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The guided pathways model, comprising 14 different practices, is a framework for comprehensive, whole-college reform undertaken by community colleges to help all students choose, enter, progress through, and complete a program of study that enables them to secure sustaining-wage employment or transfer with junior standing in a major. Since its introduction in 2015, it has been adopted by hundreds of community colleges across the United States. This paper asks whether guided pathways practices implemented at 62 community and technical colleges in three states--Tennessee, Ohio, and Washington--are associated with improvements in student outcomes during the first year of college. Specifically, using institutional survey and rich administrative data, we construct measures of adoption of guided pathways reforms to examine the association between guided pathways practices and fall-to-fall persistence, college credits earned, college math credits earned, and STEM credits earned. Our study reveals substantial variation in the adoption of guided pathways reforms across the states and across community colleges within the states over time. While we cannot establish a causal relationship between guided pathways adoption and student outcomes, we find significant positive associations between the statewide adoption of guided pathways reforms and early student outcomes in Tennessee. The observed improvements in that state are likely the result of concurrent reforms--guided pathways and others--implemented simultaneously, rather than of guided pathways reforms alone. We do not find evidence of improved student outcomes in either Ohio or Washington following the launch of statewide guided pathways initiatives. Our findings suggest that complementarities among adopted practices within and across areas of practice--rather than the adoption of individual practices or the intensity of adoption--seem to drive larger improvements in early academic success across the three states. Our study is the first of its kind to explore the potential of guided pathways reforms in contributing to improved early academic success, representing a significant descriptive contribution given that whole-college reforms in higher education are understudied.

It is now well established that the COVID-19 pandemic had a devastating and unequal impact on student achievement. Test score declines were disproportionately large for historically marginalized students, exacerbating preexisting achievement gaps and threatening educational and economic inequality. In this paper, we use longitudinal student-level NWEA MAP Growth test data to estimate differences in test score declines for students at different points on the prepandemic test distribution. We also test the extent to which students' schools and districts accounted for these differences in declines. We find significant differences in learning loss by baseline achievement, with lower-achieving student's scores dropping 0.100 SD more in math and 0.113 SD more in reading than higher-achieving students' scores. We additionally show that the school a student attended accounts for about three-quarters of this widening gap in math achievement and about one-third in reading. The findings suggest school and district-level policies may have mattered more for learning loss than individual students' experiences within schools and districts. Such nuanced information regarding the variation in the pandemic's impacts on students is critical for policymakers and practitioners designing targeted academic interventions and for tracking disparities in academic recovery. [Additional funding for this report was provided by Kenneth C. Griffin.]

In 1993, the Commonwealth of Massachusetts dramatically overhauled its K-12 education system and created a new school finance formula, building an educational accountability structure to ensure every child has access to a high-quality education. The Massachusetts Education Reform Act (MERA) established academic standards in core subjects, mandated assessments to measure student outcomes on those standards, and established a system for holding schools accountable when students failed to meet basic expectations. This system has helped Massachusetts' public schools become the highest performing in the country. Student outcomes in all tested subjects and across demographic groups have improved steadily over time, but disparities in achievement and attainment exist between the Commonwealth's most privileged students and their less privileged counterparts, many of whom are black or Hispanic. Without the MERA and its requirement to assess every student and publish aggregate academic outcomes, policymakers may not understand the extent of disparity or how to address it as student outcomes data are integral to understanding where Massachusetts' public schools have been, where they are going, and how they can get there. This paper illustrates the importance of the Massachusetts Education Reform Act and how it has positively impacted students over time. It explains why the current accountability system evolved as it did and why preserving the most important aspects of that system is critical if the state is going to fulfill its constitutional obligation to educate all children to a high common standard.

Each year, more than a million high school students nationally take college dual enrollment courses, which have been shown to increase college access and success among participants. Yet racial/ethnic and other equity gaps in dual enrollment participation are widespread. To broaden the benefits of dual enrollment, the state of Ohio passed legislation in 2017 establishing the Innovative Programs (IP) policy, allowing waivers to test-based eligibility requirements--a frequently identified barrier to equitable access--for specific high school-college partnerships providing expanded outreach and support for students underrepresented in the state's dual enrollment program. This paper describes a multiple methods study of IP we conducted to examine how these partnerships were implemented to address the needs of underrepresented students and to evaluate whether the partnerships were successful in broadening access to and success in dual enrollment, as measured by course participation, pass rates, and college matriculation after high school. We find that the IP increased participation in dual enrollment among Black and Hispanic students. And while the implementation of the policy broadened access without changing course outcomes, the impacts on college enrollment after high school were mixed. Our results underscore the importance of pairing increased access to dual enrollment with adequate financial, advising, and academic resources to promote student success in and beyond dual enrollment courses.

Graduates of special education teacher education programs can teach in a range of special education settings, raising the potential that their training can occur in very different settings than where they find their first jobs. We follow 263 completers of Moderate Disabilities programs in Massachusetts from their field placements to their early-career teaching positions and study the characteristics of their field placements and the degree to which these are aligned with their early-career teaching positions. We also assess the degree to which alignment is associated with early-career teacher turnover. We found that many of these teachers student-taught in an inclusive setting but were hired into a self-contained special education setting and vice versa, and teachers who experienced this misalignment were more likely to leave the workforce early in their careers. Teachers who student taught with a supervising practitioner without a special education license were also more likely to leave early. Findings suggest that teachers training to educate students with learning disabilities should student teach in a setting that is aligned with where they are likely to be hired, and with a supervising practitioner who is trained in special education.

Correctional education is a prevalent form of rehabilitation programming for prisoners in the United States. There is limited evidence, however, about the labor market returns to credentials received while incarcerated. Using incarceration, educational, and labor market data in Washington State, we study the labor market returns to GEDs and short-term vocational certificates earned in prison. We identify the returns to credentials by a difference-indifferences design that compares changes in earnings and employment for incarcerated persons who earn a credential to those who enroll in a program but fail to complete a GED or certificate. We estimate that GEDs increase post-incarceration earnings by about $450 per quarter and that vocational certificates increase earnings by about $250 per quarter. Degree completers have higher hourly wages, are more likely to be employed, and work more hours following release. For vocational programs, earnings increases are driven by certificates in construction and manufacturing. [The research presented presented in this report uses confidential data from the Education Research and Data Center (ERDC) located within the Washington Office of Financial Management (OFM).]

