Physical Sciences and Mathematics Commons^™

Predictions And Drivers Of Sub-Reach-Scale Annual Streamflow Permanence For The Upper Missouri River Basin: 1989–2018, Kendra E. Kaiser

Geosciences Faculty Publications and Presentations

The presence of year-round surface water in streams (i.e., streamflow permanence) is an important factor for identifying aquatic habitat availability, determining the regulatory status of streams, managing land use change, allocating water resources, and designing scientific studies. However, accurate, high resolution, and dynamic prediction of streamflow permanence that accounts for year-to-year variability at a regional extent is a major gap in modeling capability. Herein, we expand and adapt the U.S. Geological Survey (USGS) PRObability of Streamflow PERmanence (PROSPER) model from its original implementation in the Pacific Northwest (PROSPER_PNW) to the upper Missouri River basin (PROSPER_UM), a …

Analysis Of Precipitation Reversals Over The State Of Arkansas, Mallory Hoff

Crop, Soil and Environmental Sciences Undergraduate Honors Theses

Recent studies have examined hydroclimate precipitation reversals, but because it is a newly defined concept, there is minimal research available on how reversals are changing, and it has not been widely investigated. Precipitation reversal is the rapid switch between wet and dry periods or “precipitation extremes and the opposite” (McKay, 2018), based on precipitation measurements in this case. A single reversal is the immediate transition from a wet to a dry period or from a dry to a wet period. Changes in reversals have not been thoroughly reported and this gap in research creates a risk of unpredictable conditions that …

Identifying How Summer Camp Experiences Affect Children’S Environmental Literacy, Quinn Kimbell

Department of Environmental Studies: Undergraduate Student Theses

Ensuring that children are prepared for environmental issues in the future can be aided by building the skill of environmental literacy. A key factor in building this skill is having natural experiences and creating one’s connection and perception of the environment. This paper aims to identify if summer camps increase children’s perception of the environment. This was completed by testing children with an environmental perception survey (Children’s Environmental Perception Scale) before and after attending a summer camp and assessing environmental pollution knowledge through drawing (modified Draw-An-Environment Test). The rural Nebraska summer camp subjected the children to many outdoor experiences, including …

One Health In Action: Flea Control And Interpretative Education At Badlands National Park, David Eads, Lindsey Buehler, Anne Esbenshade, Jason Fly, Evan Miller, Holly Redmond, Emily Ritter, National Park Service, Sasha Wittmann, Paul Roghair, Eddie Childers

United States Geological Survey: Staff Publications

No abstract provided.

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 …

Independence Testing-Based Approach To Causal Discovery Under Measurement Error And Linear Non-Gaussian Models, Haoyue Dai, Peter Spirtes, Kun Zhang

Machine Learning Faculty Publications

Causal discovery aims to recover causal structures generating the observational data. Despite its success in certain problems, in many real-world scenarios the observed variables are not the target variables of interest, but the imperfect measures of the target variables. Causal discovery under measurement error aims to recover the causal graph among unobserved target variables from observations made with measurement error. We consider a specific formulation of the problem, where the unobserved target variables follow a linear non-Gaussian acyclic model, and the measurement process follows the random measurement error model. Existing methods on this formulation rely on non-scalable over-complete independent component …

Rare Gems: Finding Lottery Tickets At Initialization, Kartik Sreenivasan, Jy Yong Sohn, Liu Yang, Matthew Grinde, Alliot Nagle, Hongyi Wang, Eric Xing, Kangwook Lee, Dimitris Papailiopoulos

Machine Learning Faculty Publications

Large neural networks can be pruned to a small fraction of their original size, with little loss in accuracy, by following a time-consuming “train, prune, re-train” approach. Frankle & Carbin [9] conjecture that we can avoid this by training lottery tickets, i.e., special sparse subnetworks found at initialization, that can be trained to high accuracy. However, a subsequent line of work [11, 41] presents concrete evidence that current algorithms for finding trainable networks at initialization, fail simple baseline comparisons, e.g., against training random sparse subnetworks. Finding lottery tickets that train to better accuracy compared to simple baselines remains an open …

Physical Controls On The Hydrology Of Perennially Ice-Covered Lakes, Taylor Valley, Antarctica (1996-2013), J. M. Cross, Andrew G. Fountain, M. J. Hoffman, M. K. Obryk

