Search

The expansion of early childhood education (ECE) and increased spending have benefited children and supported families. However, these investments have not addressed inequities within the ECE workforce. ECE providers face economic insecurity, earning an average of $14 per hour, which is below a living wage. In rural communities, this median wage drops to $11.42, and in Tennessee, it is even lower at under $10 per hour. Women of color, especially in rural areas, are disproportionately affected by poor compensation and benefits. To address these issues, this project partners with an organization called Tennessee Early Childhood Training Alliance (TECTA) to understand the experiences of ECE providers in an effort to raise awareness of: (1) the benefits of the TECTA program and the resources they provide; (2) the key challenges and barriers they navigate on the pathway to their education; and (3) the need for program expansion to enable opportunities for social and economic mobility. This study underscores the need for systemic changes to support ECE providers, particularly in rural areas and other marginalized communities. By addressing economic insecurity, professional recognition, training disparities, and policy inconsistencies, we can create a more equitable and effective ECE workforce.

Elementary and secondary district leaders seeking to recruit teachers and diversify their teaching workforce will find useful, research-based strategies in this brief from the Region 4 Comprehensive Center. The brief focuses on ways to develop work-based learning programs in secondary schools that engage students in considering teacher careers. Drawing on insights from 14 programs, this brief summarizes five useful strategies of teacher-focused work-based learning programs, including targeted courses, cohort model and peer supports, strategic counseling, dual enrollment, and field experiences. It also provides helpful background into the historic challenges around recruiting and retaining teachers of color.

We investigate the effect of a constant static bias force on the dynamically induced shape morphing of a pre-buckled bistable beam, focusing on the beam's ability to change its vibration to be near different stable states under harmonic excitation. Our study explores four categories of oscillatory motions: switching, reverting, vacillating, and intra-well in the parameter space. We aim to achieve transitions between stable states of the pre-buckled bistable beam with minimal excitation amplitude. Our findings demonstrate the synergistic effects between dynamic excitation and static bias force, showing a broadening of the non-fractal region for switching behavior (i.e., switching from the first stable state to the second stable state) in the parameter space. This study advances the understanding of the dynamics of key structural components for multi-stable mechanical metamaterials, offering new possibilities for novel designs in adaptive applications.

Simulation is a crucial step in ensuring accurate, efficient, and realistic Connected and Autonomous Vehicles (CAVs) testing and validation. As the adoption of CAV accelerates, the integration of real-world data into simulation environments becomes increasingly critical. Among various technologies utilized by CAVs, Vehicle-to-Everything (V2X) communication plays a crucial role in ensuring a seamless transmission of information between CAVs, infrastructure, and other road users. However, most existing studies have focused on developing and testing communication protocols, resource allocation strategies, and data dissemination techniques in V2X. There is a gap where real-world V2X data is integrated into simulations to generate diverse and high-fidelity traffic scenarios. To fulfill this research gap, we leverage real-world Signal Phase and Timing (SPaT) data from Roadside Units (RSUs) to enhance the fidelity of CAV simulations. Moreover, we developed an algorithm that enables Autonomous Vehicles (AVs) to respond dynamically to real-time traffic signal data, simulating realistic V2X communication scenarios. Such high-fidelity simulation environments can generate multimodal data, including trajectory, semantic camera, depth camera, and bird's eye view data for various traffic scenarios. The generated scenarios and data provide invaluable insights into AVs' interactions with traffic infrastructure and other road users. This work aims to bridge the gap between theoretical research and practical deployment of CAVs, facilitating the development of smarter and safer transportation systems.

For the discretization of symmetric, divergence-conforming stress tensors in continuum mechanics, we prove inf-sup stability bounds which are uniform in polynomial degree and mesh size for the Hu--Zhang finite element in two dimensions. This is achieved via an explicit construction of a bounded right inverse of the divergence operator, with the crucial component being the construction of bounded Poincar\'e operators for the stress elasticity complex which are polynomial-preserving, in the Bernstein--Gelfand--Gelfand framework of the finite element exterior calculus. We also construct $hp$-bounded projection operators satisfying a commuting diagram property and $hp$-stable Hodge decompositions. Numerical examples are provided.

Large vision-language models (VLMs) have become state-of-the-art for many computer vision tasks, with in-context learning (ICL) as a popular adaptation strategy for new ones. But can VLMs learn novel concepts purely from visual demonstrations, or are they limited to adapting to the output format of ICL examples? We propose a new benchmark we call Spatial Visual Ambiguity Tasks (SVAT) that challenges state-of-the-art VLMs to learn new visuospatial tasks in-context. We find that VLMs fail to do this zero-shot, and sometimes continue to fail after finetuning. However, adding simpler data to the training by curriculum learning leads to improved ICL performance., Comment: 13 pages, 4 figures. Code released at https://github.com/groundlight/vlm-visual-demonstrations

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

Given a graph $H$, we say a graph $G$ is properly rainbow $H$-saturated if there is a proper edge-coloring of $G$ which contains no rainbow copy of $H$, but adding any edge to $G$ makes such an edge-coloring impossible. The proper rainbow saturation number, denoted $\text{sat}^*(n,H)$, is the minimum number of edges in an $n$-vertex rainbow $H$-saturated graph. We determine the proper rainbow saturation number for paths up to an additive constant and asymptotically determine $\text{sat}^*(n,K_4)$. In addition, we bound $\text{sat}^*(n,H)$ when $H$ is a larger clique, tree of diameter at least 4, or odd cycle.

