Can the Query-based Object Detector Be Designed with Fewer Stages?

Query-based object detectors have made significant advancements since the publication of DETR. However, most existing methods still rely on multi-stage encoders and decoders, or a combination of both. Despite achieving high accuracy, the multi-stage paradigm (typically consisting of 6 stages) suffers from issues such as heavy computational burden, prompting us to reconsider its necessity. In this paper, we explore multiple techniques to enhance query-based detectors and, based on these findings, propose a novel model called GOLO (Global Once and Local Once), which follows a two-stage decoding paradigm. Compared to other mainstream query-based models with multi-stage decoders, our model employs fewer decoder stages while still achieving considerable performance. Experimental results on the COCO dataset demonstrate the effectiveness of our approach

Discovery of Plant Viruses From Tea Plant (Camellia sinensis (L.) O. Kuntze) by Metagenomic Sequencing

The tea plant (Camellia sinensis (L.) O. Kuntze) is an economically important woody species. In this study, we collected 26 tea plant samples with typical discoloration symptoms from different tea gardens and performed metagenomic analysis based on next-generation sequencing. Homology annotation and PCR sequencing validation finally identified seven kinds of plant viruses from tea plant. Based on abundance distribution analysis, the two most abundant plant viruses were highlighted. Genetic characterization suggested that they are two novel virus species with relatively high homology to Blueberry necrotic ring blotch virus and American plum line pattern virus. We named the newly discovered viruses tea plant necrotic ring blotch virus (TPNRBV) and tea plant line pattern virus (TPLPV). Evolutionary relationship analysis indicated that TPNRBV and TPLPV should be grouped into the Blunervirus and the Ilarvirus genera, respectively. TPLPV might have same genome activation process with known ilarviruses based on sequence analysis. Moreover, specific primers for both viruses detection were designed and validated. The symptoms and ultrastructure of TPNRBV infected leaves were first recorded. Virus detections in the symptomatic and asymptomatic tissues from field plants showing tea plant necrotic ring blotch disease suggest that TPNRBV has a systemic movement feature. In summary, we first identified seven kinds of putative plant viruses by metagenomic analysis and report two novel viruses being latent pathogens to tea plant. The results will advance our understanding of tea plant virology and have significance for the genetic breeding of tea plants in the future

Comparison of the efficacy and safety of Transarterial chemoembolization with and without Apatinib for the treatment of BCLC stage C hepatocellular carcinoma

Abstract Background Hepatocellular carcinoma (HCC) is a common cancer worldwide, with a poor prognosis. Most patients are diagnosed at advanced stages and are only eligible for palliative therapy. Therefore, this study aimed to evaluate the safety and efficacy of transcatheter arterial chemoembolization (TACE) combined with apatinib (TACE-apatinib) treatment and TACE-alone treatment for Barcelona Clinic Liver Cancer stage C HCC. Methods We retrospectively reviewed 80 consecutive patients with BCLC stage C HCC who received TACE-apatinib or TACE-alone as the initial treatment. We compared the clinical and laboratory outcomes, imaging findings at 1 and 3 months after TACE, tumor response, time to progression (TTP), overall survival (OS), and adverse events between both groups. Results The overall response rate was higher in the TACE-apatinib group than in the TACE-alone group at 1 and 3 months after treatment (66.7% vs 39.6%, respectively, P = 0.020; 45.8% vs 17.6%, respectively, P = 0.021). The median TTP and OS in the TACE-apatinib group were longer than those of the TACE-alone group (TTP: 6.3 months vs 3.5 months, respectively, P = 0.002; OS: 13.0 months vs 9.9 months, respectively, P = 0.041). Apatinib-associated side effects such as hypertension, hand-foot syndrome, oral ulcers, proteinuria, and diarrhea were more prevalent in the TACE-apatinib group than in TACE-alone group (P < 0.05). Conclusion Compared to TACE-alone treatment, TACE-apatinib increased the TTP, OS, and tumor-response rate at 1 and 3 months after treatment of BCLC stage C HCC without any significant increase in severe adverse events

