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Liger is an adaptive optics (AO) fed imager and integral field spectrograph (IFS) designed to take advantage of the Keck All-sky Precision Adaptive-optics (KAPA) upgrade to the Keck I telescope. Liger adapts the design of the InfraRed Imaging Spectrograph (IRIS) for the Thirty Meter Telescope (TMT) to Keck by implementing a new imager and re-imaging optics. The performance of the imager is critical as it sequentially feeds the spectrograph and contains essential components such as the pupil wheel, filter wheel, and pupil viewing camera. We present the design and structural analysis of the Liger imager optical assembly including static, modal, and thermal simulations. We present the fabrication as well as the full assembly and characterization plan. The imager will be assembled bench-top in a clean room utilizing a coordinate-measuring machine (CMM) for warm alignment. To ensure optimal performance, the imager will be characterized in a test cryostat before integration with the full Liger instrument. This comprehensive approach to characterization ensures the precision and reliability of the imager, enhancing the observational capabilities of Liger and W.M. Keck Observatory.

Uncertainty is inherent to most data, including vector field data, yet it is often omitted in visualizations and representations. Effective uncertainty visualization can enhance the understanding and interpretability of vector field data. For instance, in the context of severe weather events such as hurricanes and wildfires, effective uncertainty visualization can provide crucial insights about fire spread or hurricane behavior and aid in resource management and risk mitigation. Glyphs are commonly used for representing vector uncertainty but are often limited to 2D. In this work, we present a glyph-based technique for accurately representing 3D vector uncertainty and a comprehensive framework for visualization, exploration, and analysis using our new glyphs. We employ hurricane and wildfire examples to demonstrate the efficacy of our glyph design and visualization tool in conveying vector field uncertainty.

Isosurface visualization is fundamental for exploring and analyzing 3D volumetric data. Marching cubes (MC) algorithms with linear interpolation are commonly used for isosurface extraction and visualization. Although linear interpolation is easy to implement, it has limitations when the underlying data is complex and high-order, which is the case for most real-world data. Linear interpolation can output vertices at the wrong location. Its inability to deal with sharp features and features smaller than grid cells can lead to an incorrect isosurface with holes and broken pieces. Despite these limitations, isosurface visualizations typically do not include insight into the spatial location and the magnitude of these errors. We utilize high-order interpolation methods with MC algorithms and interactive visualization to highlight these uncertainties. Our visualization tool helps identify the regions of high interpolation errors. It also allows users to query local areas for details and compare the differences between isosurfaces from different interpolation methods. In addition, we employ high-order methods to identify and reconstruct possible features that linear methods cannot detect. We showcase how our visualization tool helps explore and understand the extracted isosurface errors through synthetic and real-world data.

This paper presents a novel end-to-end framework for closed-form computation and visualization of critical point uncertainty in 2D uncertain scalar fields. Critical points are fundamental topological descriptors used in the visualization and analysis of scalar fields. The uncertainty inherent in data (e.g., observational and experimental data, approximations in simulations, and compression), however, creates uncertainty regarding critical point positions. Uncertainty in critical point positions, therefore, cannot be ignored, given their impact on downstream data analysis tasks. In this work, we study uncertainty in critical points as a function of uncertainty in data modeled with probability distributions. Although Monte Carlo (MC) sampling techniques have been used in prior studies to quantify critical point uncertainty, they are often expensive and are infrequently used in production-quality visualization software. We, therefore, propose a new end-to-end framework to address these challenges that comprises a threefold contribution. First, we derive the critical point uncertainty in closed form, which is more accurate and efficient than the conventional MC sampling methods. Specifically, we provide the closed-form and semianalytical (a mix of closed-form and MC methods) solutions for parametric (e.g., uniform, Epanechnikov) and nonparametric models (e.g., histograms) with finite support. Second, we accelerate critical point probability computations using a parallel implementation with the VTK-m library, which is platform portable. Finally, we demonstrate the integration of our implementation with the ParaView software system to demonstrate near-real-time results for real datasets., Comment: 9 pages paper + 2 page references, 8 figures, IEEE VIS 2024 paper to be published as a special issue of IEEE Transactions on Visualization and Computer Graphics (TVCG)

