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In ultracold-atom experiments, data often comes in the form of images which suffer information loss inherent in the techniques used to prepare and measure the system. This is particularly problematic when the processes of interest are complicated, such as interactions among excitations in Bose-Einstein condensates (BECs). In this paper, we describe a framework combining machine learning (ML) models with physics-based traditional analyses to identify and track multiple solitonic excitations in images of BECs. We use an ML-based object detector to locate the solitonic excitations and develop a physics-informed classifier to sort solitonic excitations into physically motivated subcategories. Lastly, we introduce a quality metric quantifying the likelihood that a specific feature is a longitudinal soliton. Our trained implementation of this framework, SolDet, is publicly available as an open-source python package. SolDet is broadly applicable to feature identification in cold-atom images when trained on a suitable user-provided dataset., Comment: 13 pages, 9 figures

As we begin to adopt approaches to help the U.S. National Institute of Standards and Technology (NIST) become a more inclusive organization, we need a way to assess the current level of inclusivity. The extent to which individuals have access to information and resources is largely associated with inclusion in the workplace, suggesting that analysis of individuals social networks may be an important component of this assessment. Network surveys provide a unique lens through which a wide range of networks, including networks of collaboration, mentorship, and trust, can be studied. As a result of such studies, efforts to improve on inclusivity can be directed towards where they are needed the most. Herein, we describe the NIST Interactions Survey a data collection tool developed for mapping and analyzing personal ego networks of NIST Federal Employees and aimed at quantifying the work- and career advice-related networks. In this study, we assess inclusivity at NIST through the lens of social network analysis to provide NIST with insights about the current work climate. With an overall response rate of approximately 25%, we find that the structure of the work- and advice-related ego networks provides important insights into the interaction patterns at NIST. An analysis of the network data confirms a siloed effect associated with the Organizational Unit affiliation of the respondents and their connections in the work and advice networks. Interestingly, we find that, while the composition of work ego networks tend to be quite aligned with what would be expected based on the workforce demographics, interactions in the advice ego networks reveal preferences associated with age and gender. In terms of gender, male employees show preference for male-dominated advice networks, which is not surprising given our predominantly male workforce. For female employees, however, we observe three distinct types of advice networks: two capturing strong preferences to seek advice from single-gender-dominated networks, be it male or female, and a third one with the gender composition quite evenly balanced. In terms of age preferences, we find that younger employees tend to seek advice from more experienced colleagues. Interestingly, no discernible differences are observed between the networks of minority and non-minority employees for either work or advice. Based on these findings, we recommend strategies for bridging the silos between the Laboratories and the rest of the Organizational Units, nurturing informal connections among female colleagues, and mapping employee networks among different minority groups (e.g. American Indian, Asian, Black, Hispanic, Native Hawaiian, Other). Although other approaches are possible, we believe that the recommendations proposed in this report will be a good start for NIST to become the inclusive organization we aspire to be.

In the fall of 2019, the National Institute of Standards and Technology (NIST) funded threes studies to better understand equity and inclusivity. The present study represents phase three of a sequential, exploratory mixed methods study designed to provide an in- depth look at the population of NIST federal employees to identify factors, attitudes, and processes that might result in gender- specific barriers at NIST. From phase 1 and the phase 2 in-depth interview qualitative results an on-line survey was designed to quantify the differences, if any, in the ways in which men and women experience work at NIST. The target population was all NIST federal employees, approximately 3,300. The sample size was 1,350, approximately 30 % of the sampling frame. The survey consisted of six sections and was disseminated by email to the sampling frame. Participation was completely voluntary. The survey was open for three weeks. 1,529 employees responded to the survey for an overall response rate of 33.2 % and 1,108 completed the survey (resulting in an overall completion rate of 72.5 %). Survey participants demographics were representative of the overall distribution of staff across Directorates, Operating Units, age, years of service, career paths, pay bands, and educational levels. The chi-square test for independence was used to test for statistically significant differences between the NIST population and the survey respondents as a whole, as well as between men and women. Statistically significant differences ( < 0.001) were found for men and women with respect to the organization's commitment to diversity and inclusivity (men are more positive than women) , on meritocracy (more women believe opportunities are based on who you know rather than most deserving employees) , and gendered experiences of being interrupted in meeting, questioning competence, and not receiving credit for ideas. More women believe they have to work harder, wait longer for promotion and opportunities for leadership. More women reported considering leaving NIST than their male counterparts. Both women and men agree they have equal opportunity to be hired, their need for work life balance is supported, that teamwork is valued, and NIST believes it is more objective than subjective and that projects are not more important than staff. Overall, the survey results align with the qualitative results and provide quantitative data on the differences in which men and women experience the culture, diversity and inclusivity of NIST.

