Incorporating graph-based techniques in the analysis of multivariate signals is becoming a standard method to understand the interdependency of activity recorded at different sites. The new research frontier in this field includes the important problem of how to assess dynamic changes of signal activity. We present a unified framework of multivariate signals and network science through the grap...

Identifying venous voxels in fMRI datasets is important to increase the specificity of fMRI analyses to microvasculature in the vicinity of the neural processes triggering the BOLD response. This is, however, difficult to achieve in particular in typical studies where magnitude images of BOLD EPI are the only data available. In this study, voxelwise functional connectivity graphs were computed ...

Within model-driven software development, model transformation has become a key activity. It refers to a variety of operations modifying a model for various purposes such as analysis, optimization, and code generation. Most of these transformations need to be bidirectional to e.g. report analysis results, or keep coherence between models. In several application-oriented papers it has been shown...

Graph theory based methods are powerful means for representing structural systems so that their geometry and topology can be understood clearly. The combination of graph theory based methods and some metaheuristics can offer effective solutions for complex engineering optimization problems. This paper presents a Charged System Search (CSS) algorithm for the free shape optimizations of thin-wall...

In recent years, the capabilities of knowledge-based systems to communicate with their users have evolved from simple interactions to complex dialogues. With this evolution comes a need to understand what makes a good dialogue. In this paper, we are concerned with dialogue coherence. We review the notion of focus, which partly explains this property, and its use for user-system communication. F...

We study the problem of sampling k-bandlimited signals on graphs. We propose two sampling strategies that consist in selecting a small subset of nodes at random. The first strategy is non-adaptive, i.e., independent of the graph structure, and its performance depends on a parameter called the graph coherence. On the contrary, the second strategy is adaptive but yields optimal results. Indeed, n...

Text coherence analysis is the most challenging task in Natural Language Processing (NLP) than other subfields of NLP, such as text generation, translation, or summarization. There are many methods them graph-based entity-based for short documents. However, long documents, existing perform low accuracy results which biggest challenge both English and Bengali. This because do not consider misspe...

Recent data-driven efforts have utilized spectral decomposition techniques to uncover the geometric self-similarity of dominant motions in logarithmic layer, and thereby validate attached eddy model. In this paper, we evaluate predictive capability stochastically forced linearized Navier–Stokes equations capturing such structural features turbulent channel flow at $Re_\tau =2003$ . We use linea...

Optical coherence tomography (OCT) is a powerful and noninvasive method for retinal imaging. In this paper, we introduce a fast segmentation method based on a new variant of spectral graph theory named diffusion maps. The research is performed on spectral domain (SD) OCT images depicting macular and optic nerve head appearance. The presented approach does not require edge-based image informatio...