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Can Video Comparison Facilitate Children's Stem Learning?, Alexis Rene Colwell Jul 2023

Can Video Comparison Facilitate Children's Stem Learning?, Alexis Rene Colwell

Theses and Dissertations

The goal of the current study was to examine the effectiveness of a video comparison activity in teaching 6- and 7-year-old children that a diagonal brace provides stability in structures, which is an important engineering principle. Children were randomly assigned to one of three groups: Comparison, Single Model, or No Training. The children in the Comparison group saw two metal towers in a training video where the researcher pushed on the towers to demonstrate that one was stable (diagonal brace tower), and one was unstable (horizontal crosspiece tower). Children in the Single Model group saw a video of the one …

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Pathogen Emergence As Complex Biological Invasion: Lessons From Dynamical Systems Modeling, Sudam Surasinghe, Marisabel Rodriguez, Victor Meszaros, Jane Molofsky, Salvador Almagro-Moreno, Brandon Ogbunugafor Jul 2023

Pathogen Emergence As Complex Biological Invasion: Lessons From Dynamical Systems Modeling, Sudam Surasinghe, Marisabel Rodriguez, Victor Meszaros, Jane Molofsky, Salvador Almagro-Moreno, Brandon Ogbunugafor

Northeast Journal of Complex Systems (NEJCS)

Infectious disease emergence has become the target of cross-disciplinary efforts
that aim to understand and predict the shape of outbreaks. The many challenges
involved with the prediction of disease emergence events is a characteristic that in-
fectious diseases share with biological invasions in many subfields of ecology (e.g.,
how certain plants are able to successfully invade a new niche). Like infectious
diseases, biological invasions by plants and animals involve interactions between
agents (pathogens and plants in their respective cases) and a recipient niche. In
this study, we examine the problem of pathogen emergence through the lens of a
framework first …

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The Validity Study Of A Channel-Based Demand Estimation Anp Model, Yasamin Salmani, Fariborz Y. Partovi Jul 2023

The Validity Study Of A Channel-Based Demand Estimation Anp Model, Yasamin Salmani, Fariborz Y. Partovi

Management Department Faculty Journal Articles

This paper suggests a solution for validating an ANP model in multi-channel retailing proposed by Salmani et al. (2018) through its application in the pet consumables industry. The purpose of the proposed ANP model is to develop a measurement for estimating the demand proportion attributed to each established sales channel. Utilizing an ANP model, channel selection customer criteria as well as the interrelations between channel structures in a multi-channel setting are quantified. This paper applies a survey-based study to collect data from random pet consumable customers. Using a compatibility measurement, per-channel proportions of demand found by ANP are compared with …

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Focus, 2023, Spring, Andrews University Jul 2023

Focus, 2023, Spring, Andrews University

Focus

FOCUS (ISSN 1077-9345) is published quarterly, free of charge, for alumni and friends of Andrews University, an institution owned and operated by the Seventh-day Adventist Church.

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Cardiac Output And Stroke Volume From The Pneumocardiogram: A New Mechanistic Insight And Practical Calculation, Charles F. Babbs Jul 2023

Cardiac Output And Stroke Volume From The Pneumocardiogram: A New Mechanistic Insight And Practical Calculation, Charles F. Babbs

Weldon School of Biomedical Engineering Faculty Working Papers

This paper presents a new and more complete mechanism for the genesis of the pneumocardiogram—a record of the small oscillations in airflow in the trachea and upper airways accompanying each heartbeat. This signal may be the basis of a simple, inexpensive and noninvasive measure of cardiac stroke volume; however, research into its practical use has languished because of poor understanding of its underlying cause and meaning. This paper explains the genesis of the pneumocardiogram, based upon net changes in intrathoracic blood volume during the cardiac cycle, together with low pass filtering of the volume signal by airway resistance and lung-chest …

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Humanities Center Annual Report 2022-23, Humanities Center, University Of San Diego Jul 2023

Humanities Center Annual Report 2022-23, Humanities Center, University Of San Diego

Humanities Center Annual Reports

No abstract provided.

