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The considerable body of data available for evaluating biometric recognition systems in Research and Development (R\&D) environments has contributed to the increasingly common problem of target performance mismatch. Biometric algorithms are frequently tested against data that may not reflect the real world applications they target. From a Testing and Evaluation (T\&E) standpoint, this domain mismatch causes difficulty assessing when improvements in State-of-the-Art (SOTA) research actually translate to improved applied outcomes. This problem can be addressed with thoughtful preparation of data and experimental methods to reflect specific use-cases and scenarios. To that end, this paper evaluates research solutions for identifying individuals at ranges and altitudes, which could support various application areas such as counterterrorism, protection of critical infrastructure facilities, military force protection, and border security. We address challenges including image quality issues and reliance on face recognition as the sole biometric modality. By fusing face and body features, we propose developing robust biometric systems for effective long-range identification from both the ground and steep pitch angles. Preliminary results show promising progress in whole-body recognition. This paper presents these early findings and discusses potential future directions for advancing long-range biometric identification systems based on mission-driven metrics.

This paper examines covariate effects on fused whole body biometrics performance in the IARPA BRIAR dataset, specifically focusing on UAV platforms, elevated positions, and distances up to 1000 meters. The dataset includes outdoor videos compared with indoor images and controlled gait recordings. Normalized raw fusion scores relate directly to predicted false accept rates (FAR), offering an intuitive means for interpreting model results. A linear model is developed to predict biometric algorithm scores, analyzing their performance to identify the most influential covariates on accuracy at altitude and range. Weather factors like temperature, wind speed, solar loading, and turbulence are also investigated in this analysis. The study found that resolution and camera distance best predicted accuracy and findings can guide future research and development efforts in long-range/elevated/UAV biometrics and support the creation of more reliable and robust systems for national security and other critical domains.

Trellis' Student Financial Wellness Survey (SFWS) seeks to document the financial well-being of post-secondary students and provide institutions with a detailed profile of the financial issues their students face, some of which could potentially impact their success in college. This report details aggregated findings for the 89 colleges and universities across the country that participated in the fall 2022 implementation of the survey. [For "Student Financial Wellness Survey: Fall 2021 Semester Results. National Aggregate Report," see ED625724.]

The Student Financial Wellness Survey (SFWS) provides a snapshot of student financial wellbeing during the fall of 2021. Over 700,000 students were surveyed from 104 schools in 25 states and 63,751 students responded. During this period Federal stimulus funding helped stabilize finances for many students, but many still struggled to make ends meet. This report is meant to amplify the lived experience of these students. Understanding these experiences will allow college administrators and policymakers to better serve students, enabling them to reach their academic potential. The Fall 2021 SFWS questionnaire went through a thorough reassessment. To reduce survey burden, many questions were removed and others rewritten based on feedback from students, school administrators, and various experts in collegiate finances. This resulted in an instrument that was easier for students to understand and quicker to complete. The report is divided into the following sections: (1) Paying for College; (2) Student Credit Card Use and Risky Borrowing; (3) Financial Decision-Making Factors and Financial Behaviors; (4) Student Financial Security; (5) Basic Needs Security; (6) Students Who Are Parents; and (7) Mental Health Challenges.

Postsecondary students face a myriad of complex financial decisions and tasks, such as financing their education, choosing a major with a return on investment, budgeting, and crafting repayment strategies for credit card and student loan debt(s). With respect to student loans, many borrowers feel "burdened by the repayment process" and express regret for having borrowed so much. Understanding students' utilization and attitudes when receiving assistance with financial decision-making can serve as a first step when evaluating on- and off-campus financial services. This brief examines data from 45,624 students at 104 higher education institutions that participated in Trellis' Fall 2021 Student Financial Wellness Survey (SFWS) with a focus on students' comfort discussing financial situations with others and students' monetary discussions with financial aid advisors. [For "Student Financial Wellness Survey: Fall 2021 Semester Results. National Aggregate Report," see ED625724.]

It is well demonstrated that basic needs insecurities (i.e., food insecurity, housing insecurity, or homelessness) are more pronounced among certain groups of historically marginalized students, including students of color at traditionally under-resourced Historically Black Colleges and University (HBCUs), Predominantly Black Institutions (PBIs), and Hispanic Serving Institutions (HSIs). This brief examines data from 14,117 students attending 26 MSIs from Trellis' Fall 2021 Student Financial Wellness Survey (SFWS)--including 19 HSIs; five HBCUs; and three PBIs. With nearly two-thirds of students from these MSIs facing one or more forms of basic needs insecurity, it is critical for college administrators, the broader campus community, and policymakers to have a full understanding of these students' lived experiences.

