Booz Allen Hamilton (United States)

ABSTRACT Objective This study longitudinally assessed how mobility across geographic regions and work–family conflict influenced spouse employment trajectories and perceptions of career hindrance. Background The unemployment rate for U.S. military spouses has remained 21% since 2015 with specific aspects of military life, such as moving, reported as negatively influencing their careers. Methods Survey data were used for multivariable regression models examining employment trajectories between baseline and follow‐up ( N = 3,149) and perceptions of career hindrance at follow‐up ( N = 2,766). Employment trajectories were categorized as follows: consistent, newly employed, and newly unemployed. Career hindrance was assessed continuously. Results Spouses who moved across geographic regions were more likely to be newly unemployed and less likely to be newly employed at follow‐up than to have consistent employment. Greater career hindrance was reported at follow‐up by spouses who moved across regions and experienced increased work–family conflict. Conclusion Findings suggest mobility was highly predictive of spouses becoming newly unemployed and reporting higher rates of career hindrance. Spouses' reports of increased military‐related work–family conflict also resulted in increased career hindrance. Implications Programs are available to assist military spouses with career development; however, employers should consider ways to help spouses maintain employment after military moves.

<div class="section abstract"><div class="htmlview paragraph">Our research focuses on developing a novel loss function that significantly improves object matching accuracy in multi-robot systems, a critical capability for Safety, Security, and Rescue Robotics (SSRR) applications. By enhancing the consistency and reliability of object identification across multiple viewpoints, our approach ensures a comprehensive understanding of environments with complex layouts and interlinked infrastructure components. We utilize ZED 2i cameras to capture diverse scenarios, demonstrating that our proposed loss function, inspired by the DETR framework, outperforms traditional methods in both accuracy and efficiency. The function’s ability to adapt to dynamic and high-risk environments, such as disaster response and critical infrastructure inspection, is further validated through extensive experiments, showing superior performance in real-time decision-making and operational effectiveness. This work not only advances the state of the art in SSRR but also addresses the practical needs of end-users, providing a more robust tool for mission-critical operations.</div></div>

A lack of accessible data has historically restricted malware analysis research, and practitioners have relied heavily on datasets provided by industry sources to advance. Existing public datasets are limited by narrow scope - most include files targeting a single platform, have labels supporting just one type of malware classification task, and make no effort to capture the evasive files that make malware detection difficult in practice. We present EMBER2024, a new dataset that enables holistic evaluation of malware classifiers. Created in collaboration with the authors of EMBER2017 and EMBER2018, the EMBER2024 dataset includes hashes, metadata, feature vectors, and labels for more than 3.2 million files from six file formats. Our dataset supports the training and evaluation of machine learning models on seven malware classification tasks, including malware detection, malware family classification, and malware behavior identification. EMBER2024 is the first to include a collection of malicious files that initially went undetected by a set of antivirus products, creating a "challenge" set to assess classifier performance against evasive malware. This work also introduces EMBER feature version 3, with added support for several new feature types. We are releasing the EMBER2024 dataset to promote reproducibility and empower researchers in the pursuit of new malware research topics.

Verifying entanglement with experimental measurements requires that we take the limitations of experimental techniques into account, while still proving that the data obtained could not have been generated from a classical source. In the energy-time degree of freedom, this challenge is exacerbated because realistic high-resolution frequency measurements are obtained as a function of light passing through arbitrary filters positioned at uneven intervals. In this work, we show how the data gathered from these kinds of measurements can be used to fully certify the degree of energy and timing correlations needed to certify energy-time entanglement without having to make special assumptions about the state or the measurement device. We accomplish this by showing how to construct a continuous-variable probability density from the data that can closely estimate, but never overestimate the correlations (and entanglement) actually present in the system, and note that these methods are applicable to all continuous-variable degrees of freedom (e.g., spatial, field quadratures, etc.). We illustrate the feasibility of these methods using frequency and timing correlations obtainable from photon pairs in spontaneous parametric down-conversion.

