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Building facade completion is an important part of digitizing the structures of buildings using computer technology. Due to the intricate textures and structures in building facade images, existing image-completion algorithms cannot accurately restore the rich texture and detailed information. In response, this paper proposes a novel network to simultaneously recover the texture and semantic structural features of building facades. By incorporating dynamic convolutions into each layer of the feature encoder, the shallow layers of the completion network can create a global receptive field, thus enhancing the model's feature-extraction capability. Additionally, a spatial attention branch is integrated into the dynamic convolution module to boost the correlation between the completion area and its surrounding edge area, resulting in improved edge clarity and accuracy of the completed facade image. Experimental results on multiple public image datasets demonstrate that the proposed model in this paper achieves state-of-the-art results when applied to real-world datasets. [ABSTRACT FROM AUTHOR]

One-stage 3D object detection using mobile light detection and ranging (LiDAR) has developed rapidly in recent years. Specifically, one-stage methods have attracted attention because of their high efficiency and light weight compared with two-stage methods. Inspired by this, we present the semantic segmentation assisted one-stage three-dimensional vehicle object detection (SSA3D), a network for the rapid detection of objects that keeps the advantages of the semantic segmentation module in the two-stage methods without increasing redundant computational load. First, we modified the sampling of the farthest point to improve the quality of the sampling points. This helps to reduce sampling outlier points and bad points that are difficult to perceive in the spatial structure information surrounding the point. Second, a neighbor attention group module is devoted to selectively add extra weight to neighbor points because of the different importance of neighbor points for the corresponding sampling point. Correctly increasing the weight is helpful to obtain richer spatial structure information. Finally, a delicate box generation module is included as a voted center point layer based on the generalized Hoff vote method and an anchor-free regression. We used the feature aggregation module as the backbone and the feature propagation module as the auxiliary network to achieve efficiency. At the same time, the auxiliary network retains the ability to extract point-wise features from the state-of-the-art semantic segmentation network. In experiments, we evaluated and tested the SSA3D on a common KITTI dataset and achieved improved performance in the class of car accuracy. [ABSTRACT FROM AUTHOR]

In recent years, deep learning methods make a big difference in side-channel attack (SCA) community especially in the profiled scenario. Multiclass classification method is the common way to complete such classification task. In this article, we propose a novel SCA method utilizing multilabel classification from bit-to-byte view. Accordingly, each leakage trace has eight labels when considering a byte. The experimental results on several datasets show that our multilabel classification method is efficient and even performs better in some situations compared with the original multiclass classification model while model complexity is much reduced. Besides, our multilabel model can be seen as ensemble of monobit models and we verify the ensemble effect experimentally. [ABSTRACT FROM AUTHOR]

XPath query is the key part of XML data processing, and its performance is usually critical for XML applications. In the process of XPath query, there is inherent seriality between query steps, which makes it difficult to parallelize the query effectively as a whole. On the other hand, although XPath query has the characteristics of data stream processing and is suitable for pipeline processing, the data flow of each query step usually varies a lot, which results in limited performance under multithreading conditions. In this paper, we propose a pipelined XPath query method (PXQ) based on cost optimization. This method uses pipelined query primitives to process query steps based on relation index. During pipeline construction, a cost estimation model based on XML statistics is proposed to estimate the cost of the query primitive and provide guidance for the creation of a pipeline phase through the partition of query primitive sequence. The pipeline construction technique makes full use of available worker threads and optimizes the load balance between pipeline stages. The experimental results show that our method can adapt to the multithreaded environment and stream processing scenarios of XPath query, and its performance is better than the existing typical query methods based on data parallelism. [ABSTRACT FROM AUTHOR]

The stock turnover values are sensitive to external factors, and remain great challenges in its prediction. The consideration is that search engine data can reflect market environment, policies and attentions on stocks. Therefore, a dual sides autoregression (DSAR) method is proposed to benefit from both observed turnover values and exogenous data. The proposed DSAR consists of linear representation stage and combination stage. In linear representation stage, the short-term patterns of turnover values and query data are represented, respectively. In combination stage, the outputs from previous stages are combined. Intensive experiments on two groups of data collections show the effectiveness of our proposed method. [ABSTRACT FROM AUTHOR]

