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Most students who begin at a community college do not complete their desired credential. Many former students fail to graduate due to various barriers rather than their academic performance. To encourage previously successful non-completers to re-enroll and eventually graduate, a growing number of community colleges have implemented re-enrollment campaigns focused on former students who have already made substantial progress toward graduation. In this study, we randomly assigned over 27,000 former community college students to a control group, "information-only" treatment group, or "information and one-course waiver" treatment group to examine whether re-enrollment campaigns can improve their likelihood of long-term persistence and credential completion. Although we showed in earlier work that the "information and one-course waiver" treatment had a positive impact on former students' likelihood of re-enrollment, our findings reveal the re-enrollment intervention has no effect on students' likelihood of long-term persistence or credential completion for the pooled sample or any subgroup of interest, including low-income students, racially minoritized students, or adult students. Simply put, this particular re-enrollment intervention including one-time, one-course tuition waivers increased former students' likelihood of re-enrollment but was not an effective lever to increase "long-term" academic outcomes among previously successful community college students who departed early without earning a credential.

Mobile robots operating indoors must be prepared to navigate challenging scenes that contain transparent surfaces. This paper proposes a novel method for the fusion of acoustic and visual sensing modalities through implicit neural representations to enable dense reconstruction of transparent surfaces in indoor scenes. We propose a novel model that leverages generative latent optimization to learn an implicit representation of indoor scenes consisting of transparent surfaces. We demonstrate that we can query the implicit representation to enable volumetric rendering in image space or 3D geometry reconstruction (point clouds or mesh) with transparent surface prediction. We evaluate our method's effectiveness qualitatively and quantitatively on a new dataset collected using a custom, low-cost sensing platform featuring RGB-D cameras and ultrasonic sensors. Our method exhibits significant improvement over state-of-the-art for transparent surface reconstruction., Comment: https://umfieldrobotics.github.io/VAIR_site/

Radially extended disk winds could be the key to unlocking how protoplanetary disks accrete and how planets form and migrate. A distinctive characteristic is their nested morphology of velocity and chemistry. Here we report JWST/NIRSpec spectro-imaging of four young stars with edge-on disks in the Taurus star-forming region that demonstrate the ubiquity of this structure. In each source, a fast collimated jet traced by [Fe II] is nested inside a hollow cavity within wider lower-velocity H2 and, in one case, also CO ro-vibrational (v=1-0) emission. Furthermore, in one of our sources, ALMA CO(2-1) emission, paired with our NIRSpec images, reveals the nested wind structure extends further outward. This nested wind morphology strongly supports theoretical predictions for wind-driven accretion and underscores the need for theoretical work to assess the role of winds in the formation and evolution of planetary systems, Comment: This preprint has not undergone peer review or any post-submission improvements or corrections. The Version of Record of this article is published in Nature Astronomy and is available online at https://doi.org/10.1038/s41550-024-02385-7

Safe motion planning algorithms are necessary for deploying autonomous robots in unstructured environments. Motion plans must be safe to ensure that the robot does not harm humans or damage any nearby objects. Generating these motion plans in real-time is also important to ensure that the robot can adapt to sudden changes in its environment. Many trajectory optimization methods introduce heuristics that balance safety and real-time performance, potentially increasing the risk of the robot colliding with its environment. This paper addresses this challenge by proposing Conformalized Reachable Sets for Obstacle Avoidance With Spheres (CROWS). CROWS is a novel real-time, receding-horizon trajectory planner that generates probalistically-safe motion plans. Offline, CROWS learns a novel neural network-based representation of a spherebased reachable set that overapproximates the swept volume of the robot's motion. CROWS then uses conformal prediction to compute a confidence bound that provides a probabilistic safety guarantee on the learned reachable set. At runtime, CROWS performs trajectory optimization to select a trajectory that is probabilstically-guaranteed to be collision-free. We demonstrate that CROWS outperforms a variety of state-of-the-art methods in solving challenging motion planning tasks in cluttered environments while remaining collision-free. Code, data, and video demonstrations can be found at https://roahmlab.github.io/crows/, Comment: https://roahmlab.github.io/crows/

