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The $S$-gap shifts have a dynamically and combinatorially rich structure. Dynamical properties of the $S$-gap shift can be related to the properties of the set $S$. This interplay is particularly interesting when $S$ is not syndetic such as when $S$ is the set of prime numbers or when $S=\{2^n\}$. It is a well known result that the entropy of the $S$-gap shift is given by $h(X) = \log \lambda$, where $\lambda>0$ is the unique solution to the equation $\sum_{n \in S} \lambda^{-(n+1)}=1$. Fix a point $w$ of the full shift $\{1,2, \dots, k\}^\mathbb{Z}$. We introduce the $(S,w)$-gap shift which is a generalization of the $S$-gap shift consisting of sequences in $\{0,1, \dots, k\}^\mathbb{Z}$ in which any two $0$'s are separated by a word $u$ appearing in $w$ such that $|u|\in S$. We extend the formula for the entropy of the $S$-gap shift to a formula describing the entropy of this new class of shift spaces. Additionally we investigate the dynamical properties including irreducibility and mixing of this generalization of the $S$-gap shift.

The SoccerNet 2024 challenges represent the fourth annual video understanding challenges organized by the SoccerNet team. These challenges aim to advance research across multiple themes in football, including broadcast video understanding, field understanding, and player understanding. This year, the challenges encompass four vision-based tasks. (1) Ball Action Spotting, focusing on precisely localizing when and which soccer actions related to the ball occur, (2) Dense Video Captioning, focusing on describing the broadcast with natural language and anchored timestamps, (3) Multi-View Foul Recognition, a novel task focusing on analyzing multiple viewpoints of a potential foul incident to classify whether a foul occurred and assess its severity, (4) Game State Reconstruction, another novel task focusing on reconstructing the game state from broadcast videos onto a 2D top-view map of the field. Detailed information about the tasks, challenges, and leaderboards can be found at https://www.soccer-net.org, with baselines and development kits available at https://github.com/SoccerNet., Comment: 7 pages, 1 figure

Occluded Person Re-Identification (ReID) is a metric learning task that involves matching occluded individuals based on their appearance. While many studies have tackled occlusions caused by objects, multi-person occlusions remain less explored. In this work, we identify and address a critical challenge overlooked by previous occluded ReID methods: the Multi-Person Ambiguity (MPA) arising when multiple individuals are visible in the same bounding box, making it impossible to determine the intended ReID target among the candidates. Inspired by recent work on prompting in vision, we introduce Keypoint Promptable ReID (KPR), a novel formulation of the ReID problem that explicitly complements the input bounding box with a set of semantic keypoints indicating the intended target. Since promptable re-identification is an unexplored paradigm, existing ReID datasets lack the pixel-level annotations necessary for prompting. To bridge this gap and foster further research on this topic, we introduce Occluded-PoseTrack ReID, a novel ReID dataset with keypoints labels, that features strong inter-person occlusions. Furthermore, we release custom keypoint labels for four popular ReID benchmarks. Experiments on person retrieval, but also on pose tracking, demonstrate that our method systematically surpasses previous state-of-the-art approaches on various occluded scenarios. Our code, dataset and annotations are available at https://github.com/VlSomers/keypoint_promptable_reidentification.

Tracking and identifying athletes on the pitch holds a central role in collecting essential insights from the game, such as estimating the total distance covered by players or understanding team tactics. This tracking and identification process is crucial for reconstructing the game state, defined by the athletes' positions and identities on a 2D top-view of the pitch, (i.e. a minimap). However, reconstructing the game state from videos captured by a single camera is challenging. It requires understanding the position of the athletes and the viewpoint of the camera to localize and identify players within the field. In this work, we formalize the task of Game State Reconstruction and introduce SoccerNet-GSR, a novel Game State Reconstruction dataset focusing on football videos. SoccerNet-GSR is composed of 200 video sequences of 30 seconds, annotated with 9.37 million line points for pitch localization and camera calibration, as well as over 2.36 million athlete positions on the pitch with their respective role, team, and jersey number. Furthermore, we introduce GS-HOTA, a novel metric to evaluate game state reconstruction methods. Finally, we propose and release an end-to-end baseline for game state reconstruction, bootstrapping the research on this task. Our experiments show that GSR is a challenging novel task, which opens the field for future research. Our dataset and codebase are publicly available at https://github.com/SoccerNet/sn-gamestate.

In recent years, machine learning has become crucial in remote sensing analysis, particularly in the domain of Land-use/Land-cover (LULC). The synergy of machine learning and satellite imagery analysis has demonstrated significant productivity in this field, as evidenced by several studies. A notable challenge within this area is the semantic segmentation mapping of land usage over extensive territories, where the accessibility of accurate land-use data and the reliability of ground truth land-use labels pose significant difficulties. For example, providing a detailed and accurate pixel-wise labeled dataset of the Flanders region, a first-level administrative division of Belgium, can be particularly insightful. Yet there is a notable lack of regulated, formalized datasets and workflows for such studies in many regions globally. This paper introduces a comprehensive approach to addressing these gaps. We present a densely labeled ground truth map of Flanders paired with Sentinel-2 satellite imagery. Our methodology includes a formalized dataset division and sampling method, utilizing the topographic map layout 'Kaartbladversnijdingen,' and a detailed semantic segmentation model training pipeline. Preliminary benchmarking results are also provided to demonstrate the efficacy of our approach.

High entropy alloys (HEAs) have attracted substantial interest in recent years. Thus far, most investigations have focused on their applications as structural materials rather than functional materials. In this paper, we show that FeMnNiAlCr HEAs can potentially be applied as both a structural and functional material for high-efficiency concentrated solar thermal power (CSP) systems working at >700 degrees C. The HEA itself would be used in high-temperature tubing to carry working fluids, while its surface oxide would act as a high-efficiency solar thermal absorber. These HEAs have demonstrated yield strengths 2-3x greater than that of stainless steel at 700 degrees C and a creep lifetime >800 h at 700 degrees C under a typical CSP tubing mechanical load of 35 MPa. Their Mn-rich surface oxides maintain a high optical-to-thermal conversion efficiency of ~87% under 1000x solar concentration for 20 simulated day-night thermal cycles between 750 degrees C and environmental temperature. These HEAs have also sustained immersion in unpurified bromide molten salts for 14 days at 750{\deg}C with <2% weight loss, in contrast to 70% weight loss from a 316 stainless steel reference. The simultaneous achievement of promising mechanical, optical, and thermochemical properties in this FeMnNiAlCr system opens the door to new applications of HEAs in solar energy harvesting.

The SoccerNet 2023 challenges were the third annual video understanding challenges organized by the SoccerNet team. For this third edition, the challenges were composed of seven vision-based tasks split into three main themes. The first theme, broadcast video understanding, is composed of three high-level tasks related to describing events occurring in the video broadcasts: (1) action spotting, focusing on retrieving all timestamps related to global actions in soccer, (2) ball action spotting, focusing on retrieving all timestamps related to the soccer ball change of state, and (3) dense video captioning, focusing on describing the broadcast with natural language and anchored timestamps. The second theme, field understanding, relates to the single task of (4) camera calibration, focusing on retrieving the intrinsic and extrinsic camera parameters from images. The third and last theme, player understanding, is composed of three low-level tasks related to extracting information about the players: (5) re-identification, focusing on retrieving the same players across multiple views, (6) multiple object tracking, focusing on tracking players and the ball through unedited video streams, and (7) jersey number recognition, focusing on recognizing the jersey number of players from tracklets. Compared to the previous editions of the SoccerNet challenges, tasks (2-3-7) are novel, including new annotations and data, task (4) was enhanced with more data and annotations, and task (6) now focuses on end-to-end approaches. More information on the tasks, challenges, and leaderboards are available on https://www.soccer-net.org. Baselines and development kits can be found on https://github.com/SoccerNet.