Projected Gradient Descent (PGD) under the L∞ ball has become one of the defacto methods used in adversarial robustness evaluation for computer vision (CV) due to its reliability and efficacy, making a strong and easy-to-implement iterative baseline. However, PGD is computationally demanding to apply, especially when using thousands of iterations is the current best-practice recommendation to generate an adversarial example for a single image. In this work, we introduce a simple novel method for early termination of PGD based on cycle detection by exploiting the geometry of how PGD is implemented in practice and show that it can produce large speedup factors while providing the exact same estimate of model robustness as standard PGD. This method substantially speeds up PGD without sacrificing any attack strength, enabling evaluations of robustness that were previously computationally intractable.

As machine learning algorithms increasingly influence decisions in our daily lives, understanding these models becomes critical. This paper explores the philosophical and practical elements of model interpretability, focusing on dense neural networks, which are often perceived as “black boxes.” LIME, SHAP, Partial Dependence Plots, Counterfactual Explanations, and Surrogate Models are applied to a dense neural network trained on the California Housing Dataset to evaluate their effectiveness in explaining the model's behavior. These methods provide insights into feature importance, global versus local model behavior, and potential decision-making logic. However, many aspects of neural networks remain unknown, such as complex feature interactions, internal decision pathways, and the specific roles of hidden layers. The paper highlights the persistent unknowns within the black box of neural networks and outlines potential avenues for future research. Ultimately, enhancing the understanding of these models is key to the trustability and usability of these large-scale machine learning models.

The newly established United States Greenhouse Gas Center (U.S. GHG Center) is a multi-agency partnership between the National Aeronautics and Space Administration (NASA), the Environmental Protection Agency (EPA), the National Oceanic and Atmospheric Administration (NOAA) and the National Institute of Standards and Technology (NIST) that aims to accelerate the production and delivery of actionable, trusted greenhouse gas (GHG) information from the federal government and non-public sector to a variety of users through a coordinated data system, reflecting transparency and open source science principles in both data and methods. The US GHG Center acts as an enabler of collaboration with networks of interagency, international, intergovernmental and private sector partners to increase confidence in setting, assessing, and meeting climate change mitigation goals, with a preliminary focus on carbon dioxide and methane. The US GHG Center is also a critical element in the implementation of the “National Strategy to Advance an Integrated US Greenhouse Gas Measurement, Monitoring, and Information System”.  Initial focus areas include 1) Gridded anthropogenic greenhouse gas emissions, 2) Natural sources and sinks, and 3) New Observations for tracking large emission events. The US GHG Center web portal includes a prototype data catalogue, exploratory data analysis capabilities, a collaborative science environment for data analysis and exploration, as well as an interactive visual interface for storytelling. Examples of products currently available on the GHG Center portal include methane and carbon dioxide concentration anomalies and emissions from airborne and space-based instruments, including from NASA’s Earth Surface Mineral Dust Source Investigation (EMIT) imaging spectrometer in orbit on the International Space Station, EPA’s gridded U.S. anthropogenic methane greenhouse gas inventory data, gridding methodologies and visualizations, NOAA’s Observation Package (ObsPack) data products that bring together atmospheric greenhouse gas observations from a variety of sampling platforms, as well as multi-model land flux and ecosystem exchange estimates. 

<title>ABSTRACT</title> <p>One of the biggest challenges in developing TRA is the determination of the critical technology elements (CTEs). This paper outlines a systematic process used to identify all potential technologies developed by the contractors during the Technology Development (TD) Phase and applies criteria for selection of CTEs. To reduce the subjectivity in the assessment, the relevant technical requirements for each CTE that are important to the customers will be established. These requirements must be met to demonstrate the level of maturity required before entering the Engineering & Manufacturing Development (EMD) Phase. The maturity of a CTE cannot be evaluated in isolation. The paper also includes other system requirements that the CTEs must satisfy before the overall system can be evaluated. A major defense ACAT vehicle acquisition program, Joint Light Tactical Vehicle (JLTV), was used to demonstrate the TRA process in preparation for the MS-B Review with the Assistant Secretary of the Army for Acquisition, Logistics, and Technology (ASA(ALT)) and the Assistant Secretary of Defense for Research & Engineering (ASD(R&E)) within the Office of the Secretary of Defense (OSD).</p>