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This report presents selected findings from the 2020/22 Beginning Postsecondary Students Longitudinal Study (BPS:20/22), a study conducted by the National Center for Education Statistics (NCES) within the U.S. Department of Education. The findings in this report are based on data about a nationally representative sample of all first-time beginning students (FTBs) who entered postsecondary education for the first time during the 2019-20 academic year. BPS:20/22 is the first follow-up of a cohort of FTBs originally identified during the 2019-20 National Postsecondary Student Aid Study (NPSAS:20) and covers 3 years of students' experiences, from their first year of postsecondary education in the 2019-20 academic year through the 2021-22 academic year. The BPS:20/22 sample consisted of approximately 37,330 total students, representing the approximately 3.3 million undergraduates who were first-time postsecondary students enrolled in the 2019-20 academic year. Data from BPS can address questions about students' postsecondary enrollment patterns, continued enrollment in college, highest postsecondary credential earned, social and academic experiences and activities, and employment history. To facilitate analyses of student choices and postsecondary enrollment and credential attainment, the BPS:20/22 survey collected information on teacher effectiveness, as well as frequency and types of communication and interactions with family and friends, faculty members, and other students, which were also included in the NPSAS:20 survey. Additionally, in response to the COVID-19 pandemic, several survey questions asked respondents to report on employment and education disruptions and personal experiences "as a result of the coronavirus pandemic" in spring 2020 and in the 2020-21 academic year. Respondents indicated their perception of whether an experience was the result of the coronavirus pandemic. The report provides information on first-time postsecondary students' attainment and persistence rates at any institution 3 years after they began postsecondary education, along with their education and personal experiences during the COVID-19 pandemic. In this report, attainment represents the students' highest postsecondary credential earned, whereas persistence describes enrollment during the 2021-22 academic year for students who had not earned a postsecondary credential. As a result, students who earned a postsecondary credential and enrolled for additional postsecondary education are accounted for in attainment rates. It is important to note that BPS:20/22 data reflect first-time postsecondary students' experiences over 3 academic years, when many students are still working toward a postsecondary credential. The 2022/25 Beginning Postsecondary Students Longitudinal Study, which is designed to follow up with students 6 years after they began postsecondary education, will provide more comprehensive data about postsecondary attainment. This First Look shows some of the ways that BPS:20/22 data can be used. Selected findings are not intended to emphasize any particular issue, and the report does not discuss all the information presented in the tables. Readers should not infer causality from the data in this report. Although many of the factors discussed are related to each other, this report does not explore these relationships. The student- and institution-level factors in this report are just a few of the several hundred that will be available in the BPS:20/22 data.

This paper evaluates the suitability of Apache Arrow, Parquet, and ORC as formats for subsumption in an analytical DBMS. We systematically identify and explore the high-level features that are important to support efficient querying in modern OLAP DBMSs and evaluate the ability of each format to support these features. We find that each format has trade-offs that make it more or less suitable for use as a format in a DBMS and identify opportunities to more holistically co-design a unified in-memory and on-disk data representation. Notably, for certain popular machine learning tasks, none of these formats perform optimally, highlighting significant opportunities for advancing format design. Our hope is that this study can be used as a guide for system developers designing and using these formats, as well as provide the community with directions to pursue for improving these common open formats.

Understanding of the formation and evolution of the Solar System requires understanding key and common materials found on and in planetary bodies. Mineral mixing and its implications on planetary body formation is a topic of high interest to the planetary science community. Previous work establishes a case for the use of Optical PhotoThermal InfraRed (O-PTIR) in planetary science and introduces and demonstrates the technique's capability to study planetary materials. In this paper, we performed a measurement campaign on granular materials relevant to planetary science, such as minerals found in lunar and martian soils. These laboratory measurements serve to start a database of O-PTIR measurements. We also present FTIR absorption measurements of the materials we observed in O-PTIR for comparison purposes. We find that the O-PTIR technique suffers from granular orientation effects similar to other IR techniques, but in most cases, is is directly comparable to commonly used absorption spectroscopy techniques. We conclude that O-PTIR would be an excellent tool for the purpose of planetary material identification during in-situ investigations on regolith and bedrock surfaces., Comment: This paper follows, "Photothermal Spectroscopy for Planetary Sciences: Mid-IR Absorption Made Easy," (arXiv:2409.11626) and is part 2 of a series. This work catalogues O-PTIR Mid-IR measurements of various planetary materials. This is a pre-print