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Study And Evaluation Of Parameters Influencing Parkison’S Disease Using 3d Cnn, Caroline El Fiorenza J, V. Sellam Jul 2023

Study And Evaluation Of Parameters Influencing Parkison’S Disease Using 3d Cnn, Caroline El Fiorenza J, V. Sellam

Applied Mathematics & Information Sciences

A diagnostic method of determining protein structure in persons for Parkinson’s disease (PD) would be a 3D imaging scan. Convolutional Neural Networks (CNNs), which are efficient when used with spatial data, are suitable candidates for automating this diagnostic procedure to assist medical workers. The protein structure of PD patients was evaluated or diagnosed using a Improved Faster Recurrent Convolutional Neural Network (IFRCNN) ordinal model in this article. A data preprocessing technique was modified to operate PD because IFRCNNs require big datasets to operate acceptably. We take into account the Improved Shortest Paths Ordinal Graph-based Oversampling (ISP-OGO) technique that employs a …

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Sixth-Kind Chebyshev Collocation Treatment Of Fractional Fredholm-Volterra Integro-Differential Equations, Mahmoud M. Mokhtar Jul 2023

Sixth-Kind Chebyshev Collocation Treatment Of Fractional Fredholm-Volterra Integro-Differential Equations, Mahmoud M. Mokhtar

Applied Mathematics & Information Sciences

We propose a spectral technique for solving mixed nonlinear and linear Fredholm-Volterra integro-differential equations. Our method involves transforming the problem using Simpson’s integration formula and expanding the solution as a series of sixth- kind Chebyshev polynomials. The convergence rate and estimated chopping error are analyzed, and numerical examples are provided to demonstrate the effectiveness and accuracy of the approach.

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Three Hundred Years Of Helping Others: Maria Gaetana Agnesi On Exponents, Kenneth M. Monks Jul 2023

Three Hundred Years Of Helping Others: Maria Gaetana Agnesi On Exponents, Kenneth M. Monks

Pre-calculus and Trigonometry

No abstract provided.

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Three Hundred Years Of Helping Others: Maria Gaetana Agnesi On The Rational Root Theorem, Kenneth M. Monks Jul 2023

Three Hundred Years Of Helping Others: Maria Gaetana Agnesi On The Rational Root Theorem, Kenneth M. Monks

Pre-calculus and Trigonometry

No abstract provided.

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Three Hundred Years Of Helping Others: Maria Gaetana Agnesi On Simplifying Radicals, Kenneth M. Monks Jul 2023

Three Hundred Years Of Helping Others: Maria Gaetana Agnesi On Simplifying Radicals, Kenneth M. Monks

Pre-calculus and Trigonometry

No abstract provided.

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Three Hundred Years Of Helping Others: Maria Gaetana Agnesi On Precalculus, Kenneth M. Monks Jul 2023

Three Hundred Years Of Helping Others: Maria Gaetana Agnesi On Precalculus, Kenneth M. Monks

Pre-calculus and Trigonometry

No abstract provided.

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Lagrange’S Study Of Wilson’S Theorem, Carl Lienert Jul 2023

Lagrange’S Study Of Wilson’S Theorem, Carl Lienert

Number Theory

No abstract provided.

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Taylor: A Magazine For Taylor University Alumni, Parents And Friends (Summer 2023), Taylor University Jul 2023

Taylor: A Magazine For Taylor University Alumni, Parents And Friends (Summer 2023), Taylor University

The Taylor Magazine (1963-Present)

The Summer 2023 edition of Taylor Magazine, published by Taylor University in Upland, Indiana.

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Target-Based Offensive Language Identification, Marcos Zampieri, Skye Morgan, Kai North, Tharindu Ranasinghe, Austin Simmons, Paridhi Khandelwal, Sara Rosenthal, Preslav Nakov Jul 2023

Target-Based Offensive Language Identification, Marcos Zampieri, Skye Morgan, Kai North, Tharindu Ranasinghe, Austin Simmons, Paridhi Khandelwal, Sara Rosenthal, Preslav Nakov

Natural Language Processing Faculty Publications

We present TBO, a new dataset for Target-based Offensive language identification. TBO contains post-level annotations regarding the harmfulness of an offensive post and token-level annotations comprising of the target and the offensive argument expression. Popular offensive language identification datasets for social media focus on annotation taxonomies only at the post level and more recently, some datasets have been released that feature only token-level annotations. TBO is an important resource that bridges the gap between post-level and token-level annotation datasets by introducing a single comprehensive unified annotation taxonomy. We use the TBO taxonomy to annotate post-level and token-level offensive language on …

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Investigation Of Vitamin D Metabolites Using Different Ion Mobility-Mass Spectrometry (Im-Ms) Methods, Selena Kingsley, Christopher D. Chouinard Jul 2023

Investigation Of Vitamin D Metabolites Using Different Ion Mobility-Mass Spectrometry (Im-Ms) Methods, Selena Kingsley, Christopher D. Chouinard

Chemistry Summer Research Program

No abstract provided.

