High school athletes and athletic leaders gain higher testscores

Does participating in high school athletics programs help or hinder students from getting good grades? In new research, Ryan Yeung finds a link between academic achievement and athletic participation in high school. Using data from a study begun in 1980, he finds that those students who were athletes or athletic leaders had grades between 8 and 13 percent higher than those that were non-athletes. He argues that the skills developed as a participant or leader on an athletic team are also useful in the academic sphere

The Effect of Immigrant Composition on Student Achievement: Evidence from New York City

There has been a large body of recent literature focused on the effects of school composition on student outcomes. These studies have focused on peer group characteristics such as achievement, gender composition, ethnic and racial composition, and socioeconomic composition. This area of research has been commonly called peer effects. A relatively unexplored area of peer effects research involves the effect of immigrant children on their schoolmates. Because of the heterogeneity between immigrant groups, this study focuses on East Asian and Dominican immigrant children. As these two groups are on opposite sides of the socioeconomic spectrum, comparing results of the two analyses should provide a reasonably complete picture of immigrant composition effects. The data for this study come from New York City. New York City is arguably the ideal place to study immigration. Immigrants from though out the world attend New York City schools. While New York remains an outlier, it is quickly becoming the norm. In recent decades, various parts of the country that have not experienced large waves of immigration are doing so now. The experience of New York has potential to inform the larger debate on the cost of providing public services to immigrants. If immigrant children have negative effects on their schoolmates, they will increase the cost of education. On the other hand, if they have positive effects, they can serve as a positive externality and reduce the cost of public education. The estimation of peer effects is a daunting challenge. One of the most challenging of these problems is called the selection problem. The selection problem occurs because immigrant children are not randomly assigned to classes, schools, or neighborhoods. To overcome this problem, this study uses credibly exogenous variation that occurs as a student progresses with a cohort within a school. The results suggest that both East Asian and Dominican immigrant composition has a negative and significant effect on student achievement. This effect occurs for all subgroups and for both English-Language Arts and mathematics. Surprisingly, this immigrant composition effect is not driven by ELL status. This coefficient can be considered something of a reduced-form measure of immigrant composition effect. Regressions that control for other country variables suggest that schools with growth in East Asian and Dominican immigrant composition also have growth in other forms of immigrant composition. When including these other variables, the results suggest a cultural effect. East Asian immigrants have positive effects in mathematics while Dominican immigrants continue to have negative effects, though at smaller magnitudes. These results suggest that culture matters. As a matter of policy though, given that immigrants move together, it is not practical to separate specific ethnic immigrant effects. Rather policy recommendation should look at the reduced form effects. Potential policy recommendations include additional resources for immigrant education such as English as a second language and civics classes or newcomer schools. Ethnographic research on how immigrant children interact with their classmates and schools could also be valuable in deciphering the exact mechanism behind this negative effect

PRELIMINARY DEVELOPMENT, TESTING, AND OPTIMIZATION OF AN ALL-REFRACTORY PARTICLE HEATING RECEIVER

The solar energy receiver is an essential component of particle-based Concentrating Solar Power (CSP) plants. Particle based CSP systems promise higher operating temperatures and more cost-effective thermal energy storage than existing gas and molten salt systems. Two general types of Particle Heating Receivers (PHR) are under development: the free-falling curtain concept being developed by Sandia National Labs (SNL) and an obstructed flow concept being developed by King Saud University (KSU) and Georgia Institute of Technology (GIT). This research focuses on the work that has been done to improve and implement the obstructed flow PHR concept. Recent work has been devoted to developing a Discrete Structure Refractory Particle Heat Receiver (DS-RPHR) suitable for cavity installation in a 6.6 MWth pre-commercial CSP demonstration plant. The simplest suitable configuration is 5 flat ceramic plates, or absorber panels, arranged in an arc, with a 15° angle of inclination, to improve particle retention in the system. To increase particle residence time various obstructions have been considered; and in the design considered in this thesis, quartz rods are placed onto the back plane of the DS-RPHR. A lab scale prototype has been constructed and tested with induced particle flow. This design has been extensively modeled using both SolTrace and ANSYS FLUENT to evaluate thermal and optical performance. This paper will discuss integration of SolTRACE generated heat flux, free convection, and thermal performance analysis in ANSYS FLUENT. The results of this work will offer a predictive model to calculate the thermal losses in the DS-RPHR. Overall modeling and experimental results show the suitability of the DS-RPHR design for potential use in the proposed 6.6 MWth pre-commercial demonstration plant.M.S

