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Although most teachers acknowledge the importance of taking investigative approached in students' science learning experiences, implementing them in high-school classes can be challenging for teachers. In this work, we analyzed data from multiple sources from a teaching Community of Practice (CoP) to investigate (a) barriers to using open-ended labs in physics classes, (b) shifts in teachers' beliefs about the use of open-ended labs in their classes during teachers' engagement in a physics teacher CoP in a partnership program, and (c) a case study of one teachers whose shifts in perceptions about open-ended labs led to her successful implementation of an open-ended lab in her class. The findings confirm the existence of well-known psychological and structural barriers that can prevent teachers from adopting investigative approaches in teaching physics labs. Moreover, we learned how the interaction of these barriers further complicates the adoption of open-ended approaches in physics classes. The study also revealed a significant gap between teachers' current practices and their desired method for conducting labs, particularly in terms of structured versus open-ended approaches. The case study offered deeper insights into how shifts in teaching practices occur through changes in perceptions within a supportive CoP.

With the advent of self-driving labs promising to synthesize large numbers of new materials, new automated tools are required for checking potential duplicates in existing structural databases before a material can be claimed as novel. To avoid duplication, we rigorously define the novelty metric of any periodic material as the smallest distance to its nearest neighbor among already known materials. Using ultra-fast structural invariants, all such nearest neighbors can be found within seconds on a typical computer even if a given crystal is disguised by changing a unit cell, perturbing atoms, or replacing chemical elements. This real-time novelty check is demonstrated by finding near-duplicates of the 43 materials produced by Berkeley's A-lab in the world's largest collections of inorganic structures, the Inorganic Crystal Structure Database and Materials Project. To help future self-driving labs successfully identify novel materials, we propose navigation maps of the materials space where any new structure can be quickly located by its invariant descriptors similar to a geographic location on Earth., Comment: 22 pages, 9 figures. The latest version is maintained at http://kurlin.org/projects/computational-materials-science/navigation-maps.pdf

Open Radio Access Networks (RAN) aim to bring a paradigm shift to telecommunications industry, by enabling an open, intelligent, virtualized, and multi-vendor interoperable RAN ecosystem. At the center of this movement, O-RAN ALLIANCE defines the O-RAN architecture and standards, so that companies around the globe can use these specifications to create innovative and interoperable solutions. To accelerate the adoption of O-RAN products, rigorous testing of O-RAN Radio Unit (O-RU) and other O-RAN products plays a key role. O-RAN ALLIANCE has approved around 20 Open Testing and Integration Centres (OTICs) globally. OTICs serve as vendor-neutral platforms for providing the testing and integration services, with the vision that an O-RAN product certified in any OTIC is accepted in other parts of the world. To demonstrate the viability of such a certified-once-and-use-everywhere approach, one theme in the O-RAN Global PlugFest Spring 2024 is to demonstrate consistent and repeatable testing for the open fronthaul interface across multiple labs. Towards this, Japan OTIC and Asia Pacific OTIC in Singapore have teamed up together with an O-RU vendor and Keysight Technology. Our international team successfully completed all test cases defined by O-RAN ALLIANCE for O-RU conformance testing. In this paper, we share our journey in achieving this outcome, focusing on the challenges we have overcome and the lessons we have learned through this process., Comment: Published version at RitiRAN Workshop - co-located with IEEE VTC Fall 2024

Recent development in Artificial Intelligence (AI) models has propelled their application in scientific discovery, but the validation and exploration of these discoveries require subsequent empirical experimentation. The concept of self-driving laboratories promises to automate and thus boost the experimental process following AI-driven discoveries. However, the transition of experimental protocols, originally crafted for human comprehension, into formats interpretable by machines presents significant challenges, which, within the context of specific expert domain, encompass the necessity for structured as opposed to natural language, the imperative for explicit rather than tacit knowledge, and the preservation of causality and consistency throughout protocol steps. Presently, the task of protocol translation predominantly requires the manual and labor-intensive involvement of domain experts and information technology specialists, rendering the process time-intensive. To address these issues, we propose a framework that automates the protocol translation process through a three-stage workflow, which incrementally constructs Protocol Dependence Graphs (PDGs) that approach structured on the syntax level, completed on the semantics level, and linked on the execution level. Quantitative and qualitative evaluations have demonstrated its performance at par with that of human experts, underscoring its potential to significantly expedite and democratize the process of scientific discovery by elevating the automation capabilities within self-driving laboratories., Comment: In Advances in Neural Information Processing Systems (NeurIPS'24)

