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In order to serve the American public and the missions of its diverse agencies, the United Stated Federal Government utilizes the Federal Acquisition Regulation (FAR) (https://www.acquisition.gov/) to purchase necessary goods and services from private industry. Integral to FAR is the publishing of Requests for Information (RFIs) and Requests for Proposals (RFPs). As a consultancy within the Federal Information Technology (IT) space, INTEGRITYOne Partners, Inc. (IOP) has responded to dozens of RFIs and RFPs over its nearly twenty-five years in existence. These RFI and RFP responses represent a tremendous amount of IOP’s intellectual capital and reflect the firm’s extensive IT and Program Management expertise. Often containing solution templates, IOP proprietary practices, and detailed capability statements, portions of these assets can be re-used or modified to respond to multiple government solicitations. To keep up with the growing database of these assets, IOP must transition their process of retrieving relevant documents from manual to automated.

Fires and burning are the chief causes of particulate matter (PM2.5), a key measurement of air quality in communities and cities worldwide. This work develops a live fire tracking platform to show active reported fires from over twenty cities in the U.S., as well as predict their smoke paths and impacts on the air quality of regions within their range. Specifically, our close to real-time tracking and predictions culminates in a digital twin to protect public health and inform the public of fire and air quality risk. This tool tracks fire incidents in real-time, utilizes the 3D building footprints of Austin to simulate smoke outputs, and predicts fire incident smoke falloffs within the complex city environment. Results from this study include a complete fire and smoke digital twin model for Austin. We work in cooperation with the City of Austin Fire Department to ensure the accuracy of our forecast and also show that air quality sensor density within our cities cannot validate urban fire presence. We additionally release code and methodology to replicate these results for any city in the world. This work paves the path for similar digital twin models to be developed and deployed to better protect the health and safety of citizens., Comment: 8 pages, 8 figures, conference

Language models are not accurate in numerical problems. Their architecture does not allow for anything less than a probabilistic next word. This paper introduces ComputeGPT: an approach of creating a chat model able to answer computational problems through running on-demand code. ComputeGPT converts each question to relevant code, runs the code, and returns the computed answer as part of the chat. We combine this approach with a local browser-based Python interpretation and fine-tuned prompts in order to achieve state-of-the-art efficiency on numerical problems and provide a suitable front-end and safe environment for the code to be executed in., Comment: 12 pages, 2 figures

Self Driving Labs (SDLs) that combine automation of experimental procedures with autonomous decision making are gaining popularity as a means of increasing the throughput of scientific workflows. The task of identifying quantities of supplied colored pigments that match a target color, the color matching problem, provides a simple and flexible SDL test case, as it requires experiment proposal, sample creation, and sample analysis, three common components in autonomous discovery applications. We present a robotic solution to the color matching problem that allows for fully autonomous execution of a color matching protocol. Our solution leverages the WEI science factory platform to enable portability across different robotic hardware, the use of alternative optimization methods for continuous refinement, and automated publication of results for experiment tracking and post-hoc analysis.

Doping-induced solubility control (DISC) patterning is a recently developed technique that uses the change in polymer solubility upon doping, along with an optical dedoping process, to achieve high-resolution optical patterning. DISC patterning can produce features smaller than predicted by the diffraction limit; however, no mechanism has been proposed to explain such high resolution. Here, we use diffraction to spatially modulate the light intensity and determine the dissolution rate, revealing a superlinear dependence on light intensity. This rate law is independent of wavelength, indicating that patterning resolution is not dominated by an optical dedoping reaction, as was previously proposed. Instead we show here that the optical patterning mechanism is primarily controlled by the thermal profile generated by the laser. To quantify this effect, the thermal profile and dissolution rate are modeled using a finite-element model and compared against patterned line cross sections as a function of wavelength, laser intensity, and dwell time. Our model reveals that although the laser-generated thermal profile is broadened considerably beyond the profile of the laser, the highly temperature dependent dissolution rate results in selective dissolution near the peak of the thermal profile. Therefore, the key factor in achieving super-resolution patterning is a strongly temperature dependent dissolution rate, a common feature of many polymers. In addition to suggesting several routes to improved resolution, our model also demonstrates that doping is not required for optical patterning of conjugated polymers, as was previously believed. Instead, we demonstrate that superlinear resolution optical patterning should be attainable in any conjugated polymer simply by tuning the solvent quality during patterning, thus extending the applicability of our method to a wide class of materials. We demonstrate the generality of photothermal patterning by writing sub-400 nm features into

