Search

A crucial question in active patient care is determining if a treatment is having the desired effect, especially when changes are subtle over short periods. We propose using inter-patient data to train models that can learn to detect these fine-grained changes within a single patient. Specifically, can a model trained on multi-patient scans predict subtle changes in an individual patient's scans? Recent years have seen increasing use of deep learning (DL) in predicting diseases using biomedical imaging, such as predicting COVID-19 severity using lung ultrasound (LUS) data. While extensive literature exists on successful applications of DL systems when well-annotated large-scale datasets are available, it is quite difficult to collect a large corpus of personalized datasets for an individual. In this work, we investigate the ability of recent computer vision models to learn fine-grained differences while being trained on data showing larger differences. We evaluate on an in-house LUS dataset and a public ADNI brain MRI dataset. We find that models pre-trained on clips from multiple patients can better predict fine-grained differences in scans from a single patient by employing contrastive learning., Comment: Under review at ISBI 2025 conference

We present MELODI, a novel memory architecture designed to efficiently process long documents using short context windows. The key principle behind MELODI is to represent short-term and long-term memory as a hierarchical compression scheme across both network layers and context windows. Specifically, the short-term memory is achieved through recurrent compression of context windows across multiple layers, ensuring smooth transitions between windows. In contrast, the long-term memory performs further compression within a single middle layer and aggregates information across context windows, effectively consolidating crucial information from the entire history. Compared to a strong baseline - the Memorizing Transformer employing dense attention over a large long-term memory (64K key-value pairs) - our method demonstrates superior performance on various long-context datasets while remarkably reducing the memory footprint by a factor of 8.

The goal of this study was to provide insights on the extent to which high school students receive support for college and career planning, the mechanisms through which they receive this information, and how this varies across students and settings using data from a multi-state, multi-study project focused on advising for postsecondary transitions. Conducted by a collaborative team from three organizations--MDRC, the Research Alliance for New York City Schools, and University of North Carolina at Greensboro's Early College Research Center--this study examines advising structures and practices across 17 high schools in North Carolina and New York City, representing diverse school types, including career and technical education (CTE)-focused schools as well as comprehensive schools offering CTE for elective credit. Findings indicate that advising practices were largely shaped by each school's academic focus and staff expectations. CTE-focused schools, for example, typically were dually focused on preparing students for postsecondary education and careers through CTE programs that included structured partnerships with colleges and employers. In contrast, comprehensive schools varied in their emphasis on college and career advising and the degree to which students were expected to participate in career exploration and preparation activities. Some comprehensive schools expected or encouraged higher levels of CTE participation, whereas others offered CTE without requiring students to participate or commit to a pathway. These schools also differed in how career advising was integrated into the overall approach to postsecondary planning, with some focusing primarily on college preparation and others helping students develop career knowledge and skills as part of a broader college and workforce readiness strategy. Additionally, the study highlights the important role that advisors and other school personnel, particularly CTE teachers, play in helping students link their interests with available school-supported preparation activities and underscores the benefits of personalized, proactive advising tailored to individual student needs. [This paper was created by the Early College Research Center, UNC Greensboro.]

This paper presents a synthesis of three collaboratively conducted studies exploring the relationship between career-focused advising and the postsecondary transition with an emphasis on students enrolled in Career and Technical Education (CTE). The studies included a survey administered to high school seniors in New York City schools, an impact study of career coaches in North Carolina, and a qualitative study looking at implementation of advising in New York City and North Carolina. Key themes coming from the three studies included: 1) postsecondary transition advising is a schoolwide phenomenon; 2) the content and intensity shifts over a student's time in high school; 3) advising tends to focus more on college with less attention paid to career opportunities except in settings with an explicit career emphasis; 4) students with more advantaged backgrounds tend to participate in advising at higher levels; 5) higher participation in advising is associated with an increase in CTE-related activities; 6) college-focused advising is associated with higher enrollment in four-year schools while career-focused advising is associated with higher enrollment in two-year and lower enrollment in four-year institutions; and 7) additional research on how advising outcomes differ by student characteristics is needed. This article also summarizes key methodological takeaways about doing research related to advising. [This paper was created by the Early College Research Center, UNC Greensboro.]

The spin frequencies of neutron stars in low-mass X-ray binaries may be limited by the emission of gravitational waves. A candidate for producing such steady emission is a mass asymmetry, or "mountain", sourced by temperature asymmetries in the star's crust. A number of studies have examined temperature-induced shifts in the crustal capture layers between one nuclear species and another to produce this asymmetry, with the presence of capture layers in the deep crust being needed to produce the required mass asymmetries. However, modern equation of state calculations cast doubt on the existence of such deep capture layers. Motivated by this, we investigated an alternative source of temperature dependence in the equation of state, coming from the pressure supplied by the solid crustal lattice itself. We show that temperature-induced perturbations in this pressure, while small, may be significant. We therefore advocate for more detailed calculations, self-consistently calculating both the temperature asymmetries, the perturbations in crustal lattice pressure, and the consequent mass asymmetries, to establish if this is a viable mechanism for explaining the observed distribution of low-mass X-ray binary spin frequencies. Furthermore, the crustal lattice pressure mechanism does not require accretion, extending the possibility for such thermoelastic mountains to include both accreting and isolated neutron stars., Comment: 10 pages, 5 figures. To be published in MNRAS

Chemical reaction network theory is a field of applied mathematics concerned with modeling chemical systems, and can be used in other contexts such as in systems biology to study cellular signaling pathways or epidemiology to study the effect of human interaction on the spread of disease. In this paper, we seek to understand a chemical reaction network's equilibrium points through the lens of algebraic geometry by computing the positive part of the steady-state variety defined by polynomial equations arising from the assumption of mass-action kinetics. We provide a systematic classification of positive steady-state varieties produced by 2-species, 2-reaction networks, grounded in combinatorial and algebraic properties. While some (restricted) techniques exist to fully understand the ideal defining the positive steady-state variety, this computation presents a significant challenge in general. Our classification theorems provide a simplification of previous criteria, and aim to provide a foundation for future analysis of larger networks.

