Digital Commons Network^™

Cryogrinding And Physical Analysis Of A Thermoplastic Polyurethane Elastomer, Timothy E. Whitesell, Marcia Reeves, David L. Helm, Joseph S. Thrasher

Chemistry Summer Research Program

No abstract provided.

Stimuli-Responsive Self-Folding Bilayer Polymer Thin Films, Astrid Garcia, Dimuthu Edirisinghe, Bogdan Oliy, George Chumanov

Chemistry Summer Research Program

No abstract provided.

Boosting Employee Morale Through Team Building, Nancy Marquez

IPS/BAS 495 Undergraduate Capstone Projects

This project focuses on the creation of a team building manual, suggestions of team building activities that can be used in the workplace for both in-office and virtual teams, and results of a pilot activity. PowerPoint outlines the process of creating a Manual for management, implementing a pilot activity, measuring results and obtaining feedback from a team of 45 employees working remotely and onsite across 3 states.

PDF Manual shows each page of the OneNote manual delivered to management at the conclusion of the project. Resources on managing cliques in the medical office and silos in the workplace are linked. …

Change The World One Worm At A Time!, David C. Goff

IPS/BAS 495 Undergraduate Capstone Projects

My Capstone Project aims to blend the old with the new. It's about taking tried-and-true farming practices and breathing new life into them with innovative, sustainable solutions. It seeks to balance making a living from the land and caring for it for future generations.

The project's core revolves around Organic Worm Tea, a natural product we've been developing and testing. This process involves rolling up our sleeves, getting our hands dirty, and observing the tea's effect on various farms, including those run by immigrant farmers who bring unique farming traditions from their home countries.

But this project isn't just about …

Exploring Model Dynamics For Accumulative Poisoning Discovery, Jianing Zhu, Xiawei Guo, Jiangchao Yao, Chao Du, Li He, Shuo Yuan, Tongliang Liu, Liang Wang, Bo Han

Machine Learning Faculty Publications

Adversarial poisoning attacks pose huge threats to various machine learning applications. Especially, the recent accumulative poisoning attacks show that it is possible to achieve irreparable harm on models via a sequence of imperceptible attacks followed by a trigger batch. Due to the limited data-level discrepancy in real-time data streaming, current defensive methods are indiscriminate in handling the poison and clean samples. In this paper, we dive into the perspective of model dynamics and propose a novel information measure, namely, Memorization Discrepancy, to explore the defense via the model-level information. By implicitly transferring the changes in the data manipulation to that …

Causal Discovery With Latent Confounders Based On Higher-Order Cumulants, Ruichu Cai, Zhiyi Huang, Wei Chen, Zhifeng Hao, Kun Zhang

Machine Learning Faculty Publications

Causal discovery with latent confounders is an important but challenging task in many scientific areas. Despite the success of some overcomplete independent component analysis (OICA) based methods in certain domains, they are computationally expensive and can easily get stuck into local optima. We notice that interestingly, by making use of higher-order cumulants, there exists a closed-form solution to OICA in specific cases, e.g., when the mixing procedure follows the One-Latent-Component structure. In light of the power of the closed-form solution to OICA corresponding to the One-Latent-Component structure, we formulate a way to estimate the mixing matrix using the higher-order cumulants, …

Feature Expansion For Graph Neural Networks, Jiaqi Sun, Lin Zhang, Guangyi Chen, Peng Xu, Kun Zhang, Yujiu Yang

Machine Learning Faculty Publications

Graph neural networks aim to learn representations for graph-structured data and show impressive performance, particularly in node classification. Recently, many methods have studied the representations of GNNs from the perspective of optimization goals and spectral graph theory. However, the feature space that dominates representation learning has not been systematically studied in graph neural networks. In this paper, we propose to fill this gap by analyzing the feature space of both spatial and spectral models. We decompose graph neural networks into determined feature spaces and trainable weights, providing the convenience of studying the feature space explicitly using matrix space analysis. In …

Team Thesyllogist At Semeval-2023 Task 3: Language-Agnostic Framing Detection In Multi-Lingual Online News: A Zero-Shot Transfer Approach, Osama Mohammed Afzal, Preslav Nakov

