The interaction between paternalistic leadership and achievement goals in predicting athletes’ sportspersonship

Paternalistic leadership, which is a prevalent leadership style in business contexts in non-Western cultures, is characterized by three dimensions: authoritarianism, benevolence, and morality. The current study of 252 Taiwanese intercollegiate athletes (Mage=20.91 years) explored this leadership style in a sports setting and examined the extent to which the interaction of paternalistic leadership and achievement goals predicted athletes’ sportspersonship. Participants completed the Paternalistic Leadership in Sport Questionnaire, Task and Ego Orientation in Sport Questionnaire, and Multidimensional Sportspersonship Orientation Scale. Athletes’ ego-orientation and perceived authoritarian leadership were related to lower levels of sportspersonship. In contrast, task-orientation, benevolent leadership, and moral leadership predicted higher levels of sportspersonship and confirmed findings reported in the research literature. Hierarchical regression analyses revealed that authoritarianism moderated the relationship between ego, orientation and sportspersonship. Future sports research should consider paternalistic leadership as an alternative approach when investigating coach-athlete relationships and the influence of coaches’ leadership on athletes’ growth and moral responses

The interaction between paternalistic leadership and achievement goals in predicting athletes’ sportspersonship

Paternalistic leadership, which is a prevalent leadership style in business contexts in non-Western cultures, is characterized by three dimensions: authoritarianism, benevolence, and morality. The current study of 252 Taiwanese intercollegiate athletes (Mage=20.91 years) explored this leadership style in a sports setting and examined the extent to which the interaction of paternalistic leadership and achievement goals predicted athletes’ sportspersonship. Participants completed the Paternalistic Leadership in Sport Questionnaire, Task and Ego Orientation in Sport Questionnaire, and Multidimensional Sportspersonship Orientation Scale. Athletes’ ego-orientation and perceived authoritarian leadership were related to lower levels of sportspersonship. In contrast, task-orientation, benevolent leadership, and moral leadership predicted higher levels of sportspersonship and confirmed findings reported in the research literature. Hierarchical regression analyses revealed that authoritarianism moderated the relationship between ego, orientation and sportspersonship. Future sports research should consider paternalistic leadership as an alternative approach when investigating coach-athlete relationships and the influence of coaches’ leadership on athletes’ growth and moral responses

Automatic view plane prescription for cardiac magnetic resonance imaging via supervision by spatial relationship between views

Background: View planning for the acquisition of cardiac magnetic resonance (CMR) imaging remains a demanding task in clinical practice. Purpose: Existing approaches to its automation relied either on an additional volumetric image not typically acquired in clinic routine, or on laborious manual annotations of cardiac structural landmarks. This work presents a clinic-compatible, annotation-free system for automatic CMR view planning. Methods: The system mines the spatial relationship, more specifically, locates the intersecting lines, between the target planes and source views, and trains deep networks to regress heatmaps defined by distances from the intersecting lines. The intersection lines are the prescription lines prescribed by the technologists at the time of image acquisition using cardiac landmarks, and retrospectively identified from the spatial relationship. As the spatial relationship is self-contained in properly stored data, the need for additional manual annotation is eliminated. In addition, the interplay of multiple target planes predicted in a source view is utilized in a stacked hourglass architecture to gradually improve the regression. Then, a multi-view planning strategy is proposed to aggregate information from the predicted heatmaps for all the source views of a target plane, for a globally optimal prescription, mimicking the similar strategy practiced by skilled human prescribers. Results: The experiments include 181 CMR exams. Our system yields the mean angular difference and point-to-plane distance of 5.68 degrees and 3.12 mm, respectively. It not only achieves superior accuracy to existing approaches including conventional atlas-based and newer deep-learning-based in prescribing the four standard CMR planes but also demonstrates prescription of the first cardiac-anatomy-oriented plane(s) from the body-oriented scout.Comment: Medical Physics. arXiv admin note: text overlap with arXiv:2109.1171

Force-EvT: A Closer Look at Robotic Gripper Force Measurement with Event-based Vision Transformer