Laboratory accidents pose significant risks to human life and property, underscoring the importance of robust safety protocols. Despite advancements in safety training, laboratory personnel may still unknowingly engage in unsafe practices. With the increasing reliance on large language models (LLMs) for guidance in various fields, including laboratory settings, there is a growing concern about their reliability in critical safety-related decision-making. Unlike trained human researchers, LLMs lack formal lab safety education, raising questions about their ability to provide safe and accurate guidance. Existing research on LLM trustworthiness primarily focuses on issues such as ethical compliance, truthfulness, and fairness but fails to fully cover safety-critical real-world applications, like lab safety. To address this gap, we propose the Laboratory Safety Benchmark (LabSafety Bench), a comprehensive evaluation framework based on a new taxonomy aligned with Occupational Safety and Health Administration (OSHA) protocols. This benchmark includes 765 multiple-choice questions verified by human experts, assessing LLMs and vision language models (VLMs) performance in lab safety contexts. Our evaluations demonstrate that while GPT-4o outperforms human participants, it is still prone to critical errors, highlighting the risks of relying on LLMs in safety-critical environments. Our findings emphasize the need for specialized benchmarks to accurately assess the trustworthiness of LLMs in real-world safety applications., Comment: 50 pages, 19 figures

Experiments in the field of quantum optics often require very low concentrations of dust particles in the laboratory, but the complexity of working routines precludes operation within a proper clean room. Research teams have established a multitude of different approaches, precaution measures, and habits to keep the delicate optics setups free of contamination. Here, we systematically quantify dust particle concentration during day-to-day operation of a quantum optics lab, assess the effectiveness of various measures, and give practical recommendations., Comment: 4 pages, 4 figures, comments welcome!

In 2018 the Center for Universal Education (CUE) at Brookings launched a series of Real-time Scaling Labs (RTSL) to generate more evidence and provide practical recommendations on how to expand, deepen, and sustain the impact of education initiatives leading to transformative change in education systems, especially for the most disadvantaged children and youth. The purpose of this report is to look across all six of the RTSL cases to analyze common themes, insights, and lessons learned about the process of scaling as well as interesting divergences, and to offer considerations for others looking to learn from or build on this work. This report is intended for governments, education implementers, donors, and researchers who are interested in collaborative approaches to scaling impact in education. Section 1 examines key scaling lessons that emerged across all of the RTSL cases and analyzes scaling drivers (key levers, forces, or factors critical to making progress toward the scaling goal) at three different units of analysis to explore the way scaling happens at the system, institution, and individual levels. The second section analyzes how the RTSL model worked in practice, its strengths and challenges, and how it contributed to scaling. The report concludes with a set of core recommendations for governments, implementing organizations, donors, and researchers developed in consultation with the RTSL learning community. [This report was written with assistance from Brad Olsen and Katie Portnoy.]

As frontier AI models advance, policy proposals for safe AI development are gaining increasing attention from researchers and policymakers. This paper explores the current integration in the AI supply chain, focusing on vertical relationships and strategic partnerships among AI labs, cloud providers, chip manufacturers, and lithography companies. It aims to lay the groundwork for a deeper understanding of the implications of various governance interventions, including antitrust measures. The study has two main contributions. First, it profiles 25 leading companies in the AI supply chain, analyzing 300 relationships and noting 80 significant mergers and acquisitions along with 40 antitrust cases. Second, we discuss potential market definitions and the integration drivers based on the observed trends. The analysis reveals predominant horizontal integration through natural growth rather than acquisitions and notable trends of backward vertical integration in the semiconductor supply chain. Strategic partnerships are also significant downstream, especially between AI companies and cloud providers, with large tech companies often pursuing conglomerate integration by acquiring specialized AI startups or forming alliances with frontier AI labs. To further understand the strategic partnerships in the industry, we provide three brief case studies featuring companies like OpenAI and Nvidia. We conclude by posing open research questions on market dynamics and possible governance interventions, such as licensing and safety audits., Comment: This is a preliminary version of the paper. Comments are welcome. E-mail: t6aguirre@gmail.com