Automatic object detection in aerial imagery is being increasingly adopted in many applications, such as traffic monitoring, smart cities, and disaster assistance. In keypoint-based detectors, the prediction modules are usually generated from a fixed feature map scale. This configuration significantly limits the ability to detect multiscale objects in aerial scenes. The corner selection module in these detectors often ignores that a category in an aerial image is relatively unitary. In this article, a novel network, called the multiscale keypoint detection network (MKD-Net), is proposed to address these challenges. MKD-Net fuses multiscale layers to generate multiple feature maps for objects of different sizes. During the inference phase, both feature maps can be exploited for predicting corners. Moreover, a category attention module is designed to reduce the channel noise for a single-category scene. Experiments on benchmarks PASCAL VOC and DOTA show promising performance of MKD-Net compared with the baseline network. The code is available on https://github.com/jason-su/MKD-NET. [ABSTRACT FROM AUTHOR]

The LiDAR point cloud filtering algorithm based on marginal points, is performed well in complex cityscapes. However, it is very time consuming for detecting marginal points. To improve the efficiency for LiDAR point cloud filtering, a parallel marginal point detecting algorithm is proposed based on GPGPU. The algorithm is followed by four steps: parallel indexing K nearest neighbor points, parallel plane fitting, parallel calculating projection points and parallel calculating points' angle. For the step of parallel indexing k nearest neighbor points, an insertion sorting algorithm based on queue priority is proposed to reduce the number of judging amount substantially. The experiment results show that GPU parallel computing can improve the LiDAR point cloud filtering efficiency effectively. The acceleration ratio can reach about 9 when data size is above 128 megabytes. [ABSTRACT FROM AUTHOR]

In 2011, Li presented clockwise collision analysis on nonprotected Advanced Encryption Standard (AES) hardware implementation. In this brief, we first propose a new clockwise collision attack, called fault rate analysis (FRA), on masked AES. Then, we analyze the critical and noncritical paths of the S-box and find that, for its three input bytes, namely, the input value, the input mask, and the output mask, the path relating to the output mask is much shorter than those relating to the other two inputs. Therefore, some sophisticated glitch cycles can be chosen such that the values in the critical path of the whole S-box are destroyed but this short path is not affected. As a result, the output mask does not offer protection to the S-box, which leads to a more efficient attack. Compared with three attacks on masking countermeasures at the Workshop on Cryptographic Hardware and Embedded Systems 2010 and 2011, our method only costs about 8% of their time and 4% of their storage space. [ABSTRACT FROM AUTHOR]

The semantic segmentation of small objects in point clouds is currently one of the most demanding tasks in photogrammetry and remote sensing applications. Multi-resolution feature extraction and fusion can significantly enhance the ability of object classification and segmentation, so it is widely used in the image field. For this motivation, we propose a point cloud semantic segmentation network based on multi-scale feature fusion (MSSCN) to aggregate the feature of a point cloud with different densities and improve the performance of semantic segmentation. In our method, random downsampling is first applied to obtain point clouds of different densities. A Spatial Aggregation Net (SAN) is then employed as the backbone network to extract local features from these point clouds, followed by concatenation of the extracted feature descriptors at different scales. Finally, a loss function is used to combine the different semantic information from point clouds of different densities for network optimization. Experiments were conducted on the S3DIS and ScanNet datasets, and our MSSCN achieved accuracies of 89.80% and 86.3%, respectively, on these datasets. Our method showed better performance than the recent methods PointNet, PointNet++, PointCNN, PointSIFT, and SAN. [ABSTRACT FROM AUTHOR]

Semantic segmentation of 3D point clouds plays a vital role in autonomous driving, 3D maps, and smart cities, etc. Recent work such as PointSIFT shows that spatial structure information can improve the performance of semantic segmentation. Motivated by this phenomenon, we propose Spatial Aggregation Net (SAN) for point cloud semantic segmentation. SAN is based on multi-directional convolution scheme that utilizes the spatial structure information of point cloud. Firstly, Octant-Search is employed to capture the neighboring points around each sampled point. Secondly, we use multi-directional convolution to extract information from different directions of sampled points. Finally, max-pooling is used to aggregate information from different directions. The experimental results conducted on ScanNet database show that the proposed SAN has comparable results with state-of-the-art algorithms such as PointNet, PointNet++, and PointSIFT, etc. In particular, our method has better performance on flat, small objects, and the edge areas that connect objects. Moreover, our model has good trade-off in segmentation accuracy and time complexity. [ABSTRACT FROM AUTHOR]