Recent advancements in robotics, including applications like self-driving cars, unmanned systems, and medical robots, have had a significant impact on the job market. On one hand, big robotics companies offer training programs based on the job requirements. However, these training programs may not be as beneficial as general robotics programs offered by universities or community colleges. On the other hand, community colleges and universities face challenges with required resources, especially qualified instructors, to offer students advanced robotics education. Furthermore, the diverse backgrounds of undergraduate students present additional challenges. Some students bring extensive industry experiences, while others are newcomers to the field. To address these challenges, we propose a student-centered personalized learning framework for robotics. This framework allows a general instructor to teach undergraduate-level robotics courses by breaking down course topics into smaller components with well-defined topic dependencies, structured as a graph. This modular approach enables students to choose their learning path, catering to their unique preferences and pace. Moreover, our framework's flexibility allows for easy customization of teaching materials to meet the specific needs of host institutions. In addition to teaching materials, a frequently-asked-questions document would be prepared for a general instructor. If students' robotics questions cannot be answered by the instructor, the answers to these questions may be included in this document. For questions not covered in this document, we can gather and address them through collaboration with the robotics community and course content creators. Our user study results demonstrate the promise of this method in delivering undergraduate-level robotics education tailored to individual learning outcomes and preferences., Comment: 11 pages, 3 figures, 1 table, 2024 ASEE Conference

To aid in prediction of turbulent boundary layer flows over rough surfaces, a new model is proposed to estimate hydrodynamic roughness based solely on geometric surface information. The model is based on a fluid-mechanics motivated geometric parameter called the wind-shade factor. Sheltering is included using a rapid algorithm adapted from the landscape shadow literature, while local pressure drag is estimated using a piecewise potential flow approximation. Similarly to evaluating traditional surface parameters such as skewness or average slope magnitude, the wind-shade factor is purely geometric and can be evaluated efficiently from knowing the surface elevation map and the mean flow direction. The wind-shade roughness model is applied to over 100 different surfaces available in a public roughness database and some others, and the predicted sandgrain-roughness heights are compared to measured values. Effects of various model ingredients are analyzed, and transitionally rough surfaces are treated by adding a term representing the viscous stress component., Comment: Paper submitted to J. Fluid Mechanics for publication, currently under review. Contains Notebooks but links will work only once it is published

This paper explores the use of large language models (LLMs) to score and explain short-answer assessments in K-12 science. While existing methods can score more structured math and computer science assessments, they often do not provide explanations for the scores. Our study focuses on employing GPT-4 for automated assessment in middle school Earth Science, combining few-shot and active learning with chain-of-thought reasoning. Using a human-in-the-loop approach, we successfully score and provide meaningful explanations for formative assessment responses. A systematic analysis of our method's pros and cons sheds light on the potential for human-in-the-loop techniques to enhance automated grading for open-ended science assessments., Comment: In press at EAAI-24: The 14th Symposium on Educational Advances in Artificial Intelligence

In recent years, large language models (LLMs) have become incredibly popular, with ChatGPT for example being used by over a billion users. While these models exhibit remarkable language understanding and logical prowess, a notable challenge surfaces in the form of "hallucinations." This phenomenon results in LLMs outputting misinformation in a confident manner, which can lead to devastating consequences with such a large user base. However, we question the appropriateness of the term "hallucination" in LLMs, proposing a psychological taxonomy based on cognitive biases and other psychological phenomena. Our approach offers a more fine-grained understanding of this phenomenon, allowing for targeted solutions. By leveraging insights from how humans internally resolve similar challenges, we aim to develop strategies to mitigate LLM hallucinations. This interdisciplinary approach seeks to move beyond conventional terminology, providing a nuanced understanding and actionable pathways for improvement in LLM reliability.

The efficiency of phonon-mediated heat transport is limited by the intrinsic atomistic properties of materials, seemingly providing an upper limit to heat transfer in materials and across their interfaces. The typical speeds of conductive transport, which are inherently limited by the chemical bonds and atomic masses, dictate how quickly heat will move in solids. Given that phonon-polaritons, or coupled phonon-photon modes, can propagate at speeds approaching 1 percent of the speed of light - orders of magnitude faster than transport within a pure diffusive phonon conductor - we demonstrate that volume-confined, hyperbolic phonon-polariton(HPhP) modes supported by many biaxial polar crystals can couple energy across solid-solid interfaces at an order of magnitude higher rates than phonon-phonon conduction alone. Using pump-probe thermoreflectance with a mid-infrared, tunable, probe pulse with sub-picosecond resolution, we demonstrate remote and spectrally selective excitation of the HPhP modes in hexagonal boron nitride in response to radiative heating from a thermally emitting gold source. Our work demonstrates a new avenue for interfacial heat transfer based on broadband radiative coupling from a hot spot in a gold film to hBN HPhPs, independent of the broad spectral mismatch between the pump(visible) and probe(mid-IR) pulses employed. This methodology can be used to bypass the intrinsically limiting phonon-phonon conductive pathway, thus providing an alternative means of heat transfer across interfaces. Further, our time-resolved measurements of the temperature changes of the HPhP modes in hBN show that through polaritonic coupling, a material can transfer heat across and away from an interface at rates orders of magnitude faster than diffusive phonon speeds intrinsic to the material, thus demonstrating a pronounced thermal transport enhancement in hBN via phonon-polariton coupling.

