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 $\text{AP}^m_{novel}$ and 44.7 $\text{AP}^m_{all}$ on OV-LVIS, surpassing the previous SoTA by 4.2 $\text{AP}^m_{novel}$ and 9.8 $\text{AP}^m_{all}$. 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