We study expansiveness properties of positive measure subsets of ergodic $\mathbb{Z}^d$-actions along two different types of structured subsets of $\mathbb{Z}^d$, namely, cyclic subgroups and images of integer polynomials. We prove quantitative expansiveness properties in both cases and strengthen combinatorial results obtained by Bj\"orklund and Fish in arXiv:2401.03724, and Bulinski and Fish in arXiv:2102.05862. Our methods unify and strengthen earlier approaches used in arXiv:2401.03724 and arXiv:2102.05862 and to our surprise, also yield a counterexample to a certain pinned variant of the polynomial Bogolyubov theorem.

High-definition (HD) maps provide environmental information for autonomous driving systems and are essential for safe planning. While existing methods with single-frame input achieve impressive performance for online vectorized HD map construction, they still struggle with complex scenarios and occlusions. We propose MemFusionMap, a novel temporal fusion model with enhanced temporal reasoning capabilities for online HD map construction. Specifically, we contribute a working memory fusion module that improves the model's memory capacity to reason across a history of frames. We also design a novel temporal overlap heatmap to explicitly inform the model about the temporal overlap information and vehicle trajectory in the Bird's Eye View space. By integrating these two designs, MemFusionMap significantly outperforms existing methods while also maintaining a versatile design for scalability. We conduct extensive evaluation on open-source benchmarks and demonstrate a maximum improvement of 5.4% in mAP over state-of-the-art methods. The project page for MemFusionMap is https://song-jingyu.github.io/MemFusionMap, Comment: Accepted to WACV 2025

Neural Radiance Fields and Gaussian Splatting have transformed the field of computer vision by enabling photo-realistic representation of complex scenes. Despite this success, they have seen only limited use in real-world robotics tasks such as trajectory optimization. Two key factors have contributed to this limited success. First, it is challenging to reason about collisions in radiance models. Second, it is difficult to perform inference of radiance models fast enough for real-time trajectory synthesis. This paper addresses these challenges by proposing SPLANNING, a risk-aware trajectory optimizer that operates in a Gaussian Splatting model. This paper first derives a method for rigorously upper-bounding the probability of collision between a robot and a radiance field. Second, this paper introduces a normalized reformulation of Gaussian Splatting that enables the efficient computation of the collision bound in a Gaussian Splat. Third, a method is presented to optimize trajectories while avoiding collisions with a scene represented by a Gaussian Splat. Experiments demonstrate that SPLANNING outperforms state-of-the-art methods in generating collision-free trajectories in highly cluttered environments. The proposed system is also tested on a real-world robot manipulator. A project page is available at https://roahmlab.github.io/splanning., Comment: First two authors contributed equally. Project Page: https://roahmlab.github.io/splanning

Let $E$ be an elliptic curve defined over $\mathbb{Q}$ with conductor $N$ and $p\nmid 2N$ a prime. Let $L$ be an imaginary quadratic field with $p$ split. We prove the existence of $p$-adic zeta element for $E$ over $L$, encoding two different $p$-adic $L$-functions associated to $E$ over $L$ via explicit reciprocity laws at the primes above $p$. We formulate a main conjecture for $E$ over $L$ in terms of the zeta element, mediating different main conjectures in which the $p$-adic $L$-functions appear, and prove some results toward them. The zeta element has various applications to the arithmetic of elliptic curves. This includes a proof of main conjecture for semistable elliptic curves $E$ over $\mathbb{Q}$ at supersingular primes $p$, as conjectured by Kobayashi in 2002. It leads to the $p$-part of the conjectural Birch and Swinnerton-Dyer (BSD) formula for such curves of analytic rank zero or one, and enables us to present the first infinite families of non-CM elliptic curves for which the BSD conjecture is true. We provide further evidence towards the BSD conjecture: new cases of $p$-converse to the Gross--Zagier and Kolyvagin theorem, and $p$-part of the BSD formula for ordinary primes $p$. Along the way, we give a proof of a conjecture of Perrin-Riou connecting Beilinson--Kato elements with rational points., Comment: A main result supersedes the preprint arXiv:1411.6352