The relentless pursuit of miniaturization and performance enhancement in electronic devices has led to a fundamental challenge in the field of circuit design and simulation: how to accurately account for the inherent stochastic nature of certain devices. While conventional deterministic models have served as indispensable tools for circuit designers, they fall short when it comes to capture the subtle yet critical variability exhibited by many electronic components. In this paper, we present an innovative approach that transcends the limitations of traditional modeling techniques by harnessing the power of machine learning, specifically Mixture Density Networks (MDNs), to faithfully represent and simulate the stochastic behavior of electronic devices. We demonstrate our approach to model heater cryotrons, where the model is able to capture the stochastic switching dynamics observed in the experiment. Our model shows 0.82% mean absolute error for switching probability. This paper marks a significant step forward in the quest for accurate and versatile compact models, poised to drive innovation in the realm of electronic circuits.

Big data applications are on the rise, and so is the number of data centers. The ever-increasing massive data pool needs to be periodically backed up in a secure environment. Moreover, a massive amount of securely backed-up data is required for training binary convolutional neural networks for image classification. XOR and XNOR operations are essential for large-scale data copy verification, encryption, and classification algorithms. The disproportionate speed of existing compute and memory units makes the von Neumann architecture inefficient to perform these Boolean operations. Compute-in-memory (CiM) has proved to be an optimum approach for such bulk computations. The existing CiM-based XOR/XNOR techniques either require multiple cycles for computing or add to the complexity of the fabrication process. Here, we propose a CMOS-based hardware topology for single-cycle in-memory XOR/XNOR operations. Our design provides at least 2 times improvement in the latency compared with other existing CMOS-compatible solutions. We verify the proposed system through circuit/system-level simulations and evaluate its robustness using a 5000-point Monte Carlo variation analysis. This all-CMOS design paves the way for practical implementation of CiM XOR/XNOR at scaled technology nodes., Comment: 12 pages, 6 figures, 1 table

Recent policy efforts have attempted to increase the number of dual enrollment courses offered within Career and Technical Education pathways and there is evidence to suggest that this practice is widespread. However, there is very little research on student participation in CTE dual enrollment and on its impacts. This study examines participation in the CTE dual enrollment pathway in North Carolina, finding that about 9% of North Carolina students participated in CTE dual enrollment courses in 11th or 12th grade and disparities in participation among subgroups were less than for college transfer dual enrollment courses. Using a quasi-experimental approach to examine the effect of the program, the study found that participation is CTE was positively associated with college credits earned in high school, graduation from high school, and overall enrollment in college within one year after high school. The study also examined results by subgroup.

In this paper, we explore combinatorial properties of the posets associated with Kohnert polynomials. In particular, we determine a sufficient condition guaranteeing when such ``Kohnert posets'' are bounded and two necessary conditions for when they are ranked. Moreover, we apply the aforementioned conditions to find complete characterizations of when Kohnert posets are bounded and when they are ranked in special cases, including those associated with Demazure characters.

This paper presents a work-in-progress on a learn-ing system that will provide robotics students with a personalized learning environment. This addresses both the scarcity of skilled robotics instructors, particularly in community colleges and the expensive demand for training equipment. The study of robotics at the college level represents a wide range of interests, experiences, and aims. This project works to provide students the flexibility to adapt their learning to their own goals and prior experience. We are developing a system to enable robotics instruction through a web-based interface that is compatible with less expensive hardware. Therefore, the free distribution of teaching materials will empower educators. This project has the potential to increase the number of robotics courses offered at both two- and four-year schools and universities. The course materials are being designed with small units and a hierarchical dependency tree in mind; students will be able to customize their course of study based on the robotics skills they have already mastered. We present an evaluation of a five module mini-course in robotics. Students indicated that they had a positive experience with the online content. They also scored the experience highly on relatedness, mastery, and autonomy perspectives, demonstrating strong motivation potential for this approach., Comment: 5 pages, 2 figures, conference

This study presents an experimental dataset documenting the evolution of a turbulent boundary layer downstream of a rough-to-smooth surface transition. To investigate the effect of upstream flow conditions, two groups of experiments are conducted. For the \emph{Group-Re} cases, a nominally constant viscous-scaled equivalent sand grain roughness $k_{s0}^+\approx160$ is maintained on the rough surface, while the friction Reynolds number $Re_{\tau 0}$ ranges from 7100 to 21000. For the \emph{Group-ks} cases, $Re_{\tau 0}\approx14000$ is maintained while $k_{s0}^+$ ranges from 111 to 228. The wall-shear stress on the downstream smooth surface is measured directly using oil-film interferometry to redress previously reported uncertainties in the skin-friction coefficient recovery trends. In the early development following the roughness transition, the flow in the internal layer is not in equilibrium with the wall-shear stress. This conflicts with the common practise of modelling the mean velocity profile as two log laws below and above the internal layer height, as first proposed by Elliott (\textit{Trans. Am. Geophys. Union}, vol. 39, 1958, pp 1048--1054). As a solution to this, the current data are used to model the recovering mean velocity semi-empirically by blending the corresponding rough-wall and smooth-wall profiles. The over-energised large-scale motions leave a strong footprint in the near-wall region of the energy spectrum, the frequency and magnitude of which exhibit dependence on $Re_{\tau 0}$ and $k_{s0}^+$ respectively. The energy distribution in near-wall small scales is mostly unaffected by the presence of the outer flow with rough-wall characteristics, which can be used as a surrogate measure to extract the local friction velocity.

