In the real world, a realistic setting for computer vision or multimedia recognition problems is that we have some classes containing lots of training data and many classes contain a small amount of training data. Therefore, how to use frequent classes to help learning rare classes for which it is harder to collect the training data is an open question. Learning with Shared Information is an emerging topic in machine learning, computer vision and multimedia analysis. There are different level of components that can be shared during concept modeling and machine learning stages, such as sharing generic object parts, sharing attributes, sharing transformations, sharing regularization parameters and sharing training examples, etc. Regarding the specific methods, multi-task learning, transfer learning and deep learning can be seen as using different strategies to share information. These learning with shared information methods are very effective in solving real-world large-scale problems. This special issue aims at gathering the recent advances in learning with shared information methods and their applications in computer vision and multimedia analysis. Both state-of-the-art works, as well as literature reviews, are welcome for submission. Papers addressing interesting real-world computer vision and multimedia applications are especially encouraged. Topics of interest include, but are not limited to:  • Multi-task learning or transfer learning for large-scale computer vision and multimedia analysis • Deep learning for large-scale computer vision and multimedia analysis • Multi-modal approach for large-scale computer vision and multimedia analysis • Different sharing strategies, e.g., sharing generic object parts, sharing attributes, sharing transformations, sharing regularization parameters and sharing training examples, • Real-world computer vision and multimedia applications based on learning with shared information, e.g., event detection, object recognition, object detection, action recognition, human head pose estimation, object tracking, location-based services, semantic indexing. • New datasets and metrics to evaluate the benefit of the proposed sharing ability for the specific computer vision or multimedia problem. • Survey papers regarding the topic of learning with shared information.  Authors who are unsure whether their planned submission is in scope may contact the guest editors prior to the submission deadline with an abstract, in order to receive feedback.

IEEE transactions on pattern analysis and machine intelligence

Transportation research. part c, emerging technologies

We analyze the betweenness centrality (BC) of nodes in large complex networks. In general, the BC is increasing with connectivity as a power law with an exponent $\eta$. We find that for trees or networks with a small loop density $\eta=2$ while a larger density of loops leads to $\eta<2$. For scale-free networks characterized by an exponent $\gamma$ which describes the connectivity distribution decay, the BC is also distributed according to a power law with a non universal exponent $\delta$. We show that this exponent $\delta$ must satisfy the exact bound $\delta\geq (\gamma+1)/2$. If the scale free network is a tree, then we have the equality $\delta=(\gamma+1)/2$.

Betweenness Centrality in Large Complex Networks

https://www.tandfonline.com/doi/pdf/10.1080/01441647.2020.1857886

Public transport planning adaption under the COVID-19 pandemic crisis: literature review of research needs and directions

In this paper, we investigate the day-to-day regularity of urban congestion patterns. We first partition link speed data every 10 min into 3D clusters that propose a parsimonious sketch of the congestion pulse. We then gather days with similar patterns and use consensus clustering methods to produce a unique global pattern that fits multiple days, uncovering the day-to-day regularity. We show that the network of Amsterdam over 35 days can be synthesized into only 4 consensual 3D speed maps with 9 clusters. This paves the way for a cutting-edge systematic method for travel time predictions in cities. By matching the current observation to historical consensual 3D speed maps, we design an efficient real-time method that successfully predicts 84% trips travel times with an error margin below 25%. The new concept of consensual 3D speed maps allows us to extract the essence out of large amounts of link speed observations and as a result reveals a global and previously mostly hidden picture of traffic dynamics at the whole city scale, which may be more regular and predictable than expected.

/pdf/revealing-the-day-to-day-regularity-of-urban-congestion-3vjrt3nh5t.pdf

Revealing the day-to-day regularity of urban congestion patterns with 3D speed maps.

The fundamental challenge of the origin-destination (OD) matrix estimation problem is that it is severely under-determined. In this paper we propose a new data driven OD estimation method for cases where a supply pattern in the form of speeds and flows is available. We show that with these input data, we do not require an iterative dynamic network loading procedure that results in an equilibrium assignment, nor do we need an assumption on the kind of equilibrium that emerges from this process. The minimal number of ingredients which are needed are (a) a method to estimate / predict production and attraction time series; (b) a method to compute the N shortest paths from each OD zone to the next; and (c) two—possibly OD-specific—assumptions on the magnitude of N; and on the proportionality of path flows between these origins and destinations, respectively. The latter constitutes the most important behavioral assumption in our method, which relates to how we assume travelers have chosen their routes between OD pairs. We choose a proportionality factor that is inversely proportional to realized travel time, where we incorporate a penalty for path overlap. For large networks, these ingredients may be insufficient to solve the resulting system of equations. We show how additional constraints can be derived directly from the data by using principal component analysis, with which we exploit the fact that temporal patterns of production and attraction are similar across the network. Experimental results on a toy network and a large city network (Santander, Spain) show that our OD estimation method works satisfactorily, given a reasonable choice of N, and the use of so-called 3D supply patterns, which provide a compact representation of the supply dynamics over the entire network. Inclusion of topological information makes the method scalable both in terms of network size and for different topologies. Although we use a neural network to predict production and attraction in our experiments (which implies ground-truth OD data were needed), there are straight-forward paths to improve the method using additional data, such as demographic data, household survey data, social media and or movement traces, which could support estimating such ground-truth baseline production and attraction patterns. The proposed framework would fit very nicely in an online traffic modeling and control framework, and we see many paths to further refine and improve the method.

