cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.7274/25541062.v1
title: Preconditioning Hybrid Discontinuous Galerkin Schemes for Incompressible Flow and Linear Elasticity Problems

description: This dissertation presents a study on the development, analysis, and application of advanced numerical methods for solving partial differential equations (PDEs), focusing primarily on the Hybrid Discontinuous Galerkin (HDG) schemes. The research introduces innovative HDG schemes, including a monolithic divergence-conforming HDG (H(div)-HDG) scheme for linear fluid-structure interaction (FSI) problems, and proposes robust block-diagonal preconditioners and geometric multigrid algorithms to address the computational challenges associated with these methods.
The work begins by reviewing H(div)-HDG schemes for Stokes and linear elasticity equations, subsequently proposing a novel monolithic scheme for FSI problems that ensures exactly divergence-free fluid velocity approximations and maintains energy stability. An optimal a priori error analysis and convergence rates are validated through numerical experiments. A significant part of the dissertation is devoted to overcoming the computational inefficiency in solving the condensed symmetric and indefinite global linear systems arising from these schemes. A uniform block-diagonal preconditioner is developed, demonstrating robustness with respect to mesh size and model parameters.
Further, the dissertation extends to the construction of optimal geometric multigrid algorithms for the lowest order HDG schemes with numerical integration, emphasizing the equivalence between the global linear system and modified Crouzeix-Raviart discretizations. This foundational work facilitates the development of an hp-multigrid preconditioner for higher-order H(div)-HDG schemes applied to generalized Stokes and Navier-Stokes equations, showcasing robustness across mesh sizes and augmented Lagrangian parameters, and insensitivity to polynomial order variations.
The research findings underscore the efficiency and adaptability of HDG methods in handling complex PDEs, contributing to the field of computational mathematics and numerical analysis. The dissertation concludes with discussions on future research directions, including the exploration of efficient parallel implementations and the extension of robust preconditioning techniques to more complex equations.
DOI: 10.15468/dl.sdmwwg
title: Occurrence Download

description: A dataset containing 3786 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Human Observation",
 "OccurrenceStatus is Present",
 "TaxonKey is Ursus maritimus Phipps, 1774"
 ]
}

 The dataset includes 3786 records from 31 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0007877-241107131044228/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.7274/25565250
title: Advances in Differential Privacy Concepts and Methods

description: Differential privacy (DP) formalizes privacy guarantees in a rigorous mathematical framework and is a state-of-the-art concept in data privacy research. The DP mechanisms ensure the privacy of each individual in a sensitive dataset while releasing useful information about the whole population in that dataset. Since its debut in 2006, significant advancements in DP theory, methodologies, and applications have been made; new research topics and questions have been proposed and studied. This dissertation aims to contribute to the advancement of DP concepts and methods in the robustness of DP mechanisms to privacy attacks, privacy amplification through subsampling, and DP guarantees of procedures with their intrinsic randomness. Specifically, this dissertation consists of three research projects on DP. The first project explores the protection potency of DP mechanisms against homogeneity attacks (HA) by providing analytical relations between measures of disclosure risk from HA and privacy loss parameters, which will assist practitioners in understanding the abstract concepts of DP by putting them in a concrete privacy attack model and offer a perspective for choosing privacy loss parameters. The second project proposes a class of subsampling methods ``MUltistage Sampling Technique (MUST)'' for privacy amplification. It provides the privacy composition analysis over repeated applications of MUST via the Fourier accountant algorithm. The utility experiments show that MUST demonstrates comparable utility and stability in privacy-preserving outputs compared to one-stage subsampling methods at similar privacy loss while improving the computational efficiency of algorithms requiring complex function calculations on distinct data points. MUST can be seamlessly integrated into stochastic optimization algorithms or procedures involving parallel or simultaneous subsampling when DP guarantees are necessary. The third project investigates the inherent DP guarantees in Bayesian posterior sampling. It provides a new privacy loss bound in releasing a single posterior sample with any prior given a bounded log ratio of the likelihood kernels based on two neighboring data sets. The new bound is tighter than the existing bounds and consistent with the likelihood principle. Experiments show that the privacy-preserving synthetic data released from Bayesian models leveraging the inherently private posterior samples are of improved utility compared to those generated by sanitizing the original information through explicit DP mechanisms.
DOI: 10.7274/25565250.v1
title: Advances in Differential Privacy Concepts and Methods

