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Accurate prostate cancer diagnosis remains challenging. Even when using MRI, radiologists exhibit low specificity and significant inter-observer variability, leading to potential delays or inaccuracies in identifying clinically significant cancers. This leads to numerous unnecessary biopsies and risks of missing clinically significant cancers. Here we present prostate vision contrastive network (ProViCNet), prostate organ-specific vision foundation models for Magnetic Resonance Imaging (MRI) and Trans-Rectal Ultrasound imaging (TRUS) for comprehensive cancer detection. ProViCNet was trained and validated using 4,401 patients across six institutions, as a prostate cancer detection model on radiology images relying on patch-level contrastive learning guided by biopsy confirmed radiologist annotations. ProViCNet demonstrated consistent performance across multiple internal and external validation cohorts with area under the receiver operating curve values ranging from 0.875 to 0.966, significantly outperforming radiologists in the reader study (0.907 versus 0.805, p<0.001) for mpMRI, while achieving 0.670 to 0.740 for TRUS. We also integrated ProViCNet with standard PSA to develop a virtual screening test, and we showed that we can maintain the high sensitivity for detecting clinically significant cancers while more than doubling specificity from 15% to 38% (p<0.001), thereby substantially reducing unnecessary biopsies. These findings highlight that ProViCNet's potential for enhancing prostate cancer diagnosis accuracy and reduce unnecessary biopsies, thereby optimizing diagnostic pathways., Comment: 44pages

Symplectic integrators offer vastly superior performance over traditional numerical techniques for conservative dynamical systems, but their application to \emph{dissipative} systems is inherently difficult due to dissipative systems' lack of symplectic structure. Leveraging the intrinsic variational structure of higher-order dynamics, this paper presents a general technique for applying existing symplectic integration schemes to dissipative systems, with particular emphasis on viscous fluids modeled by the Navier-Stokes equations. Two very simple such schemes are developed here. Not only are these schemes unconditionally stable for dissipative systems, they also outperform traditional methods with a similar degree of complexity in terms of accuracy for a given time step. For example, in the case of viscous flow between two infinite, flat plates, one of the schemes developed here is found to outperform both the implicit Euler method and the explicit fourth-order Runge-Kutta method in predicting the velocity profile. To the authors' knowledge, this is the very first time that a symplectic integration scheme has been applied successfully to the Navier-Stokes equations. We interpret the present success as direct empirical validation of the canonical Hamiltonian formulation of the Navier-Stokes problem recently published by Sanders~\emph{et al.} More sophisticated symplectic integration schemes are expected to exhibit even greater performance. It is hoped that these results will lead to improved numerical methods in computational fluid dynamics., Comment: 18 pages, 7 figures

As the descendants of stars with masses less than 8 M$_{\odot}$ on the main sequence, white dwarfs provide a unique way to constrain planetary occurrence around intermediate-mass stars (spectral types BAF) that are otherwise difficult to measure with radial-velocity or transit surveys. We update the analysis of more than 250 ultraviolet spectra of hot ($13{,}000$ K $< T_{\mathrm{eff}} <$ $30{,}000$ K), young (less than $800$ Myr) white dwarfs collected by the Hubble Space Telescope, which reveals that more than 40% of all white dwarfs show photospheric silicon and sometimes carbon, signpost for the presence of remnant planetary systems. However, the fraction of white dwarfs with metals significantly decreases for massive white dwarfs (M$_{\rm WD}~>$ 0.8 M$_{\odot}$), descendants of stars with masses greater than 3.5 M$_{\odot}$ on the main sequence, as just $11^{+6}_{-4}$% exhibit metal pollution. In contrast, $44\pm6$% of a subset of white dwarfs (M$\rm _{WD}~<$ 0.7 M$_{\odot}$) unbiased by the effects of radiative levitation are actively accreting planetary debris. While the population of massive white dwarfs is expected to be influenced by the outcome of binary evolution, we do not find merger remnants to broadly affect our sample. We connect our measured occurrence rates of metal pollution on massive white dwarfs to empirical constraints into planetary formation and survival around stars with masses greater than 3.5 M$_{\odot}$ on the main sequence., Comment: 21 pages, 5 figures, accepted to The Astrophysical Journal (ApJ)

Marine robots must maintain precise control and ensure safety during tasks like ocean monitoring, even when encountering unpredictable disturbances that affect performance. Designing algorithms for uncrewed surface vehicles (USVs) requires accounting for these disturbances to control the vehicle and ensure it avoids obstacles. While adaptive control has addressed USV control challenges, real-world applications are limited, and certifying USV safety amidst unexpected disturbances remains difficult. To tackle control issues, we employ a model reference adaptive controller (MRAC) to stabilize the USV along a desired trajectory. For safety certification, we developed a reachability module with a moving horizon estimator (MHE) to estimate disturbances affecting the USV. This estimate is propagated through a forward reachable set calculation, predicting future states and enabling real-time safety certification. We tested our safe autonomy pipeline on a Clearpath Heron USV in the Charles River, near MIT. Our experiments demonstrated that the USV's MRAC controller and reachability module could adapt to disturbances like thruster failures and drag forces. The MRAC controller outperformed a PID baseline, showing a 45%-81% reduction in RMSE position error. Additionally, the reachability module provided real-time safety certification, ensuring the USV's safety. We further validated our pipeline's effectiveness in underway replenishment and canal scenarios, simulating relevant marine tasks., Comment: 35 pages, 23 figures, 6 tables

This paper addresses the problem of adaptively controlling the bias parameter in nonlinear opinion dynamics (NOD) to allocate agents into groups of arbitrary sizes for the purpose of maximizing collective rewards. In previous work, an algorithm based on the coupling of NOD with an multi-objective behavior optimization was successfully deployed as part of a multi-robot system in an autonomous task allocation field experiment. Motivated by the field results, in this paper we propose and analyze a new task allocation model that synthesizes NOD with an evolutionary game framework. We prove sufficient conditions under which it is possible to control the opinion state in the group to a desired allocation of agents between two tasks through an adaptive bias using decentralized feedback. We then verify the theoretical results with a simulation study of a collaborative evolutionary division of labor game., Comment: v1) To appear at the 2024 IEEE Conference on Decision and Control (CDC) in Milan, Italy. 8 Pages, 5 Figures. v2) Fixed typo. v3) Fixed typo and corrected axis on Figure 4b

Colorectal cancer (CRC) is the third most common cause of cancer diagnosed in the United States and the second leading cause of cancer-related death among both genders. Notably, CRC is the leading cause of cancer in younger men less than 50 years old. Colonoscopy is considered the gold standard for the early diagnosis of CRC. Skills vary significantly among endoscopists, and a high miss rate is reported. Automated polyp segmentation can reduce the missed rates, and timely treatment is possible in the early stage. To address this challenge, we introduce \textit{\textbf{\ac{FANetv2}}}, an advanced encoder-decoder network designed to accurately segment polyps from colonoscopy images. Leveraging an initial input mask generated by Otsu thresholding, FANetv2 iteratively refines its binary segmentation masks through a novel feedback attention mechanism informed by the mask predictions of previous epochs. Additionally, it employs a text-guided approach that integrates essential information about the number (one or many) and size (small, medium, large) of polyps to further enhance its feature representation capabilities. This dual-task approach facilitates accurate polyp segmentation and aids in the auxiliary classification of polyp attributes, significantly boosting the model's performance. Our comprehensive evaluations on the publicly available BKAI-IGH and CVC-ClinicDB datasets demonstrate the superior performance of FANetv2, evidenced by high dice similarity coefficients (DSC) of 0.9186 and 0.9481, along with low Hausdorff distances of 2.83 and 3.19, respectively. The source code for FANetv2 is available at https://github.com/xxxxx/FANetv2.