Geology Faculty Publications and Presentations

The McMurdo Dry Valleys, Antarctica, are a polar desert populated with numerous closed-watershed, perennially ice-covered lakes primarily fed by glacial melt. Lake levels have varied by as much as 8 m since 1972 and are currently rising after a decade of decreasing. Precipitation falls as snow, so lake hydrology is dominated by energy available to melt glacier ice and to sublimate lake ice. To understand the energy and hydrologic controls on lake level changes and to explain the variability between neighboring lakes, only a few kilometers apart, we model the hydrology for the three largest lakes in Taylor Valley. We …

Towards Reinterpreting Neural Topic Models Via Composite Activations, Jia Peng Lim, Hady Wirawan Lauw

Research Collection School Of Computing and Information Systems

Most Neural Topic Models (NTM) use a variational auto-encoder framework producing K topics limited to the size of the encoder’s output. These topics are interpreted through the selection of the top activated words via the weights or reconstructed vector of the decoder that are directly connected to each neuron. In this paper, we present a model-free two-stage process to reinterpret NTM and derive further insights on the state of the trained model. Firstly, building on the original information from a trained NTM, we generate a pool of potential candidate “composite topics” by exploiting possible co-occurrences within the original set of …

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 …

Iowa Waste Reduction Center Newsletter, December 2022, University Of Northern Iowa. Iowa Waste Reduction Center.

Iowa Waste Reduction Center Newsletter

In this Issue:

--- Best Wishes for the New Year
--- The Impact of the Inflation Reduction Act on the Energy Industry - Webinar Recording
--- Air Permit Applications & Emissions Inventory Reporting - Electronic Reporting Submittal Requirements begin Jan. 1, 2023
--- January 31, 2023 Reporting Deadlines - Grain Facility PM10 PTE
--- January 31, 2023 Reporting Deadlines - 6X Certification and Compliance Report
--- Goodbye Megan and Devon
--- Free Energy Assessments
--- Industry News
--- Connect with Us

Identity Term Sampling For Measuring Gender Bias In Training Data, Nasim Sobhani, Sarah Jane Delany

Conference Papers

Predictions from machine learning models can reflect biases in the data on which they are trained. Gender bias has been identified in natural language processing systems such as those used for recruitment. The development of approaches to mitigate gender bias in training data typically need to be able to isolate the effect of gender on the output to see the impact of gender. While it is possible to isolate and identify gender for some types of training data, e.g. CVs in recruitment, for most textual corpora there is no obvious gender label. This paper proposes a general approach to measure …

Draft - Sieve Study Report December 2022, Appendices, Ramboll

Silver Bow Creek/Butte Area Superfund Site

No abstract provided.

The Effectiveness Of Using Python Programming Approach In Teaching Ffnancial Analytics, Clarence Goh, Yuanto Kusnadi, Gary Pan

Research Collection School Of Accountancy

This study presents a learning method and challenges regarding implementing a Python programming approach in teaching financial analytics to graduate accounting students. The advent of Big Data, as well as related applications and technologies, has significantly changed the process and practice of accounting. This has led to essential changes in the construction and teaching content of accounting education. While there have been several studies examining how data analytics is embedded in the accounting curriculum, the majority of the teaching cases in accounting focus on analysis and communication with Excel as the principal tool, with very few covering the necessary steps …

Are We Building Back Better?, Fabian M. Dayrit

Chemistry Faculty Publications

No abstract provided.

A Study Of The Structural, Optical, And Ferroelectric Characteristics Of Pb-Ge-Te Nanocrystalline Alloys As Potential Candidates For Memory Devices And Near-Infrared (Nir) Applications, Asmaa Elkhodary, Salah M. Elsheikh, Hosny A. Omar, Manal A. Mahdy, Iman A. Mahdy

Basic Science Engineering

Pb_50-xGe_xTe₅₀ (x = 15, 20, 25, 30 at. %) nanocrystalline bulk alloys were prepared using solid-state direct reaction. X-ray diffraction and high-resolution transmission electron microscopy (HR-TEM) analysis of the reference structure (Ge = 15 at.%) revealed a slightly distorted cubic structure, with a lattice parameter of 6.43 Å and an inter-axis unit cell angle of 88.69◦. Atomic force images’ analysis and histograms displayed a homogenous particle size distribution in the nanoscale for all samples. Density measurements showed a gradual decrease from 7.89 to 6.98 g/cm3 with increasing Ge content in agreement with the calculated values. …