With the rapid growth of scholarly archives, researchers subscribe to "paper alert" systems that periodically provide them with recommendations of recently published papers that are similar to previously collected papers. However, researchers sometimes struggle to make sense of nuanced connections between recommended papers and their own research context, as existing systems only present paper titles and abstracts. To help researchers spot these connections, we present PaperWeaver, an enriched paper alerts system that provides contextualized text descriptions of recommended papers based on user-collected papers. PaperWeaver employs a computational method based on Large Language Models (LLMs) to infer users' research interests from their collected papers, extract context-specific aspects of papers, and compare recommended and collected papers on these aspects. Our user study (N=15) showed that participants using PaperWeaver were able to better understand the relevance of recommended papers and triage them more confidently when compared to a baseline that presented the related work sections from recommended papers., Comment: Accepted to CHI 2024

Citation text plays a pivotal role in elucidating the connection between scientific documents, demanding an in-depth comprehension of the cited paper. Constructing citations is often time-consuming, requiring researchers to delve into extensive literature and grapple with articulating relevant content. To address this challenge, the field of citation text generation (CTG) has emerged. However, while earlier methods have primarily centered on creating single-sentence citations, practical scenarios frequently necessitate citing multiple papers within a single paragraph. To bridge this gap, we propose a method that leverages Large Language Models (LLMs) to generate multi-citation sentences. Our approach involves a single source paper and a collection of target papers, culminating in a coherent paragraph containing multi-sentence citation text. Furthermore, we introduce a curated dataset named MCG-S2ORC, composed of English-language academic research papers in Computer Science, showcasing multiple citation instances. In our experiments, we evaluate three LLMs LLaMA, Alpaca, and Vicuna to ascertain the most effective model for this endeavor. Additionally, we exhibit enhanced performance by integrating knowledge graphs from target papers into the prompts for generating citation text. This research underscores the potential of harnessing LLMs for citation generation, opening a compelling avenue for exploring the intricate connections between scientific documents., Comment: 14 pages, 7 figures, 11th International Conference, BDA 2023, Delhi, India

For undergraduate students of computing, learning to solve complex practical problems in a team is an essential skill for their future careers. This skill is needed in various fields, such as in cybersecurity and IT governance. Tabletop exercises are an innovative teaching method used in practice for training teams in incident response and evaluation of contingency plans. However, tabletop exercises are not yet widely established in university education. This paper presents data and teaching experience from a cybersecurity course that introduces tabletop exercises in classrooms using a novel technology: INJECT Exercise Platform (IXP), a web-based learning environment for delivering and evaluating the exercises. This technology substantially improves the prior practice, since tabletop exercises worldwide have usually been conducted using pen and paper. Unlike in traditional tabletop exercises, which are difficult to evaluate manually, IXP provides insights into students' behavior and learning based on automated analysis of interaction data. We demonstrate IXP's capabilities and evolution by comparing exercise sessions hosted throughout three years at different stages of the platform's readiness. The analysis of student data is supplemented by the discussion of the lessons learned from employing IXP in computing education contexts. The data analytics enabled a detailed comparison of the teams' performance and behavior. Instructors who consider innovating their classes with tabletop exercises may use IXP and benefit from the insights in this paper., Comment: Published in ACM ITiCSE 2024 conference proceedings, see https://doi.org/10.1145/3649217.3653639

In the paper [3] the value of the invariant denoted by d and often called "rank" was misstated for a narrow class of examples of ECS manifolds: they were identified as having d = 1 instead of the correct value d = 2. The error was repeated, by citing [3], in [1], [2] and [4] -- [7]. We briefly describe the class in question, explain why it has d = 2, and list the required corrections of the affected papers., Comment: 4 pages, to be included in a future paper as an appendix

This paper introduces a new method of paper circuit fabrication that overcomes design barriers and increases flexibility in circuit design. Conventional circuit boards rely on thin traces, which limits the complexity and accuracy when applied to paper circuits. To address this issue, we propose a method that uses large conductive zones in paper circuits and performs subtractive processing during their fabrication. This approach eliminates design barriers and allows for more flexibility in circuit design. We introduce PaperCAD, a software tool that simplifies the design process by converting traditional circuit design to paper circuit design. We demonstrate our technique by creating two paper circuit boards. Our approach has the potential to promote the development of new applications for paper circuits., Comment: ACM CHI 2023 Workshop 02: Beyond Prototyping Boards: Future Paradigms for Electronics Toolkits

This paper explores the implementation of embedded magnets to enhance paper-based interactions. The integration of magnets in paper-based interactions simplifies the fabrication process, making it more accessible for building soft robotics systems. We discuss various interaction patterns achievable through this approach and highlight their potential applications., Comment: ACM DIS 2023 Workshop 02: Soft Robotics and Programmable Materials for Human-Computer Interaction

Behavioural types provide a promising way to achieve lightweight, language-integrated verification for communication-centric software. However, a large barrier to the adoption of behavioural types is that the current state of the art expects software to be written using the same tools and typing discipline throughout a system, and has little support for components over which a developer has no control. This position paper describes the outcomes of a working group discussion at Dagstuhl Seminar 24051 (Next-Generation Protocols for Heterogeneous Systems). We propose a methodology for integrating multiple behaviourally-typed components, written in different languages. Our proposed approach involves an extensible protocol description language, a session IR that can describe data transformations and boundary monitoring and which can be compiled into program-specific session proxies, and finally a session middleware to aid session establishment. We hope that this position paper will stimulate discussion on one of the most pressing challenges facing the widespread adoption of behavioural typing., Comment: In Proceedings PLACES 2024, arXiv:2404.03712

Context: Annually, ICSE acknowledges a range of papers, a subset of which are paired with research artifacts such as source code, datasets, and supplementary materials, adhering to the Open Science Policy. However, no prior systematic inquiry dives into gauging the influence of ICSE papers using artifact attributes. Objective: We explore the mutual impact between artifacts and their associated papers presented at ICSE over ten years. Method: We collect data on usage attributes from papers and their artifacts, conduct a statistical assessment to identify differences, and analyze the top five papers in each attribute category. Results: There is a significant difference between paper citations and the usage of associated artifacts. While statistical analyses show no notable difference between paper citations and GitHub stars, variations exist in views and/or downloads of papers and artifacts. Conclusion: We provide a thorough overview of ICSE's accepted papers from the last decade, emphasizing the intricate relationship between research papers and their artifacts. To enhance the assessment of artifact influence in software research, we recommend considering key attributes that may be present in one platform but not in another., Comment: This paper has been accepted for publication and presentation at The 22nd IEEE/ACIS International Conference on Software Engineering, Management and Applications (SERA 2024) to be held in Honolulu, USA on May 30-June 1, 2024

