Kirigami Metamaterials for Reconfigurable Toroidal Circular Dichroism

The ancient paper craft of kirigami has recently emerged as a potential tool for the design of functional materials. Inspired by the kirigami concept, we propose a class of kirigami-based metamaterials whose electromagnetic functionalities can be switched between nonchiral and chiral states by stretching the predesigned split-ring resonator array. Single-band, dual-band and broadband circular polarizers with reconfigurable performance are experimentally demonstrated with maximum circular dichroisms of 0.88, 0.94 and 0.92, respectively. The underlying mechanism is explained and calculated via detailed analysis of the excited multipoles, including the electric, magnetic, and toroidal dipoles and quadrupole. Our approach enables tailoring the electromagnetic functionalities in kirigami patterns and provides an alternate avenue for reconfigurable optical metadevices with exceptional mechanical properties

Experimental observation of superscattering

Superscattering, induced by degenerate resonances, breaks the fundamental single-channel limit of scattering cross section of subwavelength structures; in principle, an arbitrarily large total cross section can be achieved via superscattering. It thus provides a unique way to strengthen the light-matter interaction at the subwavelength scale, and has many potential applications in sensing, energy harvesting, bio-imaging (such as magnetic resonance imaging), communication and optoelectronics. However, the experimental demonstration of superscattering remains an open challenge due to its vulnerability to structural imperfections and intrinsic material losses. Here we report the first experimental evidence for superscattering, by demonstrating the superscattering simultaneously in two different frequency regimes through both the far-field and near-field measurements. The underlying mechanism for the observed superscattering is the degenerate resonances of confined surface waves, by utilizing a subwavelength metasurface-based multilayer structure. Our work paves the way towards practical applications based on superscattering

Experimental Observation of Superscattering

Superscattering, induced by degenerate resonances, breaks the fundamental single-channel limit of the scattering cross section of subwavelength structures; in principle, an arbitrarily large total cross section can be achieved via superscattering. It thus provides a unique way to strengthen the light-matter interaction at the subwavelength scale, and has many potential applications in sensing, energy harvesting, bioimaging (such as magnetic resonance imaging), communication, and optoelectronics. However, the experimental demonstration of superscattering remains an open challenge due to its vulnerability to structural imperfections and intrinsic material losses. Here we report the first experimental evidence for superscattering by demonstrating the superscattering simultaneously in two different frequency regimes through both the far-field and near-field measurements. The underlying mechanism for the observed superscattering is the degenerate resonances of confined surface waves, by utilizing a subwavelength metasurface-based multilayer structure. Our work paves the way towards practical applications based on superscattering

MAP-SNN: Mapping Spike Activities with Multiplicity, Adaptability, and Plasticity into Bio-Plausible Spiking Neural Networks

Spiking Neural Network (SNN) is considered more biologically realistic and power-efficient as it imitates the fundamental mechanism of the human brain. Recently, backpropagation (BP) based SNN learning algorithms that utilize deep learning frameworks have achieved good performance. However, bio-interpretability is partially neglected in those BP-based algorithms. Toward bio-plausible BP-based SNNs, we consider three properties in modeling spike activities: Multiplicity, Adaptability, and Plasticity (MAP). In terms of multiplicity, we propose a Multiple-Spike Pattern (MSP) with multiple spike transmission to strengthen model robustness in discrete time-iteration. To realize adaptability, we adopt Spike Frequency Adaption (SFA) under MSP to decrease spike activities for improved efficiency. For plasticity, we propose a trainable convolutional synapse that models spike response current to enhance the diversity of spiking neurons for temporal feature extraction. The proposed SNN model achieves competitive performances on neuromorphic datasets: N-MNIST and SHD. Furthermore, experimental results demonstrate that the proposed three aspects are significant to iterative robustness, spike efficiency, and temporal feature extraction capability of spike activities. In summary, this work proposes a feasible scheme for bio-inspired spike activities with MAP, offering a new neuromorphic perspective to embed biological characteristics into spiking neural networks

Go beyond End-to-End Training: Boosting Greedy Local Learning with Context Supply