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Development Of A Rapid Sars-Cov-2 Nucleocapsid Assay Using Buoyant And Magnetic (Bam) Beads, Spasenija Radenovic, Zahra Karimpourkalou, Chaunlei Wang, Jeffrey N. Anker Jul 2023

Development Of A Rapid Sars-Cov-2 Nucleocapsid Assay Using Buoyant And Magnetic (Bam) Beads, Spasenija Radenovic, Zahra Karimpourkalou, Chaunlei Wang, Jeffrey N. Anker

Chemistry Summer Research Program

No abstract provided.

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Isolation Of Bovine Milk-Derived Exosomes Via A Polyester Capillary- Channeled Polymer (C-Cp) Fiber Stationary Phase, Jerisa Pimentel, Carolina Mata, R. Kenneth Marcus Jul 2023

Isolation Of Bovine Milk-Derived Exosomes Via A Polyester Capillary- Channeled Polymer (C-Cp) Fiber Stationary Phase, Jerisa Pimentel, Carolina Mata, R. Kenneth Marcus

Chemistry Summer Research Program

No abstract provided.

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Improved Logical Reasoning Of Language Models Via Differentiable Symbolic Programming, Hanlin Zhang, Jiani Huang, Ziyang Li, Mayur Naik, Eric Xing Jul 2023

Improved Logical Reasoning Of Language Models Via Differentiable Symbolic Programming, Hanlin Zhang, Jiani Huang, Ziyang Li, Mayur Naik, Eric Xing

Machine Learning Faculty Publications

Pre-trained large language models (LMs) struggle to perform logical reasoning reliably despite advances in scale and compositionality. In this work, we tackle this challenge through the lens of symbolic programming. We propose DSR-LM, a Differentiable Symbolic Reasoning framework where pre-trained LMs govern the perception of factual knowledge, and a symbolic module performs deductive reasoning. In contrast to works that rely on hand-crafted logic rules, our differentiable symbolic reasoning framework efficiently learns weighted rules and applies semantic loss to further improve LMs. DSR-LM is scalable, interpretable, and allows easy integration of prior knowledge, thereby supporting extensive symbolic programming to robustly derive …

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Bertnet: Harvesting Knowledge Graphs With Arbitrary Relations From Pretrained Language Models, Shibo Hao, Bowen Tan, Kaiwen Tang, Bin Ni, Xiyan Shao, Hengzhe Zhang, Eric P. Xing, Zhiting Hu Jul 2023

Bertnet: Harvesting Knowledge Graphs With Arbitrary Relations From Pretrained Language Models, Shibo Hao, Bowen Tan, Kaiwen Tang, Bin Ni, Xiyan Shao, Hengzhe Zhang, Eric P. Xing, Zhiting Hu

Machine Learning Faculty Publications

It is crucial to automatically construct knowledge graphs (KGs) of diverse new relations to support knowledge discovery and broad applications. Previous KG construction methods, based on either crowdsourcing or text mining, are often limited to a small predefined set of relations due to manual cost or restrictions in text corpus. Recent research proposed to use pretrained language models (LMs) as implicit knowledge bases that accept knowledge queries with prompts. Yet, the implicit knowledge lacks many desirable properties of a full-scale symbolic KG, such as easy access, navigation, editing, and quality assurance. In this paper, we propose a new approach of …

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Phase-Aware Adversarial Defense For Improving Adversarial Robustness, Dawei Zhou, Nannan Wang, Heng Yang, Xinbo Gao, Tongliang Liu Jul 2023

Phase-Aware Adversarial Defense For Improving Adversarial Robustness, Dawei Zhou, Nannan Wang, Heng Yang, Xinbo Gao, Tongliang Liu

