Ultra-broad band perfect absorption realized by phonon-photon resonance in periodic polar dielectric material based pyramid structure

In this research, a mid-infrared wide-angle ultra-broadband perfect absorber which composed of pyramid grating structure has been comprehensively studied. The structure was operated in the reststrahlem band of SiC and with the presence of surface phonon resonance(SPhR), the perfect absorption was observed in the region between 10.25 and 10.85 $\mu m$. We explain the mechanism of this structure with the help of PLC circuit model due to the independence of magnetic polaritons. More over, by studying the resonance behavior of different wavelength, we bridged the continuous perfect absorption band and the discret peak in 11.05 $\mu m$(emerge two close absorption band together) by modification of the geometry. The absorption band has been sufficiently broadened. More over, both 1-D and 2-D periodic structure has been considered and the response of different incident angles and polarized angles have been studied and a omnidirectional and polarization insensitive structure can be realized which may be a candidate of several sensor applications in meteorology. The simulation was conducted by the Rigorous Coupled Wave Method(RCWA)

Communication-Efficient Decentralized Federated Learning via One-Bit Compressive Sensing

Decentralized federated learning (DFL) has gained popularity due to its practicality across various applications. Compared to the centralized version, training a shared model among a large number of nodes in DFL is more challenging, as there is no central server to coordinate the training process. Especially when distributed nodes suffer from limitations in communication or computational resources, DFL will experience extremely inefficient and unstable training. Motivated by these challenges, in this paper, we develop a novel algorithm based on the framework of the inexact alternating direction method (iADM). On one hand, our goal is to train a shared model with a sparsity constraint. This constraint enables us to leverage one-bit compressive sensing (1BCS), allowing transmission of one-bit information among neighbour nodes. On the other hand, communication between neighbour nodes occurs only at certain steps, reducing the number of communication rounds. Therefore, the algorithm exhibits notable communication efficiency. Additionally, as each node selects only a subset of neighbours to participate in the training, the algorithm is robust against stragglers. Additionally, complex items are computed only once for several consecutive steps and subproblems are solved inexactly using closed-form solutions, resulting in high computational efficiency. Finally, numerical experiments showcase the algorithm's effectiveness in both communication and computation

Real Time Scanning-Modeling System for Architecture Design and Construction

The disconnection between architectural form and materiality has become an important issue in recent years. Architectural form is mainly decided by the designer, while material data is often treated as an afterthought which doesn’t factor in decision-making directly. This study proposes a new, real-time scanning-modeling system for computational design and autonomous robotic construction. By using cameras to scan the raw materials, this system would get related data and build 3D models in real time. These data would be used by a computer to calculate rational outcomes and help a robot make decisions about its construction paths and methods. The result of an application pavilion shows that data of raw materials, architectural design, and robotic construction can be integrated into a digital chain. The method and gain of the material-oriented design approach are discussed and future research on using different source materials is laid out

Audit and Improve Robustness of Private Neural Networks on Encrypted Data

Performing neural network inference on encrypted data without decryption is one popular method to enable privacy-preserving neural networks (PNet) as a service. Compared with regular neural networks deployed for machine-learning-as-a-service, PNet requires additional encoding, e.g., quantized-precision numbers, and polynomial activation. Encrypted input also introduces novel challenges such as adversarial robustness and security. To the best of our knowledge, we are the first to study questions including (i) Whether PNet is more robust against adversarial inputs than regular neural networks? (ii) How to design a robust PNet given the encrypted input without decryption? We propose PNet-Attack to generate black-box adversarial examples that can successfully attack PNet in both target and untarget manners. The attack results show that PNet robustness against adversarial inputs needs to be improved. This is not a trivial task because the PNet model owner does not have access to the plaintext of the input values, which prevents the application of existing detection and defense methods such as input tuning, model normalization, and adversarial training. To tackle this challenge, we propose a new fast and accurate noise insertion method, called RPNet, to design Robust and Private Neural Networks. Our comprehensive experiments show that PNet-Attack reduces at least $2.5\times$ queries than prior works. We theoretically analyze our RPNet methods and demonstrate that RPNet can decrease $\sim 91.88\%$ attack success rate.Comment: 10 pages, 10 figure

Are Diffusion Models Vulnerable to Membership Inference Attacks?

Diffusion-based generative models have shown great potential for image synthesis, but there is a lack of research on the security and privacy risks they may pose. In this paper, we investigate the vulnerability of diffusion models to Membership Inference Attacks (MIAs), a common privacy concern. Our results indicate that existing MIAs designed for GANs or VAE are largely ineffective on diffusion models, either due to inapplicable scenarios (e.g., requiring the discriminator of GANs) or inappropriate assumptions (e.g., closer distances between synthetic samples and member samples). To address this gap, we propose Step-wise Error Comparing Membership Inference (SecMI), a query-based MIA that infers memberships by assessing the matching of forward process posterior estimation at each timestep. SecMI follows the common overfitting assumption in MIA where member samples normally have smaller estimation errors, compared with hold-out samples. We consider both the standard diffusion models, e.g., DDPM, and the text-to-image diffusion models, e.g., Latent Diffusion Models and Stable Diffusion. Experimental results demonstrate that our methods precisely infer the membership with high confidence on both of the two scenarios across multiple different datasets. Code is available at https://github.com/jinhaoduan/SecMI.Comment: To appear in ICML 202