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Search Publications by

Alden A. Dima (Fed)

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Displaying 1 - 25 of 41

Adapting natural language processing for technical text

June 29, 2021
Alden A. Dima, Sarah Lukens, Melinda Hodkiewicz, Thurston Sexton, Michael Brundage
Despite recent dramatic successes, Natural Language Processing (NLP) is not ready to address a variety of real-world problems. Its reliance on large standard corpora, a training and evaluation paradigm that favors the learning of shallow heuristics, and

A Visual Analytics Approach for the Diagnosis of Heterogeneous and Multidimensional Machine Maintenance Data

May 10, 2021
Xiaoyu Zhang, Takanori Fujiwara, Senthil K. Chandrasegaran, Michael Brundage, Thurston Sexton, Alden A. Dima, Kwan-Liu Ma
Analysis of large, high-dimensional, and heterogeneous datasets is challenging as no one technique is suitable for visualizing and clustering such data in order to make sense of the underlying information. For instance, heterogeneous logs detailing machine

A Controlled Vocabulary and Metadata Schema for Materials Science Data Discovery

April 29, 2021
Andrea M. Medina-Smith, Chandler Becker, Raymond L. Plante, Laura Bartolo, Alden A. Dima, James A. Warren, Robert Hanisch
The International Materials Resource Registries (IMRR) working group of the Research Data Alliance (RDA) was created to spur initial development of a federated registry system to allow for easier discovery and access to materials data. As part of this

Implementing a Registry Federation for Materials Science Data Discovery

April 13, 2021
Raymond L. Plante, Chandler Becker, Andrea M. Medina-Smith, Kevin G. Brady, Alden A. Dima, Benjamin J. Long, Laura M. Bartolo, Robert Hanisch
As a result of a number of national initiatives, we are seeing rapid growth in the data important to materials science that are available over the web. Consequently, it is becoming increasingly difficult for researchers to learn what data are available and

Requirements Analysis of Large Policy Corpora

January 5, 2021
Alden A. Dima, Aaron Massey
Regulators, policy makers, and consumers are interested in proactively identifying services with acceptable or compliant data use policies, privacy policies, and terms of service. Academic requirements engineering researchers and legal scholars have

Technical Language Processing: Unlocking Maintenance Knowledge

December 11, 2020
Michael P. Brundage, Thurston B. Sexton, Melinda Hodkiewicz, Alden A. Dima, Sarah Lukens
Out-of-the-box natural-language processing (NLP) pipelines need re-imagining to understand and meet the requirements of the engineering sector. Text-based documents account for a significant portion of data collected during the life cycle of asset


June 26, 2020
Thurston Sexton, Michael Brundage, Alden A. Dima, Michael Sharp
The Manufacturing Science and Engineering Conference (MSEC) in 2020 is the 15th annual conference put on by the Manufacturing Engineering Division (MED) of ASME. MED and ASME MSEC focuses on manufacturing sciences, technology, and applications, including

An Informatics Infrastructure for the Materials Genome Initiative

July 6, 2016
Alden A. Dima, Sunil K. Bhaskarla, Chandler A. Becker, Mary C. Brady, Carelyn E. Campbell, Philippe J. Dessauw, Robert J. Hanisch, Ursula R. Kattner, Kenneth G. Kroenlein, Adele P. Peskin, Raymond L. Plante, Guillaume Sousa Amaral, Zachary T. Trautt, James A. Warren, Sharief S. Youssef, Sheng Yen Li, Pierre Francois Rigodiat, Marcus W. Newrock
A materials data infrastructure that enables the sharing and transformation of a wide range of materials data is an essential part of achieving the goals of the Materials Genome Initiative. We describe two high-level requirements of such an infrastructure

Shape Descriptors Comparison for Cell Tracking

October 15, 2015
Michael P. Majurski, Christopher Zheng, Joe Chalfoun, Alden A. Dima, Mary C. Brady
New microscope technologies are enabling the acquisition of large volumes of live cell image data. Accurate temporal object tracking is required to facilitate the analysis of this data. One principle component of cell tracking is correspondence, matching

Evaluation of Low-Contrast Detectability of Iterative Reconstruction across Multiple Institutions, CT Scanner Manufacturers, and Radiation Exposure Levels

February 19, 2015
Adele P. Peskin, Ganesh Saiprasad, James J. Filliben, Alden A. Dima
Purpose: To evaluate the impact of Iterative Reconstruction (IR) compared with Filtered Back Projection (FBP) using low-contrast objects in phantom images across vendors and exposure levels. Materials and Methods: Randomized repeat scans of the Gammex 464

Structural Materials Data Demonstration Project: Resource for Thermal Process Modeling

July 1, 2014
Carelyn E. Campbell, Ursula R. Kattner, Alden A. Dima, Scott Henry, Larry A. Berardinis, Tom Searles, Laura M. Bartolo
Integrated Computational Materials Engineering (ICME)requires the availability of supporting data for modeling across multiple length scales. It also requires robust data schema and input-output protocols. To address these needs, a team working under a

Segmenting Time-lapse Phase Contrast Images of Adjacent NIH 3T3 Cells

January 15, 2013
Joe Chalfoun, Alden A. Dima, Marcin Kociolek, Michael W. Halter, Antonio Cardone, Adele P. Peskin, Peter Bajcsy, Mary C. Brady
We present a new method for segmenting phase contrast images of NIH 3T3 fibroblast cells that is accurate even when cells are in contact. The problem of segmentation, when cells are in contact, poses a challenge to the accurate automation of cell counting

Big Data Issues in Quantitative Imaging

August 29, 2012
Mary C. Brady, Alden A. Dima, Charles D. Fenimore, James J. Filliben, John Lu, Adele Peskin, Mala Ramaiah, Ganesh Saiprasad, Ram D. Sriram

Comparison of segmentation algorithms for fluorescence microscopy images of cells

June 14, 2011
Alden A. Dima, John T. Elliott, James J. Filliben, Michael W. Halter, Adele P. Peskin, Javier Bernal, Marcin Kociolek, Mary C. Brady, Hai C. Tang, Anne L. Plant
Segmentation results from nine different segmentation techniques applied to two different cell lines and five different sets of imaging conditions were compared. Significant variability in the results of segmentation was observed that was due solely to

Predicting Segmentation Accuracy for Biological Cell Images

December 15, 2010
Adele P. Peskin, Alden A. Dima, Joe Chalfoun, John T. Elliott
We have performed segmentation procedures on a large number of images from two mammalian cell lines that were seeded at low density, in order to study trends in the segmentation results and make predictions about cellular features that affect segmentation

Predicting Segmentation Accuracy for Biological Cell Images

November 29, 2010
Adele P. Peskin, Alden A. Dima, Joe Chalfoun
We have performed image segmentations on a very large number of images, using a wide variety of imaging conditions and cell lines, in order to study trends in the segmentation results and make predictions about segmentation accuracy. Comparing results from