Accurate field boundary delineation is a critical challenge in digital agriculture, impacting everything from crop monitoring to resource management. Existing methods often struggle with noise and fail to generalize across varied landscapes, particularly when dealing with cloud cover in optical remote sensing. In response, this study presents a new approach that leverages time series data from Sentinel-2 (S2) and Sentinel-1 (S1) imagery to improve performance under diverse cloud conditions, without the need for manual cloud filtering. We introduce a 3D Vision Transformer architecture specifically designed for satellite image time series, incorporating a memory-efficient attention mechanism. Two models are proposed: PTAViT3D, which handles either S2 or S1 data independently, and PTAViT3D-CA, which fuses both datasets to enhance accuracy. Both models are evaluated under sparse and dense cloud coverage by exploiting spatio-temporal correlations. Our results demonstrate that the models can effectively delineate field boundaries, even with partial (S2 or S2 and S1 data fusion) or dense cloud cover (S1), with the S1-based model providing performance comparable to S2 imagery in terms of spatial resolution. A key strength of this approach lies in its capacity to directly process cloud-contaminated imagery by leveraging spatio-temporal correlations in a memory-efficient manner. This methodology, used in the ePaddocks product to map Australia's national field boundaries, offers a robust, scalable solution adaptable to varying agricultural environments, delivering precision and reliability where existing methods falter. Our code is available at https://github.com/feevos/tfcl., Comment: under review

If two agents disagree in their decisions, we may suspect they are not both correct. This intuition is formalized for evaluating agents that have carried out a binary classification task. Their agreements and disagreements on a joint test allow us to establish the only group evaluations logically consistent with their responses. This is done by establishing a set of axioms (algebraic relations) that must be universally obeyed by all evaluations of binary responders. A complete set of such axioms are possible for each ensemble of size N. The axioms for $N = 1, 2$ are used to construct a fully logical alarm - one that can prove that at least one ensemble member is malfunctioning using only unlabeled data. The similarities of this approach to formal software verification and its utility for recent agendas of safe guaranteed AI are discussed., Comment: 17 pages, 7 figures, under review

Spatial prediction problems often use Gaussian process models, which can be computationally burdensome in high dimensions. Specification of an appropriate covariance function for the model can be challenging when complex non-stationarities exist. Recent work has shown that pre-computed spatial basis functions and a feed-forward neural network can capture complex spatial dependence structures while remaining computationally efficient. This paper builds on this literature by tailoring spatial basis functions for use in convolutional neural networks. Through both simulated and real data, we demonstrate that this approach yields more accurate spatial predictions than existing methods. Uncertainty quantification is also considered.

Unstable periodic orbits (UPOs) are believed to be the underlying dynamical structures of spatio-temporal chaos and turbulence. Finding these UPOs is however notoriously difficult. Matrix-free loop convergence algorithms deform entire space-time fields (loops) until they satisfy the evolution equations. Initial guesses for these robust variational convergence algorithms are thus periodic space-time fields in a high-dimensional state space, rendering their generation highly challenging. Usually guesses are generated with recurrency methods, which are most suited to shorter and more stable periodic orbits. Here we propose an alternative, data-driven method for generating initial guesses: while the dimension of the space used to discretize fluid flows is prohibitively large to construct suitable initial guesses, the dissipative dynamics will collapse onto a chaotic attractor of far lower dimension. We use an autoencoder to obtain a low-dimensional representation of the discretized physical space for the one-dimensional Kuramoto-Sivashinksy equation, in chaotic and hyperchaotic regimes. In this low-dimensional latent space, we construct loops based on the latent POD modes with random periodic coefficients, which are then decoded to physical space and used as initial guesses. These loops are found to be realistic initial guesses and, together with variational convergence algorithms, these guesses help us to quickly converge to UPOs. We further attempt to 'glue' known UPOs in the latent space to create guesses for longer ones. This gluing procedure is successful and points towards a hierarchy of UPOs where longer UPOs shadow sequences of shorter ones.

Progress in gravitational-wave astronomy depends upon having sensitive detectors with good data quality. Since the end of the LIGO-Virgo-KAGRA third Observing run in March 2020, detector-characterization efforts have lead to increased sensitivity of the detectors, swifter validation of gravitational-wave candidates and improved tools used for data-quality products. In this article, we discuss these efforts in detail and their impact on our ability to detect and study gravitational-waves. These include the multiple instrumental investigations that led to reduction in transient noise, along with the work to improve software tools used to examine the detectors data-quality. We end with a brief discussion on the role and requirements of detector characterization as the sensitivity of our detectors further improves in the future Observing runs., Comment: 35 pages, 18 figures

The Birman-Williams theorem gives a connection between the collection of unstable periodic orbits (UPOs) contained within a chaotic attractor and the topology of that attractor, for three-dimensional systems. In certain cases, the fractal dimension of a chaotic attractor in a partial differential equation (PDE) is less than three, even though that attractor is embedded within an infinite-dimensional space. Here we study the Kuramoto-Sivashinsky PDE at the onset of chaos. We use two different dimensionality-reduction techniques - proper orthogonal decomposition and an autoencoder neural network - to find two different approximate embeddings of the chaotic attractor into three dimensions. By finding the projection of the attractor's UPOs in these reduced spaces and examining their linking numbers, we construct templates for the branched manifold which encodes the topological properties of the attractor. The templates obtained using two different dimensionality reduction methods mirror each other. Hence, the organization of the periodic orbits is identical (up to a global change of sign) and consistent symbolic names for low-period UPOs are derived. This is strong evidence that the dimensional reduction is robust, in this case, and that an accurate topological characterization of the chaotic attractor of the chaotic PDE has been achieved.