This paper presents and evaluates a set of methods to classify individual Scopus publications using their references back to the second generation, where each publication can be assigned fractionally into up to five ASJC (All Science Journal Classifications) categories, excluding the Multidisciplinary area and the miscellaneous categories. Based on proposals by Glanzel et al. (1999a, 1999b, 2021), some additional parameters are established that allow different results to be obtained depending on how category membership is weighted or how the acceptance thresholds for multiple assignments are established. Various classifications are obtained, and then compared with each other, with the original ASJC Scopus journal classification, and with the AAC (Authors Assignation Collection) classification of a previous study (Alvarez-Llorente et al., 2023) in which the papers corresponding authors assign them the most appropriate categories. Classifications in which a high threshold is set for allowing assignments to multiple categories, combined with the use of first- and second-generation references and averaging over the number of references, provide the most promising results, improving over other reference-based reclassification proposals in terms of granularity, and over the Scopus classification itself in such aspects as the homogeneity of the publications assigned to a category. They also show greater coincidence with the AAC classification.

This white paper presents the current status of the R&D for Analysis Facilities (AFs) and attempts to summarize the views on the future direction of these facilities. These views have been collected through the High Energy Physics (HEP) Software Foundation's (HSF) Analysis Facilities forum, established in March 2022, the Analysis Ecosystems II workshop, that took place in May 2022, and the WLCG/HSF pre-CHEP workshop, that took place in May 2023. The paper attempts to cover all the aspects of an analysis facility.

This paper explored the temporal characteristics of clinical citations of biomedical papers, including how long it takes to receive its first clinical citation (the initial stage) and how long it takes to receive two or more clinical citations after its first clinical citation (the build-up stage). Over 23 million biomedical papers in PubMed between 1940 and 2013 and their clinical citations are used as the research data. We divide these biomedical papers into three groups and four categories from clinical citation level and translational science perspectives. We compare the temporal characteristics of biomedical papers of different groups or categories. From the perspective of clinical citation level, the results show that highly clinically cited papers had obvious advantages of receiving clinical citations over medium and lowly clinically cited papers in both the initial and build-up stages. Meanwhile, as the number of clinical citations increased in the build-up stage, the difference in the length of time to receive the corresponding number of clinical citations among the three groups of biomedical papers significantly increased. From the perspective of translational science, the results reveal that biomedical papers closer to clinical science more easily receive clinical citations than papers closer to basic science in both the initial and build-up stages. Moreover, we found that highly clinically cited papers had the desperate advantage of receiving clinical citations over even the clinical guidelines or clinical trials. The robustness analysis of the two aspects demonstrates the reliability of our results.

Scientific publishing lays the foundation of science by disseminating research findings, fostering collaboration, encouraging reproducibility, and ensuring that scientific knowledge is accessible, verifiable, and built upon over time. Recently, there has been immense speculation about how many people are using large language models (LLMs) like ChatGPT in their academic writing, and to what extent this tool might have an effect on global scientific practices. However, we lack a precise measure of the proportion of academic writing substantially modified or produced by LLMs. To address this gap, we conduct the first systematic, large-scale analysis across 950,965 papers published between January 2020 and February 2024 on the arXiv, bioRxiv, and Nature portfolio journals, using a population-level statistical framework to measure the prevalence of LLM-modified content over time. Our statistical estimation operates on the corpus level and is more robust than inference on individual instances. Our findings reveal a steady increase in LLM usage, with the largest and fastest growth observed in Computer Science papers (up to 17.5%). In comparison, Mathematics papers and the Nature portfolio showed the least LLM modification (up to 6.3%). Moreover, at an aggregate level, our analysis reveals that higher levels of LLM-modification are associated with papers whose first authors post preprints more frequently, papers in more crowded research areas, and papers of shorter lengths. Our findings suggests that LLMs are being broadly used in scientific writings.

In 2024, we will hold a research paper competition (the third Human Understanding AI Paper Challenge) for the research and development of artificial intelligence technologies to understand human daily life. This document introduces the datasets that will be provided to participants in the competition, and summarizes the issues to consider in data processing and learning model development., Comment: 7 pages, 3 figures

Examining limitations is a crucial step in the scholarly research reviewing process, revealing aspects where a study might lack decisiveness or require enhancement. This aids readers in considering broader implications for further research. In this article, we present a novel and challenging task of Suggestive Limitation Generation (SLG) for research papers. We compile a dataset called LimGen, encompassing 4068 research papers and their associated limitations from the ACL anthology. We investigate several approaches to harness large language models (LLMs) for producing suggestive limitations, by thoroughly examining the related challenges, practical insights, and potential opportunities. Our LimGen dataset and code can be accessed at https://github.com/armbf/LimGen., Comment: 16 pages, 3 figures

This non-archival index is not complete, as some accepted papers chose to opt-out of inclusion. The list of all accepted papers is available on the workshop website.

Paper is a cheap, recyclable, and clean material that is often used to make practical tools. Traditional tool design either relies on simulation or physical analysis, which is often inaccurate and time-consuming. In this paper, we propose PaperBot, an approach that directly learns to design and use a tool in the real world using paper without human intervention. We demonstrated the effectiveness and efficiency of PaperBot on two tool design tasks: 1. learning to fold and throw paper airplanes for maximum travel distance 2. learning to cut paper into grippers that exert maximum gripping force. We present a self-supervised learning framework that learns to perform a sequence of folding, cutting, and dynamic manipulation actions in order to optimize the design and use of a tool. We deploy our system to a real-world two-arm robotic system to solve challenging design tasks that involve aerodynamics (paper airplane) and friction (paper gripper) that are impossible to simulate accurately., Comment: Project Website: https://paperbot.cs.columbia.edu/

Communicating design implications is common within the HCI community when publishing academic papers, yet these papers are rarely read and used by designers. One solution is to use design cards as a form of translational resource that communicates valuable insights from papers in a more digestible and accessible format to assist in design processes. However, creating design cards can be time-consuming, and authors may lack the resources/know-how to produce cards. Through an iterative design process, we built a system that helps create design cards from academic papers using an LLM and text-to-image model. Our evaluation with designers (N=21) and authors of selected papers (N=12) revealed that designers perceived the design implications from our design cards as more inspiring and generative, compared to reading original paper texts, and the authors viewed our system as an effective way of communicating their design implications. We also propose future enhancements for AI-generated design cards.

