Open Access. Powered by Scholars. Published by Universities.®
Physical Sciences and Mathematics Commons™
Open Access. Powered by Scholars. Published by Universities.®
- Discipline
-
- Computer Sciences (2051)
- Engineering (390)
- Life Sciences (191)
- Environmental Sciences (177)
- Chemistry (163)
-
- Physics (143)
- Earth Sciences (118)
- Social and Behavioral Sciences (111)
- Medicine and Health Sciences (89)
- Computer Engineering (83)
- Electrical and Computer Engineering (80)
- Statistics and Probability (77)
- Oceanography and Atmospheric Sciences and Meteorology (71)
- Education (67)
- Sustainability (63)
- Mathematics (53)
- Civil and Environmental Engineering (48)
- Analytical Chemistry (46)
- Water Resource Management (46)
- Atmospheric Sciences (44)
- Soil Science (43)
- Materials Science and Engineering (39)
- Mechanical Engineering (38)
- Artificial Intelligence and Robotics (37)
- Agriculture (36)
- Aerospace Engineering (33)
- Applied Mathematics (33)
- Databases and Information Systems (33)
- Oil, Gas, and Energy (32)
- Keyword
-
- Applied sciences (190)
- Pure sciences (125)
- Earth sciences (51)
- Biological sciences (34)
- Social sciences (34)
-
- Health and environmental sciences (27)
- Education (23)
- Sustainability (18)
- Climate change (15)
- Agriculture (14)
- Information Technology (14)
- Machine learning (14)
- Internet of Things (12)
- Mass spectrometry (11)
- Soil (11)
- Broadband antennas (10)
- Communication and the arts (10)
- Dielectric (10)
- Dipole antennas (10)
- Energy (10)
- Internet of Underground Things (10)
- Moisture (10)
- Permittivity (10)
- Underground Communications (10)
- Underground communication (10)
- Wireless Underground Channel (10)
- Machine Learning (9)
- Security (9)
- Simulation (8)
- Cybersecurity (7)
- Publication Year
- Publication
-
- Department of Computer Science Technical Reports (1721)
- Open Access Dissertations (348)
- Open Access Theses (217)
- The Summer Undergraduate Research Fellowship (SURF) Symposium (120)
- Cyber Center Publications (43)
-
- MODVIS Workshop (38)
- Faculty Publications (34)
- The Journal of Purdue Undergraduate Research (34)
- The 8th International Conference on Physical and Numerical Simulation of Materials Processing (19)
- Discovery Undergraduate Interdisciplinary Research Internship (13)
- Purdue Polytechnic Masters Theses (12)
- Department of Earth, Atmospheric, and Planetary Sciences Faculty Publications (11)
- 2011 Symposium on Data-Driven Approaches to Droughts (10)
- I-GUIDE Forum (10)
- Department of Electrical and Computer Engineering Faculty Publications (9)
- Purdue Road School (9)
- Purdue Journal of Service-Learning and International Engagement (7)
- Student Papers in Public Policy (7)
- Department of Biological Sciences Faculty Publications (6)
- Department of Computer Graphics Technology Degree Theses (6)
- Libraries Faculty and Staff Presentations (6)
- Engagement & Service-Learning Summit (5)
- Libraries Faculty and Staff Scholarship and Research (5)
- Birck and NCN Publications (4)
- Charleston Library Conference (4)
- Department of Chemistry Faculty Publications (4)
- Journal of Aviation Technology and Engineering (4)
- Purdue Energetics Research Center Articles (4)
- CERIAS Technical Reports (3)
- Department of Computer Science Faculty Publications (3)
- Publication Type
Articles 1 - 30 of 2785
Full-Text Articles in Physical Sciences and Mathematics
Digitizing Delphi: Educating Audiences Through Virtual Reconstruction, Kate Koury
Digitizing Delphi: Educating Audiences Through Virtual Reconstruction, Kate Koury
The Journal of Purdue Undergraduate Research
Implementing a 3D model into a virtual space allows the general public to engage critically with archaeological processes. There are many unseen decisions that go into reconstructing an ancient temple. Analysis of available materials and techniques, predictions of how objects were used, decisions of what sources to reference, puzzle piecing broken remains together, and even educated guesses used to fill gaps in information often go unobserved by the public. This work will educate users about those choices by allowing the side-by-side comparison of conflicting theories on the reconstruction of the Tholos at Delphi, which is an ideal site because of …
Promises And Risks Of Applying Ai Medical Imaging To Early Detection Of Cancers, And Regulation For Ai Medical Imaging, Yiyao Zhang
The Journal of Purdue Undergraduate Research
No abstract provided.
