Digital Commons Network^™

(R1980) Effect Of Climate Change On Brain Tumor, Pardeep Kumar, Sarita Jha, Rajiv Aggarwal, Govind Kumar Jha

Applications and Applied Mathematics: An International Journal (AAM)

In this paper, we introduce a new dynamical model addressing the variation in climate condition due the presence of microorganisms. We also introduce a new dynamical model of cancer growth which includes three interactive cell populations with drug free environment, namely tumor cells, healthy host cells, and immune effector cells. In this, we considered the super growth of tumor cells. For the choice of certain parameters, both of the systems exhibit chaotic behavior. The aim of this work is to design the controller to control the chaos and to provide sufficient conditions which achieve synchronization of two non-identical systems, which …

Camelira: An Arabic Multi-Dialect Morphological Disambiguator, Ossama Obeid, Go Inoue, Nizar Habash

Computer Vision Faculty Publications

We present Camelira, a web-based Arabic multi-dialect morphological disambiguation tool that covers four major variants of Arabic: Modern Standard Arabic, Egyptian, Gulf, and Levantine. Camelira offers a user-friendly web interface that allows researchers and language learners to explore various linguistic information, such as part-of-speech, morphological features, and lemmas. Our system also provides an option to automatically choose an appropriate dialect-specific disambiguator based on the prediction of a dialect identification component. Camelira is publicly accessible at http://camelira.camel-lab.com.

Pasta: Table-Operations Aware Fact Verification Via Sentence-Table Cloze Pre-Training, Zihui Gu, Ju Fan, Nan Tang, Preslav Nakov, Xiaoman Zhao, Xiaoyong Du

Natural Language Processing Faculty Publications

Fact verification has attracted a lot of research attention recently, e.g., in journalism, marketing, and policymaking, as misinformation and disinformation online can sway one's opinion and affect one's actions. While fact-checking is a hard task in general, in many cases, false statements can be easily debunked based on analytics over tables with reliable information. Hence, table-based fact verification has recently emerged as an important and growing research area. Yet, progress has been limited due to the lack of datasets that can be used to pre-train language models (LMs) to be aware of common table operations, such as aggregating a column …

Greener: Graph Neural Networks For News Media Profiling, Panayot Panayotov, Utsav Shukla, Husrev T. Sencar, Mohamed Nabeel, Preslav Nakov

Natural Language Processing Faculty Publications

We study the problem of profiling news media on the Web with respect to their factuality of reporting and bias. This is an important but under-studied problem related to disinformation and “fake news” detection, but it addresses the issue at a coarser granularity compared to looking at an individual article or an individual claim. This is useful as it allows to profile entire media outlets in advance. Unlike previous work, which has focused primarily on text (e.g., on the articles published by the target website, or on the textual description in their social media profiles or in Wikipedia), here we …

Asdot: Any-Shot Data-To-Text Generation With Pretrained Language Models, Jiannan Xiang, Zhengzhong Liu, Yucheng Zhou, Eric P. Xing, Zhiting Hu

Machine Learning Faculty Publications

Data-to-text generation is challenging due to the great variety of the input data in terms of domains (e.g., finance vs sports) or schemata (e.g., diverse predicates). Recent end-to-end neural methods thus require substantial training examples to learn to disambiguate and describe the data. Yet, real-world data-to-text problems often suffer from various data-scarce issues: one may have access to only a handful of or no training examples, and/or have to rely on examples in a different domain or schema. To fill this gap, we propose Any-Shot Data-to-Text (ASDOT), a new approach flexibly applicable to diverse settings by making efficient use of …

(R1953) M-Regression Estimation With The K Nearest Neighbors Smoothing Under Quasi-Associated Data In Functional Statistics, Bellatrach Nadjet, Bouabsa Wahiba, Attouch Mohammed Kadi, Fetitah Omar

Applications and Applied Mathematics: An International Journal (AAM)

The main goal of this paper is to study the non parametric M-estimation under quasi-associated sequence with the k Nearest Neighbor’s method shortly (kNN). We construct an estimator of this nonparametric function and we study its asymptotic properties. Furthermore, a comparison study based on simulated data is also provided to illustrate the highly sensitive of the kNN approach to the presence of even a small proportion of outliers in the data.

