Integrated placement and routing of relay nodes for fault-tolerant hierarchical sensor networks

In two-tiered sensor networks, using higher-powered relay nodes as cluster heads has been shown to lead to further improvements in network performance. Placement of such relay nodes focuses on achieving specified coverage and connectivity requirements with as few relay nodes as possible. Existing placement strategies typically are unaware of energy dissipation due to routing and are not capable of optimizing the routing scheme and placement concurrently. We, in this thesis, propose an integrated integer linear program (ILP) formulation that determines the minimum number of relay nodes, along with their locations and a suitable communication strategy such that the network has a guaranteed lifetime as well as ensuring the pre-specified level of coverage (ks) and connectivity (kr). We also present an intersection based approach for creating the initial set of potential relay node positions, which are used by our ILP, and evaluate its performance under different conditions. Experimental results on networks with hundreds of sensor nodes show that our approach leads to significant improvement over existing energy-unaware placement schemes

The Impact of Motivation and Prevention Factors on Game Addiction

Adolescents\u27 addiction to game has a negative impact on the aberrance of adolescents. Although limited research has been done on the cause of game addiction, no research has been conducted on the effectiveness of prevention measures. In this paper, we propose a model to study the impact of both the motivation and prevention factors on game addiction. Surveys were conducted among middle school students in Shanghai, with 623 valid responses. The analysis results show that among all prevention factors, only attention switch has significant negative impact on game addiction, however, dissuasion and parental monitoring have positive correlation with game addiction. The rational, resource shortage and cost have no significant impacts on game addiction. The analysis results also show that among all motivation factors, mechanics, relationship and escapism have significant positive impact on addiction

A CONCEPTUAL FRAMEWORK FOR MOBILE GROUP SUPPORT SYSTEMS

The rapid development of wireless communication and mobile devices has created a great opportunity to support mobile group coordination at a more efficient level than before. This article presents a framework for Mobile Group Support Systems (MGSS) that considers four dimensions: supporting whom, supporting what, where to support and how to support. A good MGSS design should take consideration with the characteristics of each dimension: the system should be able to support mobile users working jointly with members from multiple parties; using available and advanced mobile technology, the system should be able to support context freedom, context dependent, and ad hoc coordination under dynamic, uncertain, frequent disrupting, time and space stretched and fluid context. To meet these requirements, we discuss the issues related to three basic functions of MGSS: mobile communication, group coordination, and context awareness

Vision Transformer Advanced by Exploring Intrinsic Inductive Bias

The vision models have experienced a paradigm shift from convolutional neural networks (CNNs) to transformers. Compared with convolutions, transformers can capture both short- and long-range dependencies, making them more adaptable for extensive datasets. However, this adaptability comes at a cost: vision transformers are data-hungry and prone to overfitting with limited training data, restricting their applications in various vision tasks. This thesis aims to mitigate these shortcomings through advancements in architectural design and training methodologies, encompassing a comprehensive assessment involving various vision tasks. We investigate the data-hungry nature of transformers due to their lack of inductive bias. Our proposed remedy involves the incorporation of convolution blocks with multi-head self-attention (MHSA) mechanisms within each transformer block. This integration injects the inductive bias into the architecture, formulating the ViTAE model. Moreover, we present an innovative self-supervised learning approach, RegionCL, which bolsters the training process by emphasizing local information via region swapping. What’s more, a ViTPose-G model, based on ViTAE-G, is introduced and demonstrates exceptional performance in pose estimation tasks across various datasets

Vision Transformer with Quadrangle Attention

Window-based attention has become a popular choice in vision transformers due to its superior performance, lower computational complexity, and less memory footprint. However, the design of hand-crafted windows, which is data-agnostic, constrains the flexibility of transformers to adapt to objects of varying sizes, shapes, and orientations. To address this issue, we propose a novel quadrangle attention (QA) method that extends the window-based attention to a general quadrangle formulation. Our method employs an end-to-end learnable quadrangle regression module that predicts a transformation matrix to transform default windows into target quadrangles for token sampling and attention calculation, enabling the network to model various targets with different shapes and orientations and capture rich context information. We integrate QA into plain and hierarchical vision transformers to create a new architecture named QFormer, which offers minor code modifications and negligible extra computational cost. Extensive experiments on public benchmarks demonstrate that QFormer outperforms existing representative vision transformers on various vision tasks, including classification, object detection, semantic segmentation, and pose estimation. The code will be made publicly available at \href{https://github.com/ViTAE-Transformer/QFormer}{QFormer}.Comment: 15 pages, the extension of the ECCV 2022 paper (VSA: Learning Varied-Size Window Attention in Vision Transformers

VSA: Learning Varied-Size Window Attention in Vision Transformers

Attention within windows has been widely explored in vision transformers to balance the performance, computation complexity, and memory footprint. However, current models adopt a hand-crafted fixed-size window design, which restricts their capacity of modeling long-term dependencies and adapting to objects of different sizes. To address this drawback, we propose \textbf{V}aried-\textbf{S}ize Window \textbf{A}ttention (VSA) to learn adaptive window configurations from data. Specifically, based on the tokens within each default window, VSA employs a window regression module to predict the size and location of the target window, i.e., the attention area where the key and value tokens are sampled. By adopting VSA independently for each attention head, it can model long-term dependencies, capture rich context from diverse windows, and promote information exchange among overlapped windows. VSA is an easy-to-implement module that can replace the window attention in state-of-the-art representative models with minor modifications and negligible extra computational cost while improving their performance by a large margin, e.g., 1.1\% for Swin-T on ImageNet classification. In addition, the performance gain increases when using larger images for training and test. Experimental results on more downstream tasks, including object detection, instance segmentation, and semantic segmentation, further demonstrate the superiority of VSA over the vanilla window attention in dealing with objects of different sizes. The code will be released https://github.com/ViTAE-Transformer/ViTAE-VSA.Comment: 23 pages, 13 tables, and 5 figure

Rethinking Hierarchies in Pre-trained Plain Vision Transformer

Self-supervised pre-training vision transformer (ViT) via masked image modeling (MIM) has been proven very effective. However, customized algorithms should be carefully designed for the hierarchical ViTs, e.g., GreenMIM, instead of using the vanilla and simple MAE for the plain ViT. More importantly, since these hierarchical ViTs cannot reuse the off-the-shelf pre-trained weights of the plain ViTs, the requirement of pre-training them leads to a massive amount of computational cost, thereby incurring both algorithmic and computational complexity. In this paper, we address this problem by proposing a novel idea of disentangling the hierarchical architecture design from the self-supervised pre-training. We transform the plain ViT into a hierarchical one with minimal changes. Technically, we change the stride of linear embedding layer from 16 to 4 and add convolution (or simple average) pooling layers between the transformer blocks, thereby reducing the feature size from 1/4 to 1/32 sequentially. Despite its simplicity, it outperforms the plain ViT baseline in classification, detection, and segmentation tasks on ImageNet, MS COCO, Cityscapes, and ADE20K benchmarks, respectively. We hope this preliminary study could draw more attention from the community on developing effective (hierarchical) ViTs while avoiding the pre-training cost by leveraging the off-the-shelf checkpoints. The code and models will be released at https://github.com/ViTAE-Transformer/HPViT.Comment: Tech report, work in progres