Many multi-agent systems require inter-agent communication to properly achieve their goal. By learning the communication protocol alongside the action protocol using multi-agent reinforcement learning techniques, the agents gain the flexibility to determine which information should be shared. However, when the number of agents increases we need to create an encoding of the information contained in these messages. In this paper, we investigate the effect of increasing the amount of information that should be contained in a message and increasing the number of agents. We evaluate these effects on two different message encoding methods, the mean message encoder and the attention message encoder. We perform our experiments on a matrix environment. Surprisingly, our results show that the mean message encoder consistently outperforms the attention message encoder. Therefore, we analyse the communication protocol used by the agents that use the mean message encoder and can conclude that the agents use a combination of an exponential and a logarithmic function in their communication policy to avoid the loss of important information after applying the mean message encoder., Comment: Paper accepted to the BNAIC/BeNeLearn 2022 conference

Researchers agree that for children who attend early care and education programs, high-quality early learning experiences are critical for promoting academic, social-emotional, and executive function skills, particularly for children of color and children from families with low incomes. Rigorous research testing the effects of interventions to enhance classroom quality has found that providing professional development to teachers, including ongoing training and coaching, can improve classroom quality and teachers' instructional practices as well as children's outcomes. These benefits are particularly noticeable when professional development is focused on a specific curriculum or set of teacher practices. Yet, again, questions remain. When and for which children do quality enhancement efforts have the most impact? When and for which early care and education centers and teachers can quality enhancement efforts be implemented successfully? Answering these questions will help the early care and education field target investments in ways that are more likely to support classroom quality and help all children succeed. The "Variations in Implementation of Quality Interventions (VIQI): Examining the Quality-Child Outcomes Relationship in Child Care and Early Education" project aims to tackle these open questions and build substantial new evidence to inform policies and practices in Head Start and community-based child care centers. The VIQI project is a large-scale, rigorous study that examines the link between classroom quality and preschool children's developmental outcomes. It builds on and extends prior research--including the "Child Care and Early Education Quality Features, Thresholds and Dosage and Child Outcomes (Q-DOT) project." This prior work suggests that when classroom quality is in the higher portion of the quality range, links between quality and children's outcomes are stronger than when classroom quality is in the lower portion of the quality range. This brief describes the VIQI project, its key research questions, and the conceptual framework underlying it.

Whole slide image (WSI) assessment is a challenging and crucial step in cancer diagnosis and treatment planning. WSIs require high magnifications to facilitate sub-cellular analysis. Precise annotations for patch- or even pixel-level classifications in the context of gigapixel WSIs are tedious to acquire and require domain experts. Coarse-grained labels, on the other hand, are easily accessible, which makes WSI classification an ideal use case for multiple instance learning (MIL). In our work, we propose a novel embedding-based Dual-Query MIL pipeline (DQ-MIL). We contribute to both the embedding and aggregation steps. Since all-purpose visual feature representations are not yet available, embedding models are currently limited in terms of generalizability. With our work, we explore the potential of dynamic meta-embedding based on cutting-edge self-supervised pre-trained models in the context of MIL. Moreover, we propose a new MIL architecture capable of combining MIL-attention with correlated self-attention. The Dual-Query Perceiver design of our approach allows us to leverage the concept of self-distillation and to combine the advantages of a small model in the context of a low data regime with the rich feature representation of a larger model. We demonstrate the superior performance of our approach on three histopathological datasets, where we show improvement of up to 10% over state-of-the-art approaches.

The level of maturity reached by robust control theory techniques nowadays contributes to a considerable minimization of the development time of an end-to-end control design of a spacecraft system. The advantage offered by this framework is twofold: all system uncertainties can be included from the very beginning of the design process; the validation and verification (V\&V) process is improved by fast detection of worst-case configurations that could escape to a classical sample-based Monte Carlo simulation campaign. Before proceeding to the control synthesis and analysis, a proper uncertain plant model has to be available in order to push these techniques to their limits of performance. In this spirit, the Satellite Dynamics Toolbox Library (SDTlib) offers many features to model a spacecraft system in a multi-body fashion on SIMULINK. Parametric models can be easily built in a Linear Fractional Transformation (LFT) form by including uncertainties and varying parameters with minimal number of repetitions. Uncertain Linear Time Invariant (LTI) and uncertain Linear Parameter-Varying (LPV) controllers can then be synthesized and analyzed in a straightforward way. The authors present in this article a tutorial, that can be downloaded at https://nextcloud.isae.fr/index.php/s/XDfRfHntejHTmmp, to show how to deal with an end-to-end robust design of a spacecraft mission and to provide to researchers a benchmark to test their own algorithms.

Spreading processes, e.g. epidemics, wildfires and rumors, are often modeled on static networks. However, their underlying network structures, e.g. changing contacts in social networks, different weather forecasts for wildfires, are due to ever-changing circumstances inherently time-varying in nature. In this paper, we therefore, propose an optimization framework for sparse resource allocation for control of spreading processes over temporal networks with known connectivity patterns. We use convex optimization, in particular exponential cone programming, and dynamic programming techniques to bound and minimize the risk of an undetected outbreak by allocating budgeted resources each time step. We demonstrate with misinformation, epidemic and wildfire examples how the method can provide targeted allocation of resources., Comment: Conference submission, 8 pages. arXiv admin note: text overlap with arXiv:2110.07755

The 1$^{\text{st}}$ Workshop on Maritime Computer Vision (MaCVi) 2023 focused on maritime computer vision for Unmanned Aerial Vehicles (UAV) and Unmanned Surface Vehicle (USV), and organized several subchallenges in this domain: (i) UAV-based Maritime Object Detection, (ii) UAV-based Maritime Object Tracking, (iii) USV-based Maritime Obstacle Segmentation and (iv) USV-based Maritime Obstacle Detection. The subchallenges were based on the SeaDronesSee and MODS benchmarks. This report summarizes the main findings of the individual subchallenges and introduces a new benchmark, called SeaDronesSee Object Detection v2, which extends the previous benchmark by including more classes and footage. We provide statistical and qualitative analyses, and assess trends in the best-performing methodologies of over 130 submissions. The methods are summarized in the appendix. The datasets, evaluation code and the leaderboard are publicly available at https://seadronessee.cs.uni-tuebingen.de/macvi., Comment: MaCVi 2023 was part of WACV 2023. This report (38 pages) discusses the competition as part of MaCVi

Occluded person re-identification (ReID) is a person retrieval task which aims at matching occluded person images with holistic ones. For addressing occluded ReID, part-based methods have been shown beneficial as they offer fine-grained information and are well suited to represent partially visible human bodies. However, training a part-based model is a challenging task for two reasons. Firstly, individual body part appearance is not as discriminative as global appearance (two distinct IDs might have the same local appearance), this means standard ReID training objectives using identity labels are not adapted to local feature learning. Secondly, ReID datasets are not provided with human topographical annotations. In this work, we propose BPBreID, a body part-based ReID model for solving the above issues. We first design two modules for predicting body part attention maps and producing body part-based features of the ReID target. We then propose GiLt, a novel training scheme for learning part-based representations that is robust to occlusions and non-discriminative local appearance. Extensive experiments on popular holistic and occluded datasets show the effectiveness of our proposed method, which outperforms state-of-the-art methods by 0.7% mAP and 5.6% rank-1 accuracy on the challenging Occluded-Duke dataset. Our code is available at https://github.com/VlSomers/bpbreid.

