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Across the nation, states and school districts face a persistent shortage of educators with expertise in promoting both the English proficiency and academic achievement of English learner (EL) students. To help improve educators' qualifications and classroom instruction for ELs, the National Professional Development (NPD) program has awarded grants for EL-focused educator professional development projects since 2002. This U.S. Department of Education-funded program allows grantees to serve the varied types of educators who work with ELs, including those preparing to join the educator workforce, and encourages grantees to focus on professional development topics and approaches supported by rigorous research evidence. In addition, the NPD program encourages grantees to engage in evaluation activities, including performance measurement and rigorous evaluations of project effectiveness, that may inform project improvement and contribute to evidence building. This study examines the extent to which NPD grantees implemented their projects in ways aligned with these program objectives, drawing primarily on a 2021 survey of all 2016 and 2017 NPD grantees.

The report "How a Federal Grant Program Is Training and Supporting Educators of English Learners" examines the extent to which National Professional Development (NPD) grantees implemented their projects in ways aligned with program objectives, drawing primarily on a survey of all 2016 and 2017 NPD grantees. This document provides background information about the NPD program (Appendix A), the data sources and measures used in this study (Appendix B), supporting statistical details and supplemental findings related to the findings presented in the report (Appendix C), and findings from additional analyses that are not discussed in the report but may help readers better understand the findings (Appendix D).

Organization and ordering from interactions in many-body systems underlies our understanding of phases of classical and quantum matter. Magnetism has played a particularly foundational role in the study of many-body phases. Here, we explore the collective magnetism that emerges from two laser-coupled momentum modes of a scalar bosonic quantum gas. We employ adiabatic state preparation and explore the collective magnetization response to an applied bias potential, finding that the relative increase of interactions leads to an enhanced and muted response for the ground state and excited state, respectively. We further find evidence for significant $Z_2$ symmetry breaking of the sample magnetization for the ground state, consistent with the expected beyond-mean-field behavior. These results suggest that the nonlinear interactions of scalar Bose condensates could provide a simple, direct path towards the squeezing of momentum states for quantum sensing., Comment: 6 pages, 4 figures ; Supplementary Materials document included as ancillary file

Large foundation models have shown strong open-world generalization to complex problems in vision and language, but similar levels of generalization have yet to be achieved in robotics. One fundamental challenge is the lack of robotic data, which are typically obtained through expensive on-robot operation. A promising remedy is to leverage cheaper, off-domain data such as action-free videos, hand-drawn sketches or simulation data. In this work, we posit that hierarchical vision-language-action (VLA) models can be more effective in utilizing off-domain data than standard monolithic VLA models that directly finetune vision-language models (VLMs) to predict actions. In particular, we study a class of hierarchical VLA models, where the high-level VLM is finetuned to produce a coarse 2D path indicating the desired robot end-effector trajectory given an RGB image and a task description. The intermediate 2D path prediction is then served as guidance to the low-level, 3D-aware control policy capable of precise manipulation. Doing so alleviates the high-level VLM from fine-grained action prediction, while reducing the low-level policy's burden on complex task-level reasoning. We show that, with the hierarchical design, the high-level VLM can transfer across significant domain gaps between the off-domain finetuning data and real-robot testing scenarios, including differences on embodiments, dynamics, visual appearances and task semantics, etc. In the real-robot experiments, we observe an average of 20% improvement in success rate across seven different axes of generalization over OpenVLA, representing a 50% relative gain. Visual results are provided at: https://hamster-robot.github.io/, Comment: to be published in ICLR 2025

Managing the security of employee work computers has become increasingly important as today's work model shifts to remote and hybrid work plans. In this paper, we explore the recent 2022 LastPass data breach, in which the attacker obtained sensitive customer data by exploiting a software vulnerability on a DevSecOps engineer's computer. We discuss the methodology of the attacker as well as the impact this incident had on LastPass and its customers. Next, we expand upon the impact the breach had on LastPass as well as its customers. From this, we propose solutions for preparing for and mitigating similar attacks in the future. The aim of this paper is to shed light on the LastPass incident and provide methods for companies to secure their employee base, both nationally and internationally. With a strong security structure, companies can vastly reduce the chances of falling victim to a similar attack.

Sophisticated learning architectures, e.g., Transformers, present a unique opportunity for robots to understand complex vehicle-terrain kinodynamic interactions for off-road mobility. While internet-scale data are available for Natural Language Processing (NLP) and Computer Vision (CV) tasks to train Transformers, real-world mobility data are difficult to acquire with physical robots navigating off-road terrain. Furthermore, training techniques specifically designed to process text and image data in NLP and CV may not apply to robot mobility. In this paper, we propose VertiFormer, a novel data-efficient multi-task Transformer model trained with only one hour of data to address such challenges of applying Transformer architectures for robot mobility on extremely rugged, vertically challenging, off-road terrain. Specifically, VertiFormer employs a new learnable masked modeling and next token prediction paradigm to predict the next pose, action, and terrain patch to enable a variety of off-road mobility tasks simultaneously, e.g., forward and inverse kinodynamics modeling. The non-autoregressive design mitigates computational bottlenecks and error propagation associated with autoregressive models. VertiFormer's unified modality representation also enhances learning of diverse temporal mappings and state representations, which, combined with multiple objective functions, further improves model generalization. Our experiments offer insights into effectively utilizing Transformers for off-road robot mobility with limited data and demonstrate our efficiently trained Transformer can facilitate multiple off-road mobility tasks onboard a physical mobile robot., Comment: 9 figures, url: https://github.com/mhnazeri/VertiFormer

Training of large language models (LLMs) is typically distributed across a large number of accelerators to reduce training time. Since internal states and parameter gradients need to be exchanged at each and every single gradient step, all devices need to be co-located using low-latency high-bandwidth communication links to support the required high volume of exchanged bits. Recently, distributed algorithms like DiLoCo have relaxed such co-location constraint: accelerators can be grouped into ``workers'', where synchronizations between workers only occur infrequently. This in turn means that workers can afford being connected by lower bandwidth communication links without affecting learning quality. However, in these methods, communication across workers still requires the same peak bandwidth as before, as the synchronizations require all parameters to be exchanged across all workers. In this paper, we improve DiLoCo in three ways. First, we synchronize only subsets of parameters in sequence, rather than all at once, which greatly reduces peak bandwidth. Second, we allow workers to continue training while synchronizing, which decreases wall clock time. Third, we quantize the data exchanged by workers, which further reduces bandwidth across workers. By properly combining these modifications, we show experimentally that we can distribute training of billion-scale parameters and reach similar quality as before, but reducing required bandwidth by two orders of magnitude.