Gliomas are the most common malignant primary brain tumors in adults and one of the deadliest types of cancer. There are many challenges in treatment and monitoring due to the genetic diversity and high intrinsic heterogeneity in appearance, shape, histology, and treatment response. Treatments include surgery, radiation, and systemic therapies, with magnetic resonance imaging (MRI) playing a key role in treatment planning and post-treatment longitudinal assessment. The 2024 Brain Tumor Segmentation (BraTS) challenge on post-treatment glioma MRI will provide a community standard and benchmark for state-of-the-art automated segmentation models based on the largest expert-annotated post-treatment glioma MRI dataset. Challenge competitors will develop automated segmentation models to predict four distinct tumor sub-regions consisting of enhancing tissue (ET), surrounding non-enhancing T2/fluid-attenuated inversion recovery (FLAIR) hyperintensity (SNFH), non-enhancing tumor core (NETC), and resection cavity (RC). Models will be evaluated on separate validation and test datasets using standardized performance metrics utilized across the BraTS 2024 cluster of challenges, including lesion-wise Dice Similarity Coefficient and Hausdorff Distance. Models developed during this challenge will advance the field of automated MRI segmentation and contribute to their integration into clinical practice, ultimately enhancing patient care., Comment: 10 pages, 4 figures, 1 table

White dwarf stars have been used for decades as precise and accurate age indicators. This work presents a test of the reliability of white dwarf total ages when spectroscopic observations are available. We conduct follow-up spectroscopy of 148 individual white dwarfs in widely separated double-white-dwarf (WD+WD) binaries. We supplement the sample with 264 previously published white dwarf spectra, as well as 1292 high-confidence white dwarf spectral types inferred from their Gaia XP spectra. We find that spectroscopic fits to optical spectra do not provide noticeable improvement to the age agreement among white dwarfs in wide WD+WD binaries. The median age agreement is $\approx$$1.5\sigma$ for both photometrically and spectroscopically determined total ages, for pairs of white dwarfs with each having a total age uncertaintiy $<$ 20\%. For DA white dwarfs, we further find that photometrically determined atmospheric parameters from spectral energy distribution fitting give better total age agreement ($1.0\sigma$, 0.2 Gyr, or 14\% of the binary's average total age) compared to spectroscopically determined parameters from Balmer-line fits (agreement of $1.5\sigma$, 0.3 Gyr, or 28\% of binary's average total age). We find further evidence of a significant merger fraction among wide WD+WD binaries: across multiple spectroscopically identified samples, roughly 20\% are inconsistent with a monotonically increasing initial-final mass relation. We recommend the acquisition of an identification spectrum to ensure the correct atmospheric models are used in photometric fits in order to determine the most accurate total age of a white dwarf star., Comment: 38 pages, 17 figures, submitted to ApJ

White dwarf stars are ubiquitous in the Galaxy, and are essential to understanding stellar evolution. While most white dwarfs are photometrically stable and reliable flux standards, some can be highly variable, which can reveal unique details about the endpoints of low-mass stellar evolution. In this study we characterize a sample of high-confidence white dwarfs with multi-epoch photometry from Gaia Data Release 3. We compare these Gaia light curves with light curves from the Zwicky Transiting Facility and the Transiting Exoplanet Survey Satellite to see when Gaia data independently can accurately measure periods of variability. From this sample, 105 objects have variability periods measured from the Gaia light curves independently, with periods as long as roughly 9.5 d and as short as 256.2 s (roughly 4 min), including seven systems with periods shorter than 1000 s. We discover 86 new objects from the 105 target sample, including pulsating, spotted, and binary white dwarfs, and even a new 68.4 min eclipsing cataclysmic variable. The median amplitude of the absolute photometric variability we confirm from Gaia independently is 1.4%, demonstrating that Gaia epoch photometry is capable of measuring short-term periods even when observations are sparse., Comment: 22 pages, 8 figures, 3 tables, accepted for publication in ApJ; updated headers in table 3 to reflect correct units of amplitude; updated formatting for readability

Gyrochronology, a valuable tool for determining ages of low-mass stars where other techniques fail, relies on accurate calibration. We present a sample of 185 wide ($>$$100$ au) white dwarf + main sequence (WD + MS) binaries. Total ages of WDs are computed using all-sky survey photometry, Gaia parallaxes, and WD atmosphere models. Using a magnetic braking law calibrated against open clusters, along with assumptions about initial conditions and angular momentum transport, we construct gyrochrones to predict the rotation periods of MS stars. Both data and models show that, at the fully convective boundary (FCB), MS stars with WD ages up to 7.5 Gyr and within a $<50$ K effective temperature range experience up to a threefold increase in rotation period relative to stars slightly cooler than the FCB. We suggest that rapid braking at this boundary is driven by a sharp rise in the convective overturn timescale ($\tau_{\mathrm{cz}}$) caused by structural changes between partially and fully convective stars and the $^3 \textrm{He}$ instability occurring at this boundary. While the specific location in mass (or temperature) of this feature varies with model physics, we argue that its existence remains consistent. Stars along this feature exhibit rotation periods that can be mapped, within 1$\sigma$, to a range of gyrochrones spanning $\approx 6$ Gyr. Due to current temperature errors ($\simeq$$50$ K), this implies that a measured rotation period cannot be uniquely associated to a single gyrochrone, implying that gyrochronology may not be feasible for M dwarfs very close to the FCB., Comment: Accepted to ApJ. 31 pages, 20 figures, 3 tables

This paper reports a new hierarchical architecture for modeling autonomous multi-robot systems (MRSs): a nonlinear dynamical opinion process is used to model high-level group choice, and multi-objective behavior optimization is used to model individual decisions. Using previously reported theoretical results, we show it is possible to design the behavior of the MRS by the selection of a relatively small set of parameters. The resulting behavior - both collective actions and individual actions - can be understood intuitively. The approach is entirely decentralized and the communication cost scales by the number of group options, not agents. We demonstrated the effectiveness of this approach using a hypothetical `explore-exploit-migrate' scenario in a two hour field demonstration with eight unmanned surface vessels (USVs). The results from our preliminary field experiment show the collective behavior is robust even with time-varying network topology and agent dropouts., Comment: v1) 7 pages, 7 figures. v2) To appear at the 2024 IEEE International Conference on Robotics and Automation (ICRA) in Yokohama, Japan. v3) Fixed typos and added publication info