Memory Bias for Threat-Related Information in Social Anxiety

Biases in what is committed to memory and ultimately remembered are a key feature in individuals plagued by high levels of social anxiety. By selectively remembering the unfavourable aspects of past social situations, early work has suggested that those high in social anxiety may have enhanced memory for threat-related information. Other research has suggested that because threatening material is also most often an unwanted, irrelevant distraction to the individual’s task at hand, the effects of threat-relatedness have not been properly disentangled from the effects of task-relevance. In other words, although memories of social blunders are threat-related, as long as the individual is not intentionally attempting to re-experience their failures, these memories can also be seen as irrelevant distractors. Some have even suggested that highly socially anxious individuals may instead have enhanced memory for all distractors, not simply threat-related ones. Over the course of this thesis, the aims were to investigate the conditions under which a memory bias in high social anxiety is produced or eliminated, as well as to probe for the potential cognitive mechanisms underlying the bias. In Experiment 1, the interaction between threat-relatedness and task-relevance was examined in individuals high compared to low in social anxiety. Using a target-distractor paradigm, participants either saw a series of neutral or threatening target words, which they were asked to commit to memory; each target was simultaneously paired with a neutral or threatening distractor word. Highly socially anxious participants showed enhanced memory for threat-related distractors on a subsequent recognition test, but only when the targets they were asked to commit to memory were also threat-related. Such a result suggests that it is only when they are primed, or in a socially threatening mindset, that a memory bias for threat-specific, and irrelevant information, emerges. Prior to becoming part of long-term memory, information is first maintained in one’s working memory buffer. Thus, it stands to reason that any bias in the processing of threatening material may originate at the level of working memory. In Experiment 2, a series of word span tasks was administered to participants that were high or low in social anxiety. All participants performed three word span tasks, each with a unique word list constructed to have a different level of threat-relatedness: neutral, general threat, or social threat. We found that those high relative to low in social anxiety had reduced working memory capacity for words related to socially threatening concepts, compared to words related to neutral or generally threatening concepts. These findings suggest a bias exists in terms of what is maintained in a working memory buffer, which could explain how long-term memories are ultimately distorted in favour of threatening information. In Experiment 3, the specificity of the working memory bias was further examined. We aimed to rule out a deficit in ability to cluster semantically similarity words, as an alternative explanation for the reduction to working memory capacity observed in Experiment 2. Even after introducing an additional word span task that was both neutral and semantically similar, highly socially anxious individuals still only showed reduced working memory capacity for socially threatening words. In demonstrating that long-term and working memory biases only emerge under specific conditions, the current research suggests that the characterization of individuals with high social anxiety as having poor general attentional control is inaccurate. This thesis specifies the precise conditions, and potential mechanism of action, that lead to a memory bias in such individuals

The Persistence of Involuntary Memory: Analyzing Phenomenology, Links to Mental Health, and Content

