Physical Sciences and Mathematics Commons^™

Model Selection Through Cross-Validation For Supervised Learning Tasks With Manifold Data, Derek Brown

The Journal of Purdue Undergraduate Research

No abstract provided.

Deep Q-Learning Framework For Quantitative Climate Change Adaptation Policy For Florida Road Network Due To Extreme Precipitation, Orhun Aydin

I-GUIDE Forum

Climate change-induced extreme weather and increasing population are increasing the pressure on the global aging road networks. Adaptation requires designing interventions and alterations to the road networks that consider future dynamics of flooding and increased traffic due to the growing population. This paper introduces a reinforcement learning approach to designing interventions for Florida's road network under future traffic and climate projections. Three climate models and a tide and surge model are used to create flooding and coastal inundation projections, respectively. The optimal sequence of decisions for adapting Florida's road network to minimize flooding-related disruptions is solved by using a graph-based …

Reducing Uncertainty In Sea-Level Rise Prediction: A Spatial-Variability-Aware Approach, Subhankar Ghosh, Shuai An, Arun Sharma, Jayant Gupta, Shashi Shekhar, Aneesh Subramanian

I-GUIDE Forum

Given multi-model ensemble climate projections, the goal is to accurately and reliably predict future sea-level rise while lowering the uncertainty. This problem is important because sea-level rise affects millions of people in coastal communities and beyond due to climate change's impacts on polar ice sheets and the ocean. This problem is challenging due to spatial variability and unknowns such as possible tipping points (e.g., collapse of Greenland or West Antarctic ice-shelf), climate feedback loops (e.g., clouds, permafrost thawing), future policy decisions, and human actions. Most existing climate modeling approaches use the same set of weights globally, during either regression or …

Public Acceptance Of Guidance And Regulations For Space Flight Participation, Cory Trunkhill, Robert Joslin, Joseph Keebler

Journal of Aviation Technology and Engineering

Space flight participants are not professional astronauts and not subject to the rules and guidance covering space flight crewmembers. Ordinal logistic regression of survey data was utilized to explore public acceptance of current medical screening recommendations and regulations for safety risk and implied liability for civil space flight participation. Independent variables constituted participant demographic representations while dependent variables represented current Federal Aviation Administration guidance and regulations. Odds ratios were derived based on the demographic categories to interpret likelihood of acceptance for the criteria. Significant likely acceptance of guidance and regulations was found for five of twelve demographic variables influencing public …

Evaluating Models Of Scanpath Prediction, Matthias Kümmerer, Matthias Bethge

MODVIS Workshop

No abstract provided.

The Impact Of Service Dogs On Objective And Perceived Sleep Quality For Veterans With Ptsd, Madhuri Vempati, Elise A. Miller, Sarah C. Leighton, Leanne O. Nieforth, Marguerite O’Haire

Discovery Undergraduate Interdisciplinary Research Internship

One in four post-9/11 veterans (Fulton et al., 2015) have been diagnosed with posttraumatic stress disorder (PTSD), facing sleep disruptions as one of their most common symptoms. Service dogs have become an increasingly popular complementary intervention and anecdotes suggest they may impact sleep for veterans with PTSD. There is a need for empirical investigation into these claims through measurement and analysis of sleep quality.

The purpose of this study was to longitudinally investigate the impact of service dogs on sleep quality through both objective and subjective measures.

Participants in the treatment group (n=92) received a service dog after baseline, while …

A Two-Layer Model Explains Higher-Order Feature Selectivity Of V2 Neurons, Timothy D. Oleskiw, Justin D. Lieber, J. Anthony Movshon, Eero P. Simoncelli

MODVIS Workshop

Neurons in cortical area V2 respond selectively to higher-order visual features, such as the quasi-periodic structure of natural texture. However, a functional account of how V2 neurons build selectivity for complex natural image features from their inputs – V1 neurons locally tuned for orientation and spatial frequency – remains elusive.