The Impact Of Accessible Data On Cyberstalking, Elise Kwan
The Impact Of Accessible Data On Cyberstalking, Elise Kwan
The Journal of Purdue Undergraduate Research
No abstract provided.
Model Selection Through Cross-Validation For Supervised Learning Tasks With Manifold Data, Derek Brown
Model Selection Through Cross-Validation For Supervised Learning Tasks With Manifold Data, Derek Brown
The Journal of Purdue Undergraduate Research
No abstract provided.
The Effects Of Wildfire Aerosol Emissions On Air Quality, Emma Braun, Audrey Shirley
The Effects Of Wildfire Aerosol Emissions On Air Quality, Emma Braun, Audrey Shirley
The Journal of Purdue Undergraduate Research
No abstract provided.
Upleft: Pick Up Leftovers, Uplift Those In Need, Veronica Galles
Upleft: Pick Up Leftovers, Uplift Those In Need, Veronica Galles
The Journal of Purdue Undergraduate Research
No abstract provided.
Characterizing Differential Reflectivity Calibration Dependence On Environmental Temperature Using The X-Band Teaching And Research Radar (Xtrra): Looking For A Relationship Between Temperature And Differential Reflectivity Bias, Emma Miller
The Journal of Purdue Undergraduate Research
Calibration scans are important for the maintenance of data and the quality of the information that radars output. In this study we looked for a temperature dependency in a full year’s worth of differential reflectivity (ZDR) calibration scan data collected by the X-band Teaching and Research Radar (XTRRA) located near the Purdue University campus. In a vertically pointing calibration scan, the radar scans the drops from below while rotating. From this angle, the overall shape will be circular, which corresponds to a ZDR value of approximately 0 dB. To process the data for the year 2021, a Python script was …
Machine Learning Of Big Data: A Gaussian Regression Model To Predict The Spatiotemporal Distribution Of Ground Ozone, Jerry Gu
The Journal of Purdue Undergraduate Research
Tracking pollution levels on the ground is important to the environment and public health. One of the pollutants of concern is ozone, which, at high concentrations, can cause respiratory and cardiovascular problems. The National Center for Atmospheric Research (NCAR) has published valuable ozone data obtained from ground-based sensors installed at selected locations. Because it is unfeasible to measure the exact ozone levels everywhere at any time, it would be valuable to predict the temporal-spatial distributions of ozone concentration based on existing data. This would help us better understand the patterns and trends in the data and make better decisions to …
A Computational Profile Of Invasive Lionfish In Belize: A New Insight On A Destructive Species, Joshua E. Balan
A Computational Profile Of Invasive Lionfish In Belize: A New Insight On A Destructive Species, Joshua E. Balan
The Journal of Purdue Undergraduate Research
Since their discovery in the region in 2009, invasive Indonesian-native lionfish have been taking over the Belize Barrier Reef. As a result, populations of local species have dwindled as they are either eaten or outcompeted by the invaders. This has led to devastating losses ecologically and economically; massive industries in the local nations, such as fisheries and tourism, have suffered greatly. Attempting to combat this, local organizations, from nonprofits to ecotourism companies, have been manually spear-hunting them on scuba dives to cull the population. One such company, Reef Conservation Institute (ReefCI), operating out of Tom Owens Caye outside of Placencia, …
Clouds In The Ancient Lunar Atmosphere: Water Ice Nucleation On Aerosol Simulants, Mariana C. Aguilar
Clouds In The Ancient Lunar Atmosphere: Water Ice Nucleation On Aerosol Simulants, Mariana C. Aguilar
The Journal of Purdue Undergraduate Research
Today’s moon is vastly different from what it was 3 billion years ago. At that time, it was home to a collisional atmosphere formed through massive amounts of volcanism, releasing enough subsurface gas to sustain surface pressures of up to 1 kPa. Observations of our solar system have taught us that all dense atmospheres are host to clouds and aerosols, and we expect the Moon’s to be no different. Knowing when, where, and under what conditions cloud particles form is important for understanding the evolution of the lunar atmosphere, how it reacted to temperature gradients, and how it cycled volatiles. …