LP 791-18 d is a temperate Earth-sized planet orbiting a late M dwarf, surrounded by an interior super-Earth (LP 791-18 b, $R_P$ = 1.2 $R_{\oplus}$, $P=0.95$ days) and an exterior sub-Neptune (LP 791-18 c, $R_P$ = 2.5 $R_{\oplus}$, $P=4.99$ days). Dynamical interactions between LP 791-18 d and c produce transit timing variations (TTVs) that can be used to constrain the planet masses and eccentricities. These interactions can also force a non-zero eccentricity for LP 791-18 d, which raises its internal temperature through tidal heating and could drive volcanic outgassing. We present three new transit observations of LP 791-18 c with Palomar/WIRC, including the most precise TTV measurements ($<$ 6 seconds) of this planet to date. We fit these times with a TTV model to obtain updated constraints on the mass, eccentricity, and tidal heat flux of LP 791-18 d. We reduce the mass uncertainty by more than a factor of two ($M_d$ = 0.91 $\pm$ 0.19 $M_{\oplus}$). We perform an updated fit assuming tidally damped free eccentricities and find $e_d = 0.0011^{+0.0010}_{-0.0008}$ and $e_c = 0.0001 \pm 0.0001$, consistent with circular orbits. We find that the observed TTVs are not sensitive to $e \leq$ $\sim$0.01. Without a tidally damped eccentricity prior, $e_d = 0.056^{+0.015}_{-0.014}$, much higher than the eccentricity predicted by n-body simulations incorporating the effects of dynamical excitation and tidal damping. We predict the timing of upcoming JWST secondary eclipse observations for LP 791-18 d, which could tightly constrain the eccentricity and tidal quality factor of this Earth-sized exoplanet., Comment: 14 pages, 8 figures, 4 tables, submitted to The Astronomical Journal

We construct a class of monoids, called origami monoids, motivated by Jones monoids and by strand organization in DNA origami structures. Two types of basic building blocks of DNA origami closely associated with the graphical representation of Jones monoids are identified and are taken as generators for the origami monoid. Motivated by plausible modifications of the DNA origami structures and the relations of the well studied Jones monoids, we then identify a set of relations that characterize the origami monoid. These relations expand the relations of the Jones monoids and include a new set of relations called contextual commutation. With contextual commutation, certain generators commute only when found within a given context. We prove that the origami monoids are finite and propose a normal form representation of their elements. We establish a correspondence between the Green's classes of the origami monoid and the Green's classes of a direct product of Jones monoids.

The state-of-the-art in biometric recognition algorithms and operational systems has advanced quickly in recent years providing high accuracy and robustness in more challenging collection environments and consumer applications. However, the technology still suffers greatly when applied to non-conventional settings such as those seen when performing identification at extreme distances or from elevated cameras on buildings or mounted to UAVs. This paper summarizes an extension to the largest dataset currently focused on addressing these operational challenges, and describes its composition as well as methodologies of collection, curation, and annotation., Comment: 9 pages, 11 figures, 2 tables

Many active galaxies harbor powerful relativistic jets, however, the detailed mechanisms of their formation and acceleration remain poorly understood. To investigate the area of jet acceleration and collimation with the highest available angular resolution, we study the innermost region of the bipolar jet in the nearby low-ionization nuclear emission-line region (LINER) galaxy NGC 1052. We combined observations of NGC 1052 taken with VLBA, GMVA, and EHT over one week in the spring of 2017. For the first time, NGC 1052 was detected with the EHT, providing a size of the central region in-between both jet bases of 250 RS (Schwarzschild radii) perpendicular to the jet axes. This size estimate supports previous studies of the jets expansion profile which suggest two breaks of the profile at around 300 RS and 10000 RS distances to the core. Furthermore, we estimated the magnetic field to be 1.25 Gauss at a distance of 22 {\mu}as from the central engine by fitting a synchrotron-self absorption spectrum to the innermost emission feature, which shows a spectral turn-over at about 130 GHz. Assuming a purely poloidal magnetic field, this implies an upper limit on the magnetic field strength at the event horizon of 26000 Gauss, which is consistent with previous measurements. The complex, low-brightness, double-sided jet structure in NGC 1052 makes it a challenge to detect the source at millimeter (mm) wavelengths. However, our first EHT observations have demonstrated that detection is possible up to at least 230 GHz. This study offers a glimpse through the dense surrounding torus and into the innermost central region, where the jets are formed. This has enabled us to finally resolve this region and provide improved constraints on its expansion and magnetic field strength., Comment: 22 pages, 10 figures, published in A&A

We study transportation cost spaces over finite metric spaces, also known as Lipschitz free spaces. Our work is motivated by a core problem posed by S. Dilworth, D. Kutzarova and M. Ostrovskii, namely, find a condition on a metric space $M$ equivalent to the Banach-Mazur distance between the transportation cost space over $M$ and $\ell_1^N$ of the corresponding dimension, which we call the $\ell_1^N$-distortion of $M$. In this regard, some examples have been studied like the $N\times N$ grid by Naor and Schechtman (2007) and the Laakso and diamond graphs by Dilworth, Kutzarova and Ostrovskii (2020), later studied by Baudier, Gartland and Schlumprecht (2023). We present here three main results. Firstly, we give a partial solution to this problem relating to the tree-like structure of the metric space. For that purpose, we develop a new technique that could potentially lead to a complete solution of the problem and utilize it to find an asymptotically tight upper bound of the $\ell_1^N$-distortion of the Laakso graphs, fully solving an open problem raised by Dilworth, Kutzarova and Ostrovskii. Finally, we apply our technique to prove that finite hyperbolic approximations of doubling metric spaces have uniformly bounded $\ell_1^N$-distortion., Comment: 57 pages, 13 figures