Group work is commonly adopted in university science laboratories. In this study, we characterize student discourse group roles in inquiry-based science labs. The instructional context for the study was a summer program hosted at a private research university in the eastern United States. The program was designed as a bridge program for matriculating students who were first generation and/or deaf or hard-of-hearing (DHH). Accommodations such as interpreters and technology were provided for DHH students. We analyzed 15 students' discourse moves in five lab activities from the video recordings, resulting in a total of 40 student-lab units. We developed codes to describe student discourse moves: asking a question, proposing an idea, participating in discussion, chatting off-task, and talking to instructor. Through a cluster analysis using those 40 student-lab units on our quantified discourse moves, we identified four discourse styles, high on-task high social, high on-task low social, low on-task low social, and low on-task high social. The results show that individual students tend to demonstrate varying discourse styles in different lab activities; students' discourse styles within the same groups tend to be aligned with their group members. Moreover, no difference was observed in discourse styles between genders, but DHH students were observed to participate significantly less in group discourse compared to their non-DHH peers. We suggest that group-level interventions that specifically target discourse are needed to promote productive and equitable small-group discourse in university science labs.

This review article provides an overview of research on the topic of gender equity in educational physics labs. As many institutions and instructors seek to evolve or transform physics lab learning, it is important that changes are made that improve equity for all students along multiple axes of identity, including gender. The studies highlighted in this review article describe the existence of complex gender-based differences, e.g., in opportunities to tinker with lab equipment, as well as differences in grades, conceptual understanding, and motivational outcomes across a broad range of lab curricula and contexts. The studies also illustrate and explore social interactions and structures that can impact students' experiences based on their gender identities. Although there has been less scholarship focused on proposals to reduce gender-based inequities in labs, this review article also provides an overview of some relevant proposals as well as associated research results. This overview of research on gender equity in physics labs helps to make clear that future scholarship on equity in physics labs should adopt gender frameworks that allow researchers to transcend binary gender identities and student deficit framing of research results. Likewise, a case is made that future research is needed on equity along other axes of identity, as well as research that accounts for intersectionality of different identities, in the physics lab context., Comment: Published 30 Jan 2024

In this work, we support experts in the safety domain with safer dismantling of drug labs, by deploying robots for the initial inspection. Being able to act on the discovered environment is key to enabling this (semi-)autonomous inspection, e.g. to open doors or take a closer at suspicious items. Our approach addresses this with a novel environmental representation, the Behavior-Oriented Situational Graph, where we extend on the classical situational graph by merging a perception-driven backbone with prior actionable knowledge via a situational affordance schema. Linking situations to robot behaviors facilitates both autonomous mission planning and situational understanding of the operator. Planning over the graph is easier and faster, since it directly incorporates actionable information, which is critical for online mission systems. Moreover, the representation allows the human operator to seamlessly transition between different levels of autonomy of the robot, from remote control to behavior execution to full autonomous exploration. We test the effectiveness of our approach in a real-world drug lab scenario at a Dutch police training facility using a mobile Spot robot and use the results to iterate on the system design., Comment: IEEE ICRA Workshop on Field Robotics 2024

This paper investigates the communication styles and structures of Twitter (X) communities within the vaccination context. While mainstream research primarily focuses on the echo-chamber phenomenon, wherein certain ideas are reinforced and participants are isolated from opposing opinions, this study reveals the presence of diverse communication styles across various communities. In addition to the communities exhibiting echo-chamber behavior, this research uncovers communities with distinct communication patterns. By shedding light on the nuanced nature of communication within social networks, this study emphasizes the significance of understanding the diversity of perspectives within online communities.

For decades, three-dimensional C-arm Cone-Beam Computed Tomography (CBCT) imaging system has been a critical component for complex vascular and nonvascular interventional procedures. While it can significantly improve multiplanar soft tissue imaging and provide pre-treatment target lesion roadmapping and guidance, the traditional workflow can be cumbersome and time-consuming, especially for less experienced users. To streamline this process and enhance procedural efficiency overall, we proposed a visual perception system, namely AutoCBCT, seamlessly integrated with an angiography suite. This system dynamically models both the patient's body and the surgical environment in real-time. AutoCBCT enables a novel workflow with automated positioning, navigation and simulated test-runs, eliminating the need for manual operations and interactions. The proposed system has been successfully deployed and studied in both lab and clinical settings, demonstrating significantly improved workflow efficiency.

