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This eBook is a collection of articles from a Frontiers Research Topic. Frontiers Research Topics are very popular trademarks of the Frontiers Journals Series: they are collections of at least ten articles, all centered on a particular subject. With their unique mix of varied contributions from Original Research to Review Articles, Frontiers Research Topics unify the most influential researchers, the latest key findings and historical advances in a hot research area! Find out more on how to host your own Frontiers Research Topic or contribute to one as an author by contacting the Frontiers Editorial Office: frontiersin.org/about/contact

multi-locus genome-wide association study --- mixed linear model --- mrMLM --- FASTmrMLM --- FASTmrEMMA --- ISIS EM-BLASSO --- pLARmEB --- pKWmEB --- complex traits --- omics big dataset

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[Cancer is the second most common cause of death worldwide and encompasses highly variable clinical and biological scenarios. Some of the current clinical challenges are (i) early diagnosis of the disease and (ii) precision medicine, which allows for treatments targeted to specific clinical cases. The ultimate goal is to optimize the clinical workflow by combining accurate diagnosis with the most suitable therapies. Toward this, large-scale machine learning research can define associations among clinical, imaging, and multi-omics studies, making it possible to provide reliable diagnostic and prognostic biomarkers for precision oncology. Such reliable computer-assisted methods (i.e., artificial intelligence) together with clinicians’ unique knowledge can be used to properly handle typical issues in evaluation/quantification procedures (i.e., operator dependence and time-consuming tasks). These technical advances can significantly improve result repeatability in disease diagnosis and guide toward appropriate cancer care. Indeed, the need to apply machine learning and computational intelligence techniques has steadily increased to effectively perform image processing operations—such as segmentation, co-registration, classification, and dimensionality reduction—and multi-omics data integration.]

melanoma detection --- deep learning --- transfer learning --- ensemble classification --- 3D-CNN --- immunotherapy --- radiomics --- self-attention --- breast imaging --- microwave imaging --- image reconstruction --- segmentation --- unsupervised machine learning --- k-means clustering --- Kolmogorov-Smirnov hypothesis test --- statistical inference --- performance metrics --- contrast source inversion --- brain tumor segmentation --- magnetic resonance imaging --- survey --- brain MRI image --- tumor region --- skull stripping --- region growing --- U-Net --- BRATS dataset --- incoherent imaging --- clutter rejection --- breast cancer detection --- MRgFUS --- proton resonance frequency shift --- temperature variations --- referenceless thermometry --- RBF neural networks --- interferometric optical fibers --- breast cancer --- risk assessment --- machine learning --- texture --- mammography --- medical imaging --- imaging biomarkers --- bone scintigraphy --- prostate cancer --- semisupervised classification --- false positives reduction --- computer-aided detection --- breast mass --- mass detection --- mass segmentation --- Mask R-CNN --- dataset partition --- brain tumor --- classification --- shallow machine learning --- breast cancer diagnosis --- Wisconsin Breast Cancer Dataset --- feature selection --- dimensionality reduction --- principal component analysis --- ensemble method --- n/a

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Because of the accelerating progress in biometrics research and the latest nation-state threats to security, this book's publication is not only timely but also much needed. This volume contains seventeen peer-reviewed chapters reporting the state of the art in biometrics research: security issues, signature verification, fingerprint identification, wrist vascular biometrics, ear detection, face detection and identification (including a new survey of face recognition), person re-identification, electrocardiogram (ECT) recognition, and several multi-modal systems. This book will be a valuable resource for graduate students, engineers, and researchers interested in understanding and investigating this important field of study.

