MobilityGraphs: Visual Analysis of Mass Mobility Dynamics via Spatio-Temporal Graphs and Clustering

Learning more about people mobility is an important task for official decision makers and urban planners. Mobility data sets characterize the variation of the presence of people in different places over time as well as movements (or flows) of people between the places. The analysis of mobility data is challenging due to the need to analyze and compare spatial situations (i.e., presence and flows of people at certain time moments) and to gain an understanding of the spatio-temporal changes (variations of situations over time). Traditional flow visualizations usually fail due to massive clutter. Modern approaches offer limited support for investigating the complex variation of the movements over longer time periods

Spin-Glass State in CuGa2O4\rm CuGa_2O_4

Magnetic susceptibility, magnetization, specific heat and positive muon spin relaxation (\musr) measurements have been used to characterize the magnetic ground-state of the spinel compound $\rm CuGa_2O_4$. We observe a spin-glass transition of the S=1/2 $\rm Cu^{2+}$ spins below $\rm T_f=2.5K$ characterized by a cusp in the susceptibility curve which suppressed when a magnetic field is applied. We show that the magnetization of $\rm CuGa_2O_4$ depends on the magnetic histo Well below $\rm T_f$, the muon signal resembles the dynamical Kubo-Toyabe expression reflecting that the spin freezing process in $\rm CuGa_2O_4$ results Gaussian distribution of the magnetic moments. By means of Monte-Carlo simulati we obtain the relevant exchange integrals between the $\rm Cu^{2+}$ spins in this compound.Comment: 6 pages, 16 figure

An Interdisciplinary Model for Graphical Representation

International audienceThe paper questions whether data-driven and problem-driven models are sufficient for a software to automatically represent a meaningful graphi-cal representation of scientific findings. The paper presents descriptive and prescriptive case studies to understand the benefits and the shortcomings of existing models that aim to provide graphical representations of data-sets. First, the paper considers data-sets coming from the field of software metrics and shows that existing models can provide the expected outcomes for descriptive scientific studies. Second, the paper presents data-sets coming from the field of human mobility and sustainable development, and shows that a more comprehensive model is needed in the case of prescriptive scientific fields requiring interdisciplinary research. Finally, an interdisciplinary problem-driven model is proposed to guide the software users, and specifically scientists, to produce meaningful graphical representation of research findings. The proposal is indeed based not only on a data-driven and/or problem-driven model but also on the different knowledge domains and scientific aims of the experts, who can provide the information needed for a higher-order structure of the data, supporting the graphical representation output

HardVis: Visual Analytics to Handle Instance Hardness Using Undersampling and Oversampling Techniques

Despite the tremendous advances in machine learning (ML), training with imbalanced data still poses challenges in many real-world applications. Among a series of diverse techniques to solve this problem, sampling algorithms are regarded as an efficient solution. However, the problem is more fundamental, with many works emphasizing the importance of instance hardness. This issue refers to the significance of managing unsafe or potentially noisy instances that are more likely to be misclassified and serve as the root cause of poor classification performance. This paper introduces HardVis, a visual analytics system designed to handle instance hardness mainly in imbalanced classification scenarios. Our proposed system assists users in visually comparing different distributions of data types, selecting types of instances based on local characteristics that will later be affected by the active sampling method, and validating which suggestions from undersampling or oversampling techniques are beneficial for the ML model. Additionally, rather than uniformly undersampling/oversampling a specific class, we allow users to find and sample easy and difficult to classify training instances from all classes. Users can explore subsets of data from different perspectives to decide all those parameters, while HardVis keeps track of their steps and evaluates the model's predictive performance in a test set separately. The end result is a well-balanced data set that boosts the predictive power of the ML model. The efficacy and effectiveness of HardVis are demonstrated with a hypothetical usage scenario and a use case. Finally, we also look at how useful our system is based on feedback we received from ML experts

HardVis: Visual Analytics to Handle Instance Hardness Using Undersampling and Oversampling Techniques

Despite the tremendous advances in machine learning (ML), training with imbalanced data still poses challenges in many real-world applications. Among a series of diverse techniques to solve this problem, sampling algorithms are regarded as an efficient solution. However, the problem is more fundamental, with many works emphasizing the importance of instance hardness. This issue refers to the significance of managing unsafe or potentially noisy instances that are more likely to be misclassified and serve as the root cause of poor classification performance.This paper introduces HardVis, a visual analytics system designed to handle instance hardness mainly in imbalanced classification scenarios. Our proposed system assists users in visually comparing different distributions of data types, selecting types of instances based on local characteristics that will later be affected by the active sampling method, and validating which suggestions from undersampling or oversampling techniques are beneficial for the ML model. Additionally, rather than uniformly undersampling/oversampling a specific class, we allow users to find and sample easy and difficult to classify training instances from all classes. Users can explore subsets of data from different perspectives to decide all those parameters, while HardVis keeps track of their steps and evaluates the models predictive performance in a test set separately. The end result is a well-balanced data set that boosts the predictive power of the ML model. The efficacy and effectiveness of HardVis are demonstrated with a hypothetical usage scenario and a use case. Finally, we also look at how useful our system is based on feedback we received from ML experts