Metamaterial Broadband Angular Selectivity

We demonstrate how broadband angular selectivity can be achieved with stacks of one-dimensionally periodic photonic crystals, each consisting of alternating isotropic layers and effective anisotropic layers, where each effective anisotropic layer is constructed from a multilayered metamaterial. We show that by simply changing the structure of the metamaterials, the selective angle can be tuned to a broad range of angles; and, by increasing the number of stacks, the angular transmission window can be made as narrow as desired. As a proof of principle, we realize the idea experimentally in the microwave regime. The angular selectivity and tunability we report here can have various applications such as in directional control of electromagnetic emitters and detectors.Comment: 5 pages, 5 figure

Optical Broadband Angular Selectivity

Light selection based purely on the angle of propagation is a long-standing scientific challenge. In angularly selective systems, however, the transmission of light usually also depends on the light frequency. We tailored the overlap of the band gaps of multiple one-dimensional photonic crystals, each with a different periodicity, in such a way as to preserve the characteristic Brewster modes across a broadband spectrum. We provide theory as well as an experimental realization with an all–visible spectrum, p-polarized angularly selective material system. Our method enables transparency throughout the visible spectrum at one angle—the generalized Brewster angle—and reflection at every other viewing angle.Massachusetts Institute of Technology. Institute for Soldier Nanotechnologies (Contract W911NF-13-D0001)United States. Dept. of Energy. Solid-State Solar-Thermal Energy Conversion Center (Grant DE-SC0001299

RFAConv: Innovating Spatital Attention and Standard Convolutional Operation

Spatial attention has been widely used to improve the performance of convolutional neural networks by allowing them to focus on important information. However, it has certain limitations. In this paper, we propose a new perspective on the effectiveness of spatial attention, which is that it can solve the problem of convolutional kernel parameter sharing. Despite this, the information contained in the attention map generated by spatial attention is not sufficient for large-size convolutional kernels. Therefore, we introduce a new attention mechanism called Receptive-Field Attention (RFA). While previous attention mechanisms such as the Convolutional Block Attention Module (CBAM) and Coordinate Attention (CA) only focus on spatial features, they cannot fully address the issue of convolutional kernel parameter sharing. In contrast, RFA not only focuses on the receptive-field spatial feature but also provides effective attention weights for large-size convolutional kernels. The Receptive-Field Attention convolutional operation (RFAConv), developed by RFA, represents a new approach to replace the standard convolution operation. It offers nearly negligible increment of computational cost and parameters, while significantly improving network performance. We conducted a series of experiments on ImageNet-1k, MS COCO, and VOC datasets, which demonstrated the superiority of our approach in various tasks including classification, object detection, and semantic segmentation. Of particular importance, we believe that it is time to shift focus from spatial features to receptive-field spatial features for current spatial attention mechanisms. By doing so, we can further improve network performance and achieve even better results. The code and pre-trained models for the relevant tasks can be found at https://github.com/Liuchen1997/RFAConv.Comment: 14 pages, 5 figure

AKConv: Convolutional Kernel with Arbitrary Sampled Shapes and Arbitrary Number of Parameters

Neural networks based on convolutional operations have achieved remarkable results in the field of deep learning, but there are two inherent flaws in standard convolutional operations. On the one hand, the convolution operation be confined to a local window and cannot capture information from other locations, and its sampled shapes is fixed. On the other hand, the size of the convolutional kernel is fixed to k $\times$ k, which is a fixed square shape, and the number of parameters tends to grow squarely with size. It is obvious that the shape and size of targets are various in different datasets and at different locations. Convolutional kernels with fixed sample shapes and squares do not adapt well to changing targets. In response to the above questions, the Alterable Kernel Convolution (AKConv) is explored in this work, which gives the convolution kernel an arbitrary number of parameters and arbitrary sampled shapes to provide richer options for the trade-off between network overhead and performance. In AKConv, we define initial positions for convolutional kernels of arbitrary size by means of a new coordinate generation algorithm. To adapt to changes for targets, we introduce offsets to adjust the shape of the samples at each position. Moreover, we explore the effect of the neural network by using the AKConv with the same size and different initial sampled shapes. AKConv completes the process of efficient feature extraction by irregular convolutional operations and brings more exploration options for convolutional sampling shapes. Object detection experiments on representative datasets COCO2017, VOC 7+12 and VisDrone-DET2021 fully demonstrate the advantages of AKConv. AKConv can be used as a plug-and-play convolutional operation to replace convolutional operations to improve network performance. The code for the relevant tasks can be found at https://github.com/CV-ZhangXin/AKConv.Comment: 10 pages, 5 figure