The problem of classifying high-dimensional shapes in real-world data grows in complexity as the dimension of the space increases. For the case of identifying convex shapes of different geometries, a new classification framework has recently been proposed in which the intersections of a set of one-dimensional representations, called rays, with the boundaries of the shape are used to identify the specific geometry. This ray-based classification (RBC) has been empirically verified using a synthetic dataset of two- and three-dimensional shapes (Zwolak et al. in Proceedings of Third Workshop on Machine Learning and the Physical Sciences (NeurIPS 2020), Vancouver, Canada [December 11, 2020], arXiv:2010.00500, 2020) and, more recently, has also been validated experimentally (Zwolak et al., PRX Quantum 2:020335, 2021). Here, we establish a bound on the number of rays necessary for shape classification, defined by key angular metrics, for arbitrary convex shapes. For two dimensions, we derive a lower bound on the number of rays in terms of the shape's length, diameter, and exterior angles. For convex polytopes in $\mathbb{R}^N$, we generalize this result to a similar bound given as a function of the dihedral angle and the geometrical parameters of polygonal faces. This result enables a different approach for estimating high-dimensional shapes using substantially fewer data elements than volumetric or surface-based approaches., 10 pages, 5 figures

Quantum dots (QDs) defined with electrostatic gates are a leading platform for a scalable quantum computing implementation. However, with increasing numbers of qubits, the complexity of the control parameter space also grows. Traditional measurement techniques, relying on complete or near-complete exploration via two-parameter scans (images) of the device response, quickly become impractical with increasing numbers of gates. Here we propose to circumvent this challenge by introducing a measurement technique relying on one-dimensional projections of the device response in the multidimensional parameter space. Dubbed the ``ray-based classification (RBC) framework,'' we use this machine learning approach to implement a classifier for QD states, enabling automated recognition of qubit-relevant parameter regimes. We show that RBC surpasses the 82 % accuracy benchmark from the experimental implementation of image-based classification techniques from prior work while reducing the number of measurement points needed by up to 70 %. The reduction in measurement cost is a significant gain for time-intensive QD measurements and is a step forward toward the scalability of these devices. We also discuss how the RBC-based optimizer, which tunes the device to a multiqubit regime, performs when tuning in the two-dimensional and three-dimensional parameter spaces defined by plunger and barrier gates that control the QDs.This work provides experimental validation of both efficient state identification and optimization with machine learning techniques for non-traditional measurements in quantum systems with high-dimensional parameter spaces and time-intensive measurements., 9 pages, 4 figures

Most data in cold-atom experiments comes from images, the analysis of which is limited by our preconceptions of the patterns that could be present in the data. We focus on the well-defined case of detecting dark solitons -- appearing as local density depletions in a Bose-Einstein condensate (BEC) -- using a methodology that is extensible to the general task of pattern recognition in images of cold atoms. Studying soliton dynamics over a wide range of parameters requires the analysis of large datasets, making the existing human-inspection-based methodology a significant bottleneck. Here we describe an automated classification and positioning system for identifying localized excitations in atomic BECs utilizing deep convolutional neural networks to eliminate the need for human image examination. Furthermore, we openly publish our labeled dataset of dark solitons, the first of its kind, for further machine learning research., Comment: 17 pages, 5 figures

The current practice of manually tuning quantum dots (QDs) for qubit operation is a relatively time-consuming procedure that is inherently impractical for scaling up and applications. In this work, we report on the in situ implementation of a recently proposed autotuning protocol that combines machine learning (ML) with an optimization routine to navigate the parameter space. In particular, we show that a ML algorithm trained using exclusively simulated data to quantitatively classify the state of a double-QD device can be used to replace human heuristics in the tuning of gate voltages in real devices. We demonstrate active feedback of a functional double-dot device operated at millikelvin temperatures and discuss success rates as a function of the initial conditions and the device performance. Modifications to the training network, fitness function, and optimizer are discussed as a path toward further improvement in the success rate when starting both near and far detuned from the target double-dot range.