online signature verification --- shape contexts --- function features --- SC-DTW --- symbolic representation --- two-stage method --- finger features --- multimodal recognition --- local coding --- Gabor filter --- LGS --- human identification --- biomarker --- ECG --- machine learning --- Physionet --- Lviv Biometric Dataset --- biometry --- identification --- bloodstream --- image recognition --- multi-biometrics --- bit planes --- block --- mutual information --- cross-device --- dorsal hand vein recognition --- person re-identification --- superpixel --- temporally aligned pooling --- walking cycle --- automatic recognition --- face --- voice --- body motion --- autism spectrum disorder (ASD) --- assessment --- intervention --- curve similarity --- curve similarity model --- curve similarity transformation --- similarity distance --- segmentation matching --- evolutionary computation --- finger vein recognition --- hand vein recognition --- contactless acquisition device --- public vascular pattern dataset --- biometric recognition performance evaluation --- face verification --- optical correlation --- Hausdorff distance --- image classification --- face detection --- depth map ensemble --- filtering --- geometric deep learning --- ear detection --- structured prediction --- semantic segmentation --- rotation equivariance --- Gaussian mixture model --- superpixels --- face recognition systems --- person identification --- biometric systems --- survey --- automatic signature verification --- touch-screen sensor --- data quality --- enrollment phase --- performance assessment --- augmented signature --- security enhancement --- mobile conditions --- biometric recognition --- visible light iris images --- image quality assessment --- image covariates --- quality filtering --- vascular biometric recognition --- wrist vein recognition --- contactless dataset --- pattern recognition --- infrared camera --- non-contact devices --- Scale-Invariant Feature Transform (SIFT®) --- Speeded Up Robust Features (SURF®) --- Oriented FAST and Rotated BRIEF (ORB) --- fingerprint --- presentation attack detection --- deep learning

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The convergence of big data and geospatial computing has brought forth challenges and opportunities to Geographic Information Science with regard to geospatial data management, processing, analysis, modeling, and visualization. This book highlights recent advancements in integrating new computing approaches, spatial methods, and data management strategies to tackle geospatial big data challenges and meanwhile demonstrates opportunities for using big data for geospatial applications. Crucial to the advancements highlighted in this book is the integration of computational thinking and spatial thinking and the transformation of abstract ideas and models to concrete data structures and algorithms.

task --- workflow --- geospatial problem-solving --- knowledge base --- social media --- big data --- fine-grained emotion classification --- spatio-temporal analysis --- hazard mitigation --- missing road --- city blocks --- topology --- big mobile navigation trajectory data --- geographic knowledge representation --- geographic knowledge graph --- formalization --- GeoKG --- overlay analysis --- shape complexity --- massive data --- cloud --- parallel computing --- geovisual analytics --- machine learning --- smart card data --- transit corridor --- mobility community --- trip --- CA Markov --- land-use change prediction --- Hadoop --- MapReduce --- cloud computing --- ETL --- ELT --- sensor data --- IoT --- geospatial big data --- climate science --- metadata --- web cataloging service --- big geospatial data --- geospatial cyberinfrastructure --- topographic surface --- terrain modeling --- global terrain dataset --- geospatial computing --- cyberGIS --- GeoAI --- spatial thinking

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To promote scientific understanding of surface processes in East Asia, we have published details of the CMADS dataset in the journal, Water, and expect that users around the world will learn about CMADS datasets while promoting the development of hydrometeorological disciplines in East Asia. We hope and firmly believe that scientific development in East Asia and our understanding of this typical region will be further advanced.

sensitivity analysis --- non-point source pollution models --- reservoirs --- operation rule --- East Asia --- climate variability --- Qinghai-Tibet Plateau (TP) --- potential evapotranspiration --- precipitation --- capacity distribution --- GLUE --- soil temperature --- land use change --- JBR --- CFSR --- Jinsha River Basin --- impact --- runoff --- CMADS --- hydrological modeling --- aggregated reservoir --- reanalysis products --- Lijiang River --- spatio-temporal --- uncertainty --- total nitrogen --- Han River --- streamflow simulation --- meteorological --- CMADS-ST --- Erhai Lake Basin --- uncertainty analysis --- Biliuhe reservoir --- hydrological --- bayesian model averaging --- blue and green water flows --- SUFI-2 --- TMPA-3B42V7 --- statistical analysis --- satellite-derived rainfall --- streamflow --- satellite-based products --- Xiang River basin --- SWAT hydrological simulation --- PERSIANN-CDR --- hydrological processes --- SUFI2 --- CMADS dataset --- ParaSol --- hydrological modelling --- accumulation --- meteorological input uncertainty --- soil moisture content --- Yellow River --- SWAT --- Noah LSM-HMS --- sediment yield --- Yalong River --- TRMM --- Penman-Monteith --- IMERG --- PERSIANN --- hydrological elements --- freeze–thaw period --- land-use change --- parameter sensitivity --- China --- reservoir parameters --- soil moisture --- sloping black soil farmland --- hydrological model --- SWAT model --- hydrologic model