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Person re-identification(Re-ID) aims to retrieve pedestrians under different cameras. Compared with image-based Re-ID, video-based Re-ID extracts features from video sequences that contain both spatial features and temporal features. Existing methods usually focus on the most attractive image parts, and this will lead to redundant spatial description and insufficient temporal description. Other methods that take temporal clues into consideration usually ignore misalignment between frames and only focus on a fixed length of one given sequence. In this study, we proposed a Reciprocal Global Temporal Convolution Network with Adaptive Alignment(AA-RGTCN). The structure could address the drawback of misalignment between frames and model discriminative temporal representation. Specifically, the Adaptive Alignment block is designed to shift each frame adaptively to its best position for temporal modeling. Then, we proposed the Reciprocal Global Temporal Convolution Network to model robust temporal features across different time intervals along both normal and inverted time order. The experimental results show that our AA-RGTCN can achieve 85.9% mAP and 91.0% Rank-1 on MARS, 90.6% Rank-1 on iLIDS-VID, and 96.6% Rank-1 on PRID-2011, indicating we could gain better performance than other state-of-the-art approaches.

Key to the success of legumes is the ability to form and maintain optimal symbiotic nodules that enable them to balance the trade-off between symbiosis and plant growth. Cytokinin is essential for homeostatic regulation of nodulation, but the mechanism remains incompletely understood. Here, we show that a B-type response regulator GmRR11d mediates systemic inhibition of nodulation. GmRR11d is induced by rhizobia and low level cytokinin, and GmRR11d can suppress the transcriptional activity of GmNSP1 on GmNIN1a to inhibit soybean nodulation. GmRR11d positively regulates cytokinin response and its binding on the GmNIN1a promoter is enhanced by cytokinin. Intriguingly, rhizobial induction of GmRR11d and its function are dependent upon GmNARK that is a CLV1-like receptor kinase and inhibits nodule number in shoots. Thus, GmRR11d governs a transcriptional program associated with nodulation attenuation and cytokinin response activation essential for systemic regulation of nodulation. Cytokinin is essential for regulation of nodulation. Here, the authors identified a B-type response regulator GmRR11d that governs a transcriptional program associated with nodulation and cytokinin activation essential for systemic regulation of nodulation.

The Chimp Optimization Algorithm (ChOA) is a heuristic algorithm proposed in recent years. It models the cooperative hunting behaviour of chimpanzee populations in nature and can be used to solve numerical as well as practical engineering optimization problems. ChOA has the problems of slow convergence speed and easily falling into local optimum. In order to solve these problems, this paper proposes a novel chimp optimization algorithm with refraction learning (RL-ChOA). In RL-ChOA, the Tent chaotic map is used to initialize the population, which improves the population’s diversity and accelerates the algorithm’s convergence speed. Further, a refraction learning strategy based on the physical principle of light refraction is introduced in ChOA, which is essentially an Opposition-Based Learning, helping the population to jump out of the local optimum. Using 23 widely used benchmark test functions and two engineering design optimization problems proved that RL-ChOA has good optimization performance, fast convergence speed, and satisfactory engineering application optimization performance.

Convolutional neural networks (CNNs) have been widely applied in the fields of medical tasks because they can achieve high accuracy in many fields using a large number of parameters and operations. However, many applications designed for auxiliary checks or help need to be deployed into portable devices, where the huge number of operations and parameters of a standard CNN can become an obstruction. MobileNet adopts a depthwise separable convolution to replace the standard convolution, which can greatly reduce the number of operations and parameters while maintaining a relatively high accuracy. Such highly structured models are very suitable for FPGA implementation in order to further reduce resource requirements and improve efficiency. Many other implementations focus on performance more than on resource requirements because MobileNets has already reduced both parameters and operations and obtained significant results. However, because many small devices only have limited resources they cannot run MobileNet-like efficient networks in a normal way, and there are still many auxiliary medical applications that require a high-performance network running in real-time to meet the requirements. Hence, we need to figure out a specific accelerator structure to further reduce the memory and other resource requirements while running MobileNet-like efficient networks. In this paper, a MobileNet accelerator is proposed to minimize the on-chip memory capacity and the amount of data that is transferred between on-chip and off-chip memory. We propose two configurable computing modules: Pointwise Convolution Accelerator and Depthwise Convolution Accelerator, to parallelize the network and reduce the memory requirement with a specific dataflow model. At the same time, a new cache usage method is also proposed to further reduce the use of the on-chip memory. We implemented the accelerator on Xilinx XC7Z020, deployed MobileNetV2 on it, and achieved 70.94 FPS with 524.25 KB on-chip memory usage under 150 MHz.

In traditional Chinese medicine theory, Scutellaria baicalensis Georgi [Lamiaceae; Scutellariae radix] (SR) is bitter and cold in nature. It enters the lung, gallbladder, spleen, large intestine, and small intestine meridians. It clears heat and dries dampness, purges fire and detoxifies, stops bleeding, and stabilizes pregnancy. It excels at clearing lung fire and upper-body heat. Flavonoids, the primary active compound of SR, undergo metabolism in vivo through Phase I and Phase II reactions as well as intestinal flora-mediated processes. Modern pharmacological research indicates that flavonoid compounds exhibit diverse biological activities in immune modulation, antiviral, anti-inflammatory, antibacterial, and antitumor effects. In recent years, novel formulations such as nanomedicines and liposomes have garnered increasing attention to enhance their stability and bioavailability. This review systematically summarizes the research progress on flavonoid compounds in SR, comprehensively elaborating on their phytochemistry, extraction methods, separation and purification techniques, in vivo metabolism, immunological and pharmacological effects, toxicity, and novel dosage forms. It provides theoretical foundations and practical references for the further research, development, and rational application of these compounds.

