the tensile strength would not be satisfied. The research results provide effective guarantees for the construction and operation of steep embankment reinforcement engineering, which is meaningful for safety engineering

Physical Adversarial Attack meets Computer Vision: A Decade Survey

Although Deep Neural Networks (DNNs) have achieved impressive results in computer vision, their exposed vulnerability to adversarial attacks remains a serious concern. A series of works has shown that by adding elaborate perturbations to images, DNNs could have catastrophic degradation in performance metrics. And this phenomenon does not only exist in the digital space but also in the physical space. Therefore, estimating the security of these DNNs-based systems is critical for safely deploying them in the real world, especially for security-critical applications, e.g., autonomous cars, video surveillance, and medical diagnosis. In this paper, we focus on physical adversarial attacks and provide a comprehensive survey of over 150 existing papers. We first clarify the concept of the physical adversarial attack and analyze its characteristics. Then, we define the adversarial medium, essential to perform attacks in the physical world. Next, we present the physical adversarial attack methods in task order: classification, detection, and re-identification, and introduce their performance in solving the trilemma: effectiveness, stealthiness, and robustness. In the end, we discuss the current challenges and potential future directions.Comment: 32 pages. Under Revie

Milk fat globule membrane promotes brain development in piglets by enhancing the connection of white matter fiber trace

IntroductionBrain development during infancy is crucial for later health and development. Although Milk Fat Globule Membrane (MFGM) has been demonstrated to enhance brain development, further investigation is needed to determine the optimal dose.MethodsIn this study, 80 piglets aged 2 days were randomly assigned to four groups: Control group, MFGM-L (1.74 g MFGM per 100 g diet), MFGM-M (4.64 g MFGM per 100 g diet), and MFGM-H (6.09 g MFGM per 100 g diet). Daily body weight and milk intake of the piglets were recorded until 31 days postnatal. Learning and memory abilities were evaluated using the spatial T-maze test on day 15. MRI analysis was conducted to assess functional and structural changes in brain tissues. Additionally, mRNA and protein expression of brain-derived neurotrophic factor (BDNF) and neurotrophin-3 (NTF-3) in the hippocampus and prefrontal cortex were evaluated.ResultsThe results indicated that the MFGM supplemented diet significantly improved the accuracy of the piglets in the T-maze test, with the MFGM-L group exhibiting the best performance. MRI showed no volumetric differences in the gray and white matter between the groups. However, the fractional anisotropy in the left and right hippocampus of piglets in the MFGM-L group was significantly higher than in the other three groups. Furthermore, there was a strong correlation between the accuracy of the T-maze test and hippocampal fractional anisotropy.DiscussionThe MFGM supplemented diet also increased the expression of BDNF in the cerebral cortex. However, the changes in BDNF were not consistent with the results of the T-maze test. In conclusion, adding 1.74 g MFGM per 100 g diet can significantly improve neonatal piglets’ learning and memory abilities, potentially by enhancing the connection of white matter fiber bundles in the brain