cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.7274/25595235.v1
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.dnzxps
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Troglodytes troglodytes (Linnaeus, 1758)",
 "Year is 39",
 "Country is Netherlands"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0007879-241107131044228/datasets/export for details.
DOI: 10.7274/25596300
title: Optimization Techniques for Inference and Parameter Recovery

description: Applications involving data often contain uncertainty due to imprecise measurements or prior numerical computations. Inverse problems in statistics and machine learning arise when one seeks to estimate underlying parameters of noisy outputs. This is often achieved through posterior distribution approximation for sample generation, which can be a challenging task when characterized by multiple modes. We present an adaptive annealing scheduler AdaAnn to facilitate this task, which automatically adjusts temperature increments based on expected change in the Kullback-Leibler divergence between two annealed distributions. Incorporating this technique with a surrogate model for computationally expensive model evaluations led to the development of LINFA, a Python Library for Inference with Normalizing Flow and Annealing. Additionally, inexact parameters can lead to uncertainty in the structure of a solution set when solving parameterized polynomial systems in numerical algebraic geometry. We present methodologies for robust interpretation regarding the number of finite solutions, existence of higher-dimensional components, and number of irreducible components, along with their multiplicities, by searching for nearby parameters on exceptional sets. This dissertation discusses several numerical examples, including applications in ordinary differential equations and kinematics.
DOI: 10.7274/25596300.v1
title: Optimization Techniques for Inference and Parameter Recovery

description: Applications involving data often contain uncertainty due to imprecise measurements or prior numerical computations. Inverse problems in statistics and machine learning arise when one seeks to estimate underlying parameters of noisy outputs. This is often achieved through posterior distribution approximation for sample generation, which can be a challenging task when characterized by multiple modes. We present an adaptive annealing scheduler AdaAnn to facilitate this task, which automatically adjusts temperature increments based on expected change in the Kullback-Leibler divergence between two annealed distributions. Incorporating this technique with a surrogate model for computationally expensive model evaluations led to the development of LINFA, a Python Library for Inference with Normalizing Flow and Annealing. Additionally, inexact parameters can lead to uncertainty in the structure of a solution set when solving parameterized polynomial systems in numerical algebraic geometry. We present methodologies for robust interpretation regarding the number of finite solutions, existence of higher-dimensional components, and number of irreducible components, along with their multiplicities, by searching for nearby parameters on exceptional sets. This dissertation discusses several numerical examples, including applications in ordinary differential equations and kinematics.
DOI: 10.7274/26131414
title: Applications of Modern Machine Learning Approaches to Address Real World Problems

description: In an era with increasingly available complex real-world data, various quantitative methods have been developed to extract valuable insights from the data. Machine learning (ML) techniques have proven to be instrumental in modeling such intricate data. This dissertation encompasses a collection of illustrative examples that showcase the efficacy of ML models in analyzing data from various domains.

First, I introduce an innovative l0 regularization technique, coupled with Tucker decomposition, in the framework of tensor regression (TR) and apply it to simulated linear, binomial, and Poisson data and a real human face image dataset for age prediction. The results suggest improved predictions by TR with l0 regularization compared to other decomposition-based TR approaches, with or without regularization, while also being able to identify important predictors. 

Second, I investigate the shift in sentiment during and after the COVID-19 pandemic utilizing college subreddit data and examine the effects of different community-level factors on the sentiment. A pre-trained Robustly Optimized BERT pre-training approach (RoBERTa) was used to learn text embedding from the Reddit messages, and a graph attention network (GAT) was leveraged to learn the relational information among posted messages. I applied model stacking to combine the prediction
probabilities from RoBERTa and GAT to yield the final classification on sentiment and used a generalized
linear mixed-effects model to estimate the effects of various covariates. It's found that the odds of negative sentiments in years 2020, 2021, and 2022 increased statistically significantly compared to the year 2019, with the year 2020 having the highest increase. Factors including in-person learning, larger enrollment numbers, being public rather than private school, and very high research activities also increase the odds of negative sentiments statistically significantly.

Third, my collaborators and I leverage the CodeBERT model to predict simulated running time within gem5, a simulation framework for various computer architecture configurations. We generate a dataset that contains both C code scripts and their simulated running time in gem5. We applied the CodeBERT model in three distinct ways to predict the simulated running time and achieved a mean absolute error of 0.546 in regression, and an accuracy of 0.696 in classification. To our knowledge, this is the first work that uses ML models to predict gem5 simulation metrics.

In summary, the work in this dissertation and the findings from each of the three projects demonstrate the effectiveness of various ML techniques in different learning tasks using real-world data of different types in various domains.
DOI: 10.7274/26131414.v1
title: Applications of Modern Machine Learning Approaches to Address Real World Problems

description: In an era with increasingly available complex real-world data, various quantitative methods have been developed to extract valuable insights from the data. Machine learning (ML) techniques have proven to be instrumental in modeling such intricate data. This dissertation encompasses a collection of illustrative examples that showcase the efficacy of ML models in analyzing data from various domains.