Large Language Models (LLMs) trained on code are revolutionizing the software development process. Increasingly, code LLMs are being integrated into software development environments to improve the productivity of human programmers, and LLM-based agents are beginning to show promise for handling complex tasks autonomously. Realizing the full potential of code LLMs requires a wide range of capabilities, including code generation, fixing bugs, explaining and documenting code, maintaining repositories, and more. In this work, we introduce the Granite series of decoder-only code models for code generative tasks, trained with code written in 116 programming languages. The Granite Code models family consists of models ranging in size from 3 to 34 billion parameters, suitable for applications ranging from complex application modernization tasks to on-device memory-constrained use cases. Evaluation on a comprehensive set of tasks demonstrates that Granite Code models consistently reaches state-of-the-art performance among available open-source code LLMs. The Granite Code model family was optimized for enterprise software development workflows and performs well across a range of coding tasks (e.g. code generation, fixing and explanation), making it a versatile all around code model. We release all our Granite Code models under an Apache 2.0 license for both research and commercial use., Comment: Corresponding Authors: Rameswar Panda, Ruchir Puri; Equal Contributors: Mayank Mishra, Matt Stallone, Gaoyuan Zhang

In this paper, we present a comprehensive evaluation to establish a robust and efficient framework for Lagrangian-based particle tracing using deep neural networks (DNNs). Han et al. (2021) first proposed a DNN-based approach to learn Lagrangian representations and demonstrated accurate particle tracing for an analytic 2D flow field. In this paper, we extend and build upon this prior work in significant ways. First, we evaluate the performance of DNN models to accurately trace particles in various settings, including 2D and 3D time-varying flow fields, flow fields from multiple applications, flow fields with varying complexity, as well as structured and unstructured input data. Second, we conduct an empirical study to inform best practices with respect to particle tracing model architectures, activation functions, and training data structures. Third, we conduct a comparative evaluation of prior techniques that employ flow maps as input for exploratory flow visualization. Specifically, we compare our extended model against its predecessor by Han et al. (2021), as well as the conventional approach that uses triangulation and Barycentric coordinate interpolation. Finally, we consider the integration and adaptation of our particle tracing model with different viewers. We provide an interactive web-based visualization interface by leveraging the efficiencies of our framework, and perform high-fidelity interactive visualization by integrating it with an OSPRay-based viewer. Overall, our experiments demonstrate that using a trained DNN model to predict new particle trajectories requires a low memory footprint and results in rapid inference. Following best practices for large 3D datasets, our deep learning approach using GPUs for inference is shown to require approximately 46 times less memory while being more than 400 times faster than the conventional methods., Comment: Accepted by Pacific Vis 2024

The latest generation of high-resolution spectrograph instruments on 10m-class telescopes continue to pursue challenging science cases. Consequently, ever more precise calibration methods are necessary to enable trail-blazing science methodology. We present the High-resolution Infrared SPectrograph for Exoplanet Characterization (HISPEC) Calibration Unit (CAL), designed to facilitate challenging science cases such as Doppler imaging of exoplanet atmospheres, precision radial velocity, and high-contrast high-resolution spectroscopy of nearby exoplanets. CAL builds upon the heritage from the pathfinder instrument Keck Planet Imager and Characterizer (KPIC) and utilizes four near-infrared (NIR) light sources encoded with wavelength information that are coupled into single-mode fibers. They can be used synchronously during science observations or asynchronously during daytime calibrations. A hollow cathode lamp (HCL) and a series of gas absorption cells provide absolute calibration from 0.98 {\mu}m to 2.5 {\mu}m. A laser frequency comb (LFC) provides stable, time-independent wavelength information during observation and CAL implements a lower finesse astro-etalon as a backup for the LFC. Design lessons from instrumentation like HISPEC will serve to inform the requirements for similar instruments for the ELTs in the future., Comment: 16 pages, 14 figures

High resolution tactile sensing has great potential in autonomous mobile robotics, particularly for legged robots. One particular area where it has significant promise is the traversal of challenging, varied terrain. Depending on whether an environment is slippery, soft, hard or dry, a robot must adapt its method of locomotion accordingly. Currently many multi-legged robots, such as Boston Dynamic's Spot robot, have preset gaits for different surface types, but struggle over terrains where the surface type changes frequently. Being able to automatically detect changes within an environment would allow a robot to autonomously adjust its method of locomotion to better suit conditions, without requiring a human user to manually set the change in surface type. In this paper we report on the first detailed investigation of the properties of a particular bio-inspired tactile sensor, the TacTip, to test its suitability for this kind of automatic detection of surface conditions. We explored different processing techniques and a regression model, using a custom made rig for data collection to determine how a robot could sense directional and general force on the sensor in a variety of conditions. This allowed us to successfully demonstrate how the sensor can be used to distinguish between soft, hard, dry and (wet) slippery surfaces. We further explored a neural model to classify specific surface textures. Pin movement (the movement of optical markers within the sensor) was key to sensing this information, and all models relied on some form of temporal information. Our final trained models could successfully determine the direction the sensor is heading in, the amount of force acting on it, and determine differences in the surface texture such as Lego vs smooth hard surface, or concrete vs smooth hard surface., Comment: 6 pages + references, under review for ICRA 2024

