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2. Towards Aggregated Asynchronous Checkpointing

Author

Gossman, Mikaila J., Nicolae, Bogdan, Calhoun, Jon C., Cappello, Franck, and Smith, Melissa C.

Subjects
Computer Science - Distributed, Parallel, and Cluster Computing
Abstract

High-Performance Computing (HPC) applications need to checkpoint massive amounts of data at scale. Multi-level asynchronous checkpoint runtimes like VELOC (Very Low Overhead Checkpoint Strategy) are gaining popularity among application scientists for their ability to leverage fast node-local storage and flush independently to stable, external storage (e.g., parallel file systems) in the background. Currently, VELOC adopts a one-file-per-process flush strategy, which results in a large number of files being written to external storage, thereby overwhelming metadata servers and making it difficult to transfer and access checkpoints as a whole. This paper discusses the viability and challenges of designing aggregation techniques for asynchronous multi-level checkpointing. To this end we implement and study two aggregation strategies, their limitations, and propose a new aggregation strategy specifically for asynchronous multi-level checkpointing., Comment: Accepted submission to the SuperCheck Workshop at the SuperComputing Conference held in St. Louis, MO. November 14-19, 2021(SC'21)

Published
2021

4. Speech Enhancement Using Multi-Stage Self-Attentive Temporal Convolutional Networks

Author

Lin, Ju, van Wijngaarden, Adriaan J., Wang, Kuang-Ching, and Smith, Melissa C.

Subjects
Electrical Engineering and Systems Science - Audio and Speech Processing, Computer Science - Sound
Abstract

Multi-stage learning is an effective technique to invoke multiple deep-learning modules sequentially. This paper applies multi-stage learning to speech enhancement by using a multi-stage structure, where each stage comprises a self-attention (SA) block followed by stacks of temporal convolutional network (TCN) blocks with doubling dilation factors. Each stage generates a prediction that is refined in a subsequent stage. A fusion block is inserted at the input of later stages to re-inject original information. The resulting multi-stage speech enhancement system, in short, multi-stage SA-TCN, is compared with state-of-the-art deep-learning speech enhancement methods using the LibriSpeech and VCTK data sets. The multi-stage SA-TCN system's hyper-parameters are fine-tuned, and the impact of the SA block, the fusion block and the number of stages are determined. The use of a multi-stage SA-TCN system as a front-end for automatic speech recognition systems is investigated as well. It is shown that the multi-stage SA-TCN systems perform well relative to other state-of-the-art systems in terms of speech enhancement and speech recognition scores., Comment: Preprint

Published
2021

5. Cellular State Transformations using Generative Adversarial Networks

Author

Targonski, Colin, Shealy, Benjamin T., Smith, Melissa C., and Feltus, F. Alex

Subjects
Quantitative Biology - Quantitative Methods, Computer Science - Machine Learning
Abstract

We introduce a novel method to unite deep learning with biology by which generative adversarial networks (GANs) generate transcriptome perturbations and reveal condition-defining gene expression patterns. We find that a generator conditioned to perturb any input gene expression profile simulates a realistic transition between source and target RNA expression states. The perturbed samples follow a similar distribution to original samples from the dataset, also suggesting these are biologically meaningful perturbations. Finally, we show that it is possible to identify the genes most positively and negatively perturbed by the generator and that the enriched biological function of the perturbed genes are realistic. We call the framework the Transcriptome State Perturbation Generator (TSPG), which is open source software available at https://github.com/ctargon/TSPG., Comment: 11 pages, 5 figures

Published
2019

7. Generalized Value Iteration Networks: Life Beyond Lattices

Author

Niu, Sufeng, Chen, Siheng, Guo, Hanyu, Targonski, Colin, Smith, Melissa C., and Kovačević, Jelena

Subjects
Computer Science - Learning, Computer Science - Artificial Intelligence
Abstract

In this paper, we introduce a generalized value iteration network (GVIN), which is an end-to-end neural network planning module. GVIN emulates the value iteration algorithm by using a novel graph convolution operator, which enables GVIN to learn and plan on irregular spatial graphs. We propose three novel differentiable kernels as graph convolution operators and show that the embedding based kernel achieves the best performance. We further propose episodic Q-learning, an improvement upon traditional n-step Q-learning that stabilizes training for networks that contain a planning module. Lastly, we evaluate GVIN on planning problems in 2D mazes, irregular graphs, and real-world street networks, showing that GVIN generalizes well for both arbitrary graphs and unseen graphs of larger scale and outperforms a naive generalization of VIN (discretizing a spatial graph into a 2D image)., Comment: 14 pages, conference

