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Min Deng

Publications and source records attributed to Min Deng.

32 records · Page 2Linked to original sources
arXiv2019

Measuring Road Network Topology Vulnerability by Ricci Curvature

Describing the basic properties of road network systems, such as their robustness, vulnerability, and reliability, has been a very important research topic in the field of urban transportation. Current research mainly uses several statistical indicators of complex networks to analyze the road network systems. However, these methods are essentially node-based. These node-based methods are more concerned with the number of connections between nodes, and lack of consideration for interactions. So, this leads to the well-known node paradox problem, and their ability of characterizing the local and intrinsic properties of a network is weak. From the perspective of network intrinsic geometry, this paper proposes a method for measuring road network vulnerability using a discrete Ricci curvature, which can identify the key sections of a road network and indicate its fragile elements. The results show that our method performs better than complex network statistics on measuring the vulnerability of a road network. Additionally, it can characterize the evolution of the road network vulnerability among different periods of time in the same city through our method. Finally, we compare our method with the previous method of centrality and show the different between them. This article provides a new perspective on a geometry to analyze the vulnerability of a road network and describes the inherent nature of the vulnerability of a road system from a new perspective. It also contributes to enriching the analytical methods of complex road networks.

cs.SI↗
arXiv2018

T-GCN: A Temporal Graph ConvolutionalNetwork for Traffic Prediction

Accurate and real-time traffic forecasting plays an important role in the Intelligent Traffic System and is of great significance for urban traffic planning, traffic management, and traffic control. However, traffic forecasting has always been considered an open scientific issue, owing to the constraints of urban road network topological structure and the law of dynamic change with time, namely, spatial dependence and temporal dependence. To capture the spatial and temporal dependence simultaneously, we propose a novel neural network-based traffic forecasting method, the temporal graph convolutional network (T-GCN) model, which is in combination with the graph convolutional network (GCN) and gated recurrent unit (GRU). Specifically, the GCN is used to learn complex topological structures to capture spatial dependence and the gated recurrent unit is used to learn dynamic changes of traffic data to capture temporal dependence. Then, the T-GCN model is employed to traffic forecasting based on the urban road network. Experiments demonstrate that our T-GCN model can obtain the spatio-temporal correlation from traffic data and the predictions outperform state-of-art baselines on real-world traffic datasets. Our tensorflow implementation of the T-GCN is available at https://github.com/lehaifeng/T-GCN.

cs.LG↗
arXiv2018

Learning to Measure Change: Fully Convolutional Siamese Metric Networks for Scene Change Detection

A critical challenge problem of scene change detection is that noisy changes generated by varying illumination, shadows and camera viewpoint make variances of a scene difficult to define and measure since the noisy changes and semantic ones are entangled. Following the intuitive idea of detecting changes by directly comparing dissimilarities between a pair of features, we propose a novel fully Convolutional siamese metric Network(CosimNet) to measure changes by customizing implicit metrics. To learn more discriminative metrics, we utilize contrastive loss to reduce the distance between the unchanged feature pairs and to enlarge the distance between the changed feature pairs. Specifically, to address the issue of large viewpoint differences, we propose Thresholded Contrastive Loss (TCL) with a more tolerant strategy to punish noisy changes. We demonstrate the effectiveness of the proposed approach with experiments on three challenging datasets: CDnet, PCD2015, and VL-CMU-CD. Our approach is robust to lots of challenging conditions, such as illumination changes, large viewpoint difference caused by camera motion and zooming. In addition, we incorporate the distance metric into the segmentation framework and validate the effectiveness through visualization of change maps and feature distribution. The source code is available at https://github.com/gmayday1997/ChangeDet.

cs.CV↗
arXiv2018

Broad Feshbach resonances in ultracold alkali-metal systems

A comprehensive search for "broad" Feshbach resonances (FRs) in all possible combinations of stable alkali-metal atoms is carried out, using a multi-channel quantum-defect theory assisted by the analytic wave functions for a long-range van-der-Waals potential. A number of new "broad" $s$-, $p$- and $d$-wave FRs in the lowest-energy scattering channels, which are stable against two-body dipolar spin-flip loss, are predicted and characterized. Our results also show that "broad" FRs of $p$- or $d$-wave type that are free of two-body loss do not exist between fermionic alkali-metal atoms for magnetic field up to 1000\,G. These findings constitute helpful guidance on efforts towards experimental study of high-partial-wave coupling induced many-body physics.