Gradient Learning under Tilted Empirical Risk Minimization

Gradient Learning (GL), aiming to estimate the gradient of target function, has attracted much attention in variable selection problems due to its mild structure requirements and wide applicability. Despite rapid progress, the majority of the existing GL works are based on the empirical risk minimization (ERM) principle, which may face the degraded performance under complex data environment, e.g., non-Gaussian noise. To alleviate this sensitiveness, we propose a new GL model with the help of the tilted ERM criterion, and establish its theoretical support from the function approximation viewpoint. Specifically, the operator approximation technique plays the crucial role in our analysis. To solve the proposed learning objective, a gradient descent method is proposed, and the convergence analysis is provided. Finally, simulated experimental results validate the effectiveness of our approach when the input variables are correlated

Deep Transfer Learning for Ni-Based Superalloys Microstructure Recognition on &gamma;&prime; Phase

Ni-based superalloys are widely used to manufacture the critical hot-end components of aviation jet engines and various industrial gas turbines. The analysis of Ni-based superalloys microstructures is an important research task during the design and development of superalloys. The material microstructure information can only be understood by experts in the long history. Image segmentation and recognition are developing techniques for accelerating the microstructure analysis automatically. Although deep learning techniques have achieved satisfactory performance, they usually suffer from generalization, i.e., performing worse on a new dataset. In this paper, a deep transfer learning method which just needs a small number of labeled images is proposed to perform the microstructure recognition on &gamma;&prime; phase. To evaluate the effectiveness, we homely prepare two Ni-based superalloys at temperatures 900 &deg;C and 1000 &deg;C, and manually annotate two datasets named as W-900 and W-1000. Experimental results demonstrate that the proposed method only needs 3 and 5 labeled images to achieve state-of-the-art segmentation accuracy during the transfer from W-900 to W-1000 and the transfer from W-1000 to W-900, while enjoying the advantage of fast convergence. In addition, a simple and effective software for the Ni-based superalloys microstructure recognition on &gamma;&prime; phase is developed to improve the efficiency of materials experts, which will greatly facilitate the design of new Ni-base superalloys and even other multicomponent alloys

Tailoring impedance match and enhancing microwave absorption of Fe3O4/Bi24Fe2O39/Bi hollow porous microrods by controlling their composition

A self-assembly/precipitate conversion/decomposition process was developed for the controllable synthesis of Fe3O4/Bi24Fe2O39/Bi hollow porous microrods (HPMRs). The results demonstrated that the crystal size, component, and performances of HPMRs could be effectively modulated via changing Fe2+/Bi3+ molar ratio (γ). Fe3O4/Bi24Fe2O39/Bi HPMRs exhibited ferromagnetic behavior at room temperature. As Bi and Bi24Fe2O39 contents increased with γ, the saturation magnetization Ms and attenuation constantly decreased, whereas coercivity Hc and impedance matching ratio increased. Compounding Fe3O4 with small quantities of Bi and Bi24Fe2O39 into HPMRs can significantly enhance microwave absorption. Fe3O4/Bi24Fe2O39/Bi HPMRs formed at γ = 1:0.25 exhibited the optimum microwave absorption performance. The minimum RL was − 47.3 dB at 8.72 GHz, corresponding to 2.4 mm sample thickness. The absorption band with the reflection loss below − 20 dB was up to 14.0 GHz for the absorber with a thickness of 1.4 − 8.0 mm. The results demonstrate that the introduction of electromagnetic transparent materials (Bi24Fe2O39 or Bi) can improve the microwave absorption performances of Fe3O4 composites owing to enhanced impedance matching rather than attenuation constant. Keywords: Fe3O4/Bi24Fe2O39/Bi composites, Synthesis, Hollow porous microrods, Magnetic property, Composition-dependent microwave absorptio