Published
2017

12. Agricultural Research Service Weed Science Research: Past, Present, and Future

Author

Young, Stephen L., primary, Anderson, James V., additional, Baerson, Scott R., additional, Bajsa-Hirschel, Joanna, additional, Blumenthal, Dana M., additional, Boyd, Chad S., additional, Boyette, Clyde D., additional, Brennan, Eric B., additional, Cantrell, Charles L., additional, Chao, Wun S., additional, Chee-Sanford, Joanne C., additional, Clements, Charlie D., additional, Dray, F. Allen, additional, Duke, Stephen O., additional, Eason, Kayla M., additional, Fletcher, Reginald S., additional, Fulcher, Michael R., additional, Gaskin, John F., additional, Grewell, Brenda J., additional, Hamerlynck, Erik P., additional, Hoagland, Robert E., additional, Horvath, David P., additional, Law, Eugene P., additional, Madsen, John D., additional, Martin, Daniel E., additional, Mattox, Clint, additional, Mirsky, Steven B., additional, Molin, William T., additional, Moran, Patrick J., additional, Mueller, Rebecca C., additional, Nandula, Vijay K., additional, Newingham, Beth A., additional, Pan, Zhiqiang, additional, Porensky, Lauren M., additional, Pratt, Paul D., additional, Price, Andrew J., additional, Rector, Brian G., additional, Reddy, Krishna N., additional, Sheley, Roger L., additional, Smith, Lincoln, additional, Smith, Melissa C., additional, Snyder, Keirith A., additional, Tancos, Matthew A., additional, West, Natalie M., additional, Wheeler, Gregory S., additional, Williams, Martin M., additional, Wolf, Julie, additional, Wonkka, Carissa L., additional, Wright, Alice A., additional, Xi, Jing, additional, and Ziska, Lew H., additional

Published
2023
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15. Assessing the status of biological control of air potato (Dioscorea bulbifera) in the southeastern USA.

Author

Manrique, Veronica, Kraus, Emily, Schaffer, Charity, Diaz, Rodrigo, Kelm, Cassandra, Poffenberger, Ryan, Rohrig, Eric, Murray, Rosie, David, Aaron, Smith, Melissa C., Lake, Ellen, Rayamajhi, Min B., Leidi, Jorge, Allen Dray Jr., F., Minteer, Carey R., Le Falchier, Emily, Mass, John, and Hight, Stephen

Subjects
YAMS, POTATOES, BIOLOGICAL pest control agents, SCIENCE journalism, INSECT rearing, CHRYSOMELIDAE
Abstract

Air potato, Dioscorea bulbifera L. (Dioscoreaceae), is an invasive vine that climbs into vegetation outcompeting native species and altering ecosystems in the southeastern USA. The biological control agent, Lilioceris cheni Gressitt and Kimoto (Coleoptera: Chrysomelidae), has been released in Florida since 2011 and in other states including Georgia (2015), Louisiana (2016), Texas (2017), Mississippi (2018), and Alabama (2018) to manage air potato. Despite the wide distribution of the weed and mass rearing efforts, there are no studies documenting the establishment of L. cheni outside of Florida. The objectives of this study were to document the beetle release efforts by several agencies and determine the establishment and impact of L. cheni across the southeastern USA. Using community science reports and field surveys, we documented the distribution of air potato and L. cheni. Results demonstrated that the distribution of air potato extends to temperate regions from South Carolina and Georgia to subtropical regions of Louisiana, Texas, and Florida. Surveys showed that the beetle is present across all states with releases, and numbers fluctuated between 6 and 37 beetles counted per 30-surveyor-minutes. Most sites sampled had moderate to high beetle damage (scales of 3–4 out of 5), and air potato cover was low to moderate (2–3 out of 5). However, air potato was still abundant in some areas, which suggests the need for additional beetle releases and/or the use of other tactics to achieve management goals. [ABSTRACT FROM AUTHOR]

Published
2023
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27. Addressing noise in co-expression network construction

Author

Burns, Joshua J R, primary, Shealy, Benjamin T, additional, Greer, Mitchell S, additional, Hadish, John A, additional, McGowan, Matthew T, additional, Biggs, Tyler, additional, Smith, Melissa C, additional, Feltus, F Alex, additional, and Ficklin, Stephen P, additional

Published
2021
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29. Complete and Resilient Documentation for Operational Medical Environments Leveraging Mobile Hands-free Technology in a Systems Approach: Experimental Study

Author

Woo, MinJae, primary, Mishra, Prabodh, additional, Lin, Ju, additional, Kar, Snigdhaswin, additional, Deas, Nicholas, additional, Linduff, Caleb, additional, Niu, Sufeng, additional, Yang, Yuzhe, additional, McClendon, Jerome, additional, Smith, D Hudson, additional, Shelton, Stephen L, additional, Gainey, Christopher E, additional, Gerard, William C, additional, Smith, Melissa C, additional, Griffin, Sarah F, additional, Gimbel, Ronald W, additional, and Wang, Kuang-Ching, additional