The planar three-gluon form factor for the chiral stress tensor operator in planar maximally supersymmetric Yang-Mills theory is an analog of the Higgs-to-three-gluon scattering amplitude in QCD. The amplitude (symbol) bootstrap program has provided a wealth of high-loop perturbative data about this form factor, with results up to eight loops available. The symbol of the form factor at $L$ loops is given by words of length $2L$ in six letters with associated integer coefficients. In this paper, we analyze this data, describing patterns of zero coefficients and relations between coefficients. We find many sequences of words whose coefficients are given by closed-form expressions which we expect to be valid at any loop order. Moreover, motivated by our previous machine-learning analysis, we identify simple recursion relations that relate the coefficient of a word to the coefficients of particular lower-loop words. These results open an exciting door for understanding scattering amplitudes at all loop orders., Comment: 28 pages, 1 figure, 5 tables

The 2017 observing campaign of the Event Horizon Telescope (EHT) delivered the first very long baseline interferometry (VLBI) images at the observing frequency of 230 GHz, leading to a number of unique studies on black holes and relativistic jets from active galactic nuclei (AGN). In total, eighteen sources were observed: the main science targets, Sgr A* and M87 along with various calibrators. We investigated the morphology of the sixteen AGN in the EHT 2017 data set, focusing on the properties of the VLBI cores: size, flux density, and brightness temperature. We studied their dependence on the observing frequency in order to compare it with the Blandford-K\"onigl (BK) jet model. We modeled the source structure of seven AGN in the EHT 2017 data set using linearly polarized circular Gaussian components and collected results for the other nine AGN from dedicated EHT publications, complemented by lower frequency data in the 2-86 GHz range. Then, we studied the dependences of the VLBI core flux density, size, and brightness temperature on the frequency measured in the AGN host frame. We compared the observations with the BK jet model and estimated the magnetic field strength dependence on the distance from the central black hole. Our results indicate a deviation from the standard BK model, particularly in the decrease of the brightness temperature with the observing frequency. Either bulk acceleration of the jet material, energy transfer from the magnetic field to the particles, or both are required to explain the observations.

We present high-resolution e-MERLIN and EVN (e-VLBI) observations of a radio source associated with Dyson Sphere candidate G, identified as part of Project Hephaistos. The radio source, VLASS J233532.86-000424.9, is resolved into three compact components and shows the typical characteristics of a radio-loud active galactic nucleus (AGN). In particular, the European VLBI Network (EVN) observations show that it has a brightness temperature in excess of $10^{8}$~K. No radio emission is detected at the position of the M-dwarf star. This result confirms our earlier hypothesis, that at least some of the Dyson Sphere candidates of project Hephaistos are contaminated by obscured, background AGN, lying close to the line of sight of otherwise normal galactic stars. High-resolution radio observations of other Dyson Sphere candidates can be useful in distinguishing truly promising candidates from those contaminated by background sources., Comment: 6 pages, 2 figures, 3 tables, published in MNRAS Letters

Past millimeter-wave galaxy surveys have probed the brightest starburst galaxies only and suffered heavily from confusion. The interpretation of existing surveys has also been hindered by the lack of reliable redshift indicators for measuring distances for the entire sample. Thanks to recent advances in mm-wave detector technologies we can now overcome these limitations, and conduct the first truly volumetric surveys of star-forming galaxies at mm-wavelengths down to the L* luminosities of typical galaxies, with ~1000 redshift slices spanning most of the Cosmic star-forming volume (z ~ 1--12) with nearly uniform mass and luminosity selection. We describe an instrument concept capable of delivering such surveys with the technologies available today, which can be built and operated on a ground-based mm-wave facility in the near future. Such spectrometer cameras can resolve and redshift identify up to to 25,000 star-forming galaxies per year even when operated on a 10-m class telescope. On a larger aperture it can do the same faster or probe even deeper. We propose a loose, open-source collaboration to design, build, and operate one or several such cameras through the shared contributions of leading experts and telescopes from around the globe., Comment: 10 pages, 1 figure, SPIE Conference 13101

Using the Event Horizon Telescope (EHT), the gravitationally lensed rings around the supermassive black holes (SMBHs) in Messier 87 (M87) and Sagittarius A* (Sgr A*) have now been successfully imaged at a resolution under 10 gravitational radii (R$_{\rm g}$ $ = \rm{GM/c^2}$). To expand studies beyond M87 and Sgr A*, we have constructed the Event Horizon and Environs (ETHER) sample, a comprehensive database encompassing approximately 3.15 million SMBH mass estimates, $\sim$ 20,000 Very-Long Baseline Interferometry (VLBI) radio flux densities, and $\sim$ 36,000 hard X-ray flux densities. This database is designed to identify and optimize target selection for the EHT and its upgrades on the ground and in space. We have identified a Gold Sample (GS) of nearby low-luminosity Active Galactic Nuclei (AGNs) within it that are ideal for studying jet bases and potentially imaging black hole shadows. We observed 27 of these AGNs using the EHT from 2022 to 2024, providing an opportunity to resolve and image accretion flows and jets at resolutions of $\leq$ 100 R$_{\rm g}$. Only a few SMBHs have sufficiently high enough flux density to be imaged at scales of $\leq$ 50 R$_{\rm g}$ with the present EHT. Among these are M87, Sgr A*, NGC4594 (Sombrero/M104), NGC4261, and NGC4374 (Messier 84/M84). Of these, NGC4261, Sombrero, and M84 have been observed and/or are scheduled for deep imaging with EHT+ALMA from 2023 to 2025. Sombrero, NGC4261, M84, NGC4278, and NGC5232 are clearly detected in our EHT+ALMA observations in 2022, indicating that the 230 GHz flux density from the accretion flows is significantly high. Ongoing imaging of the ETHER GS will enable measurements of black hole mass and spin, help constrain General Relativity, and enrich our understanding of jet launching and accretion inflows across a broad multi-parameter space, including black hole mass, spin, accretion rate, and orientation., Comment: 9 pages, 6 figures, 1 table, published in Proceedings of the 16th EVN Symposium, Ed. E. Ros, P. Benke, S.A. Dzib, I. Rottmann, & J.A. Zensus, Bonn: Max-Planck-Institut f\"ur Radioastronomie, 2024, pages 75-84, https://cloud.mpifr-bonn.mpg.de/index.php/s/BkX2CC2Xjn2aKR4

