Development of a resource-efficient FPGA-based neural network regression model for the ATLAS muon trigger upgrades

In this paper, a resource-efficient FPGA-based neural network regression model is developed for potential applications in the future hardware muon trigger system of the ATLAS experiment at the Large Hadron Collider (LHC). Effective real-time selection of muon candidates is the cornerstone of the ATLAS physics programme. With the planned upgrades, the entirely new FPGA-based hardware muon trigger system will be installed in 2025-2026 that will process full muon detector data within a 10 ${\mu}s$ latency window. The planned large FPGA devices should have sufficient spare resources to allow deployment of machine learning methods for improving identification of muon candidates and searching for new exotic particles. Our model promises to improve the rejection of the dominant source of background events in the central detector region, which are due to muon candidates with low transverse momenta. This neural network was implemented in the hardware description language using 65 digital signal processors and about 10,000 lookup tables. The simulated network latency and deadtime are 245 and 60 ns, respectively, when implemented in the FPGA device using a 400 MHz clock frequency. These results are well within the requirements of the future ATLAS muon trigger system, therefore opening a possibility for deploying machine learning methods for data taking by the ATLAS experiment at the High Luminosity LHC.Comment: 12 pages, 17 figure

Interaction-free, single-pixel quantum imaging with undetected photons

A typical imaging scenario requires three basic ingredients: 1. a light source that emits light, which in turn interacts and scatters off the object of interest; 2. detection of the light being scattered from the object and 3. a detector with spatial resolution. These indispensable ingredients in typical imaging scenarios may limit their applicability in the imaging of biological or other sensitive specimens due to unavailable photon-starved detection capabilities and inevitable damage induced by interaction. Here, we propose and experimentally realize a quantum imaging protocol that alleviates all three requirements. By embedding a single-photon Michelson interferometer into a nonlinear interferometer based on induced coherence and harnessing single-pixel imaging technique, we demonstrate interaction-free, single-pixel quantum imaging of a structured object with undetected photons. Thereby, we push the capability of quantum imaging to the extreme point in which no interaction is required between object and photons and the detection requirement is greatly reduced. Our work paves the path for applications in characterizing delicate samples with single-pixel imaging at silicon-detectable wavelengths

Effect of Wu Zhi San supplementation in LPS-induced intestinal inflammation and barrier damage in broilers

Intestinal inflammation and barrier damage can inhibit the absorption and transportation of nutrients in the small intestine, and lead to various chronic diseases. Wu Zhi San (WZS) is a traditional Chinese formula composed of Schisandrae, Anemarrhenae, Lonicerae, and Glycyrrhizae that was made to cure intestinal inflammation and barrier damage in broilers. To evaluate the protective effect of WZS on intestinal inflammation and barrier damage of broilers under lipopolysaccharide (LPS) stress, a total of 200 one-day-old broilers were randomly divided into five groups, namely, the CON group, LPS group, and three WZS groups (WZS-H, WZS-M, and WZS-L). The groups were designed for stress phase I (days 15, 17, 19, and 21) and stress phase II (days 29, 31, 33, and 35). The protective effect of WZS on the intestinal tract was evaluated by measuring the levels of serum myeloperoxidase (MPO), diamine oxidase (DAO), super oxide dismutase (SOD), and serum D-lactate (D-LA) and the expression of inflammatory factors in jejunum. The results showed that the diet supplemented with WZS could significantly reduce serum MPO, DAO, and D-LA levels and jejunal CD in broilers (p < 0.05), increase serum SOD levels and jejunal VH (p < 0.05), significantly downregulate the expression of NF-κB, TLR4, MyD88, and inflammatory cytokines (TNF-α, IL-1β, IL-6, and IL-10), and upregulate Claudin-1, Occludin-1, and ZO-1 in broiler jejunum mucosa (p < 0.05). On the other hand, WZS could significantly reduce the protein expression of NF-κB (p65) in broiler jejunum (p < 0.05). These results indicate that supplementing WZS in the diet can reduce intestinal inflammation and alleviate intestinal barrier damage, and by inhibiting the NF-κB/TLR4/MyD88 signaling pathway, supplementation with WZS intervenes in LPS-induced stress injury in broilers

Research on bearing capacity of cross-type truss boom with variable cross-section of Crawler cranes

The web crossed truss boom is one of the commonly used truss boom structures of crawler cranes. However, the existing calculations fail to consider the limiting effect of the web members' bending resistance on the chord members, and cannot give full play to the load-bearing capacity of the existing structure. This paper takes the top section of the Crawler crane truss boom as the research object. The single-span truss theoretical model is established according to Timoshenko's elastic stability theory. And the theoretical critical load of the variable cross-section boom is obtained with full consideration of the limitation of the web member's bending resistance on the chord members. The finite element method simulation model is compared and verified. Compared with a large number of simulation experiments and theoretical calculations, it can be concluded that the theoretical calculations in this article are highly consistent with the simulation results, verified the assumptions that the web members' bending resistance help to improve the bending resistance of the chord members, and this will provide certain reference to the engineering designers

Quantum storage of entangled photons at telecom wavelengths in a crystal

The quantum internet -- in synergy with the internet that we use today -- promises an enabling platform for next-generation information processing, including exponentially speed-up distributed computation, secure communication, and high-precision metrology. The key ingredients for realizing such a global network are the distribution and storage of quantum entanglement. As quantum networks are likely to be based on existing fibre networks, telecom-wavelength entangled photons and corresponding quantum memories are of central interest. Recently, ${\rm ^{167}Er^{3+}}$ ions have been identified as a promising candidate for an efficient, broadband quantum memory at telecom wavelength. However, to date, no storage of entangled photons, the crucial step of quantum memory using these ions, has been reported. Here, we demonstrate the storage and recall of the entangled state of two telecom photons generated from an integrated photonic chip based on silicon nitride. Combining the natural narrow linewidth of the entangled photons and long storage time of ${\rm ^{167}Er^{3+}}$ ions, we achieve storage time of 400 ns, more than one order of magnitude longer than in previous works. Successful storage of entanglement in the crystal is certified by a violation of an entanglement witness by more than 12 standard deviations (-0.161 $\pm$ 0.012) at 400 ns storage time. These results pave the way for realizing quantum networks based on solid-state devices.Comment: 15 pages, 11 figure

Multiscale Feature Filtering Network for Image Recognition System in Unmanned Aerial Vehicle

For unmanned aerial vehicle (UAV), object detection at different scales is an important component for the visual recognition. Recent advances in convolutional neural networks (CNNs) have demonstrated that attention mechanism remarkably enhances multiscale representation of CNNs. However, most existing multiscale feature representation methods simply employ several attention blocks in the attention mechanism to adaptively recalibrate the feature response, which overlooks the context information at a multiscale level. To solve this problem, a multiscale feature filtering network (MFFNet) is proposed in this paper for image recognition system in the UAV. A novel building block, namely, multiscale feature filtering (MFF) module, is proposed for ResNet-like backbones and it allows feature-selective learning for multiscale context information across multiparallel branches. These branches employ multiple atrous convolutions at different scales, respectively, and further adaptively generate channel-wise feature responses by emphasizing channel-wise dependencies. Experimental results on CIFAR100 and Tiny ImageNet datasets reflect that the MFFNet achieves very competitive results in comparison with previous baseline models. Further ablation experiments verify that the MFFNet can achieve consistent performance gains in image classification and object detection tasks