Satellite imagery analytics have numerous human development and disaster response applications, particularly when time series methods are involved. For example, quantifying population statistics is fundamental to 67 of the 231 United Nations Sustainable Development Goals Indicators, but the World Bank estimates that over 100 countries currently lack effective Civil Registration systems. To help address this deficit and develop novel computer vision methods for time series data, we present the Multi-Temporal Urban Development SpaceNet (MUDS, also known as SpaceNet 7) dataset. This open source dataset consists of medium resolution (4.0m) satellite imagery mosaics, which includes 24 images (one per month) covering >100 unique geographies, and comprises >40,000 km2 of imagery and exhaustive polygon labels of building footprints therein, totaling over 11M individual annotations. Each building is assigned a unique identifier (i.e. address), which permits tracking of individual objects over time. Label fidelity exceeds image resolution; this "omniscient labeling" is a unique feature of the dataset, and enables surprisingly precise algorithmic models to be crafted. We demonstrate methods to track building footprint construction (or demolition) over time, thereby directly assessing urbanization. Performance is measured with the newly developed SpaceNet Change and Object Tracking (SCOT) metric, which quantifies both object tracking as well as change detection. We demonstrate that despite the moderate resolution of the data, we are able to track individual building identifiers over time. This task has broad implications for disaster preparedness, the environment, infrastructure development, and epidemic prevention., Comment: 8 pages, 10 figures, 3 tables

In hopes of boosting achievement for students with low math skills, a large number of schools and districts have employed the policy of “double dose” math courses (an additional math instruction period during the school day) in middle or early high school. We know very little about the effectiveness of this intervention and whether previously-reported impacts are generalizable to different settings and implementation structures. In the current dissertation, I use a mixed methods approach across three studies to focus on an individual school district that implemented, discontinued, and then re-implemented double dose math courses as an intervention for struggling middle school students. In Chapter 1, I analyze the effectiveness of the original implementation of seventh-grade support courses on math achievement for low performing math students and find no significant impacts for students very close to the enrollment cutoff but modest, positive intent-to-treat effects on standardized test scores across all eligible students. I also find increased achievement gains for treatment students with the lowest prior math achievement and English Language Learners. In Chapter 2, I develop a case study on the decision-making processes of the school district administrators within this time period using research on organizational sensemaking (Vaughan, 1996; Weick, 1995) and collective sensemaking in educational settings (Coburn, 2001) as a theoretical framework. Decision making regarding the policy was shown to be sensitive to changing state policy and curricular standards, student test scores, and pressures from resources and organizational structure. In Chapter 3, I document the changes to the intervention’s implementation structure and test the impact of the new version. I find modest, positive benefits on standardized test scores for treated students (especially intervention students with the highest previous math achievement) but no impacts on math course grades or failure rat