We study a target-following variation of online resource allocation. As in classical resource allocation, the decision-maker must assign sequentially arriving jobs to one of multiple available resources. However, in addition to the assignment costs incurred from these decisions, the decision-maker is also penalized for deviating from exogenously given, nonstationary target allocations throughout the horizon. The goal is to minimize the total expected assignment and deviation penalty costs incurred throughout the horizon when the distribution of assignment costs is unknown. In contrast to traditional online resource allocation, in our setting the timing of allocation decisions is critical due to the nonstationarity of allocation targets. Examples of practical problems that fit this framework include many physical resource settings where capacity is time-varying, such as manual warehouse processes where staffing levels change over time, and assignment of packages to outbound trucks whose departure times are scheduled throughout the day. We first show that naive extensions of state-of-the-art algorithms for classical resource allocation problems can fail dramatically when applied to target-following resource allocation. We then propose a novel ``proxy assignment" primal-dual algorithm for the target-following online resource allocation problem that uses current arrivals to simulate the effect of future arrivals. We prove that our algorithm incurs the optimal $O(\sqrt{T})$ regret bound when the assignment costs of the arriving jobs are drawn i.i.d. from a fixed distribution. We demonstrate the practical performance of our approach by conducting numerical experiments on synthetic datasets, as well as real-world datasets from retail fulfillment operations.

We report on the fault-tolerant operation of logical qubits on a neutral atom quantum computer, with logical performance surpassing physical performance for multiple circuits including Bell states (12x error reduction), random circuits (15x), and a prototype Anderson Impurity Model ground state solver for materials science applications (up to 6x, non-fault-tolerantly). The logical qubits are implemented via the [[4, 2, 2]] code (C4). Our work constitutes the first complete realization of the benchmarking protocol proposed by Gottesman 2016 [1] demonstrating results consistent with fault-tolerance. In light of recent advances on applying concatenated C4/C6 detection codes to achieve error correction with high code rates and thresholds, our work can be regarded as a building block towards a practical scheme for fault tolerant quantum computation. Our demonstration of a materials science application with logical qubits particularly demonstrates the immediate value of these techniques on current experiments.

Organic electrochemical transistors are extensively studied for applications ranging from bioelectronics to analog and neuromorphic computing. Despite significant advances, the fundamental interactions between the polymer semiconductor channel and the electrolyte, which critically determine the device performance, remain underexplored. Here, we examine the coupling between the benchmark semiconductor PEDOT:PSS and ionic liquids to explain the bistable and non-volatile behavior observed in OECTs. Using X-ray scattering and spectroscopy techniques, we demonstrate how the electrolyte modifies the channel composition, enhances molecular order, and reshapes the electronic and energetic landscape. Notably, the observed bistability arises from asymmetric and path-dependent energetics during doping and dedoping, resulting in two distinct, stable states, driven by a direct interaction between the electronic and ionic charge carriers. These findings highlight a compelling method to control charge carrier dynamics via the electrolyte, positioning it as a powerful yet underutilized tool for enabling novel device functionalities.

Ultrashort pulsed lasers (USPL) can produce thin columns of plasma in air via femtosecond filamentation, and these plasmas have been found to generate broadband TeraHertz (THz) and Radio Frequency (RF) radiation. A recent theory argues that the currents driven at the boundary of the plasma excite a Surface Plasmon Polariton (SPP) surface wave (in particular a Sommerfeld-Goubau wave given the cylindrical symmetry), which proceeds to detach from the end of the plasma to become the RF pulse. We have performed near-field measurements of these plasmas with a D-dot probe, and find an excellent agreement with this theory. The radial field dependence is precisely fit by a Hankel function, with an outer length scale in agreement with plasma conductivity and radius, and a measured longitudinal drift in frequency maxima closely matches both SPP simulations and analytic expectations., Comment: 8 pages, 7 figures

The Atacama Millimeter/Submillimeter Array (ALMA) remains unparalleled in sensitivity at radio frequencies above 35 GHz. In this paper, we explore ALMA's potential for narrowband technosignature detection, considering factors such as the interferometer's undistorted field of view, signal dilution due to significant drift rates at high frequencies and the possibility of spectral confusion. We present the first technosignature survey using archival ALMA data in Band 3, focusing on two spectral windows centred on 90.642 GHz and 93.151 GHz. Our survey places new limits at these frequencies on the prevalence of extraterrestrial transmitters for 28 galactic stars, selected from the Gaia DR3 catalogue. We employ a stellar 'bycatch' method to sample these objects within the undistorted field of view of four ALMA calibrators. For the closest star in our sample, we find no evidence of transmitters with EIRP_min > 7 x 10^17 W. To the best of our knowledge, this represents the first technosignature search conducted using ALMA data., Comment: 9 pages, 5 figures, submitted to MNRAS

Due to the inherent lack of transparency in deep neural networks, it is challenging for deep reinforcement learning (DRL) agents to gain trust and acceptance from users, especially in safety-critical applications such as medical diagnosis and military operations. Existing methods for explaining an agent's decision either require to retrain the agent using models that support explanation generation or rely on perturbation-based techniques to reveal the significance of different input features in the decision making process. However, retraining the agent may compromise its integrity and performance, while perturbation-based methods have limited performance and lack knowledge accumulation or learning capabilities. Moreover, since each perturbation is performed independently, the joint state of the perturbed inputs may not be physically meaningful. To address these challenges, we introduce $\textbf{VisionMask}$, a standalone explanation model trained end-to-end to identify the most critical regions in the agent's visual input that can explain its actions. VisionMask is trained in a self-supervised manner without relying on human-generated labels. Importantly, its training does not alter the agent model, hence preserving the agent's performance and integrity. We evaluate VisionMask on Super Mario Bros (SMB) and three Atari games. Compared to existing methods, VisionMask achieves a 14.9% higher insertion accuracy and a 30.08% higher F1-Score in reproducing original actions from the selected visual explanations. We also present examples illustrating how VisionMask can be used for counterfactual analysis.