Natural Language Processing Faculty Publications

We describe our system for SemEval-2022 Task 3 subtask 2 which on detecting the frames used in a news article in a multi-lingual setup. We propose a multi-lingual approach based on machine translation of the input, followed by an English prediction model. Our system demonstrated good zero-shot transfer capability, achieving micro-F1 scores of 53% for Greek (4th on the leaderboard) and 56.1% for Georgian (3rd on the leaderboard), without any prior training on translated data for these languages. Moreover, our system achieved comparable performance on seven other languages, including German, English, French, Russian, Italian, Polish, and Spanish. Our results demonstrate …

Semeval-2023 Task 3: Detecting The Category, The Framing, And The Persuasion Techniques In Online News In A Multi-Lingual Setup, Jakub Piskorski, Nicolas Stefanovitch, Giovanni Da San Martino, Preslav Nakov

Natural Language Processing Faculty Publications

We describe SemEval-2023 task 3 on Detecting the Category, the Framing, and the Persuasion Techniques in Online News in a Multilingual Setup: the dataset, the task organization process, the evaluation setup, the results, and the participating systems. The task focused on news articles in nine languages (six known to the participants upfront: English, French, German, Italian, Polish, and Russian), and three additional ones revealed to the participants at the testing phase: Spanish, Greek, and Georgian). The task featured three subtasks: (1) determining the genre of the article (opinion, reporting, or satire), (2) identifying one or more frames used in an …

Multilingual Multifaceted Understanding Of Online News In Terms Of Genre, Framing And Persuasion Techniques, Jakub Piskorski, Nicolas Stefanovitch, Nikolaos Nikolaidis, Giovanni Da San Martino, Preslav Nakov

Natural Language Processing Faculty Publications

We present a new multilingual multifacet dataset of news articles, each annotated for genre (objective news reporting vs. opinion vs. satire), framing (what key aspects are highlighted), and persuasion techniques (logical fallacies, emotional appeals, ad hominem attacks, etc.). The persuasion techniques are annotated at the span level, using a taxonomy of 23 fine-grained techniques grouped into 6 coarse categories. The dataset contains 1,612 news articles covering recent news on current topics of public interest in six European languages (English, French, German, Italian, Polish, and Russian), with more than 37k annotated spans of persuasion techniques. We describe the dataset and the …

Leaving Taormina, Eustacia Leone

Emerald City

No abstract provided.

Portrait: August 25, 1967, Bruce Petronio

Emerald City

No abstract provided.

Last Groove, Diamond Braxton

Emerald City

No abstract provided.

Truhsh: Developing Ntru Cryptosystem In Terms Of Security And Performance, Hiba Shakir Salman, Hassan Rashed Yassein

Applied Mathematics & Information Sciences

The rapid advancement of technology, the creation of new communication channels, the globalization of some aspects of society, and the reliance on networks for the distribution of different types of data have all increased the risk of information leakage and unauthorized access, making it urgent to maintain information security. In this work, we presented the improvement of the NTRU system, which is still effective, by introducing a new system called TRUHSH based on a new algebra called hexat, in addition to a new mathematical construction of the encryption stages, while giving high security and efficient performance.

Model Transferability With Responsive Decision Subjects, Yatong Chen, Zeyu Tang, Kun Zhang, Yang Liu

Machine Learning Faculty Publications

Given an algorithmic predictor that is accurate on some source population consisting of strategic human decision subjects, will it remain accurate if the population respond to it? In our setting, an agent or a user corresponds to a sample (X, Y ) drawn from a distribution D and will face a model h and its classification result h(X). Agents can modify X to adapt to h, which will incur a distribution shift on (X, Y ). Our formulation is motivated by applications where the deployed machine learning models are subjected to human agents, and will ultimately face responsive and interactive …

Detecting Out-Of-Distribution Data Through In-Distribution Class Prior, Xue Jiang, Feng Liu, Zhen Fang, Hong Chen, Tongliang Liu, Feng Zheng, Bo Han