Reinforcement Learning (RL) has increasingly become a preferred method over traditional rule-based systems in diverse human-in-the-loop (HITL) applications due to its adaptability to the dynamic nature of human interactions. However, integrating RL in such settings raises significant privacy concerns, as it might inadvertently expose sensitive user information. Addressing this, our paper focuses on developing PAPER-HILT, an innovative, adaptive RL strategy through exploiting an early-exit approach designed explicitly for privacy preservation in HITL environments. This approach dynamically adjusts the tradeoff between privacy protection and system utility, tailoring its operation to individual behavioral patterns and preferences. We mainly highlight the challenge of dealing with the variable and evolving nature of human behavior, which renders static privacy models ineffective. PAPER-HILT's effectiveness is evaluated through its application in two distinct contexts: Smart Home environments and Virtual Reality (VR) Smart Classrooms. The empirical results demonstrate PAPER-HILT's capability to provide a personalized equilibrium between user privacy and application utility, adapting effectively to individual user needs and preferences. On average for both experiments, utility (performance) drops by 24%, and privacy (state prediction) improves by 31%., Comment: 18 pages

The Wide-field Spectroscopic Telescope (WST) is proposed as a new facility dedicated to the efficient delivery of spectroscopic surveys. This white paper summarises the initial concept as well as the corresponding science cases. WST will feature simultaneous operation of a large field-of-view (3 sq. degree), a high multiplex (20,000) multi-object spectrograph (MOS) and a giant 3x3 sq. arcmin integral field spectrograph (IFS). In scientific capability these requirements place WST far ahead of existing and planned facilities. Given the current investment in deep imaging surveys and noting the diagnostic power of spectroscopy, WST will fill a crucial gap in astronomical capability and work synergistically with future ground and space-based facilities. This white paper shows that WST can address outstanding scientific questions in the areas of cosmology; galaxy assembly, evolution, and enrichment, including our own Milky Way; origin of stars and planets; time domain and multi-messenger astrophysics. WST's uniquely rich dataset will deliver unforeseen discoveries in many of these areas. The WST Science Team (already including more than 500 scientists worldwide) is open to the all astronomical community. To register in the WST Science Team please visit https://www.wstelescope.com/for-scientists/participate, Comment: 194 pages, 66 figures. Comments are welcome (wstelescope@gmail.com)

This paper discusses the effectiveness of leveraging Chatbot: Generative Pre-trained Transformer (ChatGPT) versions 3.5 and 4 for analyzing research papers for effective writing of scientific literature surveys. The study selected the \textit{Application of Artificial Intelligence in Breast Cancer Treatment} as the research topic. Research papers related to this topic were collected from three major publication databases Google Scholar, Pubmed, and Scopus. ChatGPT models were used to identify the category, scope, and relevant information from the research papers for automatic identification of relevant papers related to Breast Cancer Treatment (BCT), organization of papers according to scope, and identification of key information for survey paper writing. Evaluations performed using ground truth data annotated using subject experts reveal, that GPT-4 achieves 77.3\% accuracy in identifying the research paper categories and 50\% of the papers were correctly identified by GPT-4 for their scopes. Further, the results demonstrate that GPT-4 can generate reasons for its decisions with an average of 27\% new words, and 67\% of the reasons given by the model were completely agreeable to the subject experts.

After last year's successful BabyLM Challenge, the competition will be hosted again in 2024/2025. The overarching goals of the challenge remain the same; however, some of the competition rules will be different. The big changes for this year's competition are as follows: First, we replace the loose track with a paper track, which allows (for example) non-model-based submissions, novel cognitively-inspired benchmarks, or analysis techniques. Second, we are relaxing the rules around pretraining data, and will now allow participants to construct their own datasets provided they stay within the 100M-word or 10M-word budget. Third, we introduce a multimodal vision-and-language track, and will release a corpus of 50% text-only and 50% image-text multimodal data as a starting point for LM model training. The purpose of this CfP is to provide rules for this year's challenge, explain these rule changes and their rationale in greater detail, give a timeline of this year's competition, and provide answers to frequently asked questions from last year's challenge.

Galaxy cluster mergers are representative of a wide range of physics, making them an excellent probe of the properties of dark matter and the ionized plasma of the intracluster medium. To date, most studies have focused on mergers occurring in the plane of the sky, where morphological features can be readily identified. To allow study of mergers with arbitrary orientation, we have assembled multi-probe data for the eight-cluster ICM-SHOX sample sensitive to both morphology and line of sight velocity. The first ICM-SHOX paper (Silich+2023) provided an overview of our methodology applied to one member of the sample, MACS J0018.5+1626, in order to constrain its merger geometry. That work resulted in an exciting new discovery of a velocity space decoupling of its gas and dark matter distributions. In this work, we describe the availability and quality of multi-probe data for the full ICM-SHOX galaxy cluster sample. These datasets will form the observational basis of an upcoming full ICM-SHOX galaxy cluster sample analysis., Comment: 6 pages, 6 figures; published in Proc. of the mm Universe 2023 conference, EPJ Web of conferences, EDP Sciences

Failure Mode, Effects and Criticality Analysis (FMECA) is one of the safety analysis methods recommended by most of the international standards. The classical FMECA is made in a form of a table filled in either manually or by using safety analysis tools. In both cases, the design engineers have to choose the trade-offs between safety and other development constraints. In the case of complex cyber-physical systems (CPS) with thousands of specified constraints, this may lead to severe problems and significantly impact the overall criticality of CPS. In this paper, we propose to adopt optimization techniques to automate the decision making process conducted after FMECA of CPS. We describe a multi-agent based optimization method which extends classical FMECA for offering optimal solutions in terms of criticality and development constraints of CPS., Comment: 13 pages, 2 figures, 1 table, "2nd International Workshop on Emerging Ideas and Trends in Engineering of Cyber-Physical Systems, part of Cyber-Physical Systems Week, April 2015, Seattle, USA"

These notes aim to provide a deeper insight on the specifics of the paper "Refined criteria toward boundedness in an attraction-repulsion chemotaxis system with nonlinear productions" by A. Columbu, S. Frassu and G. Viglialoro [\textit{Appl. Anal.} 2024, 103:2, 415--431].