cond-mat.quant-gas↗
arXiv2017

Observation of "broad" d-wave Feshbach resonances with a triplet structure

High partial-wave ($l\ge2$) Feshbach resonance (FR) in an ultracold mixture of $^{85}$Rb-$^{87}$Rb atoms is investigated experimentally aided by a partial-wave insensitive analytic multichannel quantum-defect theory (MQDT). Two "broad" resonances from coupling between d-waves in both the open and closed channels are observed and characterized. One of them shows a fully resolved triplet structure with splitting ratio well explained by the perturbation to the closed channel due to interatomic spin-spin interaction. These tunable "broad" d-wave resonances, especially the one in the lowest-energy open channel, could find important applications in simulating d-wave coupling dominated many-body systems. In addition, we find that there is generally a time and temperature requirement, associated with tunneling through the angular momentum barrier, to establish and observe resonant coupling in nonzero partial waves.

cond-mat.quant-gas↗
arXiv2017

On the Selective and Invariant Representation of DCNN for High-Resolution Remote Sensing Image Recognition

Human vision possesses strong invariance in image recognition. The cognitive capability of deep convolutional neural network (DCNN) is close to the human visual level because of hierarchical coding directly from raw image. Owing to its superiority in feature representation, DCNN has exhibited remarkable performance in scene recognition of high-resolution remote sensing (HRRS) images and classification of hyper-spectral remote sensing images. In-depth investigation is still essential for understanding why DCNN can accurately identify diverse ground objects via its effective feature representation. Thus, we train the deep neural network called AlexNet on our large scale remote sensing image recognition benchmark. At the neuron level in each convolution layer, we analyze the general properties of DCNN in HRRS image recognition by use of a framework of visual stimulation-characteristic response combined with feature coding-classification decoding. Specifically, we use histogram statistics, representational dissimilarity matrix, and class activation mapping to observe the selective and invariance representations of DCNN in HRRS image recognition. We argue that selective and invariance representations play important roles in remote sensing images tasks, such as classification, detection, and segment. Also selective and invariance representations are significant to design new DCNN liked models for analyzing and understanding remote sensing images.

cs.CV↗
arXiv2017

A Probabilistic Embedding Clustering Method for Urban Structure Detection

Urban structure detection is a basic task in urban geography. Clustering is a core technology to detect the patterns of urban spatial structure, urban functional region, and so on. In big data era, diverse urban sensing datasets recording information like human behaviour and human social activity, suffer from complexity in high dimension and high noise. And unfortunately, the state-of-the-art clustering methods does not handle the problem with high dimension and high noise issues concurrently. In this paper, a probabilistic embedding clustering method is proposed. Firstly, we come up with a Probabilistic Embedding Model (PEM) to find latent features from high dimensional urban sensing data by learning via probabilistic model. By latent features, we could catch essential features hidden in high dimensional data known as patterns; with the probabilistic model, we can also reduce uncertainty caused by high noise. Secondly, through tuning the parameters, our model could discover two kinds of urban structure, the homophily and structural equivalence, which means communities with intensive interaction or in the same roles in urban structure. We evaluated the performance of our model by conducting experiments on real-world data and experiments with real data in Shanghai (China) proved that our method could discover two kinds of urban structure, the homophily and structural equivalence, which means clustering community with intensive interaction or under the same roles in urban space.

cs.SI↗
arXiv2017

What do We Learn by Semantic Scene Understanding for Remote Sensing imagery in CNN framework?