Deploying autonomous systems in safety critical settings necessitates methods to verify their safety properties. This is challenging because real-world systems may be subject to disturbances that affect their performance, but are unknown a priori. This work develops a safety-verification strategy wherein data is collected online and incorporated into a reachability analysis approach to check in real-time that the system avoids dangerous regions of the state space. Specifically, we employ an optimization-based moving horizon estimator (MHE) to characterize the disturbance affecting the system, which is incorporated into an online reachability calculation. Reachable sets are calculated using a computational graph analysis tool to predict the possible future states of the system and verify that they satisfy safety constraints. We include theoretical arguments proving our approach generates reachable sets that bound the future states of the system, as well as numerical results demonstrating how it can be used for safety verification. Finally, we present results from hardware experiments demonstrating our approach's ability to perform online reachability calculations for an unmanned surface vehicle subject to currents and actuator failures., Comment: 6 pages, 7 figures

We provide the first step toward developing a hierarchical control-estimation framework to actively plan robot trajectories for anomaly detection in confined spaces. The space is represented globally using a directed region graph, where a region is a landmark that needs to be visited (inspected). We devise a fast mixing Markov chain to find an ergodic route that traverses this graph so that the region visitation frequency is proportional to its anomaly detection uncertainty, while satisfying the edge directionality (region transition) constraint(s). Preliminary simulation results show fast convergence to the ergodic solution and confident estimation of the presence of anomalies in the inspected regions.

In this paper we report an experimental evaluation of three popular methods for online system identification of unmanned surface vehicles (USVs) which were implemented as an ensemble: certifiably stable shallow recurrent neural network (RNN), adaptive identification (AID), and recursive least squares (RLS). The algorithms were deployed on eight USVs for a total of 30 hours of online estimation. During online training the loss function for the RNN was augmented to include a cost for violating a sufficient condition for the RNN to be stable in the sense of contraction stability. Additionally we described an efficient method to calculate the equilibrium points of the RNN and classify the associated stability properties about these points. We found the AID method had lowest mean absolute error in the online prediction setting, but a weighted ensemble had lower error in offline processing., Comment: v1) 8 Pages, 5 Figures, To appear at 2023 RSJ/IEEE Conference on Intelligent Robotics and Systems (IROS) in Detroit, Michigan, USA, v2) corrected error in references

On 26 September 2022 the Double Asteroid Redirection Test (DART) spacecraft impacted Dimorphos, a satellite of the asteroid 65803 Didymos. Because it is a binary system, it is possible to determine how much the orbit of the satellite changed, as part of a test of what is necessary to deflect an asteroid that might threaten Earth with an impact. In nominal cases, pre-impact predictions of the orbital period reduction ranged from ~8.8 - 17.2 minutes. Here we report optical observations of Dimorphos before, during and after the impact, from a network of citizen science telescopes across the world. We find a maximum brightening of 2.29 $\pm$ 0.14 mag upon impact. Didymos fades back to its pre-impact brightness over the course of 23.7 $\pm$ 0.7 days. We estimate lower limits on the mass contained in the ejecta, which was 0.3 - 0.5% Dimorphos' mass depending on the dust size. We also observe a reddening of the ejecta upon impact., Comment: Accepted by Nature

This paper reports an investigation into the problem of rapid identification of a channel that crosses a body of water using one or more Unmanned Surface Vehicles (USV). A new algorithm called Proposal Based Adaptive Channel Search (PBACS) is presented as a potential solution that improves upon current methods. The empirical performance of PBACS is compared to lawnmower surveying and to Markov decision process (MDP) planning with two state-of-the-art reward functions: Upper Confidence Bound (UCB) and Maximum Value Information (MVI). The performance of each method is evaluated through comparison of the time it takes to identify a continuous channel through an area, using one, two, three, or four USVs. The performance of each method is compared across ten simulated bathymetry scenarios and one field area, each with different channel layouts. The results from simulations and field trials indicate that on average multi-vehicle PBACS outperforms lawnmower, UCB, and MVI based methods, especially when at least three vehicles are used., Comment: 8 pages, 9 figures, (v1) Submitted to IEEE International Conference on Robotics and Automation (ICRA) 2023, (v2) Updated figures, Submitted to IEEE Robotics and Automation Letters (RA-L), (v3) Preprint accepted by IEEE Robotics and Automation Letters (RA-L)

Many complex vehicular systems, such as large marine vessels, contain confined spaces like water tanks, which are critical for the safe functioning of the vehicles. It is particularly hazardous for humans to inspect such spaces due to limited accessibility, poor visibility, and unstructured configuration. While robots provide a viable alternative, they encounter the same set of challenges in realizing robust autonomy. In this work, we specifically address the problem of detecting foreign object debris (FODs) left inside the confined spaces using a visual mapping-based system that relies on Mahalanobis distance-driven comparisons between the nominal and online maps for local outlier identification. Simulation trials show extremely high recall but low precision for the outlier identification method. The assistance of remote humans is, therefore, taken to deal with the precision problem by going over the close-up robot camera images of the outlier regions. An online survey is conducted to show the usefulness of this assistance process. Physical experiments are also reported on a GPU-enabled mobile robot platform inside a scaled-down, prototype tank to demonstrate the feasibility of the FOD detection system.

White dwarf (WD) stars evolve simply and predictably, making them reliable age indicators. However, self-consistent validation of the methods for determining WD total ages has yet to be widely performed. This work uses 1565 wide ( > 100 au) WD+WD binaries and 24 new triples containing at least two WDs to test the accuracy and validity of WD total age determinations. For these 1589 wide double-WD binaries and triples, we derive total ages of each WD using photometric data from all-sky surveys, in conjunction with Gaia parallaxes and current hydrogen-atmosphere WD models. Ignoring initial-to-final-mass relations and considering only WD cooling ages, we find that roughly 21-36% of the more massive WDs in a system have a shorter cooling age. Since more massive WDs should be born as more massive main-sequence stars, we attribute this unphysical disagreement as evidence of prior mergers or the presence of an unresolved companion, suggesting that roughly 21-36% of wide WD+WD binaries were once triples. Among the 423 wide WD+WD pairs that pass high-fidelity cuts, we find that 25% total age uncertainties are generally appropriate for WDs with masses > 0.63 Msun and temperatures < 12,000 K, and provide suggested inflation factors for age uncertainties for higher-mass WDs. Overall, WDs return reliable stellar ages, but we detail cases where total ages are least reliable, especially for WDs < 0.63 Msun., Comment: 24 pages, 10 figures. Submitted to ApJ. Catalog available at https://sites.bu.edu/buwd/files/2022/05/Table_A1.csv