Impact Of Digital Twins And Metaverse On Cities: History, Current Situation, And Application Perspectives, Zhihan Lv, Wen Long Shang, Mohsen Guizani

Machine Learning Faculty Publications

To promote the expansion and adoption of Digital Twins (DTs) in Smart Cities (SCs), a detailed review of the impact of DTs and digitalization on cities is made to assess the progression of cities and standardization of their management mode. Combined with the technical elements of DTs, the coupling effect of DTs technology and urban construction and the internal logic of DTs technology embedded in urban construction are discussed. Relevant literature covering the full range of DTs technologies and their applications is collected, evaluated, and collated, relevant studies are concatenated, and relevant accepted conclusions are summarized by modules. First, the …

Resel: N-Ary Relation Extraction From Scientific Text And Tables By Learning To Retrieve And Select, Yuchen Zhuang, Yinghao Li, Jerry Junyang Cheung, Yue Yu, Yingjun Mou, Xiang Chen, Le Song, Chao Zhang

Machine Learning Faculty Publications

We study the problem of extracting N-ary relation tuples from scientific articles. This task is challenging because the target knowledge tuples can reside in multiple parts and modalities of the document. Our proposed method RESEL decomposes this task into a two-stage procedure that first retrieves the most relevant paragraph/table and then selects the target entity from the retrieved component. For the high-level retrieval stage, RESEL designs a simple and effective feature set, which captures multilevel lexical and semantic similarities between the query and components. For the low-level selection stage, RESEL designs a cross-modal entity correlation graph along with a multi-view …

Efficient (Soft) Q-Learning For Text Generation With Limited Good Data, Han Guo, Bowen Tan, Zhengzhong Liu, Eric P. Xing, Zhiting Hu

Machine Learning Faculty Publications

Maximum likelihood estimation (MLE) is the predominant algorithm for training text generation models. This paradigm relies on direct supervision examples, which is not applicable to many emerging applications, such as generating adversarial attacks or generating prompts to control language models. Reinforcement learning (RL) on the other hand offers a more flexible solution by allowing users to plug in arbitrary task metrics as reward. Yet previous RL algorithms for text generation, such as policy gradient (on-policy RL) and Q-learning (off-policy RL), are often notoriously inefficient or unstable to train due to the large sequence space and the sparse reward received only …

Amp: Automatically Finding Model Parallel Strategies With Heterogeneity Awareness, Dacheng Li, Hongyi Wang, Eric Xing, Hao Zhang

Machine Learning Faculty Publications

Scaling up model sizes can lead to fundamentally new capabilities in many machine learning (ML) tasks. However, training big models requires strong distributed system expertise to carefully design model-parallel execution strategies that suit the model architectures and cluster setups. In this paper, we develop AMP, a framework that automatically derives such strategies. AMP identifies a valid space of model parallelism strategies and efficiently searches the space for high-performed strategies, by leveraging a cost model designed to capture the heterogeneity of the model and cluster specifications. Unlike existing methods, AMP is specifically tailored to support complex models composed of uneven layers …

Unpaired Image-To-Image Translation With Density Changing Regularization, Shaoan Xie, Qirong Ho, Kun Zhang

Machine Learning Faculty Publications

Unpaired image-to-image translation aims to translate an input image to another domain such that the output image looks like an image from another domain while important semantic information are preserved. Inferring the optimal mapping with unpaired data is impossible without making any assumptions. In this paper, we make a density changing assumption where image patches of high probability density should be mapped to patches of high probability density in another domain. Then we propose an efficient way to enforce this assumption: we train the flows as density estimators and penalize the variance of density changes. Despite its simplicity, our method …

On Pac Learning Halfspaces In Non-Interactive Local Privacy Model With Public Unlabeled Data, Jinyan Su, Jinhui Xu, Di Wang

Machine Learning Faculty Publications

In this paper, we study the problem of PAC learning halfspaces in the non-interactive local differential privacy model (NLDP). To breach the barrier of exponential sample complexity, previous results studied a relaxed setting where the server has access to some additional public but unlabeled data. We continue in this direction. Specifically, we consider the problem under the standard setting instead of the large margin setting studied before. Under different mild assumptions on the underlying data distribution, we propose two approaches that are based on the Massart noise model and self-supervised learning and show that it is possible to achieve sample …