First, I introduce an innovative l0 regularization technique, coupled with Tucker decomposition, in the framework of tensor regression (TR) and apply it to simulated linear, binomial, and Poisson data and a real human face image dataset for age prediction. The results suggest improved predictions by TR with l0 regularization compared to other decomposition-based TR approaches, with or without regularization, while also being able to identify important predictors. 

Second, I investigate the shift in sentiment during and after the COVID-19 pandemic utilizing college subreddit data and examine the effects of different community-level factors on the sentiment. A pre-trained Robustly Optimized BERT pre-training approach (RoBERTa) was used to learn text embedding from the Reddit messages, and a graph attention network (GAT) was leveraged to learn the relational information among posted messages. I applied model stacking to combine the prediction
probabilities from RoBERTa and GAT to yield the final classification on sentiment and used a generalized
linear mixed-effects model to estimate the effects of various covariates. It's found that the odds of negative sentiments in years 2020, 2021, and 2022 increased statistically significantly compared to the year 2019, with the year 2020 having the highest increase. Factors including in-person learning, larger enrollment numbers, being public rather than private school, and very high research activities also increase the odds of negative sentiments statistically significantly.

Third, my collaborators and I leverage the CodeBERT model to predict simulated running time within gem5, a simulation framework for various computer architecture configurations. We generate a dataset that contains both C code scripts and their simulated running time in gem5. We applied the CodeBERT model in three distinct ways to predict the simulated running time and achieved a mean absolute error of 0.546 in regression, and an accuracy of 0.696 in classification. To our knowledge, this is the first work that uses ML models to predict gem5 simulation metrics.

In summary, the work in this dissertation and the findings from each of the three projects demonstrate the effectiveness of various ML techniques in different learning tasks using real-world data of different types in various domains.
DOI: 10.15468/dl.wasprx
title: Occurrence Download

description: A dataset containing 470 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Indonesia",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Physeter macrocephalus Linnaeus, 1758"
 ]
}

 The dataset includes 470 records from 4 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0007880-241107131044228/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.mfgh2u
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Dendrocopos major (Linnaeus, 1758)",
 "Year is 39",
 "Country is Netherlands"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0007881-241107131044228/datasets/export for details.
DOI: 10.7274/27569475
title: A Study of Singularities in Homotopy Continuation for Kinematic Applications

description: Task requirements and positions of robotic kinematic systems can be represented by a system of polynomial equations. The use of homotopy continuation, a numerical method from algebraic geometry, solves these systems and analyzes their solution sets. However, these systems are plagued by branch points, and homotopy paths passing within proximity to these points experience ill-conditioning leading to computational burdens and even numerical failures. Although branch points negatively impact homotopy continuation methods, one can leverage knowledge regarding these points for solving and analyzing kinematic systems. First, we present a holistic consideration of the presence of branch points in homotopy systems of generic cases and an applied kinematic problem. We study the distribution of the image of branch points, ramification points, compactified to the Riemann sphere for select combinations of start and end systems of a homotopy. We compute metrics to assess the uniformity of the ramification point distribution and observe methods that impact that distribution, including the use of specially structured start systems and scaling coefficients. Next, we consider the complete computation of solution sets for the four-bar optimal path synthesis problem in kinematics. As task requirements become more complex, so do their resulting systems, ultimately leading one to consider an optimization formulation such as the least-squares approximation. Homotopy continuation methods, namely the use of random monodromy loops, can be employed to these polynomial formulations to yield a nearly, if not totally, complete solution set starting from a single seed solution. We consider three scenarios of the optimal path synthesis with no, one, and two pivots pre-specified and applied examples for each scenario. Lastly, we propose a method for kinematic path planning that incorporates topological ideas into computing a radius graph representation of the workspace for a five-bar mechanism. The workspace of a five-bar mechanism is a manifold possessing extra folds that, when projected into a 2D view, appears to admit additional internal boundaries. The mechanism satisfies additional constraints at these boundaries, but only a portion of those boundaries should be avoided due to unfavorable transmission qualities displayed by the five-bar. Our method identifies and removes these boundaries in a workspace discretization while preserving characteristics of the workspace manifold. We sample a sufficient number of points from the configuration space such that we preserve topological features of the manifold. Then, through homotopy applications, we identify and remove problematic boundaries from our viable workspace discretization for use in path-planning algorithms. We apply our method to two path-planning examples and show the resulting path and joint actuations taken by the five-bar mechanism.
DOI: 10.7274/27569475.v1
title: A Study of Singularities in Homotopy Continuation for Kinematic Applications