Deep Learning Approaches For Chaotic Dynamics And High-Resolution Weather Simulations In The Us Midwest, Vlada Volyanskaya, Kabir Batra, Shubham Shrivastava
Deep Learning Approaches For Chaotic Dynamics And High-Resolution Weather Simulations In The Us Midwest, Vlada Volyanskaya, Kabir Batra, Shubham Shrivastava
Discovery Undergraduate Interdisciplinary Research Internship
Weather prediction is indispensable across various sectors, from agriculture to disaster forecasting, deeply influencing daily life and work. Recent advancement of AI foundation models for weather and climate predictions makes it possible to perform a large number of predictions in reasonable time to support timesensitive policy- and decision-making. However, the uncertainty quantification, validation, and attribution of these models have not been well explored, and the lack of knowledge can eventually hinder the improvement of their prediction accuracy and precision. Our project is embarking on a two-fold approach leveraging deep learning techniques (LSTM and Transformer) architectures. Firstly, we model the Lorenz …
Les Expositions Turnus, Une Page D’Histoire Transnationale Des Beaux-Arts En Suisse À La Fin Du Xixe Siècle. Et Comment Découvrir Les Humanités Numériques, Béatrice Joyeux-Prunel
Les Expositions Turnus, Une Page D’Histoire Transnationale Des Beaux-Arts En Suisse À La Fin Du Xixe Siècle. Et Comment Découvrir Les Humanités Numériques, Béatrice Joyeux-Prunel
Artl@s Bulletin
Cet article présente le travail de la classe d’introduction aux humanités numériques de l’Université de Genève sur les expositions Turnus en Suisse à partir des années 1840. Près de 50 catalogues ont été retranscrits, décrits et structurés à l’aide de scripts Python, puis géolocalisés. Les données ont été ajoutées à BasArt, le répertoire mondial de catalogues d’expositions d’Artl@s (https://artlas.huma-num.fr/map). Elles permettent de mieux comprendre les premières années de ces expositions et leurs dynamiques locales, fédérales et internationales. Le Turnus fut une plaque tournante pour les artistes suisses, voire un tremplin vers le marché européen de l’art.
Closing The Gap: Leveraging Aes-Ni To Balance Adversarial Advantage And Honest User Performance In Argon2i, Nicholas Harrell, Nathaniel Krakauer
Closing The Gap: Leveraging Aes-Ni To Balance Adversarial Advantage And Honest User Performance In Argon2i, Nicholas Harrell, Nathaniel Krakauer
CERIAS Technical Reports
The challenge of providing data privacy and integrity while maintaining efficient performance for honest users is a persistent concern in cryptography. Attackers exploit advances in parallel hardware and custom circuit hardware to gain an advantage over regular users. One such method is the use of Application-Specific Integrated Circuits (ASICs) to optimize key derivation function (KDF) algorithms, giving adversaries a significant advantage in password guessing and recovery attacks. Other examples include using graphical processing units (GPUs) and field programmable gate arrays (FPGAs). We propose a focused approach to close the gap between adversarial advantage and honest user performance by leveraging the …
Complex Impacts Of Wars On Global Sustainable Development In A Metacoupled World, Qutu Jiang, Zhenci Xu, Yuanzheng Cui, Jianguo Liu
Complex Impacts Of Wars On Global Sustainable Development In A Metacoupled World, Qutu Jiang, Zhenci Xu, Yuanzheng Cui, Jianguo Liu
I-GUIDE Forum
Wars and armed conflicts have had profound impacts on local and global sustainable development in an interconnected world. However, evidence on the impacts of wars is fragmented and little attention has been paid to the impacts on the 17 UN’s Sustainable Development Goals (SDGs), a unifying framework for achieving global sustainable development. This perspective synthesizes the scattered information to provide a holistic analysis and highlight the applications of remote sensing in assessing the impacts of wars on global sustainable development in a metacoupling world. Wars have complex impacts on all 17 SDGs, which cascade beyond conflict zones and spillover to …
Geospatial Data Integration Middleware For Exploratory Analytics Addressing Regional Natural Resource Grand Challenges In The Us Mountain West, Shannon Albeke, Nicholas Case, Samantha Ewers, Jeffrey Hamerlinck, William Kirkpatrick, Jerod Merkle, Luke Todd