We consider the motion of two massive particles along a straight line. A lighter particle bounces back and forth between a heavier particle and a stationary wall, with all collisions being ideally elastic. It is known that if the lighter particle moves much faster than the heavier one, and the kinetic energies of the particles are of the same order, then the product of the speed of the lighter particle and the distance between the heavier particle and the wall is an adiabatic invariant: its value remains approximately constant over a long period. We show that the value of this adiabatic invariant, calculated at the collisions of the lighter particle with the wall, is a constant of motion (i.e., {an exact adiabatic invariant}). On the other hand, the value of this adiabatic invariant at the collisions between the particles slowly linearly in time decays with each collision. The model we consider is a highly simplified version of the classical adiabatic piston problem, where the lighter particle represents a gas particle, and the heavier particle represents the piston., Comment: 6 pages

Multimodal large language models (LLMs) have achieved notable success across various domains, while research in the medical field has largely focused on unimodal images. Meanwhile, current general-domain multimodal models for videos still lack the capabilities to understand and engage in conversations about surgical videos. One major contributing factor is the absence of datasets in the surgical field. In this paper, we create a new dataset, Surg-QA, consisting of 102,000 surgical video-instruction pairs, the largest of its kind so far. To build such a dataset, we propose a novel two-stage question-answer generation pipeline with LLM to learn surgical knowledge in a structured manner from the publicly available surgical lecture videos. The pipeline breaks down the generation process into two stages to significantly reduce the task complexity, allowing us to use a more affordable, locally deployed open-source LLM than the premium paid LLM services. It also mitigates the risk of LLM hallucinations during question-answer generation, thereby enhancing the overall quality of the generated data. We further train LLaVA-Surg, a novel vision-language conversational assistant capable of answering open-ended questions about surgical videos, on this Surg-QA dataset, and conduct comprehensive evaluations on zero-shot surgical video question-answering tasks. We show that LLaVA-Surg significantly outperforms all previous general-domain models, demonstrating exceptional multimodal conversational skills in answering open-ended questions about surgical videos. We will release our code, model, and the instruction-tuning dataset.

Significant work has been done on advancing localization and mapping in underwater environments. Still, state-of-the-art methods are challenged by low-texture environments, which is common for underwater settings. This makes it difficult to use existing methods in diverse, real-world scenes. In this paper, we present TURTLMap, a novel solution that focuses on textureless underwater environments through a real-time localization and mapping method. We show that this method is low-cost, and capable of tracking the robot accurately, while constructing a dense map of a low-textured environment in real-time. We evaluate the proposed method using real-world data collected in an indoor water tank with a motion capture system and ground truth reference map. Qualitative and quantitative results validate the proposed system achieves accurate and robust localization and precise dense mapping, even when subject to wave conditions. The project page for TURTLMap is https://umfieldrobotics.github.io/TURTLMap., Comment: Accepted to IROS 2024

We construct split anti-cyclotomic Euler systems for Galois representations attached to certain RACSDC automorphic representations on the group $\mathrm{GL}_n\times\mathrm{GL}_{n+1}$. As a result, we make progress towards certain rank 1 cases of the Beilinson--Bloch--Kato conjecture for those representations., Comment: 25 pages

We prove an inverse of Furstenberg's correspondence principle stating that for all measure preserving systems $(X,\mu,T)$ and $A\subset X$ measurable there exists a set $E \subset \mathbb{N}$ such that \[ \mu\left( \bigcap_{i=1}^k T^{-n_i}A\right) = \lim_{N\to \infty} \frac{\left|\left( \bigcap_{i=1}^k (E-n_i) \right)\cap \{0,\dots,N-1\}\right|}{N}\] for all $k,n_1,\dots,n_k \in \mathbb{N}$. As a corollary we show that a set $R\subset \mathbb{N}$ is a set of nice recurrence if and only if it is nicely intersective. Together, the inverse of Furstenberg's correspondence principle and it's corollary partially answer two questions of Moreira.

