This Looks Like Those: Illuminating Prototypical Concepts Using Multiple Visualizations

We present ProtoConcepts, a method for interpretable image classification combining deep learning and case-based reasoning using prototypical parts. Existing work in prototype-based image classification uses a ``this looks like that'' reasoning process, which dissects a test image by finding prototypical parts and combining evidence from these prototypes to make a final classification. However, all of the existing prototypical part-based image classifiers provide only one-to-one comparisons, where a single training image patch serves as a prototype to compare with a part of our test image. With these single-image comparisons, it can often be difficult to identify the underlying concept being compared (e.g., ``is it comparing the color or the shape?''). Our proposed method modifies the architecture of prototype-based networks to instead learn prototypical concepts which are visualized using multiple image patches. Having multiple visualizations of the same prototype allows us to more easily identify the concept captured by that prototype (e.g., ``the test image and the related training patches are all the same shade of blue''), and allows our model to create richer, more interpretable visual explanations. Our experiments show that our ``this looks like those'' reasoning process can be applied as a modification to a wide range of existing prototypical image classification networks while achieving comparable accuracy on benchmark datasets

Single View Refractive Index Tomography with Neural Fields

Refractive Index Tomography is an inverse problem in which we seek to reconstruct a scene's 3D refractive field from 2D projected image measurements. The refractive field is not visible itself, but instead affects how the path of a light ray is continuously curved as it travels through space. Refractive fields appear across a wide variety of scientific applications, from translucent cell samples in microscopy to fields of dark matter bending light from faraway galaxies. This problem poses a unique challenge because the refractive field directly affects the path that light takes, making its recovery a non-linear problem. In addition, in contrast with traditional tomography, we seek to recover the refractive field using a projected image from only a single viewpoint by leveraging knowledge of light sources scattered throughout the medium. In this work, we introduce a method that uses a coordinate-based neural network to model the underlying continuous refractive field in a scene. We then use explicit modeling of rays' 3D spatial curvature to optimize the parameters of this network, reconstructing refractive fields with an analysis-by-synthesis approach. The efficacy of our approach is demonstrated by recovering refractive fields in simulation, and analyzing how recovery is affected by the light source distribution. We then test our method on a simulated dark matter mapping problem, where we recover the refractive field underlying a realistic simulated dark matter distribution

Estimated Muscle Loads During Squat Exercise in Microgravity Conditions

Loss of muscle mass in microgravity is one of the primary factors limiting long-term space flight. NASA researchers have developed a number of exercise devices to address this problem. The most recent is the Advanced Resistive Exercise Device (ARED), which is currently used by astronauts on the International Space Station (ISS) to emulate typical free-weight exercises in microgravity. ARED exercise on the ISS is intended to reproduce Earth-level muscle loads, but the actual muscle loads produced remain unknown as they cannot currently be measured directly. In this study we estimated muscle loads experienced during squat exercise on ARED in microgravity conditions representative of Mars, the moon, and the ISS. The estimates were generated using a subject-specific musculoskeletal computer model and ARED exercise data collected on Earth. The results provide insight into the capabilities and limitations of the ARED machine

IRIS-EDA: An Integrated RNA-Seq Interpretation System for Gene Expression Data Analysis