Machine Learning Faculty Publications

Given a pre-trained in-distribution (ID) model, the inference-time out-of-distribution (OOD) detection aims to recognize OOD data during the inference stage. However, some representative methods share an unproven assumption that the probability that OOD data belong to every ID class should be the same, i.e., these OOD-to-ID probabilities actually form a uniform distribution. In this paper, we show that this assumption makes the above methods incapable when the ID model is trained with class-imbalanced data. Fortunately, by analyzing the causal relations between ID/OOD classes and features, we identify several common scenarios where the OOD-to-ID probabilities should be the ID-class-prior distribution and …

Development Of An Occupation-Based Program For Hispanic Women With Breast Cancer, Tara Licon, Leslie Khan-Farooqi, Mary Ann Smith

Summer 2023 Virtual OTD Capstone Symposium

Currently, available programs for breast cancer patients do not consider the specific needs of Hispanic women or their preferred occupations. The goal of the capstone project was to develop an occupational therapy (OT) program that is personalized to Hispanic women with breast cancer at every stage of their recovery. The program was informed by lifestyle redesign. After reviewing the literature and conducting a needs assessment the capstone student created a program titled ‘Beautiful, Bold, Breast Cancer’ which contains five detailed treatment sessions.

Orgsp Quarterly: An Orgsp Newsletter From Lincoln Memorial University, Vol 8.2, Lincoln Memorial University, Office Of Research Grants And Sponsored Projects

ORGSP Quarterly

This quarter's issue focuses on summer research. Learn about the excellent work of LMU's faculty, staff, and students.

Improving Patient Outcomes: Decreasing Central Line Blood Stream Infections, Erin Sevilla Rn

Research and Education Celebration 2023 - Nursing Research Posters

No abstract provided.

A Randomized Controlled Trial Of The Impact Of Titration Instruction Orders On Time To Achieving Hemodynamic Stability In A Simulated Setting, Sue Scott Rn, Cidalia Vital Rn, Gina St. Jean Rn

Research and Education Celebration 2023 - Nursing Research Posters

No abstract provided.

S3 Discharge Unit’S Impact On Capacity, Safety, Quality And Patient Experience, Debra Baker, Alicia Meacham, Karen Plante

Research and Education Celebration 2023 - Nursing Research Posters

No abstract provided.

A Companion Guide For The Discipleship To Missional Community (D2mc) Program, Jedidiah T. Maschke

Doctor of Ministry Major Applied Project

This Doctor of Ministry (DMin.) Major Applied Project (MAP) offers a companion guide for the discipleship training program called D2MC (Discipleship to Missional Community) taught by PLI, a leadership development organization that works primarily with the LCMS. The D2MC program is based on the training pioneered by Mike Breen through 3DM. The companion guide shows how the methods and practices taught by PLI are soundly based in Scripture and confessional Lutheran theology and offers some personal insights and devotional thoughts. The companion guide was distributed and field tested with volunteer participants through and with the approval of PLI. Participants in …

The Good, The Bad, And The Ugly Of Us Antitrust, Maurice E. Stucke

Scholarly Works

This article examines the bad and ugly as the US federal agencies seek to rejuvenate competition. The bad is legislative hiatus to update the antitrust laws for the digital economy. The ugly is when courts push their own economic beliefs, without regard for the congressional intent and aims of the antitrust laws. Regardless of who wins, the rule of law (and those most dependent on the antitrust law) suffer. To correct America’s market power problem, the article proposes restoring the constitutional balance, where the courts adjudicate, the legislature legislates, and enforcers enforce.

How To Make Decision Under Interval Uncertainty: Description Of All Reasonable Partial Orders On The Set Of All Intervals, Tiago M. Costa, Olga Kosheleva, Vladik Kreinovich

Departmental Technical Reports (CS)

In many practical situations, we need to make a decision while for each alternative, we only know the corresponding value of the objective function with interval uncertainty. To help a decision maker in this situation, we need to know the (in general, partial) order on the set of all intervals that corresponds to the preferences of the decision maker. For this purpose, in this paper, we provide a description of all such partial orders -- under some reasonable conditions. It turns out that each such order is characterized by two linear inequalities relating the endpoints of the corresponding intervals, and …

Which Fuzzy Implications Operations Are Polynomial? A Theorem Proves That This Can Be Determined By A Finite Set Of Inequalities, Sebastia Massanet, Olga Kosheleva, Vladik Kreinovich