Hydrodynamic transport effectively describes the collective dynamics of fluids with well-defined thermodynamic quantities. With enhanced electron-electron interactions at elevated temperatures, the collective behavior of electrons in graphene with minimal impurities can be depicted as a hydrodynamic flow of charges. In this new regime, the well-known rules of Ohmic transport based on a single electron picture no longer apply, necessitating the consideration of collective electron dynamics. In particular, the hydrodynamic analogues of Joule heating and thermal transport require consideration of the viscous motion of the electron fluid, which has a direct impact on energy dissipation and heat generation by the fluidic motion of charge. In this work, we probe graphene hydrodynamics with thermal transport and find two distinct, qualitative signatures: thermal conductivity suppression below the Wiedemann-Franz value and viscous heating leading to magnetically-induced redistribution of temperature. We find these two effects are coincident in temperature and density, providing robust qualitative signatures of hydrodynamics, despite arising from two distinct aspects of this new regime: microscopic momentum conservation due to electron-electron scattering, and geometry-dependent viscous dissipation. Our results mark the first observation of viscous electronic heating in an electron fluid, providing insight for thermal management in electronic hydrodynamic devices and offering a new methodology for identifying hydrodynamic states in other systems., Comment: 36 pages, 9 figures, including supplementary information

We report on the critical influence of small-scale flow structures (e.g., fronts, vortices, and waves) that immediately arise in hot-exoplanet atmosphere simulations initialized with a resting state. A hot, 1:1 spin-orbit synchronized Jupiter is used here as a clear example; but, the phenomenon is generic and important for any type of a hot synchronized planet -- gaseous, oceanic, or telluric. When the early-time structures are not captured in simulations (due to, e.g., poor resolution and/or too much dissipation), the flow behavior is markedly different at later times -- in an observationally significant way; for example, the flow at large-scale is smoother and much less dynamic. This results in the temperature field, and its corresponding thermal flux, to be incorrectly predicted in numerical simulations, even when the quantities are spatially averaged., Comment: 9 pages, 4 figures, submitted

We present new Chandra X-ray observations of TAP 26, a ~17 Myr old magnetically-active weak-lined T Tauri star that has been reported to host a massive planet in a 10.8 day orbit. At a separation of a = 0.097 AU the planet will be exposed to intense X-ray and UV radiation from the star. The first observation caught the star in a state of elevated X-ray emission with variability on a timescale of a few hours and an X-ray temperature kTx ~ 2 - 4 keV. Two subsequent observations 5-10 days later showed slow variability and a lower X-ray flux and temperature (kTx ~ 1 keV). We characterize the X-ray emission and estimate the X-ray ionization and heating rates that will need to be incorporated into realistic models of the planet's atmosphere., Comment: 12 pages, 2 tables, 3 figures

Let $E/\mathbb{Q}$ be an elliptic curve of conductor $N$, $p$ an odd prime of good ordinary reduction such that $E[p]$ is an irreducible $G_{\mathbb{Q}}$-module, and $K$ an imaginary quadratic field with all primes dividing $Np$ split. We prove Iwasawa Main Conjectures for the $\mathbb{Z}_p$-cyclotomic and $\mathbb{Z}_p$-anticyclotomic deformation of $E$ over $\mathbb{Q}$ and $K$ respectively, dispensing with any of the ramification hypotheses on $E[p]$ in previous works. Using base change, the proofs are based on Wan's divisibility towards a three-variable main conjecture for $E$ over a quartic CM field containing $K$. As an application, we prove cases of the two-variable main conjecture for $E$ over $K$. The one-variable main conjectures imply the $p$-part of the conjectural Birch and Swinnerton-Dyer formula if ${\rm ord}_{s=1}L(E,s)\leq 1$. They are also an ingredient in the proof of Kolyvagin's conjecture and its cyclotomic variant in our joint work with Grossi \cite{BCGS}.