A Cybersecurity Assessment Of Health Data Ecosystems, Michelle N. Halsey

Cyber Operations and Resilience Program Graduate Projects

This paper is an exploratory study that investigates data collected and used by health plans and reviews the laws and regulations governing this data to identify the gaps in protections and provide recommendations for eliminating these gaps. Health insurance companies collect a wide array of data about the people they insure, data that is often only peripherally relevant to the service these companies provide. The data environment currently consists of seven categories of data: personal health information, summary health information, personally identifiable information, financial information, professional information, biometric information, and lifestyle data or social indicators of health. Much of this …

Experimental Identification Of Aminomethanol (Nh2ch2oh)—The Key Intermediate In The Strecker Synthesis, Santosh K. Singh, Cheng Zhu, Jesse La Jeunesse, Ryan C. Fortenberry, Ralf I. Kaiser

Faculty and Student Publications

The Strecker Synthesis of (a)chiral α-amino acids from simple organic compounds, such as ammonia (NH3), aldehydes (RCHO), and hydrogen cyanide (HCN) has been recognized as a viable route to amino acids on primordial earth. However, preparation and isolation of the simplest hemiaminal intermediate – the aminomethanol (NH2CH2OH)– formed in the Strecker Synthesis to even the simplest amino acid glycine (H2NCH2COOH) has been elusive. Here, we report the identification of aminomethanol prepared in low-temperature methylamine (CH3NH2) – oxygen (O2) ices upon exposure to energetic electrons. Isomer-selective photoionization time-of-flight mass spectrometry (PI-ReTOF-MS) facilitated the gas phase detection of aminomethanol during the temperature …

Politicizing The Pandemic: Covid-19 And Its Impact On The Nigerian Economy, John Shola Olanrewaju, Agaptus Nwozor, Ajibola Abdulwasiu Abdulrahaman

Faculty and Student Open Access Publications

This study aims to examine the connection between international politics that characterized the outbreak of COVID-19, mainly between the United States of America and China and the devastating impact of the second wave outbreak on the Nigerian economy. At the initial stage of the pandemic on the Chinese mainland, the economy of China experienced a significant decline. The efficacy of China’s response to COVID-19 led to a fractional economic recovery during the pandemic. However, the internationalization of COVID-19 was greeted with a second wave in most advanced countries, such as the United States of America, Italy, and the United Kingdom, …

A Damped Newton Method Achieves Global O(1/K2) And Local Quadratic Convergence Rate, Slavomír Hanzely, Dmitry Kamzolov, Dmitry Pasechnyuk, Alexander Gasnikov, Peter Richtárik, Martin Takáč

Machine Learning Faculty Publications

In this paper, we present the first stepsize schedule for Newton method resulting in fast global and local convergence guarantees. In particular, a) we prove an O (1/k2) global rate, which matches the state-of-the-art global rate of cubically regularized Newton method of Polyak and Nesterov (2006) and of regularized Newton method of Mishchenko (2021) and Doikov and Nesterov (2021), b) we prove a local quadratic rate, which matches the best-known local rate of second-order methods, and c) our stepsize formula is simple, explicit, and does not require solving any subproblem. Our convergence proofs hold under affine-invariance assumptions closely related to …

Automs: Automatic Model Selection For Novelty Detection With Error Rate Control, Yifan Zhang, Haiyan Jiang, Haojie Ren, Changliang Zou, Dejing Dou

Machine Learning Faculty Publications

Given an unsupervised novelty detection task on a new dataset, how can we automatically select a “best” detection model while simultaneously controlling the error rate of the best model? For novelty detection analysis, numerous detectors have been proposed to detect outliers on a new unseen dataset based on a score function trained on available clean data. However, due to the absence of labeled anomalous data for model evaluation and comparison, there is a lack of systematic approaches that are able to select the “best” model/detector (i.e., the algorithm as well as its hyperparameters) and achieve certain error rate control simultaneously. …

