Mercer 大学卫生科学中心

Validated learning plays a critical role in the lean startup approach to entrepreneurship education. We argue that, compared to validated learning from business generalists, student entrepreneurs who seek feedback from technical specialists develop superior minimum viable products (MVPs). We further suggest that the different effects of feedback from business generalists and technical specialists on the quality of MVPs are mediated by student entrepreneurs' failure analysis. Using a between-subjects, randomized field experiment with a longitudinal design, we find our hypotheses supported. The results indicate that validated learning from technical specialists, in addition to business generalists, should be incorporated into the lean startup pedagogy to guide student entrepreneurs for better MVPs.

Aggressive driving behaviors such as tailgating and cutting off pose serious highway safety risks, especially for trucks. Timely detection of these behaviors can enable real-time interventions (e.g., automated driver warnings or vehicle safety system activation) to prevent crashes. This study presents a machine learning approach to detect tailgating and cut-off events using data from a high-fidelity driving simulator. Forty participants drove a truck in mild and heavy traffic scenarios within a connected vehicle (CV) environment, providing rich data for analysis. We fused four data sources-vehicle kinematics, CV-based metrics, road characteristics, and driver demographics-into five feature combinations to evaluate their predictive power. Four classification models (Artificial Neural Network, Support Vector Machine, Random Forest, and XGBoost) were trained on these feature sets. Performance evaluation across traffic scenarios shows that models leveraging CV data significantly outperform those using only traditional data, achieving high accuracy in identifying aggressive behaviors. Integrating CV features with conventional kinematic data substantially improved tailgating and cutting-off detection, underscoring the promise of CV technology for enhancing highway safety.

IceCube recently reported the observation of TeV neutrinos from the nearby Seyfert galaxy NGC 1068, and the corresponding neutrino flux is significantly higher than the upper limit implied by observations of GeV-TeV gamma rays. This suggests that neutrinos are produced near the supermassive black hole, where the radiation density is high enough to obscure gamma rays. We use a set of muon neutrinos with interaction vertices inside the detector, which have good sensitivity to sources in the southern sky, from IceCube data recorded between 2011 and 2021. We then search for individual and collective neutrino signals from 14 Seyfert galaxies in the southern sky selected from the Swift Burst Alert Telescope AGN Spectroscopic Survey. Using the correlations between keV X-rays and TeV neutrinos predicted by disk-corona models, and assuming production characteristics similar to NGC 1068, a collective neutrino signal search reveals an excess of 6.7(-3.2)(+4.0) events, which is inconsistent with background expectations at the 3 sigma level of significance. In this Letter, we present new independent evidence that Seyfert galaxies contribute to the extragalactic flux of high-energy neutrinos.

We establish finite-sample closed-loop stability guarantees for Model Predictive Path Integral (MPPI) control applied to discrete-time Linear Time-Invariant (LTI) systems with additive Gaussian process disturbances. The key observation is that, for unconstrained LTI/quadratic systems with the DARE terminal cost, the exact finite-horizon MPC law has the same first control action as the infinite-horizon LQR law for every planning horizon. Thus, finite-sample MPPI can be analyzed as a stochastic perturbation of LQR. First, we show that the MPPI control law approximates the LQR feedback with high probability. The approximation error decomposes into a Monte Carlo term that decreases with the sample count and an infinite-sample temperature bias that persists at finite temperature but vanishes as the temperature is reduced. The resulting constants are written in terms of the horizon-dependent stacked cost matrices, making explicit that the finite-sample certificate is parametrized by the selected planning horizon. Second, we use a Lyapunov perturbation argument to prove practical exponential stability in expectation. On sample paths that remain in a compact Lyapunov sublevel set over a finite operating horizon, the expected state norm decays exponentially up to three residual floors: a process-noise floor, an MPPI approximation floor, and a confidence floor from the per-step sampling failure probability. The sufficient sample threshold is explicit and computable from the DARE solution, LQR stability margin, MPPI sampling parameters, temperature, and planning horizon. In the joint limit of infinite samples and vanishing temperature bias, the result recovers the stochastic LQR stability bound.

To facilitate its goal of fostering department transformation, the Partnership for Undergraduate Life Sciences Education (PULSE) organizes some of its work into five geographic regions and leverages the expertise of the PULSE Fellows in each region. This article examines how these Regional Networks have driven department-level changes through outreach activities, use of the PULSE Rubrics as a diagnostic tool to help departments to advance the Vision and Change recommendations, and implementation of the PULSE framework to address local needs and challenges. We present impact data through Regional Network activities and identify commonalities in successes and challenges across all the geographic regions. Through reports from the Northeast, Southeast, Southwest, Northwest, and Midwest Great Plains, we highlight each Regional Network’s accomplishments in influencing the adoption of PULSE strategies to implement the Vision and Change recommendations to transform departments.