Transiting giant exoplanets around M-dwarf stars (GEMS) are rare, owing to the low-mass host stars. However, the all-sky coverage of TESS has enabled the detection of an increasingly large number of them to enable statistical surveys like the \textit{Searching for GEMS} survey. As part of this endeavour, we describe the observations of six transiting giant planets, which includes precise mass measurements for two GEMS (K2-419Ab, TOI-6034b) and statistical validation for four systems, which includes validation and mass upper limits for three of them (TOI-5218b, TOI-5616b, TOI-5634Ab), while the fourth one -- TOI-5414b is classified as a `likely planet'. Our observations include radial velocities from the Habitable-zone Planet Finder on the Hobby-Eberly Telescope, and MAROON-X on Gemini-North, along with photometry and high-contrast imaging from multiple ground-based facilities. In addition to TESS photometry, K2-419Ab was also observed and statistically validated as part of the K2 mission in Campaigns 5 and 18, which provides precise orbital and planetary constraints despite the faint host star and long orbital period of $\sim 20.4$ days. With an equilibrium temperature of only 380 K, K2-419Ab is one of the coolest known well-characterized transiting planets. TOI-6034 has a late F-type companion about 40\arcsec~away, making it the first GEMS host star to have an earlier main-sequence binary companion. These confirmations add to the existing small sample of confirmed transiting GEMS., Comment: Accepted in AJ

We implemented a control system for ultracold atom experiments. The system includes hardware modules that generate synchronized experiment signals of different kinds, and a protocol to communicate with all the modules. We also implemented software that can automatically generate experiment sequences from declarative tables of parameters with variations. Both the hardware and the software are open-source for adaptation and customization in other experiment platforms.

In this work, we investigate the radius maximizing reliability for binary modulation on conjugate-reciprocal zeros (BMOCZ) implemented with both maximum likelihood (ML) and direct zero-testing (DiZeT) decoders. We first show that the optimal radius for BMOCZ is a function of the employed decoder and that the radius maximizing the minimum distance between polynomial zeros does not maximize the minimum distance of the final code. While maximizing zero separation offers an almost optimal solution with the DiZeT decoder, simulations show that the ML decoder outperforms the DiZeT decoder in both additive white Gaussian noise (AWGN) and fading channels when the radius is chosen to maximize codeword separation. Finally, we analyze different sequence-to-subcarrier mappings for BMOCZ-based orthogonal frequency division multiplexing (OFDM). We highlight a flexible time-frequency OFDM waveform that avoids distortion introduced by a frequency-selective channel at the expense of a higher peak-to-average power ratio (PAPR)., Comment: This work has been accepted for presentation at IEEE MILCOM 2024

Optical trapping is having ever-increasing impact in science $-$ particularly biophysics, photonics and most recently in quantum optomechanics $-$ owing to its superior capability for manipulating nanoscale structures and materials. However, essentially all experimental optical trapping studies in the optical dipole regime have, to date, been dominated by the interaction between a material's electric polarizability, $\alpha_{e}$, and the electric part of the incident electromagnetic field, and therefore described by electric field intensity gradient forces. Optical trapping based on optical magnetic light-matter interactions has not been experimentally addressed despite it's immediate extension of the boundaries of optical trapping research and applications. This paper addresses this long-standing deficiency through the realization of optical magnetic trapping of large index of refraction (i.e., Si) nanoparticles and also presents a formalism for quantitative understanding of the experimental findings. Our experimental optical trapping results require including optical magnetic polarizability, $\alpha_{m}$, and electric-magnetic scattering forces associated with the Photonic Hall effect that are qualitatively and quantitatively validated by Maxwell stress tensor calculations. Our findings bring new opportunities for nanoparticle manipulation, potentially relax the limitations Ashkin claimed based on the optical Earnshaw's theorem, motivate optical matter formation by optical magnetic interactions, and suggest new N-body effects and symmetry breaking to drive dynamics of optical matter systems.

Context. We have developed deep learning (DL) and AI-based tools to search extant narrow-band wide-field H$\alpha$ surveys of the Galactic Plane for elusive planetary nebulae (PNe) which are hidden in dense star fields towards the Galactic center. They are faint, low-surface brightness, usually resolved sources, which are not discovered by previous automatic searches that depend on photometric data for point-like sources. These sources are very challenging to find by traditional visual inspection in such crowded fields and many have been missed. We have successfully adopted a novel 'Swin-Transformer' AI algorithm, which we described in detail in the preceding Techniques paper (Paper I). Aims. Here, we present preliminary results from our first spectroscopic follow-up run for 31 top-quality PN candidates found by the algorithm from the high-resolution H$\alpha$ survey VPHAS+. This survey has not yet undergone extensive manual, systematic searching. Methods. Our candidate PNe were observed with the SpUpNIC spectrograph on the 1.9 m telescope at the South African Astronomical Observatory (SAAO) in June 2023. We performed standard IRAF spectroscopic reduction and then followed our normal HASH PN identification and classification procedures. Results. Our reduced spectra confirmed that these candidates include 22 true, likely, and possible PNe (70.97\%), 3 emission-line galaxies, 2 emission-line stars, 2 late-type star contaminants, and 2 other H$\alpha$ sources including a newly identified detached fragment of SNR RCW 84. We present the imaging and spectral data of these candidates and a preliminary analysis of their properties. These data provide strong input to help evaluate and refine the behavior of the AI algorithm when searching for PNe in wide-field H$\alpha$ surveys.