Factored Adaptation For Non-Stationary Reinforcement Learning, Fan Feng, Biwei Huang, Kun Zhang, Sara Magliacane

Machine Learning Faculty Publications

Dealing with non-stationarity in environments (e.g., in the transition dynamics) and objectives (e.g., in the reward functions) is a challenging problem that is crucial in real-world applications of reinforcement learning (RL). While most current approaches model the changes as a single shared embedding vector, we leverage insights from the recent causality literature to model non-stationarity in terms of individual latent change factors, and causal graphs across different environments. In particular, we propose Factored Adaptation for Non-Stationary RL (FANS-RL), a factored adaption approach that learns jointly both the causal structure in terms of a factored MDP, and a factored representation of …

Classroom Climate, Culture, Parenting Skills, And Student Grit, Karen Ochoa

Dissertations

The education system has noted that student motivation and perseverance are lacking, yet the focus on standardized testing over the last decade has increased the rigor and expectations in the classroom. As such, educational researchers are always looking for new and inventive ways to enhance student achievement without increasing the financial burden on the schools. Grit is the non-cognitive skill that a person processes that enable them to work hard, endure, and persevere through challenging times. A person who exhibits grit often succeeds over those who have the cognitive ability but do not have grit. Children in countries with collectivist …

The Casnr L.I.N.K.S. Newsletter: Learning Innovation Network For K-12 Schools, Edition 11, December 2022, Bailey Feit, Tammy Mittelstet

CASNR L.I.N.K.S. Newsletter

The purpose of this L.I.N.K.S. newsletter is to provide a monthly update of opportunities and resources for K-12 schools to connect with the University of Nebraska's CASNR Food, Energy, Water, and Societal Systems (FEWS²) programs and partners.

This edition includes:

Teachers' corner, K-12 resources

Links to partners' newsletters

Top 5 success stories

FEWSS 2022 highlighted impact

Looking toward 2023

CASNR focus areas

Send your students to campus!

Scholarship opportunities

A Look At The Effectiveness Of High School Chemistry Curriculum In Preparing Students For Act, Shanna Nesser

Dissertations

In the ever-changing world, students are challenged with cultivating the skills and knowledge needed to handle the pace and level of understanding required to excel in their future. The foundation for a student's future begins in their formative years, but high school is a prime environment for nurturing the applied, critical thinking, and problem-solving skills needed to move forward into independent, adult life. Mississippi schools are ranked by an accountability score, which is used to determine fund allocation and the development of improvement plans. This score is compiled by looking at various state-tested courses, College and Career Readiness Standards (MS …

Data Processing Under Fuzzy Uncertainty: Towards More Accurate Algorithms, Marina Tuyako Mizukoshi, Weldon Lodwick, Martine Ceberio, Olga Kosheleva, Vladik Kreinovich

Departmental Technical Reports (CS)

Data that we process comes either from measurements or from experts -- or from the results of previous data processing that were also based on measurements and/or expert estimates. In both cases, the data is imprecise. To gauge the accuracy of the results of data processing, we need to take the corresponding data uncertainty into account. In this paper, we describe a new algorithm for taking fuzzy uncertainty into account, an algorithm that, for small number of inputs, leads to the same or even better accuracy than the previously proposed methods.

(R2020) Dynamical Study And Optimal Harvesting Of A Two-Species Amensalism Model Incorporating Nonlinear Harvesting, Manoj Kumar Singh, Poonam .

Applications and Applied Mathematics: An International Journal (AAM)

This study proposes a two-species amensalism model with a cover to protect the first species from the second species, with the assumption that the growth of the second species is governed by nonlinear harvesting. Analytical and numerical analyses have both been done on this suggested ecological model. Boundedness and positivity of the solutions of the model are examined. The existence of feasible equilibrium points and their local stability have been discussed. In addition, the parametric conditions under which the proposed system is globally stable have been determined. It has also been shown, using the Sotomayor theorem, that under certain parametric …

Supercontinuum Light Generation Via Non-Linear Effects In Hollow-Core Fiber, Skyler Gulati

