Will Sentiment Analysis Need Subculture? A New Data Augmentation Approach

The renowned proverb that "The pen is mightier than the sword" underscores the formidable influence wielded by text expressions in shaping sentiments. Indeed, well-crafted written can deeply resonate within cultures, conveying profound sentiments. Nowadays, the omnipresence of the Internet has fostered a subculture that congregates around the contemporary milieu. The subculture artfully articulates the intricacies of human feelings by ardently pursuing the allure of novelty, a fact that cannot be disregarded in the sentiment analysis. This paper strives to enrich data through the lens of subculture, to address the insufficient training data faced by sentiment analysis. To this end, a new approach of subculture-based data augmentation (SCDA) is proposed, which engenders six enhanced texts for each training text by leveraging the creation of six diverse subculture expression generators. The extensive experiments attest to the effectiveness and potential of SCDA. The results also shed light on the phenomenon that disparate subculture expressions elicit varying degrees of sentiment stimulation. Moreover, an intriguing conjecture arises, suggesting the linear reversibility of certain subculture expressions. It is our fervent aspiration that this study serves as a catalyst in fostering heightened perceptiveness towards the tapestry of information, sentiment and culture, thereby enriching our collective understanding.Comment: JASIS

Exploring the law of text geographic information

Textual geographic information is indispensable and heavily relied upon in practical applications. The absence of clear distribution poses challenges in effectively harnessing geographic information, thereby driving our quest for exploration. We contend that geographic information is influenced by human behavior, cognition, expression, and thought processes, and given our intuitive understanding of natural systems, we hypothesize its conformity to the Gamma distribution. Through rigorous experiments on a diverse range of 24 datasets encompassing different languages and types, we have substantiated this hypothesis, unearthing the underlying regularities governing the dimensions of quantity, length, and distance in geographic information. Furthermore, theoretical analyses and comparisons with Gaussian distributions and Zipf's law have refuted the contingency of these laws. Significantly, we have estimated the upper bounds of human utilization of geographic information, pointing towards the existence of uncharted territories. Also, we provide guidance in geographic information extraction. Hope we peer its true countenance uncovering the veil of geographic information.Comment: IP

FSNet: Redesign Self-Supervised MonoDepth for Full-Scale Depth Prediction for Autonomous Driving

Predicting accurate depth with monocular images is important for low-cost robotic applications and autonomous driving. This study proposes a comprehensive self-supervised framework for accurate scale-aware depth prediction on autonomous driving scenes utilizing inter-frame poses obtained from inertial measurements. In particular, we introduce a Full-Scale depth prediction network named FSNet. FSNet contains four important improvements over existing self-supervised models: (1) a multichannel output representation for stable training of depth prediction in driving scenarios, (2) an optical-flow-based mask designed for dynamic object removal, (3) a self-distillation training strategy to augment the training process, and (4) an optimization-based post-processing algorithm in test time, fusing the results from visual odometry. With this framework, robots and vehicles with only one well-calibrated camera can collect sequences of training image frames and camera poses, and infer accurate 3D depths of the environment without extra labeling work or 3D data. Extensive experiments on the KITTI dataset, KITTI-360 dataset and the nuScenes dataset demonstrate the potential of FSNet. More visualizations are presented in \url{https://sites.google.com/view/fsnet/home}Comment: 12 pages. conditionally accepted by IEEE T-AS

CenterLineDet: Road Lane CenterLine Graph Detection With Vehicle-Mounted Sensors by Transformer for High-definition Map Creation