We made single-unit recordings in area V2 in two fixating rhesus macaques. We presented stimuli composed of multiple superimposed grating patches that localize contrast energy in space, orientation, and scale. V2 activity is modeled via a two-layer linear-nonlinear network, optimized to use a sparse combination of V1-like outputs to account …

Rattle Detection – An Automotive Case Study, Orla Hartley

International Conference on Lean Six Sigma

This case study showcases the use of statistical tools to develop an objective Squeak and Rattle (S&R) measurement and detection test for End Of Line (EOL) sign off in an automotive manufacturing environment. Audio Induced S&R is an unwanted vibration within the vehicle caused by the sound system, impacting on customer perception of vehicle quality. Testing for S&R in an automotive environment has a key challenge; how to robustly detect a rattle at the EOL and thus prevent plant escapes to the customer. The objective test developed used microphones and analysers in order to replace an e subjective listening test. …

A Comparison Of The Localized Aviation Mos Program (Lamp) And Terminal Aerodrome Forecast (Taf) Accuracy For General Aviation, Douglas D. Boyd, Thomas A. Guinn

Journal of Aviation Technology and Engineering

Background. For general aviation (GA) pilots, operations in instrument meteorological conditions (IMC) carry an elevated risk of a fatal accident. As to whether a general aviation flight can be safely undertaken, aerodrome-specific forecasts (TAF, LAMP) provide guidance. Although LAMP forecasts are more common for GA-frequented aerodromes, nevertheless, the FAA recommends that for such aerodromes (and for which a TAF is not issued) the airman uses the TAF generated for the geographically closest airport for pre-flight weather evaluation. Herein, for non-TAF-issuing airports, the LAMP (sLAMP) predictive accuracy for visual (VFR) and instrument (IFR) flight rules flight category was determined.

Method. sLAMP …

Prerequisite Course Recommendation Based On Course Description And Students’ Grades, Haozhe Zhou

The Journal of Purdue Undergraduate Research

No abstract provided.

Estimating Vehicular Traffic Intensity With Deep Learning And Semantic Segmentation, Logan Bradley-Trietsch

The Journal of Purdue Undergraduate Research

No abstract provided.

A Differential Geometry-Based Machine Learning Algorithm For The Brain Age Problem, Justin Asher, Khoa Tan Dang, Maxwell Masters

The Journal of Purdue Undergraduate Research

No abstract provided.

Predicting Postoperative Delirium Risk For Intracranial Surgery: A Statistical Machine Learning Approach, Juliet Aygun, Alaina Bartfeld, Sahana Rayan

The Journal of Purdue Undergraduate Research

No abstract provided.

High Wind Alerts: A System Created With Observations From The X-Band Teaching And Research Radar, Lauren Warner

The Journal of Purdue Undergraduate Research

Following the August 13, 2011, Indiana State Fair stage collapse tragedy, caused by a wind gust from an approaching thunderstorm, Purdue University enforced a wind speed restriction of 30 mph (13 m s-1) for tents at outdoor events. During these events, volunteers stand outside with handheld anemometers, measuring and reporting when the wind speeds exceed this limit. In this study, we report testing of a new system to automate high-wind alerts based on observations from a Doppler radar, the X-band Teaching and Research Radar (XTRRA), near Purdue’s campus. XTRRA scans over campus at low elevations approximately every 5 minutes. Using …

An Animal-Assisted Intervention Study In The Nursing Home: Lessons Learned, Lonneke G. J. A. Schuurmans, Inge Noback, Jos M. G. A. Schols, Marie-Jose Enders-Slegers

People and Animals: The International Journal of Research and Practice

AAI studies in the nursing home pose a specific set of challenges. In this article the practical and ethical issues encountered during a Dutch psychogeriatric nursing home AAI study are addressed with the aim of sharing our experiences for future researchers as well as AAI practitioners in general.

In our study we compared three groups of clients with dementia who participated in group sessions of either visiting dog teams, visiting FurReal Friend robot animals, or visiting students (control group) and monitored the effect on social interaction and neuropsychiatric symptoms through video analysis and questionnaires. We encountered the following four categories …

Selecting Maximally-Predictive Deep Features To Explain What Drives Fixations In Free-Viewing, Matthias Kümmerer, Thomas S.A. Wallis, Matthias Bethge

MODVIS Workshop

No abstract provided.