The JETSCAPE Collaboration reports a new determination of the jet transport parameter $\hat{q}$ in the Quark-Gluon Plasma (QGP) using Bayesian Inference, incorporating all available inclusive hadron and jet yield suppression data measured in heavy-ion collisions at RHIC and the LHC. This multi-observable analysis extends the previously published JETSCAPE Bayesian Inference determination of $\hat{q}$, which was based solely on a selection of inclusive hadron suppression data. JETSCAPE is a modular framework incorporating detailed dynamical models of QGP formation and evolution, and jet propagation and interaction in the QGP. Virtuality-dependent partonic energy loss in the QGP is modeled as a thermalized weakly-coupled plasma, with parameters determined from Bayesian calibration using soft-sector observables. This Bayesian calibration of $\hat{q}$ utilizes Active Learning, a machine--learning approach, for efficient exploitation of computing resources. The experimental data included in this analysis span a broad range in collision energy and centrality, and in transverse momentum. In order to explore the systematic dependence of the extracted parameter posterior distributions, several different calibrations are reported, based on combined jet and hadron data; on jet or hadron data separately; and on restricted kinematic or centrality ranges of the jet and hadron data. Tension is observed in comparison of these variations, providing new insights into the physics of jet transport in the QGP and its theoretical formulation., Comment: 20 pages, 10 figures, 2 tables, submitted to PRC; updated acknowledgements

The complexity and heterogeneity of data in many real-world applications pose significant challenges for traditional machine learning and signal processing techniques. For instance, in medicine, effective analysis of diverse physiological signals is crucial for patient monitoring and clinical decision-making and yet highly challenging. We introduce MedTsLLM, a general multimodal large language model (LLM) framework that effectively integrates time series data and rich contextual information in the form of text to analyze physiological signals, performing three tasks with clinical relevance: semantic segmentation, boundary detection, and anomaly detection in time series. These critical tasks enable deeper analysis of physiological signals and can provide actionable insights for clinicians. We utilize a reprogramming layer to align embeddings of time series patches with a pretrained LLM's embedding space and make effective use of raw time series, in conjunction with textual context. Given the multivariate nature of medical datasets, we develop methods to handle multiple covariates. We additionally tailor the text prompt to include patient-specific information. Our model outperforms state-of-the-art baselines, including deep learning models, other LLMs, and clinical methods across multiple medical domains, specifically electrocardiograms and respiratory waveforms. MedTsLLM presents a promising step towards harnessing the power of LLMs for medical time series analysis that can elevate data-driven tools for clinicians and improve patient outcomes., Comment: published in Proceedings of Machine Learning Research, MLHC 2024

With the commissioning of the refurbished adaptive secondary mirror (ASM) for the 6.5-meter MMT Observatory under way, special consideration had to be made to properly calibrate the mirror response functions to generate an interaction matrix (IM). The commissioning of the ASM is part of the MMT Adaptive optics exoPlanet characterization System (MAPS) upgrade the observatory's legacy adaptive optics (AO) system. Unlike most AO systems, MAPS employs a convex ASM which prevents the introduction of a calibration source capable of simultaneously illuminating its ASM and wavefront sensor (WFS). This makes calibration of the AO system a significant hurdle in commissioning. To address this, we have employed a hybrid calibration strategy we call the Efficient Synthesis of Calibrations for Adaptive Optics through Pseudo-synthetic and Empirical methods (ESCAPE). ESCAPE combines the DO-CRIME on-sky calibration method with the SPRINT method for computing pseudo-synthetic calibration matrices. To monitor quasi-static system change, the ESCAPE methodology rapidly and continuously generates pseudo-synthetic calibration matrices using continual empirical feedback in either open or closed-loop. In addition, by measuring the current IM in the background while in close-loop, we are also able to measure the optical gains for pyramid wavefront sensor (PyWFS) systems. In this paper, we will provide the mathematical foundation of the ESCAPE calibration strategy and on-sky results from its application in calibrating the MMT Observatory's ASM. Additionally, we will showcase the validation of our approach from our AO testbed and share preliminary on-sky results from MMT., Comment: 16 pages, 9 figures, Submission to SPIE Adaptive Optics Systems IX

Polymers are a primary building block in many biomaterials, often interacting with anisotropic backgrounds. While previous studies have considered polymer dynamics within nematic solvents, rarely are the the effects of anisotropic viscosity and polymer elongation differentiated. Here, we study polymers embedded in nematic liquid crystals with isotropic viscosity via numerical simulations, to explicitly investigate the effect of nematicity on macromolecular conformation and how conformation alone can produce anisotropic dynamics. We employ a hybrid technique that captures nematic orientation, thermal fluctuations and hydrodynamic interactions. The coupling of the polymer backbone to the nematic field elongates the polymer, producing anisotropic diffusion even in nematic solvents with isotropic viscosity. For intermediate coupling, the competition between background anisotropy and macrmolecular entropy leads to hairpins - sudden kinks along the backbone of the polymer. Experiments of DNA embedded in a solution of rod-like fd viruses qualitatively support the role of hairpins in establishing characteristic conformational features that govern polymer dynamics. Hairpin diffusion along the backbone exponentially slows as coupling increases. Better understanding two-way coupling between polymers and their surroundings could allow the creation of more biomimetic composite materials., Comment: 13 pages, 7 figures, 2 appendices

