We introduce DEIM, an innovative and efficient training framework designed to accelerate convergence in real-time object detection with Transformer-based architectures (DETR). To mitigate the sparse supervision inherent in one-to-one (O2O) matching in DETR models, DEIM employs a Dense O2O matching strategy. This approach increases the number of positive samples per image by incorporating additional targets, using standard data augmentation techniques. While Dense O2O matching speeds up convergence, it also introduces numerous low-quality matches that could affect performance. To address this, we propose the Matchability-Aware Loss (MAL), a novel loss function that optimizes matches across various quality levels, enhancing the effectiveness of Dense O2O. Extensive experiments on the COCO dataset validate the efficacy of DEIM. When integrated with RT-DETR and D-FINE, it consistently boosts performance while reducing training time by 50%. Notably, paired with RT-DETRv2, DEIM achieves 53.2% AP in a single day of training on an NVIDIA 4090 GPU. Additionally, DEIM-trained real-time models outperform leading real-time object detectors, with DEIM-D-FINE-L and DEIM-D-FINE-X achieving 54.7% and 56.5% AP at 124 and 78 FPS on an NVIDIA T4 GPU, respectively, without the need for additional data. We believe DEIM sets a new baseline for advancements in real-time object detection. Our code and pre-trained models are available at https://www.shihuahuang.cn/DEIM/.

Subsurface chlorophyll maxima (SCM) is a crucial ecological indicator for marine ecosystems. Previous studies have indicated that this phenomenon is globally widespread. Although the biogeochemical Argo assimilation results have yielded positive results, the sparse data prevents them from being effectively used in oceanographic operations. Considering the dependence of ocean parameter, a deep learning model termed AT-GRU based on gated recurrent units is proposed. By incorporating an attention mechanism, the model can effectively address missing data in the biogeochemical-Argo (BGC-Argo) profiles, achieving the transition of data from sparse to dense (Sparse2Dense) and improving the accuracy of estimating subsurface chlorophyll-a (Chla) concentration. Specifically, the dataset of satellite remote sensing data and the associated BGC-Argo profiles is first established. AT-GRU is employed to reconstruct Chla concentration profiles from 1 to 300 m, utilizing several sources of ocean surface data. Next, an in-depth investigation is conducted to determine the characteristics of SCM. The objective is to enable wider research on SCM by analyzing vertical Chla profiles in four geographical locations. Finally, the general improvement of skill performance metrics, with R-squared reaching 0.84, demonstrates the feasibility of the proposed methodology through extensive experiments. In addition, we apply AT-GRU to global surface satellite data from January 2023 and compare the results with numerical modeling data to further validate the performance. This study presents promising opportunities for leveraging artificial intelligence in subsurface oceanic phenomena with the idea of Sparse2Dense and holds significant implications for the field of marine ecology.

Currently, there are two representative non-volatile dual in-line memory module (NVDIMM) interfaces: a proprietary Intel DDR-T and the JEDEC NVDIMM-P, which are not supported by existing platforms. Adoption of new platform is costly and measuring its efficiency of migrating to the new platform is much more complex. This study is an alternative way of them—finding a new memory device that can be supported by all existing systems. In this paper, we propose an NVDIMM architecture with several system-wide mechanisms to allow the synchronous DDR4 memory interfaces to support non-deterministic (asynchronous) timing. The proposed memory architecture is implemented as a real device prototype, and also evaluated using synthetic and real workloads on an x86-64 server system.

Half wavelength AC transmission (HWACT) is a promising technology with unique advantages in the field of large power transmission over long distance. However, there is little study on the grid connection of HWACT, which shall be an important technical point for its engineering application. This paper deals with the point-to-grid connection of an HWACT line. Firstly, the requirements of grid connection are reviewed, after that the quasi-synchronization is identified for the further study. Then the point-to-grid connection of HWACT under various connection conditions is simulated using PSCAD/EMTDC. The point-to-grid connection characteristics between the HWACT line and the conventional line are compared and analyzed. The simulation results show that when there is only a phase angle error and both the voltage error and the frequency error are zero, the point-to-grid connection characteristics are different. The point-to-grid connection characteristics are the same, provided that there is only a small voltage or frequency error on both sides of the grid-connection switch.

Half-wavelength AC transmission (HWACT) possessed economic advantages due to its characteristics. However, the problem of secondary arc current limits its development and application. The neutral reactor is not suitable for HWACT and high speed grounding switch (HSGS) has many drawbacks. In this paper, the characteristics of secondary arc current in HWACT are studied in detail. Then, active suppression method for HWACT secondary arc current is proposed, which is based on a converter. Simulation study in PSCAD/EMTDC is carried out to provide its theory feasibility. The simulation results show that the proposed method is able to limit secondary arc current, speed up the arc extinction and improve the system's fault recovery efficiency in HWACT.