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Articles 342841 - 342870 of 5211493
Full-Text Articles in Entire DC Network
Screaming For Nipples, Angela Lazarus
[Untitled], Hulsey
Must, Hulsey
Apology, Arlene Hampton
At Circe's Farm, Matt Layne
Mama … Show Me The Way, Linda Barton
Emily, Tom Brown
Thurisaz, Carla Delane Wood
[Untitled], Jinny Pearce
Zach, Jenny Duvall
A Madness Of Two Minds, Carol Case
[Untitled], Karen Graffeo
The Barnacle, Emily Grimes
Principles Of Flight, Jennifer Horne
Biological Clock, Robert Mcduff
Astarte 2 (Front Matter), Astarte Staff
Power Play, Arlene Hampton
Astarte 2 (Complete Issue), Astarte Staff
Astarte 2 (Complete Issue), Astarte Staff
Astarte (literary magazine)
No abstract provided.
Data Analysis On Predicting The Top 12 Fantasy Football Players By Position, Alan Abadzic, Jacquelyn Cheun, Milan Patel
Data Analysis On Predicting The Top 12 Fantasy Football Players By Position, Alan Abadzic, Jacquelyn Cheun, Milan Patel
SMU Data Science Review
Fantasy football enthusiasts rely on rankings populated by their platform of choice to draft winning teams and make strategic roster decisions. This study presents a comprehensive analysis of player performance data to forecast the top 12 fantasy points performers per position for the upcoming season. Leveraging machine learning techniques and historical data, our model identifies key performance indicators and trends to inform player evaluations. Insights gleaned from positional trends, breakout candidates, risk assessment, and matchup analysis offer a competitive edge. By addressing limitations, ethical considerations, and avenues for future research, this study contributes to the advancement of fantasy sports analysis …
An Analysis Of Drivers Of The Federal Funds Rate, Stephen Johnson, Neha Dixit, Martin Selzer Ph.D.
An Analysis Of Drivers Of The Federal Funds Rate, Stephen Johnson, Neha Dixit, Martin Selzer Ph.D.
SMU Data Science Review
The Federal Funds Rate (FFR) is a tool used by the Federal Reserve to set monetary policy on borrowing costs for consumers and businesses. The Fed’s primary motivation with the FFR is to control macroeconomic factors such as inflation and unemployment. Over time, policy stances for the Fed have varied in response to events such as the Great Recession, and more recently the COVID-19 pandemic. In light of the Fed’s actions following these events, there is intensified debate over which macroeconomic factors should be prioritized, and what magnitude of change is sufficient to warrant action. Additionally, when action is taken …
Enhancing Imputation Accuracy: A Multi-Faceted Approach For Missing Data In Chicago Arrest Records, Steve Bramhall, Jae Chung, Nicholas Mueller
Enhancing Imputation Accuracy: A Multi-Faceted Approach For Missing Data In Chicago Arrest Records, Steve Bramhall, Jae Chung, Nicholas Mueller
SMU Data Science Review
This paper introduces a novel approach to enhance the imputation process for missing data, utilizing crime records from Chicago with arrests as the target feature. Robust imputation techniques are crucial in the era of burgeoning datasets for generating reliable insights. Our core objective is to present an innovative method that improves imputation techniques, augmenting model performance and bolstering the reliability of analytical outcomes. Leveraging numeric crime data, we establish a Gradient Boosting (GBM) baseline model, then introduce ensemble methods including Random Forest and Decision Trees for further refinement. By systematically exploring multiple imputation processes, we establish a baseline for comparative …
Applying Transfer Learning And Existing Eeg Datasets To Identify Patients With Als, Nibhrat Lohia, Chris Mathew, Garrett Shankel
Applying Transfer Learning And Existing Eeg Datasets To Identify Patients With Als, Nibhrat Lohia, Chris Mathew, Garrett Shankel
SMU Data Science Review
Convolutional neural networks (CNNs) have helped medical practitioners re-design the diagnosis of electroencephalogram (EEG) recordings, resulting in a level of accuracy and efficiency that matches or exceeds experts in the field. This study aims to explore the application of VGG19, an algorithm based on CNN architecture, to diagnose small subsets of neurodegenerative diseases, specifically amyotrophic lateral sclerosis (ALS), a disease that reduces the nervous system’s ability to control the muscles of the body. Since EEG recordings are scarce for ALS, this study will use a larger collection of EEG recordings taken on patients with more common neurological diseases, including but …
Geospatial Temporal Crime Prediction Using Convolution And Lstm Neural Networks: Enhancing The Las Vegas Cardiff Model, Corey D. Holmes, Christian Orji, Chris Papesh
Geospatial Temporal Crime Prediction Using Convolution And Lstm Neural Networks: Enhancing The Las Vegas Cardiff Model, Corey D. Holmes, Christian Orji, Chris Papesh
SMU Data Science Review