This paper examines recovery of the wall-shear stress of a turbulent boundary layer that has undergone a sudden transition from a rough to a smooth surface. Early works of Antonia and Luxton questioned the reliability of standard smooth-wall methods to measure wall-shear stress in such conditions, and subsequent studies show significant disagreement depending on the approach used to determine the wall-shear stress downstream. Here we address this by utilising a collection of experimental databases at Re_\tau \approx 4100 that have access to both `direct' and `indirect' measures of the wall-shear stress to understand the recovery to equilibrium conditions to the new surface. Our results reveal that the viscous region (z^+\lesssim 4) recovers almost immediately to an equilibrium state with the new wall conditions, however, the buffer region and beyond takes several boundary layer thicknesses before recovering to equilibrium conditions, which is longer than previously thought. A unique direct numerical simulation database of a wall-bounded flow with a rough-to-smooth wall transition is employed to confirm these findings. In doing so, we present evidence that any estimate of the wall-shear stress from the mean velocity profile in the buffer region or further away from the wall tends to underestimate its magnitude in the near vicinity of the rough-to-smooth transition, and this is likely to be partly responsible for the large scatter of recovery lengths to equilibrium conditions reported in the literature. Our results also reveal that the smaller energetic scales in the near-wall region recover to an equilibrium state associated with the new wall conditions within one boundary layer thickness downstream of the transition, while the larger energetic scales exhibit an over-energised state for several boundary layer thicknesses downstream of the transition.

We show that for each fixed non-constant complex polynomial $P$ of the plane there exists a homeomorphism $h$ such that $P\circ h$ is a Lipschitz quotient mapping. This corrects errors in the construction given earlier by Johnson et. al. [Michigan Math. J. $\textbf{47}$ (2000), 15-31]. Further we introduce a stronger notion of pointwise co-Lipschitzness and characterise its equivalence to the standard pointwise definition whilst also highlighting its relevance to a long-standing conjecture concerning Lipschitz quotient mappings $\mathbb{R}^n\to\mathbb{R}^n, n\geq 3$.

We investigate the underlying physics behind the change in amplitude modulation coefficient in non-canonical wall-bounded flows in the framework of the inner-outer interaction model (IOIM) (Baars et al., Phys. Rev. Fluids 1 (5), 054406). The IOIM captures the amplitude modulation effect, and here we focus on extending the model to non-canonical flows. An analytical relationship between the amplitude modulation coefficient and IOIM parameters is derived, which is shown to capture the increasing trend of the amplitude modulation coefficient with an increasing Reynolds number in a smooth-wall dataset. This relationship is then applied to classify and interpret the non-canonical turbulent boundary layer results reported in previous works. We further present the case study of a turbulent boundary layer after a rough-to-smooth change. Both single-probe and two-probe hotwire measurements are performed to acquire streamwise velocity time series in the recovering flow on the downstream smooth wall. An increased coherence between the large-scale motions and the small-scale envelope in the near-wall region is attributed to the stronger footprints of the over-energetic large-scale motions in the outer layer, whereas the near-wall cycle and its amplitude sensitivity to the superposed structures are similar to that of a canonical smooth-wall flow. These results indicate that the rough-wall structures above the internal layer interact with the near-wall cycle in a similar manner as the increasingly energetic structures in a high-Reynolds number smooth-wall boundary layer.

Project EQuIPD--Engaging Quality Instruction through Professional Development--sought to establish and test for efficacy a professional development model to produce highly qualified teachers in STEM practices for all children, especially for students who are in traditionally underserved schools and districts within the State of Florida. The project did this by providing two years of intensive professional development that helped teachers use system thinking to infuse technology-based inquiry into their regular instructional practices. The project also prepared the teachers, using a train-the-trainer model, so that they could train other teachers and sustain the work moving forward. This brief provides a summary of key findings from the impact study of Project EQuIPD. If specific information on program implementation and on the impact study--including the methodology and detailed impact findings--can be found in the technical report, "Improving Teachers' Instruction: The Impact of Project EQuIPD. Final External Evaluation Report" (ED618488).

This report represents the final results of an evaluation of Project EQuIPD--Engaging Quality Instruction through Professional Development. The project provided two years of intensive professional development to help teachers learn to think more strategically about how to improve student outcomes using technology-based inquiry instruction. The project also prepared participating teachers to train their colleagues. Led by the University of Florida, Project EQuIPD was a three-year grant awarded through the U.S. Department of Education's Supporting Effective Educator Development (SEED) Program. The project was implemented in 121 schools in ten counties in Florida. The impact of the project was studied with a randomized controlled trial. Key findings from the evaluation include: (1) The program was able to implement all intended activities, modifying some of the delivery methods in response to the COVID-19 pandemic; (2) On a survey, treatment teachers reported statistically significantly higher levels of knowledge and higher implementation of EQuIPD instructional practices than control teacher; (3) Observations of teachers' instruction showed no statistically significant overall differences between treatment and control teachers, although treatment teachers scored higher on implementation of inquiry practices than control teachers; (4) There was no significant difference in teacher retention or overall attendance between the treatment and control groups; and (5) There were no differences between treatment and control groups on overall measures of student achievement. The COVID-19 pandemic likely impacted the project's impacts and it is possible that results would have been different if the context had been different. [For the summary report, see ED626193.]

