Delving into Motion-Aware Matching for Monocular 3D Object Tracking

Recent advances of monocular 3D object detection facilitate the 3D multi-object tracking task based on low-cost camera sensors. In this paper, we find that the motion cue of objects along different time frames is critical in 3D multi-object tracking, which is less explored in existing monocular-based approaches. In this paper, we propose a motion-aware framework for monocular 3D MOT. To this end, we propose MoMA-M3T, a framework that mainly consists of three motion-aware components. First, we represent the possible movement of an object related to all object tracklets in the feature space as its motion features. Then, we further model the historical object tracklet along the time frame in a spatial-temporal perspective via a motion transformer. Finally, we propose a motion-aware matching module to associate historical object tracklets and current observations as final tracking results. We conduct extensive experiments on the nuScenes and KITTI datasets to demonstrate that our MoMA-M3T achieves competitive performance against state-of-the-art methods. Moreover, the proposed tracker is flexible and can be easily plugged into existing image-based 3D object detectors without re-training. Code and models are available at https://github.com/kuanchihhuang/MoMA-M3T.Comment: Accepted by ICCV 2023. Code is available at https://github.com/kuanchihhuang/MoMA-M3

Research on the Application of Online and Offline Mixed Teaching Mode of Marketing Course Based on the BOPPPS Model

BOPPPS teaching fully integrates the advantages of online self-study and offline courses. This kind of teaching has been widely used in college education, and has proved to have a positive effect on improving students’ ability to solve problems. It also has a significant effect on improving students’ sense of self-efficacy, stimulating learning interest and improving their ability to learn independently in practice. During the implementation of the research, the team explored and practiced the online and offline mixed teaching mode of marketing course with the wisdom tree teaching platform, and built teaching resources for students to learn and discuss on their own, which is a reference for future online mixed teaching

Real value prediction of protein solvent accessibility using enhanced PSSM features

<p>Abstract</p> <p>Background</p> <p>Prediction of protein solvent accessibility, also called accessible surface area (ASA) prediction, is an important step for tertiary structure prediction directly from one-dimensional sequences. Traditionally, predicting solvent accessibility is regarded as either a two- (exposed or buried) or three-state (exposed, intermediate or buried) classification problem. However, the states of solvent accessibility are not well-defined in real protein structures. Thus, a number of methods have been developed to directly predict the real value ASA based on evolutionary information such as position specific scoring matrix (PSSM).</p> <p>Results</p> <p>This study enhances the PSSM-based features for real value ASA prediction by considering the physicochemical properties and solvent propensities of amino acid types. We propose a systematic method for identifying residue groups with respect to protein solvent accessibility. The amino acid columns in the PSSM profile that belong to a certain residue group are merged to generate novel features. Finally, support vector regression (SVR) is adopted to construct a real value ASA predictor. Experimental results demonstrate that the features produced by the proposed selection process are informative for ASA prediction.</p> <p>Conclusion</p> <p>Experimental results based on a widely used benchmark reveal that the proposed method performs best among several of existing packages for performing ASA prediction. Furthermore, the feature selection mechanism incorporated in this study can be applied to other regression problems using the PSSM. The program and data are available from the authors upon request.</p

Accuracy of hysteroscopic biopsy, compared to dilation and curettage, as a predictor of final pathology in patients with endometrial cancer