This brief examines data from 19,934 students with self-reported educational debt in Trellis' Fall 2021 Student Financial Wellness Survey (SFWS), with special emphasis on their financial security and attitudes toward their loans, debt, and debt forgiveness. Nearly two-thirds of borrowers had accumulated more debt than expected, and 36 percent did not feel that their total debt was manageable. These students were also uncertain of their ability to eventually repay their loans--only 28 percent were confident they would be able to pay off the debt acquired while in school.

When students are food insecure, they often encounter adverse health, social, and academic outcomes, including harm to cognitive functions, mental health, and academic performance. Additionally, compared to food secure peers, students struggling with food insecurity are 43 percent less likely to graduate from college with a two- or four-year degree. With recent estimates of food insecurity among postsecondary students as high as 45 percent, many colleges and universities have responded by implementing on-campus food distribution centers (i.e., food pantries or closets) to better assist students struggling to meet their basic needs. These pantries often provide more than food to students, including personal hygiene products, toiletries, diapers, personnel trained to assist applicants with applying to the Supplemental Nutrition Assistance Program (SNAP) and other benefit programs, and more. Ninety-one of the 104 colleges and universities who participated in Trellis' Fall 2021 Student Financial Wellness Survey (SFWS) had at least one food pantry available to students. To better understand student awareness and utilization of this resource, this brief examines data from 49,946 students attending a school with one or more food pantries (as of November 1, 2021) and who responded to the corresponding question: "Does your school have a food pantry or food closet on campus?"

College students are unable to meet their basic needs (food, housing, utilities, etc.) at greater rates than the general adult population of the United States. When students lack enough to eat and/or are unable to secure safe, stable housing, they often face tolls to their psychological and physical health. Additionally, compared to peers who are basic needs secure, students struggling with basic needs insecurities often encounter adverse academic outcomes, including poor grades, enrollment disruptions, academic dismissal, or suspension, etc. Even in a non-pandemic academic setting, these challenges can severely complicate a student's ability to focus on schoolwork and being academically successful. Unfortunately, research suggests the pandemic has exacerbated instances of basic needs insecurity among college students, especially among vulnerable groups. This brief examines data from 20,095 students with one or more forms of basic needs insecurity (BNI) (i.e., food insecurity, housing insecurity, homelessness) from Trellis' Fall 2020 Student Financial Wellness Survey (SFWS) with a special lens on their mental health. With more than one-third of all postsecondary students experiencing mental health challenges before the onset of the COVID-19 pandemic, and many more living with food and housing insecurity, having a full understanding of students' lived experiences during the past year is critical. [For "Student Financial Wellness Survey: Fall 2020 Semester Results. National Aggregate Report," see ED616426.]

As the COVID-19 pandemic has proved, maintaining student financial wellness is key to keeping students enrolled. The Student Financial Wellness Survey (SFWS) was administered a year and a half into the pandemic, during a time when vaccinations were allowing schools to return to in-person learning, but the pandemic was continuing to have a strong impact. The SFWS is a self-reported, online survey that documents the financial well-being and student success indicators of postsecondary students across the nation. The Fall 2021 SFWS included many questions from prior implementations as well as new questions on topics including emergency aid, food pantry awareness and use, mental health, and expectations of student loan forgiveness. This report details findings from the Fall 2021 implementation at 24 Texas community colleges.

Individual participant data (IPD) meta-analyses of randomised trials are considered a reliable way to assess participant-level treatment effect modifiers but may not make the best use of the available data. Traditionally, effect modifiers are explored one covariate at a time, which gives rise to the possibility that evidence of treatment-covariate interaction may be due to confounding from a different, related covariate. We aimed to evaluate current practice when estimating treatment-covariate interactions in IPD meta-analysis, specifically focusing on involvement of additional covariates in the models. We reviewed 100 IPD meta-analyses of randomised trials, published between 2015 and 2020, that assessed at least one treatment-covariate interaction. We identified four approaches to handling additional covariates: (1) Single interaction model (unadjusted): No additional covariates included (57/100 studies); (2) Single interaction model (adjusted): Adjustment for the main effect of at least one additional covariate (35/100); (3) Multiple interactions model: Adjustment for at least one two-way interaction between treatment and an additional covariate (3/100); and (4) Three-way interaction model: Three-way interaction formed between treatment, the additional covariate and the potential effect modifier (5/100). IPD is not being utilised to its fullest extent. In an exemplar dataset, we demonstrate how these approaches can lead to different conclusions. Researchers should adjust for additional covariates when estimating interactions in IPD meta-analysis providing they adjust their main effects, which is already widely recommended. Further, they should consider whether more complex approaches could provide better information on who might benefit most from treatments, improving patient choice and treatment policy and practice., Comment: 23 pages, 3 figures, 4 tables