description: Differential privacy (DP) formalizes privacy guarantees in a rigorous mathematical framework and is a state-of-the-art concept in data privacy research. The DP mechanisms ensure the privacy of each individual in a sensitive dataset while releasing useful information about the whole population in that dataset. Since its debut in 2006, significant advancements in DP theory, methodologies, and applications have been made; new research topics and questions have been proposed and studied. This dissertation aims to contribute to the advancement of DP concepts and methods in the robustness of DP mechanisms to privacy attacks, privacy amplification through subsampling, and DP guarantees of procedures with their intrinsic randomness. Specifically, this dissertation consists of three research projects on DP. The first project explores the protection potency of DP mechanisms against homogeneity attacks (HA) by providing analytical relations between measures of disclosure risk from HA and privacy loss parameters, which will assist practitioners in understanding the abstract concepts of DP by putting them in a concrete privacy attack model and offer a perspective for choosing privacy loss parameters. The second project proposes a class of subsampling methods ``MUltistage Sampling Technique (MUST)'' for privacy amplification. It provides the privacy composition analysis over repeated applications of MUST via the Fourier accountant algorithm. The utility experiments show that MUST demonstrates comparable utility and stability in privacy-preserving outputs compared to one-stage subsampling methods at similar privacy loss while improving the computational efficiency of algorithms requiring complex function calculations on distinct data points. MUST can be seamlessly integrated into stochastic optimization algorithms or procedures involving parallel or simultaneous subsampling when DP guarantees are necessary. The third project investigates the inherent DP guarantees in Bayesian posterior sampling. It provides a new privacy loss bound in releasing a single posterior sample with any prior given a bounded log ratio of the likelihood kernels based on two neighboring data sets. The new bound is tighter than the existing bounds and consistent with the likelihood principle. Experiments show that the privacy-preserving synthetic data released from Bayesian models leveraging the inherently private posterior samples are of improved utility compared to those generated by sanitizing the original information through explicit DP mechanisms.
DOI: 10.15468/dl.6k2r82
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Lissotriton vulgaris (Linnaeus, 1758)",
 "Year is 38",
 "Country is Netherlands"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0007878-241107131044228/datasets/export for details.
DOI: 10.7274/25575060
title: Graph-Based Approaches for Prediction and Similarity Analysis

description: This thesis explores graph-based approaches for prediction and similarity analysis problems within networks and hypergraphs. While existing algorithms for link prediction in networks predominantly target the existence or weights of edges, our study expands the scope by delving into the prediction of both vertex and edge weights using metric geometry and machine learning approaches. Additionally, our investigation extends into weight prediction in higher-order networks, often referred to as hypergraphs. We propose a novel notion of neighborhood for hyperedges, utilizing the topological structures of hypergraphs and weights of hyperedges from a given training set. We construct metric spaces on the set of hyperedges based on the neighborhood information. Furthermore, we explore the practical application of graph similarity algorithms in DNA sequence analysis, introducing an accurate and computationally efficient approach to analyze the similarities among DNA sequences. Our proposed methods were tested on diverse real-world datasets and yielded promising results. The main implication of our research is offering a more comprehensive framework for prediction tasks in networks and hypergraphs, providing alternative avenues to gain a deeper understanding of the intricate relationships within complex networks.
DOI: 10.7274/25575060.v1
title: Graph-Based Approaches for Prediction and Similarity Analysis