Recognizing places from an opposing viewpoint during a return trip is a common experience for human drivers. However, the analogous robotics capability, visual place recognition (VPR) with limited field of view cameras under 180 degree rotations, has proven to be challenging to achieve. To address this problem, this paper presents Same Place Opposing Trajectory (SPOT), a technique for opposing viewpoint VPR that relies exclusively on structure estimated through stereo visual odometry (VO). The method extends recent advances in lidar descriptors and utilizes a novel double (similar and opposing) distance matrix sequence matching method. We evaluate SPOT on a publicly available dataset with 6.7-7.6 km routes driven in similar and opposing directions under various lighting conditions. The proposed algorithm demonstrates remarkable improvement over the state-of-the-art, achieving up to 91.7% recall at 100% precision in opposing viewpoint cases, while requiring less storage than all baselines tested and running faster than all but one. Moreover, the proposed method assumes no a priori knowledge of whether the viewpoint is similar or opposing, and also demonstrates competitive performance in similar viewpoint cases., Comment: Accepted to ICRA 2024, project website: https://umautobots.github.io/spot

Terrain classification is an important problem for mobile robots operating in extreme environments as it can aid downstream tasks such as autonomous navigation and planning. While RGB cameras are widely used for terrain identification, vision-based methods can suffer due to poor lighting conditions and occlusions. In this paper, we propose the novel use of Ground Penetrating Radar (GPR) for terrain characterization for mobile robot platforms. Our approach leverages machine learning for surface terrain classification from GPR data. We collect a new dataset consisting of four different terrain types, and present qualitative and quantitative results. Our results demonstrate that classification networks can learn terrain categories from GPR signals. Additionally, we integrate our GPR-based classification approach into a multimodal semantic mapping framework to demonstrate a practical use case of GPR for surface terrain classification on mobile robots. Overall, this work extends the usability of GPR sensors deployed on robots to enable terrain classification in addition to GPR's existing scientific use cases., Comment: Accepted to the 2024 Conference on Computer Vision and Pattern Recognition (CVPR) Perception Beyond the Visible Spectrum Workshop

We study the properties of double white dwarf (DWD) mergers by performing hydrodynamic simulations using the new and improved adaptive mesh refinement code Octo-Tiger. We follow the orbital evolution of DWD systems of mass ratio q=0.7 for tens of orbits until and after the merger to investigate them as a possible origin for R Coronae Borealis (RCB) type stars. We reproduce previous results, finding that during the merger, the Helium WD donor star is tidally disrupted within 20-80 minutes since the beginning of the simulation onto the accretor Carbon-Oxygen WD, creating a high temperature shell around the accretor. We investigate the possible Helium burning in this shell and the merged object's general structure. Specifically, we are interested in the amount of Oxygen-16 dredged-up to the hot shell and the amount of Oxygen-18 produced. This is critical as the discovery of very low Oxygen-16 to Oxygen-18 ratios in RCB stars pointed out the merger scenario as a favorable explanation for their origin. A small amount of hydrogen in the donor may help keep the Oxygen-16 to Oxygen-18 ratios within observational bounds, even if moderate dredge-up from the accretor occurs. In addition, we perform a resolution study to reconcile the difference found in the amount of Oxygen-16 dredge-up between smoothed-particle hydrodynamics and grid-based simulations., Comment: 32 pages, Accepted to MNRAS

The goal of this white paper is to provide a snapshot of the data availability and data needs primarily for the Ariel space mission, but also for related atmospheric studies of exoplanets and brown dwarfs. It covers the following data-related topics: molecular and atomic line lists, line profiles, computed cross-sections and opacities, collision-induced absorption and other continuum data, optical properties of aerosols and surfaces, atmospheric chemistry, UV photodissociation and photoabsorption cross-sections, and standards in the description and format of such data. These data aspects are discussed by addressing the following questions for each topic, based on the experience of the "data-provider" and "data-user" communities: (1) what are the types and sources of currently available data, (2) what work is currently in progress, and (3) what are the current and anticipated data needs. We present a GitHub platform for Ariel-related data, with the goal to provide a go-to place for both data-users and data-providers, for the users to make requests for their data needs and for the data-providers to link to their available data. Our aim throughout the paper is to provide practical information on existing sources of data whether in databases, theoretical, or literature sources., Comment: 58 pages, submitted to RAS Techniques and Instruments (RASTI). The authors welcome feedback: corresponding author emails can be found as footnotes on page 2