149 research outputs found
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
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