Despite its great scientific and technological importance, wall-bounded turbulence is an unresolved problem in classical physics that requires new perspectives to be tackled. One of the key strategies has been to study interactions among the energy-containing coherent structures in the flow. Such interactions are explored in this study for the first time using an explainable deep-learning method. The instantaneous velocity field obtained from a turbulent channel flow simulation is used to predict the velocity field in time through a U-net architecture. Based on the predicted flow, we assess the importance of each structure for this prediction using the game-theoretic algorithm of SHapley Additive exPlanations (SHAP). This work provides results in agreement with previous observations in the literature and extends them by revealing that the most important structures in the flow are not necessarily the ones with the highest contribution to the Reynolds shear stress. We also apply the method to an experimental database, where we can identify completely new structures based on their importance score. This framework has the potential to shed light on numerous fundamental phenomena of wall-bounded turbulence, including novel strategies for flow control.

Background: Deep learning (DL) can extract predictive and prognostic biomarkers from routine pathology slides in colorectal cancer. For example, a DL test for the diagnosis of microsatellite instability (MSI) in CRC has been approved in 2022. Current approaches rely on convolutional neural networks (CNNs). Transformer networks are outperforming CNNs and are replacing them in many applications, but have not been used for biomarker prediction in cancer at a large scale. In addition, most DL approaches have been trained on small patient cohorts, which limits their clinical utility. Methods: In this study, we developed a new fully transformer-based pipeline for end-to-end biomarker prediction from pathology slides. We combine a pre-trained transformer encoder and a transformer network for patch aggregation, capable of yielding single and multi-target prediction at patient level. We train our pipeline on over 9,000 patients from 10 colorectal cancer cohorts. Results: A fully transformer-based approach massively improves the performance, generalizability, data efficiency, and interpretability as compared with current state-of-the-art algorithms. After training on a large multicenter cohort, we achieve a sensitivity of 0.97 with a negative predictive value of 0.99 for MSI prediction on surgical resection specimens. We demonstrate for the first time that resection specimen-only training reaches clinical-grade performance on endoscopic biopsy tissue, solving a long-standing diagnostic problem. Interpretation: A fully transformer-based end-to-end pipeline trained on thousands of pathology slides yields clinical-grade performance for biomarker prediction on surgical resections and biopsies. Our new methods are freely available under an open source license., Comment: Updated Figure 2 and Table A.5

The spin-distribution of accreting neutron stars in low-mass X-ray binary (LMXB) systems shows a concentration of pulsars well below the Keplarian break-up limit. It has been suggested that their spin frequencies may be limited by the emission of gravitational waves, due to the presence of large-scale asymmetries in the internal temperature profile of the star. These temperature asymmetries have been demonstrated to lead to a non-axisymmetric mass-distribution, or `mountain', that generates gravitational waves at twice the spin frequency. The presence of a toroidal magnetic field in the interior of accreting neutron stars has been shown to introduce such anisotropies in the star's thermal conductivity, by restricting the flow of heat orthogonal to the magnetic field and establishing a non-axisymmetric temperature distribution within the star. We revisit this mechanism, extending the computational domain from (only) the crust to the entire star, incorporating more realistic microphysics, and exploring different choices of outer boundary condition. By allowing a magnetic field to permeate the core of the neutron star, we find that the likely level of temperature asymmetry in the inner crust ($\rho \sim 10^{13}$ g cm$^{-3}$) can be up to 3 orders of magnitude greater than the previous estimate, improving prospects for one day detecting continuous gravitational radiation. We also show that temperature asymmetries sufficiently large to be interesting for gravitational wave emission can be generated in strongly accreting neutron stars if crustal magnetic fields can reach $\sim 10^{12}$ G., Comment: 26 pages, 21 figures. Updated to match version accepted by MNRAS (mainly additional discussion)

Prediction markets are a popular, prominent, and successful structure for a collective intelligence platform. However the exact mechanism by which information known to the participating traders is incorporated into the market price is unknown. Kyle (1985) detailed a model for price formation in continuous auctions with information distributed heterogeneously amongst market participants. This paper demonstrates a novel method derived from the Kyle model applied to data from a field experiment prediction market. The method is able to identify traders whose trades have price impact that adds a significant amount of information to the market price. Traders who are not identified as informed in aggregate have price impact consistent with noise trading. Results are reproduced on other prediction market datasets. Ultimately the results provide strong evidence in favor of the Kyle model in a field market setting, and highlight an under-discussed advantage of prediction markets over alternative group forecasting mechanisms: that the operator of the market does not need to have information on the distribution of information amongst participating traders.

Contemporary artificial neural networks (ANN) are trained end-to-end, jointly learning both features and classifiers for the task of interest. Though enormously effective, this paradigm imposes significant costs in assembling annotated task-specific datasets and training large-scale networks. We propose to decouple feature learning from downstream lung ultrasound tasks by introducing an auxiliary pre-task of visual biomarker classification. We demonstrate that one can learn an informative, concise, and interpretable feature space from ultrasound videos by training models for predicting biomarker labels. Notably, biomarker feature extractors can be trained from data annotated with weak video-scale supervision. These features can be used by a variety of downstream Expert models targeted for diverse clinical tasks (Diagnosis, lung severity, S/F ratio). Crucially, task-specific expert models are comparable in accuracy to end-to-end models directly trained for such target tasks, while being significantly lower cost to train.