With the rapid development of autonomous vehicles, there witnesses a booming demand for high-definition maps (HD maps) that provide reliable and robust prior information of static surroundings in autonomous driving scenarios. As one of the main high-level elements in the HD map, the road lane centerline is critical for downstream tasks, such as prediction and planning. Manually annotating lane centerline HD maps by human annotators is labor-intensive, expensive and inefficient, severely restricting the wide application and fast deployment of autonomous driving systems. Previous works seldom explore the centerline HD map mapping problem due to the complicated topology and severe overlapping issues of road centerlines. In this paper, we propose a novel method named CenterLineDet to create the lane centerline HD map automatically. CenterLineDet is trained by imitation learning and can effectively detect the graph of lane centerlines by iterations with vehicle-mounted sensors. Due to the application of the DETR-like transformer network, CenterLineDet can handle complicated graph topology, such as lane intersections. The proposed approach is evaluated on a large publicly available dataset Nuscenes, and the superiority of CenterLineDet is well demonstrated by the comparison results. This paper is accompanied by a demo video and a supplementary document that are available at \url{https://tonyxuqaq.github.io/projects/CenterLineDet/}.Comment: Under revie

V2HDM-Mono: A Framework of Building a Marking-Level HD Map with One or More Monocular Cameras

Marking-level high-definition maps (HD maps) are of great significance for autonomous vehicles, especially in large-scale, appearance-changing scenarios where autonomous vehicles rely on markings for localization and lanes for safe driving. In this paper, we propose a highly feasible framework for automatically building a marking-level HD map using a simple sensor setup (one or more monocular cameras). We optimize the position of the marking corners to fit the result of marking segmentation and simultaneously optimize the inverse perspective mapping (IPM) matrix of the corresponding camera to obtain an accurate transformation from the front view image to the bird's-eye view (BEV). In the quantitative evaluation, the built HD map almost attains centimeter-level accuracy. The accuracy of the optimized IPM matrix is similar to that of the manual calibration. The method can also be generalized to build HD maps in a broader sense by increasing the types of recognizable markings

Effects of propofol combined with remifentanil on hemodynamics and stress response in children undergoing surgery for oral cancers, tonsil and adenoid surgery

727-733The anesthetic medication to sedate a child during general anesthesia (GA) for oral cancer, adenoidectomy or tonsillectomy is associated with operative complications such as hemodynamic instability and long postoperative recovery period. The current advancement enables combination of different anesthetic medications to decrease operative or postoperative complications associated with GA. In this study assessed the effects of propofol combined with remifentanil on hemodynamics and stress response in children undergoing oral cancer, tonsil and adenoid surgery. Propofol combined with remifentanil is beneficial to anesthesia for children undergoing oral cancer tonsil and adenoid surgery, manifested as stable hemodynamics, rapid recovery, low inflammatory and stress responses, and mild adverse reactions. A total of 106 eligible children treated from May 2017 to December 2019 were randomly divided into observation and control groups (n=53). Observation group was anesthetized by propofol plus remifentanil, while control group was anesthetized by propofol plus esketamine. Mean arterial pressure (MAP), heart rate (HR), serum C-reactive protein (CRP), interleukin-6 (IL-6), tumor necrosis factor-α (TNF-α), epinephrine (E), cortisol (Cor), CD3+, CD4+ helper and CD8+ inhibitory T lymphocytes, and CD4+/CD8+were compared before anesthesia induction (T1), immediately after intubation (T2), at the beginning of operation (T3), at the end of operation (T4) and 5 min after extubation (T5). Time of anesthetic recovery and adverse reactions after extubation were observed. MAP and HR significantly rose at T2 compared with those at T1. After maintenance of anesthesia, MAP and HR were significantly lower in observation group than those in control group. Serum CRP, IL-6 and TNF-α levels rose with time. E and Cor levels rose from T1 to T4 and declined at T5, with significant differences at each time point. CRP, IL-6, TNF-α, E and Cor levels were lower in observation group from T3 to T5. At T4 and T5, CD3+, CD4+levels and CD4+/CD8+ declined, whileCD8+level rose compared with those at other three time points. Time of recovery of autonomous respiration and limbs and duration from anesthetic withdrawal to extubation were significantly shorter in observation group. Observation group had lower incidence rate of dysphoria during recovery