Hypothesis: Pre-operative cochlear implant (CI) electrode array (EL) insertion plans created by automated image analysis methods can improve positioning of slim pre-curved EL. Background: This study represents the first evaluation of a system for patient-customized EL insertion planning for a slim pre-curved EL. Methods: Twenty-one temporal bone specimens were divided into experimental and control groups and underwent cochlear implantation. For the control group, the surgeon performed a traditional insertion without an insertion plan. For the experimental group, customized insertion plans guided entry site, trajectory, curl direction, and base insertion depth. An additional 35 clinical insertions from the same surgeon were analyzed, 7 of which were conducted using the insertion plans. EL positioning was analyzed using post-operative imaging auto-segmentation techniques, allowing measurement of angular insertion depth (AID), mean modiolar distance (MMD), and scalar position. Results: In the cadaveric temporal bones, 3 scalar translocations, including 2 foldovers, occurred in 14 control group insertions. In the clinical insertions, translocations occurred in 2 of 28 control cases. No translocations or folds occurred in the 7 experimental temporal bone and the 7 experimental clinical insertions. Among the non-translocated cases, overall AID and MMD were 401(41) degrees and 0.34(0.13) mm for the control insertions. AID and MMD for the experimental insertions were 424(43) degrees and 0.34(0.09) mm overall and were 432(19) and 0.30(0.07) mm for cases where the planned insertion depth was achieved. Conclusions: Trends toward improved EL positioning within scala tympani were observed when EL insertion plans are used. Variability in MMD was significantly reduced (0.07mm vs 0.13 mm, p=0.039) when the planned depth was achieved., Comment: First two listed authors are co-first authors

Natural scenes are of key interest for visual perception. Previous work on natural scenes has frequently focused on collections of discrete images with considerable physical differences from stimulus to stimulus. For many purposes it would, however, be desirable to have sets of natural images that vary smoothly along a continuum (for example in order to measure quantitative properties such as thresholds or precisions). This problem has typically been addressed by morphing a source into a target image. However, this approach yields transitions between images that primarily follow their low-level physical features and that can be semantically unclear or ambiguous. Here, in contrast, we used a different approach (Stable Diffusion XL) to synthesise a custom stimulus set of photorealistic images that are characterized by gradual transitions where each image is a clearly interpretable but unique exemplar from the same category. We developed natural scene stimulus sets from six categories with 18 objects each. For each object we generated 10 graded variants that are ordered along a perceptual continuum. We validated the image set psychophysically in a large sample of participants, ensuring that stimuli for each exemplar have varying levels of perceptual confusability. This image set is of interest for studies on visual perception, attention and short- and long-term memory., Comment: 29 pages, 7 Figures, 5 tables

The understanding of the formation and evolution of the solar system still has many unanswered questions. Formation of solids in the solar system, mineral and organic mixing, and planetary body creation are all topics of interest to the community. Studying these phenomena is often performed through observations, remote sensing, and in-situ analysis, but there are limitations to the methods. Limitations such as IR diffraction limits, spatial resolution issues, and spectral resolution issues can prevent detection of organics, detection and identification of cellular structures, and the disentangling of granular mixtures. Optical-PhotoThermal InfraRed (O-PTIR) spectroscopy is a relatively new method of spectroscopy currently used in fields other than planetary sciences. O-PTIR is a non-destructive, highly repeatable, and fast form of measurement capable of reducing these limitations. Using a dual laser system with an IR source tuned to the mid-IR wavelength we performed laboratory O-PTIR measurements to compare O-PTIR data to existing IR absorption data and laboratory FTIR measurements for planetary materials. We do this for the purpose of introducing O-PTIR to the planetary science community. The technique featured here would serve to better measurements of planetary bodies during in-situ analysis. We find that, unlike other fields where O-PTIR produces almost one-to-one measurements with IR absorption measurements of the same material, granular materials relevant to planetary science do not. However, we do find that the materials compared were significantly close and O-PTIR was still capable of identifying materials relevant to planetary science., Comment: This paper is an introductory of O-PTIR spectroscopy into planetary science. It is also going to be part 1 of a series. This is a pre-print