Robotic grippers are receiving increasing attention in various industries as essential components of robots for interacting and manipulating objects. While significant progress has been made in the past, conventional rigid grippers still have limitations in handling irregular objects and can damage fragile objects. We have shown that soft grippers offer deformability to adapt to a variety of object shapes and maximize object protection. At the same time, dynamic vision sensors (e.g., event-based cameras) are capable of capturing small changes in brightness and streaming them asynchronously as events, unlike RGB cameras, which do not perform well in low-light and fast-moving environments. In this paper, a dynamic-vision-based algorithm is proposed to measure the force applied to the gripper. In particular, we first set up a DVXplorer Lite series event camera to capture twenty-five sets of event data. Second, motivated by the impressive performance of the Vision Transformer (ViT) algorithm in dense image prediction tasks, we propose a new approach that demonstrates the potential for real-time force estimation and meets the requirements of real-world scenarios. We extensively evaluate the proposed algorithm on a wide range of scenarios and settings, and show that it consistently outperforms recent approaches.Comment: 6 pages, 5 figure

Application of 25 MHz B-Scan Ultrasonography to Determine the Integrity of the Posterior Capsule in Posterior Polar Cataract

Purpose. To report the application of 25 MHz B-scan ultrasonography (MHzB) to determine the integrity of the posterior capsule (PC) in posterior polar cataract (PPC). Methods. Patients with whom PPC was clinically diagnosed using slit lamp microscopy who underwent 25 MHzB before phacoemulsification were retrospectively reviewed. The status of the PC was determined by 25 MHzB before phacoemulsification and confirmed during cataract surgery. Results. In total, 21 eyes in 14 clinically diagnosed PPC patients were enrolled in this study. Out of 25 MHzB images, 19 PCs were found to be intact, while 2 showed dehiscence before cataract surgery. During phacoemulsification, 17 PCs were observed to be intact, while 4 PCs showed posterior capsule rupture (PCR). These 4 PCR cases included the above 2 eyes, in which preexisting dehiscence was detected by 25 MHzB. The other 2 PCR cases showed high reflectivity between high echoes in posterior opacities and the PC, indicating synechia between the PPC and PC. Conclusion. This is the first report to show that 25 MHzB can be used to clearly visualize the status of the PC in PPC. These results, in turn, could be used to select the appropriate treatment and to thereby avoid further complications during PPC surgery

Perspectives on Privacy in the Post-Roe Era: A Mixed-Methods of Machine Learning and Qualitative Analyses of Tweets

Abortion is a controversial topic that has long been debated in the US. With the recent Supreme Court decision to overturn Roe v. Wade, access to safe and legal reproductive care is once again in the national spotlight. A key issue central to this debate is patient privacy, as in the post-HITECH Act era it has become easier for medical records to be electronically accessed and shared. This study analyzed a large Twitter dataset from May to December 2022 to examine the public's reactions to Roe v. Wade's overruling and its implications for privacy. Using a mixed-methods approach consisting of computational and qualitative content analysis, we found a wide range of concerns voiced from the confidentiality of patient-physician information exchange to medical records being shared without patient consent. These findings may inform policy making and healthcare industry practices concerning medical privacy related to reproductive rights and women's health.Comment: Paper accepted for the proceedings of the 2023 American Medical Informatics Association Annual Symposium (AMIA

LiDAR-Forest Dataset: LiDAR Point Cloud Simulation Dataset for Forestry Application

The popularity of LiDAR devices and sensor technology has gradually empowered users from autonomous driving to forest monitoring, and research on 3D LiDAR has made remarkable progress over the years. Unlike 2D images, whose focused area is visible and rich in texture information, understanding the point distribution can help companies and researchers find better ways to develop point-based 3D applications. In this work, we contribute an unreal-based LiDAR simulation tool and a 3D simulation dataset named LiDAR-Forest, which can be used by various studies to evaluate forest reconstruction, tree DBH estimation, and point cloud compression for easy visualization. The simulation is customizable in tree species, LiDAR types and scene generation, with low cost and high efficiency.Comment: 5 page

M2fNet: Multi-modal Forest Monitoring Network on Large-scale Virtual Dataset

Forest monitoring and education are key to forest protection, education and management, which is an effective way to measure the progress of a country's forest and climate commitments. Due to the lack of a large-scale wild forest monitoring benchmark, the common practice is to train the model on a common outdoor benchmark (e.g., KITTI) and evaluate it on real forest datasets (e.g., CanaTree100). However, there is a large domain gap in this setting, which makes the evaluation and deployment difficult. In this paper, we propose a new photorealistic virtual forest dataset and a multimodal transformer-based algorithm for tree detection and instance segmentation. To the best of our knowledge, it is the first time that a multimodal detection and segmentation algorithm is applied to large-scale forest scenes. We believe that the proposed dataset and method will inspire the simulation, computer vision, education, and forestry communities towards a more comprehensive multi-modal understanding.Comment: 5 page