Object Detection Combining CNN and Adaptive Color Prior Features.

When compared with the traditional manual design method, the convolutional neural network has the advantages of strong expressive ability and it is insensitive to scale, light, and deformation, so it has become the mainstream method in the object detection field. In order to further improve the accuracy of existing object detection methods based on convolutional neural networks, this paper draws on the characteristics of the attention mechanism to model color priors. Firstly, it proposes a cognitive-driven color prior model to obtain the color prior features for the known types of target samples and the overall scene, respectively. Subsequently, the acquired color prior features and test image color features are adaptively weighted and competed to obtain prior-based saliency images. Finally, the obtained saliency images are treated as features maps and they are further fused with those extracted by the convolutional neural network to complete the subsequent object detection task. The proposed algorithm does not need training parameters, has strong generalization ability, and it is directly fused with convolutional neural network features at the feature extraction stage, thus has strong versatility. Experiments on the VOC2007 and VOC2012 benchmark data sets show that the utilization of cognitive-drive color priors can further improve the performance of existing object detection algorithms.

Efficient and Practical Correlation Filter Tracking.

Visual tracking is a basic task in many applications. However, the heavy computation and low speed of many recent trackers limit their applications in some computing power restricted scenarios. On the other hand, the simple update scheme of most correlation filter-based trackers restricts their robustness during target deformation and occlusion. In this paper, we explore the update scheme of correlation filter-based trackers and propose an efficient and adaptive training sample update scheme. The training sample extracted in each frame is updated to the training set according to its distance between existing samples measured with a difference hashing algorithm or discarded according to tracking result reliability. In addition, we expand our new tracker to long-term tracking. On the basis of the proposed model updating mechanism, we propose a new tracking state discrimination mechanism to accurately judge tracking failure, and resume tracking after the target is recovered. Experiments on OTB-2015, Temple Color 128 and UAV123 (including UAV20L) demonstrate that our tracker performs favorably against state-of-the-art trackers with light computation and runs over 100 fps on desktop computer with Intel i7-8700 CPU(3.2 GHz).

Coupling a Germanium Hut Wire Hole Quantum Dot to a Superconducting Microwave Resonator.

Realizing a strong coupling between spin and resonator is an important issue for scalable quantum computation in semiconductor systems. Benefiting from the advantages of a strong spin-orbit coupling strength and long coherence time, the Ge hut wire, which is proposed to be site-controlled grown for scalability, is considered to be a promising candidate to achieve this goal. Here we present a hybrid architecture in which an on-chip superconducting microwave resonator is coupled to the holes in a Ge quantum dot. The charge stability diagram can be obtained from the amplitude and phase responses of the resonator independently from the DC transport measurement. Furthermore, we estimate the hole-resonator coupling rate of gc/2π = 148 MHz in the single quantum dot-resonator system and estimate the spin-resonator coupling rate gs/2π to be in the range 2-4 MHz. We anticipate that strong coupling between hole spins and microwave photons in a Ge hut wire is feasible with optimized schemes in the future.

Coupling Two Distant Double Quantum Dots with a Microwave Resonator.

We fabricated a hybrid device with two distant graphene double quantum dots (DQDs) and a microwave resonator. A nonlinear response is observed in the resonator reflection amplitude when the two DQDs are jointly tuned to the vicinity of the degeneracy points. This observation can be well fitted by the Tavis-Cummings (T-C) model which describes two two-level systems coupling with one photonic field. Furthermore, the correlation between the DC currents in the two DQDs is studied. A nonzero cross-current correlation is observed which has been theoretically predicted to be an important sign of nonlocal coupling between two distant systems. Our results explore T-C physics in electronic transport and also contribute to the study of nonlocal transport and future implementations of remote electronic entanglement.

Charge Number Dependence of the Dephasing Rates of a Graphene Double Quantum Dot in a Circuit QED Architecture.

We use an on-chip superconducting resonator as a sensitive meter to probe the properties of graphene double quantum dots at microwave frequencies. Specifically, we investigate the charge dephasing rates in a circuit quantum electrodynamics architecture. The dephasing rates strongly depend on the number of charges in the dots, and the variation has a period of four charges, over an extended range of charge numbers. Although the exact mechanism of this fourfold periodicity in dephasing rates is an open problem, our observations hint at the fourfold degeneracy expected in graphene from its spin and valley degrees of freedom.

Curative effect of the total saponins of Panax japonicus (TSPJ) on type 2 diabetes: Focusing on VEGFA.

OBJECTIVE: The objective of this study was to assess the impact of the total saponins of Panax japonicus (TSPJ) on Type 2 diabetes mellitus (T2DM). RESULTS: The intervention of TSPJ was found to have the ability to reverse physiological indicators associated with T2DM, while also enhancing the expression of genes involved in glucose metabolism and intestinal homeostasis. Additionally, alterations in the composition of the gut microbiota were observed. Based on the findings of experimental results and network pharmacology analysis, it is evident that vascular endothelial growth factor A (VEGFA) serves as a prominent shared target between TSPJ and diabetes. The outcomes observed in T2DM mice overexpressing VEGFA align with those observed in T2DM mice treated with TSPJ. CONCLUSIONS: TSPJ administration and VEGFA overexpression yield similar effects on T2DM in mice. Thus, in terms of mechanism, by upregulating the expression of VEGFA, TSPJ may ameliorate metabolic imbalance, preserve intestinal homeostasis, and lessen the symptoms of type 2 diabetes. The findings demonstrated the viability of using VEGFA as a type 2 diabetes therapy option and offered important insights into the therapeutic mechanisms by TSPJ in the management of T2DM. To determine the exact mechanisms behind the effects of TSPJ and VEGFA and to assess their potential therapeutic uses, more research efforts are necessary.