Remote labs are a groundbreaking development in the education industry, providing students with access to laboratory education anytime, anywhere. However, most remote labs are costly and difficult to scale, especially in developing countries. With this as a motivation, this paper proposes a new remote labs (RLabs) solution that includes two use case experiments: Vanishing Rod and Focal Length. The hardware experiments are built at a low-cost by retrofitting Internet of Things (IoT) components. They are also made portable by designing miniaturised and modular setups. The software architecture designed as part of the solution seamlessly supports the scalability of the experiments, offering compatibility with a wide range of hardware devices and IoT platforms. Additionally, it can live-stream remote experiments without needing dedicated server space for the stream. The software architecture also includes an automation suite that periodically checks the status of the experiments using computer vision (CV). RLabs is qualitatively evaluated against seven non-functional attributes - affordability, portability, scalability, compatibility, maintainability, usability, and universality. Finally, user feedback was collected from a group of students, and the scores indicate a positive response to the students' learning and the platform's usability., Comment: 30 pages, 7 tables and 20 figures. Submitted to ACM Transactions on IoT

In the digital realm, rich data serves as a crucial source of insights into the complexities of social, political, and economic landscapes. Addressing the growing need for high-quality information on events and the imperative to combat hate speech, this research led to the establishment of the Shared Task on Climate Activism Stance and Hate Event Detection at CASE 2024. Focused on climate activists contending with hate speech on social media, our study contributes to hate speech identification from tweets. Analyzing three sub-tasks - Hate Speech Detection (Sub-task A), Targets of Hate Speech Identification (Sub-task B), and Stance Detection (Sub-task C) - Team Z-AGI Labs evaluated various models, including LSTM, Xgboost, and LGBM based on Tf-Idf. Results unveiled intriguing variations, with Catboost excelling in Subtask-B (F1: 0.5604) and Subtask-C (F1: 0.7081), while LGBM emerged as the top-performing model for Subtask-A (F1: 0.8684). This research provides valuable insights into the suitability of classical machine learning models for climate hate speech and stance detection, aiding informed model selection for robust mechanisms., Comment: Authors weren't supposed to upload given organisational agreements

Designing and developing web-enabled remote laboratories for pedagogical purposes is not an easy task. Often, developers (generally, educators who know the subjects they teach but lack of the technical and programming skills required to build Internet-based educational applications) end up discarding the idea of exploring these new teaching and learning experiences mainly due to the technical issues that must be mastered. To tackle this problem, authors present a novel technique that allows developers to create remote labs in a quick, didactical, and straightforward way. This framework is based on the use of two well-known software tools in the scope of engineering education, Easy Java Simulations and LabVIEW. The development exploits a new feature of Easy Java Simulations known as EJS-elements that enables Java developers to create and integrate their own authoring libraries (elements) into EJS, thus increasing its application possibilities. Particularly, the EJS element here presented allows to LabVIEW programs be controlled from EJS applications through a communication network. This paper presents the element creation details and how this can be used to create web-enabled experimentation environments for educational purposes. A step by step example of development of a remote lab for automatic control education is described.

In this paper we introduce a proposal to provide students in labs with an alternative to the traditional visible range spectrophotometers, whose acquisition and maintenance entails high costs, based on smartphones. Our solution faced two aspects. On the one hand, the software for the smartphone, able to perform the typical functionalities of the traditional spectrophotometers. On the other hand, the portable peripheral support needed to capture the images to be analyzed in the smartphone. The promising results allow this solution to be applied in Bring Your Own Devices (BYOD) contexts.

Human choice prediction in economic contexts is crucial for applications in marketing, finance, public policy, and more. This task, however, is often constrained by the difficulties in acquiring human choice data. With most experimental economics studies focusing on simple choice settings, the AI community has explored whether LLMs can substitute for humans in these predictions and examined more complex experimental economics settings. However, a key question remains: can LLMs generate training data for human choice prediction? We explore this in language-based persuasion games, a complex economic setting involving natural language in strategic interactions. Our experiments show that models trained on LLM-generated data can effectively predict human behavior in these games and even outperform models trained on actual human data.