A data driven method for OD matrix estimation

Classification of congestion patterns is important in many areas in traffic planning and management, ranging from policy appraisal, database design, to prediction and real-time control. One of the key constraints in applying machine learning techniques for classification is the availability of sufficient data (traffic patterns) with clear and undisputed labels, e.g. traffic pattern X or Y. The challenge is that labelling traffic patterns (e.g. combinations of congested and freely flow areas over time and space) is highly subjective. In our view this means that assessment of how well algorithms label the data should also include a qualitative component that focuses on what the found patterns really mean for traffic flow operations and applications. In this study, we investigate the application of clustering analysis to obtain labels automatically from the data, where we indeed first qualitatively assess how meaningful the found labels are, and subsequently test quantitatively how well the labels separate the resulting feature space. By transforming traffic measurements (speeds) into (colored) images, two different approaches are proposed to extract the features of a large number of traffic patterns for clustering: point-based and area-based. The point-based approach is widely applied in the image processing literature, and explores local interest points in images (i.e. where large changes occur in color intensity); whereas a new area-based approach combines domain knowledge with Watershed segmentation to partition the images into different spatial-temporal segments from which domain specific features, such as wide moving jam patterns, are extracted. The results show that the Watershed segmentation separates the traffic (congestion) patterns into more meaningful and separable classes, comparable to those that have been proposed in the literature. Since there is no ground-truth set of labels, the quantitative assessment tests how well both methods are able to separate the respective feature spaces they construct for the (large) database of traffic patterns. We argue that the more crisp this separation is; the better the labelling has turned out. For this quantitative comparison we train a multinomial classifier that maps unseen patterns to the labels discovered by each of the two labeling approaches. The most important result is that the classifier using the area-based feature vector achieves the highest average levels of confidence in its decisions to classify patterns, implying a highly separable feature vector space. We argue this is good news! Not only does the combination of image processing (Watershed) and domain knowledge (traffic flow characteristics) lead to meaningful labels that can be automatically retrieved from large databases of data; this method also leads to a more efficient separation of the resulting feature space. Our next endeavor is to further refine and use this method to develop a search engine for the (rapidly growing) 200 TB historical database of traffic data hosted by the Dutch National Datawarehouse (NDW).

/pdf/feature-extraction-and-clustering-analysis-of-highway-1xsi1iqi3h.pdf

Feature extraction and clustering analysis of highway congestion

Nowadays, the deployment of sensing technology permits to collect massive 1 spatio-temporal data in urban cities. These data can provide comprehensive traffic state conditions for an urban network and for a particular day. However, they are often too numerous and too detailed to be of direct use, particularly for applications like delivery tour planning, trip advisors and dynamic route guidance. A rough estimation of travel times and their variability may be sufficient if the information is available at the full city scale. The concept of spatio-temporal speed cluster map is a promising avenue for these applications.

/pdf/spatiotemporal-partitioning-of-transportation-network-using-1lwg91tycd.pdf

Spatiotemporal Partitioning of Transportation Network Using Travel Time Data

In large-scale urban agglomerations, heavy rail in the form of metro and commuter train serves as the backbone of the metropolitan public transport network. The objective of this paper is to investigate whether networks with strikingly different structure and development pattern exhibit different robustness properties in the event of random and targeted attacks. We adopt a complex network theory approach, investigating network performances under alternative sequential disruption scenarios corresponding to the successive closure of either stations or track segments. We also investigate the case where the removal of a network node or link implies the closure of all traversing lines Network performance is measured both in terms of the capacity of the network to function in terms of connectivity as well as the additional impedance induced for those that remain connected. An aggregate robustness indicator based on the integral of the deterioration of network performance is adopted. Three exemplary networks are selected, the urban rail networks of London, Shanghai and Randstad. These three networks offer showcases of short and long development patterns, mono- and polycentric agglomeration structures, including the largest and the oldest metropolitan heavy rail networks. The polycentric network of the Randstad was found the least robust in this analysis when compared to the more monocentric networks of London and Shanghai. The London network is in general more robust than the Shanghai network thanks to the presence of cycles beyond the core. Our findings provide more nuanced evidence on the relation between network structure and development pattern, and its robustness.

/pdf/metropolitan-rail-network-robustness-rcjk7s22wm.pdf

Panchamy Krishnakumari

Papers

Revealing the day-to-day regularity of urban congestion patterns with 3D speed maps.

A data driven method for OD matrix estimation

Feature extraction and clustering analysis of highway congestion

Spatiotemporal Partitioning of Transportation Network Using Travel Time Data

Metropolitan rail network robustness