An acoustic metamaterial lens for acoustic point-to-point communication in air

Acoustic metamaterials have become a novel and effective way to control sound waves and design acoustic devices. In this study, we design a 3D acoustic metamaterial lens (AML) to achieve point-to-point acoustic communication in air: any acoustic source (i.e. a speaker) in air enclosed by such an AML can produce an acoustic image where the acoustic wave is focused (i.e. the field intensity is at a maximum, and the listener can receive the information), while the acoustic field at other spatial positions is low enough that listeners can hear almost nothing. Unlike a conventional elliptical reflective mirror, the acoustic source can be moved around inside our proposed AML. Numerical simulations are given to verify the performance of the proposed AML

Active thermal metasurfaces for remote heating/cooling by mimicking negative thermal conductivity

Remote temperature control can be obtained by a long-focus thermal lens that can focus heat fluxes into a spot far away from the back surface of the lens and create a virtual thermal source/sink in the background material, around which the temperature field distribution can be remotely controlled by changing the parameters of the thermal lens. However, due to the lack of negative thermal conductivity, the existing thermal lenses have extremely short focal lengths and cannot be used to remotely control the temperature field around the virtual thermal source/sink. In this study, we theoretically propose a general approach to equivalently realize negative thermal conductivity by elaborately distributed active thermal metasurface (ATMS), then use the proposed ATMS to implement a novel thermal lens with long focal length designed by transformation thermodynamics, and experimentally verify the performance of the designed long-focus thermal lens with measured focal length f=19.8mm for remote heating/cooling. The proposed method expands the scope of the thermal conductivity and open up new ways to realize unprecedented thermal effects with effective negative thermal conductivity, such as "thermal surface plasmon polaritons", thermal superlens, thermal tunneling effect, and thermal invisible gateway

Adaptive Integration of Partial Label Learning and Negative Learning for Enhanced Noisy Label Learning

There has been significant attention devoted to the effectiveness of various domains, such as semi-supervised learning, contrastive learning, and meta-learning, in enhancing the performance of methods for noisy label learning (NLL) tasks. However, most existing methods still depend on prior assumptions regarding clean samples amidst different sources of noise (\eg, a pre-defined drop rate or a small subset of clean samples). In this paper, we propose a simple yet powerful idea called \textbf{NPN}, which revolutionizes \textbf{N}oisy label learning by integrating \textbf{P}artial label learning (PLL) and \textbf{N}egative learning (NL). Toward this goal, we initially decompose the given label space adaptively into the candidate and complementary labels, thereby establishing the conditions for PLL and NL. We propose two adaptive data-driven paradigms of label disambiguation for PLL: hard disambiguation and soft disambiguation. Furthermore, we generate reliable complementary labels using all non-candidate labels for NL to enhance model robustness through indirect supervision. To maintain label reliability during the later stage of model training, we introduce a consistency regularization term that encourages agreement between the outputs of multiple augmentations. Experiments conducted on both synthetically corrupted and real-world noisy datasets demonstrate the superiority of NPN compared to other state-of-the-art (SOTA) methods. The source code has been made available at {\color{purple}{\url{https://github.com/NUST-Machine-Intelligence-Laboratory/NPN}}}.Comment: accepted by AAAI 202