The proliferation of scholarly publications underscores the necessity for reliable tools to navigate scientific literature. OpenAlex, an emerging platform amalgamating data from diverse academic sources, holds promise in meeting these evolving demands. Nonetheless, our investigation uncovered a flaw in OpenAlex's portrayal of publication status, particularly concerning retractions. Despite accurate metadata sourced from Crossref database, OpenAlex consolidated this information into a single boolean field, "is_retracted," leading to misclassifications of papers. This challenge not only impacts OpenAlex users but also extends to users of other academic resources integrating the OpenAlex API. The issue affects data provided by OpenAlex in the period between 22 Dec 2023 and 19 Mar 2024. Anyone using data from this period should urgently check it and replace it if necessary.

This is a summary paper of a use case of a Robotdog dedicated to guide visually impaired people in complex environment like a smart intersection. In such scenarios, the Robotdog has to autonomously decide whether it is safe to cross the intersection or not in order to further guide the human. We leverage data sharing and collaboration between the Robotdog and other autonomous systems operating in the same environment. We propose a system architecture for autonomous systems through a separation of a collaborative decision layer, to enable collective decision making processes, where data about the environment, relevant to the Robotdog decision, together with evidences for trustworthiness about other systems and the environment are shared.

The lack of experimental data on the direct-drive pressure leads us to the conclusion that, the published experimental confirmation of driving pressure boosting and smoothing for hybrid-drive inertial fusion is not credible., Comment: Comment on https://doi.org/10.1038/s41467-023-41477-2

OO Peg is a detached eclipsing binary system containing two late-A-type stars in a circular orbit with a period of 2.985 d. Using published spectroscopic results and a light curve from the Transiting Exoplanet Survey Satellite (TESS) we determine their masses to be 1.69 +/- 0.09 and 1.74 +/- 0.06 Msun, and their radii to be 2.12 +/- 0.03 and 1.91 +/- 0.03 Rsun. The TESS data are of high quality, but discrepancies in the radial velocities from two sources prevent a precise mass measurement. The primary star is definitively hotter, larger and more luminous than its companion, but its mass is lower (albeit to a significance of only 1.1 sigma). Using published apparent magnitudes and temperatures, we find a distance of 238.8 +/- 6.1 pc, in agreement with the Gaia DR3 parallax. Although both components are in the delta Scuti instability strip, we find no evidence of pulsations. More extensive spectroscopy is needed to improve our understanding of the system., Comment: Accepted for publication in The Observatory. 12 pages, 4 tables, 3 black/white figures

Version 11 of the CHIANTI database and software package is presented. Advanced ionization equilibrium models have been added for low charge states of seven elements (C, N, O, Ne, Mg, Si and S), and represent a significant improvement especially when modelling the solar transition region. The models include the effects of higher electron density and charge transfer on ionization and recombination rates. As an illustration of the difference these models make, a synthetic spectrum is calculated for an electron pressure of 7$\times 10^{15}$ cm$^{-3}$ K and compared with an active region observation from HRTS. Increases are seen of factors of two to five in the predicted radiances of the strongest lines in the UV from Si IV, C IV, and N V, compared to the previous modelling using the coronal approximation. Much better agreement (within 20\%) with the observation is found for the majority of the lines. The new atomic models better equip both those who are studying the transition region and those who are interpreting emission from higher density astrophysical and laboratory plasma. In addition to the advanced models, several ion datasets have been added or updated, and data for the radiative recombination energy loss rate have been updated., Comment: 19 pages, 5 figures, 1 table, submitted to ApJ

We present a full general relativistic analytic solution for a radiation-pressure supported equilibrium fluid torus orbiting a rotating neutron star (NS). Previously developed analytical methods are thoroughly applied in the Hartle-Thorne geometry, including the effects of both the NS's angular momentum and quadrupole moment. The structure, size and shape of the torus are explored, focusing especially on the critically thick solution - the cusp tori. For the astrophysically relevant range of NS parameters, we examine how our findings differ from those obtained for the Schwarzschild spacetime. The solutions for rotating stars display signatures of the interplay between relativistic and Newtonian effects where the impact of NS angular momentum and quadrupole moment are almost counterbalanced at the given radius. Nevertheless, the spacetime parameters still strongly influence the size of tori, which can be shown in a coordinate-independent way. Finally, we discuss the importance of the size of the central neutron star, determining whether or not the surrounding torus may exist. We provide a set of tools in a Wolfram Mathematica code, which poses a basis allowing for a further investigation of the impact of the NSs' superdense matter equation of state on the spectral and temporal behaviour of accretion tori., Comment: 7 pages, 5 figures, submitted to A&A

The four characteristic oscillation frequencies of accretion flows are, in addition to the Keplerian orbital frequency, often discussed in the context of the time variability of the black hole and neutron star (NS) low-mass X-ray binaries (LMXBs). These are namely the frequencies of the axisymmetric radial and vertical epicyclic oscillations, and the frequencies of non-axisymmetric oscillations corresponding to the periastron (radial) and Lense-Thirring (vertical) precessions. In this context, we investigate the effect of the quadrupole moment of a slowly rotating NS and provide complete formulae for calculating these oscillation and precession frequencies, as well as their convenient approximations. Simple formulae corresponding to the geodesic limit of a slender torus (and test particle motion) and the limit of a marginally overflowing torus (torus exhibiting a critical cusp) are presented, and furthermore, more general approximate formulae are included to allow calculations for arbitrarily thick tori. We provide the Wolfram Mathematica code used for our calculations together with C++ and PYTHON codes for calculations of the frequencies. Our formulae can be used for various calculations describing the astrophysical signatures of the NSs' superdense matter equation of state. For instance, we demonstrate that, even for a given fixed number of free parameters, a model accounting for fluid flow precession better matches the frequencies of twin-peak quasiperiodic oscillations observed in NS LMXBs than a model using geodesic precession., Comment: 17 pages, 6 figures, submitted to A&A