As the COVID-19 pandemic is proving, maintaining student financial wellness is key to keeping students enrolled. The Student Financial Wellness Survey (SFWS) was open from October 2020 to November 2020. The COVID-19 pandemic and the associated economic crisis were prominent concerns for students as they navigated a mostly remote learning experience for a fall semester unlike any before. Respondents seemed eager to share their experiences, resulting in the highest response rate for the SFWS to date--9.7 percent. Trellis collected information about the entire population surveyed--i.e., gender, age, enrollment intensity--and used it to weigh the results, reflecting the total populations of the participating institutions. There may be lingering response biases based on characteristics uncaptured in the study, some of which may be meaningful when interpreting the results. Sixty-two institutions participated in the survey--46 two-year institutions, 12 public four-years, and 4 private four-years. While the SFWS is not nationally representative, the participating institutions represent a wide range of institutions from 13 different states. In partnership with participating institutions, web-based survey invitations were sent to 394,240 undergraduate students, of whom 37,936 responded. A detailed description of survey characteristics, comparison groups, tests for representativeness, methodology, and other research notes can be found in the appendices. [For the 2019 report, see ED610029.]

Face recognition technology has advanced significantly in recent years due largely to the availability of large and increasingly complex training datasets for use in deep learning models. These datasets, however, typically comprise images scraped from news sites or social media platforms and, therefore, have limited utility in more advanced security, forensics, and military applications. These applications require lower resolution, longer ranges, and elevated viewpoints. To meet these critical needs, we collected and curated the first and second subsets of a large multi-modal biometric dataset designed for use in the research and development (R&D) of biometric recognition technologies under extremely challenging conditions. Thus far, the dataset includes more than 350,000 still images and over 1,300 hours of video footage of approximately 1,000 subjects. To collect this data, we used Nikon DSLR cameras, a variety of commercial surveillance cameras, specialized long-rage R&D cameras, and Group 1 and Group 2 UAV platforms. The goal is to support the development of algorithms capable of accurately recognizing people at ranges up to 1,000 m and from high angles of elevation. These advances will include improvements to the state of the art in face recognition and will support new research in the area of whole-body recognition using methods based on gait and anthropometry. This paper describes methods used to collect and curate the dataset, and the dataset's characteristics at the current stage.

Recent global growth in the interest of smart cities has led to trillions of dollars of investment toward research and development. These connected cities have the potential to create a symbiosis of technology and society and revolutionize the cost of living, safety, ecological sustainability, and quality of life of societies on a world-wide scale. Some key components of the smart city construct are connected smart grids, self-driving cars, federated learning systems, smart utilities, large-scale public transit, and proactive surveillance systems. While exciting in prospect, these technologies and their subsequent integration cannot be attempted without addressing the potential societal impacts of such a high degree of automation and data sharing. Additionally, the feasibility of coordinating so many disparate tasks will require a fast, extensible, unifying framework. To that end, we propose FaRO2, a completely reimagined successor to FaRO1, built from the ground up. FaRO2 affords all of the same functionality as its predecessor, serving as a unified biometric API harness that allows for seamless evaluation, deployment, and simple pipeline creation for heterogeneous biometric software. FaRO2 additionally provides a fully declarative capability for defining and coordinating custom machine learning and sensor pipelines, allowing the distribution of processes across otherwise incompatible hardware and networks. FaRO2 ultimately provides a way to quickly configure, hot-swap, and expand large coordinated or federated systems online without interruptions for maintenance. Because much of the data collected in a smart city contains Personally Identifying Information (PII), FaRO2 also provides built-in tools and layers to ensure secure and encrypted streaming, storage, and access of PII data across distributed systems.