Published
2021
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33. Complete and Resilient Documentation for Operational Medical Environments Leveraging Mobile Hands-free Technology in a Systems Approach: Experimental Study (Preprint)

Author

Woo, MinJae, primary, Mishra, Prabodh, additional, Lin, Ju, additional, Kar, Snigdhaswin, additional, Deas, Nicholas, additional, Linduff, Caleb, additional, Niu, Sufeng, additional, Yang, Yuzhe, additional, McClendon, Jerome, additional, Smith, D Hudson, additional, Shelton, Stephen L, additional, Gainey, Christopher E, additional, Gerard, William C, additional, Smith, Melissa C, additional, Griffin, Sarah F, additional, Gimbel, Ronald W, additional, and Wang, Kuang-Ching, additional

Published
2021
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34. Systematic Evaluation and Enhancement of Speech Recognition in Operational Medical Environments

Author

Kar, Snigdhaswin, primary, Mishra, Prabodh, additional, Lin, Ju, additional, Woo, Min-Jae, additional, Deas, Nicholas, additional, Linduff, Caleb, additional, Niu, Sufeng, additional, Yang, Yuzhe, additional, McClendon, Jerome, additional, Smith, D. Hudson, additional, Smith, Melissa C., additional, Gimbel, Ronald W., additional, and Wang, Kuang-Ching, additional

Published
2021
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35. A generalized deep learning approach for local structure identification in molecular simulations† †Electronic supplementary information (ESI) available: All details of molecular simulations and our implementation of the Geiger and Dellago22 network are provided in the ESI. Code containing an implementation of the PointNet along with an example is freely available in a Github repository (https://github.com/rsdefever/GenStrIde). See DOI: 10.1039/c9sc02097g

Author

DeFever, Ryan S., Targonski, Colin, Hall, Steven W., Smith, Melissa C., and Sarupria, Sapna

Subjects
Computer Science::Machine Learning, Chemistry
Abstract

We demonstrate a PointNet-based deep learning approach to classify local structure in molecular simulations, learning features directly from atomic coordinates., Identifying local structure in molecular simulations is of utmost importance. The most common existing approach to identify local structure is to calculate some geometrical quantity referred to as an order parameter. In simple cases order parameters are physically intuitive and trivial to develop (e.g., ion-pair distance), however in most cases, order parameter development becomes a much more difficult endeavor (e.g., crystal structure identification). Using ideas from computer vision, we adapt a specific type of neural network called a PointNet to identify local structural environments in molecular simulations. A primary challenge in applying machine learning techniques to simulation is selecting the appropriate input features. This challenge is system-specific and requires significant human input and intuition. In contrast, our approach is a generic framework that requires no system-specific feature engineering and operates on the raw output of the simulations, i.e., atomic positions. We demonstrate the method on crystal structure identification in Lennard-Jones (four different phases), water (eight different phases), and mesophase (six different phases) systems. The method achieves as high as 99.5% accuracy in crystal structure identification. The method is applicable to heterogeneous nucleation and it can even predict the crystal phases of atoms near external interfaces. We demonstrate the versatility of our approach by using our method to identify surface hydrophobicity based solely upon positions and orientations of surrounding water molecules. Our results suggest the approach will be broadly applicable to many types of local structure in simulations.

Published
2019

43. Classical biological control and apparent competition: Evaluating a waterhyacinth invaded community module

Author

Tipping, Philip W., Smith, Melissa C., Lake, Ellen C., Minteer, Carey R., Goode, Ashley B. C., Foley, Jeremiah R., Gettys, Lyn A., and Entomology

Subjects
apparent competition, Megamelus davisi, Nuphar advena, food and beverages, biological control, waterhyacinth, indirect effects, Megamelus scutellaris
Abstract