Machine Learning Faculty Publications

Deep neural networks have been found to be vulnerable to adversarial noise. Recent works show that exploring the impact of adversarial noise on intrinsic components of data can help improve adversarial robustness. However, the pattern closely related to human perception has not been deeply studied. In this paper, inspired by the cognitive science, we investigate the interference of adversarial noise from the perspective of image phase, and find ordinarily-trained models lack enough robustness against phase-level perturbations. Motivated by this, we propose a joint adversarial defense method: a phase-level adversarial training mechanism to enhance the adversarial robustness on the phase pattern; …

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High-Probability Bounds For Stochastic Optimization And Variational Inequalities: The Case Of Unbounded Variance, Abdurakhmon Sadiev, Marina Danilova, Eduard Gorbunov, Samuel Horváth, Gauthier Gidel, Pavel Dvurechensky, Alexander Gasnikov, Peter Richtárik Jul 2023

High-Probability Bounds For Stochastic Optimization And Variational Inequalities: The Case Of Unbounded Variance, Abdurakhmon Sadiev, Marina Danilova, Eduard Gorbunov, Samuel Horváth, Gauthier Gidel, Pavel Dvurechensky, Alexander Gasnikov, Peter Richtárik

Machine Learning Faculty Publications

During recent years the interest of optimization and machine learning communities in high-probability convergence of stochastic optimization methods has been growing. One of the main reasons for this is that high-probability complexity bounds are more accurate and less studied than in-expectation ones. However, SOTA high-probability non-asymptotic convergence results are derived under strong assumptions such as the boundedness of the gradient noise variance or of the objective's gradient itself. In this paper, we propose several algorithms with high-probability convergence results under less restrictive assumptions. In particular, we derive new high-probability convergence results under the assumption that the gradient/operator noise has bounded …

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Analysis Of Predictive Performance And Reliability Of Classifiers For Quality Assessment Of Medical Evidence Revealed Important Variation By Medical Area, Simon Šuster, Timothy Baldwin, Karin Verspoor Jul 2023

Analysis Of Predictive Performance And Reliability Of Classifiers For Quality Assessment Of Medical Evidence Revealed Important Variation By Medical Area, Simon Šuster, Timothy Baldwin, Karin Verspoor

Natural Language Processing Faculty Publications

Objectives: A major obstacle in deployment of models for automated quality assessment is their reliability. To analyze their calibration and selective classification performance. Study Design and Setting: We examine two systems for assessing the quality of medical evidence, EvidenceGRADEr and RobotReviewer, both developed from Cochrane Database of Systematic Reviews (CDSR) to measure strength of bodies of evidence and risk of bias (RoB) of individual studies, respectively. We report their calibration error and Brier scores, present their reliability diagrams, and analyze the risk–coverage trade-off in selective classification. Results: The models are reasonably well calibrated on most quality criteria (expected calibration error …

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Bertastic At Semeval-2023 Task 3: Fine-Tuning Pretrained Multilingual Transformers – Does Order Matter?, Tarek Mahmoud, Preslav Nakov Jul 2023

Bertastic At Semeval-2023 Task 3: Fine-Tuning Pretrained Multilingual Transformers – Does Order Matter?, Tarek Mahmoud, Preslav Nakov

Natural Language Processing Faculty Publications

The naïve approach for fine-tuning pretrained deep learning models on downstream tasks involves feeding them mini-batches of randomly sampled data. In this paper, we propose a more elaborate method for fine-tuning Pretrained Multilingual Transformers (PMTs) on multilingual data. Inspired by the success of curriculum learning approaches, we investigate the significance of fine-tuning PMTs on multilingual data in a sequential fashion language by language. Unlike the curriculum learning paradigm where the model is presented with increasingly complex examples, we do not adopt a notion of “easy” and “hard” samples. Instead, our experiments draw insight from psychological findings on how the human …

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Conformal Prediction For Federated Uncertainty Quantification Under Label Shift, Vincent Plassier, Mehdi Makni, Aleksandr Rubashevskii, Eric Moulines, Maxim Panov Jul 2023

Conformal Prediction For Federated Uncertainty Quantification Under Label Shift, Vincent Plassier, Mehdi Makni, Aleksandr Rubashevskii, Eric Moulines, Maxim Panov

Machine Learning Faculty Publications

Federated Learning (FL) is a machine learning framework where many clients collaboratively train models while keeping the training data decentralized. Despite recent advances in FL, the uncertainty quantification topic (UQ) remains partially addressed. Among UQ methods, conformal prediction (CP) approaches provides distribution-free guarantees under minimal assumptions. We develop a new federated conformal prediction method based on quantile regression and take into account privacy constraints. This method takes advantage of importance weighting to effectively address the label shift between agents and provides theoretical guarantees for both valid coverage of the prediction sets and differential privacy. Extensive experimental studies demonstrate that this …