Traditional end-to-end (E2E) training of deep networks necessitates storing intermediate activations for back-propagation, resulting in a large memory footprint on GPUs and restricted model parallelization. As an alternative, greedy local learning partitions the network into gradient-isolated modules and trains supervisely based on local preliminary losses, thereby providing asynchronous and parallel training methods that substantially reduce memory cost. However, empirical experiments reveal that as the number of segmentations of the gradient-isolated module increases, the performance of the local learning scheme degrades substantially, severely limiting its expansibility. To avoid this issue, we theoretically analyze the greedy local learning from the standpoint of information theory and propose a ContSup scheme, which incorporates context supply between isolated modules to compensate for information loss. Experiments on benchmark datasets (i.e. CIFAR, SVHN, STL-10) achieve SOTA results and indicate that our proposed method can significantly improve the performance of greedy local learning with minimal memory and computational overhead, allowing for the boost of the number of isolated modules. Our codes are available at https://github.com/Tab-ct/ContSup.Comment: 9 figures, 12 table

Gambogic acid targets HSP90 to alleviate DSS-induced colitis via inhibiting the necroptosis of intestinal epithelial cells

Abnormal elevations in the mortality of intestinal epithelial cells (IECs) are indicative of intestinal inflammation. Necroptosis of IECs represents a pro-inflammatory form of cell death, and modulation of IECs necroptosis may mitigate subsequent intestinal inflammation and preserve the integrity of the intestinal barrier. Currently, safe and effective preventive measures are lacking. In the Traditional Chinese Medicine theory, necroptosis of IECs leads to the destruction of the intestinal barrier in a manner associated with “heat and toxicity”, exacerbating intestinal inflammation. Heat shock protein 90 (HSP90) has been identified as a regulator of key proteins involved in necroptosis signal pathway including RIPK1/3 and MLKL. Gambogic acid (GA), the primary active compound found in Garcinia hanburii Hook. f., a traditional Chinese medicine used for detoxification and hemostasis, has not been studied for its potential therapeutic effects in ulcerative colitis previously. This study investigated the protective effects of GA on dextran sodium sulfate (DSS)-induced colitis in mice, as well as the underlying molecular mechanisms. GA was observed to significantly ameliorate DSS-induced enteritis and enhance intestinal barrier function. Concurrently, it reduced the phosphorylated expression levels of RIPK1/3 and MLKL. The underlying mechanism may be related to the suppression of HSP90 expression

An intelligent fault diagnosis method using variable weight artificial immune recognizers (V-AIR)

The Artificial Immune Recognition System (AIRS), which has been proved to be a successful classification method in the field of Artificial Immune Systems, has been used in many classification problems and gained good classification effect. However, the network inhibition mechanisms used in these methods are based on the threshold inhibition and the cells with low affinity will be deleted directly from the network, which will misrepresent the key features of the data set for not considering the density information within the data. In this paper, we utilize the concept of data potential field and propose a new weight optimizing network inhibition algorithm called variable weight artificial immune recognizer (V-AIR) where we replace the network inhibiting mechanism based on affinity with the inhibiting mechanism based on weight optimizing. The concept of data potential field was also used to describe the data distribution around training samples and the pattern of a training data belongs to the class with the largest potential field. At last, we used this algorithm to rolling bearing analog fault diagnosis and reciprocating compressor valves fault diagnosis, which get a good classification effect

SHARED MENTAL MODELS AS MODERATORS OF TEAM PROCESS-PERFORMANCE RELATIONSHIPS

The effects of shared mental models on the relationship between episodic team behavioral processes and performance were investigated, while teams were using an expenmentally stimulated construction project planning program. The results indicated that episodic team processes made positive contributions to the team performance. Furthermore, a hierarchical linear regression indicated that the convergence of shared teamwork mental models moderated the effects of team processes on team performance Specifically, the positive Impact of team processes on performance was found to be improved for those teams who shared more similar teamwork mental models than for teams who hold fewer similar teamwork mental models. Potential implications and relevant impacts on future research are discussed

Experimental Realization of an Extreme-Parameter Omnidirectional Cloak

An ideal transformation-based omnidirectional cloak always relies on metamaterials with extreme parameters, which were previously thought to be too difficult to realize. For such a reason, in previous experimental proposals of invisibility cloaks, the extreme parameters requirements are usually abandoned, leading to inherent scattering. Here, we report on the first experimental demonstration of an omnidirectional cloak that satisfies the extreme parameters requirement, which can hide objects in a homogenous background. Instead of using resonant metamaterials that usually involve unavoidable absorptive loss, the extreme parameters are achieved using a nonresonant metamaterial comprising arrays of subwavelength metallic channels manufactured with 3D metal printing technology. A high level transmission of electromagnetic wave propagating through the present omnidirectional cloak, as well as significant reduction of scattering field, is demonstrated both numerically and experimentally. Our work may also inspire experimental realizations of the other full-parameter omnidirectional optical devices such as concentrator, rotators, and optical illusion apparatuses