The X-ray polarization observations made possible with the Imaging X-ray Polarimetry Explorer (IXPE) offer new ways of probing high-energy emission processes in astrophysical jets from blazars. Here we report on the first X-ray polarization observation of the blazar S4 0954+65 in a high optical and X-ray state. During our multi-wavelength campaign on the source, we detected an optical flare whose peak coincided with the peak of an X-ray flare. This optical-X-ray flare most likely took place in a feature moving along the parsec-scale jet, imaged at 43 GHz by the Very Long Baseline Array. The 43 GHz polarization angle of the moving component underwent a rotation near the time of the flare. In the optical band, prior to the IXPE observation, we measured the polarization angle to be aligned with the jet axis. In contrast, during the optical flare the optical polarization angle was perpendicular to the jet axis; after the flare, it reverted to being parallel to the jet axis. Due to the smooth behavior of the optical polarization angle during the flare, we favor shocks as the main acceleration mechanism. We also infer that the ambient magnetic field lines in the jet were parallel to the jet position angle. The average degree of optical polarization during the IXPE observation was (14.3$\pm$4.1)%. Despite the flare, we only detected an upper limit of 14% (at 3$\sigma$ level) on the X-ray polarization degree; although a reasonable assumption on the X-ray polarization angle results in an upper limit of 8.8% ($3\sigma$). We model the spectral energy distribution (SED) and spectral polarization distribution (SPD) of S4 0954+65 with leptonic (synchrotron self-Compton) and hadronic (proton and pair synchrotron) models. The constraints we obtain with our combined multi-wavelength polarization observations and SED modeling tentatively disfavor hadronic models for the X-ray emission in S4 0954+65., Comment: Submitted to A&A, 16 pages, 5 figures, and 7 tables

We demonstrate the suitability of high dynamic range, high-speed, neuromorphic event-based, dynamic vision sensors for metallic additive manufacturing and welding for in-process monitoring applications. In-process monitoring to enable quality control of mission critical components produced using metallic additive manufacturing is of high interest. However, the extreme light environment and high speed dynamics of metallic melt pools have made this a difficult environment in which to make measurements. Event-based sensing is an alternative measurement paradigm where data is only transmitted/recorded when a measured quantity exceeds a threshold resolution. The result is that event-based sensors consume less power and less memory/bandwidth, and they operate across a wide range of timescales and dynamic ranges. Event-driven driven imagers stand out from conventional imager technology in that they have a very high dynamic range of approximately 120 dB. Conventional 8 bit imagers only have a dynamic range of about 48 dB. This high dynamic range makes them a good candidate for monitoring manufacturing processes that feature high intensity light sources/generation such as metallic additive manufacturing and welding. In addition event based imagers are able to capture data at timescales on the order of 100 {\mu}s, which makes them attractive to capturing fast dynamics in a metallic melt pool. In this work we demonstrate that event-driven imagers have been shown to be able to observe tungsten inert gas (TIG) and laser welding melt pools. The results of this effort suggest that with additional engineering effort, neuromorphic event imagers should be capable of 3D geometry measurements of the melt pool, and anomaly detection/classification/prediction., Comment: This work is a derivative work of a conference proceedings paper submitted to the International Modal Analysis Conference 2024, and is subject to some copyright restrictions associated with the Society of Experimental Mechanics. A variation of this paper is also published in the Weapons Engineering Symposium and Journal (WESJ) which is not publically accessible

We present a differentiable optimization-based framework for Task and Motion Planning (TAMP) that is massively parallelizable on GPUs, enabling thousands of sampled seeds to be optimized simultaneously. Existing sampling-based approaches inherently disconnect the parameters by generating samples for each independently and combining them through composition and rejection, while optimization-based methods struggle with highly non-convex constraints and local optima. Our method treats TAMP constraint satisfaction as optimizing a batch of particles, each representing an assignment to a plan skeleton's continuous parameters. We represent the plan skeleton's constraints using differentiable cost functions, enabling us to compute the gradient of each particle and update it toward satisfying solutions. Our use of GPU parallelism better covers the parameter space through scale, increasing the likelihood of finding the global optima by exploring multiple basins through global sampling. We demonstrate that our algorithm can effectively solve a highly constrained Tetris packing problem using a Franka arm in simulation and deploy our planner on a real robot arm. Website: https://williamshen-nz.github.io/gpu-tamp, Comment: 2-page paper presented at the CoRL 2024 Workshop on Differentiable Optimization Everywhere

We present images of IRAS~08235--4316 with the Dark Energy Camera Plane Survey (DECaPS; spanning 0.398--1.034$\,\mu$m, at ${\sim}1''$ resolution) and the Submillimeter Array (SMA; at 1.38\,mm/217\,GHz, at ${\sim}1.9''\times1.2''$ resolution), a YSO located in the Vela constellation near to the Puppis boundary, detected in a systematic search for new large/extended emission sources. The DECaPS data show an asymmetric bi--polar morphology with a large angular extent of ${\sim}7.1''$ separated by a dark lane, characteristic of highly--inclined protoplanetary disks and less-evolved YSOs with outflows. The SMA data show an extended continuum structure along the optical dark lane with a smaller angular extent of ${\sim}4.6''$. The detected $^{12}$CO J=2--1 emission tentatively shows a velocity gradient along the position angle of the dark lane/millimeter continuum, that may trace rotating gas. Additional $^{12}$CO emission is present which could trace infalling/outflowing gas, and/or a nearby gas cloud. We estimate a distance to IRAS~08235--4316 of at least ${\sim}191\,$pc. Supported by additional SED modelling, we infer IRAS~08235--4316 to be a newly discovered class~I YSO with an outflow, host to an embedded protoplanetary disk, with a large millimeter radius of ${\sim}440$\,au and dust mass ${\gtrsim}11\,M_\oplus$., Comment: Accepted for publication in AJ 14 pages (10 page paper), 6 figures