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A Statistical Framework For Automating Resonance Detection: Modelling Pion Proton Collision Activity, Shahnaz Abdul Hameed Jul 2023

A Statistical Framework For Automating Resonance Detection: Modelling Pion Proton Collision Activity, Shahnaz Abdul Hameed

2023 REYES Proceedings

In this paper, we analyze π− − p elastic collision data from the Particle Data Group (PDG), by creating a general framework to study resonance activity: automating peak detection, extrapolating, parametrizing thresholds, filtering resonances and further comparing and extracting characteristics, to identify Delta (Δ) baryons. We then analyse experimental Energy vs Phase-Shift (δ) data for the collision π+ +π− → π− +π+, model the T matrix from a curve fitted polynomial representation of the K−1 matrix, simulate its Riemann sheets and analyse it to identify the characteristics of ρ0(770) meson, as well as estimate their uncertainties. …

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How To Best Retrain A Neural Network If We Added One More Input Variable, Saeid Tizpaz-Niari, Vladik Kreinovich Jul 2023

How To Best Retrain A Neural Network If We Added One More Input Variable, Saeid Tizpaz-Niari, Vladik Kreinovich

Departmental Technical Reports (CS)

Often, once we have trained a neural network to estimate the value of a quantity y based on the available values of inputs x1, ..., xn, we learn to measure the values of an additional quantity that have some influence on y. In such situations, it is desirable to re-train the neural network, so that it will be able to take this extra value into account. A straightforward idea is to add a new input to the first layer and to update all the weights based on the patterns that include the values of the new input. The problem with …

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Topological Explanation Of Why Complex Numbers Are Needed In Quantum Physics, Julio C. Urenda, Vladik Kreinovich Jul 2023

Topological Explanation Of Why Complex Numbers Are Needed In Quantum Physics, Julio C. Urenda, Vladik Kreinovich

Departmental Technical Reports (CS)

In quantum computing, we only use states in which all amplitudes are real numbers. So why do we need complex numbers with non-zero imaginary part in quantum physics in general? In this paper, we provide a simple topological explanation for this need, explanation based on the Second Law of Thermodynamics.

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The Appropriateness Of Outlier Exclusion Approaches Depends On The Expected Contamination: Commentary On André (2022), Daniel Villanova Jul 2023

The Appropriateness Of Outlier Exclusion Approaches Depends On The Expected Contamination: Commentary On André (2022), Daniel Villanova

Marketing Faculty Publications and Presentations

In a recent article, André (2022) addressed the decision to exclude outliers using a threshold across conditions or within conditions and offered a clear recommendation to avoid within-conditions exclusions because of the possibility for large false-positive inflation. In this commentary, I note that André’s simulations did not include the situation for which within-conditions exclusion has previously been recommended—when across-conditions exclusion would exacerbate selection bias. Examining test performance in this situation confirms the recommendation for within-conditions exclusion in such a circumstance. Critically, the suitability of exclusion criteria must be considered in relationship to assumptions about data-generating mechanisms.

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On The Blow Up Criterion For The 3d Nematic Liquid Crystal Flows Involving The Second Eigenvalue Of The Deformation Tensor, Ines Ben Omrane Jul 2023

On The Blow Up Criterion For The 3d Nematic Liquid Crystal Flows Involving The Second Eigenvalue Of The Deformation Tensor, Ines Ben Omrane

Applied Mathematics & Information Sciences

In this paper, we study the blow up criterion of the smooth solutions to the three-dimensional incompressible nematic liquid crystal flows in terms of $\lambda _{2}^{+}$ in the multiplier space $\dot{X}_{1}$ and $\nabla d$ in $BMO$. It is shown that the solution $(u,d)$ can be extended beyond $t=T$ if 􏰖 T  􏰇􏰇 λ + ( · , t ) 􏰇􏰇 2 2   2 X ̇ 1 + ∥ ∇ d ( · , t ) ∥ B M O  d t < ∞ . 0 ln(e+∥∇u(·,t)∥X ̇1) ln(e+∥∇d(·,t)∥BMO)

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