The application of social network analysis (SNA) has recently grown prevalent in science, technology, engineering, and mathematics education research. Research on classroom networks has led to greater understandings of student persistence in physics majors, changes in their career-related beliefs (e.g., physics interest), and their academic success. In this paper, we aim to provide a practitioner's guide to carrying out research using SNA, including how to develop data collection instruments, set up protocols for gathering data, as well as identify network methodologies relevant to a wide range of research questions beyond what one might find in a typical primer. We illustrate these techniques using student anxiety data from active-learning physics classrooms. We explore the relationship between students' physics anxiety and the social networks they participate in throughout the course of a semester. We find that students' with greater numbers of outgoing interactions are more likely to experience negative anxiety shifts even while we control for {\it pre} anxiety, gender, and final course grade. We also explore the evolution of student networks and find that the second half of the semester is a critical period for participating in interactions associated with decreased physics anxiety. Our study further supports the benefits of dynamic group formation strategies that give students an opportunity to interact with as many peers as possible throughout a semester. To complement our guide to SNA in education research, we also provide a set of tools for letting other researchers use this approach in their work -- the {\it SNA toolbox} -- that can be accessed on GitHub., 17 pages, 5 figures

The current practice of manually tuning quantum dots (QDs) for qubit operation is a relatively time-consuming procedure that is inherently impractical for scaling up and applications. In this work, we report on the {\it in situ} implementation of a recently proposed autotuning protocol that combines machine learning (ML) with an optimization routine to navigate the parameter space. In particular, we show that a ML algorithm trained using exclusively simulated data to quantitatively classify the state of a double-QD device can be used to replace human heuristics in the tuning of gate voltages in real devices. We demonstrate active feedback of a functional double-dot device operated at millikelvin temperatures and discuss success rates as a function of the initial conditions and the device performance. Modifications to the training network, fitness function, and optimizer are discussed as a path toward further improvement in the success rate when starting both near and far detuned from the target double-dot range., Comment: 9 pages, 7 figures

Research shows that community plays a central role in learning, and strong community engages students and aids in student persistence. Thus, understanding the function and structure of communities in learning environments is essential to education. We use social network analysis to explore the community dynamics of students in a pre-matriculation, two-week summer program. Unlike previous network analysis studies in PER, we build our networks from classroom video that has been coded for student interactions using labeled, directed ties. We define 3 types of interaction: on task interactions (regarding the assigned task), on topic interactions (having to do with science, technology, engineering, and mathematics (STEM)), and off topic interactions (unrelated to the assignment or STEM). To study the development of community in this program, we analyze the shift in conversation topicality over the course of the program. Conversations are more on-task toward the end of the program and we propose that this conversational shift represents a change in student membership within their forming community., 4 pages, 3 figures

We propose a novel application of Social Network Analysis (SNA) using classroom video data as a means of quantitatively and visually exploring the collaborations between students. The context for our study was a summer program that works with first generation students and deaf/hard-of-hearing students to engage in authentic science practice and develop a supportive community. We applied SNA to data from one activity during the two-week program to test our approach and as a means to begin to assess whether the goals of the program are being met. We used SNA to identify groups that were interacting in unexpected ways and then to highlight how individuals were contributing to the overall group behavior. We plan to expand our new use of SNA to video data on a larger scale.