The American Association of Physics Teachers (AAPT) Committee on Laboratories assembled a task force whose charge was to write an open letter to the physics education community calling for increased investment in accessible lab courses. Contributors to this paper include students, staff, and faculty with and without disabilities who expressed interest in the open letter. In this document, we recognize the need for making physics laboratories more accessible in all spaces (e.g., high school courses, graduate level courses, research labs). We focus on the experiences of students with disabilities in physics lab courses at the undergraduate level because that is the context for which the writing team had the most collective experience. The intended audiences for this document consist of undergraduate physics students, staff, and faculty, especially those who have direct stake in laboratory courses; physics departments; and member societies, including AAPT. We begin by presenting our motivation for the document and the importance of accessibility and diversity in education and the workforce. We start with the broader context of accessibility, narrowing our focus to physics education and the current state of affairs and availability of accessible resources. Accessibility is then discussed in the specific context of physics laboratory courses, focusing on how barriers are created and can be lowered. In exploring ideas and strategies for improving accessibility, we recognize that the development of multiple pathways for laboratory investigation creates opportunities to expand learning opportunities for more students in physics lab programs., Comment: 51 pages; Endorsed by the AAPT Board of Directors on January 5, 2022

Deep discriminative approaches like random forests and deep neural networks have recently found applications in many important real-world scenarios. However, deploying these learning algorithms in safety-critical applications raises concerns, particularly when it comes to ensuring confidence calibration for both in-distribution and out-of-distribution data points. Many popular methods for in-distribution (ID) calibration, such as isotonic and Platt's sigmoidal regression, exhibit excellent ID calibration performance. However, these methods are not calibrated for the entire feature space, leading to overconfidence in the case of out-of-distribution (OOD) samples. On the other end of the spectrum, existing out-of-distribution (OOD) calibration methods generally exhibit poor in-distribution (ID) calibration. In this paper, we address ID and OOD calibration problems jointly. We leveraged the fact that deep models, including both random forests and deep-nets, learn internal representations which are unions of polytopes with affine activation functions to conceptualize them both as partitioning rules of the feature space. We replace the affine function in each polytope populated by the training data with a Gaussian kernel. Our experiments on both tabular and vision benchmarks show that the proposed approaches obtain well-calibrated posteriors while mostly preserving or improving the classification accuracy of the original algorithm for ID region, and extrapolate beyond the training data to handle OOD inputs appropriately.

We report the discovery of 74 new pulsating DA white dwarf stars, or ZZ Cetis, from the data obtained by the Transiting Exoplanet Survey Satellite (TESS) mission, from Sectors 1 to 39, corresponding to the first 3 cycles. This includes objects from the Southern Hemisphere (Sectors 1-13 and 27-39) and the Northern Hemisphere (Sectors 14-26), observed with 120 s- and 20 s-cadence. Our sample likely includes 13 low-mass and one extremely low-mass white dwarf candidate, considering the mass determinations from fitting Gaia magnitudes and parallax. In addition, we present follow-up time series photometry from ground-based telescopes for 11 objects, which allowed us to detect a larger number of periods. For each object, we analysed the period spectra and performed an asteroseismological analysis, and we estimate the structure parameters of the sample, i.e., stellar mass, effective temperature and hydrogen envelope mass. We estimate a mean asteroseismological mass of _~ 0.635 +/-0.015 Msun, excluding the candidate low or extremely-low mass objects. This value is in agreement with the mean mass using estimates from Gaia data, which is ~ 0.631 +/- 0.040 Msun, and with the mean mass of previously known ZZ Cetis of = 0.644 +/-0.034 Msun. Our sample of 74 new bright ZZ~Cetis increases the number of known ZZ~Cetis by $\sim$20 per cent., Comment: Accepted for publication in MNRAS

Learning is a process wherein a learning agent enhances its performance through exposure of experience or data. Throughout this journey, the agent may encounter diverse learning environments. For example, data may be presented to the leaner all at once, in multiple batches, or sequentially. Furthermore, the distribution of each data sample could be either identical and independent (iid) or non-iid. Additionally, there may exist computational and space constraints for the deployment of the learning algorithms. The complexity of a learning task can vary significantly, depending on the learning setup and the constraints imposed upon it. However, it is worth noting that the current literature lacks formal definitions for many of the in-distribution and out-of-distribution learning paradigms. Establishing proper and universally agreed-upon definitions for these learning setups is essential for thoroughly exploring the evolution of ideas across different learning scenarios and deriving generalized mathematical bounds for these learners. In this paper, we aim to address this issue by proposing a chronological approach to defining different learning tasks using the provably approximately correct (PAC) learning framework. We will start with in-distribution learning and progress to recently proposed lifelong or continual learning. We employ consistent terminology and notation to demonstrate how each of these learning frameworks represents a specific instance of a broader, more generalized concept of learnability. Our hope is that this work will inspire a universally agreed-upon approach to quantifying different types of learning, fostering greater understanding and progress in the field.

This paper addresses the long-standing open problem of observability of mass and inertia plant parameters in the adaptive identification (AID) of second-order nonlinear models of 6 degree-of-freedom rigid-body dynamical systems subject to externally applied forces and moments. Although stable methods for AID of plant parameters for this class of systems, as well numerous approaches to stable model-based direct adaptive trajectory-tracking control of such systems, have been reported, these studies have been unable to prove analytically that the adaptive parameter estimates converge to the true plant parameter values. This paper reports necessary and sufficient conditions for the uniform complete observability (UCO) of 6-DOF plant inertial parameters for a stable adaptive identifier for this class of systems. When the UCO condition is satisfied, the adaptive parameter estimates are shown to converge to the true plant parameter values. To the best of our knowledge this is the first reported proof for this class of systems of UCO of plant parameters and for convergence of adaptive parameter estimates to true parameter values. We also report a numerical simulation study of this AID approach which shows that (a) the UCO condition can be met for fully-actuated plants as well as underactuated plants with the proper choice of control input and (b) convergence of adaptive parameter estimates to the true parameter values. We conjecture that this approach can be extended to include other parameters that appear rigid body plant models including parameters for drag, buoyancy, added mass, bias, and actuators., Comment: To appear in published proceedings of the 2021 IEEE International Conference on Robotics and Automation (ICRA), Xian, China, May 30 - June 5, 2021. 6 Pages, 2 Figures. *Replacement (v2) submitted to include author's middle initials in metadata. *Replacement (v3) submitted to include publication information

The precise calibration of the strain readout of the LIGO gravitational wave observatories is paramount to the accurate interpretation of gravitational wave events. This calibration is traditionally done by imparting a known force on the test masses of the observatory via radiation pressure. Here we describe the implementation of an alternative calibration scheme: the Newtonian Calibrator. This system uses a rotor consisting of both quadrupole and hexapole mass distributions to apply a time-varying gravitational force on one of the observatory's test masses. The force produced by this rotor can be predicted to $<1\%$ relative uncertainty and is well-resolved in the readout of the observatory. This system currently acts as a cross-check of the existing absolute calibration system.