The two main competing theories proposed to explain the formation of massive ($>10$M$_\odot$) stars -- competitive accretion and monolithic core collapse -- make different observable predictions for the environment of the massive stars during, and immediately after, their formation. Proponents of competitive accretion have long predicted that the most massive stars should have a different spatial distribution to lower-mass stars, either through the stars being mass segregated, or being in areas of higher relative densities, or sitting deeper in gravitational potential wells. We test these predictions by analysing a suite of SPH simulations where star clusters form massive stars via competitive accretion with and without feedback. We find that the most massive stars have higher relative densities, and sit in deeper potential wells, only in simulations in which feedback is not present. When feedback is included, only half of the simulations have the massive stars residing in deeper potential wells, and there are no other distinguishing signals in their spatial distributions. Intriguingly, in our simple models for monolithic core collapse, the massive stars may also end up in deeper potential wells, because if massive cores fragment the stars are still massive, and dominate their local environs. We find no robust diagnostic test in the spatial distributions of massive stars that can distinguish their formation mechanisms, and so other predictions for distinguishing between competitive accretion and monolithic collapse are required., Comment: 18 pages, 7 figures, with an Appendix containing a further 18 figures showing all the SPH simulation results. Accepted for publication in ApJ

Artificial Intelligence (AI) technologies have profoundly transformed the field of remote sensing, revolutionizing data collection, processing, and analysis. Traditionally reliant on manual interpretation and task-specific models, remote sensing has been significantly enhanced by the advent of foundation models--large-scale, pre-trained AI models capable of performing a wide array of tasks with unprecedented accuracy and efficiency. This paper provides a comprehensive survey of foundation models in the remote sensing domain, covering models released between June 2021 and June 2024. We categorize these models based on their applications in computer vision and domain-specific tasks, offering insights into their architectures, pre-training datasets, and methodologies. Through detailed performance comparisons, we highlight emerging trends and the significant advancements achieved by these foundation models. Additionally, we discuss the technical challenges, practical implications, and future research directions, addressing the need for high-quality data, computational resources, and improved model generalization. Our research also finds that pre-training methods, particularly self-supervised learning techniques like contrastive learning and masked autoencoders, significantly enhance the performance and robustness of foundation models in remote sensing tasks such as scene classification, object detection, and other applications. This survey aims to serve as a resource for researchers and practitioners by providing a panorama of advances and promising pathways for continued development and application of foundation models in remote sensing.

We present a data-driven approach for learning MDP policies that are robust across stochastic environments whose transition probabilities are defined by parameters with an unknown distribution. We produce probably approximately correct (PAC) guarantees for the performance of these learned policies in a new, unseen environment over the unknown distribution. Our approach is based on finite samples of the MDP environments, for each of which we build an approximation of the model as an interval MDP, by exploring a set of generated trajectories. We use the built approximations to synthesise a single policy that performs well (meets given requirements) across the sampled environments, and furthermore bound its risk (of not meeting the given requirements) when deployed in an unseen environment. Our procedure offers a trade-off between the guaranteed performance of the learned policy and the risk of not meeting the guarantee in an unseen environment. Our approach exploits knowledge of the environment's state space and graph structure, and we show how additional knowledge of its parametric structure can be leveraged to optimize learning and to obtain tighter guarantees from less samples. We evaluate our approach on a diverse range of established benchmarks, demonstrating that we can generate highly performing and robust policies, along with guarantees that tightly quantify their performance and the associated risk.

The Sun drives a supersonic wind which inflates a giant plasma bubble in our very local interstellar neighborhood, the heliosphere. It is bathed in an extremely variable background of energetic ions and electrons which originate from a number of sources. Solar energetic particles (SEPs) are accelerated in the vicinity of the Sun, whereas shocks driven by solar disturbances are observed to accelerate energetic storm particles (ESPs). Moreover, a dilute population with a distinct composition forms the anomalous cosmic rays (ACRs) which are of a mixed interstellar-heliospheric origin. Particles are also accelerated at planetary bow shocks. We will present recent observations of energetic particles by Solar Orbiter and Parker Solar Probe, as well as other spacecraft that allow us to study the acceleration and transport of energetic particles at multiple locations in the inner heliosphere., Comment: 10 pages, one figure, proceedings IAU Symposium 388

TeNPy (short for 'Tensor Network Python') is a python library for the simulation of strongly correlated quantum systems with tensor networks. The philosophy of this library is to achieve a balance of readability and usability for new-comers, while at the same time providing powerful algorithms for experts. The focus is on MPS algorithms for 1D and 2D lattices, such as DMRG ground state search, as well as dynamics using TEBD, TDVP, or MPO evolution. This article is a companion to the recent version 1.0 release of TeNPy and gives a brief overview of the package., Comment: v2: updated funding acknowledgement

In this work, we introduce Gemma 2, a new addition to the Gemma family of lightweight, state-of-the-art open models, ranging in scale from 2 billion to 27 billion parameters. In this new version, we apply several known technical modifications to the Transformer architecture, such as interleaving local-global attentions (Beltagy et al., 2020a) and group-query attention (Ainslie et al., 2023). We also train the 2B and 9B models with knowledge distillation (Hinton et al., 2015) instead of next token prediction. The resulting models deliver the best performance for their size, and even offer competitive alternatives to models that are 2-3 times bigger. We release all our models to the community.