description: This thesis explores graph-based approaches for prediction and similarity analysis problems within networks and hypergraphs. While existing algorithms for link prediction in networks predominantly target the existence or weights of edges, our study expands the scope by delving into the prediction of both vertex and edge weights using metric geometry and machine learning approaches. Additionally, our investigation extends into weight prediction in higher-order networks, often referred to as hypergraphs. We propose a novel notion of neighborhood for hyperedges, utilizing the topological structures of hypergraphs and weights of hyperedges from a given training set. We construct metric spaces on the set of hyperedges based on the neighborhood information. Furthermore, we explore the practical application of graph similarity algorithms in DNA sequence analysis, introducing an accurate and computationally efficient approach to analyze the similarities among DNA sequences. Our proposed methods were tested on diverse real-world datasets and yielded promising results. The main implication of our research is offering a more comprehensive framework for prediction tasks in networks and hypergraphs, providing alternative avenues to gain a deeper understanding of the intricate relationships within complex networks.
DOI: 10.7274/25595235
title: Probabilistic Machine Learning Methods for Spatio-Temporal Data

description: This dissertation presents multiple novel methodological advancements in the realm of machine learning (ML) for spatio-temporal data applications. Traditional machine learning approaches typically have difficultly producing both accurate point predictions and adequate uncertainty quantification for these data, especially in instances where the data themselves are sampled at a fine temporal scale. This is due to the fact that inference on these complex ML models is notably difficult and can impose a significant computational burden. The challenge of forecasting spatio-temporal data is further heightened when attempting to ensure the forecast themselves obey any known physical laws which dictate or influence the underlying data structure.

We explore the current challenges in properly quantifying the uncertainty of forecasts for spatio-temporal data applications stemming from contemporary ML models. Methods are introduced to not only calibrate the uncertainty estimates such that proper coverage is achieved but also so there is a realistic expansion of the uncertainty through time. These contemporary ML models are also adapted such that the physical processes present throughout that data are used to inform the learning procedures, so that the forecasts themselves are influenced to be more physically compliant. We demonstrate the power in combining ML models in an ensemble to improve model accuracy in predicting nonstationary, complex temporal data. Finally, a general comparison is made to explore the benefits and drawbacks of ML approaches to time-series forecasting versus the popular and standard statistical approaches, and as a guide to explain how these newfound advanced ML modelling techniques are not necessarily meant to act as a universal best approach for prediction and forecasting.
DOI: 10.15468/dl.6c8dez
title: Occurrence Download

description: A dataset containing 11342770 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is China",
 "OccurrenceStatus is Present"
 ]
}

 The dataset includes 11342770 records from 4257 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0007860-241107131044228/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.13140/rg.2.2.29695.33441
title: INFOGRAFIK ANALISIS FAKTOR TEKANAN MEMPENGARUHI TEKANAN PELAJAR KOLEJ KOMUNITI JOHOR KETIKA PKP
DOI: 10.5517/ccss1n7
title: CCDC 738099: Experimental Crystal Structure Determination

description: Related Article: S.M.Yu, Y.J.Song, K.C.Kim, C.Kim, Y.Kim|2009|Acta Crystallogr.,Sect.E:Struct.Rep.Online|65|m678|doi:10.1107/S1600536809018728
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccsxbnn
title: CCDC 742222: Experimental Crystal Structure Determination

description: Related Article: R.W.Seidel, I.M.Oppel|2009|Acta Crystallogr.,Sect.C:Cryst.Struct.Commun.|65|m235|doi:10.1107/S0108270109015418
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.13140/rg.2.2.10860.28803
title: شهادة مشاركة في دورة برنامج خطة صناعة المعرفة
DOI: 10.5517/ccsxbpp
title: CCDC 742223: Experimental Crystal Structure Determination

description: Related Article: Ying-Lin Chen, Ben-Zhen Li, Ping Yang, Jian-Zhong Wu|2009|Acta Crystallogr.,Sect.C:Cryst.Struct.Commun.|65|m238|doi:10.1107/S0108270109018812
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccsxbqq
title: CCDC 742224: Experimental Crystal Structure Determination

description: Related Article: A.M.Atria, G.Corsini, L.Gonzalez, M.T.Garland, R.Baggio|2009|Acta Crystallogr.,Sect.C:Cryst.Struct.Commun.|65|m241|doi:10.1107/S0108270109019490
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccsxbrr
title: CCDC 742225: Experimental Crystal Structure Determination