Coprime and nested arrays are sparse arrays with enhanced degrees of freedom, which can be exploited in direction of arrival estimation using algorithms such as product processing, min processing, and MUSIC. This paper applies the minimum norm method for direction of arrival estimation. Comparison of the root mean squared errors and probabilities of resolution of the minimum norm method with MUSIC for a given linear coprime or nested array demonstrates the superiority of the minimum norm method. Specifically, minimum norm method exhibits lower mean squared error, narrower peaks at the locations of the true sources, and a lower noise floor in the spatial spectral estimate. This work also formulates two different minimum norm methods for planar sparse arrays: direct and linear. Comparison of the linear minimum norm method with the linear MUSIC for planar arrays also demonstrates higher accuracy of the minimum norm method., Comment: The paper has been accepted for publication in IEEE ICECCE 2021. The information will be updated once the conference paper is published

We present follow-up photometry and spectroscopy of ZTF J0328$-$1219 strengthening its status as a white dwarf exhibiting transiting planetary debris. Using TESS and Zwicky Transient Facility photometry, along with follow-up high speed photometry from various observatories, we find evidence for two significant periods of variability at 9.937 and 11.2 hr. We interpret these as most likely the orbital periods of different debris clumps. Changes in the detailed dip structures within the light curves are observed on nightly, weekly, and monthly timescales, reminiscent of the dynamic behavior observed in the first white dwarf discovered to harbor a disintegrating asteroid, WD 1145+017. We fit previously published spectroscopy along with broadband photometry to obtain new atmospheric parameters for the white dwarf, with $M_{\star} = 0.731 \pm 0.023\,M_{\odot}$, $T_{\mathrm{eff}} = 7630 \pm 140\,$K, and $\mathrm{[Ca/He]}=-9.55\pm0.12$. With new high-resolution spectroscopy, we detect prominent and narrow Na D absorption features likely of circumstellar origin, with velocities $21.4\pm1.0$ km s$^{-1}$ blue-shifted relative to atmospheric lines. We attribute the periodically modulated photometric signal to dusty effluents from small orbiting bodies such as asteroids or comets, but are unable to identify the most likely material that is being sublimated, or otherwise ejected, as the environmental temperatures range from roughly 400K to 600K., Comment: 24 pages, 10 figures, 9 tables, accepted to ApJ

We construct from Gaia eDR3 an extensive catalog of spatially resolved binary stars within $\approx$ 1 kpc of the Sun, with projected separations ranging from a few au to 1 pc. We estimate the probability that each pair is a chance alignment empirically, using the Gaia catalog itself to calculate the rate of chance alignments as a function of observables. The catalog contains 1.3 (1.1) million binaries with >90% (>99%) probability of being bound, including 16,000 white dwarf -- main sequence (WD+MS) binaries and 1,400 WD+WD binaries. We make the full catalog publicly available, as well as the queries and code to produce it. We then use this sample to calibrate the published Gaia DR3 parallax uncertainties, making use of the binary components' near-identical parallaxes. We show that these uncertainties are generally reliable for faint stars ($G\gtrsim 18$), but are underestimated significantly for brighter stars. The underestimates are generally $\le 30\%$ for isolated sources with well-behaved astrometry, but are larger (up to 80%) for apparently well-behaved sources with a companion within $\lesssim 4$ arcsec, and much larger for sources with poor astrometric fits. We provide an empirical fitting function to inflate published $\sigma_{\varpi}$ values for isolated sources. The public catalog offers wide ranging follow-up opportunities: from calibrating spectroscopic surveys, to precisely constraining ages of field stars, to the masses and the initial-final mass relation of white dwarfs, to dynamically probing the Galactic tidal field., Comment: Accepted to MNRAS. Catalog available at https://zenodo.org/record/4435257

We present a novel method to detect variable astrophysical objects and transient phenomena using anomalous excess scatter in repeated measurements from public catalogs of Gaia DR2 and Zwicky Transient Facility (ZTF) DR3 photometry. We first provide a generalized, all-sky proxy for variability using only Gaia DR2 photometry, calibrated to white dwarf stars. To ensure more robust candidate detection, we further employ a method combining Gaia with ZTF photometry and alerts. To demonstrate the efficacy, we apply this latter technique to a sample of roughly $12,100$ white dwarfs within 200 pc centered on the ZZ Ceti instability strip, where hydrogen-atmosphere white dwarfs are known to pulsate. Through inspecting the top $1\%$ samples ranked by these methods, we demonstrate that both the Gaia-only and ZTF-informed techniques are highly effective at identifying known and new variable white dwarfs, which we verify using follow-up, high-speed photometry. We confirm variability in all 33 out of 33 ($100\%$) observed white dwarfs within our top $1\%$ highest-ranked candidates, both inside and outside the ZZ Ceti instability strip. In addition to dozens of new pulsating white dwarfs, we also identify five white dwarfs highly likely to show transiting planetary debris; if confirmed, these systems would more than triple the number of white dwarfs known to host transiting debris., Comment: 30 pages, 14 figures, revised and accepted to ApJ on March 11, 2021

The psychosis spectrum is associated with structural dysconnectivity concentrated in transmodal association cortex. However, understanding of this pathophysiology has been limited by an exclusive focus on the direct connections to a region. Using Network Control Theory, we measured variation in both direct and indirect structural connections to a region to gain new insights into the pathophysiology of the psychosis spectrum. We used psychosis symptom data and structural connectivity in 1,068 youths aged 8 to 22 years from the Philadelphia Neurodevelopmental Cohort. Applying a Network Control Theory metric called average controllability, we estimated each brain region's capacity to leverage its direct and indirect structural connections to control linear brain dynamics. Next, using non-linear regression, we determined the accuracy with which average controllability could predict negative and positive psychosis spectrum symptoms in out-of-sample testing. We also compared prediction performance for average controllability versus strength, which indexes only direct connections to a region. Finally, we assessed how the prediction performance for psychosis spectrum symptoms varied over the functional hierarchy spanning unimodal to transmodal cortex. Average controllability outperformed strength at predicting positive psychosis spectrum symptoms, demonstrating that indexing indirect structural connections to a region improved prediction performance. Critically, improved prediction was concentrated in association cortex for average controllability, whereas prediction performance for strength was uniform across the cortex, suggesting that indexing indirect connections is crucial in association cortex. Examining inter-individual variation in direct and indirect structural connections to association cortex is crucial for accurate prediction of positive psychosis spectrum symptoms.

We show that quantum entanglement between causally separated regions of a nucleon in antineutrino-nucleon scattering manifests itself as a thermal component in the resulting pion momentum distribution. For antineutrino scattering coherently from the (carbon) nucleus as a whole, this thermal component is absent, as expected by our quantum entanglement thermalization proposition. These phenomena, which have been observed in proton-proton collisions at the Large Hadron Collider, and in electromagnetic deep inelastic scattering, are now for the first time shown to exist in electroweak interactions as well., Comment: 8 pages, 3 figures

Canonical distances such as Euclidean distance often fail to capture the appropriate relationships between items, subsequently leading to subpar inference and prediction. Many algorithms have been proposed for automated learning of suitable distances, most of which employ linear methods to learn a global metric over the feature space. While such methods offer nice theoretical properties, interpretability, and computationally efficient means for implementing them, they are limited in expressive capacity. Methods which have been designed to improve expressiveness sacrifice one or more of the nice properties of the linear methods. To bridge this gap, we propose a highly expressive novel decision forest algorithm for the task of distance learning, which we call Similarity and Metric Random Forests (SMERF). We show that the tree construction procedure in SMERF is a proper generalization of standard classification and regression trees. Thus, the mathematical driving forces of SMERF are examined via its direct connection to regression forests, for which theory has been developed. Its ability to approximate arbitrary distances and identify important features is empirically demonstrated on simulated data sets. Last, we demonstrate that it accurately predicts links in networks., Comment: Submitted to NeurIPS 2020