New phosphors Ca2Mg(BO3)2: Ce3+ were synthesized by the solid-state reaction method at a high temperature. The phase purity was characterized by powder X-ray diffraction (XRD). The ultraviolet-visible (UV-Vis) optical properties of Ce3+ have been investigated, and the lowest 5d levels, the emission, and the Stokes shifts of Ce3+ in the host lattice were identified. In addition, its concentration quenching process was also studied. The results show that Ce3+ ions enter Ca2+ sites with only one emission in a UV-Vis range and that the optimum doping concentration is x = 0.05. The excitation and emission spectra were evaluated to clearly reveal luminescence features.

Entity linking and predicate matching are two core tasks in the Chinese Knowledge Base Question Answering (CKBQA). Compared with the English entity linking task, the Chinese entity linking is extremely complicated, making accurate Chinese entity linking difficult. Meanwhile, strengthening the correlation between entities and predicates is the key to the accuracy of the question answering system. Therefore, we put forward a Bidirectional Encoder Representation from Transformers and transfer learning Knowledge Base Question Answering (BAT-KBQA) framework, which is on the basis of feature-enhanced Bidirectional Encoder Representation from Transformers (BERT), and then perform a Named Entity Recognition (NER) task, which is appropriate for Chinese datasets using transfer learning and the Bidirectional Long Short-Term Memory-Conditional Random Field (BiLSTM-CRF) model. We utilize a BERT-CNN (Convolutional Neural Network) model for entity disambiguation of the problem and candidate entities; based on the set of entities and predicates, a BERT-Softmax model with answer entity predicate features is introduced for predicate matching. The answer ultimately chooses to integrate entities and predicates scores to determine the definitive answer. The experimental results indicate that the model, which is developed by us, considerably enhances the overall performance of the Knowledge Base Question Answering (KBQA) and it has the potential to be generalizable. The model also has better performance on the dataset supplied by the NLPCC-ICCPOL2016 KBQA task with a mean F1 score of 87.74% compared to BB-KBQA.

New phosphors Ca 2 Mg(BO 3 ) 2 : Ce 3+ were synthesized by the solid‐state reaction method at a high temperature. The phase purity was characterized by powder X‐ray diffraction (XRD). The ultraviolet‐visible (UV‐Vis) optical properties of Ce 3+ have been investigated, and the lowest 5d levels, the emission, and the Stokes shifts of Ce 3+ in the host lattice were identified. In addition, its concentration quenching process was also studied. The results show that Ce 3+ ions enter Ca 2+ sites with only one emission in a UV‐Vis range and that the optimum doping concentration is x  = 0.05. The excitation and emission spectra were evaluated to clearly reveal luminescence features.

Symbiotic nitrogen fixation (SNF) provides nitrogen for soybean. A primary challenge in enhancing yield through efficient SNF lies in striking a balance between its high energy consumption and plant growth. However, the systemic transcriptional reprogramming during nodulation remains limited. Here, this work conducts a comprehensive RNA‐seq of the roots, cotyledons and leaves of inoculated‐soybean. This work finds 88,814 mRNAs and 6,156 noncoding RNAs (ncRNAs) across various organs. Notably, this work identifies 6,679 nodulation‐regulated mRNAs (NR‐mRNAs), 1,681 long noncoding RNAs (lncRNAs) (NR‐lncRNAs), and 59 miRNAs (NR‐miRNAs). The majority of these NR‐RNAs are associated with plant‐microbial interaction and exhibit high organ specificity. Roots display the highest abundance of NR‐ncRNAs and the most dynamic crosstalk between NR‐lncRNAs and NR‐miRNAs in a GmNARK‐dependent manner. This indicates that while each tissue responds uniquely, GmNARK serves as a primary regulator of the transcriptional control of nodulated‐plants. Furthermore, this work proves that lnc‐NNR6788 and lnc‐NNR7059 promote nodulation by regulating their target genes. This work also shows that the nodulation‐ and GmNARK‐regulated (NNR) lnc‐NNR4481 negatively regulates nodulation through miR172c within a competing endogenous RNA (ceRNA) network. The spatial organ‐type transcriptomic atlas establishes a benchmark and provides a valuable resource for integrative analyses of the mechanism underlying of nodulation and plant growth balance. Symbiotic nitrogen fixation promotes soybean growth, but maintaining energy balance is challenging. RNA‐seq of three organs of inoculated soybean identifies 88,814 mRNAs and 6,156 ncRNAs. Roots exhibit the most NR‐ncRNAs and dynamic crosstalk controlled by GmNARK. Lnc‐NNR6788 and lnc‐NNR7059 boost nodulation, whereas lnc‐NNR4481 inhibits it in a ceRNA network. This atlas facilitates the understanding of balancing nodulation and growth.