Purpose: To identify ocular hypertension (OHT) subtypes with different trends of visual field (VF) progression based on unsupervised machine learning and to discover factors associated with fast VF progression. Participants: A total of 3133 eyes of 1568 ocular hypertension treatment study (OHTS) participants with at least five follow-up VF tests were included in the study. Methods: We used a latent class mixed model (LCMM) to identify OHT subtypes using standard automated perimetry (SAP) mean deviation (MD) trajectories. We characterized the subtypes based on demographic, clinical, ocular, and VF factors at the baseline. We then identified factors driving fast VF progression using generalized estimating equation (GEE) and justified findings qualitatively and quantitatively. Results: The LCMM model discovered four clusters (subtypes) of eyes with different trajectories of MD worsening. The number of eyes in clusters were 794 (25%), 1675 (54%), 531 (17%) and 133 (4%). We labelled the clusters as Improvers, Stables, Slow progressors, and Fast progressors based on their mean of MD decline, which were 0.08, -0.06, -0.21, and -0.45 dB/year, respectively. Eyes with fast VF progression had higher baseline age, intraocular pressure (IOP), pattern standard deviation (PSD) and refractive error (RE), but lower central corneal thickness (CCT). Fast progression was associated with calcium channel blockers, being male, heart disease history, diabetes history, African American race, stroke history, and migraine headaches.

In inverse problems, one attempts to infer spatially variable functions from indirect measurements of a system. To practitioners of inverse problems, the concept of "information" is familiar when discussing key questions such as which parts of the function can be inferred accurately and which cannot. For example, it is generally understood that we can identify system parameters accurately only close to detectors, or along ray paths between sources and detectors, because we have "the most information" for these places. Although referenced in many publications, the "information" that is invoked in such contexts is not a well understood and clearly defined quantity. Herein, we present a definition of information density that is based on the variance of coefficients as derived from a Bayesian reformulation of the inverse problem. We then discuss three areas in which this information density can be useful in practical algorithms for the solution of inverse problems, and illustrate the usefulness in one of these areas -- how to choose the discretization mesh for the function to be reconstructed -- using numerical experiments.

Liger is a next-generation near-infrared imager and integral field spectrograph (IFS) planned for the W.M. Keck Observatory. Liger is designed to take advantage of improved adaptive optics (AO) from the Keck All-Sky Precision Adaptive Optics (KAPA) upgrade currently underway. Liger operates at 0.84-2.45 $\mu$m with spectral resolving powers of R$\sim$4,000-10,000. Liger makes use of a sequential imager and spectrograph design allowing for simultaneous observations. There are two spectrograph modes: a lenslet with high spatial sampling of 14 and 31 mas, and a slicer with 75 and 150 mas sampling with an expanded field of view. Two pick-off mirrors near the imager detector direct light to these two IFS channels. We present the design and structural analysis for the imager detector and IFS pick-off mirror mounting assembly that will be used to align and maintain stability throughout its operation. A piezoelectric actuator will be used to step through $\rm3\,mm$ of travel during alignment of the instrument to determine the optimal focus for both the detector and pick-off mirrors which will be locked in place during normal operation. We will demonstrate that the design can withstand the required gravitational and shipping loads and can be aligned within the positioning tolerances for the optics., Comment: 10 pages, 7 figures, 3 tables