The nuclear spins of low-density implanted Ga atoms in Ge are interesting candidates for solid state-based qubits. To date, activation studies of implanted Ga in Ge have focused on high densities. Here we extend activation studies into the low-density regime. We use spreading resistance profiling and secondary ion mass spectrometry to derive electrical activation of Ga ions implanted into Ge as a function of rapid thermal anneal temperature and implant density. We show that for our implant conditions the activation is best for anneal temperatures between 400 and 650 $^\circ$C, with a maximum activation of 64% at the highest fluence. Below 400 $^\circ$C, remaining implant damage results in defects that act as superfluous carriers, and above 650 $^\circ$C, surface roughening and loss of Ga ions are observed. The activation increased monotonically from 10% to 64% as the implant fluence increased from $6\times10^{10}$ to $6\times10^{12}$ cm$^{-2}$. The results provide thermal anneal conditions to be used for initial studies of using low-density Ga atoms in Ge as nuclear spin qubits., Comment: 7 pages, 4 figures

Language models typically need to be trained or finetuned in order to acquire new knowledge, which involves updating their weights. We instead envision language models that can simply read and memorize new data at inference time, thus acquiring new knowledge immediately. In this work, we extend language models with the ability to memorize the internal representations of past inputs. We demonstrate that an approximate kNN lookup into a non-differentiable memory of recent (key, value) pairs improves language modeling across various benchmarks and tasks, including generic webtext (C4), math papers (arXiv), books (PG-19), code (Github), as well as formal theorems (Isabelle). We show that the performance steadily improves when we increase the size of memory up to 262K tokens. On benchmarks including code and mathematics, we find that the model is capable of making use of newly defined functions and theorems during test time., Comment: Published as a conference paper at ICLR 2022 (spotlight)

We introduce the Block-Recurrent Transformer, which applies a transformer layer in a recurrent fashion along a sequence, and has linear complexity with respect to sequence length. Our recurrent cell operates on blocks of tokens rather than single tokens during training, and leverages parallel computation within a block in order to make efficient use of accelerator hardware. The cell itself is strikingly simple. It is merely a transformer layer: it uses self-attention and cross-attention to efficiently compute a recurrent function over a large set of state vectors and tokens. Our design was inspired in part by LSTM cells, and it uses LSTM-style gates, but it scales the typical LSTM cell up by several orders of magnitude. Our implementation of recurrence has the same cost in both computation time and parameter count as a conventional transformer layer, but offers dramatically improved perplexity in language modeling tasks over very long sequences. Our model out-performs a long-range Transformer XL baseline by a wide margin, while running twice as fast. We demonstrate its effectiveness on PG19 (books), arXiv papers, and GitHub source code. Our code has been released as open source., Comment: Update to NeurIPS camera-ready version

This document describes PPfT school-wide value-added measure by showing how to gain access to your SAS Education Value-Added Assessment System (EVAAS) account, stepping through the school growth measures, and discussing how growth is scored in PPfT appraisal.

A large-scale spanwise and wall-normal array of sonic anemometers in the atmospheric surface layer is used to acquire all three components of instantaneous fluctuating velocity as well as temperature in a range of stability conditions. These data permit investigation of the three-dimensional statistical structure of turbulence structures. The present work extends the view of a self-similar range of wall-attached turbulence structures to the atmospheric surface layer under unstable and near-neutral stability conditions, and includes the statistical structure in both the wall-normal and spanwise directions in relation to the streamwise wavelength. Results suggest that the self-similar wall-attached structures have similar aspect ratios between streamwise/wall-normal scales and streamwise/spanwise scales such that $\lambda_x/\Delta z : \lambda_x/\Delta y \approx 1$ for both near-neutral and unstable conditions. By analysing the phase shift between synchronized measurements, in the spectral domain, it is quantified how the structure inclination angle varies with stability. Under the most unstable conditions, coherent structures of $\lambda_x/\delta = 1$ are inclined at angles as high as $65^\circ$ relative to the solid boundary, while larger scales of $\lambda_x/\delta = 6$ exhibit inclination angles of approximately $35^\circ$. For near-neutral stability conditions, the angle tends towards $12^\circ$ for all scales. It is noted that in the near-neutral condition, the structure inclination angle and the aspect ratio -- and thus the statistical modeling of coherent structures in the ASL -- are highly sensitive to the value of the stability parameter.

Following the findings in \cite{wangsawijaya2020}, we re-examine the turbulent boundary layers developing over surfaces with spanwise heterogeneous roughness of various roughness wavelengths $0.32 \leq S/\overline{\delta} \leq 3.63$, where $S$ is the width of the roughness strips and $\overline{\delta}$ is the spanwise-averaged boundary-layer thickness. The heterogeneous cases induce counter-rotating secondary flows, and these are compared to the large-scale turbulent structures that occur naturally over the smooth wall. Both appear as meandering elongated high- and low-momentum streaks in the instantaneous flow field. Results suggest that the secondary flows might be spanwise-locked turbulent structures, with $S/\overline{\delta}$ governing the strength of the turbulent structures and possibly the efficacy of the surface in locking the structures in place (most effective when $S/\overline{\delta} \approx 1$). Conditional averages of the fluctuating velocity fields of both spanwise heterogeneous and smooth wall cases result in structures that are strongly reminiscent of the streak-vortex instability model. Secondary flows and large-scale structures coexist in the limits where either $S/\overline{\delta} \gg 1$ or $S/\overline{\delta} \ll 1$, where the secondary flows scale on $\delta$ or $S$, respectively. When $S/\overline{\delta} \gg 1$, the secondary flows are locked about the roughness transition, while relatively unaltered large-scale structures occur further from the transition. In the case where $S/\overline{\delta} \ll 1$, $S$-scaled secondary flows are confined close to the surface, coexisting with unaltered larger scale turbulent structures that penetrate much deeper into the layer., Comment: Currently under review for publication in J. Fluid Mech

Open citation data can improve the transparency and robustness of scientific portfolio analysis, improve science policy decision-making, stimulate downstream commercial activity, and increase the discoverability of scientific articles. Once sparsely populated, public-domain citation databases crossed a threshold of one billion citations in February 2021, during the COVID-19 pandemic. Shortly thereafter, the threshold of one billion public-domain citations from the Crossref database alone. Since the relative advantage of withholding data in closed databases has diminished with the flood of public-domain data, this likely constitutes an irreversible change in the citation data ecosystem. The successes of this movement can guide future open data efforts.