The micromixing time of impinging thin liquid sheets depends upon the energy dissipation rate. The kinetic energy released by the impingement has been previously studied and was found to be a function of the coefficient of restitution of the collision. In this work, the volume within which the released kinetic energy is dissipated was investigated. The volume of energy dissipation was determined by measuring the time required for the velocity of the single sheet (prior to the collision) to be reduced to the velocity in the mixed sheet (after the collision). Although different, the velocity in single sheets and the velocity in mixed sheets has been previously shown to be constant. High-speed video was used to measure the velocity of features, generated in the front single sheet, as they passed through the impingement zone and into the mixed sheet. The experimental results showed that the time required for the velocity change was approximately equal to the residence time of liquid in the impingement zone. A new equation for the energy dissipation rate was developed and compared with the energy dissipation rate derived from turbulence energy-cascade theory. This comparison showed that the large-eddy turnover time was approximately equal to the residence time in the impingement zone; a result that is in accordance with the notion from turbulence energy-cascade theory that large, energy-containing eddies lose their energy within approximately one large-eddy turnover time. Within the impingement zone, the large-eddy kinetic energy was found to decay exponentially with time., Comment: Accepted for publication in Chemical Engineering Science

Human-robot interaction is now an established discipline. Dozens of HRI courses exist at universities worldwide, and some institutions even offer degrees in HRI. However, although many students are being taught HRI, there is no agreed-upon curriculum for an introductory HRI course. In this workshop, we aimed to reach community consensus on what should be covered in such a course. Through interactive activities like panels, breakout discussions, and syllabus design, workshop participants explored the many topics and pedagogical approaches for teaching HRI. This collection of articles submitted to the workshop provides examples of HRI courses being offered worldwide.

We respond to David Steigmann's discussion of our paper "A general theory for anisotropic Kirchhoff-Love shells with in-plane bending of embedded fibers, Math. Mech. Solids, 28(5):1274-1317" (arXiv:2101.03122). His discussion allows us to clarify two misleading statements in our original paper, and confirm that its formulation is fully consistent with the formulation of Steigmann. We also demonstrate that some of our original statements criticized by Steigmann are not wrong., Comment: 7 pages, 1 figure

The Comments are devoted to the paper 'Derivation of lump solutions to a variety of Boussinesq equations with distinct dimensions' (Int J Numer Methods Heat Fluid Flow. 2022;32:3072{3082), in which three new generalizations of the classical Boussinesq equation are suggested that were further investigated in several papers. Here it is shown that the equations derived in the above paper are not presented in their canonical forms. It turns out that all three equations can be essentially simplified by the standard technique widely used for linear and quasi-linear PDEs. As a result, it is proved that the equations suggested are not multidimensional generalizations of the Boussinesq equation., Comment: Comment on doi:10.1108/HFF-12-2021-0786

This study explores the fusion of a field-effect transistor (FET), a paper-based analytical cartridge, and the computational power of deep learning (DL) for quantitative biosensing via kinetic analyses. The FET sensors address the low sensitivity challenge observed in paper analytical devices, enabling electrical measurements with kinetic data. The paper-based cartridge eliminates the need for surface chemistry required in FET sensors, ensuring economical operation (cost < $0.15/test). The DL analysis mitigates chronic challenges of FET biosensors such as sample matrix interference, by leveraging kinetic data from target-specific bioreactions. In our proof-of-concept demonstration, our DL-based analyses showcased a coefficient of variation of < 6.46% and a decent concentration measurement correlation with an r2 value of > 0.976 for cholesterol testing when blindly compared to results obtained from a CLIA-certified clinical laboratory. These integrated technologies can create a new generation of FET-based biosensors, potentially transforming point-of-care diagnostics and at-home testing through enhanced accessibility, ease-of-use, and accuracy., Comment: 18 pages, 4 figures

Jailbreaking is an emerging adversarial attack that bypasses the safety alignment deployed in off-the-shelf large language models (LLMs). A considerable amount of research exists proposing more effective jailbreak attacks, including the recent Greedy Coordinate Gradient (GCG) attack, jailbreak template-based attacks such as using "Do-Anything-Now" (DAN), and multilingual jailbreak. In contrast, the defensive side has been relatively less explored. This paper proposes a lightweight yet practical defense called SELFDEFEND, which can defend against all existing jailbreak attacks with minimal delay for jailbreak prompts and negligible delay for normal user prompts. Our key insight is that regardless of the kind of jailbreak strategies employed, they eventually need to include a harmful prompt (e.g., "how to make a bomb") in the prompt sent to LLMs, and we found that existing LLMs can effectively recognize such harmful prompts that violate their safety policies. Based on this insight, we design a shadow stack that concurrently checks whether a harmful prompt exists in the user prompt and triggers a checkpoint in the normal stack once a token of "No" or a harmful prompt is output. The latter could also generate an explainable LLM response to adversarial prompts. We demonstrate our idea of SELFDEFEND works in various jailbreak scenarios through manual analysis in GPT-3.5/4. We also list three future directions to further enhance SELFDEFEND., Comment: Fixed the bibliography reference issue in our LLM jailbreak defense vision paper submitted on 24 Feb 2024

Due to the rapid pace of research publications, keeping up to date with all the latest related papers is very time-consuming, even with daily feed tools. There is a need for automatically generated, short, customized literature reviews of sets of papers to help researchers decide what to read. While several works in the last decade have addressed the task of explaining a single research paper, usually in the context of another paper citing it, the relationship among multiple papers has been ignored; prior works have focused on generating a single citation sentence in isolation, without addressing the expository and transition sentences needed to connect multiple papers in a coherent story. In this work, we explore a feature-based, LLM-prompting approach to generate richer citation texts, as well as generating multiple citations at once to capture the complex relationships among research papers. We perform an expert evaluation to investigate the impact of our proposed features on the quality of the generated paragraphs and find a strong correlation between human preference and integrative writing style, suggesting that humans prefer high-level, abstract citations, with transition sentences between them to provide an overall story.