The SoccerNet 2022 challenges were the second annual video understanding challenges organized by the SoccerNet team. In 2022, the challenges were composed of 6 vision-based tasks: (1) action spotting, focusing on retrieving action timestamps in long untrimmed videos, (2) replay grounding, focusing on retrieving the live moment of an action shown in a replay, (3) pitch localization, focusing on detecting line and goal part elements, (4) camera calibration, dedicated to retrieving the intrinsic and extrinsic camera parameters, (5) player re-identification, focusing on retrieving the same players across multiple views, and (6) multiple object tracking, focusing on tracking players and the ball through unedited video streams. Compared to last year's challenges, tasks (1-2) had their evaluation metrics redefined to consider tighter temporal accuracies, and tasks (3-6) were novel, including their underlying data and annotations. More information on the tasks, challenges and leaderboards are available on https://www.soccer-net.org. Baselines and development kits are available on https://github.com/SoccerNet., Comment: Accepted at ACM MMSports 2022

With the recent development of Deep Learning applied to Computer Vision, sport video understanding has gained a lot of attention, providing much richer information for both sport consumers and leagues. This paper introduces DeepSportradar-v1, a suite of computer vision tasks, datasets and benchmarks for automated sport understanding. The main purpose of this framework is to close the gap between academic research and real world settings. To this end, the datasets provide high-resolution raw images, camera parameters and high quality annotations. DeepSportradar currently supports four challenging tasks related to basketball: ball 3D localization, camera calibration, player instance segmentation and player re-identification. For each of the four tasks, a detailed description of the dataset, objective, performance metrics, and the proposed baseline method are provided. To encourage further research on advanced methods for sport understanding, a competition is organized as part of the MMSports workshop from the ACM Multimedia 2022 conference, where participants have to develop state-of-the-art methods to solve the above tasks. The four datasets, development kits and baselines are publicly available.

The learning losses caused by the COVID-19 pandemic make this an important time to identify strategies that can help students accelerate their learning. The Strategic Adolescent Reading Intervention (STARI) is a year-long supplemental reading intervention that is intentionally designed to address the motivational barriers of middle school students who are reading below grade level. STARI is intended to be used for a full class period (45 minutes minimum), at least three days a week, in addition to their regular English Language Arts class. STARI has a Tier 1 (strong) ESSA evidence rating based on a student-level randomized experiment conducted in SY 2013-14 in a northeastern state, which found that STARI had statistically significant positive effects on students' reading skills, including their word recognition, efficiency of basic reading comprehension, and morphological awareness. To explore the replicability of these results, the effect of STARI was evaluated in 11 middle schools in two urban school districts in SY 2021-22, a school year when instruction was still periodically disrupted by the COVID-19 pandemic. Eligible students were randomly assigned to the STARI class (n=183) or to a business-as-usual elective or reading class at their school (n=215). Students' English Language Arts (ELA) state test scores and their course marks at the end of the program year were obtained from school districts, and students' reading skills and reading motivation and self-efficacy were measured using a reading assessment and survey administered in Spring 2022. In one of the two study districts, the findings are challenging to interpret due to low response rates related to the COVID-19 pandemic; in the other study district, response rates were higher and more balanced across research groups. In that district, students in the STARI group had higher ELA state test scores than students in the control group and the difference between research groups is statistically significant at the 10 percent level (n=154, effect size=0.32, p- value=0.071). Students in the STARI group also had consistently higher scores on subtests of reading skills than students in the control group (n=80, effect size=0.15 to 0.32). Although not statistically significant, the magnitude of these effects mirrors the findings from the prior evaluation of STARI, suggesting that STARI is a promising strategy for helping struggling adolescent readers catch up across different settings, including a time period when instruction was still challenging because of the pandemic. An upcoming large-scale evaluation of STARI will continue to build evidence on its implementation and effectiveness in additional settings and for different subgroups of students.

Technical debt denotes shortcuts taken during software development, mostly for the sake of expedience. When such shortcuts are admitted explicitly by developers (e.g., writing a TODO/Fixme comment), they are termed as Self-Admitted Technical Debt or SATD. There has been a fair amount of work studying SATD management in Open Source projects, but SATD in industry is relatively unexplored. At the same time, there is no work focusing on developers' perspectives towards SATD and its management. To address this, we conducted an exploratory case study in cooperation with an industrial partner to study how they think of SATD and how they manage it. Specifically, we collected data by identifying and characterizing SATD in different sources (issues, source code comments, and commits) and carried out a series of interviews with 12 software practitioners. The results show: 1) the core characteristics of SATD in industrial projects; 2) developers' attitudes towards identified SATD and statistics; 3) triggers for practitioners to introduce and repay SATD; 4) relations between SATD in different sources; 5) practices used to manage SATD; 6) challenges and tooling ideas for SATD management.

In 2010, Congress authorized the Maternal, Infant, and Early Childhood Home Visiting (MIECHV) Program by enacting section 511 of the Social Security Act, 42 U.S.C. § 711, which also appropriated funding for fiscal years 2010 through 2014. Subsequently enacted laws extended funding for the program through fiscal year 2022. The program is administered by the Health Resources and Services Administration (HRSA) in collaboration with the Administration for Children and Families (ACF) within the U.S. Department of Health and Human Services (HHS). The initiation of the MIECHV Program began a major expansion of evidence-based home visiting programs for families living in at-risk communities. The legislation authorizing MIECHV recognized that there was considerable evidence about the effectiveness of home visiting, but also required an evaluation of MIECHV in its early years, which became the Mother and Infant Home Visiting Program Evaluation (MIHOPE). The overarching goal of MIHOPE is to learn whether families and children benefit from MIECHV-funded early childhood home visiting programs, and if so, how. ACF and HRSA were interested in ensuring that any additional follow-ups build on information from the earlier waves of data collection to the greatest extent possible, and that any proposed follow-up points build on one another. This long-term follow-up phase is called MIHOPE-LT. Following the introduction in Chapter 1, Chapter 2 presents results from the first follow-up with MIHOPE families, which occurred around the time children were 15 months of age. Chapter 3 summarizes evidence from existing long-term studies of the four MIHOPE home visiting models, on the effects of home visiting and benefit-cost analyses. Chapter 4 presents the research questions that MIHOPE-LT is designed to answer, the proposed timing of potential follow-up points, the sampling the study may use, and the data sources and outcome areas that may be examined. Chapter 5 describes the first MIHOPE-LT data collection effort, which began in January 2019 and is occurring approximately five years after women enrolled in MIHOPE, when their children are kindergarten age. This chapter describes plans for the kindergarten data collection effort, focusing on the constructs to be examined. The report concludes with Chapter 6, which highlights the contributions that MIHOPE-LT is expected to make to the research evidence on home visiting and discusses challenges the study could face in collecting data over many years as well as ways these challenges can be addressed. [This report was produced in partnership with Columbia University.]