We report on a study of the effects of laboratory activities that model fictitious laws of physics in a virtual reality environment on (1) students' epistemology about the role of experimental physics in class and in the world; (2) students' self-efficacy; and (3) the quality of student engagement with the lab activities. We create opportunities for students to practice physics as a means of creating and validating new knowledge by simulating real and fictitious physics in virtual reality (VR). This approach seeks to steer students away from a confirmation mindset in labs by eliminating any form of prior or outside models to confirm. We refer to the activities using this approach as Novel Observations in Mixed Reality (NOMR) labs. We examined NOMR's effects in 100-level and 200-level undergraduate courses. Using pre-post measurements we find that after NOMR labs, students in both populations were more expertlike in their epistemology about experimental physics and held stronger self-efficacy about their abilities to do the kinds of things experimental physicists do. Through the lens of the psychological theory of flow, we found that students engage as productively with NOMR labs as with traditional hands-on labs. This engagement persisted after the novelty of VR in the classroom wore off, suggesting that these effects are due to the pedagogical design rather than the medium of the intervention. We conclude that these NOMR labs offer an approach to physics laboratory instruction that centers the development of students' understanding of and comfort with the authentic practice of science., Comment: 20 pages, 9+1 figures (Table III is an image because the first author was defeated by LaTeX's tables). This is the version sent for a second round of reviews at Physical Review: Physics Education Research for the Special Collection on Laboratory Instruction, having been edited in response to reviewers' suggestions and critiques from the first round

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

This report presents a practical approach to teaching quantum computing to Electrical Engineering & Computer Science (EECS) students through dedicated hands-on programming labs. The labs cover a diverse range of topics, encompassing fundamental elements, such as entanglement, quantum gates and circuits, as well as advanced algorithms including Quantum Key Distribution, Deutsch and Deutsch-Jozsa Algorithms, Simon's algorithm, and Grover's algorithm. As educators, we aim to share our teaching insights and resources with fellow instructors in the field. The full lab handouts and program templates are provided for interested instructors. Furthermore, the report elucidates the rationale behind the design of each experiment, enabling a deeper understanding of quantum computing., Comment: Program templates have been updated based on Qiskit version 1.2. Latex handouts are located in the subdir 'handouts_latex,' and program templates can be found in the 'pub' folder

In recent years, discussions of responsible AI practices have seen growing support for "participatory AI" approaches, intended to involve members of the public in the design and development of AI systems. Prior research has identified a lack of standardised methods or approaches for how to use participatory approaches in the AI development process. At present, there is a dearth of evidence on attitudes to and approaches for participation in the sites driving major AI developments: commercial AI labs. Through 12 semi-structured interviews with industry practitioners and subject-matter experts, this paper explores how commercial AI labs understand participatory AI approaches and the obstacles they have faced implementing these practices in the development of AI systems and research. We find that while interviewees view participation as a normative project that helps achieve "societally beneficial" AI systems, practitioners face numerous barriers to embedding participatory approaches in their companies: participation is expensive and resource intensive, it is "atomised" within companies, there is concern about exploitation, there is no incentive to be transparent about its adoption, and it is complicated by a lack of clear context. These barriers result in a piecemeal approach to participation that confers no decision-making power to participants and has little ongoing impact for AI labs. This papers contribution is to provide novel empirical research on the implementation of public participation in commercial AI labs, and shed light on the current challenges of using participatory approaches in this context., Comment: Accepted to 2023 FAccT Conference

Academic Search is a timeless challenge that the field of Information Retrieval has been dealing with for many years. Even today, the search for academic material is a broad field of research that recently started working on problems like the COVID-19 pandemic. However, test collections and specialized data sets like CORD-19 only allow for system-oriented experiments, while the evaluation of algorithms in real-world environments is only available to researchers from industry. In LiLAS, we open up two academic search platforms to allow participating research to evaluate their systems in a Docker-based research environment. This overview paper describes the motivation, infrastructure, and two systems LIVIVO and GESIS Search that are part of this CLEF lab., Comment: Manuscript version of the CLEF 2020 proceedings paper

In today's world, where societal challenges in the areas of digitalization, demographic change and sustainability are becoming increasingly complex, new innovation structures are needed to meet these challenges. Living Labs or also Real World Laboratories prove to be such. Through their applied methods such as co-creation, they integrate users into research, making it more user-centric. Which other research infrastructures exist and how they can be differentiated is presented in this paper on the basis of a systematic literature research. Furthermore, methods for user integration are examined and provided in the form of an overview.

