Clustering is an efficient way to group data into different classes on basis of the internal and
previously unknown schemes inherent of the data. With the development of the location …

Recent advances in sensor and mobile devices have enabled an unprecedented increase
in the availability and collection of urban trajectory data, thus increasing the demand for …

Significant evidence supports the view that dopamine shapes learning by encoding reward
prediction errors. However, it is unknown whether striatal targets receive tailored dopamine …

This paper examines the relationship among CO2 emissions, energy use, and GDP in
Russia using annual data ranging from 1990 to 2020. We first conduct time-series analyses …

Unlike coverage-based fuzzing that gives equal attention to every part of a code, directed
fuzzing aims to direct a fuzzer to a specific target in the code, eg, the code with potential …

Time series clustering is an active research area with applications in a wide range of fields.
One key component in cluster analysis is determining a proper dissimilarity measure …

Computing trajectory similarity is a fundamental operation in movement analytics, required
in search, clustering, and classification of trajectories, for example. Yet the range of different …

The proliferation of trajectory data in various application domains has inspired tremendous
research efforts to analyze large-scale trajectory data from a variety of aspects. A …

Sum-of-square based error formulations may be difficult to implement on an inverse analysis
consisting of multiple tension-compression hysteresis loops. Five alternative measures of …

We present a unified approach for simultaneously clustering and discovering outliers in
data. Our approach is formalized as a generalization of the k-MEANS problem. We prove …