End-to-end one-shot path-planning algorithm for an autonomous vehicle based on a convolutional neural network considering traversability cost

Path planning plays an important role in navigation and motion planning for robotics and automated driving applications. Most existing methods use iterative frameworks to calculate and plan the optimal path from the starting point to the endpoint. Iterative planning algorithms can be slow on large maps or long paths. This work introduces an end-to-end path-planning algorithm based on a fully convolutional neural network (FCNN) for grid maps with the concept of the traversability cost, and this trains a general path-planning model for 10 × 10 to 80 × 80 square and rectangular maps. The algorithm outputs the lowest-cost path while considering the cost and the shortest path without considering the cost. The FCNN model analyzes the grid map information and outputs two probability maps, which show the probability of each point in the lowest-cost path and the shortest path. Based on the probability maps, the actual optimal path is reconstructed by using the highest probability method. The proposed method has superior speed advantages over traditional algorithms. On test maps of different sizes and shapes, for the lowest-cost path and the shortest path, the average optimal rates were 72.7% and 78.2%, the average success rates were 95.1% and 92.5%, and the average length rates were 1.04 and 1.03, respectively

Solid-surface vitrification is an appropriate and convenient method for cryopreservation of isolated rat follicles

<p>Abstract</p> <p>Background</p> <p>Cryopreservation of isolated follicles may be a potential option to restore fertility in young women with cancer, because it can prevent the risks of cancer transmission. Several freezing protocols are available, including slow-rate freezing, open-pulled straws vitrification (OPS) and solid-surface vitrification (SSV, a new freezing technique). The purpose of our study was to investigate the effects of these freezing procedures on viability, ultrastructure and developmental capacity of isolated rat follicles.</p> <p>Methods</p> <p>Isolated follicles from female Sprague-Dawley rats were randomly assigned to SSV, OPS and slow-rate freezing groups for cryopreservation. Follicle viability assessment and ultrastructural examination were performed after thawing. In order to study the developmental capacity of thawed follicles, we performed <it>in vitro </it>culture with a three-dimensional (3D) system by alginate hydrogels.</p> <p>Results</p> <p>Our results showed that the totally viable rate of follicles vitrified by SSV (64.76%) was slightly higher than that of the OPS group (62.38%) and significantly higher than that of the slow-rate freezing group (52.65%; <it>P </it>< 0.05). The ultrastructural examination revealed that morphological alterations were relatively low in the SSV group compared to the OPS and slow-rate freezing groups. After <it>in vitro </it>culture within a 3D system using alginate hydrogels, we found the highest increase (28.90 ± 2.21 μm) in follicle diameter in follicles from the SSV group. The estradiol level in the SSV group was significantly higher than those in the OPS and slow-rate freezing groups at the end of a 72-hr culture period (<it>P </it>< 0.05).</p> <p>Conclusions</p> <p>Our results suggest that the SSV method is an appropriate and convenient method for cryopreservation of isolated rat follicles compared with the conventional slow-rate freezing method and the OPS method.</p

MuseCoco: Generating Symbolic Music from Text

Generating music from text descriptions is a user-friendly mode since the text is a relatively easy interface for user engagement. While some approaches utilize texts to control music audio generation, editing musical elements in generated audio is challenging for users. In contrast, symbolic music offers ease of editing, making it more accessible for users to manipulate specific musical elements. In this paper, we propose MuseCoco, which generates symbolic music from text descriptions with musical attributes as the bridge to break down the task into text-to-attribute understanding and attribute-to-music generation stages. MuseCoCo stands for Music Composition Copilot that empowers musicians to generate music directly from given text descriptions, offering a significant improvement in efficiency compared to creating music entirely from scratch. The system has two main advantages: Firstly, it is data efficient. In the attribute-to-music generation stage, the attributes can be directly extracted from music sequences, making the model training self-supervised. In the text-to-attribute understanding stage, the text is synthesized and refined by ChatGPT based on the defined attribute templates. Secondly, the system can achieve precise control with specific attributes in text descriptions and offers multiple control options through attribute-conditioned or text-conditioned approaches. MuseCoco outperforms baseline systems in terms of musicality, controllability, and overall score by at least 1.27, 1.08, and 1.32 respectively. Besides, there is a notable enhancement of about 20% in objective control accuracy. In addition, we have developed a robust large-scale model with 1.2 billion parameters, showcasing exceptional controllability and musicality