RarePlanes is a unique open-source machine learning dataset that incorporates both real and synthetically generated satellite imagery. The RarePlanes dataset specifically focuses on the value of synthetic data to aid computer vision algorithms in their ability to automatically detect aircraft and their attributes in satellite imagery. Although other synthetic/real combination datasets exist, RarePlanes is the largest openly-available very-high resolution dataset built to test the value of synthetic data from an overhead perspective. Previous research has shown that synthetic data can reduce the amount of real training data needed and potentially improve performance for many tasks in the computer vision domain. The real portion of the dataset consists of 253 Maxar WorldView-3 satellite scenes spanning 112 locations and 2,142 km^2 with 14,700 hand-annotated aircraft. The accompanying synthetic dataset is generated via AI.Reverie's simulation platform and features 50,000 synthetic satellite images simulating a total area of 9331.2 km^2 with ~630,000 aircraft annotations. Both the real and synthetically generated aircraft feature 10 fine grain attributes including: aircraft length, wingspan, wing-shape, wing-position, wingspan class, propulsion, number of engines, number of vertical-stabilizers, presence of canards, and aircraft role. Finally, we conduct extensive experiments to evaluate the real and synthetic datasets and compare performances. By doing so, we show the value of synthetic data for the task of detecting and classifying aircraft from an overhead perspective., Comment: To appear in WACV 2021 - 11 pages

Within the remote sensing domain, a diverse set of acquisition modalities exist, each with their own unique strengths and weaknesses. Yet, most of the current literature and open datasets only deal with electro-optical (optical) data for different detection and segmentation tasks at high spatial resolutions. optical data is often the preferred choice for geospatial applications, but requires clear skies and little cloud cover to work well. Conversely, Synthetic Aperture Radar (SAR) sensors have the unique capability to penetrate clouds and collect during all weather, day and night conditions. Consequently, SAR data are particularly valuable in the quest to aid disaster response, when weather and cloud cover can obstruct traditional optical sensors. Despite all of these advantages, there is little open data available to researchers to explore the effectiveness of SAR for such applications, particularly at very-high spatial resolutions, i.e. <1m Ground Sample Distance (GSD). To address this problem, we present an open Multi-Sensor All Weather Mapping (MSAW) dataset and challenge, which features two collection modalities (both SAR and optical). The dataset and challenge focus on mapping and building footprint extraction using a combination of these data sources. MSAW covers 120 km^2 over multiple overlapping collects and is annotated with over 48,000 unique building footprints labels, enabling the creation and evaluation of mapping algorithms for multi-modal data. We present a baseline and benchmark for building footprint extraction with SAR data and find that state-of-the-art segmentation models pre-trained on optical data, and then trained on SAR (F1 score of 0.21) outperform those trained on SAR data alone (F1 score of 0.135)., Comment: To appear in CVPR EarthVision Proceedings, 10 pages, 7 figures

Identification of road networks and optimal routes directly from remote sensing is of critical importance to a broad array of humanitarian and commercial applications. Yet while identification of road pixels has been attempted before, estimation of route travel times from overhead imagery remains a novel problem, particularly for off-nadir overhead imagery. To this end, we extract road networks with travel time estimates from the SpaceNet MVOI dataset. Utilizing the CRESIv2 framework, we demonstrate the ability to extract road networks in various observation angles and quantify performance at 27 unique nadir angles with the graph-theoretic APLS_length and APLS_time metrics. A minimal gap of 0.03 between APLS_length and APLS_time scores indicates that our approach yields speed limits and travel times with very high fidelity. We also explore the utility of incorporating all available angles during model training, and find a peak score of APLS_time = 0.56. The combined model exhibits greatly improved robustness over angle-specific models, despite the very different appearance of road networks at extremely oblique off-nadir angles versus images captured from directly overhead., Comment: 4 pages, 5 figures. To appear at the 2020 IEEE International Geoscience and Remote Sensing Symposium

We describe a parallel algorithm that computes persistent homology, an algebraic descriptor of a filtered topological space. Our algorithm is distinguished by operating on a spatial decomposition of the domain, as opposed to a decomposition with respect to the filtration. We rely on a classical construction, called the Mayer--Vietoris blowup complex, to glue global topological information about a space from its disjoint subsets. We introduce an efficient algorithm to perform this gluing operation, which may be of independent interest, and describe how to process the domain hierarchically. We report on a set of experiments that help assess the strengths and identify the limitations of our method.

