5,779 research outputs found
The Importance of Safety Production and Humanistic Management in Petroleum Project
Oil and gas industry has higher standard of safety requirements in production unit due to its high-risk nature of products. Production safety management is the most important component of petroleum project management. With the integration of humanistic management, the smoothness of project operations and the safety of personnel, facilities and products are guaranteed. Therefore, it is necessary to investigate the production safety policies in the aspects of humanistic management. Implementation of production safety and humanistic management protocols can effectively reduce the risk factors; thereby improve economic efficiency of oil and gas companies
Real-time Data Flow Control for CBM-TOF Super Module Quality Evaluation
Super module assembled with MRPC detectors is the component unit of TOF (Time
of Flight) system for the Compressed Baryonic Matter (CBM) experiment. Quality
of super modules needs to be evaluated before it is applied in CBM-TOF. Time
signals exported from super module are digitalized at TDC (Time to Digital
Converter) station. Data rate is up to 6 Gbps at each TDC station, which brings
a tremendous pressure for data transmission in real time. In this paper, a
real-time data flow control method is designed. In this control method, data
flow is divided into 3 types: scientific data flow, status data flow and
control data flow. In scientific data flow, data of each TDC station is divided
into 4 sub-flows, and then is read out by a parallel and hierarchical network,
which consists of multiple readout mother boards and daughter boards groups. In
status data flow, status data is aggregated into a specific readout mother
board. Then it is uploaded to DAQ via readout daughter board. In control data
flow, control data is downloaded to all circuit modules in the opposite
direction of status data flow. Preliminary test result indicated data of STS
was correctly transmitted to DAQ with no error and three type data flows were
control orderly in real time. This data flow control method can meet the
quality evaluation requirement of supper module in CBM-TOF
CoDet: Co-Occurrence Guided Region-Word Alignment for Open-Vocabulary Object Detection
Deriving reliable region-word alignment from image-text pairs is critical to
learn object-level vision-language representations for open-vocabulary object
detection. Existing methods typically rely on pre-trained or self-trained
vision-language models for alignment, which are prone to limitations in
localization accuracy or generalization capabilities. In this paper, we propose
CoDet, a novel approach that overcomes the reliance on pre-aligned
vision-language space by reformulating region-word alignment as a co-occurring
object discovery problem. Intuitively, by grouping images that mention a shared
concept in their captions, objects corresponding to the shared concept shall
exhibit high co-occurrence among the group. CoDet then leverages visual
similarities to discover the co-occurring objects and align them with the
shared concept. Extensive experiments demonstrate that CoDet has superior
performances and compelling scalability in open-vocabulary detection, e.g., by
scaling up the visual backbone, CoDet achieves 37.0 and
44.7 on OV-LVIS, surpassing the previous SoTA by 4.2
and 9.8 . Code is available at
https://github.com/CVMI-Lab/CoDet.Comment: Accepted by NeurIPS 202
Client-Level Differential Privacy via Adaptive Intermediary in Federated Medical Imaging
Despite recent progress in enhancing the privacy of federated learning (FL)
via differential privacy (DP), the trade-off of DP between privacy protection
and performance is still underexplored for real-world medical scenario. In this
paper, we propose to optimize the trade-off under the context of client-level
DP, which focuses on privacy during communications. However, FL for medical
imaging involves typically much fewer participants (hospitals) than other
domains (e.g., mobile devices), thus ensuring clients be differentially private
is much more challenging. To tackle this problem, we propose an adaptive
intermediary strategy to improve performance without harming privacy.
Specifically, we theoretically find splitting clients into sub-clients, which
serve as intermediaries between hospitals and the server, can mitigate the
noises introduced by DP without harming privacy. Our proposed approach is
empirically evaluated on both classification and segmentation tasks using two
public datasets, and its effectiveness is demonstrated with significant
performance improvements and comprehensive analytical studies. Code is
available at: https://github.com/med-air/Client-DP-FL.Comment: Accepted by 26th International Conference on Medical Image Computing
and Computer Assisted Intervention (MICCAI'23
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