Next-Generation Sequencing has made available substantial amounts of large-scale Omics data, providing unprecedented opportunities to understand complex biological systems. Specifically, the value of RNA-Sequencing (RNA-Seq) data has been confirmed in inferring how gene regulatory systems will respond under various conditions (bulk data) or cell types (single-cell data). RNA-Seq can generate genome-scale gene expression profiles that can be further analyzed using correlation analysis, co-expression analysis, clustering, differential gene expression (DGE), among many other studies. While these analyses can provide invaluable information related to gene expression, integration and interpretation of the results can prove challenging. Here we present a tool called IRIS-EDA, which is a Shiny web server for expression data analysis. It provides a straightforward and user-friendly platform for performing numerous computational analyses on user-provided RNA-Seq or Single-cell RNA-Seq (scRNA-Seq) data. Specifically, three commonly used R packages (edgeR, DESeq2, and limma) are implemented in the DGE analysis with seven unique experimental design functionalities, including a user-specified design matrix option. Seven discovery-driven methods and tools (correlation analysis, heatmap, clustering, biclustering, Principal Component Analysis (PCA), Multidimensional Scaling (MDS), and t-distributed Stochastic Neighbor Embedding (t-SNE)) are provided for gene expression exploration which is useful for designing experimental hypotheses and determining key factors for comprehensive DGE analysis. Furthermore, this platform integrates seven visualization tools in a highly interactive manner, for improved interpretation of the analyses. It is noteworthy that, for the first time, IRIS-EDA provides a framework to expedite submission of data and results to NCBI’s Gene Expression Omnibus following the FAIR (Findable, Accessible, Interoperable and Reusable) Data Principles. IRIS-EDA is freely available at http://bmbl.sdstate.edu/IRIS/

Analysis of the RNA Binding Specificity Landscape of C5 Protein Reveals Structure and Sequence Preferences that Direct RNase P Specificity.

RNA binding proteins (RBPs) are typically involved in non-equilibrium cellular processes, and specificity can arise from differences in ground state, transition state, or product states of the binding reactions for alternative RNAs. Here, we use high-throughput methods to measure and analyze the RNA association kinetics and equilibrium binding affinity for all possible sequence combinations in the precursor tRNA binding site of C5, the essential protein subunit of Escherichia coli RNase P. The results show that the RNA sequence specificity of C5 arises due to favorable RNA-protein interactions that stabilize the transition state for association and bound enzyme-substrate complex. Specificity is further impacted by unfavorable RNA structure involving the C5 binding site in the ground state. The results illustrate a comprehensive quantitative approach for analysis of RNA binding specificity, and show how both RNA structure and sequence preferences of an essential protein subunit direct the specificity of a ribonucleoprotein enzyme

AI Enabled Maneuver Identification via the Maneuver Identification Challenge

Artificial intelligence (AI) has enormous potential to improve Air Force pilot training by providing actionable feedback to pilot trainees on the quality of their maneuvers and enabling instructor-less flying familiarization for early-stage trainees in low-cost simulators. Historically, AI challenges consisting of data, problem descriptions, and example code have been critical to fueling AI breakthroughs. The Department of the Air Force-Massachusetts Institute of Technology AI Accelerator (DAF-MIT AI Accelerator) developed such an AI challenge using real-world Air Force flight simulator data. The Maneuver ID challenge assembled thousands of virtual reality simulator flight recordings collected by actual Air Force student pilots at Pilot Training Next (PTN). This dataset has been publicly released at Maneuver-ID.mit.edu and represents the first of its kind public release of USAF flight training data. Using this dataset, we have applied a variety of AI methods to separate "good" vs "bad" simulator data and categorize and characterize maneuvers. These data, algorithms, and software are being released as baselines of model performance for others to build upon to enable the AI ecosystem for flight simulator training.Comment: 10 pages, 7 figures, 4 tables, accepted to and presented at I/ITSE

Possibilities of coal–gas substitution in East Asia: A comparison among China, Japan and South Korea

Natural gas is currently playing an increasingly significant role in low carbon development, as it provides a credible pathway to meet rising energy demand while emitting fewer greenhouse gases than from using other fossil fuels such as coal and oil. In this paper, a log linear trans-log production function model is established to investigate inter-fuel elasticity of substitution between coal, oil, natural gas and electricity in China, Japan and South Korea, respectively. In order to overcome the problem of multicollinearity, the ridge regression approach is therefore adopted to estimate the parameters of the function. Results show elasticity estimates of both coal–gas substitution and coal–electricity substitution to be positive over 1985–2012, suggesting that these two energy input pairs are substitutes at least to some extent. It also reveals that relatively higher substitution possibilities between coal and natural gas, and less opportunities to substitute coal with other fuels in China. In addition, the model results also suggest the elasticities of coal–gas substitution in China are much larger than that in Japan and South Korea, indicating there is higher possibilities of coal–gas substitution in China