Geospatial Data Integration Middleware For Exploratory Analytics Addressing Regional Natural Resource Grand Challenges In The Us Mountain West, Shannon Albeke, Nicholas Case, Samantha Ewers, Jeffrey Hamerlinck, William Kirkpatrick, Jerod Merkle, Luke Todd
I-GUIDE Forum
This paper describes CyberGIS-based research and development aimed at improving geospatial data integration and visual analytics to better understand the impact of regional climate change on water availability in the U.S. Rocky Mountains. Two Web computing applications are presented. DEVISE - Derived Environmental Variability Indices Spatial Extractor, streamlines utilization of environmental data for better-informed wildlife decisions by biologists and game managers. The WY-Adapt platform aims to enhance predictive understanding of climate change impacts on water availability through two modules: “Current Conditions” and “Future Scenarios”. It integrates high-resolution models of the biophysical environment and human interactions, providing a robust framework for …
Large-Scale Google Street View Images For Urban Change Detection, Fangzheng Lyu, Xinlin Ma, Yan Song, Eric Zhu, Shaowen Wang
Large-Scale Google Street View Images For Urban Change Detection, Fangzheng Lyu, Xinlin Ma, Yan Song, Eric Zhu, Shaowen Wang
I-GUIDE Forum
Urbanization has entered a new phase characterized by urban changes occurring at a micro-scale and “under the roof”, as opposed to external modifications. These changes, known as urban retrofitting, involve the incorporation of novel technologies or features into pre-existing systems to promote sustainability. Given the limitations of remote sensing images in identifying such urban changes, novel tools need to be developed for detecting urban retrofitting. In this study, we first build a pipeline to collect large-scale time-series urban street view images from Google Street View in Mecklenburg County, North Carolina. And we examine the feasibility of utilizing the acquired dataset …
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 …
Graph Transformer Network For Flood Forecasting With Heterogeneous Covariates, Jimeng Shi, Vitalii Stebliankin, Zhaonan Wang, Shaowen Wang, Giri Narasimhan
Graph Transformer Network For Flood Forecasting With Heterogeneous Covariates, Jimeng Shi, Vitalii Stebliankin, Zhaonan Wang, Shaowen Wang, Giri Narasimhan
I-GUIDE Forum
Floods can be very destructive causing heavy damage to life, property, and livelihoods. Global climate change and the consequent sea-level rise have increased the occurrence of extreme weather events, resulting in elevated and frequent flood risk. Therefore, accurate and timely flood forecasting in coastal river systems is critical to facilitate good flood management. However, the computational tools currently used are either slow or inaccurate. In this paper, we propose a Flood prediction tool using Graph Transformer Network (FloodGTN) for river systems. More specifically, FloodGTN learns the spatio-temporal dependencies of water levels at different monitoring stations using Graph Neural Networks (GNNs) …
Solving Geospatial Problems Under Extreme Time Constraints: A Call For Inclusive Geocomputational Education, Coline C. Dony
Solving Geospatial Problems Under Extreme Time Constraints: A Call For Inclusive Geocomputational Education, Coline C. Dony
I-GUIDE Forum
To prepare our next generation to face geospatial problems that have extreme time constraints (e.g., disasters, climate change) we need to create educational pathways that help students develop their geocomputational thinking skills. First, educators are central in helping us create those pathways, therefore, we need to clearly convey to them why and in which contexts this thinking is necessary. For that purpose, a new definition for geocomputational thinking is suggested that makes it clear that this thinking is needed for geospatial problems that have extreme time constraints. Secondly, we can not further burden educators with more demands, rather we should …
Curriculum Design Of Artificial Intelligence And Sustainability In Secondary School, Jinyi Cai, Mei-Po Kwan, Chunyu Hou, Dong Liu, Yeung Yam
Curriculum Design Of Artificial Intelligence And Sustainability In Secondary School, Jinyi Cai, Mei-Po Kwan, Chunyu Hou, Dong Liu, Yeung Yam
I-GUIDE Forum