Use Of Machine Learning And Natural Language Processing To Enhance Traffic Safety Analysis, Md Abu Sayed

Theses and Dissertations

Despite significant advances in vehicle technologies, safety data collection and analysis, and engineering advancements, tens of thousands of Americans die every year in motor vehicle crashes. Alarmingly, the trend of fatal and serious injury crashes appears to be heading in the wrong direction. In 2021, the actual rate of fatalities exceeded the predicted rate. This worrisome trend prompts and necessitates the development of advanced and holistic approaches to determining the causes of a crash (particularly fatal and major injuries). These approaches range from analyzing problems from multiple perspectives, utilizing available data sources, and employing the most suitable tools and technologies …

Unraveling Deformation Mechanisms And Kinematics In Metasedimentary Rocks Along The Southern Iberian Shear Zone, Kayla P. Kopinski

Theses and Dissertations

The primary goal of this project is to document deformation mechanisms in quartz-rich rocks across a 100 m wide ductile shear zone to evaluate whether strain localization at the brittle-ductile transition is accompanied by variations in active or dominant deformation mechanisms. A secondary goal of this project is to evaluate whether the kinematic framework varies across a shear zone with a major rheological boundary. The Southern Iberian Shear Zone (SISZ), a major terrane bounding shear zone within the Iberian Massif, is an ideal location to study these questions because it is a regional scale shear zone currently exposed at the …

Automated Feature Extraction From Large Cardiac Electrophysiological Data Sets, And A Population Dynamics Approach To The Distribution Of Space Debris In Low-Earth Orbit, John Jurkiewicz

Theses and Dissertations

We present two applications of mathematics to relevant real-world situations.

In the first chapter, we discuss an automated method for the extraction of useful data from large file-size readings of cardiac data. We begin by describing the history of electrophysiology and the background of the work's setting, wherein a new multi-electrode array-based application for the long-term recording of action potentials from electrogenic cells makes large-scale readings of relevant data possible, opening the way for exciting cardiac electrophysiology studies in health and disease. With hundreds of simultaneous electrode recordings being acquired over a period of days, the main challenge becomes achieving …

Strain Development And Partitioning Across A Transpressional Shear Zone Along A Quartzite- Metagabbro Contact In The Black Hills Uplift, South Dakota, Eric L. Schuemann

Theses and Dissertations

The Nemo region of South Dakota’s Black Hills offers an ideal location to study transpressional shear zones because it hosts an exposed Archean lithological boundary between two contrasting rheological units, the Boxelder Creek Quartzite (BCQ), a rift-depositional quartzite, and the Blue Draw Metagabbro (BDM), a metagabbro sill, deformed within a ductile shear zone that represents the beginning of main-phase formation of the North American continent as we know it today. The tectonic setting of the Black Hills is at the eastern edge of the Archean Wyoming province, located near the Trans-Hudson Orogeny suture zone that formed between the Wyoming and …

A Protocol To Build Trust With Black Box Models, Timothy K. Thielke

Theses and Dissertations

Data scientists are more widely using artificial intelligence and machine learning (ML) algorithms today despite the general mistrust associated with them due to the lack of contextual understanding of the domain occurring within the algorithm. Of the many types of ML algorithms, those that use non-linear activation functions are especially regarded with suspicion because of the lack of transparency and intuitive understanding of what is occurring within the black box of the algorithm. In this thesis, we set out to create a protocol to delve into the black box of an ML algorithm set to predict synoptic severe weather patterns …

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 …

A Framework For Assessing The 4th Rank Dispersivity Tensor Under Anisotropic Axial Symmetries, Xiang Fan

Dissertations

Multi-dimensional expansion of the advection-dispersion equation necessitated the representation of dispersivity as a 4th rank tensor. This tensorial form of dispersivity has 81 terms in three-dimensions with a maximum of 36 independent terms that may be used to describe Fickian spreading of a dissolved contaminant plume according to intrinsic properties of a porous medium. The complexity of the 4th rank tensor has led to the common practice of simplifying the tensor to only 2 or 3 independent terms by assuming isotropic conditions, although isotropic porous media are uncommon in nature as many natural geologic systems exhibit pronounced anisotropy. A broad …