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In this paper, we propose a new method to achieve automatic matching of multi-scale roads under the constraints of smaller scale data. The matching process is: Firstly, meshes are extracted from two different scales road data. Secondly, several basic meshes in the larger scale road network will be merged into a composite one which is matched with one mesh in the smaller scale road network, to complete the N∶1(N>1) and 1∶1 matching. Thirdly, meshes of the two different scale road data with M∶N(M>1,N>1) matching relationships will be matched. Finally, roads will be classified into two categories under the constraints of meshes: mesh boundary roads and mesh internal roads, and then matchings between the two scales meshes will be carried out within their own categories according to the matching relationships. The results show that roads of different scales will be more precisely matched using the proposed method.

Objective: The purpose of this study was to investigate the prognostic value of the family history of cancer (FHC) in predicting survival and clinicopathological features in oral squamous cell carcinoma (OSCC) patients. Materials and Methods: This single‐institution study utilized data from 610 patients undergoing surgery from 2014 to 2020 that was prospectively collected and cataloged for research purposes. All patients underwent standard surgery with/without radiotherapy or chemoradiotherapy. We statistically evaluated whether FHC was associated with changes in disease‐free survival (DFS) and disease‐specific survival (DSS). Results: Among 610 patients, 141 (23.1%) reported a family history of cancer. The distribution of clinicopathological characteristics was balanced between FHC‐positive and FHC‐negative OSCC patients. FHC‐positive patients had decreased DFS (p = 0.005) and DSS (p = 0.018) compared to FHC‐negative patients. Conclusions: FHC‐positive OSCC patients have a poorer prognosis. FHC positivity is an independent predictor of negative outcomes based on DFS and DSS. FHC should be a consideration in screening, evaluating, counseling, and treating OSCC patients. [ABSTRACT FROM AUTHOR]

Due to its high computational complexity, graph-based methods have limited applicability in large-scale multiview clustering tasks. To address this issue, many accelerated algorithms, especially anchor graph-based methods and indicator learning-based methods, have been developed and made a great success. Nevertheless, since the restrictions of the optimization strategy, these accelerated methods still need to approximate the discrete graph-cutting problem to a continuous spectral embedding problem and utilize different discretization strategies to obtain discrete sample categories. To avoid the loss of effectiveness and efficiency caused by the approximation and discretization, we establish a discrete fast multiview anchor graph clustering (FMAGC) model that first constructs an anchor graph of each view and then generates a discrete cluster indicator matrix by solving the discrete multiview graph-cutting problem directly. Since the gradient descent-based method makes it hard to solve this discrete model, we propose a fast coordinate descent-based optimization strategy with linear complexity to solve it without approximating it as a continuous one. Extensive experiments on widely used normal and large-scale multiview datasets show that FMAGC can improve clustering effectiveness and efficiency compared to other state-of-the-art baselines.

With the purpose of providing an external human-machine interaction platform for the elderly in need, a novel facial surface electromyography based silent speech recognition system was developed. In this study, we propose a deep learning architecture named Parallel-Inception Convolutional Neural Network (PICNN), and employ up-to-date feature extraction method log Mel frequency spectral coefficients (MFSC). To better meet the requirements of our target users, a 100-class dataset containing daily life-related demands was designed and generated for the comparative experiments. According to experimental results, the highest recognition accuracy of 88.44% was achieved by proposed recognition framework based on MFSC and PICNN, exceeding the performance of state-of-the-art deep learning algorithms such as CNN, VGGNet and Inception CNN (3.22%, 4.09% and 1.19%, respectively). These findings suggest that the newly developed silent speech approach holds promise to provide a more reliable communication channel, and the application scenery of speech recognition technology has been expanded at the same time.