Does technological change destroy or create jobs? New technologies may replace human workers, but can simultaneously create jobs if workers are needed to use these technologies or if new economic activities emerge. Furthermore, technology-driven productivity growth may increase disposable income, stimulating a demand-induced expansion of employment. To synthesize the existing knowledge on this question, we systematically review the empirical literature on the past four decades of technological change and its impact on employment, distinguishing between five broad technology categories (ICT, Robots, Innovation, TFP-style, Other). Overall, we find across studies that the labor-displacing effect of technology appears to be more than offset by compensating mechanisms that create or reinstate labor. This holds for most types of technology, suggesting that previous anxieties over widespread technology-driven unemployment lack an empirical base, at least so far. Nevertheless, low-skill, production, and manufacturing workers have been adversely affected by technological change, and effective up- and reskilling strategies should remain at the forefront of policy making along with targeted social support systems.

In this paper we propose a method for sparse dynamic allocation of resources to bound the risk of spreading processes, such as epidemics and wildfires, using convex optimization and dynamic programming techniques. Here, risk is defined as the risk of an outbreak, i.e. the product of the probability of an outbreak occurring over a time interval and the future impact of that outbreak, and we can allocate budgeted resources each time step to bound or minimize the risk. Our method in particular provides sparsity of resources, which is important due to the large network structures involved with spreading processes and has advantages when resources can not be distributed widely., Comment: Conference submission. arXiv admin note: text overlap with arXiv:2107.05878, arXiv:2003.07555

In this paper, we propose an optimization framework that combines surveillance schedules and sparse control to bound the risk of spreading processes such as epidemics and wildfires. Here, risk is considered the risk of an undetected outbreak, i.e. the product of the probability of an outbreak and the impact of that outbreak, and we can bound or minimize the risk by resource allocation and persistent monitoring schedules. The presented framework utilizes the properties of positive systems and convex optimization to provide scalable algorithms for both surveillance and intervention purposes. We demonstrate with different spreading process examples how the method can incorporate different parameters and scenarios such as a vaccination strategy for epidemics and the effect of vegetation, wind and outbreak rate on a wildfire in persistent monitoring scenarios., Comment: Journal submission

The effect of surface atom vibrations for H$_2$ scattering from a Cu(111) surface at different temperatures is being investigated for hydrogen molecules in their rovibrational ground state ($v$=0, $j$=0). We assume weakly correlated interactions between molecular degrees of freedom and surface modes through a Hartree product type wavefunction. While constructing the six dimensional effective Hamiltonian, we employ: (a) a chemically accurate potential energy surface according to the Static Corrugation Model [Wijzenbroek and Somers, J. Chem. Phys. 137, 054703 (2012)]; (b) normal mode frequencies and displacement vectors calculated with different surface atom interaction potentials within a cluster approximation; (c) initial state distributions for the vibrational modes according to Bose-Einstein probability factors. We carry out 6D quantum dynamics with the so-constructed effective Hamiltonian, and analyze sticking and state-to-state scattering probabilities. The surface atom vibrations affect the chemisorption dynamics. The results show physically meaningful trends both for reaction as well as scattering probabilities compared to experimental and other theoretical results., Comment: 38 pages, 10 figures

Children's school readiness as they enter kindergarten is an important predictor of their academic success and ultimately their long-term health and economic outcomes. Research shows that participation in high-quality early childhood education helps preschoolers gain the foundational language and literacy skills as well as the social-emotional competencies they need to be ready for kindergarten. Even so, free or affordable high-quality preschool options remain limited for families in many underserved communities. Jumpstart is a nonprofit organization that aims to address the school readiness gap. It partners with early childhood education centers across the country to increase their capacity to provide high-quality language and literacy instruction to children in underserved communities. This report presents the findings from an evaluation of Jumpstart's Foster Grandparent Program (JFG) in Los Angeles and Compton, California, which operated from fall 2011 to summer 2019, where Jumpstart's services were delivered by older adults. The evaluation, which focuses on the 2017-2018 academic year, examines whether the JFG program was implemented as intended and to what extent the presence of volunteers enhanced children's educational and developmental experience in the classroom. This evaluation found that: (1) The Foster Grandparents had consistently high attendance at training sessions and reported feeling adequately prepared to provide services to children; (2) The Foster Grandparents demonstrated a strong commitment to the program, and children experienced an increased number of adults in the classroom; (3) The Foster Grandparents implemented the curriculum-based sessions regularly and as intended; (4) The Foster Grandparents devoted a high number of hours to child-centered time (CCT); and (5) Children who received services from volunteers made gains in language and literacy development. [This report was written with Alec Gilfillan and Sara Staszak.]

In summer 2015, roughly six years after the Great Recession of 2007-2009 ended, the American media began reporting on an emerging teacher shortage. Public schools were reinstating programs and classes that they had cut during the recession, and student enrollment was increasing. Meanwhile, enrollment in teacher training programs was decreasing. Teacher shortages are especially problematic for schools serving mainly students from low-income families and communities of color. Teach For America (TFA), founded in 1989, has grown to become one of the largest providers of educators in the country for high-needs, underresourced schools. In 2015, TFA was awarded a Supporting Effective Educator Development (SEED) grant from the U.S. Department of Education to redesign its summer training for new teachers, or "corps members" (CMs). In summer 2016, TFA piloted a redesigned training model, developed specifically for adult learners, that incorporated more rigorous academic standards and training methods and practices with an eye toward college and career readiness. This report presents the findings from a study that examined how the redesigned training was implemented in a TFA training site (program group) and how it compared with the usual training at other TFA sites (comparison group). It also examined the promise of the redesigned training to improve the short-term outcomes of the first cohort of TFA teachers to receive it -- that is, CMs' use of the new instructional strategies and teaching practices, their perceptions of the value of the training, their commitment to teaching and to equity, and their retention rates in the TFA program. The lessons from this study have helped TFA address the challenges that were encountered during implementation and to strengthen the redesigned training model.

Maple trees (genus $\textit{Acer}$) accomplish the task of distributing objects to a wide area by producing seeds which are carried by the wind as they slowly descend to the ground, known as samaras. With the goal of supporting engineering applications, such as gathering environmental data over a broad area, we developed 3D-printed artificial samaras. Here, we compare the behavior of both natural and artificial samaras in both still-air laboratory experiments and wind dispersal experiments in the field. We show that the artificial samaras are able to replicate (within 1 standard deviation) the behavior of natural samaras in a lab setting. We further introduce the notion of windage to compare dispersal behavior, and show that the natural samara has the highest mean windage, corresponding to the longest flights during both high wind and low wind experimental trials. This research provides a bioinspired design for the dispersed deployment of sensors and provides a better understanding of wind-dispersal of both natural and artificial samaras.