Although adolescents experience an array of out-of-school time (OST) settings, research has primarily focused on these settings in isolation. This study examined time in four OST settings (unsupervised time with peers, paid employment, sports, and nonsports organized activities) in relation to adolescent functioning at age 15 and the end of high school. Individual fixed effects analyses provided a more rigorous control of selection into OST activities by controlling for time-invariant observable and unobservable characteristics. More unsupervised time with peers predicted increases in risky and externalizing behaviors, whereas increases in paid employment predicted gains in work orientation and self-identity. Time in organized sports was associated with increased positive self-identity, highlighting the value of expanding consideration of multiple OST contexts and selection effects.

Eversion refers to an organism’s ability to turn itself inside out. For example, a sea cucumber can eject its internal organs to distract predators, sacrificing vital functions for survival. Similarly, introverts temporarily evert their personalities to function in extroverted contexts. This performance is simultaneously action and reaction, assertion and retreat. This transformation can be physically, emotionally, and mentally taxing. Cultural, educational, and professional environments do not often provide introverts the sanctuary necessary to revitalize themselves. Everted Sanctuaries VI communicates about the complex needs of introverts. A transformed book functions as a metaphor for the often uncomfortable process of becoming temporarily extroverted. Ubiquitous exteriors part to reveal internal intricacies—beautiful, but unintended consequences of the contortions necessary to fit in. Everted Sanctuaries VI explores the boundaries between speaking out and blending in. For introverts, speaking out is both necessity and liability. Introverts are required to transcend their own self-silencing in cultures preoccupied with speaking. Words may be methodically processed, slowly contemplated, carefully considered, or explosively released. The act of expressing oneself can be exhilarating, empowering and freeing but also isolating, frightening, exhausting, and damaging. Whether cloistered in the distance of writing or birthed through the raw immediacy of speaking, the velocity, volume, and frequency of words shape our individual and collective identities.

Recently a large number of graph separator problems have been proven to be \textsc{NP-Hard}. Amazingly we have found that $\alpha$-Subgraph-Balanced-Vertex-Separator, an important variant, has been overlooked. In this work ``Yet Another Graph Partitioning Problem is NP-Hard" we present the surprising result that $\alpha$-Subgraph-Balanced-Vertex-Separator is $NP$-Hard. This is despite the fact that the constraints of our new problem are harder to satisfy than the original problem.

In this work we investigate the parallel computation of homology using the Mayer-Vietoris principle. We present a two stage approach for parallelizing persistence. In the first stage, we produce a cover of the input cell complex by overlapping subspaces. In the second stage, we use this cover to build the Mayer-Vietoris blowup complex, a topological space, which organizes the various subspaces needed for employing the Mayer-Vietoris principle. Next, we compute the homology of each subspace in the blowup complex in parallel and then glue these results together in serial. We show how to use the persistence algorithm to organize these computations. In the first stage, any algorithm can be used to produce a cover of the input complex. We describe an algorithm for producing a cover of a space with a simple structure and bounded overlap based on graph partitions. Additionally, we present a simplistic model for the problem of finding covers appropriate for parallel algorithms and show that finding such covers is NP-Hard. Finally, we present a second parallel homology algorithm. This algorithm avoids the explicit construction of the blowup complex saving space. We implement our algorithms for multicore computers, and compare them against each other as well as existing serial and parallel algorithms with a suite of experiments. We achieve roughly 8x speedup of the homology computations on a 10-dimensional complex with about 46 million simplices using 11 cores., Comment: For source code and data sets, see http://www.ctl.appliedtopology.org

During the last decade, pseudospectral methods for optimal control, the focus of this tutorial session, have been rapidly developed as a powerful tool to enable new applications that were previously considered impossible due to the complicated nature of these problems. The purpose of this tutorial section is to introduce this advanced technology to a wider community of control system engineering. We bring in experts of pseudospectral methods from academia, industry, and military and DoD to present topics covering a large spectrum of pseudospectral methods, including the theoretical foundation, numerical techniques of pseudospectral optimal control, and military/industry applications.