In lifelong learning, data are used to improve performance not only on the present task, but also on past and future (unencountered) tasks. While typical transfer learning algorithms can improve performance on future tasks, their performance on prior tasks degrades upon learning new tasks (called forgetting). Many recent approaches for continual or lifelong learning have attempted to maintain performance on old tasks given new tasks. But striving to avoid forgetting sets the goal unnecessarily low. The goal of lifelong learning should be to use data to improve performance on both future tasks (forward transfer) and past tasks (backward transfer). In this paper, we show that a simple approach -- representation ensembling -- demonstrates both forward and backward transfer in a variety of simulated and benchmark data scenarios, including tabular, vision (CIFAR-100, 5-dataset, Split Mini-Imagenet, and Food1k), and speech (spoken digit), in contrast to various reference algorithms, which typically failed to transfer either forward or backward, or both. Moreover, our proposed approach can flexibly operate with or without a computational budget.

The EXtreme PREcision Spectrograph (EXPRES) is a new Doppler spectrograph designed to reach a radial velocity measurement precision sufficient to detect Earth-like exoplanets orbiting nearby, bright stars. We report on extensive laboratory testing and on-sky observations to quantitatively assess the instrumental radial velocity measurement precision of EXPRES, with a focused discussion of individual terms in the instrument error budget. We find that EXPRES can reach a single-measurement instrument calibration precision better than 10 cm/s, not including photon noise from stellar observations. We also report on the performance of the various environmental, mechanical, and optical subsystems of EXPRES, assessing any contributions to radial velocity error. For atmospheric and telescope related effects, this includes the fast tip-tilt guiding system, atmospheric dispersion compensation, and the chromatic exposure meter. For instrument calibration, this includes the laser frequency comb (LFC), flat-field light source, CCD detector, and effects in the optical fibers. Modal noise is mitigated to a negligible level via a chaotic fiber agitator, which is especially important for wavelength calibration with the LFC. Regarding detector effects, we empirically assess the impact on radial velocity precision due to pixel-position non-uniformities (PPNU) and charge transfer inefficiency (CTI). EXPRES has begun its science survey to discover exoplanets orbiting G-dwarf and K-dwarf stars, in addition to transit spectroscopy and measurements of the Rossiter-McLaughlin effect., Comment: 39 pages, 30 figures, accepted to AJ

The EXtreme PREcision Spectrograph (EXPRES) is an environmentally stabilized, fiber-fed, $R=137,500$, optical spectrograph. It was recently commissioned at the 4.3-m Lowell Discovery Telescope (LDT) near Flagstaff, Arizona. The spectrograph was designed with a target radial-velocity (RV) precision of 30$\mathrm{~cm~s^{-1}}$. In addition to instrumental innovations, the EXPRES pipeline, presented here, is the first for an on-sky, optical, fiber-fed spectrograph to employ many novel techniques---including an "extended flat" fiber used for wavelength-dependent quantum efficiency characterization of the CCD, a flat-relative optimal extraction algorithm, chromatic barycentric corrections, chromatic calibration offsets, and an ultra-precise laser frequency comb for wavelength calibration. We describe the reduction, calibration, and radial-velocity analysis pipeline used for EXPRES and present an example of our current sub-meter-per-second RV measurement precision, which reaches a formal, single-measurement error of 0.3$\mathrm{~m~s^{-1}}$ for an observation with a per-pixel signal-to-noise ratio of 250. These velocities yield an orbital solution on the known exoplanet host 51 Peg that matches literature values with a residual RMS of 0.895$\mathrm{~m~s^{-1}}$.

In systems neuroscience, most models posit that brain regions communicate information under constraints of efficiency. Yet, evidence for efficient communication in structural brain networks characterized by hierarchical organization and highly connected hubs remains sparse. The principle of efficient coding proposes that the brain transmits maximal information in a metabolically economical or compressed form to improve future behavior. To determine how structural connectivity supports efficient coding, we develop a theory specifying minimum rates of message transmission between brain regions to achieve an expected fidelity, and we test five predictions from the theory based on random walk communication dynamics. In doing so, we introduce the metric of compression efficiency, which quantifies the trade-off between lossy compression and transmission fidelity in structural networks. In a large sample of youth (n = 1,042; age 8-23 years), we analyze structural networks derived from diffusion weighted imaging and metabolic expenditure operationalized using cerebral blood flow. We show that structural networks strike compression efficiency trade-offs consistent with theoretical predictions. We find that compression efficiency prioritizes fidelity with development, heightens when metabolic resources and myelination guide communication, explains advantages of hierarchical organization, links higher input fidelity to disproportionate areal expansion, and shows that hubs integrate information by lossy compression. Lastly, compression efficiency is predictive of behavior--beyond a conventional metric--for cognitive domains including executive function, memory, complex reasoning, and social cognition. Our findings elucidate how macroscale connectivity supports efficient coding, and serve to foreground communication processes that utilize random walk dynamics constrained by network connectivity.

Decision forests (Forests), in particular random forests and gradient boosting trees, have demonstrated state-of-the-art accuracy compared to other methods in many supervised learning scenarios. In particular, Forests dominate other methods in tabular data, that is, when the feature space is unstructured, so that the signal is invariant to a permutation of the feature indices. However, in structured data lying on a manifold (such as images, text, and speech) deep networks (Networks), specifically convolutional deep networks (ConvNets), tend to outperform Forests. We conjecture that at least part of the reason for this is that the input to Networks is not simply the feature magnitudes, but also their indices. In contrast, naive Forest implementations fail to explicitly consider feature indices. A recently proposed Forest approach demonstrates that Forests, for each node, implicitly sample a random matrix from some specific distribution. These Forests, like some classes of Networks, learn by partitioning the feature space into convex polytopes corresponding to linear functions. We build on that approach and show that one can choose distributions in a manifold-aware fashion to incorporate feature locality. We demonstrate the empirical performance on data whose features live on three different manifolds: a torus, images, and time-series. Moreover, we demonstrate its strength in multivariate simulated settings and also show superiority in predicting surgical outcome in epilepsy patients and predicting movement direction from raw stereotactic EEG data from non-motor brain regions. In all simulations and real data, Manifold Oblique Random Forest (MORF) algorithm outperforms approaches that ignore feature space structure and challenges the performance of ConvNets. Moreover, MORF runs fast and maintains interpretability and theoretical justification., Comment: Updated manuscript based on review at SIMODS

As the ratio between the rate of computation and rate with which data can be retrieved from various layers of memory continues to deteriorate, a question arises: Will the current best algorithms for computing matrix-matrix multiplication on future CPUs continue to be (near) optimal? This paper provides compelling analytical and empirical evidence that the answer is "no". The analytical results guide us to a new family of algorithms of which the current state-of-the-art "Goto's algorithm" is but one member. The empirical results, on architectures that were custom built to reduce the amount of bandwidth to main memory, show that under different circumstances, different and particular members of the family become more superior. Thus, this family will likely start playing a prominent role going forward.