We present geometric and dynamical modeling of the broad line region for the multi-wavelength reverberation mapping campaign focused on NGC 5548 in 2014. The dataset includes photometric and spectroscopic monitoring in the optical and ultraviolet, covering the H$\beta$, C IV, and Ly$\alpha$ broad emission lines. We find an extended disk-like H$\beta$ BLR with a mixture of near-circular and outflowing gas trajectories, while the C IV and Ly$\alpha$ BLRs are much less extended and resemble shell-like structures. There is clear radial structure in the BLR, with C IV and Ly$\alpha$ emission arising at smaller radii than the H$\beta$ emission. Using the three lines, we make three independent black hole mass measurements, all of which are consistent. Combining these results gives a joint inference of $\log_{10}(M_{\rm BH}/M_\odot) = 7.64^{+0.21}_{-0.18}$. We examine the effect of using the $V$ band instead of the UV continuum light curve on the results and find a size difference that is consistent with the measured UV-optical time lag, but the other structural and kinematic parameters remain unchanged, suggesting that the $V$ band is a suitable proxy for the ionizing continuum when exploring the BLR structure and kinematics. Finally, we compare the H$\beta$ results to similar models of data obtained in 2008 when the AGN was at a lower luminosity state. We find that the size of the emitting region increased during this time period, but the geometry and black hole mass remain unchanged, which confirms that the BLR kinematics suitably gauge the gravitational field of the central black hole., Comment: 26 pages, 19 figures, 1 table, accepted for publication in ApJ

In this letter we propose a method for sparse allocation of resources to control spreading processes -- such as epidemics and wildfires -- using convex optimization, in particular exponential cone programming. Sparsity of allocation has advantages in situations where resources cannot easily be distributed over a large area. In addition, we introduce a model of risk to optimize the product of the likelihood and the future impact of an outbreak. We demonstrate with a simplified wildfire example that our method can provide more targeted resource allocation compared to previous approaches based on geometric programming., Comment: 6 pages, conference submission

We report velocity-delay maps for prominent broad emission lines, Ly_alpha, CIV, HeII and H_beta, in the spectrum of NGC5548. The emission-line responses inhabit the interior of a virial envelope. The velocity-delay maps reveal stratified ionization structure. The HeII response inside 5-10 light-days has a broad single-peaked velocity profile. The Ly_alpha, CIV, and H_beta responses peak inside 10 light-days, extend outside 20 light-days, and exhibit a velocity profile with two peaks separated by 5000 km/s in the 10 to 20 light-day delay range. The velocity-delay maps show that the M-shaped lag vs velocity structure found in previous cross-correlation analysis is the signature of a Keplerian disk with a well-defined outer edge at R=20 light-days. The outer wings of the M arise from the virial envelope, and the U-shaped interior of the M is the lower half of an ellipse in the velocity-delay plane. The far-side response is weaker than that from the near side, so that we see clearly the lower half, but only faintly the upper half, of the velocity--delay ellipse. The delay tau=(R/c)(1-sin(i))=5 light-days at line center is from the near edge of the inclined ring, giving the inclination i=45 deg. A black hole mass of M=7x10^7 Msun is consistent with the velocity-delay structure. A barber-pole pattern with stripes moving from red to blue across the CIV and possibly Ly_alpha line profiles suggests the presence of azimuthal structure rotating around the far side of the broad-line region and may be the signature of precession or orbital motion of structures in the inner disk. Further HST observations of NGC 5548 over a multi-year timespan but with a cadence of perhaps 10 days rather than 1 day could help to clarify the nature of this new AGN phenomenon., Comment: 19 pages, 9 figures, ApJ in press

One-dimensional stellar evolution models have been successful at representing the structure and evolution of stars in diverse astrophysical contexts, but complications have been noted in the context of young, magnetically active stars, as well as close binary stars with significant tidal interactions. Numerous puzzles are associated with pre-main sequence and active main-sequence stars, relating to their radii, their colors, certain elemental abundances, and the coevality of young clusters, among others. A promising explanation for these puzzles is the distorting effects of magnetic activity and starspots on the structure of active stars. To assist the community in evaluating this hypothesis, we present the Stellar Parameters Of Tracks with Starspots (SPOTS) models, a grid of solar-metallicity stellar evolutionary tracks and isochrones which include a treatment of the structural effects of starspots. The models range from 0.1-1.3\msun and from spot-less to a surface covering fraction of 85\%, and are evolved from the pre-main sequence to the red giant branch (or 15~Gyr). We also produce two-temperature synthetic colors for our models using empirically-calibrated color tables. We describe the physical ingredients included in the SPOTS models and compare their predictions to other modern evolution codes. Finally, we apply these models to several open questions in the field of active stars, including the radii of young eclipsing binaries, the color scale of pre-main sequence stars, and the existence of sub-subgiants, demonstrating that our models can explain many peculiar features of active stars., Comment: 19 pages, 10 figures

We model the ultraviolet spectra of the Seyfert 1 galaxy NGC~5548 obtained with the Hubble Space Telescope during the 6-month reverberation-mapping campaign in 2014. Our model of the emission from NGC 5548 corrects for overlying absorption and deblends the individual emission lines. Using the modeled spectra, we measure the response to continuum variations for the deblended and absorption-corrected individual broad emission lines, the velocity-dependent profiles of Ly$\alpha$ and C IV, and the narrow and broad intrinsic absorption features. We find that the time lags for the corrected emission lines are comparable to those for the original data. The velocity-binned lag profiles of Ly$\alpha$ and C IV have a double-peaked structure indicative of a truncated Keplerian disk. The narrow absorption lines show delayed response to continuum variations corresponding to recombination in gas with a density of $\sim 10^5~\rm cm^{-3}$. The high-ionization narrow absorption lines decorrelate from continuum variations during the same period as the broad emission lines. Analyzing the response of these absorption lines during this period shows that the ionizing flux is diminished in strength relative to the far-ultraviolet continuum. The broad absorption lines associated with the X-ray obscurer decrease in strength during this same time interval. The appearance of X-ray obscuration in $\sim\,2012$ corresponds with an increase in the luminosity of NGC 5548 following an extended low state. We suggest that the obscurer is a disk wind triggered by the brightening of NGC 5548 following the decrease in size of the broad-line region during the preceding low-luminosity state., Comment: 50 pages, 30 figures, uses aastex62.cls. Accepted for publication in ApJ, 07/06/2019. High-level products page in MAST will go live after 7/15/2019. Replaced Figure 4 on 7/12/2019 to be more red/green color-blind friendly

In recent years, reinforcement learning has been successful in solving video games from Atari to Star Craft II. However, the end-to-end model-free reinforcement learning (RL) is not sample efficient and requires a significant amount of computational resources to achieve superhuman level performance. Model-free RL is also unlikely to produce human-like agents for playtesting and gameplaying AI in the development cycle of complex video games. In this paper, we present a hierarchical approach to training agents with the goal of achieving human-like style and high skill level in team sports games. While this is still work in progress, our preliminary results show that the presented approach holds promise for solving the posed multi-agent learning problem., Comment: Presented at ICML 2019 Workshop on Imitation, Intent, and Interaction (I3). arXiv admin note: substantial text overlap with arXiv:1903.10545

Unmanned Aerial Vehicle (UAV) path planning algorithms often assume a knowledge reward function or priority map, indicating the most important areas to visit. In this paper we propose a method to create priority maps for monitoring or intervention of dynamic spreading processes such as wildfires. The presented optimization framework utilizes the properties of positive systems, in particular the separable structure of value (cost-to-go) functions, to provide scalable algorithms for surveillance and intervention. We present results obtained for a 16 and 1000 node example and convey how the priority map responds to changes in the dynamics of the system. The larger example of 1000 nodes, representing a fictional landscape, shows how the method can integrate bushfire spreading dynamics, landscape and wind conditions. Finally, we give an example of combining the proposed method with a travelling salesman problem for UAV path planning for wildfire intervention., Comment: Accepted for ICRA 2019