description: Task requirements and positions of robotic kinematic systems can be represented by a system of polynomial equations. The use of homotopy continuation, a numerical method from algebraic geometry, solves these systems and analyzes their solution sets. However, these systems are plagued by branch points, and homotopy paths passing within proximity to these points experience ill-conditioning leading to computational burdens and even numerical failures. Although branch points negatively impact homotopy continuation methods, one can leverage knowledge regarding these points for solving and analyzing kinematic systems. First, we present a holistic consideration of the presence of branch points in homotopy systems of generic cases and an applied kinematic problem. We study the distribution of the image of branch points, ramification points, compactified to the Riemann sphere for select combinations of start and end systems of a homotopy. We compute metrics to assess the uniformity of the ramification point distribution and observe methods that impact that distribution, including the use of specially structured start systems and scaling coefficients. Next, we consider the complete computation of solution sets for the four-bar optimal path synthesis problem in kinematics. As task requirements become more complex, so do their resulting systems, ultimately leading one to consider an optimization formulation such as the least-squares approximation. Homotopy continuation methods, namely the use of random monodromy loops, can be employed to these polynomial formulations to yield a nearly, if not totally, complete solution set starting from a single seed solution. We consider three scenarios of the optimal path synthesis with no, one, and two pivots pre-specified and applied examples for each scenario. Lastly, we propose a method for kinematic path planning that incorporates topological ideas into computing a radius graph representation of the workspace for a five-bar mechanism. The workspace of a five-bar mechanism is a manifold possessing extra folds that, when projected into a 2D view, appears to admit additional internal boundaries. The mechanism satisfies additional constraints at these boundaries, but only a portion of those boundaries should be avoided due to unfavorable transmission qualities displayed by the five-bar. Our method identifies and removes these boundaries in a workspace discretization while preserving characteristics of the workspace manifold. We sample a sufficient number of points from the configuration space such that we preserve topological features of the manifold. Then, through homotopy applications, we identify and remove problematic boundaries from our viable workspace discretization for use in path-planning algorithms. We apply our method to two path-planning examples and show the resulting path and joint actuations taken by the five-bar mechanism.
DOI: 10.15468/dl.shu5cw
title: Occurrence Download

description: A dataset listing the 3 species recorded in GBIF matching the query:
{
 "and" : [
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Tremarctos ornatus (F.G.Cuvier, 1825)"
 ]
}

 The dataset's 3 records were derived from 74 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0007867-241107131044228/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.srw4u5
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Apus apus (Linnaeus, 1758)",
 "Year is 39",
 "Country is Netherlands"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0007882-241107131044228/datasets/export for details.
DOI: 10.15468/dl.yqtzun
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Haematopus ostralegus Linnaeus, 1758",
 "Year is 39",
 "Country is Netherlands"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0007883-241107131044228/datasets/export for details.
DOI: 10.15468/dl.649fut
title: Occurrence Download

description: A dataset containing 267845 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Bidens bullata L., Boerhavia coccinea Mill., Boerhaavia coccinea Mill., Bromopsis inermis (Leyss.) Holub, Broussonetia papyrifera (L.) L'Hér. ex Vent., Broussonetia papyrifera (L.) L'Hér., Buddleja davidii Franch., Capsella grandiflora (Fauché & Chaub.) Boiss., Carex vulpinoidea Michx., Carex vulpinoidea Torr.)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "Year is greater than or equal to 2000"
 ]
}

 The dataset includes 267845 records from 1745 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0007869-241107131044228/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.3r6cfp
title: Occurrence Download

description: A dataset containing 17 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((-71.88652 -37.26435,-71.16021 -37.26435,-71.16718 -36.57483,-71.63201 -36.57559,-72.04547 -36.57743,-72.04084 -36.68973,-72.04479 -36.80425,-72.04479 -37.26495,-71.88652 -37.26435))",
 "TaxonKey is Amphibia"
 ]
}

 The dataset includes 17 records from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000488-241107131044228/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.jubwhk
title: Occurrence Download

description: A dataset containing 1354 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Hyparrhenia cymbaria (L.) Stapf",
 "Continent is Africa"
 ]
}

 The dataset includes 1354 records from 60 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000489-241107131044228/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.yg2kpn
title: Occurrence Download

description: A dataset listing the 124921 species recorded in GBIF matching the query:
{
 "and" : [
 "Country is United States of America",
 "BasisOfRecord is one of (Human Observation)"
 ]
}

 The dataset's 124921 records were derived from 1239 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000435-241107131044228/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.f258wm
title: Occurrence Download

description: A dataset containing 1753 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Continent is North America",
 "Country is Canada",
 "GadmGid is CAN.1.12.9_1",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Troglodytes aedon Vieillot, 1809",
 "Year 2013-2024"
 ]
}

 The dataset includes 1753 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000491-241107131044228/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.r3uc5e
title: Occurrence Download

description: A dataset containing 378 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Cryptotis mayensis (Merriam, 1901)"
 ]
}

 The dataset includes 378 records from 21 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000490-241107131044228/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.d68v2v
title: Occurrence Download

description: A dataset containing 35136832 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Brazil",
 "OccurrenceStatus is Present"
 ]
}

 The dataset includes 35136832 records from 4052 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000440-241107131044228/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.5ajbxj
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Chenalopex pugil (Winge, 1887)"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0052881-210914110416597/datasets/export for details.