Recently, deep convolutional neural network (DCNN) achieved increasingly remarkable success and rapidly developed in the field of natural image recognition. Compared with the natural image, the scale of remote sensing image is larger and the scene and the object it represents are more macroscopic. This study inquires whether remote sensing scene and natural scene recognitions differ and raises the following questions: What are the key factors in remote sensing scene recognition? Is the DCNN recognition mechanism centered on object recognition still applicable to the scenarios of remote sensing scene understanding? We performed several experiments to explore the influence of the DCNN structure and the scale of remote sensing scene understanding from the perspective of scene complexity. Our experiment shows that understanding a complex scene depends on an in-depth network and multiple-scale perception. Using a visualization method, we qualitatively and quantitatively analyze the recognition mechanism in a complex remote sensing scene and demonstrate the importance of multi-objective joint semantic support.

cs.CV↗
arXiv2016

Prevalence of algorithm-based qualitative (ABQ) method osteoporotic vertebral fracture in elderly Chinese men and women with reference to semi-quantitative (SQ) method: Mr. Os and Ms Os. (Hong Kong) studies

Introduction: This study evaluated algorithm-based qualitative (ABQ) method for vertebral fracture (VF) evaluation with reference to semi-quantitative (SQ) method and bone mineral density (BMD) measurement. Methods: Mr. OS (Hong Kong) and Ms. OS (Hong Kong) represent the first large-scale cohort studies on bone health in elderly Chinese men and women. The current study compared Genant's SQ method and ABQ method in these two cohorts. Based on quantitative measurement, the severity of ABQ method detected fractures was additionally classified into grade-1, grad-2, and grade-3 according to SQ's deformity criteria. The radiographs of 1,954 elderly Chinese men (mean: 72.3 years) and 1,953 elderly Chinese women (mean: 72.5 years) were evaluated. Results: according to ABQ, grade-1,-2,-3 VFs accounted for 1.89%, 1.74%, 2.25% in men, and 3.33%, 3.07%, and 5.53% in women. In men and women, 15.7% (35/223) and 34.5% (48/139) of vertebrae with SQ grade-1 deformity were ABQ(+, with fracture) respectively. In men and women, 89.7% (35/39) and 66.7% (48/72) of vertebrae with ABQ grade-1 fracture had SQ grade-1 deformity. For grade-1 change, SQ (-, negative without fracture) & ABQ (+, positive with vertebral cortex line fracture) subjects tend to have a lower BMD than the SQ(+)& ABQ(-) subjects. In subjects with SQ grade-2 deformity, those were also ABQ(+) tended to have a lower BMD than those were ABQ(-). In all grades, SQ(-)&ABQ(-) subjects tended to have highest BMD, while SQ(+)&ABQ(+)subjects tended to have lowest BMD. Conclusion: ABQ method may be more sensitive to VF associated mild lower BMD than SQ method.

q-bio.TO↗
arXiv2016

Improved liver T1rho measurement precision with a breathhold black blood single shot fast spin echo acquisition: a validation study in healthy volunteers

Purpose: To explore the usability and normal T1rho value of liver parenchyma with a novel single breathhold black blood single shot fast spin echo acquisition based liver imaging sequence. Materials and Methods: In total 19 health subjects (10 males, 9 females; mean age: 37.4 yrs; range: 23-54 yrs) participated in the study. 11 subjects had liver scanned twice in the same session to access scan-rescan repeatability. 12 subjects had liver scanned twice in two sessions with 7-10 days' interval to access scan-rescan reproducibility. MR was performed with a 3.0 T scanner with dual transmitter. The MR sequence allows simultaneous acquisition of 4 spin lock times (TSLs: 0ms, 10 ms, 30 ms, 50ms) in 10 second. Inherent black blood effect of fast spin echo and double inversion recovery were utilized to achieve blood signal suppression. Results: The technique demonstrated good image quality and minimal artifacts. For liver parenchyma, Bland-Altman plot showed the scan-rescan repeatability mean difference was 0.025 ms (95% limits of agreement: -1.163 to 1.213 ms), and intraclass correlation coefficient (ICC) was 0.977. The scan-rescan reproducibility mean difference was -0.075 ms (95% limits of agreement: -3.280 to 3.310 ms), and ICC was 0.820 which is better than the ICC of 0.764 of a previous bright blood multi-breath hold gradient echo acquisition technique. The liver T1rho value was 39.9 +/- 2.4 ms (range: 36.1 - 44.2 ms), which is lower than the value of 42.8=/-2.1 ms acquired with the previous bright blood technique. Conclusion: This study validated the application of a single breathhold black blood single shot fast spin echo acquisition based for human liver T1rho imaging. The lower liver parenchyma T1rho value and higher scan rescan reproducibility may improve of the sensitivity of this technique.