Recently, there have been several high-profile achievements of agents learning to play games against humans and beat them. In this paper, we study the problem of training intelligent agents in service of game development. Unlike the agents built to "beat the game", our agents aim to produce human-like behavior to help with game evaluation and balancing. We discuss two fundamental metrics based on which we measure the human-likeness of agents, namely skill and style, which are multi-faceted concepts with practical implications outlined in this paper. We report four case studies in which the style and skill requirements inform the choice of algorithms and metrics used to train agents; ranging from A* search to state-of-the-art deep reinforcement learning. We, further, show that the learning potential of state-of-the-art deep RL models does not seamlessly transfer from the benchmark environments to target ones without heavily tuning their hyperparameters, leading to linear scaling of the engineering efforts and computational cost with the number of target domains., Comment: Accepted to IEEE Trans. Games

Background/Context: There is a convergence of evidence that the quality of children's early care and education (ECE) experiences is critical for promoting children's development (Gormley, Phillips, & Gayer, 2008). Curriculum combined with professional development is thought to be the "strongest hope" for promoting quality (Weiland & Yoshikawa, 2021). Yet, open questions remain about the effectiveness of such targeted investments to ensure that children, particularly low-income children, receive and benefit from high-quality child care and early education programming on a large scale. Purpose: This paper presents impact findings from The Expanding Children's Early Learning (ExCEL) Quality Study: Improving Preschool and Early Care and Education Instruction through Curricula, Coaching, and Training. This study aims to build evidence about the effectiveness of two theoretically distinct curricular and professional development intervention approaches by employing a rigorous experimental study design. In doing so, it aims to inform the field about which particular teacher practices may be most important to target to maximize improvements for child outcomes in ECE programming. This paper examines the following research questions: (1) What is the impact of the two intervention approaches on teacher-reported classroom practices?; and (2) What is the impact of the two intervention approaches on parent reports of children's behaviors and pre-academic skills? Setting: The study took place in 53 Head Start and community-based ECE settings in 4 localities across the U.S. in the 2019-2020 school year. Participating classrooms include 4-year-old classrooms and mixed-aged classrooms that serve 3- and 4-year-olds. Participants: The study sample consists of 145 classrooms. 1,955 parents provided consent for their children to participate in the study. The sample was racially and ethnically diverse -- 42% of children were Hispanic and 52% of children were Black. Twenty-three percent of parents had an Associate's degree or higher. Ninety-two percent of parents reported receiving assistance paying for their child's tuition at the center (via Head Start, childcare subsidy, public pre-K or other help). Intervention: The intervention approaches are: (1) A whole-child, global approach that uses Creative Curriculum (6th edition); and (2) an integrated, domain-specific approach with a specific scope and sequence that uses Connect4Learning. Teachers assigned to either of the intervention approaches received training in the summer prior to and throughout the program year. They also received ongoing coaching every other week during the program year consisting of a classroom observation and a coach-teacher meeting. Due to the pandemic, in-person professional development supports and most in-person instruction ended in March of the program year. Research Design: ExCEL Quality is a 3-group, cluster randomized controlled study where ECE centers were randomly assigned to one of two intervention approaches or to a preschool-as-usual control condition. Data Collection and Analysis: All data were collected via online surveys, fielded to parents and teachers in the summer of 2020. Teachers reported on the number of days per week that their classrooms utilized various activity settings and focused on different content areas. Parents reported on the Behavior Problem Index (Peterson & Zill, 1986), the social competence subscale of the Positive Behavior Scale (Quint, Bos, & Polit, 1997), and the early learnings skills items from the National Outcome Measure of Healthy and Ready to Learn (Paschall, Moore, Pina & Anderson, 2020). Data were analyzed using mixed models with random intercepts for center and, for child outcomes, classroom. Analyses for teacher practices outcomes controlled for teacher education, teacher experience, and classroom composition, and analyses for child outcomes controlled for the child's baseline vocabulary score (PLS-5; Zimmerman, Steiner & Pond, 2011) and child and family background characteristics. Findings/Results: See Tables 1 and 2 for results for teacher-reported classroom practices and parent-reported child outcomes, respectively. Activity Settings: Teachers reported implementing statistically significantly more small group activities in the both the whole-child, global approach group (ES=0.41, p < 0.05) and, at a trend level, the integrated, domain-specific approach group (ES=0.46, p < 0.10) than teachers in the control group. Teachers in the integrated, domain-specific approach group also reported implementing more large group activities than teachers in the control group at a trend level (ES = 0.46, p < 0.10). There are several additional moderately-sized effects (ES > 0.25) that are not statistically significant. Teachers in the integrated, domain-specific approach group reported implementing more read alouds and individual activities, and fewer choice time/centers activities, than teachers in the control group (ES's 0.27-0.55, ns). In addition, teachers in the whole-child, global approach group reported implementing more read alouds and individual activities than teachers in the control group (ES's 0.27- 0.44, ns). Content areas: There were no statistically significant differences in teachers' reports of content areas. However, there are several moderately-sized effects that are not statistically significant. Teachers in the integrated, domain-specific approach group reported implementing more language and literacy and math activities than teachers in the control group (ES's 0.25-0.46, ns). Teachers in the whole-child, global approach group reported implementing less dance, and more social studies and science, than teachers in the control group (ES's 0.26-1.01, ns). Children's behaviors: There were no statistically significant differences between the research groups on children's behaviors. All effect sizes were small in magnitude (ES's 0.01-0.06). Children's skills: Parents whose children attended centers in the integrated, domain-specific approach group reported higher child skill scores (counting, letter naming, etc.) compared to parents whose children attended centers in the control group (ES = 0.18, p < 0.05). There was no difference between child skill scores between the whole-child, global approach group and the control group (ES = 0.02, ns). Conclusions: The results of this study show promising patterns, suggesting that the two interventions approaches may differentially change the activity settings and content areas taught in classrooms. There is also some indication that the integrated, domain-specific approach may be more effective for improving children's constrained early learning skills, as reported by parents. Unfortunately, the methods used to assess outcomes in this study were limited due to the onset of the COVID-19 pandemic. Nevertheless, this study provides support for the benefits of structured curricula combined with professional development on teacher practices and child skills.

The last two decades have seen a dramatic increase in randomized controlled trials (RCTs) conducted in community colleges. Yet, there is limited empirical information on the design parameters necessary to plan the sample size for RCTs in this context. We provide empirical estimates of key design parameters, discussing lessons based on the pattern of estimates across outcomes, semesters, and studies. Nearly all RCTs in community colleges use student random assignment within blocks (colleges or cohorts). Key design parameters for the minimum detectable true effect (MDTE) are the within-block outcome standard deviation ([sigma][subscript ¦][subscript S]) and the within-block outcome variance explained by baseline covariates like student characteristics (R[superscript 2][subscript ¦][subscript S). The main analyses use student-level data from 8 to 14 RCTs including 5,649-7,099 students (depending on the outcome) with follow-up data for 3 years. A public database created for this paper includes parameter estimates for 30 RCTs including 65,637 students. Enrollment, credits earned, credential attainment, and grade point average. The within-block standard deviation ([sigma]jS) and therefore the MDTE can be much larger in later semesters for enrollment outcomes and cumulative credits earned. There is substantial variation across studies in [sigma][subscript ¦][subscript S] for degree attainment. Baseline covariates explain less than 10% of the variation in student outcomes. When planning the sample size for a study, researchers should be mindful of the follow-up period, use a range of values to calculate the MDTE for outcomes that vary across studies, and assume a value of R[superscript 2][subscript ¦][subscript S] between 0 and 0.05. [This is the online first version of an article published in "Evaluation Review."]