Modern artificial intelligence (AI) systems are powered by foundation models. This paper presents a new set of foundation models, called Llama 3. It is a herd of language models that natively support multilinguality, coding, reasoning, and tool usage. Our largest model is a dense Transformer with 405B parameters and a context window of up to 128K tokens. This paper presents an extensive empirical evaluation of Llama 3. We find that Llama 3 delivers comparable quality to leading language models such as GPT-4 on a plethora of tasks. We publicly release Llama 3, including pre-trained and post-trained versions of the 405B parameter language model and our Llama Guard 3 model for input and output safety. The paper also presents the results of experiments in which we integrate image, video, and speech capabilities into Llama 3 via a compositional approach. We observe this approach performs competitively with the state-of-the-art on image, video, and speech recognition tasks. The resulting models are not yet being broadly released as they are still under development.

Analysis of bacterial biofilms is particularly challenging and important with diverse applications from systems biology to biotechnology. Among the variety of techniques that have been applied, time-of-flight secondary ion mass spectrometry (ToF-SIMS) has many promising features in studying the surface characteristics of biofilms. ToF-SIMS offers high spatial resolution and high mass accuracy, which permit surface sensitive analysis of biofilm components. Thus, ToF-SIMS provides a powerful solution to addressing the challenge of bacterial biofilm analysis. This dataset covers ToF-SIMS analysis of Shewanella oneidensis MR-1 isolated from freshwater lake sediment in New York state. The MR-1 strain is known to have metal and sulfur reducing properties and it can be used for bioremediation and wastewater treatment. There is a current need to identify small molecules and fragments produced from bacterial biofilms. Static ToF-SIMS spectra of MR-1 were obtained using an IONTOF TOF.SIMS V instrument equipped with a 25 keV Bi3+ metal ion gun. Identified molecules and molecular fragments are compared against known biological databases and the reported peaks have at least 65 ppm mass accuracy. These molecules range from lipids and fatty acids to flavonoids, quinolones, and other naturally occurring organic compounds. It is anticipated that the spectral identification of key peaks will assist detection of metabolites, extracellular polymeric substance molecules like polysaccharides, and biologically relevant small molecules using ToF-SIMS in future surface and interface research., Comment: 7 pages, 2 figures

The Koopman Operator (KO) provides an analytical solution of dynamical systems in terms of orthogonal polynomials. This work exploits this representation to include the propagation of uncertainties, where the polynomials are modified to work with stochastic variables. Thus, a new uncertainty quantification technique is proposed, where the KO solution is expanded to include the prediction of central moments, up to an arbitrary order. The propagation of uncertainties is then expanded to develop a new filtering algorithm, where measurements are considered as additional observables in the KO mathematics. Numerical simulations in astrodynamics assess the accuracy and performance of the new methodologies., Comment: 2022 AAS/AIAA Astrodynamics Specialist Conference

Electro-optical modulation of a continuous wave laser is a highly stable way to generate frequency combs, gaining popularity in telecommunication and spectroscopic applications. These combs are generated by modulating non-linear electro-optic crystals with radio frequencies, creating equally spaced side-bands centered around the single-frequency seed laser. Electro-optic frequency comb architectures often choose between optical bandwidth (cascaded GHz combs) or higher mode density (chirped RF generation). This work demonstrates an electro-optic frequency comb with > 120 GHz of bandwidth and a 75 MHz repetition rate. The comb has three cascaded electro-optic modulators driven at sequentially lower harmonics, the last megahertz modulation dictating the repetition rate. This architecture can modulate at any individual harmonic and repetition rate without changes to the components. This comb can be used in any applications where a stable and tunable repetition rate is needed., Comment: 8 figures, 11 pages

Magnetic skyrmions are chiral spin textures which have attracted intense research for their fundamentally novel physics and potential applications as spintronic information carriers. The stability which makes them so potentially useful is a result of their underlying non-trivial topology. While skyrmions were originally predicted and observed in crystalline materials lacking inversion symmetry, some of the most promising host systems for skyrmions are multilayer thin films, where skyrmions have been stabilized at ambient conditions, which is critical for their use in real world devices. The skyrmions found in multilayer thin films have additional three-dimensional structure, with their domain wall helicities twisting through the thickness of the film to create a hybrid skyrmion composed of a Bloch-type core with N\'eel-type caps of opposite chiralities at the surfaces. In this work, we show that this three-dimensional variation creates additional knotted topological structure, providing an explanation for their exceptional stability in ambient conditions. We show that hybrid skyrmions can be described as half-integer Hopfions, and that their field lines have the knotted structure of the Hopf fibration. Furthermore, we show that the topological charge of partially twisted hybrid skyrmions can be related to the domain wall helicity at the surfaces, providing a straightforward way to connect experimental measurements to underlying topology., Comment: 8 pages, 6 figures, 1 table

The ComPair balloon instrument is a prototype of the All-sky Medium Energy Gamma-ray Observatory (AMEGO) mission concept. AMEGO aims to bridge the spectral gap in sensitivity that currently exists from $\sim$100 keV to $\sim$100 MeV by being sensitive to both Compton and pair-production events. This is made possible through the use of four subsystems working together to reconstruct events: a double-sided silicon strip detector (DSSD) Tracker, a virtual Frisch grid cadmium zinc telluride (CZT) Low Energy Calorimeter, a ceasium iodide (CsI) High Energy Calorimeter, and an anti-coincidence detector (ACD) to reject charged particle backgrounds. Composed of 10 layers of DSSDs, ComPair's Tracker is designed to measure the position of photons that Compton scatter in the silicon, as well as reconstruct the tracks of electrons and positrons from pair-production as they propagate through the detector. By using these positions, as well as the absorbed energies in the Tracker and 2 Calorimeters, the energy and direction of the incident photon can be determined. This proceeding will present the development, testing, and calibration of the ComPair DSSD Tracker and early results from its balloon flight in August 2023., Comment: SPIE Astronomical Telescopes + Instrumentation Conference