q-bio.TO↗
arXiv2016

Lumbar degenerative spondylolisthesis epidemiology: a systemic review with a focus on gender-specific and age-specific prevalence

The epidemiology of lumbar degenerative spondylolisthesis (DS) remains controversial. We performed a systemic review with the aim to have a better understanding of DS's prevalence in general population. The results showed the prevalence of DS is very gender specific and age specific. Both women and men have few DS before 50 years old, after 50 years old both women and men start to develop DS, with women having a faster developing rate than men. For elderly Chinese (>=65 yrs, mean age: 72.5 yrs), large population based studies (MsOS(Hong Kong) and MrOS (Hong Kong), females n=2000 and males n=2000) showed DS prevalence was for 25.0% for women and 19.1% for men, and the prevalence F:M (women:men) ratio was 1.3:1. The published data (MsOS(USA) and MrOS(USA) studies) seem to show elderly Caucasian American has a higher DS prevalence, being approximately 60-70% higher than elderly Chinese; however the prevalence F:M ratio was similar to elderly Chinese population. Patient data showed female patients more often received treatment than men; and preliminary data show the ratio of numbers of female patients received treatment compared with men did not differ between Northeast Asians (Chinese, Japanese, and Korean) and European and American Caucasians, being around 2:1 in elderly population. The existing data also suggest that menopause may be a contributing factor for the accelerated DS development in post-menopausal

q-bio.TO↗
arXiv2016

Queue Theory based Response Time Analyses for Geo-Information Processing Chain

Typical characteristics of remote sensing applications are concurrent tasks, such as those found in disaster rapid response. The existing composition approach to geographical information processing service chain, searches for an optimisation solution and is what can be deemed a "selfish" way. This way leads to problems of conflict amongst concurrent tasks and decreases the performance of all service chains. In this study, a non-cooperative game-based mathematical model to analyse the competitive relationships between tasks, is proposed. A best response function is used, to assure each task maintains utility optimisation by considering composition strategies of other tasks and quantifying conflicts between tasks. Based on this, an iterative algorithm that converges to Nash equilibrium is presented, the aim being to provide good convergence and maximise the utilisation of all tasks under concurrent task conditions. Theoretical analyses and experiments showed that the newly proposed method, when compared to existing service composition methods, has better practical utility in all tasks.

cs.DC↗
arXiv2015

Urban spatial-temporal activity structures: a New Approach to Inferring the Intra-urban Functional Regions via Social Media Check-In Data

Most existing literature focuses on the exterior temporal rhythm of human movement to infer the functional regions in a city, but they neglects the underlying interdependence between the functional regions and human activities which uncovers more detailed characteristics of regions. In this research, we proposed a novel model based on the low rank approximation (LRA) to detect the functional regions using the data from about 15 million check-in records during a yearlong period in Shanghai, China. We find a series of latent structures, called urban spatial-temporal activity structure (USTAS). While interpreting these structures, a series of outstanding underlying associations between the spatial and temporal activity patterns can be found. Moreover, we can not only reproduce the observed data with a lower dimensional representative but also simultaneously project both the spatial and temporal activity patterns in the same coordinate system. By utilizing the K-means clustering algorithm, five significant types of clusters which are directly annotated with a corresponding combination of temporal activities can be obtained. This provides a clear picture of how the groups of regions are associated with different activities at different time of day. Besides the commercial and transportation dominant area, we also detect two kinds of residential areas, the developed residential areas and the developing residential areas. We further verify the spatial distribution of these clusters in the view of urban form analysis. The results shows a high consistency with the government planning from the same periods, indicating our model is applicable for inferring the functional regions via social media check-in data, and can benefit a wide range of fields, such as urban planning, public services and location-based recommender systems and other purposes.

cs.SI↗