According to the Department of Justice, more than half of violent crimes go unreported to law enforcement in the United States (Kollar et al., 2018). This data gap reduces the opportunity to implement proven solutions in the areas with the greatest need. In 1996, Dr. Shepherd developed the Cardiff Model with the aim of bringing together hospitals, law enforcement, and community leaders through the sharing of data. We partnered with ongoing efforts to implement the Cardiff Model in Las Vegas, Nevada. Our goal was to provide a geospatial temporal model that can predict the next 30 days of crime. By …
Rethinking Retrieval Augmented Fine-Tuning In An Evolving Llm Landscape, Nicholas Sager, Timothy Cabaza, Matthew Cusack, Ryan Bass, Joaquin Dominguez
Rethinking Retrieval Augmented Fine-Tuning In An Evolving Llm Landscape, Nicholas Sager, Timothy Cabaza, Matthew Cusack, Ryan Bass, Joaquin Dominguez
SMU Data Science Review
This study explores the utilization of Retrieval Augmented Fine-Tuning (RAFT) to enhance the performance of Large Language Models (LLMs) in domain-specific Retrieval Augmented Generation (RAG) tasks. By integrating domain-specific information during the retrieval process, RAG aims to reduce hallucination and improve the accuracy of LLM outputs. We investigate the use of RAFT, an approach that enhances LLMs by incorporating domain-specific knowledge and effectively handling distractor documents. This paper validates previous work, which found that RAFT can considerably improve the performance of Llama2-7B in specific domains. We also expand upon previous work into new state-of-the-art open-source models and other datasets with …
Enhancing Shap With Multi-Core Parallelization And Distributed Computation, Matthew David, William Jones, Hayley Horn
Enhancing Shap With Multi-Core Parallelization And Distributed Computation, Matthew David, William Jones, Hayley Horn
SMU Data Science Review
In recent years, the adoption of complex machine learning algorithms, often perceived as “black box” models, has grown exponentially across various disciplines. However, the lack of understanding regarding how these models come to their predictions often fosters skepticism and mistrust. In response to the demand for transparency and interpretability, Explainable AI techniques, such as SHapley Additive exPlanations (SHAP), have emerged as powerful tools for comprehending and trusting these algorithms. However, SHAP has an exponential computational demand O( x2 ), where x is the number of features. This becomes increasingly problematic with the larger datasets standard in most industries. Many frameworks …
A Note From The Editor, And Some Unconventional Grammar, Steven M. Schnell
A Note From The Editor, And Some Unconventional Grammar, Steven M. Schnell
The Geographical Bulletin
A Note from the Editor, and Some Unconventional Grammar
Cover And Forewords, Steven M. Schnell
Volume 52-1 Complete Issue, Steven M. Schnell
Volume 52-1 Complete Issue, Steven M. Schnell
The Geographical Bulletin
Volume 52-1 Complete Issue
Family And Social Networks Considered In An Examination Of Exurban Migration Motivations, Brian Edward Johnson, Benjamin Schultz
Family And Social Networks Considered In An Examination Of Exurban Migration Motivations, Brian Edward Johnson, Benjamin Schultz
The Geographical Bulletin
Exurbia is growing in population and land use mainly due to internal migration from urban and suburban areas . Existing literature has identified urban decay, housing affordability, and natural amenities as key reasons why Americans move to exurbia . The literature, however, largely overlooks the potential influence of family and social networks . Drawing from previous studies in non-exurban contexts, this paper asks: are family and social networks attracting migrants to exurbia? The authors conducted in-depth, semi-structured interviews with 34 exurbanites in northern Indiana and northwest Illinois, allowing residents to describe their migration decisions without forcing participant responses into predefined …
Assessing The Impact Of Green Roofs On Urban Heat Island Mitigation: A Hardware Scale Modeling Approach, William C. Pompeii Ii, Timothy W. Hawkins
Assessing The Impact Of Green Roofs On Urban Heat Island Mitigation: A Hardware Scale Modeling Approach, William C. Pompeii Ii, Timothy W. Hawkins
The Geographical Bulletin
As urban areas and the number of people living in urban areas grow, it is important to assess different mechanisms for mitigating the resulting urban heat island . Two hardware scale models (16 ft2 footprint), identical except for their roofing material, were constructed and monitored to assess the impact of green roofs on urban temperature and humidity . During the daytime, indoor and outdoor temperatures of the green roof model were 4 .2°C and 0 .3°C cooler respectively than the black roof model . The green roof indoor temperature was 0 .7°C warmer during the nighttime . Humidity differences between …