Modeling Instruction (MI), an active-learning introductory physics curriculum, has been shown to improve student academic success. Peer-to-peer interactions play a salient role in the MI classroom. Their impact on student interest and self-efficacy -- preeminent constructs of various career theories -- has not been thoroughly explored. Our examination of three undergraduate MI courses (N=221) revealed a decrease in students' physics self-efficacy, physics interest, and general science interest. We found a positive link from physics interest to self-efficacy, and a negative relationship between science interest and self-efficacy. We tested structural equation models confirming that student interactions make positive contributions to self-efficacy. This study frames students' classroom interactions within broader career theory frameworks and suggests nuanced considerations regarding interest and self-efficacy constructs in the context of undergraduate active-learning science courses., Comment: 15 pages, 4 figures

The Modeling Instruction (MI) approach to introductory physics manifests significant increases in student conceptual understanding and attitudes toward physics. In light of these findings, we investigated changes in student self-efficacy while considering the construct's contribution to the career-decision making process. Students in the Fall 2014 and 2015 MI courses at Florida International University exhibited a decrease on each of the sources of self-efficacy and overall self-efficacy (N = 147) as measured by the Sources of Self-Efficacy in Science Courses-Physics (SOSESC-P) survey. This held true regardless of student gender or ethnic group. Given the highly interactive nature of the MI course and the drops observed on the SOSESC-P, we chose to further explore students' changes in self-efficacy as a function of three centrality measures (i.e., relational positions in the classroom social network): inDegree, outDegree, and PageRank. We collected social network data by periodically asking students to list the names of peers with whom they had meaningful interactions. While controlling for PRE scores on the SOSESC-P, bootstrapped linear regressions revealed post-self-efficacy scores to be predicted by PageRank centrality. When disaggregated by the sources of self-efficacy, PageRank centrality was shown to be directly related to students' sense of mastery experiences. InDegree was associated with verbal persuasion experiences, and outDegree with both verbal persuasion and vicarious learning experiences. We posit that analysis of social networks in active learning classrooms helps to reveal nuances in self-efficacy development., Comment: 14 pages, 2 figures

Over the past decade, machine learning techniques have revolutionized how research is done, from designing new materials and predicting their properties to assisting drug discovery to advancing cybersecurity. Recently, we added to this list by showing how a machine learning algorithm (a so-called learner) combined with an optimization routine can assist experimental efforts in the realm of tuning semiconductor quantum dot (QD) devices. Among other applications, semiconductor QDs are a candidate system for building quantum computers. The present-day tuning techniques for bringing the QD devices into a desirable configuration suitable for quantum computing that rely on heuristics do not scale with the increasing size of the quantum dot arrays required for even near-term quantum computing demonstrations. Establishing a reliable protocol for tuning that does not rely on the gross-scale heuristics developed by experimentalists is thus of great importance. To implement the machine learning-based approach, we constructed a dataset of simulated QD device characteristics, such as the conductance and the charge sensor response versus the applied electrostatic gate voltages. Here, we describe the methodology for generating the dataset, as well as its validation in training convolutional neural networks. We show that the learner's accuracy in recognizing the state of a device is ~96.5 % in both current- and charge-sensor-based training. We also introduce a tool that enables other researchers to use this approach for further research: QFlow lite - a Python-based mini-software suite that uses the dataset to train neural networks to recognize the state of a device and differentiate between states in experimental data. This work gives the definitive reference for the new dataset that will help enable researchers to use it in their experiments or to develop new machine learning approaches and concepts., Comment: 18 pages, 6 figures, 3 tables

Recent progress in building large-scale quantum devices for exploring quantum computing and simulation paradigms has relied upon effective tools for achieving and maintaining good experimental parameters, i.e. tuning up devices. In many cases, including in quantum-dot based architectures, the parameter space grows substantially with the number of qubits, and may become a limit to scalability. Fortunately, machine learning techniques for pattern recognition and image classification using so-called deep neural networks have shown surprising successes for computer-aided understanding of complex systems. In this work, we use deep and convolutional neural networks to characterize states and charge configurations of semiconductor quantum dot arrays when one can only measure a current-voltage characteristic of transport (here conductance) through such a device. For simplicity, we model a semiconductor nanowire connected to leads and capacitively coupled to depletion gates using the Thomas-Fermi approximation and Coulomb blockade physics. We then generate labelled training data for the neural networks, and find at least $90\,\%$ accuracy for charge and state identification for single and double dots purely from the dependence of the nanowire's conductance upon gate voltages. Using these characterization networks, we can then optimize the parameter space to achieve a desired configuration of the array, a technique we call `auto-tuning'. Finally, we show how such techniques can be implemented in an experimental setting by applying our approach to an experimental data set, and outline further problems in this domain, from using charge sensing data to extensions to full one and two-dimensional arrays, that can be tackled with machine learning., 15 pages, 10 figures

