Active and Tunable Metamaterials

In this chapter, we will first present the research progress on the active and tunable metamaterials based on different realization methods, such as varactor diodes, liquid crystals, superconductivity, and structural-shifting structures. Then we focus on the achievements in our research group for the tunable metamaterials by using the ferrite as the substrate of metamaterials. We will present the designs and theories of single-, dual-, and triple-band tunable metamaterials based on the ferrite and the design of metamaterial absorbers based on the ferrite. It will indicate that the proposed tunable metamaterials have many advantages compared with other active and tunable metamaterials

Role of Selenium in Viral Infections with a Major Focus on SARS-CoV-2

Viral infections have afflicted human health and despite great advancements in scientific knowledge and technologies, continue to affect our society today. The current coronavirus (COVID-19) pandemic has put a spotlight on the need to review the evidence on the impact of nutritional strategies to maintain a healthy immune system, particularly in instances where there are limited therapeutic treatments. Selenium, an essential trace element in humans, has a long history of lowering the occurrence and severity of viral infections. Much of the benefits derived from selenium are due to its incorporation into selenocysteine, an important component of proteins known as selenoproteins. Viral infections are associated with an increase in reactive oxygen species and may result in oxidative stress. Studies suggest that selenium deficiency alters immune response and viral infection by increasing oxidative stress and the rate of mutations in the viral genome, leading to an increase in pathogenicity and damage to the host. This review examines viral infections, including the novel SARS-CoV-2, in the context of selenium, in order to inform potential nutritional strategies to maintain a healthy immune system

Dynamic motion of polar skyrmions in oxide heterostructures

Polar skyrmions have been widely investigated in oxide heterostructure recently, due to their exotic properties and intriguing physical insights. Meanwhile, so far, the external field-driven motion of the polar skyrmion, akin to the magnetic counterpart, has yet to be discovered. Here, using phase-field simulations, we demonstrate the dynamic motion of the polar skyrmions with integrated external thermal, electrical, and mechanical stimuli. The external heating reduces the spontaneous polarization hence the skyrmion motion barrier, while the skyrmions shrink under the electric field, which could weaken the lattice pinning and interactions between the skyrmions. The mechanical force transforms the skyrmions into c-domain in the vicinity of the indenter center under the electric field, providing the space and driving force needed for the skyrmions to move. This study confirmed that the skyrmions are quasi-particles that can move collectively, while also providing concrete guidance for the further design of polar skyrmion-based electronic devices.Comment: 17 pages, 4 figure

A low-frequency chip-scale optomechanical oscillator with 58 kHz mechanical stiffening and more than 100th-order stable harmonics.

For the sensitive high-resolution force- and field-sensing applications, the large-mass microelectromechanical system (MEMS) and optomechanical cavity have been proposed to realize the sub-aN/Hz1/2 resolution levels. In view of the optomechanical cavity-based force- and field-sensors, the optomechanical coupling is the key parameter for achieving high sensitivity and resolution. Here we demonstrate a chip-scale optomechanical cavity with large mass which operates at ≈77.7 kHz fundamental mode and intrinsically exhibiting large optomechanical coupling of 44 GHz/nm or more, for both optical resonance modes. The mechanical stiffening range of ≈58 kHz and a more than 100th-order harmonics are obtained, with which the free-running frequency instability is lower than 10-6 at 100 ms integration time. Such results can be applied to further improve the sensing performance of the optomechanical inspired chip-scale sensors

E2Net: Resource-Efficient Continual Learning with Elastic Expansion Network

Continual Learning methods are designed to learn new tasks without erasing previous knowledge. However, Continual Learning often requires massive computational power and storage capacity for satisfactory performance. In this paper, we propose a resource-efficient continual learning method called the Elastic Expansion Network (E2Net). Leveraging core subnet distillation and precise replay sample selection, E2Net achieves superior average accuracy and diminished forgetting within the same computational and storage constraints, all while minimizing processing time. In E2Net, we propose Representative Network Distillation to identify the representative core subnet by assessing parameter quantity and output similarity with the working network, distilling analogous subnets within the working network to mitigate reliance on rehearsal buffers and facilitating knowledge transfer across previous tasks. To enhance storage resource utilization, we then propose Subnet Constraint Experience Replay to optimize rehearsal efficiency through a sample storage strategy based on the structures of representative networks. Extensive experiments conducted predominantly on cloud environments with diverse datasets and also spanning the edge environment demonstrate that E2Net consistently outperforms state-of-the-art methods. In addition, our method outperforms competitors in terms of both storage and computational requirements

CLIP-KD: An Empirical Study of Distilling CLIP Models

CLIP has become a promising language-supervised visual pre-training framework and achieves excellent performance over a wide range of tasks. This paper aims to distill small CLIP models supervised by a large teacher CLIP model. We propose several distillation strategies, including relation, feature, gradient and contrastive paradigm, to examine the impact on CLIP distillation. We show that the simplest feature mimicry with MSE loss performs best. Moreover, interactive contrastive learning and relation-based distillation are also critical in performance improvement. We apply the unified method to distill several student networks trained on 15 million (image, text) pairs. Distillation improves the student CLIP models consistently over zero-shot ImageNet classification and cross-modal retrieval benchmarks. We hope our empirical study will become an important baseline for future CLIP distillation research. The code is available at \url{https://github.com/winycg/CLIP-KD}