Twitter And Disasters: A Social Resilience Fingerprint, Benjamin A. Rachunok, Jackson B. Bennett, Roshanak Nateghi

Purdue University Libraries Open Access Publishing Fund

Understanding the resilience of a community facing a crisis event is critical to improving its adaptive capacity. Community resilience has been conceptualized as a function of the resilience of components of a community such as ecological, infrastructure, economic, and social systems, etc. In this paper, we introduce the concept of a “resilience fingerprint” and propose a multi-dimensional method for analyzing components of community resilience by leveraging existing definitions of community resilience with data from the social network Twitter. Twitter data from 14 events are analyzed and their resulting resilience fingerprints computed. We compare the fingerprints between events and show that …

Inferring Gene Regulatory Networks From A Population Of Yeast Segregants, Chen Chen, Dabao Zhang, Tony R. Hazbun, Min Zhang

Purdue University Libraries Open Access Publishing Fund

Constructing gene regulatory networks is crucial to unraveling the genetic architecture of complex traits and to understanding the mechanisms of diseases. On the basis of gene expression and single nucleotide polymorphism data in the yeast, Saccharomyces cerevisiae, we constructed gene regulatory networks using a two-stage penalized least squares method. A large system of structural equations via optimal prediction of a set of surrogate variables was established at the first stage, followed by consistent selection of regulatory effects at the second stage. Using this approach, we identified subnetworks that were enriched in gene ontology categories, revealing directional regulatory mechanisms controlling …

Deep Machine Learning For Mechanical Performance And Failure Prediction, Elijah Reber, Nickolas D. Winovich, Guang Lin

The Summer Undergraduate Research Fellowship (SURF) Symposium

Deep learning has provided opportunities for advancement in many fields. One such opportunity is being able to accurately predict real world events. Ensuring proper motor function and being able to predict energy output is a valuable asset for owners of wind turbines. In this paper, we look at how effective a deep neural network is at predicting the failure or energy output of a wind turbine. A data set was obtained that contained sensor data from 17 wind turbines over 13 months, measuring numerous variables, such as spindle speed and blade position and whether or not the wind turbine experienced …

Efvs Effects On Pilot Performance, Michael Campbell, Nsikak Udo-Imeh, Steven J. Landry

The Summer Undergraduate Research Fellowship (SURF) Symposium

Flight tests have been conducted at Purdue University using a computer-based flying simulator in an attempt to determine and measure the effects of Enhanced Flight Vision Systems (EFVS) on the performance of pilots during landing. Knowledge of these effects could help guide future design and implementation of EFVS in modern commercial aircraft, and further increase pilots’ ability to control the aircraft in low-visibility conditions. The problem that has faced researchers in the past has revolved around the difficulty in interpreting the data which is generated by these tests. The difficulty in making a generalized conclusion based on the large amount …

Consistent Saliency Benchmarking: How One Model Can Win On All Metrics, Matthias Kümmerer, Thomas S.A. Wallis, Matthias Bethge

MODVIS Workshop

No abstract provided.

Improving The Accuracy For The Long-Term Hydrologic Impact Assessment (L-Thia) Model, Anqi Zhang, Lawrence Theller, Bernard A. Engel

The Summer Undergraduate Research Fellowship (SURF) Symposium

Urbanization increases runoff by changing land use types from less impervious to impervious covers. Improving the accuracy of a runoff assessment model, the Long-Term Hydrologic Impact Assessment (L-THIA) Model, can help us to better evaluate the potential uses of Low Impact Development (LID) practices aimed at reducing runoff, as well as to identify appropriate runoff and water quality mitigation methods. Several versions of the model have been built over time, and inconsistencies have been introduced between the models. To improve the accuracy and consistency of the model, the equations and parameters (primarily curve numbers in the case of this model) …

Thermodynamics Of Coherent Structures Near Phase Transitions, Julia M. Meyer, Ivan Christov

The Summer Undergraduate Research Fellowship (SURF) Symposium

Phase transitions within large-scale systems may be modeled by nonlinear stochastic partial differential equations in which system dynamics are captured by appropriate potentials. Coherent structures in these systems evolve randomly through time; thus, statistical behavior of these fields is of greater interest than particular system realizations. The ability to simulate and predict phase transition behavior has many applications, from material behaviors (e.g., crystallographic phase transformations and coherent movement of granular materials) to traffic congestion. Past research focused on deriving solutions to the system probability density function (PDF), which is the ground-state wave function squared. Until recently, the extent to which …