This paper introduces an improved image processing method usable in capacitive imaging applications. Standard capacitive imaging tends to prefer amplitude-based images over the use of phase due to better signal-to-noise ratios. The new approach exploits the best features of both types of information by combining them to form clearer images, hence improving both defect detection and characterization in non-destructive evaluation. The methodology is demonstrated and optimized using a benchmark sample. Additional experiments on glass fibre composite sample illustrate the advantages of the technique.

The purpose of this report is to help stakeholders of Professional Pathways for Teachers (PPfT) understand and refine the methods used to measure program implementation and outcomes. This report also contains summative data on the progress of PPfT, which just completed its third year. PPfT first launched district-wide in AISD in the 2016-2017 school year, and since then the concept behind the program has been refined to that of empowering teachers and improving the quality of teaching through a multi-measure appraisal and compensation system. [For the executive summary, see ED629689.]

The purpose of the 2018-2019 CARES evaluation was to (a) help program staff conceptualize the CARES implementation work and goals; (b) define measurable indicators of implementation, outputs, and outcomes; and (c) provide summative data on the first year of program implementation. Results of the 2018-2019 CARES program evaluation activities were organized into four reporting areas: conceptualization of CARES, evaluation of implementation activities, baseline measurement of customer experiences, and exploratory correlation analyses.

The STEM Early College Expansion Project is an effort to integrate STEM strategies with the early college model and implement this in comprehensive high schools. This report summarizes findings from two separate quasi-experimental impact studies of the model in Michigan and Connecticut. Results from Michigan showed statistically significant impacts on enrollment in college-level courses and on attainment of college credits. Treatment schools in Michigan also had descriptively lower dropout rates. The Connecticut impact study had challenges with the study design that resulted in an inability to make clear causal claims about the impact.

The purpose of this Professional Pathways for Teachers (PPfT) evaluation was to examine the measurement validity and reliability of PPfT appraisal data from the 2017-2018 school year in the Austin Independent School District. The PPfT appraisal is a multi-measure system that covers three areas: instructional practices (IP), professional growth and responsibilities (PGR), and two student growth measures: a teacher-level student learning objective (SLO) measure and a campus-level school-wide value-added (SWVA) measure. A separate technical report also was published. [For the technical report, see ED629585.]

The purpose of this Professional Pathways for Teachers (PPfT) evaluation was to examine the measurement validity and reliability of PPfT appraisal data from the 2017-2018 school year in the Austin Independent School District. The PPfT appraisal is a multi-measure system that covers three areas: instructional practices (IP), professional growth and responsibilities (PGR), and two student growth measures: a teacher-level student learning objective (SLO) measure and a campus-level school-wide value-added (SWVA) measure. A separate research brief report also was published. [For the research brief, see ED629586.]

Professional Pathways for Teachers (PPfT) is a human capital system that blends three primary components: appraisal, compensation, and professional development (PD) opportunities. The purpose of PPfT is to build the capacity of Austin Independent School District (AISD) teachers through a comprehensive system of supports and compensation with the ultimate goal of having a positive impact on teacher retention and student achievement. This report presents descriptive analyses of the 2017-2018 PPfT appraisal and compensation data. A separate executive summary also was published. [For the executive summary, see ED629571.]

As a response to the 2004 Individuals with Disabilities Education Act (IDEA) federal statute, Maine enacted a rule requiring all schools to provide additional support to students who are not on track for meeting state learning standards beginning in 2012. One intent of the requirement was to improve student achievement, and another was to reduce the number of children who are identified as having special educational needs and thus require an Individualized Education Plan. Recent policy discussions, including the 2018 report of the Task Force to Identify Special Education Cost Drivers and Innovative Approaches to Services, have raised the question of how well schools implementing Response to Intervention (RTI) programs. The study found that most schools (83%) are using a universal screening assessment to identify students who need academic support. Elementary and middle schools are more likely to administer universal screening (92% and 85% respectively) than high schools (59%). A number of practitioners in schools without RTI programs or universal screening processes reported that classroom teachers were uncomfortable with providing behavior supports and escalated problems to special education staff before first trying general classroom strategies. This suggests that many classroom teachers would benefit from additional training and practice with evidence-based behavior strategies, and that this may also lessen the workload for special education teachers. The authors concluded that RTI academic and behavioral support programs are well on their way to being embedded in Maine schools, and practitioners cited numerous positive initial impacts on students and teachers. However, additional support is needed for all Maine districts to improve their programs and thus be able to offer supportive opportunities to their students.

This study examined why some seniors attending Early College High Schools did not plan to continue their education at a 4-year college immediately after graduation despite attending schools designed to provide coordinated academic and social supports with the expectation that all students would continue their education. Most students in our study (75%) did plan to continue their education at a 4-year college immediately after graduation. For the remaining students, we used cluster analysis to identify four distinct groups of non-college bound students. These groups were evaluated based on background characteristics, perceived opportunities and barriers, and schooling experiences. Results indicate that non-college bound students are diverse in their reasons for not continuing their education at a 4-year program and that any reform efforts designed to personalize opportunities and supports for these students must take into account how combinations of perceived opportunities and barriers shape their postsecondary decisions. [This paper was published in "Psychology in the Schools" v56 n7 p1117-1138 2019 (EJ1221093).]