At rest, human brain functional networks display striking modular architecture in which coherent clusters of brain regions are activated. The modular account of brain function is pervasive, reliable, and reproducible. Yet, a complementary perspective posits a core-periphery or rich-club account of brain function, where hubs are densely interconnected with one another, allowing for integrative processing. Unifying these two perspectives has remained difficult due to the fact that the methodological tools to identify modules are entirely distinct from the methodological tools to identify core-periphery structure. Here we leverage a recently-developed model-based approach -- the weighted stochastic block model -- that simultaneously uncovers modular and core-periphery structure, and we apply it to fMRI data acquired at rest in 872 youth of the Philadelphia Neurodevelopmental Cohort. We demonstrate that functional brain networks display rich meso-scale organization beyond that sought by modularity maximization techniques. Moreover, we show that this meso-scale organization changes appreciably over the course of neurodevelopment, and that individual differences in this organization predict individual differences in cognition more accurately than module organization alone. Broadly, our study provides a unified assessment of modular and core-periphery structure in functional brain networks, providing novel insights into their development and implications for behavior.

A diverse white matter network and finely tuned neuronal membrane properties allow the brain to transition seamlessly between cognitive states. However, it remains unclear how static structural connections guide the temporal progression of large-scale brain activity patterns in different cognitive states. Here, we analyze the brain's trajectories through a high-dimensional activity space at the level of single time point activity patterns from functional magnetic resonance imaging data acquired during passive visual fixation (rest) and an n-back working memory task. We find that specific state space trajectories, which represent temporal sequences of brain activity, are modulated by cognitive load and related to task performance. Using diffusion-weighted imaging acquired from the same subjects, we use tools from network control theory to show that linear spread of activity along white matter connections constrains the brain's state space trajectories at rest. Additionally, accounting for stimulus-driven visual inputs explains the different trajectories taken during the n-back task. We also used models of network rewiring to show that these findings are the result of non-trivial geometric and topological properties of white matter architecture. Finally, we examine associations between age and time-resolved brain state dynamics, revealing new insights into functional changes in the default mode and executive control networks. Overall, these results elucidate the structural underpinnings of cognitively and developmentally relevant spatiotemporal brain dynamics.

Higher socioeconomic status (SES) in childhood is associated with increased cognitive abilities, higher academic achievement, and decreased incidence of mental illness later in development. Accumulating evidence suggests that these effects may be due to changes in brain development induced by environmental factors. While prior work has mapped the associations between neighborhood SES and brain structure, little is known about the relationship between SES and intrinsic neural dynamics. Here, we capitalize upon a large community-based sample (Philadelphia Neurodevelopmental Cohort, ages 8-22 years, n=1012) to examine developmental changes in functional brain network topology as estimated from resting state functional magnetic resonance imaging data. We quantitatively characterize this topology using a local measure of network segregation known as the clustering coefficient, and find that it accounts for a greater degree of SES-associated variance than meso-scale segregation captured by modularity. While whole-brain clustering increased with age, high-SES youth displayed faster increases in clustering than low-SES youth, and this effect was most pronounced for regions in the limbic, somatomotor, and ventral attention systems. The effect of SES on developmental increases in clustering was strongest for connections of intermediate physical length, consistent with faster decreases in local connectivity in these regions in low-SES youth, and tracked changes in BOLD signal complexity in the form of regional homogeneity. Our findings suggest that neighborhood SES may fundamentally alter intrinsic patterns of inter-regional interactions in the human brain in a manner that is consistent with greater segregation of information processing in late childhood and adolescence., Comment: 9 pages, 6 figures. Cerebral Cortex (2019)

Machine learning has an emerging critical role in high-performance computing to modulate simulations, extract knowledge from massive data, and replace numerical models with efficient approximations. Decision forests are a critical tool because they provide insight into model operation that is critical to interpreting learned results. While decision forests are trivially parallelizable, the traversals of tree data structures incur many random memory accesses and are very slow. We present memory packing techniques that reorganize learned forests to minimize cache misses during classification. The resulting layout is hierarchical. At low levels, we pack the nodes of multiple trees into contiguous memory blocks so that each memory access fetches data for multiple trees. At higher levels, we use leaf cardinality to identify the most popular paths through a tree and collocate those paths in cache lines. We extend this layout with out-of-order execution and cache-line prefetching to increase memory throughput. Together, these optimizations increase the performance of classification in ensembles by a factor of four over an optimized C++ implementation and a actor of 50 over a popular R language implementation.

Optical fiber modal noise is a limiting factor for high precision spectroscopy signal-to-noise in the near-infrared and visible. Unabated, especially when using highly coherent light sources for wavelength calibration, modal noise can induce radial velocity (RV) errors that hinder the discovery of low-mass (and potentially Earth-like) planets. Previous research in this field has found sufficient modal noise mitigation through the use of an integrating sphere, but this requires extremely bright light sources, a luxury not necessarily afforded by the next generation of high-resolution optical spectrographs. Otherwise, mechanical agitation, which "mixes" the fiber's modal patterns and allows the noise to be averaged over minutes-long exposures, provides some noise reduction but the exact mechanism behind improvement in signal-to-noise and RV drift has not been fully explored or optimized by the community. Therefore, we have filled out the parameter space of modal noise agitation techniques in order to better understand agitation's contribution to mitigating modal noise and to discover a better method for agitating fibers. We find that modal noise is best suppressed by the quasi-chaotic motion of two high-amplitude agitators oscillating with varying phase for fibers with large core diameters and low azimuthal symmetry. This work has subsequently influenced the design of a fiber agitator, to be installed with the EXtreme PREcision Spectrograph, that we estimate will reduce modal-noise-induced RV error to less than 3.2 cm/s., Comment: Accepted by The Astrophysical Journal

Executive function emerges late in development and displays different developmental trends in males and females. Sex differences in executive function in youth have been linked to vulnerability to psychopathology as well as to behaviors that impinge on health. Yet, the neurobiological basis of these differences is not well understood. Here we test the hypothesis that sex differences in executive function in youth stem from sex differences in the controllability of structural brain networks as they rewire over development. Combining methods from network neuroscience and network control theory, we characterize the network control properties of structural brain networks estimated from diffusion imaging data acquired in males and females in a sample of 882 youth aged 8-22 years. We summarize the control properties of these networks by estimating average and modal controllability, two statistics that probe the ease with which brain areas can drive the network towards easy- versus difficult-to-reach states. We find that females have higher modal controllability in frontal, parietal, and subcortical regions while males have higher average controllability in frontal and subcortical regions. Furthermore, average controllability values in the medial frontal cortex and subcortex, both higher in males, are negatively related to executive function. Finally, we find that average controllability predicts sex-dependent individual differences in activation during an n-back working memory task. Taken together, our findings support the notion that sex differences in the controllability of structural brain networks can partially explain sex differences in executive function. Controllability of structural brain networks also predicts features of task-relevant activation, suggesting the potential for controllability to represent context-specific constraints on network state more generally.