From industry to government to academia, attracting and retaining science, technology, engineering, and mathematics majors is recognized as a key element of the 21$^{\text{st}}$ century knowledge economy. The ability to retain students seems to be intimately tied with understanding their immersion into the academic and social system of an institution. For instance, it has been noted that insufficient interactions with peers can lead to a low commitment to the university and, ultimately, affect one's decision about whether to drop out. Since nearly half of first-time students who leave a university by the end of the freshman year never come back to college, the importance of understanding experiences in introductory courses as a means for improving students' persistence is particularly pronounced. We investigate students' experiences in an introductory physics courses, focusing on their self-reported perception of the values of out-of-class collaborations. We find that, even though students consider the out-of-class collaborations to be important for success, it takes a relatively long time before they start practicing collaborative learning., Comment: 4 pages, 5 figures

The lack of an engaging pedagogy and the highly competitive atmosphere in introductory science courses tend to discourage students from pursuing science, technology, engineering, and mathematics (STEM) majors. Once in a STEM field, academic and social integration has been long thought to be important for students' persistence. Yet, it is rarely investigated. In particular, the relative impact of in-class and out-of-class interactions remains an open issue. Here, we demonstrate that, surprisingly, for students whose grades fall in the "middle of the pack," the out-of-class network is the most significant predictor of persistence. To do so, we use logistic regression combined with Akaike's information criterion to assess in- and out-of-class networks, grades, and other factors. For students with grades at the very top (and bottom), final grade, unsurprisingly, is the best predictor of persistence---these students are likely already committed (or simply restricted from continuing) so they persist (or drop out). For intermediate grades, though, only out-of-class closeness---a measure of one's immersion in the network---helps predict persistence. This does not negate the need for in-class ties. However, it suggests that, in this cohort, only students that get past the convenient in-class interactions and start forming strong bonds outside of class are or become committed to their studies. Since many students are lost through attrition, our results suggest practical routes for increasing students' persistence in STEM majors., Comment: 12 pages, 2 figures, 8 tables, 6 pages of Supplementary Materials

Theories developed by Tinto and Nora identify academic performance, learning gains, and involvement in learning communities as significant facets of student engagement that, in turn, support student persistence. Collaborative learning environments, such as those employed in the Modeling Instruction introductory physics course, provide structure for student engagement by encouraging peer-to-peer interactions. Because of the inherently social nature of collaborative learning, we examine student interactions in the classroom using network analysis. We use centrality---a family of measures that quantify how connected or "central" a particular student is within the classroom network---to study student engagement longitudinally. Bootstrapped linear regression modeling shows that students' centrality predicts future academic performance over and above prior GPA for three out of four centrality measures tested. In particular, we find that closeness centrality explains 28 % more of the variance than prior GPA alone. These results confirm that student engagement in the classroom is critical to supporting academic performance. Furthermore, we find that this relationship for social interactions does not emerge until the second half of the semester, suggesting that classroom community develops over time in a meaningful way.

Increasing student retention (successfully finishing a particular course) and persistence (continuing through a sequence of courses or the major area of study) is currently a major challenge for universities. While students' academic and social integration into an institution seems to be vital for student retention, research into the effect of interpersonal interactions is rare. We use network analysis as an approach to investigate academic and social experiences of students in the classroom. In particular, centrality measures identify patterns of interaction that contribute to integration into the university. Using these measures, we analyze how position within a social network in a Modeling Instruction (MI) course -- an introductory physics course that strongly emphasizes interactive learning -- predicts their persistence in taking a subsequent physics course. Students with higher centrality at the end of the first semester of MI are more likely to enroll in a second semester of MI. Moreover, we found that chances of successfully predicting individual student's persistence based on centrality measures are fairly high -- up to $75\%$, making the centrality a good predictor of persistence. These findings suggest that increasing student social integration may help in improving persistence in science, technology, engineering, and mathematics fields., 14 pages, 4 figures