Artificial Intelligence is revolutionizing numerous sectors with its transformative power, while at the same time, there is an increasing sense of urgency to address sustainability challenges. Despite the significance of both areas, secondary school curriculums still lack comprehensive integration of AI and sustainability education. This paper presents a curriculum designed to bridge this gap. The curriculum integrates progressive objectives, computational thinking competencies and system thinking components across five modules—awareness, knowledge, interaction, empowerment and ethics—to cater to varying learner levels. System thinking components help students understand sustainability in a holistic manner. Computational thinking competencies aim to cultivate computational thinkers to guide …
Reducing Uncertainty In Sea-Level Rise Prediction: A Spatial-Variability-Aware Approach, Subhankar Ghosh, Shuai An, Arun Sharma, Jayant Gupta, Shashi Shekhar, Aneesh Subramanian
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 …
A Spatiotemporal Synthesis Of High-Resolution Salinity Data With Aquaculture Applications, Dong Liang, Jeremy M. Testa, Cassie Gurbisz, Lora A. Harris
A Spatiotemporal Synthesis Of High-Resolution Salinity Data With Aquaculture Applications, Dong Liang, Jeremy M. Testa, Cassie Gurbisz, Lora A. Harris
I-GUIDE Forum
Technological advancement and the desire to better monitor shallow habitats in the Chesapeake Bay, Maryland, United States led to the initiation of several high-resolution monitoring programs such as ConMon (short for “Continuous Monitoring”) measuring oxygen, salinity, and chlorophyll-a at a 15-minute frequency. These monitoring efforts have yielded an enormous volume of data and insight into the condition of the tidal water of the Bay. But this information is underutilized in documenting the fine-scale variability of water quality, which is critical in identifying the link between water quality and ecological responses, partly due to the challenges in integrating monitoring data collected …
Cross-Scale Urban Land Cover Mapping: Empowering Classification Through Transfer Learning And Deep Learning Integration, Zhe Wang, Chao Fan, Xian Min, Shoukun Sun, Xiaogang Ma, Xiang Que
Cross-Scale Urban Land Cover Mapping: Empowering Classification Through Transfer Learning And Deep Learning Integration, Zhe Wang, Chao Fan, Xian Min, Shoukun Sun, Xiaogang Ma, Xiang Que
I-GUIDE Forum
Urban land cover mapping is essential for effective urban planning and resource management. Thanks to its ability to extract intricate features from urban datasets, deep learning has emerged as a powerful technique for urban classification. The U-net architecture has achieved state-of-the-art land cover classification performance, highlighting its potential for mapping urban trees at different spatial scales. However, deep learning approaches often require large, labeled datasets, which are challenging to acquire for specific urban contexts. Transfer learning addresses this limitation by leveraging pre-trained deep learning models on extensive datasets and adapting them to smaller urban datasets with limited labeled samples. Transfer …
Research Instrumentation Center (Ric), Ryan Hilger, Purdue University Office Of Research
Research Instrumentation Center (Ric), Ryan Hilger, Purdue University Office Of Research
University Research Core Facility Boilerplate Descriptions
No abstract provided.
Waste Treatment Facility Location For Hotel Chains, Dolores R. Santos-Peñate, Rafael R. Suárez-Vega, Carmen Florido De La Nuez
Waste Treatment Facility Location For Hotel Chains, Dolores R. Santos-Peñate, Rafael R. Suárez-Vega, Carmen Florido De La Nuez
ITSA 2022 Gran Canaria - 9th Biennial Conference: Corporate Entrepreneurship and Global Tourism Strategies After Covid 19
Tourism generates huge amounts of waste. About half of the waste generated by hotels is food and garden bio-waste. This bio-waste can be used to make compost and pellets. In turn, pellets can be used as an absorbent material in composters and as an energy source. We consider the problem of locating composting and pellet-making facilities so that the bio-waste generated by a chain of hotels can be managed at or close to the generation points. An optimization model is applied to locate the facilities and allocate the waste and products, and several scenarios are analysed. The study shows that, …
Instagram Travel Influencers Coping With Covid-19 Travel Disruption, Andrei Kirilenko, Katarzyna Emin, Karen Tavares
Instagram Travel Influencers Coping With Covid-19 Travel Disruption, Andrei Kirilenko, Katarzyna Emin, Karen Tavares
ITSA 2022 Gran Canaria - 9th Biennial Conference: Corporate Entrepreneurship and Global Tourism Strategies After Covid 19