Foundation models trained on internet-scale data, such as Vision-Language Models (VLMs), excel at performing tasks involving common sense, such as visual question answering. Despite their impressive capabilities, these models cannot currently be directly applied to challenging robot manipulation problems that require complex and precise continuous reasoning. Task and Motion Planning (TAMP) systems can control high-dimensional continuous systems over long horizons through combining traditional primitive robot operations. However, these systems require detailed model of how the robot can impact its environment, preventing them from directly interpreting and addressing novel human objectives, for example, an arbitrary natural language goal. We propose deploying VLMs within TAMP systems by having them generate discrete and continuous language-parameterized constraints that enable TAMP to reason about open-world concepts. Specifically, we propose algorithms for VLM partial planning that constrain a TAMP system's discrete temporal search and VLM continuous constraints interpretation to augment the traditional manipulation constraints that TAMP systems seek to satisfy. We demonstrate our approach on two robot embodiments, including a real world robot, across several manipulation tasks, where the desired objectives are conveyed solely through language.

In the past few years, the improved sensitivity and cadence of wide-field optical surveys have enabled the discovery of several afterglows without associated detected gamma-ray bursts (GRBs). We present the identification, observations, and multiwavelength modeling of a recent such afterglow (AT2023lcr), and model three literature events (AT2020blt, AT2021any, and AT2021lfa) in a consistent fashion. For each event, we consider the following possibilities as to why a GRB was not observed: 1) the jet was off-axis; 2) the jet had a low initial Lorentz factor; and 3) the afterglow was the result of an on-axis classical GRB (on-axis jet with physical parameters typical of the GRB population), but the emission was undetected by gamma-ray satellites. We estimate all physical parameters using afterglowpy and Markov Chain Monte Carlo methods from emcee. We find that AT2023lcr, AT2020blt, and AT2021any are consistent with on-axis classical GRBs, and AT2021lfa is consistent with both on-axis low Lorentz factor ($\Gamma_0 \approx 5 - 13$) and off-axis ($\theta_\text{obs}=2\theta_\text{jet}$) high Lorentz factor ($\Gamma_0 \approx 100$) jets., Comment: 40 pages, 18 figures, 20 tables

To enhance large language models (LLMs) for chemistry problem solving, several LLM-based agents augmented with tools have been proposed, such as ChemCrow and Coscientist. However, their evaluations are narrow in scope, leaving a large gap in understanding the benefits of tools across diverse chemistry tasks. To bridge this gap, we develop ChemAgent, an enhanced chemistry agent over ChemCrow, and conduct a comprehensive evaluation of its performance on both specialized chemistry tasks and general chemistry questions. Surprisingly, ChemAgent does not consistently outperform its base LLMs without tools. Our error analysis with a chemistry expert suggests that: For specialized chemistry tasks, such as synthesis prediction, we should augment agents with specialized tools; however, for general chemistry questions like those in exams, agents' ability to reason correctly with chemistry knowledge matters more, and tool augmentation does not always help.

In autonomous robot navigation, terrain cost assignment is typically performed using a semantics-based paradigm in which terrain is first labeled using a pre-trained semantic classifier and costs are then assigned according to a user-defined mapping between label and cost. While this approach is rapidly adaptable to changing user preferences, only preferences over the types of terrain that are already known by the semantic classifier can be expressed. In this paper, we hypothesize that a machine-learning-based alternative to the semantics-based paradigm above will allow for rapid cost assignment adaptation to preferences expressed over new terrains at deployment time without the need for additional training. To investigate this hypothesis, we introduce and study PACER, a novel approach to costmap generation that accepts as input a single birds-eye view (BEV) image of the surrounding area along with a user-specified preference context and generates a corresponding BEV costmap that aligns with the preference context. Using both real and synthetic data along with a combination of proposed training tasks, we find that PACER is able to adapt quickly to new user preferences while also exhibiting better generalization to novel terrains compared to both semantics-based and representation-learning approaches.

We explore transdisciplinary collaborations between artists and roboticists across a portfolio of artworks. Brendan Walker's Broncomatic was a breath controlled mechanical rodeo bull ride. Blast Theory's Cat Royale deployed a robot arm to play with a family of three cats for twelve days. Different Bodies is a prototype improvised dance performance in which dancers with disabilities physically manipulate two mirrored robot arms. We reflect on these to explore how artists shape robotics research through the two key strategies of improvisation and provocation. Artists are skilled at improvising extended robot experiences that surface opportunities for technology-focused design, but which also require researchers to improvise their research processes. Artists may provoke audiences into reflecting on the societal implications of robots, but at the same time challenge the established techno-centric concepts, methods and underlying epistemology of robotics research., Comment: 16th International Conference on Social Robotics (ISCR 2024)

Reconciling agricultural production with climate-change mitigation and adaptation is one of the most formidable problems in sustainability. One proposed strategy for addressing this problem is the judicious retention of trees in agricultural systems. However, the magnitude of the current and future-potential benefit that trees contribute remains uncertain, particularly in the agricultural sector where trees can also limit production. Here we help to resolve these issues across a West African region responsible for producing $\approx$60% of the world's cocoa, a crop that contributes one of the highest per unit carbon footprints of all foods. We use machine learning to generate spatially-explicit estimates of shade-tree cover and carbon stocks across the region. We find that existing shade-tree cover is low, and not spatially aligned with climate threat. But we also find enormous unrealized potential for the sector to counterbalance a large proportion of their high carbon footprint annually, without threatening production. Our methods can be applied to other globally significant commodities that can be grown in agroforests, and align with accounting requirements of carbon markets, and emerging legislative requirements for sustainability reporting.

