Object Discovery From a Single Unlabeled Image by Mining Frequent Itemset With Multi-scale Features

TThe goal of our work is to discover dominant objects in a very general setting where only a single unlabeled image is given. This is far more challenge than typical co-localization or weakly-supervised localization tasks. To tackle this problem, we propose a simple but effective pattern mining-based method, called Object Location Mining (OLM), which exploits the advantages of data mining and feature representation of pre-trained convolutional neural networks (CNNs). Specifically, we first convert the feature maps from a pre-trained CNN model into a set of transactions, and then discovers frequent patterns from transaction database through pattern mining techniques. We observe that those discovered patterns, i.e., co-occurrence highlighted regions, typically hold appearance and spatial consistency. Motivated by this observation, we can easily discover and localize possible objects by merging relevant meaningful patterns. Extensive experiments on a variety of benchmarks demonstrate that OLM achieves competitive localization performance compared with the state-of-the-art methods. We also evaluate our approach compared with unsupervised saliency detection methods and achieves competitive results on seven benchmark datasets. Moreover, we conduct experiments on fine-grained classification to show that our proposed method can locate the entire object and parts accurately, which can benefit to improving the classification results significantly

ICML - On the Number of Linear Regions of Convolutional Neural Networks

One fundamental problem in deep learning is understanding the outstanding performance of deep Neural Networks (NNs) in practice. One explanation for the superiority of NNs is that they can realize a large class of complicated functions, i.e., they have powerful expressivity. The expressivity of a ReLU NN can be quantified by the maximal number of linear regions it can separate its input space into. In this paper, we provide several mathematical results needed for studying the linear regions of CNNs, and use them to derive the maximal and average numbers of linear regions for one-layer ReLU CNNs. Furthermore, we obtain upper and lower bounds for the number of linear regions of multi-layer ReLU CNNs. Our results suggest that deeper CNNs have more powerful expressivity than their shallow counterparts, while CNNs have more expressivity than fully-connected NNs per parameter.Comment: International Conference on Machine Learning (ICML) 202

Intra-tumoural heterogeneity characterization through texture and colour analysis for differentiation of non-small cell lung carcinoma subtypes

Radiomics has shown potential in disease diagnosis, but its feasibility for non-small cell lung carcinoma (NSCLC) subtype classification is unclear. This study aims to explore the diagnosis value of texture and colour features from positron emission tomography computed tomography (PET-CT) images in differentiation of NSCLC subtypes: adenocarcinoma (ADC) and squamous cell carcinoma (SqCC). Two patient cohorts were retrospectively collected into a dataset of 341 18F-labeled 2-deoxy-2fluoro-d-glucose ([18F] FDG) PET-CT images of NSCLC tumours (125 ADC, 174 SqCC, and 42 cases with unknown subtype). Quantification of texture and colour features was performed using freehand regions of interest. The relation between extracted features and commonly used parameters such as age, gender, tumour size, and standard uptake value (SUVmax) was explored. To classify NSCLC subtypes, support vector machine algorithm was applied on these features and the classification performance was evaluated by receiver operating characteristic curve analysis. There was a significant difference between ADC and SqCC subtypes in texture and colour features (P  <  0.05); this showed that imaging features were significantly correlated to both SUVmax and tumour diameter (P  <  0.05). When evaluating classification performance, features combining texture and colour showed an AUC of 0.89 (95% CI, 0.78–1.00), colour features showed an AUC of 0.85 (95% CI, 0.71–0.99), and texture features showed an AUC of 0.68 (95% CI, 0.48–0.88). DeLong's test showed that AUC was higher for features combining texture and colour than that for texture features only (P  =  0.010), but not significantly different from that for colour features only (P  =  0.328). HSV colour features showed a similar performance to RGB colour features (P  =  0.473). The colour features are promising in the refinement of NSCLC subtype differentiation, and features combining texture and colour of PET-CT images could result in better classification performance

Distinct miRNAs associated with various clinical presentations of SARS-CoV-2 infection.

MicroRNAs (miRNAs) have been shown to play important roles in viral infections, but their associations with SARS-CoV-2 infection remain poorly understood. Here, we detected 85 differentially expressed miRNAs (DE-miRNAs) from 2,336 known and 361 novel miRNAs that were identified in 233 plasma samples from 61 healthy controls and 116 patients with COVID-19 using the high-throughput sequencing and computational analysis. These DE-miRNAs were associated with SASR-CoV-2 infection, disease severity, and viral persistence in the patients with COVID-19, respectively. Gene ontology and KEGG pathway analyses of the DE-miRNAs revealed their connections to viral infections, immune responses, and lung diseases. Finally, we established a machine learning model using the DE-miRNAs between various groups for classification of COVID-19 cases with different clinical presentations. Our findings may help understand the contribution of miRNAs to the pathogenesis of COVID-19 and identify potential biomarkers and molecular targets for diagnosis and treatment of SARS-CoV-2 infection