Hazard Assessment Of Meteoroid Impact For The Design Of Lunar Habitats, Herta Paola Montoya, Shirley Dyke, Julio A. Ramirez, Antonio Bobet, H. Jay Melosh, Daniel Gomez

The Summer Undergraduate Research Fellowship (SURF) Symposium

The design of self-sustaining lunar habitats is a challenge primarily due to the Moon’s lack of atmospheric protection and hazardous environment. To assure safe habitats that will lead to further lunar and space exploration, it is necessary to assess the different hazards faced on the Moon such as meteoroid impacts, extreme temperatures, and radiation. In particular, meteoroids pose a risk to lunar structures due to their high frequency of occurrence and hypervelocity impact. Continuous meteoroid impacts can harm structural elements and vital equipment compromising the well-being of lunar inhabitants. This study is focused on the hazard conceptualization and quantification of …

Shape Features Underlying The Perception Of Liquids, Jan Jaap R. Van Assen, Pascal Barla, Roland W. Fleming

MODVIS Workshop

No abstract provided.

Can Cone Signals In The Wild Be Predicted From The Past?, David H. Foster, Iván Marín-Franch

MODVIS Workshop

In the natural world, the past is usually a good guide to the future. If light from the sun and sky is blue earlier in the day and yellow now, then it is likely to be more yellow later, as the sun's elevation decreases. But is the light reflected from a scene into the eye as predictable as the light incident upon the scene, especially when lighting changes are not just spectral but include changes in local shadows and mutual reflections? The aim of this work was to test the predictability of cone photoreceptor signals in the wild over the …

A Framework For The Statistical Analysis Of Mass Spectrometry Imaging Experiments, Kyle Bemis

Open Access Dissertations

Mass spectrometry (MS) imaging is a powerful investigation technique for a wide range of biological applications such as molecular histology of tissue, whole body sections, and bacterial films , and biomedical applications such as cancer diagnosis. MS imaging visualizes the spatial distribution of molecular ions in a sample by repeatedly collecting mass spectra across its surface, resulting in complex, high-dimensional imaging datasets. Two of the primary goals of statistical analysis of MS imaging experiments are classification (for supervised experiments), i.e. assigning pixels to pre-defined classes based on their spectral profiles, and segmentation (for unsupervised experiments), i.e. assigning pixels to newly …

Group Transformation And Identification With Kernel Methods And Big Data Mixed Logistic Regression, Chao Pan

Open Access Dissertations

Exploratory Data Analysis (EDA) is a crucial step in the life cycle of data analysis. Exploring data with effective methods would reveal main characteristics of data and provides guidance for model building. The goal of this thesis is to develop effective and efficient methods for data exploration in the regression setting.

First, we propose to use optimal group transformations as a general approach for exploring the relationship between predictor variables X and the response Y. This approach can be considered an automatic procedure to identify the best characteristic of P( Y|X) under which the relationship …

Characterizing The Effects Of Repetitive Head Trauma In Female Soccer Athletes For Prevention Of Mild Traumatic Brain Injury, Diana Otero Svaldi

Open Access Dissertations

As participation in women’s soccer continues to grow and the longevity of female athletes’ careers continues to increase, prevention of mTBI in women’s soccer has become a major concern for female athletes as the long-term risks associated with a history of mTBI are well documented. Among women’s sports, soccer exhibits the highest concussion rates, on par with those of men’s football at the collegiate level. Head impact monitoring technology has revealed that “concussive hits” occurring directly before symptomatic injury are not predictive of mTBI, suggesting that the cumulative effect of repetitive head impacts experienced by collision sport athletes should be …

Computational Environment For Modeling And Analysing Network Traffic Behaviour Using The Divide And Recombine Framework, Ashrith Barthur

Open Access Dissertations

There are two essential goals of this research. The first goal is to design and construct a computational environment that is used for studying large and complex datasets in the cybersecurity domain. The second goal is to analyse the Spamhaus blacklist query dataset which includes uncovering the properties of blacklisted hosts and understanding the nature of blacklisted hosts over time.

The analytical environment enables deep analysis of very large and complex datasets by exploiting the divide and recombine framework. The capability to analyse data in depth enables one to go beyond just summary statistics in research. This deep analysis is …