X-ray polarization is a unique new probe of the particle acceleration in astrophysical jets made possible through the Imaging X-ray Polarimetry Explorer. Here we report on the first dense X-ray polarization monitoring campaign on the blazar Mrk 421. Our observations were accompanied by an even denser radio and optical polarization campaign. We find significant short-timescale variability in both X-ray polarization degree and angle, including a $\sim90^\circ$ angle rotation about the jet axis. We attribute this to random variations of the magnetic field, consistent with the presence of turbulence but also unlikely to be explained by turbulence alone. At the same time, the degree of lower-energy polarization is significantly lower and shows no more than mild variability. Our campaign provides further evidence for a scenario in which energy-stratified shock-acceleration of relativistic electrons, combined with a turbulent magnetic field, is responsible for optical to X-ray synchrotron emission in blazar jets., Comment: 23 pages, including 8 pages of appendices. 12 figures, 3 tables. Submitted to ApJ

Imitation learning from human demonstrations is an effective paradigm for robot manipulation, but acquiring large datasets is costly and resource-intensive, especially for long-horizon tasks. To address this issue, we propose SkillMimicGen (SkillGen), an automated system for generating demonstration datasets from a few human demos. SkillGen segments human demos into manipulation skills, adapts these skills to new contexts, and stitches them together through free-space transit and transfer motion. We also propose a Hybrid Skill Policy (HSP) framework for learning skill initiation, control, and termination components from SkillGen datasets, enabling skills to be sequenced using motion planning at test-time. We demonstrate that SkillGen greatly improves data generation and policy learning performance over a state-of-the-art data generation framework, resulting in the capability to produce data for large scene variations, including clutter, and agents that are on average 24% more successful. We demonstrate the efficacy of SkillGen by generating over 24K demonstrations across 18 task variants in simulation from just 60 human demonstrations, and training proficient, often near-perfect, HSP agents. Finally, we apply SkillGen to 3 real-world manipulation tasks and also demonstrate zero-shot sim-to-real transfer on a long-horizon assembly task. Videos, and more at https://skillgen.github.io.

Robot learning has proven to be a general and effective technique for programming manipulators. Imitation learning is able to teach robots solely from human demonstrations but is bottlenecked by the capabilities of the demonstrations. Reinforcement learning uses exploration to discover better behaviors; however, the space of possible improvements can be too large to start from scratch. And for both techniques, the learning difficulty increases proportional to the length of the manipulation task. Accounting for this, we propose SPIRE, a system that first uses Task and Motion Planning (TAMP) to decompose tasks into smaller learning subproblems and second combines imitation and reinforcement learning to maximize their strengths. We develop novel strategies to train learning agents when deployed in the context of a planning system. We evaluate SPIRE on a suite of long-horizon and contact-rich robot manipulation problems. We find that SPIRE outperforms prior approaches that integrate imitation learning, reinforcement learning, and planning by 35% to 50% in average task performance, is 6 times more data efficient in the number of human demonstrations needed to train proficient agents, and learns to complete tasks nearly twice as efficiently. View https://sites.google.com/view/spire-corl-2024 for more details., Comment: Conference on Robot Learning (CoRL) 2024

Aluminum scandium nitride (AlScN) has emerged as a highly promising material for high-temperature applications due to its robust piezoelectric, ferroelectric, and dielectric properties. This study investigates the behavior of Al0.7Sc0.3N thin films in extreme thermal environments, demonstrating functional stability up to 1000{\deg}C, making it suitable for use in aerospace, hypersonics, deep-well, and nuclear reactor systems. Tantalum silicide (TaSi2)/Al0.7Sc0.3N/TaSi2 capacitors were fabricated and characterized across a wide temperature range, revealing robust ferroelectric and dielectric properties, along with significant enhancement in piezoelectric performance. At 1000{\deg}C, the ferroelectric hysteresis loops showed a substantial reduction in coercive field from 4.3 MV/cm to 1.2 MV/cm, while the longitudinal piezoelectric coefficient increased nearly tenfold, reaching 75.1 pm/V at 800{\deg}C. Structural analysis via scanning and transmission electron microscopy confirmed the integrity of the TaSi2/Al0.7Sc0.3N interfaces, even after exposure to extreme temperatures. Furthermore, the electromechanical coupling coefficient was calculated to increase by over 500%, from 12.9% at room temperature to 82% at 700{\deg}C. These findings establish AlScN as a versatile material for high-temperature ferroelectric, piezoelectric, and dielectric applications, offering unprecedented thermal stability and functional enhancement.

We explore recursive programming with extensible data types. Row types make the structure of data types first class, and can express a variety of type system features from subtyping to modular combination of case branches. Our goal is the modular combination of recursive types and of recursive functions over them. The most significant challenge is in recursive function calls, which may need to account for new cases in a combined type. We introduce bounded algebras, Mendler-style descriptions of recursive functions in which recursive calls can happen at larger types, and show that they provide expressive recursion over extensible data types. We formalize our approach in R$\omega\mu$, a small extension of an existing row type theory with support for recursive terms and types, and mechanize the metatheory of R$\omega\mu$ via an embedding in Agda

The inherent sensitivity of quantum sensors to their physical environment can make them good reporters of parameters such as temperature, pressure, strain, and electric fields. Here, we present a molecular platform for pressure (P) and temperature (T) sensing using para-terphenyl crystals doped with pentacene. We leverage the optically detected magnetic resonance (ODMR) of the photoexcited triplet electron in the pentacene molecule, that serves as a sensitive probe for lattice changes in the host para-terphenyl due to pressure or temperature variations. We observe maximal ODMR frequency variations of df/dP=1.8 MHz/bar and df/dT=247 kHz/K, which are over 1,200 times and three times greater, respectively, than those seen in nitrogen-vacancy centers in diamond. This results in a >85-fold improvement in pressure sensitivity over best previously reported. The larger variation reflects the weaker nature of the para-terphenyl lattice, with first-principles DFT calculations indicating that even picometer-level shifts in the molecular orbitals due to P, T changes are measurable. The platform offers additional advantages including high levels of sensor doping, narrow ODMR linewidths and high contrasts, and ease of deployment, leveraging the ability for large single crystals at low cost. Overall, this work paves the way for low-cost, optically-interrogated pressure and temperature sensors and lays the foundation for even more versatile sensors enabled by synthetic tunability in designer molecular systems., Comment: 7 pages, 4 figures, 1 table