The enormous repetitive Antarctic krill genome reveals environmental adaptations and population insights

Antarctic krill (Euphausia superba) is Earth’smost abundant wild animal, and its enormous biomass is vital to the Southern Ocean ecosystem. Here, we report a 48.01-Gb chromosome-level Antarctic krill genome, whose large genome size appears to have resulted from inter-genic transposable element expansions. Our assembly reveals the molecular architecture of the Antarctic krill circadian clock and uncovers expanded gene families associated with molting and energy metabolism, providing insights into adaptations to the cold and highly seasonal Antarctic environment. Population-level genome re-sequencing from four geographical sites around the Antarctic continent reveals no clear population structure but highlights natural selection associated with environmental variables. An apparent drastic reduction in krill population size 10 mya and a subsequent rebound 100 thousand years ago coincides with climate change events. Our findings uncover the genomic basis of Antarctic krill adaptations to the Southern Ocean and provide valuable resources for future Antarctic research

Urbanization and Grain Production Pattern of China: Dynamic Effect and Mediating Mechanism

The flow and reallocation of agricultural production factors induced by urbanization play an important role in the changes of the grain production pattern (GPP). Using provincial panel data from 1996 to 2018 in China as the research sample, the center of gravity transfer–standard deviation ellipse model was applied to understand the change characteristics of GPP. Next, a dynamic spatial panel econometric model was established to test the impact of urbanization on GPP, and a spatial mediated effect model was used to identify the mediated transmission paths played by cropland utilization, planting structure adjustment, and agricultural technology progress in this impact process. The main conclusions showed that (1) the grain production COG of China transferred to the northeast, gradually resulting in a spatial pattern from the northeast to the southwest; (2) the urbanization process has a significant negative impact on the GPP, with each unit increase in urbanization resulting in a 0.30% decrease in the grain production concentration index; (3) cropland utilization, planting structural adjustment, and agricultural technology progress play significant mediating roles in the impact of urbanization on the GPP, and their mediating effects can weaken the direct negative impact of urbanization, among which the mediating effect of planting structure adjustment is the highest (13.9%). The study findings provide a new perspective for further understanding the relationship between urbanization and grain production pattern and also provide theoretical references and practical insights for improving the allocation efficiency of agricultural production factors and formulating scientific regional planning policies for grain production in the high-quality transformation of urbanization

Urbanization and Grain Production Pattern of China: Dynamic Effect and Mediating Mechanism

The flow and reallocation of agricultural production factors induced by urbanization play an important role in the changes of the grain production pattern (GPP). Using provincial panel data from 1996 to 2018 in China as the research sample, the center of gravity transfer&ndash;standard deviation ellipse model was applied to understand the change characteristics of GPP. Next, a dynamic spatial panel econometric model was established to test the impact of urbanization on GPP, and a spatial mediated effect model was used to identify the mediated transmission paths played by cropland utilization, planting structure adjustment, and agricultural technology progress in this impact process. The main conclusions showed that (1) the grain production COG of China transferred to the northeast, gradually resulting in a spatial pattern from the northeast to the southwest; (2) the urbanization process has a significant negative impact on the GPP, with each unit increase in urbanization resulting in a 0.30% decrease in the grain production concentration index; (3) cropland utilization, planting structural adjustment, and agricultural technology progress play significant mediating roles in the impact of urbanization on the GPP, and their mediating effects can weaken the direct negative impact of urbanization, among which the mediating effect of planting structure adjustment is the highest (13.9%). The study findings provide a new perspective for further understanding the relationship between urbanization and grain production pattern and also provide theoretical references and practical insights for improving the allocation efficiency of agricultural production factors and formulating scientific regional planning policies for grain production in the high-quality transformation of urbanization

Prediction for the Flexural Properties of Nanowires in Lateral Manipulation

Simple beam theory is usually employed to predict the flexural properties of nanowires (NWs) in the lateral manipulation, which results in a linear F−δ curve. However, three factors, namely, changes in the load position and direction of the AFM tip, surface effects, and the large displacement of the NW, are not considered in simple beam theory. In this work, a simple geometrical model is proposed to analyze the large deflection and rotational angle deformation of NWs in the lateral manipulation. The traditional solution to the differential equation of the deflection curve turns the solution of an integral equation. Results show that contact force does not linearly increase with increasing displacement, and the F−δ curve exists a maximum amount due to changes in the load position and direction of the AFM tip. Moreover, the rotational angle and deflection have a nonlinear relationship. Comparison of the results of this work with other authors’ measurements illustrates that the use of simple beam theory in manipulation underestimates the deflection and effective modulus of NWs. Our model provides a good approach to predict F−δ curves, rotational angles, and contact forces that closely match experimental results