Gut microbiome: New perspectives for type 2 diabetes prevention and treatment.

Type 2 diabetes mellitus (T2DM), which is distinguished by increased glucose levels in the bloodstream, is a metabolic disease with a rapidly increasing incidence worldwide. Nevertheless, the etiology and characteristics of the mechanism of T2DM remain unclear. Recently, abundant evidence has indicated that the intestinal microbiota is crucially involved in the initiation and progression of T2DM. The gut microbiome, the largest microecosystem, engages in material and energy metabolism in the human body. In this review, we concentrated on the correlation between the gut flora and T2DM. Meanwhile, we summarized the pathogenesis involving the intestinal flora in T2DM, as well as therapeutic approaches aimed at modulating the gut microbiota for the management of T2DM. Through the analysis presented here, we draw attention to further exploration of these research directions.

Healing mechanism of diabetic foot ulcers using single-cell RNA-sequencing.

Background: Diabetic foot ulcer (DFU) is one of the common and severe complications in diabetic patients, mainly caused by the interaction of various factors such as peripheral neuropathy, peripheral vascular disease, and infection. Moreover, vascular damage, disorder of tissue cells, decreased expression level of neurotrophic factor, and decreased growth factor caused by long-term exposure to a high glucose environment can also lead to prolonged or incomplete wound healing. This imposes a tremendous financial burden on the patients' family and society. Although various innovative techniques and drugs have been developed to treat DFU, the therapeutic effect is still unsatisfactory. Methods: We filtered and downloaded the single-cell dataset of diabetic patients from the Gene Expression Omnibus (GEO) website and used the Seurat package in R for creation of single-cell objects, integration, control of quality, clustering, cell type identification, differential gene analysis, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis, and intercellular communication analysis. Results: Diabetic healing-related differentially expressed gene (DEG) analysis showed that there were 1,948 differential genes between tissue stem cells in healing and non-healing wounds, of which 1,198 genes were up-regulated and 685 genes were down-regulated. The results of GO functional enrichment analysis in tissue stem cells showed that they were closely related to wound healing. The CCL2-ACKR1 signaling pathway activity in tissue stem cells influenced the biological activity of endothelial cell subpopulation, which ultimately promoted the healing of DFU wounds. Conclusions: The CCL2-ACKR1 axis is closely associated with DFU healing.

Hybrid Fine-Tuning Strategy for Few-Shot Classification.

Few-shot classification aims to enable the network to acquire the ability of feature extraction and label prediction for the target categories given a few numbers of labeled samples. Current few-shot classification methods focus on the pretraining stage while fine-tuning by experience or not at all. No fine-tuning or insufficient fine-tuning may get low accuracy for the given tasks, while excessive fine-tuning will lead to poor generalization for unseen samples. To solve the above problems, this study proposes a hybrid fine-tuning strategy (HFT), including a few-shot linear discriminant analysis module (FSLDA) and an adaptive fine-tuning module (AFT). FSLDA constructs the optimal linear classification function under the few-shot conditions to initialize the last fully connected layer parameters, which fully excavates the professional knowledge of the given tasks and guarantees the lower bound of the model accuracy. AFT adopts an adaptive fine-tuning termination rule to obtain the optimal training epochs to prevent the model from overfitting. AFT is also built on FSLDA and outputs the final optimum hybrid fine-tuning strategy for a given sample size and layer frozen policy. We conducted extensive experiments on mini-ImageNet and tiered-ImageNet to prove the effectiveness of our proposed method. It achieves consistent performance improvements compared to existing fine-tuning methods under different sample sizes, layer frozen policies, and few-shot classification frameworks.

A Ni(COD)2-free approach for the synthesis of high surface area porous aromatic frameworks.

Porous aromatic frameworks (PAFs) are attractive materials for applications where high surface area and material stability govern performance. Most of the highest surface area PAFs are synthesized using poorly scalable and costly methods involving super-stoichiometric bis(1,5-cyclooctadiene)Nickel(0) (Ni(COD)2). This communication describes a general approach for the synthesis of high surface area PAFs that does not use isolated Ni(COD)2. The method is general to at least seven microporous polymers and can be conducted on gram scales without the use of an inert atmosphere glovebox. This work is expected to improve the synthetic accessibility of these materials.

Parametric strong mode-coupling in carbon nanotube mechanical resonators.

Carbon nanotubes (CNTs) have attracted much attention for use in nanomechanical devices because of their exceptional properties, such as large resonant frequencies, low mass, and high quality factors. Here, we report the first experimental realization of parametric strong coupling between two mechanical modes on a single CNT nanomechanical resonator, by applying an extra microwave pump. This parametric pump method can be used to couple mechanical modes with arbitrary frequency differences. The properties of the mechanical resonator are detected by single-electron tunneling at low temperature, which is found to be strongly coupled to both modes. The coupling strength between the two modes can be tuned by the pump power, setting the coupling regime from weak to strong. This tunability may be useful in further phonon manipulations in carbon nanotubes.

Introduction of DC line structures into a superconducting microwave 3D cavity.

We report a technique that can noninvasively add multiple DC wires into a 3D superconducting microwave cavity for electronic devices that require DC electrical terminals. We studied the influence of our DC lines on the cavity performance systematically. We found that the quality factor of the cavity is reduced if any of the components of the electrical wires cross the cavity equipotential planes. Using this technique, we were able to incorporate a quantum dot (QD) device into a 3D cavity. We then controlled and measured the QD transport signal using the DC lines. We have also studied the heating effects of the QD by the microwave photons in the cavity.