Related papers: A Graph Based Raman Spectral Processing Technique …
We propose a new approach, the calibrated nonparametric scan statistic (CNSS), for more accurate detection of anomalous patterns in large-scale, real-world graphs. Scan statistics identify connected subgraphs that are interesting or…
Motivation. Cancer heterogeneity is observed at multiple biological levels. To improve our understanding of these differences and their relevance in medicine, approaches to link organ- and tissue-level information from diagnostic images and…
Graph machine learning has been widely explored in various domains, such as community detection, transaction analysis, and recommendation systems. In these applications, anomaly detection plays an important role. Recently, studies have…
We study the potential of graphene plasmons for spectrometer-free sensing based on surface-enhanced infrared absorption and Raman scattering. The large electrical tunability of these excitations enables an accurate identification of…
Surface-enhanced Raman spectroscopy (SERS) is a potential fast and inexpensive method of analyte quantification, which can be combined with deep learning to discover biomarker-disease relationships. This study aims to address present…
Detecting and quantifying products of cellular metabolism using Mass Spectrometry (MS) has already shown great promise in many biological and biomedical applications. The biggest challenge in metabolomics is annotation, where measured…
We propose a new data analysis method for obtaining transmission spectra of exoplanet atmospheres and brightness variation across the stellar disk from transit observations. The new method is capable of recovering exoplanet atmosphere…
Frequent Subgraph Mining (FSM) is the process of identifying common subgraph patterns that surpass a predefined frequency threshold. While FSM is widely applicable in fields like bioinformatics, chemical analysis, and social network anomaly…
Resonant Raman scattering is investigated in monolayer WS$_2$ at low temperature with the aid of an unconventional spectroscopy technique, $i.e.$, Raman scattering excitation (RSE). The RSE spectrum is made up by sweeping the excitation…
Approximation-based spectral graph neural networks, which construct graph filters with function approximation, have shown substantial performance in graph learning tasks. Despite their great success, existing works primarily employ…
Raman spectroscopy is a key tool for graphene characterization, yet its application to graphene grown on silicon carbide (SiC) is strongly limited by the intense and variable second-order Raman response of the substrate. This limitation is…
Modern materials science requires efficient processing and characterization techniques for low dimensional systems. Raman spectroscopy is an important non-destructive tool, which provides enormous information on these materials. This…
Identification of the mechanically exfoliated graphene flakes and classification of the thickness is important in the nanomanufacturing of next-generation materials and devices that overcome the bottleneck of Moore's Law. Currently,…
Graph neural networks (GNNs) have shown remarkable performance on homophilic graph data while being far less impressive when handling non-homophilic graph data due to the inherent low-pass filtering property of GNNs. In general, since…
The search for atmospheric biosignatures in Earth-like exoplanets is one of the most pressing challenges in observational astrobiology. Detecting biogenic gases in terrestrial planets requires high-resolution observations and long…
Surface enhanced Raman spectroscopy (SERS) is a precise and non-invasive analytical technique that is widely used in chemical analysis, environmental protection, food processing, pharmaceutics, and diagnostic biology. However, it is still a…
High-resolution cameras have become very helpful for plant phenotyping by providing a mechanism for tasks such as target versus background discrimination, and the measurement and analysis of fine-above-ground plant attributes. However, the…
Accurate detection and analysis of traces of persistent organic pollutants in water is important in many areas, including environmental monitoring and food quality control, due to their long environmental stability and potential…
The proliferation of new types of drugs necessitates the urgent development of faster and more accurate detection methods. Traditional detection methods have high requirements for instruments and environments, making the operation complex.…
Predicting molecular properties is essential for drug discovery, and computational methods can greatly enhance this process. Molecular graphs have become a focus for representation learning, with Graph Neural Networks (GNNs) widely used.…