Metric sparsification and operator norm localization

We study an operator norm localization property and its applications to the coarse Novikov conjecture in operator K-theory. A metric space X is said to have operator norm localization property if there exists a positive number c such that for every r>0, there is R>0 for which, if m is a positive locally finite Borel measure on X, H is a separable infinite dimensional Hilbert space and T is a bounded linear operator acting on L^2(X,m) with propagation r, then there exists an unit vector v satisfying with support of diameter at most R and such that |Tv| is larger or equal than c|T|. If X has finite asymptotic dimension, then X has operator norm localization property. In this paper, we introduce a sufficient geometric condition for the operator norm localization property. This is used to give many examples of finitely generated groups with infinite asymptotic dimension and the operator norm localization property. We also show that any sequence of expanding graphs does not possess the operator norm localization property

Efficient Fully Convolution Neural Network for Generating Pixel Wise Robotic Grasps With High Resolution Images

This paper presents an efficient neural network model to generate robotic grasps with high resolution images. The proposed model uses fully convolution neural network to generate robotic grasps for each pixel using 400 $\times$ 400 high resolution RGB-D images. It first down-sample the images to get features and then up-sample those features to the original size of the input as well as combines local and global features from different feature maps. Compared to other regression or classification methods for detecting robotic grasps, our method looks more like the segmentation methods which solves the problem through pixel-wise ways. We use Cornell Grasp Dataset to train and evaluate the model and get high accuracy about 94.42% for image-wise and 91.02% for object-wise and fast prediction time about 8ms. We also demonstrate that without training on the multiple objects dataset, our model can directly output robotic grasps candidates for different objects because of the pixel wise implementation.Comment: Submitted to ROBIO 201

Assessment of the resilience of urban tourism flow network structure based on the impact of COVID-19: A case of Chongqing

Based on the change data of network comments of tourist attractions in the central urban area of Chongqing from 2019 to 2021, the urban tourism flow network of Chongqing before and after the epidemic was constructed in stages based on the gravity model. And from the three dimensions of resistance, resilience and adaptability, the six measurement indicators of network load, stability, growth, hierarchy, matching and transmission are evaluated. The results show that: 1) Although the comprehensive indicator of network load of urban tourism flow in Chongqing is seriously impacted by the COVID-19 pandemic, the network structure of tourism flow has obvious resilience in the indicators of stability and growth. 2) COVID-19 helps to force the optimization of tourism flow network structure. The hierarchy of the urban tourism flow network structure in Chongqing tends to be flat, and the indicator of assortative shows obvious heterogeneity characteristics. 3) Transmission is a weak link in the network structure of urban tourism flow in Chongqing, which needs to be further optimized.Keywords: tourism flow; network structure; resilience evaluation; COVID-19 pandemic; Chongqin

Critical Online Information Evaluation (COIE): A comprehensive model for curriculum and assessment design

The recent evolution of technology and the Internet has transformed how individuals find and share information. Research shows that citizens of all ages and backgrounds struggle with critical online information evaluation (COIE), which could result in serious societal consequences. Although it is crucial to develop student proficiency within this key information literacy construct beginning in middle school, there is currently no interdisciplinary framework for designing COIE instruction or assessments. To address this gap, we have developed a comprehensive COIE model for curriculum developers, assessment creators, and practitioners to implement at the secondary and post-secondary level. In this paper, we provide cross-disciplinary theoretical context and empirical grounding for our model, offer guidance for its practical application in the 6-16 curriculum, and discuss metacognitive and sociocultural considerations for developing and measuring learners’ COIE proficiency

Semi-Supervised Self-Taught Deep Learning for Finger Bones Segmentation

Segmentation stands at the forefront of many high-level vision tasks. In this study, we focus on segmenting finger bones within a newly introduced semi-supervised self-taught deep learning framework which consists of a student network and a stand-alone teacher module. The whole system is boosted in a life-long learning manner wherein each step the teacher module provides a refinement for the student network to learn with newly unlabeled data. Experimental results demonstrate the superiority of the proposed method over conventional supervised deep learning methods.Comment: IEEE BHI 2019 accepte

Protein Interaction Prediction Method Based on Feature Engineering and XGBoost

Human protein interaction prediction studies occupy an important place in systems biology. The understanding of human protein interaction networks and interactome will provide important insights into the regulation of developmental, physiological and pathological processes. In this study, we propose a method based on feature engineering and integrated learning algorithms to construct protein interaction prediction models. Principal Component Analysis (PCA) and Locally Linear Embedding (LLE) dimensionality reduction methods were used to extract sequence features from the 174-dimensional human protein sequence vector after Normalized Difference Sequence Feature (NDSF) encoding, respectively. The classification performance of three integrated learning methods (AdaBoost, Extratrees, XGBoost) applied to PCA and LLE features was compared, and the best combination of parameters was found using cross-validation and grid search methods. The results show that the classification accuracy is significantly higher when using the linear dimensionality reduction method PCA than the nonlinear dimensionality reduction method LLE. the classification with XGBoost achieves a model accuracy of 99.2%, which is the best performance among all models. This study suggests that NDSF combined with PCA and XGBoost may be an effective strategy for classifying different human protein interactions