Physics Student Works

The field of non-linear optics has gained traction in the last couple decades due to the variable generation of wavelengths which are less deterministic than within traditional optics. Using non-linear mediums, including hollow-core fibers (HCF), generation of wavelengths spanning into the vacuum ultraviolet (VUV) wavelength range is possible. These short wavelengths can be utilized within electron spectroscopy-based methods of material science like angle-resolved photoemission spectroscopy (ARPES). This technique most often uses specific photoemission lines of atoms in discharge lamps, however, with the frequency dispersion capabilities of HCF, broad band creation can allow for variable wavelength selection through filtering specific wavelengths …

A Cradle-To-Gate Life Cycle Assessment Of American Hops In The Craft Beer Supply Chain, Carson Bristol

Master of Science in Environmental Sciences and Management Projects

The hop industry represents a culturally and economically significant industry in the United States due to the product’s significant role in the beer industry, but little is known about the environmental impacts associated with hop production. There exists a significant lack of specific research quantifying the emissions contribution of hop production, and the work that has been done lacks consensus in the scientific community. In an effort to overcome this, this study conducts a cradle-to-gate life cycle assessment (LCA) of aroma hop production broken into four phases, beginning with propagation of seedlings by tissue culture and ending with packaged T-90 …

Non-Localized Physical Processes Can Help Speed Up Computations, Be It Hidden Variables In Quantum Physics Or Non-Localized Energy In General Relativity, Michael Zakharevich, Olga Kosheleva, Vladik Kreinovich

Departmental Technical Reports (CS)

While most physical processes are localized -- in the sense that each event can only affect events in its close vicinity -- many physicists believe that some processes are non-local. These beliefs range from more heretic -- such as hidden variables in quantum physics -- to more widely accepted, such as the non-local character of energy in General Relativity. In this paper, we attract attention to the fact that non-local processes bring in the possibility of drastically speeding up computations.

Graph Approach To Uncertainty Quantification, Hector A. Reyes, Cliff Joslyn, Vladik Kreinovich

Departmental Technical Reports (CS)

Traditional analysis of uncertainty of the result of data processing assumes that all measurement errors are independent. In reality, there may be common factor affecting these errors, so these errors may be dependent. In such cases, the independence assumption may lead to underestimation of uncertainty. In such cases, a guaranteed way to be on the safe side is to make no assumption about independence at all. In practice, however, we may have information that a few pairs of measurement errors are indeed independent -- while we still have no information about all other pairs. Alternatively, we may suspect that for …

Systems Approach Explains Why Low Heart Rate Variability Is Correlated With Depression (And Suicidal Thoughts), Francisco Zapata, Eric Smith, Vladik Kreinovich

Departmental Technical Reports (CS)

Depression is a serious medical problem. If diagnosed early, it can usually be cured, but if left undetected, it can lead to suicidal thoughts and behavior. The early stages of depression are difficult to diagnose. Recently, researchers found a promising approach to such diagnosis -- it turns out that depression is correlated with low heart rate variability. In this paper, we show that the general systems approach can explain this empirical relation.

An Argument In Favor Of Piecewise-Constant Membership Functions, Marina Tuyako Mizukoshi, Weldon Lodwick, Martine Ceberio, Olga Kosheleva, Vladik Kreinovich

Departmental Technical Reports (CS)

Theoretically, we can have membership functions of arbitrary shape. However, in practice, at any given moment of time, we can only represent finitely many parameters in a computer. As a result, we usually restrict ourselves to finite-parametric families of membership functions. The most widely used families are piecewise linear ones, e.g., triangular and trapezoid membership functions. The problem with these families is that if we know a nonlinear relation y = f(x) between quantities, the corresponding relation between membership functions is only approximate -- since for piecewise linear membership functions for x, the resulting membership function for y is not …

Will Nanotechnology Bring In The Judgement Day?, Olga Kosheleva, Vladik Kreinovich

Departmental Technical Reports (CS)

There are many current and prospective positive aspects of nanotechnology. However, while we look forward to its future successes, we need to keep our eyes open and be prepared for what will really be a future shock: that quantum computing – an inevitable part of nanotechnology – will enable the future folks to read all our encrypted messages and thus, learn everything that we wanted to keep secret. This will be really the Judgement Day, when all our sins will be open to everyone. How we will react to it? Will this destroy our civilization? Let us hope that the …