Complex video queries can be answered by decomposing them into modular subtasks. However, existing video data management systems assume the existence of predefined modules for each subtask. We introduce VOCAL-UDF, a novel self-enhancing system that supports compositional queries over videos without the need for predefined modules. VOCAL-UDF automatically identifies and constructs missing modules and encapsulates them as user-defined functions (UDFs), thus expanding its querying capabilities. To achieve this, we formulate a unified UDF model that leverages large language models (LLMs) to aid in new UDF generation. VOCAL-UDF handles a wide range of concepts by supporting both program-based UDFs (i.e., Python functions generated by LLMs) and distilled-model UDFs (lightweight vision models distilled from strong pretrained models). To resolve the inherent ambiguity in user intent, VOCAL-UDF generates multiple candidate UDFs and uses active learning to efficiently select the best one. With the self-enhancing capability, VOCAL-UDF significantly improves query performance across three video datasets.

Advances in fifth-generation (5G) networks enable unprecedented reliability, speed, and connectivity compared to previous mobile networks. These advancements can revolutionize various sectors by supporting applications requiring real-time data processing. However, the rapid deployment and integration of 5G networks bring security concerns that must be addressed to operate these infrastructures safely. This paper reviews penetration testing approaches for identifying security vulnerabilities in 5G networks. Penetration testing is an ethical hacking technique used to simulate a network's security posture in the event of cyberattacks. This review highlights the capabilities, advantages, and limitations of recent 5G-targeting security tools for penetration testing. It examines ways adversaries exploit vulnerabilities in 5G networks, covering tactics and strategies targeted at 5G features. A key topic explored is the comparison of penetration testing methods for 5G and earlier generations. The article delves into the unique characteristics of 5G, including massive MIMO, edge computing, and network slicing, and how these aspects require new penetration testing methods. Understanding these differences helps develop more effective security solutions tailored to 5G networks. Our research also indicates that 5G penetration testing should use a multithreaded approach for addressing current security challenges. Furthermore, this paper includes case studies illustrating practical challenges and limitations in real-world applications of penetration testing in 5G networks. A comparative analysis of penetration testing tools for 5G networks highlights their effectiveness in mitigating vulnerabilities, emphasizing the need for advanced security measures against evolving cyber threats in 5G deployment.

Modern cloud has turned data services into easily accessible commodities. With just a few clicks, users are now able to access a catalog of data processing systems for a wide range of tasks. However, the cloud brings in both complexity and opportunity. While cloud users can quickly start an application by using various data services, it can be difficult to configure and optimize these services to gain the most value from them. For cloud providers, managing every aspect of an ever-increasing set of data services, while meeting customer SLAs and minimizing operational cost is becoming more challenging. Cloud technology enables the collection of significant amounts of workload traces and system telemetry. With the progress in data science (DS) and machine learning (ML), it is feasible and desirable to utilize a data-driven, ML-based approach to automate various aspects of data services, resulting in the creation of autonomous data services. This paper presents our perspectives and insights on creating autonomous data services on Azure. It also covers the future endeavors we plan to undertake and unresolved issues that still need attention., Comment: SIGMOD Companion of the 2023 International Conference on Management of Data. 2023

Large scale analytics engines have become a core dependency for modern data-driven enterprises to derive business insights and drive actions. These engines support a large number of analytic jobs processing huge volumes of data on a daily basis, and workloads are often inundated with overlapping computations across multiple jobs. Reusing common computation is crucial for efficient cluster resource utilization and reducing job execution time. Detecting common computation is the first and key step for reducing this computational redundancy. However, detecting equivalence on large-scale analytics engines requires efficient and scalable solutions that are fully automated. In addition, to maximize computation reuse, equivalence needs to be detected at the semantic level instead of just the syntactic level (i.e., the ability to detect semantic equivalence of seemingly different-looking queries). Unfortunately, existing solutions fall short of satisfying these requirements. In this paper, we take a major step towards filling this gap by proposing GEqO, a portable and lightweight machine-learning-based framework for efficiently identifying semantically equivalent computations at scale. GEqO introduces two machine-learning-based filters that quickly prune out nonequivalent subexpressions and employs a semi-supervised learning feedback loop to iteratively improve its model with an intelligent sampling mechanism. Further, with its novel database-agnostic featurization method, GEqO can transfer the learning from one workload and database to another. Our extensive empirical evaluation shows that, on TPC-DS-like queries, GEqO yields significant performance gains-up to 200x faster than automated verifiers-and finds up to 2x more equivalences than optimizer and signature-based equivalence detection approaches.