Liger is a second generation near-infrared imager and integral field spectrograph (IFS) for the W. M. Keck Observatory that will utilize the capabilities of the Keck All-sky Precision Adaptive-optics (KAPA) system. Liger operates at a wavelength range of 0.81 {\mu}m - 2.45 {\mu}m and utilizes a slicer and a lenslet array IFS with varying spatial plate scales and fields of view resulting in hundreds of modes available to the astronomer. Because of the high level of complexity in the raw data formats for the slicer and lenslet IFS modes, Liger must be designed in conjunction with a Data Reduction System (DRS) which will reduce data from the instrument in real-time and deliver science-ready data products to the observer. The DRS will reduce raw imager and IFS frames from the readout system and provide 2D and 3D data products via custom quick-look visualization tools suited to the presentation of IFS data. The DRS will provide the reduced data to the Keck Observatory Archive (KOA) and will be available to astronomers for offline post-processing of observer data. We present an initial design for the DRS and define the interfaces between observatory and instrument software systems., Comment: 12 pages, 4 figures, 2 tables

Liger is an adaptive optics (AO) fed imager and integral field spectrograph (IFS) designed to take advantage of the Keck All-sky Precision Adaptive-optics (KAPA) upgrade for the W.M. Keck Observatory. We present the design and analysis of the imager optical assembly including the spectrograph Re-Imaging Optics (RIO) which transfers the beam path from the imager focal plane to the IFS slicer module and lenslet array. Each imager component and the first two RIO mechanisms are assembled and individually aligned on the same optical plate. Baffling suppresses background radiation and scattered light, and a pupil viewing camera allows the imager detector to focus on an image of the telescope pupil. The optical plate mounts on an adapter frame for alignment of the overall system. The imager and RIO will be characterized in a cryogenic test chamber before installation in the final science cryostat., Comment: 8 pages, 5 figures

Visualization and analysis of multivariate data and their uncertainty are top research challenges in data visualization. Constructing fiber surfaces is a popular technique for multivariate data visualization that generalizes the idea of level-set visualization for univariate data to multivariate data. In this paper, we present a statistical framework to quantify positional probabilities of fibers extracted from uncertain bivariate fields. Specifically, we extend the state-of-the-art Gaussian models of uncertainty for bivariate data to other parametric distributions (e.g., uniform and Epanechnikov) and more general nonparametric probability distributions (e.g., histograms and kernel density estimation) and derive corresponding spatial probabilities of fibers. In our proposed framework, we leverage Green's theorem for closed-form computation of fiber probabilities when bivariate data are assumed to have independent parametric and nonparametric noise. Additionally, we present a nonparametric approach combined with numerical integration to study the positional probability of fibers when bivariate data are assumed to have correlated noise. For uncertainty analysis, we visualize the derived probability volumes for fibers via volume rendering and extracting level sets based on probability thresholds. We present the utility of our proposed techniques via experiments on synthetic and simulation datasets., Comment: 9 pages paper + 2 page references, 10 figures, IEEE VIS 2022 paper to be published as a special issue of IEEE Transactions on Visualization and Computer Graphics (TVCG)

Visualizing the uncertainty of ensemble simulations is challenging due to the large size and multivariate and temporal features of ensemble data sets. One popular approach to studying the uncertainty of ensembles is analyzing the positional uncertainty of the level sets. Probabilistic marching cubes is a technique that performs Monte Carlo sampling of multivariate Gaussian noise distributions for positional uncertainty visualization of level sets. However, the technique suffers from high computational time, making interactive visualization and analysis impossible to achieve. This paper introduces a deep-learning-based approach to learning the level-set uncertainty for two-dimensional ensemble data with a multivariate Gaussian noise assumption. We train the model using the first few time steps from time-varying ensemble data in our workflow. We demonstrate that our trained model accurately infers uncertainty in level sets for new time steps and is up to 170X faster than that of the original probabilistic model with serial computation and 10X faster than that of the original parallel computation., Comment: 5 pages, IEEE Vis 2022 Short Paper

Time-varying vector fields produced by computational fluid dynamics simulations are often prohibitively large and pose challenges for accurate interactive analysis and exploration. To address these challenges, reduced Lagrangian representations have been increasingly researched as a means to improve scientific time-varying vector field exploration capabilities. This paper presents a novel deep neural network-based particle tracing method to explore time-varying vector fields represented by Lagrangian flow maps. In our workflow, in situ processing is first utilized to extract Lagrangian flow maps, and deep neural networks then use the extracted data to learn flow field behavior. Using a trained model to predict new particle trajectories offers a fixed small memory footprint and fast inference. To demonstrate and evaluate the proposed method, we perform an in-depth study of performance using a well-known analytical data set, the Double Gyre. Our study considers two flow map extraction strategies as well as the impact of the number of training samples and integration durations on efficacy, evaluates multiple sampling options for training and testing and informs hyperparameter settings. Overall, we find our method requires a fixed memory footprint of 10.5 MB to encode a Lagrangian representation of a time-varying vector field while maintaining accuracy. For post hoc analysis, loading the trained model costs only two seconds, significantly reducing the burden of I/O when reading data for visualization. Moreover, our parallel implementation can infer one hundred locations for each of two thousand new pathlines across the entire temporal resolution in 1.3 seconds using one NVIDIA Titan RTX GPU., Comment: The paper has been accepted to publish by Journal of Flow Visualization and Image Processing

