ConES: Concept Embedding Search for Parameter Efficient Tuning Large Vision Language Models

Large pre-trained vision-language models have shown great prominence in transferring pre-acquired knowledge to various domains and downstream tasks with appropriate prompting or tuning. Existing prevalent tuning methods can be generally categorized into three genres: 1) prompt engineering by creating suitable prompt texts, which is time-consuming and requires domain expertise; 2) or simply fine-tuning the whole model, which is extremely inefficient; 3) prompt tuning through parameterized prompt embeddings with the text encoder. Nevertheless, all methods rely on the text encoder for bridging the modality gap between vision and language. In this work, we question the necessity of the cumbersome text encoder for a more lightweight and efficient tuning paradigm as well as more representative prompt embeddings closer to the image representations. To achieve this, we propose a Concept Embedding Search (ConES) approach by optimizing prompt embeddings -- without the need of the text encoder -- to capture the 'concept' of the image modality through a variety of task objectives. By dropping the text encoder, we are able to significantly speed up the learning process, \eg, from about an hour to just ten minutes in our experiments for personalized text-to-image generation without impairing the generation quality. Moreover, our proposed approach is orthogonal to current existing tuning methods since the searched concept embeddings can be further utilized in the next stage of fine-tuning the pre-trained large models for boosting performance. Extensive experiments show that our approach can beat the prompt tuning and textual inversion methods in a variety of downstream tasks including objection detection, instance segmentation, and image generation. Our approach also shows better generalization capability for unseen concepts in specialized domains, such as the medical domain

Numerical Model of Mixed Lubrication and Experimental Study of Reciprocating Seal Based on Inverse Lubrication Theory

Based on the sealing mechanism of the reciprocating seal, the inverse-hydrodynamic-lubrication (IHL) method was adopted in this study to solve the Reynolds equation, and a multi-field coupled reciprocating seal mixed lubrication numerical model was established. Considering seals used for aircraft actuators as an example, we obtained sealing performance parameters such as leakage and friction at different oil pressures, reciprocating speeds, and temperatures. According to the actual situation, the influence of different working condition parameters on the sealing performance of the reciprocating seal system were analyzed. A reciprocating seal test bench was designed and built, and the friction data for the reciprocating seal system under different working conditions were experimentally obtained. Through a comparative analysis of experimental data and theoretical numerical results, the numerical model and calculation results for reciprocating seal mixed lubrication were verified

Experimental Measurement Method for Contact Stress of Elastic Metal Sealing Ring Based on Pressure Sensitive Paper

As a basic mechanical component, the sealing ring is widely used in industrial, aerospatial, and other fields. In this study, an elastic metal C-shaped sealing ring with a wave structure was taken as an example, and its performance was analyzed theoretically and measured experimentally. First, an experimental study was performed on the C-ring seal. The proposed method for experimental measurement of the contact stress of the C-ring seal involved innovative use of a universal electronic testing machine and pressure sensitive paper, in conjunction with the hue⁻saturation⁻brightness (HSB) method. Based on the discoloration of the pressure sensitive paper after contact stress, computer software was used for analysis, the discoloration was digitized, and the contact stress was established. Second, a theoretical calculation model of the C-ring seal was established using ANSYS software, and a finite element theoretical calculation of the mechanical properties of the sealing ring was established. Finally, the contact stress results were compared with the model calculation results of the C-ring seal. The error between the two was small (4.8%), which proved the validity of the calculation model and the scientificity of the experimental method

Piston Rod Coating Material Study of Reciprocating Sealing Experiment Based on Sterling Seal

Sterling seal is a commonly used reciprocating seal, in which the PTFE ring of the seal and the surface material of the piston rod play an important role in the reciprocating sealing process. In this paper, a reciprocating sealing test bench was built, four sets of carbon fiber PTFE sealing rings were used to perform reciprocating sealing bench experiments with Cr-coated piston rods and DLC-coated piston rods. After the experiment, the used four sets of seals were taken as experimental samples, and a new, unused carbon fiber PTFE seal was taken as a reference sample. The surface topography, surface wear, and wear surface elements of the test specimens were measured by three-dimensional white light interference surface topography instrument, field emission environment scanning electron microscope, and field emission scanning electron microscope. Through experimental determination, it is found that the coating material is detached to form abrasive grains, which causes the surface of the sealing ring to wear. This paper also proposes optimization suggestions for the processing method of the sealing ring and the selection of the material of the piston rod coating