The rapid spread of SARS-CoV-2 caused the COVID-19 pandemic and accelerated vaccine development to prevent the spread of the virus and control the disease. Given the sustained high infectivity and evolution of SARS-CoV-2, there is an ongoing interest in developing COVID-19 serology tests to monitor population-level immunity. To address this critical need, we designed a paper-based multiplexed vertical flow assay (xVFA) using five structural proteins of SARS-CoV-2, detecting IgG and IgM antibodies to monitor changes in COVID-19 immunity levels. Our platform not only tracked longitudinal immunity levels but also categorized COVID-19 immunity into three groups: protected, unprotected, and infected, based on the levels of IgG and IgM antibodies. We operated two xVFAs in parallel to detect IgG and IgM antibodies using a total of 40 uL of human serum sample in <20 min per test. After the assay, images of the paper-based sensor panel were captured using a mobile phone-based custom-designed optical reader and then processed by a neural network-based serodiagnostic algorithm. The trained serodiagnostic algorithm was blindly tested with serum samples collected before and after vaccination or infection, achieving an accuracy of 89.5%. The competitive performance of the xVFA, along with its portability, cost-effectiveness, and rapid operation, makes it a promising computational point-of-care (POC) serology test for monitoring COVID-19 immunity, aiding in timely decisions on the administration of booster vaccines and general public health policies to protect vulnerable populations., Comment: 19 Pages, 4 Figures

Successful integration of point-of-care testing (POCT) into clinical settings requires improved assay sensitivity and precision to match laboratory standards. Here, we show how innovations in amplified biosensing, imaging, and data processing, coupled with deep learning, can help improve POCT. To demonstrate the performance of our approach, we present a rapid and cost-effective paper-based high-sensitivity vertical flow assay (hs-VFA) for quantitative measurement of cardiac troponin I (cTnI), a biomarker widely used for measuring acute cardiac damage and assessing cardiovascular risk. The hs-VFA includes a colorimetric paper-based sensor, a portable reader with time-lapse imaging, and computational algorithms for digital assay validation and outlier detection. Operating at the level of a rapid at-home test, the hs-VFA enabled the accurate quantification of cTnI using 50 uL of serum within 15 min per test and achieved a detection limit of 0.2 pg/mL, enabled by gold ion amplification chemistry and time-lapse imaging. It also achieved high precision with a coefficient of variation of < 7% and a very large dynamic range, covering cTnI concentrations over six orders of magnitude, up to 100 ng/mL, satisfying clinical requirements. In blinded testing, this computational hs-VFA platform accurately quantified cTnI levels in patient samples and showed a strong correlation with the ground truth values obtained by a benchtop clinical analyzer. This nanoparticle amplification-based computational hs-VFA platform can democratize access to high-sensitivity point-of-care diagnostics and provide a cost-effective alternative to laboratory-based biomarker testing., Comment: 23 Pages, 4 Figures, 1 Table

As new research on Large Language Models (LLMs) continues, it is difficult to keep up with new research and models. To help researchers synthesize the new research many have written survey papers, but even those have become numerous. In this paper, we develop a method to automatically assign survey papers to a taxonomy. We collect the metadata of 144 LLM survey papers and explore three paradigms to classify papers within the taxonomy. Our work indicates that leveraging graph structure information on co-category graphs can significantly outperform the language models in two paradigms; pre-trained language models' fine-tuning and zero-shot/few-shot classifications using LLMs. We find that our model surpasses an average human recognition level and that fine-tuning LLMs using weak labels generated by a smaller model, such as the GCN in this study, can be more effective than using ground-truth labels, revealing the potential of weak-to-strong generalization in the taxonomy classification task., Comment: TL;DR: We collected metadata about LLM surveys and developed a method for categorizing them into a taxonomy, indicating the superiority of graph representation learning over language models and revealing the efficacy of fine-tuning using weak labels

In October 2023, arXiv made HTML formatted papers available to readers. This was the exciting outcome of over a year of accessibility research and development with the scientific community. Currently, only 2.4% of research outputs meet accessibility guidelines. Informed by scientists who rely on assistive technology, our analysis demonstrates that offering HTML is the most impactful step arXiv can take. Scientists need HTML now, and emphasize to not let perfect be the enemy of good enough. In this paper we share with you how arXiv is achieving HTML conversions from LaTeX now, and our plans for future improvements., Comment: The 5th International Workshop on "Digitization and E-Inclusion in Mathematics and Science 2024" (DEIMS2024). https://workshop.sciaccess.net/deims2024/

Topological deep learning (TDL) is a rapidly evolving field that uses topological features to understand and design deep learning models. This paper posits that TDL may complement graph representation learning and geometric deep learning by incorporating topological concepts, and can thus provide a natural choice for various machine learning settings. To this end, this paper discusses open problems in TDL, ranging from practical benefits to theoretical foundations. For each problem, it outlines potential solutions and future research opportunities. At the same time, this paper serves as an invitation to the scientific community to actively participate in TDL research to unlock the potential of this emerging field.

TurtleRabbit is a new RoboCup SSL team from Western Sydney University. This team description paper presents our approach in navigating some of the challenges in developing a new SSL team from scratch. SSL is dominated by teams with extensive experience and customised equipment that has been developed over many years. Here, we outline our approach in overcoming some of the complexities associated with replicating advanced open-sourced designs and managing the high costs of custom components. Opting for simplicity and cost-effectiveness, our strategy primarily employs off-the-shelf electronics components and ``hobby'' brushless direct current (BLDC) motors, complemented by 3D printing and CNC milling. This approach helped us to streamline the development process and, with our open-sourced hardware design, hopefully will also lower the bar for other teams to enter RoboCup SSL in the future. The paper details the specific hardware choices, their approximate costs, the integration of electronics and mechanics, and the initial steps taken in software development, for our entry into SSL that aims to be simple yet competitive., Comment: Submitted paper as part of the qualification for RoboCup 2024

A major threat to the peer-review systems of computer science conferences is the existence of "collusion rings" between reviewers. In such collusion rings, reviewers who have also submitted their own papers to the conference work together to manipulate the conference's paper assignment, with the aim of being assigned to review each other's papers. The most straightforward way that colluding reviewers can manipulate the paper assignment is by indicating their interest in each other's papers through strategic paper bidding. One potential approach to solve this important problem would be to detect the colluding reviewers from their manipulated bids, after which the conference can take appropriate action. While prior work has developed effective techniques to detect other kinds of fraud, no research has yet established that detecting collusion rings is even possible. In this work, we tackle the question of whether it is feasible to detect collusion rings from the paper bidding. To answer this question, we conduct empirical analysis of two realistic conference bidding datasets, including evaluations of existing algorithms for fraud detection in other applications. We find that collusion rings can achieve considerable success at manipulating the paper assignment while remaining hidden from detection: for example, in one dataset, undetected colluders are able to achieve assignment to up to 30% of the papers authored by other colluders. In addition, when 10 colluders bid on all of each other's papers, no detection algorithm outputs a group of reviewers with more than 31% overlap with the true colluders. These results suggest that collusion cannot be effectively detected from the bidding using popular existing tools, demonstrating the need to develop more complex detection algorithms as well as those that leverage additional metadata (e.g., reviewer-paper text-similarity scores).