The InfraRed Imaging Spectrograph (IRIS) is the first-light client instrument for the Narrow Field Infrared Adaptive Optics System (NFIRAOS) on the Thirty Meter Telescope (TMT). Now approaching the end of its final design phase, we provide an overview of the instrument control software. The design is challenging since IRIS has interfaces with many systems at different stages of development (e.g., NFIRAOS, telescope control system, observatory sequencers), and will be built using the newly-developed TMT Common Software (CSW), which provides framework code (Java/Scala), and services (e.g., commands, telemetry). Lower-level software will be written in a combination of Java and C/C++ to communicate with hardware, such as motion controllers and infrared detectors. The overall architecture and philosophy of the IRIS software is presented, as well as a summary of the individual software components and their interactions with other systems., Comment: 20 pages, 9 figures, SPIE (2020) 11452-28

Marching squares (MS) and marching cubes (MC) are widely used algorithms for level-set visualization of scientific data. In this paper, we address the challenge of uncertainty visualization of the topology cases of the MS and MC algorithms for uncertain scalar field data sampled on a uniform grid. The visualization of the MS and MC topology cases for uncertain data is challenging due to their exponential nature and the possibility of multiple topology cases per cell of a grid. We propose the topology case count and entropy-based techniques for quantifying uncertainty in the topology cases of the MS and MC algorithms when noise in data is modeled with probability distributions. We demonstrate the applicability of our techniques for independent and correlated uncertainty assumptions. We visualize the quantified topological uncertainty via color mapping proportional to uncertainty, as well as with interactive probability queries in the MS case and entropy isosurfaces in the MC case. We demonstrate the utility of our uncertainty quantification framework in identifying the isovalues exhibiting relatively high topological uncertainty. We illustrate the effectiveness of our techniques via results on synthetic, simulation, and hixel datasets.

Analyzing the effects of ocean eddies is important in oceanology for gaining insights into transport of energy and biogeochemical particles. We present an application of statistical visualization algorithms for the analysis of the Red Sea eddy simulation ensemble. Specifically, we demonstrate the applications of statistical volume rendering and statistical Morse complex summary maps to a velocity magnitude field for studying the eddy positions in the flow dataset. In statistical volume rendering, we model per-voxel data uncertainty using noise models, such as parametric and nonparametric, and study the propagation of uncertainty into the volume rendering pipeline. In the statistical Morse complex summary maps, we derive histograms charactering uncertainty of gradient flow destinations to understand Morse complex topological variations across the ensemble. We demonstrate the utility of our statistical visualizations for an effective analysis of the potential eddy positions and their spatial uncertainty.

The InfraRed Imaging Spectrograph (IRIS) is a first-light instrument for the Thirty Meter Telescope (TMT) that will be used to sample the corrected adaptive optics field by the Narrow-Field Infrared Adaptive Optics System (NFIRAOS) with a near-infrared (0.8 - 2.4 $\mu$m) imaging camera and integral field spectrograph. To better understand IRIS science specifications we use the IRIS data simulator to characterize relative photometric precision and accuracy across the IRIS imaging camera 34"x34" field of view. Because the Point Spread Function (PSF) varies due to the effects of anisoplanatism, we use the Anisoplanatic and Instrumental Reconstruction of Off-axis PSFs for AO (AIROPA) software package to conduct photometric measurements on simulated frames using PSF-fitting as the PSF varies in single-source, binary, and crowded field use cases. We report photometric performance of the imaging camera as a function of the instrumental noise properties including dark current and read noise. Using the same methods, we conduct comparisons of photometric performance with reconstructed PSFs, in order to test the veracity of the current PSF-Reconstruction algorithms for IRIS/TMT., Comment: SPIE 2020, 16 pages, 10 figures, 4 tables