Clinical characteristics of adult inpatients with Measles in Beijing from 2010 to 2021: a retrospective analysis

Abstract Background With the measles vaccine coverage rate gradually increasing, adult patients’ epidemiological and clinical characteristics have changed. Aims To analyze the clinical characteristics of adult measles patients in Beijing Youan Hospital. Methods We retrospectively reviewed the electronic medical records of 818 patients diagnosed with measles at Beijing Youan Hospital between June 2010 and October 2021. We divided all hospitalized patients into two demographics groups, using 14 years of age as the cut-off. Results Of the adult inpatients, 110 (74.83%) were aged 20–40. There was an overall peak incidence in 2014, and yearly peaks came in April. Fever, cough, erythema, and Koplik’s spots were present in 79.59%, 82.1%, 99.3%, and 59.8% of the adult group, respectively, compared to 75.26%, 92.0%, 99.9%, and 39.0% of the pediatric group. Decreased lymphocytes and hepatic impairment were common in adults. The adult group’s median level of C-reactive protein was higher than that of the pediatric group (p < 0.05). The positive rate of measles antibody (IgM) detection was 64.6% in the adults and 78.8% in the pediatric group (p < 0.05). Of the adults, 46.9%, 8.8%, and 66% had pneumonia, gastroenteritis, and antibiotic use, compared to 89.6%, 2.7%, and 83.2% of the pediatric patients. The duration of symptoms before admission and the average length of hospital stay was approximately six days in both groups. Conclusions Koplik’s spots are more likely to be detected by clinicians in adult patients admitted to the hospital. Active surveillance is helpful for adults who are negative for IgM on admission. Although the proportion of adult measles patients with liver injury is high, the disease is generally mild. Measles significantly impacts peripheral blood lymphocytes in adults, but adults are at lower risk of concurrent pneumonia than the pediatric group. Clinicians need to pay attention to the appropriate use of antibiotics. Expanding the coverage of the measles vaccination in high-risk areas is beneficial for preventing measles in adults

A Novel Deep Learning-Based Pose Estimation Method for Robotic Grasping of Axisymmetric Bodies in Industrial Stacked Scenarios

A vision-based intelligent robotic grasping system is essential for realizing unmanned operations in industrial manufacturing, and pose estimation plays an import role in this system. In this study, deep learning was used to obtain the 6D pose of an axisymmetric body which was optimal for robotic grasping in industrial stacked scenarios. We propose a method to obtain the 6D pose of an axisymmetric body by detecting the pre-defined keypoints on the side surface. To realize this method and solve other challenges in industrial stacked scenarios, we propose a multitask real-time convolutional neural network (CNN), named Key-Yolact, which involves object detection, instance segmentation, and multiobject 2D keypoint detection. A small CNN as a decision-making subsystem was designed to score multiple predictions of Key-Yolact, and the body with the highest score is considered the best for grasping. Experiments on a self-built stacked dataset showed that Key-Yolact has a practical tradeoff between inference speed and precision. The inference speed of Key-Yolact is higher by 10 FPS, whereas its precision is decreased by only 7% when compared with the classical multitask Keypoint R-CNN. Robotic grasping experiments showed that the proposed design is effective and can be directly applied to industrial scenarios

A Novel Deep Learning-Based Pose Estimation Method for Robotic Grasping of Axisymmetric Bodies in Industrial Stacked Scenarios