The SNO+ detector operated initially as a water Cherenkov detector. The implementation of a sealed covergas system midway through water data taking resulted in a significant reduction in the activity of $^{222}$Rn daughters in the detector and allowed the lowest background to the solar electron scattering signal above 5 MeV achieved to date. This paper reports an updated SNO+ water phase $^8$B solar neutrino analysis with a total livetime of 282.4 days and an analysis threshold of 3.5 MeV. The $^8$B solar neutrino flux is found to be $\left(2.32^{+0.18}_{-0.17}\text{(stat.)}^{+0.07}_{-0.05}\text{(syst.)}\right)\times10^{6}$ cm$^{-2}$s$^{-1}$ assuming no neutrino oscillations, or $\left(5.36^{+0.41}_{-0.39}\text{(stat.)}^{+0.17}_{-0.16}\text{(syst.)} \right)\times10^{6}$ cm$^{-2}$s$^{-1}$ assuming standard neutrino oscillation parameters, in good agreement with both previous measurements and Standard Solar Model Calculations. The electron recoil spectrum is presented above 3.5 MeV.

A new framework, called x-scape, for the combined study of both hard and soft transverse momentum sectors in high energy proton-proton ($p$-$p$) and proton-nucleus ($p$-$A$) collisions is set up. A dynamical initial state is set up using the 3d-Glauber model with transverse locations of hotspots within each incoming nucleon. A hard scattering that emanates from two colliding hotspots is carried out using the Pythia generator. Initial state radiation from the incoming hard partons is carried out in a new module called I-matter, which includes the longitudinal location of initial splits. The energy-momentum of both the initial hard partons and their associated beam remnants is removed from the hot spots, depleting the energy-momentum available for the formation of the bulk medium. Outgoing showers are simulated using the matter generator, and results are presented for both cases, allowing for and not allowing for energy loss. First comparisons between this hard-soft model and single inclusive hadron and jet data from $p$-$p$ and minimum bias $p$-$Pb$ collisions are presented. Single hadron spectra in $p$-$p$ are used to carry out a limited (in number of parameters) Bayesian calibration of the model. Fair comparisons with data are indicative of the utility of this new framework. Theoretical studies of the correlation between jet $p_T$ and event activity at mid and forward rapidity are carried out., Comment: 18 pages, 15 figures

Qudits provide a resource-efficient alternative to qubits for quantum information processing. The multilevel nature of the transmon, with its individually resolvable transition frequencies, makes it an attractive platform for superconducting circuit-based qudits. In this work, we systematically analyze the trade-offs associated with encoding high-dimensional quantum information in fixed-frequency transmons. Designing high $E_J/E_C$ ratios of up to 325, we observe up to 12 levels ($d=12$) on a single transmon. Despite the decreased anharmonicity, we demonstrate process infidelities $e_f < 3 \times 10^{-3}$ for qubit-like operations in each adjacent-level qubit subspace in the lowest 10 levels. Furthermore, we achieve a 10-state readout assignment fidelity of 93.8% with the assistance of deep neural network classification of a multi-tone dispersive measurement. We find that the Hahn echo time $T_{2E}$ for the higher levels is close to the limit of $T_1$ decay, primarily limited by bosonic enhancement. We verify the recently introduced Josephson harmonics model, finding that it yields better predictions for the transition frequencies and charge dispersion. Finally, we show strong $ZZ$-like coupling between the higher energy levels in a two-transmon system. Our high-fidelity control and readout methods, in combination with our comprehensive characterization of the transmon model, suggest that the high-$E_J/E_C$ transmon is a powerful tool for exploring excited states in circuit quantum electrodynamics., Comment: 18 pages, 9 figures

Understanding the noise characteristics of high quantum efficiency silicon-based ultraviolet detectors, developed by the Microdevices Lab at the Jet Propulsion Laboratory, is critical for current and proposed UV missions using these devices. In this paper, we provide an overview of our detector noise characterization test bench that uses delta-doped, photon counting, Electron-multiplying CCDs (EMCCDs) to understand the fundamental noise properties relevant to all silicon CCDs and CMOS arrays. This work attempts to identify the source of the dark current plateau that has been previously measured with photon-counting EMCCDs and is known to be prevalent in other silicon-based arrays. It is suspected that the plateau could be due to a combination of detectable photons in the tail of blackbody radiation of the ambient instrument, low-level light leaks, and a non-temperature-dependent component that varies with substrate voltage. Our innovative test setup delineates the effect of the ambient environment during dark measurements by independently controlling the temperature of the detector and surrounding environment. We present the design of the test setup and preliminary results., Comment: Submitted for Proceedings of SPIE Astronomical Telescopes+Instrumentation 2024, Paper number: 13093-26