We comment on the paper 'Saturation and Multifractality of Lagrangian and Eulerian Scaling Exponents in Three-Dimensional Turbulence' by D. Buaria and K.R. Sreenivasan. We point out some physical interpretation of the results discussed in the paper.

What is the optimal order in which a researcher should submit their papers to journals of differing quality? I analyze a sequential search model without recall where the researcher's expected value from journal submission depends on the history of past submissions. Acceptances immediately terminate the search process and deliver some payoff, while rejections carry information about the paper's quality, affecting the researcher's belief in acceptance probability over future journals. When journal feedback does not change the paper's quality, the researcher's optimal strategy is monotone in their acceptance payoff. Submission costs distort the researcher's effective acceptance payoff, but maintain monotone optimality. If journals give feedback which can affect the paper's quality, such as through \textit{referee reports}, the search order can change drastically depending on the agent's prior belief about their paper's quality. However, I identify a set of \textit{assortative matched} conditions on feedback such that monotone strategies remain optimal whenever the agent's prior is sufficiently optimistic.

The Ward et al. (2016) Plos-One paper is an important, heavily-cited paper in the decoupling literature. The authors present evidence of 1990-2015 growth in material and energy consumption and GDP at a world level, and for selected countries. They find only relative decoupling has occurred, leading to their central claim that future absolute decoupling is implausible. However, the authors have made two key errors in their collected data: GDP data is in current prices which includes inflation, and their global material use data is the total mass of fossil energy materials. Strictly, GDP data should be in constant prices to allow for its comparison over time, and material inputs to an economy should be the sum of mineral raw materials. Amending for these errors, we find much smaller levels of energy-GDP relative decoupling, and no materials-GDP decoupling at all at a global level. We check these new results by adding data for 1900-1990 to provide a longer time series, and find consistently low (and even no) levels of global relative decoupling of material use. The central claim for materials over the implausibility of future absolute decoupling therefore not only remains valid but is reinforced by the corrected datasets., Comment: 6 pages, 4 figures

Background: The COVID-19 pandemic has caused severe impacts on health systems worldwide. Its critical nature and the increased interest of individuals and organizations to develop countermeasures to the problem has led to a surge of new studies in scientific journals. Objetive: We sought to develop a tool that incorporates, in a novel way, aspects of Information Retrieval (IR) and Extraction (IE) applied to the COVID-19 Open Research Dataset (CORD-19). The main focus of this paper is to provide researchers with a better search tool for COVID-19 related papers, helping them find reference papers and hightlight relevant entities in text. Method: We applied Latent Dirichlet Allocation (LDA) to model, based on research aspects, the topics of all English abstracts in CORD-19. Relevant named entities of each abstract were extracted and linked to the corresponding UMLS concept. Regular expressions and the K-Nearest Neighbors algorithm were used to rank relevant papers. Results: Our tool has shown the potential to assist researchers by automating a topic-based search of CORD-19 papers. Nonetheless, we identified that more fine-tuned topic modeling parameters and increased accuracy of the research aspect classifier model could lead to a more accurate and reliable tool. Conclusion: We emphasize the need of new automated tools to help researchers find relevant COVID-19 documents, in addition to automatically extracting useful information contained in them. Our work suggests that combining different algorithms and models could lead to new ways of browsing COVID-19 paper data.

This study analyzed 16,799 journal papers and 98,773 conference papers published by IEEE Xplore in 2016 to investigate the relationships among usage counts, Mendeley readership, and citations through descriptive, regression, and mediation analyses. Differences in the relationship among these metrics between journal and conference papers are also studied. Results showed that there is no significant difference between journal and conference papers in the distribution patterns and accumulation rates of the three metrics. However, the correlation coefficients of the interrelationships between the three metrics were lower in conference papers compared to journal papers. Secondly, funding, international collaboration, and open access are positively associated with all three metrics, except for the case of funding on the usage metrics of conference papers. Furthermore, early Mendeley readership is a better predictor of citations than early usage counts and performs better for journal papers. Finally, we reveal that early Mendeley readership partially mediates between early usage counts and citation counts in the journal and conference papers. The main difference is that conference papers rely more on the direct effect of early usage counts on citations. This study contributes to expanding the existing knowledge on the relationships among usage counts, Mendeley readership, and citations in journal and conference papers, providing new insights into the relationship between the three metrics through mediation analysis., Comment: 23 pages, 7 figures

Writing and genre conventions are extant to any scientific community, and CHI is no different. In this paper, we present the early phases of an AI tool we created called KITSUNE, which supports authors in placing their work into the format of a CHI paper, taking into account many conventions that are ever-present in CHI papers. We describe the development of the tool with the intent to promote discussion around how writing conventions are upheld and unquestioned by the CHI community, and how this translates to the work produced. In addition, we bring up questions surrounding how the introduction of LLMs into academic writing fundamentally change how conventions will be upheld now and in the future, Comment: 8 pages

We study the ability of LLMs to generate feedback for scientific papers and develop MARG, a feedback generation approach using multiple LLM instances that engage in internal discussion. By distributing paper text across agents, MARG can consume the full text of papers beyond the input length limitations of the base LLM, and by specializing agents and incorporating sub-tasks tailored to different comment types (experiments, clarity, impact) it improves the helpfulness and specificity of feedback. In a user study, baseline methods using GPT-4 were rated as producing generic or very generic comments more than half the time, and only 1.7 comments per paper were rated as good overall in the best baseline. Our system substantially improves the ability of GPT-4 to generate specific and helpful feedback, reducing the rate of generic comments from 60% to 29% and generating 3.7 good comments per paper (a 2.2x improvement).

This paper explores articles hosted on the arXiv preprint server with the aim to uncover valuable insights hidden in this vast collection of research. Employing text mining techniques and through the application of natural language processing methods, we examine the contents of quantitative finance papers posted in arXiv from 1997 to 2022. We extract and analyze crucial information from the entire documents, including the references, to understand the topics trends over time and to find out the most cited researchers and journals on this domain. Additionally, we compare numerous algorithms to perform topic modeling, including state-of-the-art approaches.