In daily life, memories of one’s personal past are often retrieved involuntarily (i.e., unintentionally and effortlessly). Termed involuntary autobiographical memories (IAMs), recent evidence suggests that these are often recurrent (i.e., the same event is remembered repetitively), though controversy surrounds their basic nature. Some research suggests that they are mostly positive or benign, whereas others suggest that they directly contribute to mental health disorders. Here, we show that while recurrent IAMs are common and frequent in general populations, they consistently predict symptoms of mental health disorders. In Study 1, we characterized recurrent IAMs in a large-scale survey of undergraduates. Most participants had experienced recurrent IAMs within the past year (52%), most of which were self-rated as negative in valence (52%). Experiencing negative recurrent IAMs predicted significantly more symptoms of depression, posttraumatic stress, social anxiety, and general anxiety. In Studies 2a and 2b, we examined whether age and trait emotion regulation might modulate recurrent IAMs, because older adults are well-known to have enhanced emotion regulation compared to younger adults. Results indicated that age (Study 2a) reversed the valence distribution: younger adults’ recurrent IAMs were mostly negative, whereas older adults’ were mostly positive. Further, trait emotion regulation (Study 2b) also modulated valence in a sample of younger adults: high emotion regulators were significantly less likely to report negative recurrent IAMs. Regardless of age or trait emotion regulation, experiencing negative recurrent IAMs again predicted greater symptoms of mental health disorders. In Study 3, we asked how analyzing content (e.g., written descriptions of recurrent IAMs) might expand our understanding of these memories, beyond self-reported valence ratings. We developed the first adaptation of computational methods (e.g., machine learning) to understand autobiographical memory content, enabling us to discover content categories (“topics”) in recurrent IAMs. We found that participants experienced recurrent IAMs about a variety of events, ranging from the mundane to the extreme. In Study 4, we extended this computational approach to measure how content might predict mental health above and beyond self-reported valence ratings. Results indicated that elevated symptoms of each disorder were uniquely related to recurrent IAMs about specific topics. Our results suggest that it is imprecise to say that negative recurrent IAMs are related to increased symptoms – our current work pinpoints which specific topics in recurrent IAMs predict mental health. This dissertation provides insight into the nature of recurrent IAMs in large samples of general populations. Importantly, this dissertation distinguishes how these memories and their relationships to mental health are modulated by individual differences. Finally, this dissertation provides a novel framework and methodology (e.g., computational text analysis) for analyzing autobiographical memory content in concert with phenomenology, opening avenues for research to be conducted at an unprecedented scope and scale

DeepAdjoint: An All-in-One Photonic Inverse Design Framework Integrating Data-Driven Machine Learning with Optimization Algorithms

In recent years, hybrid design strategies combining machine learning (ML) with electromagnetic optimization algorithms have emerged as a new paradigm for the inverse design of photonic structures and devices. While a trained, data-driven neural network can rapidly identify solutions near the global optimum with a given dataset's design space, an iterative optimization algorithm can further refine the solution and overcome dataset limitations. Furthermore, such hybrid ML-optimization methodologies can reduce computational costs and expedite the discovery of novel electromagnetic components. However, existing hybrid ML-optimization methods have yet to optimize across both materials and geometries in a single integrated and user-friendly environment. In addition, due to the challenge of acquiring large datasets for ML, as well as the exponential growth of isolated models being trained for photonics design, there is a need to standardize the ML-optimization workflow while making the pre-trained models easily accessible. Motivated by these challenges, here we introduce DeepAdjoint, a general-purpose, open-source, and multi-objective "all-in-one" global photonics inverse design application framework which integrates pre-trained deep generative networks with state-of-the-art electromagnetic optimization algorithms such as the adjoint variables method. DeepAdjoint allows a designer to specify an arbitrary optical design target, then obtain a photonic structure that is robust to fabrication tolerances and possesses the desired optical properties - all within a single user-guided application interface. Our framework thus paves a path towards the systematic unification of ML and optimization algorithms for photonic inverse design

Gaussian Shell Maps for Efficient 3D Human Generation

Efficient generation of 3D digital humans is important in several industries, including virtual reality, social media, and cinematic production. 3D generative adversarial networks (GANs) have demonstrated state-of-the-art (SOTA) quality and diversity for generated assets. Current 3D GAN architectures, however, typically rely on volume representations, which are slow to render, thereby hampering the GAN training and requiring multi-view-inconsistent 2D upsamplers. Here, we introduce Gaussian Shell Maps (GSMs) as a framework that connects SOTA generator network architectures with emerging 3D Gaussian rendering primitives using an articulable multi shell--based scaffold. In this setting, a CNN generates a 3D texture stack with features that are mapped to the shells. The latter represent inflated and deflated versions of a template surface of a digital human in a canonical body pose. Instead of rasterizing the shells directly, we sample 3D Gaussians on the shells whose attributes are encoded in the texture features. These Gaussians are efficiently and differentiably rendered. The ability to articulate the shells is important during GAN training and, at inference time, to deform a body into arbitrary user-defined poses. Our efficient rendering scheme bypasses the need for view-inconsistent upsamplers and achieves high-quality multi-view consistent renderings at a native resolution of $512 \times 512$ pixels. We demonstrate that GSMs successfully generate 3D humans when trained on single-view datasets, including SHHQ and DeepFashion.Comment: Project page : https://rameenabdal.github.io/GaussianShellMaps