We investigate rough-wall turbulent flows through direct numerical simulations of flow over three-dimensional transitionally rough sinusoidal surfaces. The roughness Reynolds number is fixed at $k^+=10$, where $k$ is the sinusoidal semi-amplitude, and the sinusoidal wavelength is varied, resulting in the roughness solidity, $\Lambda$ (frontal area divided by plan area) ranging from 0.05 to 0.54. The high cost of resolving both the flow around the dense roughness elements and the bulk flow is circumvented by the use of the minimal-span channel technique, recently demonstrated by Chung et al. (J. Fluid Mech., vol. 773, 2015, pp. 418-431) to accurately determine the Hama roughness function, $\Delta U^+$. Good agreement of the second-order statistics in the near-wall roughness-affected region between minimal- and full-span rough-wall channels is observed. In the sparse regime of roughness ($\Lambda \lesssim 0.15$) the roughness function increases with increasing solidity, while in the dense regime the roughness function decreases with increasing solidity. It was found that the dense regime begins when $\Lambda\gtrsim0.15-$0.18, in agreement with the literature. A model is proposed for the limit of $\Lambda\rightarrow\infty$, which is a smooth wall located at the crest of the roughness elements. This model assists with interpreting the asymptotic behaviour of the roughness, and the rough-wall data presented in this paper show that the near-wall flow is tending towards this modelled limit. The peak streamwise turbulence intensity, which is associated with the turbulent near-wall cycle, is seen to move increasingly further away from the wall with increasing solidity. In the sparse regime, increasing $\Lambda$ reduces streamwise turbulent energy associated with the near-wall cycle, while increasing $\Lambda$ in the dense regime increases turbulent energy [continued]$\ldots$, Comment: Accepted in the Journal of Fluid Mechanics

We conduct minimal-channel direct numerical simulations of turbulent flow over two-dimensional rectangular bars aligned in the spanwise direction. This roughness has been often described as $d$-type, as the roughness function $\Delta U^+$ is thought to depend only on the outer-layer length scale (pipe diameter, channel half height or boundary layer thickness). This is in contrast to conventional engineering rough surfaces, named $k$-type, for which $\Delta U^+$ depends on the roughness height, $k$. The minimal-span rough-wall channel is used to circumvent the high cost of simulating high Reynolds number flows, enabling a range of bars with varying aspect ratios to be investigated. The present results show that increasing the trough-to-crest height ($k$) of the roughness while keeping the width between roughness bars, $\mathcal{W}$, fixed in viscous units, results in non-$k$-type behaviour although this does not necessarily indicate $d$-type behaviour. Instead, for deep surfaces with $k/\mathcal{W}\gtrsim 3$, the roughness function appears to depend only on $\mathcal{W}$ in viscous units. In these situations, the flow no longer has any information about how deep the roughness is and instead can only `see' the width of the fluid gap between the bars., Comment: Accepted in the Journal of Fluid Mechanics

Opposition-control of the energetic cycle of near wall streaks in wall-bounded turbulence, using numerical approaches, has shown promise for drag reduction. For practical implementation, opposition control is only realizable if there is a degree of coherence between the sensor--actuator pairs of the control system (these sensors and actuators should typically be wall-based to avoid parasitic drag). As such, we here inspect the feasibility of real-time control of the near-wall cycle, by considering the coherence between a measurable wall-quantity, being the wall-shear stress fluctuations, and the streamwise and wall-normal velocity fluctuations in a turbulent boundary layer. Synchronized spatial and temporal velocity data from numerical simulations at $Re_\tau \approx 590$ and $ 2000$ are employed. It is shown that the spectral energy of the streamwise velocity fluctuations that is stochastically incoherent with wall signals is independent of Reynolds number in the near wall region. Consequently, the streamwise energy-fraction that is stochastically wall-coherent grows with Reynolds number due to the increasing range of energetic large scales. This thus implies that a wall-based control system has the ability to manipulate a larger portion of the total turbulence energy at off-wall locations, at higher Reynolds numbers. Coherence values of 0.55 and 0.4, which are considerably lower than the maximum possible coherence 1, were found between the streamwise and wall-normal velocity fluctuations at the near wall peak in the energy spectrogram, respectively, and the streamwise fluctuating friction velocity. This suggests that a closed-loop drag reduction scheme targeting near wall cycle of streaks alone will be of limited success in practice as the Reynolds number grows.

Acoustic metamaterials constructed from conventional base materials can exhibit exotic phenomena such as negative refractive index, extraordinary transmission-absorption and sub-wavelength imaging. These are typically achieved by combining geometrical and resonance effects. Holey-structured acoustic metamaterials have already shown potential for sub-wavelength acoustic imaging in air, where it is relatively simple to achieve the required difference in acoustic impedance needed to create resonances within the holes. However, the use of polymers for metamaterial operation in water is difficult, due to the much lower difference in acoustic impedance between water and the many polymers used, for example, in additive manufacturing. Hence, metals are commonly used. Here we show that this can be overcome by trapping air layers so that they surround each water-filled channel, making sub-wavelength imaging in water possible. The operation of such a Trapped air design is confirmed at 200-300 kHz ultrasonic frequencies via both finite element modelling and experimental measurements in a water tank. It is also shown quantitatively that the trapped air design outperforms its metal counterpart. The results indicate a way forward for exploiting additive-manufacturing for realising acoustic metamaterials in water at ultrasonic frequencies., Comment: 29 pages, 14 figures