The Communities In Schools (CIS) Model of Integrated Student Supports aims to reduce dropout rates by providing students with integrated and tiered support services based on their levels of need. The model includes preventive services that are available to all students (Level 1 services) as well as intensive, targeted, and sustained services provided through case management (Level 2 services) for the 5 percent to 10 percent of students who display significant risk factors for dropping out, such as poor academic performance, high absentee rates, or behavioral problems. This study, which is based on a quasi-experimental research design, examines the CIS model's effect on students' outcomes in elementary schools, middle schools, and high schools. The sample for this study includes 53 CIS schools in Texas and North Carolina (14 high schools, 15 middle schools, and 24 elementary schools) that started implementing the CIS model between 2005 and 2008. The study compares these CIS schools with 78 matched comparison schools (18 high schools, 24 middle schools, and 36 elementary schools). For the high schools, the main finding is that on-time graduation rates increased--and dropout rates decreased--in the study schools after the CIS model was launched. Graduation and dropout rates also improved in the comparison schools, so it is unclear whether the CIS model was "more" effective than the strategies used by the comparison schools. The findings do suggest that the CIS model may be at least as effective as these other approaches. This executive summary discusses what the CIS Model is, how it was evaluated, and if it improved school outcomes. [For the full report, see ED574040.]

While high school graduation rates are on the rise nationwide, too many students still never reach that milestone, with 7,000 on average dropping out every day. Recognizing that many students need additional support to succeed in school, Communities In Schools (CIS) works to provide and connect students with integrated support services to keep them on a path to graduation. CIS makes some services broadly available to all students at a school, and provides individual case management to those deemed most at risk of dropping out. This report presents the final implementation and impact findings from a two-year randomized controlled trial of CIS case management. This trial is half of a two-pronged national evaluation, the other half being a quasi-experimental study of the whole-school model. The report describes the implementation and effects of CIS case management in 24 mostly urban, low-income secondary schools in two states during the 2013-2014 school year. The study found that CIS case management services succeeded in getting targeted students into more support activities and improving several of their nonacademic outcomes. However, findings also showed that these services did not have a positive effect on students' attendance, academic performance, or behavior after two years. The study also estimated the effect of case management on students' nonacademic and more traditional school outcomes. The final chapter of this report includes implications for practice based on the evaluation findings. The report concludes by considering the results of this random assignment study of case management together with the results of the quasi-experimental analysis of the whole-school model. Taken together, the results suggest that whole-school models of integrated student support offer the promise of positive effects. The results also indicate, however, some areas to which support providers may need to pay close attention, to ensure that students receive services that address their specific needs and that benefit them above and beyond the services already available.

Internal mixing on the giant branch is an important process which affects the evolution of stars and the chemical evolution of the galaxy. While several mechanisms have been proposed to explain this mixing, better empirical constraints are necessary. Here, we use [C/N] abundances in 26097 evolved stars from the SDSS-IV/APOGEE-2 Data Release 14 to trace mixing and extra mixing in old field giants with -1.7< [Fe/H] < 0.1. We show that the APOGEE [C/N] ratios before any dredge-up occurs are metallicity dependent, but that the change in [C/N] at the first dredge-up is metallicity independent for stars above [Fe/H] ~ -1. We identify the position of the red giant branch (RGB) bump as a function of metallicity, note that a metallicity-dependent extra mixing episode takes place for low-metallicity stars ([Fe/H] <-0.4) 0.14 dex in log g above the bump, and confirm that this extra mixing is stronger at low metallicity, reaching $\Delta$ [C/N] = 0.58 dex at [Fe/H] = -1.4. We show evidence for further extra mixing on the upper giant branch, well above the bump, among the stars with [Fe/H] < -1.0. This upper giant branch mixing is stronger in the more metal-poor stars, reaching 0.38 dex in [C/N] for each 1.0 dex in log g. The APOGEE [C/N] ratios for red clump (RC) stars are significantly higher than for stars at the tip of the RGB, suggesting additional mixing processes occur during the helium flash or that unknown abundance zero points for C and N may exist among the red clump RC sample. Finally, because of extra mixing, we note that current empirical calibrations between [C/N] ratios and ages cannot be naively extrapolated for use in low-metallicity stars specifically for those above the bump in the luminosity function.

Circadian clocks play a pivotal role in orchestrating numerous physiological and developmental events. Waveform shapes of the oscillations of protein abundances can be informative about the underlying biochemical processes of circadian clocks. We derive a mathematical framework where waveforms do reveal hidden biochemical mechanisms of circadian timekeeping. We find that the cost of synthesizing proteins with particular waveforms can be substantially reduced by rhythmic protein half-lives over time, as supported by previous plant and mammalian data, as well as our own seedling experiment. We also find that previously-enigmatic, cyclic expression of positive arm components within the mammalian and insect clocks allows both a broad range of peak time differences between protein waveforms and the symmetries of the waveforms about the peak times. Such various peak-time differences may facilitate tissue-specific or developmental stage-specific multicellular processes. Our waveform-guided approach can be extended to various biological oscillators, including cell-cycle and synthetic genetic oscillators., Comment: Supplementary material is available at the journal website

Quantum electrodynamic fluctuations cause an attractive force between metallic surfaces. At separations where the finite speed of light affects the interaction, it is called the Casimir force. Thermal motion determines the fundamental sensitivity limits of its measurement at room temperature, but several other systematic errors contribute uncertainty as well and become more significant in air relative to vacuum. Here we discuss the viability of several measurement techniques in air (force modulation, frequency modulation, and quasi-static deflection), characterize their sensitivity and accuracy by identifying several dominant sources of uncertainty, and compare the results to the fundamental sensitivity limits dictated by thermal motion and to the uncertainty inherent to calculations of the Casimir force. Finally, we explore prospects for mitigating the sources of uncertainty to enhance the range and accuracy of Casimir force measurements., Comment: 21 pages, 16 Figures In version 2: section on frequency modulation based measurements reduced, section on the estimation of the residual hydrodynamic force added

Radius inflation continues to be explored as a peculiar occurrence among magnetically active, low-mass stars. Recently Somers et. al. 2017 showed that radius inflation among low-mass stars in the young open cluster M45 (Pleiades Cluster) is correlated to the rotation rate: faster rotators are more inflated. Here we extend that work to a sample of 68 stars of the older open Hyades Cluster. We derive the stars' spectral energy distributions to measure their bolometric fluxes. With spectroscopically defined $T_{\rm eff}$ and {\it Gaia\/} distances we calculate stellar radii using the Stefan-Boltzmann relation. We find numerous stars that exhibit significant (3-4$\sigma$) radius inflation relative to a nominal cluster isochrone. We compare these results to that of the younger Pleiades and consider radius inflation as a function of open cluster evolution. We find that unlike the Pleiades, there is not a statistically significant correlation between radius inflation and stellar rotation period. However, we do find that most inflated stars have (rapid) rotational Rossby numbers of 0.1--0.2, such that the correlation of radius inflation with Rossby number is statistically significant at 99.98\% confidence. Since the canonical rotation-activity relation of low-mass stars is understood to result from the connection between magnetic activity and surface convection, our results imply that magnetic activity within the convective layers of low-mass stars is what preferentially drives radius inflation, Comment: 13 Pages, 9 Figures, 2 Tables