Multi-view Stereo (MVS) meshes suffer from occlusions or missing data, making most surface reconstruction methods invalid. Due to data imperfections, existing methods, just like PolyFit, have made a trade-off between reconstruction accuracy and time consumption. We propose a novel approach that automatically reconstructs a building surface model from raw triangular mesh with data incompleteness. Unlike existing methods that extract high-quality primitives, our method focuses on assemblies of primitives to control the level of geometric detail in the reconstructed models. We design a topological relationship of the primitives to form a plane connection graph. To be precise, we first combine the scalability of shapes with plane primitives. Then strong and soft connections between primitives are constructed by calculating the confidence of the plane intersection in space. Furthermore, the topological relations of all primitives are encoded into an undirected graph. Finally, a watertight and manifold model is extracted from the faces of a candidate set by energy minimization. Experiments on the Helsinki 3D dataset demonstrate the superiority of our method in time consumption and reconstruction error, as measured by Hausdorff distance. Our method outperforms other primitive-based algorithms in handling non-planar structures. Even when dealing with imperfect data, a watertight model is still obtained. [Display omitted] • Due to the occlusion caused by various reasons such as trees, we designed a lightweight data structure and general strategy to rectify the building data defects. A confidence strategy combining graph structure is proposed, which reduces the time consumption compared with the state-of-art methods and captures more accurate geometric details within the reconstruction pipeline to generate building models with higher levels of detail. [ABSTRACT FROM AUTHOR]

Aim: To analyze the efficacy of surgical resection versus brain biopsy combined with postoperative chemotherapy for primary central nervous system lymphoma (PCNSL) and to discuss a clinically standardized treatment protocol., Material and Methods: Patients with a pathological diagnosis of PCNSL and subsequent chemotherapy between 2016 and 2021 at Northern Jiangsu People?s Hospital were selected and divided into groups according to whether they underwent microsurgical resection or stereotactic needle biopsy. Statistical analyses were performed to compare efficacy and safety in the two groups., Results: A total of 21 patients with PCNSL were identified, of whom 12 underwent resection and 9 underwent diagnostic stereotactic biopsy only. Compared with the resection group, the biopsy group had a higher proportion of deep tumors (55.6% vs. 8.3%, p=0.016), and the mean intraoperative bleeding was significantly reduced (13.33 ± 6.61 mL vs. 170.83 ± 101.04 ml, p < 0.001). In addition, the mean survival time of patients who died during the postoperative follow-up period was shorter (6.83 ± 1.60 vs. 18.56 ± 10.20 months, p=0.016), and the one-year survival rate was lower (33.3% vs. 83.3%, p=0.032). There was no significant difference between the two groups in terms of the mean progression-free survival time or new functional impairment after surgery., Conclusion: For PCNSL, patients who undergo surgical resection have a better outcome than those who undergo biopsy only, suggesting that when the tumor is located at a surgically resectable site, surgical resection should be actively chosen; when the tumor is located at a deep and unresectable site, brain biopsy should be chosen.

Background: China is experiencing rapid population aging, with the proportion of older adult people aged 60 and above reaching 19. 8% of the total population in 2022. With the growth of age, the physical function of older adults declines and their mental health is getting worse, with the increasing trend of empty nesting and childlessness, older adults lack information and social interaction with others and fall into social isolation, loneliness and some mental health problems, the proportion of older adults with mental health problems gradually rises and the mortality rate increases incrementally, requiring effective ways to intervene in the mental health of older adults and promote healthy aging., Aim of the Study: The present study investigated the influence of spiritual comfort older adult services on the mental health of 12,624 older adults aged 60 years or older in 23 provinces in China from 2017 to 2018, with the aim of providing a case for the development of more targeted mental health strategies for older people., Methods: Using the data from the 2018 CLHLS Survey, the influencing factors of the mental health of older people were analyzed using chi-square test and the logit regression model. The mechanism of the health care facilities and the spiritual comfort services on mental health was analyzed using the chain mediation effect., Results: The spiritual comfort services decreased the risk of negative emotion and mental health of older adults, with female (OR = 1.168), rural residents (OR = 1.385), no drinking (OR = 1.255), not exercising (OR = 1.543), not having pension insurance (OR = 1.233), and low annual household income (OR = 1.416) being the characteristics as risk factors. The mediating effect results showed that the health care facilities had a partial mediating effect between the spiritual comfort services and the mental health status of older people, and the mediating effect accounted for 40.16% of the total effect., Conclusions: The use of spiritual comfort services can effectively reduce and alleviate the adverse mental health symptoms of older people, promote guidance and health education for healthy older people and chronically ill patients, and improve the good health perception of older people in order to improve their quality of life and mental health status., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Dai, Liu, Zhang, Wang and Yang.)