Squeezed mechanical states are a valuable tool for quantum sensing and error correction in quantum computing, and a pivotal platform for tests of fundamental physics. Recently, solid state mechanical oscillators have been prepared in squeezed states using parametric interactions in both the microwave and optical regimes. It has long been predicted that a fast measurement rate comparable to the mechanical resonance frequency can prepare the oscillator under measurement into a quantum squeezed state. Despite decades of effort, this straightforward protocol is yet to be demonstrated in the quantum regime. Here, we use post-processing techniques to demonstrate preparation of a 50 ng GaAs cantilever in a conditional classical squeezed state with a minimum uncertainty (0.28 plus/minus 0.18) dB above (1.07 plus/minus 0.04 times) the zero point fluctuations, 3 orders of magnitude closer to the quantum regime in variance than the previous record. This paves the way to real-time measurement-based preparation of macroscopic oscillators in quantum squeezed states, and can be adapted to mechanical systems as large as the kg-scale test masses of the Laser Interferometer Gravitational-Wave Observatory (LIGO).

In this work, we present MedImageInsight, an open-source medical imaging embedding model. MedImageInsight is trained on medical images with associated text and labels across a diverse collection of domains, including X-Ray, CT, MRI, dermoscopy, OCT, fundus photography, ultrasound, histopathology, and mammography. Rigorous evaluations demonstrate MedImageInsight's ability to achieve state-of-the-art (SOTA) or human expert level performance across classification, image-image search, and fine-tuning tasks. Specifically, on public datasets, MedImageInsight achieves SOTA in CT 3D medical image retrieval, as well as SOTA in disease classification and search for chest X-ray, dermatology, and OCT imaging. Furthermore, MedImageInsight achieves human expert performance in bone age estimation (on both public and partner data), as well as AUC above 0.9 in most other domains. When paired with a text decoder, MedImageInsight achieves near SOTA level single image report findings generation with less than 10\% the parameters of other models. Compared to fine-tuning GPT-4o with only MIMIC-CXR data for the same task, MedImageInsight outperforms in clinical metrics, but underperforms on lexical metrics where GPT-4o sets a new SOTA. Importantly for regulatory purposes, MedImageInsight can generate ROC curves, adjust sensitivity and specificity based on clinical need, and provide evidence-based decision support through image-image search (which can also enable retrieval augmented generation). In an independent clinical evaluation of image-image search in chest X-ray, MedImageInsight outperformed every other publicly available foundation model evaluated by large margins (over 6 points AUC), and significantly outperformed other models in terms of AI fairness (across age and gender). We hope releasing MedImageInsight will help enhance collective progress in medical imaging AI research and development.

The first very long baseline interferometry (VLBI) detections at 870$\mu$m wavelength (345$\,$GHz frequency) are reported, achieving the highest diffraction-limited angular resolution yet obtained from the surface of the Earth, and the highest-frequency example of the VLBI technique to date. These include strong detections for multiple sources observed on inter-continental baselines between telescopes in Chile, Hawaii, and Spain, obtained during observations in October 2018. The longest-baseline detections approach 11$\,$G$\lambda$ corresponding to an angular resolution, or fringe spacing, of 19$\mu$as. The Allan deviation of the visibility phase at 870$\mu$m is comparable to that at 1.3$\,$mm on the relevant integration time scales between 2 and 100$\,$s. The detections confirm that the sensitivity and signal chain stability of stations in the Event Horizon Telescope (EHT) array are suitable for VLBI observations at 870$\mu$m. Operation at this short wavelength, combined with anticipated enhancements of the EHT, will lead to a unique high angular resolution instrument for black hole studies, capable of resolving the event horizons of supermassive black holes in both space and time., Comment: Corresponding author: S. Doeleman

Vision-Language Models (VLM) can generate plausible high-level plans when prompted with a goal, the context, an image of the scene, and any planning constraints. However, there is no guarantee that the predicted actions are geometrically and kinematically feasible for a particular robot embodiment. As a result, many prerequisite steps such as opening drawers to access objects are often omitted in their plans. Robot task and motion planners can generate motion trajectories that respect the geometric feasibility of actions and insert physically necessary actions, but do not scale to everyday problems that require common-sense knowledge and involve large state spaces comprised of many variables. We propose VLM-TAMP, a hierarchical planning algorithm that leverages a VLM to generate goth semantically-meaningful and horizon-reducing intermediate subgoals that guide a task and motion planner. When a subgoal or action cannot be refined, the VLM is queried again for replanning. We evaluate VLM- TAMP on kitchen tasks where a robot must accomplish cooking goals that require performing 30-50 actions in sequence and interacting with up to 21 objects. VLM-TAMP substantially outperforms baselines that rigidly and independently execute VLM-generated action sequences, both in terms of success rates (50 to 100% versus 0%) and average task completion percentage (72 to 100% versus 15 to 45%). See project site https://zt-yang.github.io/vlm-tamp-robot/ for more information.

In longitudinal studies using routinely collected data, such as electronic health records (EHRs), patients tend to have more measurements when they are unwell; this informative observation pattern may lead to bias. While semi-parametric approaches to modelling longitudinal data subject to irregular observation are known to be sensitive to misspecification of the visit process, parametric models may provide a more robust alternative. Robustness of parametric models on the outcome alone has been assessed under the assumption that the visit intensity is independent of the time since the last visit, given the covariates and random effects. However, this assumption of a memoryless visit process may not be realistic in the context of EHR data. In a special case which includes memory embedded into the visit process, we derive an expression for the bias in parametric models for the outcome alone and use this to identify factors that lead to increasing bias. Using simulation studies, we show that this bias is often small in practice. We suggest diagnostics for identifying the specific cases when the outcome model may be susceptible to meaningful bias, and propose a novel joint model of the outcome and visit processes that can eliminate or reduce the bias. We apply these diagnostics and the joint model to a study of juvenile dermatomyositis. We recommend that future studies using EHR data avoid relying only on the outcome model and instead first evaluate its appropriateness with our proposed diagnostics, applying our proposed joint model if necessary., Comment: 43 pages, 4 tables, 6 figures