A significant portion of today’s marketing is done through social media influencers, that is, through bloggers with established online credibility in a certain area who are recognized and followed by a sizable online audience. In the travel and hospitality industry, the influencer marketing is primarily done through Instagram due to its emphasis on visual images rather than texts. Covid-19 related travel restrictions and shrinking social media advertisement in travel industry have heavily impacted travel influencers, reducing their income and forcing many out of business. We present the outcomes of a study of the top 150 online travel influencers. The analysis …
The Future Of Indiana’S Water Resources: A Report From The Indiana Climate Change Impacts Assessment, Keith Cherkauer, Robert Barr, Laura C. Bowling, Kyuhyun Byun, Indrajeet Chaubey, Natalie Chin, Chun-Mei Chiu, Darren Ficklin, Alan Hamlet, Stephen Kines, Charlotte Lee, Ram Neupane, Garett Pignotti, Sanoar Rahman, Sarmistha Singh, Pandara Valappil Femeena, Tanja Williamson, Melissa Widhalm, Jeffrey Dukes
The Future Of Indiana’S Water Resources: A Report From The Indiana Climate Change Impacts Assessment, Keith Cherkauer, Robert Barr, Laura C. Bowling, Kyuhyun Byun, Indrajeet Chaubey, Natalie Chin, Chun-Mei Chiu, Darren Ficklin, Alan Hamlet, Stephen Kines, Charlotte Lee, Ram Neupane, Garett Pignotti, Sanoar Rahman, Sarmistha Singh, Pandara Valappil Femeena, Tanja Williamson, Melissa Widhalm, Jeffrey Dukes
Water Report
This report from the Indiana Climate Change Impacts Assessment (IN CCIA) applies climate change projections for the state to explore how continued changes in Indiana’s climate are going to affect all aspects of water resources, including soil water, evaporation, runoff, snow cover, streamflow, drought, and flooding. As local temperatures continue to rise and rainfall patterns shift, managing the multiple water needs of communities, natural systems, recreation, industry, and agriculture will become increasingly difficult. Ensuring that enough water is available in the right places and at the right times will require awareness of Indiana’s changing water resources and planning at regional …
Public Acceptance Of Guidance And Regulations For Space Flight Participation, Cory Trunkhill, Robert Joslin, Joseph Keebler
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 …
The Model 2.0 And Friends: An Interim Report, Garrison W. Cottrell, Martha Gahl, Shubham Kulkarni, Shashank Venkatramani, Yash Shah, Keyu Long, Xuzhe Zhi, Shivaank Agarwal, Cody Li, Jingyuan He, Thomas Fischer
The Model 2.0 And Friends: An Interim Report, Garrison W. Cottrell, Martha Gahl, Shubham Kulkarni, Shashank Venkatramani, Yash Shah, Keyu Long, Xuzhe Zhi, Shivaank Agarwal, Cody Li, Jingyuan He, Thomas Fischer
MODVIS Workshop
Last year, I reported on preliminary results of an anatomically-inspired deep learning model of the visual system and its role in explaining the face inversion effect. This year, I will report on new results and some variations on network architectures that we have explored, mainly as a way to generate discussion and get feedback. This is by no means a polished, final presentation!
We look forward to the group’s suggestions for these projects.
Automated Delineation Of Visual Area Boundaries And Eccentricities By A Cnn Using Functional, Anatomical, And Diffusion-Weighted Mri Data, Noah C. Benson, Bogeng Song, Toshikazu Miyata, Hiromasa Takemura, Jonathan Winawer
Automated Delineation Of Visual Area Boundaries And Eccentricities By A Cnn Using Functional, Anatomical, And Diffusion-Weighted Mri Data, Noah C. Benson, Bogeng Song, Toshikazu Miyata, Hiromasa Takemura, Jonathan Winawer
MODVIS Workshop
Delineating visual field maps and iso-eccentricities from fMRI data is an important but time-consuming task for many neuroimaging studies on the human visual cortex because the traditional methods of doing so using retinotopic mapping experiments require substantial expertise as well as scanner, computer, and human time. Automated methods based on gray-matter anatomy or a combination of anatomy and functional mapping can reduce these requirements but are less accurate than experts. Convolutional Neural Networks (CNNs) are powerful tools for automated medical image segmentation. We hypothesize that CNNs can define visual area boundaries with high accuracy. We trained U-Net CNNs with ResNet18 …