AbstractObjectiveTo compare the methods of transcervical resectoscopy versus dilation and curettage (D&C) for endometrial biopsy and to compare these methods for the percentage of histological upgrades at the final posthysterectomy pathology findings in endometrial cancer.Materials and methodsWe retrospectively reviewed 253 cases of uterine cancer diagnosed from May 1995 to January 2014. Included in the study were patients who received transcervical resectoscopy (TCR) or D&C biopsy as the diagnostic method and underwent laparoscopic staging at our institution. The International Federation of Gynecologists and Obstetricians (FIGO) grade in the pathological report of the biopsy and final hysterectomy were recorded. The extrauterine risk was stratified using the initial FIGO grade and depth of myometrium invasion. It was compared to the actual risk using final pathological findings.ResultsWe identified 203 cases of endometrial cancer; 18 (8.9%) patients had a higher histological grade at the final hysterectomy. Among the 203 patients, 76 patients underwent TCR biopsy and 127 underwent D&C biopsy. The histological grade was upgraded in two (2.6%) patients in the TCR group. Three (3.9%) patients had positive peritoneal washings. In the D&C group, 16 (12.6%) patients with three (2.4%) positive peritoneal washings were upgraded.ConclusionTranscervical resectoscopy could provide more precise grading information, compared to D&C (2.6% vs. 12.6%). Doctors could therefore make a more accurate staging plan, based on the preoperative risk evaluation

Using Linear Regression to Identify Critical Demographic Variables Affecting Patient Safety Culture From Viewpoints of Physicians and Nurses

Background: The issues of patient safety and healthcare quality have become increasingly important around the world since the 1990s. Many hospitals manage to reduce the number of adverse events (AEs) that can threaten patient safety in healthcare organizations. Assessing the existing patient safety culture gives hospital management a clear vision of an organization’s strengths and weaknesses. The Safety Attitudes Questionnaire, with its good psychometric properties and great internal consistency, has been used extensively to assess the patient safety culture in healthcare organizations.Objective: Physicians and nurses form the core staff of each organization. With different demographic variables, they might perceive patient safety culture differently. This study purposed to identify critical demographic variables from the viewpoints of physicians and nurses that significantly influence the patient safety culture in a regional teaching hospital in Taiwan.Methods: Linear regression with forward selection was employed in this study to focus on all physicians and nurses using results of a 2015 internal survey in the case hospital. Ten demographic variables were the independent variables, and seven dimensions of the Chinese version of the Safety Attitudes Questionnaire were dependent variables.Results: Four out of 10 demographic variables had significant impacts on 6 out of 7 dimensions (with the exception of emotional exhaustion) from the Safety Attitudes Questionnaire. “Supervisor/manager” and “experience in position” followed by “age” were viewed by physicians and nurses as the most critical variables affecting the patient safety culture in this regional teaching hospital in Taiwan.Conclusion: Assessing an organization’s current patient safety culture provides a significant value to improving patient safety. This study revealed that “supervisor/manager” and “experience in position” are the 2 most important demographic variables influencing the patient safety culture. Hospital management should take heed of the suggestions of staff members regarding these characteristics to continuously enhance their patient safety culture

Inhibition effect of a custom peptide on lung tumors

Cecropin B is a natural antimicrobial peptide and CB1a is a custom, engineered modification of it. In vitro, CB1a can kill lung cancer cells at concentrations that do not kill normal lung cells. Furthermore, in vitro, CB1a can disrupt cancer cells from adhering together to form tumor-like spheroids. Mice were xenografted with human lung cancer cells; CB1a could significantly inhibit the growth of tumors in this in vivo model. Docetaxel is a drug in present clinical use against lung cancers; it can have serious side effects because its toxicity is not sufficiently limited to cancer cells. In our studies in mice: CB1a is more toxic to cancer cells than docetaxel, but dramatically less toxic to healthy cells

Text-driven Visual Synthesis with Latent Diffusion Prior

There has been tremendous progress in large-scale text-to-image synthesis driven by diffusion models enabling versatile downstream applications such as 3D object synthesis from texts, image editing, and customized generation. We present a generic approach using latent diffusion models as powerful image priors for various visual synthesis tasks. Existing methods that utilize such priors fail to use these models' full capabilities. To improve this, our core ideas are 1) a feature matching loss between features from different layers of the decoder to provide detailed guidance and 2) a KL divergence loss to regularize the predicted latent features and stabilize the training. We demonstrate the efficacy of our approach on three different applications, text-to-3D, StyleGAN adaptation, and layered image editing. Extensive results show our method compares favorably against baselines.Comment: Project website: https://latent-diffusion-prior.github.io