A vision-based intelligent robotic grasping system is essential for realizing unmanned operations in industrial manufacturing, and pose estimation plays an import role in this system. In this study, deep learning was used to obtain the 6D pose of an axisymmetric body which was optimal for robotic grasping in industrial stacked scenarios. We propose a method to obtain the 6D pose of an axisymmetric body by detecting the pre-defined keypoints on the side surface. To realize this method and solve other challenges in industrial stacked scenarios, we propose a multitask real-time convolutional neural network (CNN), named Key-Yolact, which involves object detection, instance segmentation, and multiobject 2D keypoint detection. A small CNN as a decision-making subsystem was designed to score multiple predictions of Key-Yolact, and the body with the highest score is considered the best for grasping. Experiments on a self-built stacked dataset showed that Key-Yolact has a practical tradeoff between inference speed and precision. The inference speed of Key-Yolact is higher by 10 FPS, whereas its precision is decreased by only 7% when compared with the classical multitask Keypoint R-CNN. Robotic grasping experiments showed that the proposed design is effective and can be directly applied to industrial scenarios

Clinical characteristics of Coronavirus Disease 2019 patients in Beijing, China

The outbreak of Coronavirus Disease (COVID-19) in Wuhan have affected more than 250 countries and regions worldwide. However, most of the clinical studies have been focused on Wuhan, and little is known about the disease outside of Wuhan in China. In this retrospective cohort study, we report the early clinical features of 80 patients with COVID-19 admitted to the hospital in Beijing. The results show that 27 (33.8%) patients had severe illness. Six (7.5%) patients were admitted to the ICU, and 3 (3.8%) patients died. Forty-eight percent (39/80) of the patients had a history of living/traveling in Wuhan. Patients with severe- illness were significantly older (average age, 71 years old vs 44 years old) and had a high incidence of expectoration (59.3% vs 34.0%), shortness of breath (92.6% vs 9.4%), anorexia (51.9% vs 18.9%) and confusion(18.5% vs 0%) compared with nonsevere patients. The systolic blood pressure (median, 130 mmHg vs 120 mmHg) was higher and the oxygen saturation (median, 98.3% vs 92.0%) was significantly lower in severe patients than nonsevere patients. In addition, myoglobin (median, 56.0 ng/mL vs 35.0 ng/mL), troponin I (median, 0.02 pg/mL vs 0.01 pg/mL), C-reactive protein (median, 69.7 mg/L vs 12.9 mg/L) and neutrophils (median, 3.3×109/L vs 2.2×109/L) were significantly increased, while lymphocytes (median, 0.8×109/L vs 1.2×109/L), albumin (mean, 32.8 g/L vs 36.8 g/L) and the creatinine clearance rate (median, 91.2 vs 108.2 ml/min/1.73m2) were significantly decreased among severe patients. Our study revealed that older patients with high levels of C-reactive protein, myoglobin, troponin I, and neutrophil and high systolic blood pressure as well as low levels of lymphocytes, and albumin and a low creatinine clearance rate and oxygen saturation were more likely to have severe disease

First Report on Development of Genome-Wide Microsatellite Markers for Stock (<i>Matthiola incana</i> L.)

Stock (Matthiola incana (L.) R. Br.) is a famous annual ornamental plant with important ornamental and economic value. The lack of DNA molecular markers has limited genetic analysis, genome evolution, and marker-assisted selective breeding studies of M. incana. Therefore, more DNA markers are needed to support the further elucidation of the biology and genetics of M. incana. In this study, a high-quality genome of M. incana was initially assembled and a set of effective SSR primers was developed at the whole-genome level using genome data. A total of 45,612 loci of SSRs were identified; the di-nucleotide motifs were the most abundant (77.35%). In total, 43,540 primer pairs were designed, of which 300 were randomly selected for PCR validation, and as the success rate for amplification. In addition, 22 polymorphic SSR markers were used to analyze the genetic diversity of 40 stock varieties. Clustering analysis showed that all varieties could be divided into two clusters with a genetic distance of 0.68, which were highly consistent with their flower shape (potted or cut type). Moreover, we have verified that these SSR markers are effective and transferable within the Brassicaceae family. In this study, potential SSR molecular markers were successfully developed for 40 M. incana varieties using whole genome analysis, providing an important genetic tool for theoretical and applied research on M. incana