We present a full implementation of the parareal algorithm---an integration technique to solve differential equations in parallel---in the Julia programming language for a fully general, first-order, initial-value problem. We provide a brief overview of Julia---a concurrent programming language for scientific computing. Our implementation of the parareal algorithm accepts both coarse and fine integrators as functional arguments. We use Euler's method and another Runge-Kutta integration technique as the integrators in our experiments. We also present a simulation of the algorithm for purposes of pedagogy and as a tool for investigating the performance of the algorithm., Comment: 6 pages, 2 figures, 2 listings

Given the ubiquity of electronic displays integration in human-based systems, the impending mission critical, space-based applications of electronic displays will necessitate SEE assessment of components unique to electronic displays. A commercially available DDIC designed to drive a small form factor organic light emitting diode (OLED) was visually monitored during heavy ion irradiation to catalogue radiation induced persistent visual error signatures that require manual intervention (i.e., power cycling) to return to nominal function. These error signatures were able to be reproduced via modification of configuration register values utilizing the instruction set intended for interfacing a microcontroller with the DDIC. This approach to emulation of heavy-ion induced errors on a table-top assists with human perception-based criticality analysis as well as development of mitigation techniques.

The human brain is organized into large-scale functional modules that have been shown to evolve in childhood and adolescence. However, it remains unknown whether structural brain networks are similarly refined during development, potentially allowing for improvements in executive function. In a sample of 882 participants (ages 8-22) who underwent diffusion imaging as part of the Philadelphia Neurodevelopmental Cohort, we demonstrate that structural network modules become more segregated with age, with weaker connections between modules and stronger connections within modules. Evolving modular topology facilitated network integration, driven by age-related strengthening of hub edges that were present both within and between modules. Critically, both modular segregation and network integration were associated with enhanced executive performance, and mediated the improvement of executive functioning with age. Together, results delineate a process of structural network maturation that supports executive function in youth.

We announce the discovery of KELT-16b, a highly irradiated, ultra-short period hot Jupiter transiting the relatively bright ($V = 11.7$) star TYC 2688-1839-1. A global analysis of the system shows KELT-16 to be an F7V star with $T_\textrm{eff} = 6236\pm54$ K, $\log{g_\star} = 4.253_{-0.036}^{+0.031}$, [Fe/H] = -0.002$_{-0.085}^{+0.086}$, $M_\star = 1.211_{-0.046}^{+0.043} M_\odot$, and $R_\star = 1.360_{-0.053}^{+0.064} R_\odot$. The planet is a relatively high mass inflated gas giant with $M_\textrm{P} = 2.75_{-0.15}^{+0.16} M_\textrm{J}$, $R_\textrm{P} = 1.415_{-0.067}^{+0.084} R_\textrm{J}$, density $\rho_\textrm{P} = 1.20\pm0.18$ g cm$^{-3}$, surface gravity $\log{g_\textrm{P}} = 3.530_{-0.049}^{+0.042}$, and $T_\textrm{eq} = 2453_{-47}^{+55}$ K. The best-fitting linear ephemeris is $T_\textrm{C} = 2457247.24791\pm0.00019$ BJD$_{tdb}$ and $P = 0.9689951 \pm 0.0000024$ d. KELT-16b joins WASP-18b, -19b, -43b, -103b, and HATS-18b as the only giant transiting planets with $P < 1$ day. Its ultra-short period and high irradiation make it a benchmark target for atmospheric studies by HST, Spitzer, and eventually JWST. For example, as a hotter, higher mass analog of WASP-43b, KELT-16b may feature an atmospheric temperature-pressure inversion and day-to-night temperature swing extreme enough for TiO to rain out at the terminator. KELT-16b could also join WASP-43b in extending tests of the observed mass-metallicity relation of the Solar System gas giants to higher masses. KELT-16b currently orbits at a mere $\sim$ 1.7 Roche radii from its host star, and could be tidally disrupted in as little as a few $\times 10^{5}$ years (for a stellar tidal quality factor of $Q_*' = 10^5$). Finally, the likely existence of a widely separated bound stellar companion in the KELT-16 system makes it possible that Kozai-Lidov oscillations played a role in driving KELT-16b inward to its current precarious orbit., Comment: 16 pages, 18 Figures, 7 Tables, Accepted for publication in AJ

We present the discovery of a hot-Jupiter transiting the V=9.23 mag main-sequence A-star KELT-17 (BD+14 1881). KELT-17b is a 1.31 -0.29/+0.28 Mj, 1.525 -0.060/+0.065 Rj hot-Jupiter in a 3.08 day period orbit misaligned at -115.9 +/- 4.1 deg to the rotation axis of the star. The planet is confirmed via both the detection of the radial velocity orbit, and the Doppler tomographic detection of the shadow of the planet over two transits. The nature of the spin-orbit misaligned transit geometry allows us to place a constraint on the level of differential rotation in the host star; we find that KELT-17 is consistent with both rigid-body rotation and solar differential rotation rates (alpha < 0.30 at 2 sigma significance). KELT-17 is only the fourth A-star with a confirmed transiting planet, and with a mass of 1.635 -0.061/+0.066 Msun, effective temperature of 7454 +/- 49 K, and projected rotational velocity v sin I_* = 44.2 -1.3/+1.5 km/s; it is amongst the most massive, hottest, and most rapidly rotating of known planet hosts., Comment: 15 pages, 9 figures, accepted for publication in AJ

As the human brain develops, it increasingly supports coordinated control of neural activity. The mechanism by which white matter evolves to support this coordination is not well understood. We use a network representation of diffusion imaging data from 882 youth ages 8 to 22 to show that white matter connectivity becomes increasingly optimized for a diverse range of predicted dynamics in development. Notably, stable controllers in subcortical areas are negatively related to cognitive performance. Investigating structural mechanisms supporting these changes, we simulate network evolution with a set of growth rules. We find that all brain networks are structured in a manner highly optimized for network control, with distinct control mechanisms predicted in child versus older youth. We demonstrate that our results cannot be simply explained by changes in network modularity. This work reveals a possible mechanism of human brain development that preferentially optimizes dynamic network control over static network architecture., Comment: In press at Nature Communications

We dispel with "street wisdom" regarding the practical implementation of Strassen's algorithm for matrix-matrix multiplication (DGEMM). Conventional wisdom: it is only practical for very large matrices. Our implementation is practical for small matrices. Conventional wisdom: the matrices being multiplied should be relatively square. Our implementation is practical for rank-k updates, where k is relatively small (a shape of importance for libraries like LAPACK). Conventional wisdom: it inherently requires substantial workspace. Our implementation requires no workspace beyond buffers already incorporated into conventional high-performance DGEMM implementations. Conventional wisdom: a Strassen DGEMM interface must pass in workspace. Our implementation requires no such workspace and can be plug-compatible with the standard DGEMM interface. Conventional wisdom: it is hard to demonstrate speedup on multi-core architectures. Our implementation demonstrates speedup over conventional DGEMM even on an Intel(R) Xeon Phi(TM) coprocessor utilizing 240 threads. We show how a distributed memory matrix-matrix multiplication also benefits from these advances.