This paper presents NAVER LABS Europe's systems for Tamasheq-French and Quechua-Spanish speech translation in the IWSLT 2023 Low-Resource track. Our work attempts to maximize translation quality in low-resource settings using multilingual parameter-efficient solutions that leverage strong pre-trained models. Our primary submission for Tamasheq outperforms the previous state of the art by 7.5 BLEU points on the IWSLT 2022 test set, and achieves 23.6 BLEU on this year's test set, outperforming the second best participant by 7.7 points. For Quechua, we also rank first and achieve 17.7 BLEU, despite having only two hours of translation data. Finally, we show that our proposed multilingual architecture is also competitive for high-resource languages, outperforming the best unconstrained submission to the IWSLT 2021 Multilingual track, despite using much less training data and compute., Comment: IWSLT 2023: Tamasheq-French and Quechua-Spanish challenge winner

Most, if not all, physics undergraduate degree programs include instructional lab experiences. Physics lab instructors, both faculty and staff, are instrumental to student learning in instructional physics labs. However, the faculty-staff dichotomy belies the complex, varied, and multifaceted landscape of positions that lab instructors hold in the fabrics of physics departments. Here we present the results of a mixed methods study of the people who teach instructional labs and their professional contexts. Recruiting physics lab instructors across the US, we collected 84 survey responses and conducted 12 in-depth interviews about their job characteristics, professional identities, resources, and experiences. Our investigation reveals that lab instructors vary in terms of their official titles, job descriptions, formal duties, personal agency, and access to resources. We also identified common themes around the value of instructional labs, mismatched job descriptions, and a broad set of necessary skills and expertise. Our results suggest that instructors often occupy overlapping roles that fall in between more canonical jobs in physics departments. By understanding the professional contexts of physics lab instructors, the rest of the physics community can better promote and engage with their critical work, improving laboratory learning both for students and for the lab instructors who teach and support them.

Pathology laboratories are increasingly using digital workflows. This has the potential of increasing lab efficiency, but the digitization process also involves major challenges. Several reports have been published describing the individual experiences of specific laboratories with the digitization process. However, a comprehensive overview of the lessons learned is still lacking. We provide an overview of the lessons learned for different aspects of the digitization process, including digital case management, digital slide reading, and computer-aided slide reading. We also cover metrics used for monitoring performance and pitfalls and corresponding values observed in practice. The overview is intended to help pathologists, IT decision-makers, and administrators to benefit from the experiences of others and to implement the digitization process in an optimal way to make their own laboratory future-proof., Comment: 22 pages, 1 figure; corrected typo

Recent studies provide evidence that social constructivist pedagogical methods such as active learning, interactive engagement, and inquiry-based learning, while pedagogically more effective, can enable inequities in the classroom. By conducting a quantitative empirical examination of gender-inequitable group dynamics in two inquiry-based physics labs, we extend results of previous work. Using a survey on group work preferences and video recordings of lab sessions, we find similar patterns of gendered role-taking noted in prior studies. These results are not reducible to differences in students' preferences. We find that an intervention which employed partner agreement forms, with the goal of reducing inequities, had a positive impact on students' engagement with equipment during a first-semester lab course. Our work will inform implementation of more effective interventions in the future and emphasizes challenges faced by instructors who are dedicated to both research-based pedagogical practices and efforts to promote diversity, equity, and inclusion in their classrooms., Comment: 21 pages, 8 figures

Research on nontraditional laboratory (lab) activities in physics shows that students often expect to verify predetermined results, as takes place in traditional activities. This understanding of what is taking place, or epistemic framing, may impact their behaviors in the lab, either productively or unproductively. In this paper, we present an analysis of student epistemic framing in a nontraditional lab to understand how instructional context, specifically instructor behaviors, may shape student framing. We present video data from a lab section taught by an experienced teaching assistant (TA), with 19 students working in seven groups. We argue that student framing in this lab is evidenced by whether or not students articulate experimental predictions and by the extent to which they take up opportunities to construct knowledge (epistemic agency). We show that the TA's attempts to shift student frames generally succeed with respect to experimental predictions but are less successful with respect to epistemic agency. In part, we suggest, the success of the TA's attempts reflects whether and how they are responsive to students' current framing. This work offers evidence that instructors can shift students' frames in nontraditional labs, while also illuminating the complexities of both student framing and the role of the instructor in shifting that framing in this context., Comment: Submitted to Physical Review Physics Education Research