Open generative models are vitally important for the community, allowing for fine-tunes and serving as baselines when presenting new models. However, most current text-to-audio models are private and not accessible for artists and researchers to build upon. Here we describe the architecture and training process of a new open-weights text-to-audio model trained with Creative Commons data. Our evaluation shows that the model's performance is competitive with the state-of-the-art across various metrics. Notably, the reported FDopenl3 results (measuring the realism of the generations) showcase its potential for high-quality stereo sound synthesis at 44.1kHz., Comment: Demo: https://stability-ai.github.io/stable-audio-open-demo/ Weights: https://huggingface.co/stabilityai/stable-audio-open-1.0 Code: https://github.com/Stability-AI/stable-audio-tools. arXiv admin note: text overlap with arXiv:2404.10301

While boundary plasmas in present day tokamaks generally fall in a fluid regime, neutral species near the boundary often require kinetic models due to long mean-free-paths compared to characteristic spatial scales in the region. Monte-Carlo (MC) methods provide a complete, high-fidelity approach to solving kinetic models, and must be coupled to fluid plasma models to simulate the full plasma-neutrals system. The statistical nature of MC methods, however, prevents convergence of coupled fluid-kinetic simulations to an exact self-consistent steady-state. Moreover, this forces the use of explicit methods that can suffer from numerical errors and require huge computational resources. Correlated Monte-Carlo (CMC) methods are expected to alleviate these issues, but have historically enjoyed only mixed success. Here, a fully implicit method for coupled plasma-neutral systems is demonstrated in 1D using the UEDGE plasma code and a homemade CMC code. In particular, it is shown that ensuring the CMC method is a differentiable function of the background plasma is sufficient to employ a Jacobian-Free Newton-Krylov solver for implicit time steps. The convergence of the implicit coupling method is explored and compared with explicit coupling and uncorrelated methods. It is shown that ensuring differentiability by controlling random seeds in the MC is sufficient to achieve convergence, and that the use of implicit time-stepping methods has the potential for improved stability and runtimes over explicit coupling methods., Comment: 6 pages, 6 figures. Comments welcome!

Given a pair of $\mathbb{Z}_2$-harmonic spinors (resp. 1-forms) on closed Riemannian 3-manifolds $(Y_1, g_1)$ and $(Y_2,g_2)$, we construct $\mathbb{Z}_2$-harmonic spinors (resp. 1-forms) on the connected sum $Y_1 \# Y_2$ and the torus sum $Y_1 \cup_{T^2} Y_2$ using a gluing argument. The main tool in the proof is a parameterized version of the Nash-Moser implicit function theorem established by Donaldson and the second author. We use these results to construct an abundance of new examples of $\mathbb Z_2$-harmonic spinors and 1-forms. In particular, we prove that for every closed 3-manifold $Y$, there exist infinitely many $\mathbb{Z}_2$-harmonic spinors with singular sets representing infinitely many distinct isotopy classes of embedded links, strengthening an existence theorem of Doan-Walpuski. Moreover, combining this with previous results, our construction implies that if $b_1(Y) > 0$, there exist infinitely many $\mathrm{spin}^c$ structures on $Y$ such that the moduli space of solutions to the two-spinor Seiberg-Witten equations is non-empty and non-compact., Comment: 38 pages, comments welcome!

Flat band moir\'e graphene systems have emerged as a quintessential platform to investigate correlated phases of matter. A plethora of interaction-driven ground states have been proposed, and yet despite extensive experimental effort, there has been little direct evidence that distinguishes between the various phases, in particular near charge neutrality point. Here, we use a nanoscale scanning superconducting quantum interference device to image the local thermodynamic quantum oscillations in alternating-twist trilayer graphene at magnetic fields as low as 56 mT, which reveal ultrafine details of the density of states and of the renormalization of the single-particle band structure by Coulomb interactions. We find that the charging self-energy due to occupied electronic states, is critical in explaining the high carrier density physics. At half-filling of the conduction flat band, we observe a Stoner-like symmetry breaking, suggesting that it is the most robust mechanism in the hierarchy of phase transitions. On approaching charge neutrality, where the charging energy is negligible and exchange energy is dominant, we find the ground state to be a nematic semimetal which is favored over gapped states in the presence of heterostrain. In the revealed semimetallic phase, the flat-band Dirac cones migrate towards the mini-Brillouin zone center, spontaneously breaking the C_3 rotational symmetry. Our low-field local quantum oscillations technique presents an alluring avenue to explore the ground states of diverse strongly interacting van der Waals systems., Comment: 30 pages, 4 main text figures, 6 Extended Data figures

We present results from a search for X-ray/gamma-ray counterparts of gravitational-wave (GW) candidates from the third observing run (O3) of the LIGO-Virgo-KAGRA (LVK) network using the Swift Burst Alert Telescope (Swift-BAT). The search includes 636 GW candidates received in low latency, 86 of which have been confirmed by the offline analysis and included in the third cumulative Gravitational-Wave Transient Catalogs (GWTC-3). Targeted searches were carried out on the entire GW sample using the maximum--likelihood NITRATES pipeline on the BAT data made available via the GUANO infrastructure. We do not detect any significant electromagnetic emission that is temporally and spatially coincident with any of the GW candidates. We report flux upper limits in the 15-350 keV band as a function of sky position for all the catalog candidates. For GW candidates where the Swift-BAT false alarm rate is less than 10$^{-3}$ Hz, we compute the GW--BAT joint false alarm rate. Finally, the derived Swift-BAT upper limits are used to infer constraints on the putative electromagnetic emission associated with binary black hole mergers., Comment: 50 pages, 10 figures, 4 tables