Departmental Technical Reports (CS)

To adequately represent human reasoning in a computer-based systems, it is desirable to select fuzzy operations that are as close to human reasoning as possible. In general, every real-valued function can be approximated, with any desired accuracy, by polynomials; it is therefore reasonable to use polynomial fuzzy operations as the appropriate approximations. We thus need to select, among all polynomial operations that satisfy corresponding properties -- like associativity -- the ones that best fit the empirical data. The challenge here is that properties like associativity mean satisfying infinitely many constraints (corresponding to infinitely many possible triples of values), while most …

Fuzzy Mathematics Under Non-Minimal "And"-Operations (T-Norms): Equivalence Leads To Metric, Order Leads To Kinematic Metric, Topology Leads To Area Or Volume, Purbita Jana, Olga Kosheleva, Vladik Kreinovich

Departmental Technical Reports (CS)

Most formulas analyzed in fuzzy mathematics assume -- explicitly or implicitly -- that the corresponding "and"-operation (t-norm) is the simplest minimum operation. In this paper, we analyze what happens if instead, we use other "and"-operations. It turns out that for such operations, a fuzzification of a mathematical theory naturally leads to a more complex mathematical setting: fuzzification of equivalence relation leads to metric, fuzzification of order leads to kinematic metric, and fuzzification of topology leads to area or volume.

Why Deep Learning Is Under-Determined? Why Usual Numerical Methods For Solving Partial Differential Equations Do Not Preserve Energy? The Answers May Be Related To Chevalley-Warning Theorem (And Thus To Fermat Last Theorem), Julio C. Urenda, Olga Kosheleva, Vladik Kreinovich

Departmental Technical Reports (CS)

In this paper, we provide a possible explanation to two seemingly unrelated phenomena: (1) that in deep learning, under-determined systems of equations perform much better than the over-determined one -- which are typical in data processing, and that (2) usual numerical methods for solving partial differential equations do not preserve energy. Our explanation is related to the intuition of Fermat behind his Last Theorem and of Euler about more general statements, intuition that led to the proof of Chevalley-Warning Theorem in number theory.

Methodological Lesson Of Pythagorean Triples, Julio C. Urenda, Olga Kosheleva, Vladik Kreinovich

Departmental Technical Reports (CS)

There are many right triangles in which all three sides a, b, and c have integer lengths. The triples (a,b,c) formed by such lengths are known as Pythagorean triples. Since ancient times, it is known how to generate all Pythagorean triples: we can enumerate primitive Pythagorean triples -- in which the three numbers have no common divisors -- by considering all pairs of natural numbers m>n in which m and n have no common divisors, and taking a =m2 − n2, b = 2mn, and c = m2 + n2. Multiplying all elements of a triple by the same …

Why Resilient Modulus Is Proportional To The Square Root Of Unconfined Compressive Strength (Ucs): A Qualitative Explanation, Edgar Daniel Rodriguez Velasquez, Vladik Kreinovich

Departmental Technical Reports (CS)

The strength of the pavement is determine by its resilient modulus, i.e., by its ability to withstand (practically) instantaneous stresses caused by the passing traffic. However, the resilient modulus is not easy to measure: its measurement requires a special expensive equipment that many labs do not have. So, instead of measuring it, practitioners often measure easier-to-measure Unconfined Compressive Strength (UCS) -- that describes the effect of a continuously applied force -- and estimate the resilient modulus based on the result of this measurement. An empirical formula shows that the resilient modulus is proportional to the square root of the Unconfined …

How To Estimate Unknown Unknowns: From Cosmic Light To Election Polls, Talha Azfar, Vignesh Ponraj, Vladik Kreinovich, Nguyen Hoang Phuong

Departmental Technical Reports (CS)

In two different areas of research -- in the study of space light and in the study of voting -- the observed value of the quantity of interest is twice larger than what we would expect. That the observed value is larger makes perfect sense: there are phenomena that we do not take into account in our estimations. However, the fact that the observed value is exactly twice larger deserves explanation. In this paper, we show that Laplace Indeterminacy Principle leads to such an explanation.