As the role of information and communication technologies gradually increases in our lives, source code security becomes a significant issue to protect against malicious attempts Furthermore with the advent of data-driven techniques, there is now a growing interest in leveraging machine learning and natural language processing as a source code assurance method to build trustworthy systems Therefore training our future software developers to write secure source code is in high demand In this thesis we propose a framework including learning modules and hands on labs to guide future IT professionals towards developing secure programming habits and mitigating source code vulnerabilities at the early stages of the software development lifecycle In this thesis our goal is to design learning modules with a set of hands on labs that will introduce students to secure programming practices using source code and log file analysis tools to predict and identify vulnerabilities In a Secure Coding Education framework we will improve students skills and awareness on source code vulnerabilities detection tools and mitigation techniques integrate concepts of source code vulnerabilities from Function API and library level to bad programming habits and practices leverage deep learning NLP and static analysis tools for log file analysis to introduce the root cause of source code vulnerabilities

To close the performance gap for students with disabilities, teacher and student-focused interventions have been implemented through the Houston Independent School District (HISD) Autism and Behavior Services. The teacher-focused intervention entails support and professional development opportunities to improve teacher efficacy to close the performance gap for students with disabilities. In the 2019-2020 school year, the department implemented a new student-focused intervention with the addition of sensory labs at 30 HISD campuses. This evaluation explored teachers' perceptions of the professional development services and support provided by HISD Autism and Behavior Services Team to improve their efficacy educating students with disabilities. The evaluation also explored teachers' perceptions of and frequency of access to the sensory room, and the impact that sensory room use had on the academic and behavioral outcomes of students with disabilities and/or behavioral challenges during the 2019-2020 school year. Overall, HISD teachers who responded to the survey agreed that the quality of instructional support and information provided by the Autism and Behavior Services Team was effective. However, teachers believed that it would be beneficial if there were better follow-up and more hands-on support through modeling of instructional and behavioral strategies. Teachers who accessed the sensory rooms indicated that the observed benefit of students using the sensory room was increased focus. More than half of the teachers, 57.5 percent, observed that students were better able to follow directions after participating in the sensory room activities, and 38.8 percent observed that students were better able to spend more time on task. In terms of behavior, 55 percent of teachers observed a decrease in negative behaviors, and 27.5 percent observed that students were more motivated. Students with a disability who were at campuses with sensory rooms showed a lower mean rate of suspension (in-school and out-of-school), a higher mean rate of attendance, and a higher mean cumulative GPA than students with disabilities at campuses without sensory rooms. The mean cumulative GPA was statistically significant.

This paper describes our participation in the 2022 TREC NeuCLIR challenge. We submitted runs to two out of the three languages (Farsi and Russian), with a focus on first-stage rankers and comparing mono-lingual strategies to Adhoc ones. For monolingual runs, we start from pretraining models on the target language using MLM+FLOPS and then finetuning using the MSMARCO translated to the language either with ColBERT or SPLADE as the retrieval model. While for the Adhoc task, we test both query translation (to the target language) and back-translation of the documents (to English). Initial result analysis shows that the monolingual strategy is strong, but that for the moment Adhoc achieved the best results, with back-translating documents being better than translating queries., Comment: Notebook detailing our participation and analysis on the TREC NeuCLIR 2022

Measurement uncertainty is a critical feature of experimental research in the physical sciences, and the concepts and practices surrounding measurement uncertainty are important components of physics lab courses. However, there has not been a broadly applicable, research-based assessment tool that allows physics instructors to easily measure students' knowledge of measurement uncertainty concepts and practices. To address this need, we employed Evidence-Centered Design to create the Survey of Physics Reasoning on Uncertainty Concepts in Experiments (SPRUCE). SPRUCE is a pre-post assessment instrument intended for use in introductory (first- and second-year) physics lab courses to help instructors and researchers identify student strengths and challenges with measurement uncertainty. In this paper, we discuss the development of SPRUCE's assessment items guided by Evidence-Centered Design, focusing on how instructors' and researchers' assessment priorities were incorporated into the assessment items and how students' reasoning from pilot testing informed decisions around item answer options. We also present an example of some of the feedback an instructor would receive after implementing SPRUCE in a pre-post fashion, along with a brief discussion of how that feedback could be interpreted and acted upon., Comment: 19 pages, 5 figures, submitted as part of the Physical Review Physics Education Research Focused Collection on Instructional Labs: Improving Traditions and New Directions