The scope and complexity of interactions within community food webs necessitates their simplification to a community module scale for conducting empirical studies. An outdoor mesocosm study in the USA quantified the strengths of direct and indirect interactions between two herbivore congeners that fed on two aquatic plant species while sharing a parasitoid. Kalopolynema ema (Hymenoptera: Mymaridae) is a stenophagous native egg parasitoid that attacks the hemipteran species in this study, Megamelus davisi (Hemiptera: Delphacidae), a native herbivore that feeds on the native aquatic species Nuphar advena, and the introduced biological control agent Megamelus scutellaris (Hemiptera: Delphacidae) that feeds on the non-native aquatic species waterhyacinth, Pontederia crassipes. The presence of M. scutellaris did not significantly increase parasitism of M. davisi indicating that apparent competition was not a factor in this community module. There was no evidence of any trophic cascades caused by these interactions based on the relative growth rates of biomass and leaf area for both plant species. Synthesis and applications. The relative strengths of community interactions varied with herbivore densities suggesting that should negative indirect effects with biological control agents occur they would likely be transient and closely linked to population dynamics as influenced by abiotic factors like temperature. Despite identifying potential negative interactions using path analysis, we saw no apparent competition following the insertion of a weed biological control agent into a community that contains an ecological analog. In this example, the primary negative interaction was biotic resistance to Megamelus scutellaris from attack by Kalopolynema ema. Adopting weed management tactics that emphasize efficacy, sustainability and environmental benevolence argue for the use of classical biological control when appropriate. USDA National Institute of Food and AgricultureUnited States Department of Agriculture (USDA) [201667013-24860] USDA National Institute of Food and Agriculture, Grant/Award Number: 201667013-24860 Public domain – authored by a U.S. government employee

Published
2020

45. Looking for biocontrol in Asia for invasive plants

Author

Smith, Melissa C.

Subjects
Pests -- Biological control, Agricultural industry, Biotechnology industry, Business
Abstract

Predator and prey co-evolve as part of a natural 'arms race' for survival. When plants and animals are moved from their native habitats to new locales where they have no [...]

Published
2016

48. Upward gaze direction increases center of pressure displacement and decreases squat stability.

Author

Kohn, Michael A, Smith, Melissa C, and Goble, Daniel J

Subjects
GAZE, RESISTANCE training, STATISTICAL significance
Abstract

The purpose of this study was to quantify differences in barbell back squat stability as a function of gaze direction. Sixteen female subjects who were experienced in barbell back squatting performed six sets of three squat repetitions, randomized according to three gaze directions (DOWN, STRAIGHT, and UP). Stability was determined based on the displacement of center of pressure (COP) during each squat repetition, measured using a low-cost, portable force plate. Multiple t-tests with statistical significance set at p < 0.05 were conducted between gaze directions and revealed significant differences in stability between the DOWN versus UP conditions. Specifically, DOWN had the smallest COP displacement, with significantly larger COP displacement in the UP condition (p = 0.02). There were no significant differences when DOWN or UP was compared to STRAIGHT (p = 0.22 and p = 0.28 respectively). From a practical standpoint, these results suggest that having a downward gaze direction during barbell back squat will maximize stability and reduce the likelihood of injury. [ABSTRACT FROM AUTHOR]

Published
2022
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49. Addressing noise in co-expression network construction.

Author

Burns, Joshua J R, Shealy, Benjamin T, Greer, Mitchell S, Hadish, John A, McGowan, Matthew T, Biggs, Tyler, Smith, Melissa C, Feltus, F Alex, and Ficklin, Stephen P

Subjects
GENE regulatory networks, CONFOUNDING variables, NOISE, GENE expression, FALSE positive error
Abstract

Gene co-expression networks (GCNs) provide multiple benefits to molecular research including hypothesis generation and biomarker discovery. Transcriptome profiles serve as input for GCN construction and are derived from increasingly larger studies with samples across multiple experimental conditions, treatments, time points, genotypes, etc. Such experiments with larger numbers of variables confound discovery of true network edges, exclude edges and inhibit discovery of context (or condition) specific network edges. To demonstrate this problem, a 475-sample dataset is used to show that up to 97% of GCN edges can be misleading because correlations are false or incorrect. False and incorrect correlations can occur when tests are applied without ensuring assumptions are met, and pairwise gene expression may not meet test assumptions if the expression of at least one gene in the pairwise comparison is a function of multiple confounding variables. The 'one-size-fits-all' approach to GCN construction is therefore problematic for large, multivariable datasets. Recently, the Knowledge Independent Network Construction toolkit has been used in multiple studies to provide a dynamic approach to GCN construction that ensures statistical tests meet assumptions and confounding variables are addressed. Additionally, it can associate experimental context for each edge of the network resulting in context-specific GCNs (csGCNs). To help researchers recognize such challenges in GCN construction, and the creation of csGCNs, we provide a review of the workflow. [ABSTRACT FROM AUTHOR]

Published
2022
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50. The Biology of Casmara subagronoma (Lepidoptera: Oecophoridae), a Stem-Boring Moth of Rhodomyrtus tomentosa (Myrtaceae): Descriptions of the Previously Unknown Adult Female and Immature Stages, and Its Potential as a Biological Control Candidate

Author

Wineriter-Wright, Susan A., primary, Smith, Melissa C., additional, Metz, Mark A., additional, Makinson, Jeffrey R., additional, Brown, Bradley T., additional, Purcell, Matthew F., additional, Barr, Kane L., additional, and Pratt, Paul D., additional

Published
2020
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