Dynamic Graph Neural Networks (DGNNs) have demonstrated exceptional performance at dynamic-graph analysis tasks. However, the costs exceed those incurred by other learning tasks, to the point where deployment on large-scale dynamic graphs is infeasible. Existing distributed frameworks that facilitate DGNN training are in their early stages and experience challenges such as communication bottlenecks, imbalanced workloads, and GPU memory overflow. We introduce DynaHB, a distributed framework for DGNN training using so-called Hybrid Batches. DynaHB reduces communication by means of vertex caching, and it ensures even data and workload distribution by means of load-aware vertex partitioning. DyanHB also features a novel hybrid-batch training mode that combines vertex-batch and snapshot-batch techniques, thereby reducing training time and GPU memory usage. Next, to further enhance the hybrid batch based approach, DynaHB integrates a reinforcement learning-based batch adjuster and a pipelined batch generator with a batch reservoir to reduce the cost of generating hybrid batches. Extensive experiments show that DynaHB is capable of up to a 93× and an average of 8.06× speedups over the state-of-the-art training framework.
High efficiency video coding (HEVC) has brought outperforming efficiency for video compression. To reduce the compression artifacts of HEVC, we propose a DenseNet based approach as the in-loop filter of HEVC, which leverages multiple adjacent frames to enhance the quality of each encoded frame. Specifically, the higher-quality frames are found by a reference frame selector (RFS). Then, a deep neural network for multi-frame in-loop filter (named MIF-Net) is developed to enhance the quality of each encoded frame by utilizing the spatial information of this frame and the temporal information of its neighboring higher-quality frames. The MIF-Net is built on the recently developed DenseNet, benefiting from the improved generalization capacity and computational efficiency. Finally, experimental results verify the effectiveness of our multi-frame in-loop filter, outperforming the HM baseline and other state-of-the-art approaches.
RGB-D semantic segmentation methods conventionally use two independent encoders to extract features from the RGB and depth data. However, there lacks an effective fusion mechanism to bridge the encoders, for the purpose of fully exploiting the complementary information from multiple modalities. This paper proposes a novel bottom-up interactive fusion structure to model the interdependencies between the encoders. The structure introduces an interaction stream to interconnect the encoders. The interaction stream not only progressively aggregates modality-specific features from the encoders but also computes complementary features for them. To instantiate this structure, the paper proposes a residual fusion block (RFB) to formulate the interdependences of the encoders. The RFB consists of two residual units and one fusion unit with gate mechanism. It learns complementary features for the modality-specific encoders and extracts modality-specific features as well as cross-modal features. Based on the RFB, the paper presents the deep multimodal networks for RGB-D semantic segmentation called RFBNet. The experiments on two datasets demonstrate the effectiveness of modeling the interdependencies and that the RFBNet achieved state-of-the-art performance.
An extensive study on the in-loop filter has been proposed for a high efficiency video coding (HEVC) standard to reduce compression artifacts, thus improving coding efficiency. However, in the existing approaches, the in-loop filter is always applied to each single frame, without exploiting the content correlation among multiple frames. In this paper, we propose a multi-frame in-loop filter (MIF) for HEVC, which enhances the visual quality of each encoded frame by leveraging its adjacent frames. Specifically, we first construct a large-scale database containing encoded frames and their corresponding raw frames of a variety of content, which can be used to learn the in-loop filter in HEVC. Furthermore, we find that there usually exist a number of reference frames of higher quality and of similar content for an encoded frame. Accordingly, a reference frame selector (RFS) is designed to identify these frames. Then, a deep neural network for MIF (known as MIF-Net) is developed to enhance the quality of each encoded frame by utilizing the spatial information of this frame and the temporal information of its neighboring higher-quality frames. The MIF-Net is built on the recently developed DenseNet, benefiting from its improved generalization capacity and computational efficiency. In addition, a novel block-adaptive convolutional layer is designed and applied in the MIF-Net, for handling the artifacts influenced by coding tree unit (CTU) structure in HEVC. Extensive experiments show that our MIF approach achieves on average 11.621% saving of the Bjøntegaard delta bit-rate (BD-BR) on the standard test set, significantly outperforming the standard in-loop filter in HEVC and other state-of-the-art approaches.