Enzymatic Preparation of Quinoa Protein Peptides and Its Lipid-lowering and Uric Acid-Lowering Activity in Vitro

To study the optimal enzymatic hydrolysis conditions and uric acid-lowering activity of lipid-lowering peptides from quinoa protein, this study used quinoa as raw material to extract protein, and used pancreatic lipase inhibition rate as the activity index. The enzymatic hydrolysis process of lipid-lowering peptides was optimized by single factor experiment and response surface analysis. The pancreatic lipase inhibitory activity, sodium taurocholate binding activity, cholesterol esterase inhibitory activity, xanthine oxidase inhibitory activity and amino acid composition of quinoa protein peptides were analyzed and characterized. The results showed that the optimal enzymatic hydrolysis conditions of lipid-lowering peptides from quinoa were as follows: pH1.6, enzymatic hydrolysis temperature 42.9 ℃, substrate concentration 3.03%, enzymatic hydrolysis time 1 h and enzyme to substrate ratio 0.2%. The theoretical value of inhibition rate of pancreatic lipase was 90.43%, and the actual value was 90.93%±0.10%. The optimal enzymatic hydrolysates showed excellent effect of lowering lipid in vitro. The IC50 of pancreatic lipase inhibition rate and cholesterol esterase inhibition rate were 7.49 μg/mL and 4.73 mg/mL, respectively. Meanwhile, the EC50 of taurocholic sodium binding rate was 0.53 mg/mL. In addition, the optimal enzymatic hydrolysates showed good xanthine oxidase inhibition effect (IC50=5.97 mg/mL), indicating that it had the uric acid-lowering effect in vitro. Amino acid analysis showed that quinoa protein peptides were rich in essential amino acids (34.23%), and the percentage of hydrophobic amino acid and acidic amino acid were 34.11% and 31.66%, respectively. The quinoa protein peptides had high lipid-lowering and uric acid-lowering activities in vitro, which provided a theoretical basis for the high-value application of quinoa protein peptides

The Long-Baseline Neutrino Experiment: Exploring Fundamental Symmetries of the Universe

The preponderance of matter over antimatter in the early Universe, the dynamics of the supernova bursts that produced the heavy elements necessary for life and whether protons eventually decay --- these mysteries at the forefront of particle physics and astrophysics are key to understanding the early evolution of our Universe, its current state and its eventual fate. The Long-Baseline Neutrino Experiment (LBNE) represents an extensively developed plan for a world-class experiment dedicated to addressing these questions. LBNE is conceived around three central components: (1) a new, high-intensity neutrino source generated from a megawatt-class proton accelerator at Fermi National Accelerator Laboratory, (2) a near neutrino detector just downstream of the source, and (3) a massive liquid argon time-projection chamber deployed as a far detector deep underground at the Sanford Underground Research Facility. This facility, located at the site of the former Homestake Mine in Lead, South Dakota, is approximately 1,300 km from the neutrino source at Fermilab -- a distance (baseline) that delivers optimal sensitivity to neutrino charge-parity symmetry violation and mass ordering effects. This ambitious yet cost-effective design incorporates scalability and flexibility and can accommodate a variety of upgrades and contributions. With its exceptional combination of experimental configuration, technical capabilities, and potential for transformative discoveries, LBNE promises to be a vital facility for the field of particle physics worldwide, providing physicists from around the globe with opportunities to collaborate in a twenty to thirty year program of exciting science. In this document we provide a comprehensive overview of LBNE's scientific objectives, its place in the landscape of neutrino physics worldwide, the technologies it will incorporate and the capabilities it will possess.Comment: Major update of previous version. This is the reference document for LBNE science program and current status. Chapters 1, 3, and 9 provide a comprehensive overview of LBNE's scientific objectives, its place in the landscape of neutrino physics worldwide, the technologies it will incorporate and the capabilities it will possess. 288 pages, 116 figure