GLAM organisations have been exploring the benefits of publishing their digital collections in a wide variety of forms since the 2000s. Many institutions, and in particular libraries, have adopted the Semantic Web and Linked Data principles to their main catalogues. Recent advances in technology and innovative approaches concerning the reuse of the digital collections by means of computational access have paved the way for the creation of Labs within GLAM organisations. In this work, we present a framework to transform the datasets made available by GLAM organisations under open licenses into LOD. The framework has been applied to three metadata datasets made available by the Data Foundry at the National Library of Scotland. The results of this work are publicly available and can be applied to other domains such as digital humanities and data science., Comment: This is the preprint version (not peer-reviewed) of an article published by SAGE Journal in Journal of Information Science on June 8, 2023, available online: https://doi.org/10.1177/01655515231174386

As quantum information science and technology (QIST) is becoming more prevalent and occurring not only in research labs but also in industry, many educators are considering how best to incorporate learning about quantum mechanics into various levels of education. Although much of the focus has been on quantum concepts in non-lab courses, current work in QIST has a substantial experimental component. Many instructors of undergraduate lab courses want to provide their students the opportunity to work with quantum experiments. One common way this is done is through a sequence of quantum optics experiments often referred to as the ``single-photon experiments.'' These experiments demonstrate fundamental quantum phenomena with equipment common to research labs; however, they are resource intensive and cannot be afforded by all institutions. It is therefore imperative to know what unique affordances these experiments provide to students. As a starting point, we surveyed and interviewed instructors who use the single-photon experiments in undergraduate courses, asking how and why they use the experiments. We describe the most commonly used experiments in both quantum and beyond-first-year lab courses, the prevalence of actions the students perform, and the learning goals, ranging from conceptual knowledge to lab skills to student affect. Finally, we present some strategies from these data demonstrating how instructors have addressed the common challenges of preparing students to work with conceptually and technically complex experiments and balancing the practice of technical skills with the completion of the experiments., Comment: 18 pages, 4 figures, minor edits

This paper describes our participation to the 2022 TREC Deep Learning challenge. We submitted runs to all four tasks, with a focus on the full retrieval passage task. The strategy is almost the same as 2021, with first stage retrieval being based around SPLADE, with some added ensembling with ColBERTv2 and DocT5. We also use the same strategy of last year for the second stage, with an ensemble of re-rankers trained using hard negatives selected by SPLADE. Initial result analysis show that the strategy is still strong, but is still unclear to us what next steps should we take.

This article explains and illustrates the design of a laboratory experience for blind students to measure the inertia of simple objects, in this case, that of a disc around its axis of symmetry. Our adaptation consisted in modifying the data collection process, where we used an open-source electronic platform to convert visual signals into acoustic signals. This allows one of the blind students at our University to participate simultaneously as their classmates in the laboratory session corresponding to the mechanics unit of a standard engineering course.

Self-driving labs (SDLs) combine fully automated experiments with artificial intelligence (AI) that decides the next set of experiments. Taken to their ultimate expression, SDLs could usher a new paradigm of scientific research, where the world is probed, interpreted, and explained by machines for human benefit. While there are functioning SDLs in the fields of chemistry and materials science, we contend that synthetic biology provides a unique opportunity since the genome provides a single target for affecting the incredibly wide repertoire of biological cell behavior. However, the level of investment required for the creation of biological SDLs is only warranted if directed towards solving difficult and enabling biological questions. Here, we discuss challenges and opportunities in creating SDLs for synthetic biology., Comment: 17 pages, 3 figures. Submitted for publication in Current Opinion in Biotechnology. Updated figure 3 in this version

This report describes our approach for the Audio-Visual Diarization (AVD) task of the Ego4D Challenge 2022. Specifically, we present multiple technical improvements over the official baselines. First, we improve the detection performance of the camera wearer's voice activity by modifying the training scheme of its model. Second, we discover that an off-the-shelf voice activity detection model can effectively remove false positives when it is applied solely to the camera wearer's voice activities. Lastly, we show that better active speaker detection leads to a better AVD outcome. Our final method obtains 65.9% DER on the test set of Ego4D, which significantly outperforms all the baselines. Our submission achieved 1st place in the Ego4D Challenge 2022., Comment: Validation report for the Ego4D challenge at ECCV 2022