High Efficiency Video Coding (HEVC) significantly reduces bit-rates over the proceeding H.264 standard but at the expense of extremely high encoding complexity. In HEVC, the quad-tree partition of coding unit (CU) consumes a large proportion of the HEVC encoding complexity, due to the bruteforce search for rate-distortion optimization (RDO). Therefore, this paper proposes a deep learning approach to predict the CU partition for reducing the HEVC complexity at both intra- and inter-modes, which is based on convolutional neural network (CNN) and long- and short-term memory (LSTM) network. First, we establish a large-scale database including substantial CU partition data for HEVC intra- and inter-modes. This enables deep learning on the CU partition. Second, we represent the CU partition of an entire coding tree unit (CTU) in the form of a hierarchical CU partition map (HCPM). Then, we propose an early-terminated hierarchical CNN (ETH-CNN) for learning to predict the HCPM. Consequently, the encoding complexity of intra-mode HEVC can be drastically reduced by replacing the brute-force search with ETH-CNN to decide the CU partition. Third, an early-terminated hierarchical LSTM (ETH-LSTM) is proposed to learn the temporal correlation of the CU partition. Then, we combine ETH-LSTM and ETH-CNN to predict the CU partition for reducing the HEVC complexity for inter-mode. Finally, experimental results show that our approach outperforms other state-of-the-art approaches in reducing the HEVC complexity at both intra- and inter-modes.
A space-filling curve (SFC) maps points in a multi-dimensional space to one-dimensional points by discretizing the multi-dimensional space into cells and imposing a linear order on the cells. This way, an SFC enables computing a one-dimensional layout for multidimensional data storage and retrieval. Choosing an appropriate SFC is crucial, as different SFCs have different effects on query performance. Currently, there are two primary strategies: 1) deterministic schemes, which are computationally efficient but often yield suboptimal query performance, and 2) dynamic schemes, which consider a broad range of candidate SFCs based on cost functions but incur significant computational overhead. Despite these strategies, existing methods cannot efficiently measure the effectiveness of SFCs under heavy query workloads and numerous SFC options. To address this problem, we propose means of constant-time cost estimations that can enhance existing SFC selection algorithms, enabling them to learn more effective SFCs. Additionally, we propose an SFC learning method that leverages reinforcement learning and our cost estimations to choose an SFC pattern efficiently. Experimental studies offer evidence of the effectiveness and efficiency of the proposed means of cost estimation and SFC learning.
Researchers investigating various facets of theory of mind, sometimes referred to as mentalizing, are increasingly exploring how social group membership influences this process. To facilitate this research, we introduce the Black Reading the Mind in The Eyes task, a freely available 36-item Black RME task with an array of norming data about these stimuli. Stimuli have been created and equated to match the original Reading the Mind in the Eyes (RME) task which included only White faces. Norming data were collected in three waves that characterized the physical properties of the stimuli and also participants' subjective ratings of the stimuli. Between each round of ratings, stimuli that did not equate with the original RME task or were not distinctly recognized as Black were removed and new stimuli were incorporated in the next round until we obtained 36 distinctive Black RME targets that matched the 36 mental states used in the original RME stimulus set. Both stimulus sets were similarly difficult and subsequent testing showed that neither Black nor White participants' mentalizing accuracy varied as a function of target race. We provide instructions for obtaining the database and stimulus ratings.
Cardiovascular disease is the leading cause of mortality worldwide, and mitochondrial dysfunction is the primary contributor to these disorders. Recent studies have elaborated on selective autophagy-mitophagy, which eliminates damaged and dysfunctional mitochondria, stabilizes mitochondrial structure and function, and maintains cell survival and growth. Numerous recent studies have reported that mitophagy plays an important role in the pathogenesis of various cardiovascular diseases. This review summarizes the mechanisms underlying mitophagy and advancements in studies on the role of mitophagy in cardiovascular disease.
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