Exciton spin and related optical polarization in self-assembled InAs/In$_{0.53}$Ga$_{0.23}$Al$_{0.24}$As/InP(001) quantum dashes emitting at 1.55 {\mu}m are investigated by means of polarization- and time-resolved photoluminescence, as well as photoluminescence excitation spectroscopy, at cryogenic temperature. We investigate the influence of highly non-resonant and quasi-resonant optical spin pumping conditions on spin polarization and spin memory of the quantum dash ground state. We show that a spin pumping scheme, utilizing the longitudinal-optical-phonon-mediated coherent scattering process, can lead to the polarization degree above 50%. We discuss the role of intrinsic asymmetries in the quantum dash that influence values of the degree of polarization and its time evolution., Comment: 4 pages, 3 figures

We present the first results from a reverberation-mapping campaign undertaken during the first half of 2012, with additional data on one AGN (NGC 3227) from a 2014 campaign. Our main goals are (1) to determine the black hole masses from continuum-Hbeta reverberation signatures, and (2) to look for velocity-dependent time delays that might be indicators of the gross kinematics of the broad-line region. We successfully measure Hbeta time delays and black hole masses for five AGNs, four of which have previous reverberation mass measurements. The values measured here are in agreement with earlier estimates, though there is some intrinsic scatter beyond the formal measurement errors. We observe velocity dependent Hbeta lags in each case, and find that the patterns have changed in the intervening five years for three AGNs that were also observed in 2007., Comment: Accepted for publication on ApJ; 32 pages, 16 figures, 10 tables

The presence of certain elements within a star, and by extension its planet, strongly impacts the formation and evolution of the planetary system. The positive correlation between a host star's iron-content and the presence of an orbiting giant exoplanet has been confirmed; however, the importance of other elements in predicting giant planet occurrence is less certain despite their central role in shaping internal planetary structure. We designed and applied a machine learning algorithm to the Hypatia Catalog (Hinkel et a. 2014) to analyze the stellar abundance patterns of known host stars to determine those elements important in identifying potential giant exoplanet host stars. We analyzed a variety of different elements ensembles, namely volatiles, lithophiles, siderophiles, and Fe. We show that the relative abundances of oxygen, carbon, and sodium, in addition to iron, are influential indicators of the presence of a giant planet. We demonstrate the predictive power of our algorithm by analyzing stars with known giant planets and found that they had median 75% prediction score. We present a list of ~350 stars with no currently discovered planets that have a $\geq$90% prediction probability likelihood of hosting a giant exoplanet. We investigated archival HARPS data and found significant trends that HIP62345, HIP71803, and HIP10278 host long-period giant planet companions with estimated minimum $M_p\sin(i)$ values of 3.7, 6.8, and 8.5 M$_{J}$, respectively. We anticipate that our findings will revolutionize future target selection, the role that elements play in giant planet formation, and the determination of giant planet interior structure models., Comment: 14 pages, 8 figures, 1 Table (full csv table of planet prediction scores included in source files), accepted to the Astrophysical Journal (ApJ)

We present an analysis of a slightly eccentric ($e=0.05$), partially eclipsing long-period ($P = 69.73$ d) main sequence binary system (WOCS 12009, Sanders 1247) in the benchmark old open cluster M67. Using Kepler K2 and ground-based photometry along with a large set of new and reanalyzed spectra, we derived highly precise masses ($1.111\pm0.015$ and $0.748\pm0.005 M_\odot$) and radii ($1.071\pm0.008\pm0.003$ and $0.713\pm0.019\pm0.026 R_\odot$, with statistical and systematic error estimates) for the stars. The radius of the secondary star is in agreement with theory. The primary, however, is approximately $15\%$ smaller than reasonable isochrones for the cluster predict. Our best explanation is that the primary star was produced from the merger of two stars, as this can also account for the non-detection of photospheric lithium and its higher temperature relative to other cluster main sequence stars at the same $V$ magnitude. To understand the dynamical characteristics (low measured rotational line broadening of the primary star and the low eccentricity of the current binary orbit), we believe that the most probable (but not the only) explanation is the tidal evolution of a close binary within a primordial triple system (possibly after a period of Kozai-Lidov oscillations), leading to merger approximately 1Gyr ago. This star appears to be a future blue straggler that is being revealed as the cluster ages and the most massive main sequence stars die out., Comment: 33 pages, 11 figures, accepted to AJ, photometry files will be available with the electronic journal article

We present optical continuum lags for two Seyfert 1 galaxies, MCG+08-11-011 and NGC 2617, using monitoring data from a reverberation mapping campaign carried out in 2014. Our light curves span the ugriz filters over four months, with median cadences of 1.0 and 0.6 days for MCG+08-11-011 and NGC\,2617, respectively, combined with roughly daily X-ray and near-UV data from Swift for NGC 2617. We find lags consistent with geometrically thin accretion-disk models that predict a lag-wavelength relation of $\tau \propto \lambda^{4/3}$. However, the observed lags are larger than predictions based on standard thin-disk theory by factors of 3.3 for MCG+08-11-011 and 2.3 for NGC\,2617. These differences can be explained if the mass accretion rates are larger than inferred from the optical luminosity by a factor of 4.3 in MCG+08-11-011 and a factor of 1.3 in NGC\,2617, although uncertainty in the SMBH masses determines the significance of this result. While the X-ray variability in NGC\,2617 precedes the UV/optical variability, the long 2.6 day lag is problematic for coronal reprocessing models., Comment: Accepted to ApJ, please send comments to faus@mit.edu. 24 pages, 8 figures

The Kilodegree Extremely Little Telescope (KELT) has been surveying more than $70\%$ of the celestial sphere for nearly a decade. While the primary science goal of the survey is the discovery of transiting, large-radii planets around bright host stars, the survey has collected more than $10^6$ images, with a typical cadence between $10-30$ minutes, for more than $4$ million sources with apparent visual magnitudes in the approximate range $7

We study the occurrence of spectroscopic binaries in young star-forming regions using the INfrared Spectroscopy of Young Nebulous Clusters(IN-SYNC) survey, carried out in SDSS-III with the APOGEE spectrograph. Multi-epoch observations of thousands of low-mass stars in Orion A, NGC 2264, NGC 1333, IC 348, and the Pleiades have been carried out, yielding H-band spectra with a nominal resolution of R=22,500 for sources with H $\le$ 12 mag. Radial velocity precisions of $\sim$0.3 $km\:s^{-1}$ were achieved, which we use to identify radial velocity variations indicative of undetected companions. We use Monte Carlo simulations to assess the types of spectroscopic binaries to which we are sensitive, finding sensitivity to binaries with orbital periods $< 10^{4}$ d, for stars with $2500 {\rm K} \le T_\mathrm{eff} \le 6000 {\rm K}$ and $\it{v} \sin \it{i}$ $\le$ 100 $km\:s^{-1}$. Using Bayesian inference, we find evidence for a decline in the spectroscopic binary fraction, by a factor of 3-4 from the age of our pre-main-sequence sample to the Pleiades age . The significance of this decline is weakened if spot-induced radial-velocity jitter is strong in the sample, and is only marginally significant when comparing any one of the pre-main-sequence clusters against the Pleiades. However, the same decline in both sense and magnitude is found for each of the five pre-main-sequence clusters, and the decline reaches statistical significance of greater than 95% confidence when considering the pre-main-sequence clusters jointly. Our results suggest that dynamical processes disrupt the widest spectroscopic binaries ($P_{\rm orb} \approx 10^3 - 10^4$ d) as clusters age, indicating that this occurs early in the stars' evolution, while they still reside within their nascent clusters., Comment: 21 pages, 9 Figures