226 research outputs found
PointOcc: Cylindrical Tri-Perspective View for Point-based 3D Semantic Occupancy Prediction
Semantic segmentation in autonomous driving has been undergoing an evolution
from sparse point segmentation to dense voxel segmentation, where the objective
is to predict the semantic occupancy of each voxel in the concerned 3D space.
The dense nature of the prediction space has rendered existing efficient
2D-projection-based methods (e.g., bird's eye view, range view, etc.)
ineffective, as they can only describe a subspace of the 3D scene. To address
this, we propose a cylindrical tri-perspective view to represent point clouds
effectively and comprehensively and a PointOcc model to process them
efficiently. Considering the distance distribution of LiDAR point clouds, we
construct the tri-perspective view in the cylindrical coordinate system for
more fine-grained modeling of nearer areas. We employ spatial group pooling to
maintain structural details during projection and adopt 2D backbones to
efficiently process each TPV plane. Finally, we obtain the features of each
point by aggregating its projected features on each of the processed TPV planes
without the need for any post-processing. Extensive experiments on both 3D
occupancy prediction and LiDAR segmentation benchmarks demonstrate that the
proposed PointOcc achieves state-of-the-art performance with much faster speed.
Specifically, despite only using LiDAR, PointOcc significantly outperforms all
other methods, including multi-modal methods, with a large margin on the
OpenOccupancy benchmark. Code: https://github.com/wzzheng/PointOcc.Comment: Code is available at https://github.com/wzzheng/PointOc
A Systematic Evaluation of Federated Learning on Biomedical Natural Language Processing
Language models (LMs) like BERT and GPT have revolutionized natural language
processing (NLP). However, privacy-sensitive domains, particularly the medical
field, face challenges to train LMs due to limited data access and privacy
constraints imposed by regulations like the Health Insurance Portability and
Accountability Act (HIPPA) and the General Data Protection Regulation (GDPR).
Federated learning (FL) offers a decentralized solution that enables
collaborative learning while ensuring the preservation of data privacy. In this
study, we systematically evaluate FL in medicine across biomedical NLP
tasks using LMs encompassing corpora. Our results showed that: 1) FL
models consistently outperform LMs trained on individual client's data and
sometimes match the model trained with polled data; 2) With the fixed number of
total data, LMs trained using FL with more clients exhibit inferior
performance, but pre-trained transformer-based models exhibited greater
resilience. 3) LMs trained using FL perform nearly on par with the model
trained with pooled data when clients' data are IID distributed while
exhibiting visible gaps with non-IID data. Our code is available at:
https://github.com/PL97/FedNLPComment: Accepted by KDD 2023 Workshop FL4Data-Minin
The Role of Iron, Its Metabolism and Ferroptosis in Traumatic Brain Injury
Traumatic brain injury (TBI) is a structural and physiological disruption of brain function caused by external forces. It is a major cause of death and disability for patients worldwide. TBI includes both primary and secondary impairments. Iron overload and ferroptosis highly involved in the pathophysiological process of secondary brain injury. Ferroptosis is a form of regulatory cell death, as increased iron accumulation in the brain leads to lipid peroxidation, reactive oxygen species (ROS) production, mitochondrial dysfunction and neuroinflammatory responses, resulting in cellular and neuronal damage. For this reason, eliminating factors like iron deposition and inhibiting lipid peroxidation may be a promising therapy. Iron chelators can be used to eliminate excess iron and to alleviate some of the clinical manifestations of TBI. In this review we will focus on the mechanisms of iron and ferroptosis involving the manifestations of TBI, broaden our understanding of the use of iron chelators for TBI. Through this review, we were able to better find novel clinical therapeutic directions for further TBI study
Peixin Yang's Economic thought and its Outcomes in the 1980s, Early in China's Reform, and China's Reform Miracle
During the initial period of reform, China's prudent handling of the money—price relationship while simultaneously bringing inflation and unemployment under control had a far-reaching influence on economic theory as well as practical significance. The country's successful experience constituted a miracle beyond the dreams of authoritative international financial authorities and experts. The famous American Nobel Prize winner, the economist Milton Friedman, once said that anyone who could explain China's success could win the Nobel Prize. In today's complex world, with its recurrent financial turmoil and intensified threat of war, China's successful experience in the early phases of reform has important significance for developing socialism with Chinese characteristics and maintaining the country's economic and financial security and the interests of the great mass of the people. We should sum up this experience to strengthen confidence in our institutions, our path, and our theory
A Review of Reinforcement Learning for Natural Language Processing, and Applications in Healthcare
Reinforcement learning (RL) has emerged as a powerful approach for tackling
complex medical decision-making problems such as treatment planning,
personalized medicine, and optimizing the scheduling of surgeries and
appointments. It has gained significant attention in the field of Natural
Language Processing (NLP) due to its ability to learn optimal strategies for
tasks such as dialogue systems, machine translation, and question-answering.
This paper presents a review of the RL techniques in NLP, highlighting key
advancements, challenges, and applications in healthcare. The review begins by
visualizing a roadmap of machine learning and its applications in healthcare.
And then it explores the integration of RL with NLP tasks. We examined dialogue
systems where RL enables the learning of conversational strategies, RL-based
machine translation models, question-answering systems, text summarization, and
information extraction. Additionally, ethical considerations and biases in
RL-NLP systems are addressed
Fault Separation Based on An Excitation Operator with Application to a Quadrotor UAV
This paper presents an excitation operator based fault separation
architecture for a quadrotor unmanned aerial vehicle (UAV) subject to loss of
effectiveness (LoE) faults, actuator aging, and load uncertainty. The actuator
fault dynamics is deeply excavated, containing the deep coupling information
among the actuator faults, the system states, and control inputs. By explicitly
considering the physical constraints and tracking performance, an excitation
operator and corresponding integrated state observer are designed to estimate
separately actuator fault and load uncertainty. Moreover, a fault separation
maneuver and a safety controller are proposed to ensure the tracking
performance when the excitation operator is injected. Both comparative
simulation and flight experiments have demonstrated the effectiveness of the
proposed scheme while maintaining high levels of tracking performance
Real-time Multi-person Eyeblink Detection in the Wild for Untrimmed Video
Real-time eyeblink detection in the wild can widely serve for fatigue
detection, face anti-spoofing, emotion analysis, etc. The existing research
efforts generally focus on single-person cases towards trimmed video. However,
multi-person scenario within untrimmed videos is also important for practical
applications, which has not been well concerned yet. To address this, we shed
light on this research field for the first time with essential contributions on
dataset, theory, and practices. In particular, a large-scale dataset termed
MPEblink that involves 686 untrimmed videos with 8748 eyeblink events is
proposed under multi-person conditions. The samples are captured from
unconstrained films to reveal "in the wild" characteristics. Meanwhile, a
real-time multi-person eyeblink detection method is also proposed. Being
different from the existing counterparts, our proposition runs in a one-stage
spatio-temporal way with end-to-end learning capacity. Specifically, it
simultaneously addresses the sub-tasks of face detection, face tracking, and
human instance-level eyeblink detection. This paradigm holds 2 main advantages:
(1) eyeblink features can be facilitated via the face's global context (e.g.,
head pose and illumination condition) with joint optimization and interaction,
and (2) addressing these sub-tasks in parallel instead of sequential manner can
save time remarkably to meet the real-time running requirement. Experiments on
MPEblink verify the essential challenges of real-time multi-person eyeblink
detection in the wild for untrimmed video. Our method also outperforms existing
approaches by large margins and with a high inference speed.Comment: Accepted by CVPR 202
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