Introduction to the special section on Visual Movement Analytics

PostprintNon peer reviewe

Technical report of data mining

No abstract availabl

Democratized image analytics by visual programming through integration of deep models and small-scale machine learning

Analysis of biomedical images requires computational expertize that are uncommon among biomedical scientists. Deep learning approaches for image analysis provide an opportunity to develop user-friendly tools for exploratory data analysis. Here, we use the visual programming toolbox Orange (http://orange.biolab.si) to simplify image analysis by integrating deep-learning embedding, machine learning procedures, and data visualization. Orange supports the construction of data analysis workflows by assembling components for data preprocessing, visualization, and modeling. We equipped Orange with components that use pre-trained deep convolutional networks to profile images with vectors of features. These vectors are used in image clustering and classification in a framework that enables mining of image sets for both novel and experienced users. We demonstrate the utility of the tool in image analysis of progenitor cells in mouse bone healing, identification of developmental competence in mouse oocytes, subcellular protein localization in yeast, and developmental morphology of social amoebae

Developments in the negative-U modelling of the cuprate HTSC systems

The paper deals with the many stands that go into creating the unique and complex nature of the HTSC cuprates above Tc as below. Like its predecessors it treats charge, not spin or lattice, as prime mover, but thus taken in the context of the chemical bonding relevant to these copper oxides. The crucial shell filling, negative-U, double-loading fluctuations possible there require accessing at high valent local environment as prevails within the mixed valent, inhomogeneous two sub-system circumstance of the HTSC materials. Close attention is paid to the recent results from Corson, Demsar, Li, Johnson, Norman, Varma, Gyorffy and colleagues.Comment: 44 pages:200+ references. Submitted to J.Phys.:Condensed Matter, Sept 7 200

A graph based approach for functional urban areas delineation

International audienc

Assessment of machine learning reliability methods for quantifying the applicability domain of QSAR regression models

The vastness of chemical space and the relatively small coverage by experimental data recording molecular properties require us to identify subspaces, or domains, for which we can confidently apply QSAR models. The prediction of QSAR models in these domains is reliable, and potential subsequent investigations of such compounds would find that the predictions closely match the experimental values. Standard approaches in QSAR assume that predictions are more reliable for compounds that are "similar" to those in subspaces with denser experimental data. Here, we report on a study of an alternative set of techniques recently proposed in the machine learning community. These methods quantify prediction confidence through estimation of the prediction error at the point of interest. Our study includes 20 public QSAR data sets with continuous response and assesses the quality of 10 reliability scoring methods by observing their correlation with prediction error. We show that these new alternative approaches can outperform standard reliability scores that rely only on similarity to compounds in the training set. The results also indicate that the quality of reliability scoring methods is sensitive to data set characteristics and to the regression method used in QSAR. We demonstrate that at the cost of increased computational complexity these dependencies can be leveraged by integration of scores from various reliability estimation approaches. The reliability estimation techniques described in this paper have been implemented in an open source add-on package (https://bitbucket.org/biolab/orange-reliability ) to the Orange data mining suite