description: Related Article: A.M.Atria, G.Corsini, L.Gonzalez, M.T.Garland, R.Baggio|2009|Acta Crystallogr.,Sect.C:Cryst.Struct.Commun.|65|m241|doi:10.1107/S0108270109019490
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccsxbss
title: CCDC 742226: Experimental Crystal Structure Determination

description: Related Article: J.Kuchar, J.Cernak|2009|Acta Crystallogr.,Sect.C:Cryst.Struct.Commun.|65|m246|doi:10.1107/S0108270109017107
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.13140/rg.2.2.23757.77288
title: HTC BBDST P N20 58.007
DOI: 10.5517/ccsxbtt
title: CCDC 742227: Experimental Crystal Structure Determination

description: Related Article: A.M.Atria, G.Corsini, L.Gonzalez, M.T.Garland, R.Baggio|2009|Acta Crystallogr.,Sect.C:Cryst.Struct.Commun.|65|m250|doi:10.1107/S0108270109021404
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccsxbvv
title: CCDC 742228: Experimental Crystal Structure Determination

description: Related Article: A.M.Atria, G.Corsini, L.Gonzalez, M.T.Garland, R.Baggio|2009|Acta Crystallogr.,Sect.C:Cryst.Struct.Commun.|65|m250|doi:10.1107/S0108270109021404
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.13140/rg.2.2.11174.86080
title: H OW THE COVID 19 INFECT HUMANS PROFESSOR TAREQ ALASADI
DOI: 10.15468/dl.4kx5pw
title: Occurrence Download

description: A dataset containing 3582 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Amphibia)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "Year is greater than or equal to 1980",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Living Specimen)"
 ]
 }
 },
 "Geometry POLYGON((-67.5 0,-60 0,-60 7.5,-67.5 7.5,-67.5 0))"
 ]
}

 The dataset includes 3582 records from 30 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0041544-251009101135966/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5517/ccsxbww
title: CCDC 742229: Experimental Crystal Structure Determination

description: Related Article: Xiaowu Wang, Wanli Nie, Fang Ge, M.V.Borzov|2009|Acta Crystallogr.,Sect.C:Cryst.Struct.Commun.|65|m255|doi:10.1107/S010827010902112X
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.13140/rg.2.2.18043.03368
title: HTC HPPC OCV T25 S097.0205 1
DOI: 10.13140/rg.2.2.33404.67207
title: Reentrant Flow Shop Experiment: Unit Times instances #1-53
DOI: 10.5517/ccr79t2
title: CCDC 692224: Experimental Crystal Structure Determination

description: Related Article: J.Cernak, A.Pavlova, M.Dusek, K.Fejfarova|2009|Acta Crystallogr.,Sect.C:Cryst.Struct.Commun.|65|m260|doi:10.1107/S0108270109021131
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccsxbxx
title: CCDC 742230: Experimental Crystal Structure Determination

description: Related Article: Xiaowu Wang, Wanli Nie, Fang Ge, M.V.Borzov|2009|Acta Crystallogr.,Sect.C:Cryst.Struct.Commun.|65|m255|doi:10.1107/S010827010902112X
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccsxbyy
title: CCDC 742231: Experimental Crystal Structure Determination

description: Related Article: S.B.Novakovic, G.A.Bogdanovic, I.D.Brceski, V.M.Leovac|2009|Acta Crystallogr.,Sect.C:Cryst.Struct.Commun.|65|m263|doi:10.1107/S0108270109021970
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccsxbzz
title: CCDC 742232: Experimental Crystal Structure Determination

description: Related Article: Ming-Lin Guo, Chen-Hu Guo|2009|Acta Crystallogr.,Sect.C:Cryst.Struct.Commun.|65|m266|doi:10.1107/S0108270109021441
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.15468/dl.73x7mw
title: Occurrence Download

description: A dataset containing 35836 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Peru",
 "Year is 2023",
 "InstitutionCode is iNaturalist"
 ]
}

 The dataset includes 35836 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0041545-251009101135966/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.