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Purpose: The purpose of the study was to evaluate the implementation and impact of a computer science-integrated math curriculum, the Beauty and Joy of Computing (BJC), combined with curriculum and professional development support for Computer Science Principles (CSP) teachers, funded by an Innovation and Research (EIR) grant. Methods: The study used a randomized controlled trial design where schools from the IDEA public schools were randomly assigned to use the BJC curriculum (treatment) or, the business as usual, Code.org (comparison). The study compared AP CSP test scores and student attitudes towards CSP between students in the treatment and comparison group. Results: The study found that the average AP CSP scores were low in both the treatment and comparison groups. About one-quarter of test takers (25%) earned a passing score of 3 or higher (compared to national average of 64%). The results of the analyses show that there were no statistically significant differences between the BJC and comparison groups. The study also showed that students in the treatment group responded more favorably on all student engagement in CSP constructs than comparison students, the size of the differences between the treatment and comparison condition were small and none were statistically significant. Implications: Since the evaluation examined student outcomes in the same year that the new curriculum was implemented for this first time, finding significant impacts on student outcomes may require more time. Nevertheless, the results of this evaluation inform IDEA staff, practitioners, and other researchers on the intervention's effectiveness, and they provide insights on how to improve curriculum use to further improve student outcomes.

Purpose: The purpose of the study was to evaluate the implementation and impact of the IDEA Public Schools' Mathematics Curriculum Redesign (MCR) effort, funded by an Innovation and Research (EIR) grant. Methods: The study used a quasi-experimental matching design to examine the effect of MCR on student achievement outcomes. The study compared outcomes between the two cohorts of students at IDEA Public Schools that piloted the MCR and matched cohorts of students in IDEA schools that had delayed implementation of MCR. Results:?The study found statistically significant differences in IDEA Geometry test scores (ES = 0.39) between treatment and comparison group students in Grade 9 and on AP Calculus exam scores between treatment and comparison group students in Grade 12 (ES = 0.57). The study also found that the MCR program met the evaluation threshold for fidelity of implementation for the two out of three key program components (development of redesigned math curriculum and teacher use of math curriculum) in some years. The program did not meet the evaluation threshold for fidelity of implementation for the third key program components (teacher training and professional development to use redesigned math curriculum) in any of the years. Implications:?Because curriculum development and implementation are ongoing and iterative, realizing all of the benefits is not immediate and may take more time to achieve desired results. Continuous improvement of the curriculum and consistent supports to teachers will contribute to continued benefits for teachers and students while moving toward reaching anticipated student outcomes.

The purpose of the addendum is to document the findings from the Year 2 student achievement analyses of a teacher-level randomized experiment, which was designed to examine the implementation and impact of a scalable version of the 2-year MyTeachingPartner Secondary (MTP-S) program as part of a federally-funded Education Innovation and Research (EIR) project. Due to COVID-caused delays, we were unable to include findings about the impact of MTP-S on Year 2 student achievement in the final report produced at the end of the EIR project (Wayne et al., 2023). With the support of a grant from the William T. Grant Foundation, we were able to complete the collection and analyses of Year 2 student achievement data presented in this addendum. Similar to the Year 1 student achievement findings presented in our EIR final project report, the Year 2 student achievement findings did not reveal any significant program impact on students' math or English language arts achievement at the end of the 2-year program. Findings from this study need to be interpreted with caution given study limitations resulting largely from the influence of the pandemic.

Recent research has explored the memorization capacity of multi-head attention, but these findings are constrained by unrealistic limitations on the context size. We present a novel proof for language-based Transformers that extends the current hypothesis to any context size. Our approach improves upon the state-of-the-art by achieving more effective exact memorization with an attention layer, while also introducing the concept of approximate memorization of distributions. Through experimental validation, we demonstrate that our proposed bounds more accurately reflect the true memorization capacity of language models, and provide a precise comparison with prior work., Comment: 16 pages, 6 figures, submitted to AISTATS 2025

This paper investigates the application of Video Foundation Models (ViFMs) for generating robot data summaries to enhance intermittent human supervision of robot teams. We propose a novel framework that produces both generic and query-driven summaries of long-duration robot vision data in three modalities: storyboards, short videos, and text. Through a user study involving 30 participants, we evaluate the efficacy of these summary methods in allowing operators to accurately retrieve the observations and actions that occurred while the robot was operating without supervision over an extended duration (40 min). Our findings reveal that query-driven summaries significantly improve retrieval accuracy compared to generic summaries or raw data, albeit with increased task duration. Storyboards are found to be the most effective presentation modality, especially for object-related queries. This work represents, to our knowledge, the first zero-shot application of ViFMs for generating multi-modal robot-to-human communication in intermittent supervision contexts, demonstrating both the promise and limitations of these models in human-robot interaction (HRI) scenarios., Comment: This work has been submitted to the IEEE RAL for possible publication

Visual navigation takes inspiration from humans, who navigate in previously unseen environments using vision without detailed environment maps. Inspired by this, we introduce a novel no-RL, no-graph, no-odometry approach to visual navigation using feudal learning to build a three tiered agent. Key to our approach is a memory proxy map (MPM), an intermediate representation of the environment learned in a self-supervised manner by the high-level manager agent that serves as a simplified memory, approximating what the agent has seen. We demonstrate that recording observations in this learned latent space is an effective and efficient memory proxy that can remove the need for graphs and odometry in visual navigation tasks. For the mid-level manager agent, we develop a waypoint network (WayNet) that outputs intermediate subgoals, or waypoints, imitating human waypoint selection during local navigation. For the low-level worker agent, we learn a classifier over a discrete action space that avoids local obstacles and moves the agent towards the WayNet waypoint. The resulting feudal navigation network offers a novel approach with no RL, no graph, no odometry, and no metric map; all while achieving SOTA results on the image goal navigation task., Comment: arXiv admin note: substantial text overlap with arXiv:2402.12498

We present the Brain Treebank, a large-scale dataset of electrophysiological neural responses, recorded from intracranial probes while 10 subjects watched one or more Hollywood movies. Subjects watched on average 2.6 Hollywood movies, for an average viewing time of 4.3 hours, and a total of 43 hours. The audio track for each movie was transcribed with manual corrections. Word onsets were manually annotated on spectrograms of the audio track for each movie. Each transcript was automatically parsed and manually corrected into the universal dependencies (UD) formalism, assigning a part of speech to every word and a dependency parse to every sentence. In total, subjects heard over 38,000 sentences (223,000 words), while they had on average 168 electrodes implanted. This is the largest dataset of intracranial recordings featuring grounded naturalistic language, one of the largest English UD treebanks in general, and one of only a few UD treebanks aligned to multimodal features. We hope that this dataset serves as a bridge between linguistic concepts, perception, and their neural representations. To that end, we present an analysis of which electrodes are sensitive to language features while also mapping out a rough time course of language processing across these electrodes. The Brain Treebank is available at https://BrainTreebank.dev/, Comment: 36 pages, 17 figures; Accepted at NeurIPS Dataset and Benchmarks 2024

Complexity is an important metric for appropriate characterization of different classes of irregular signals, observed in the laboratory or in nature. The literature is already rich in the description of such measures using a variety of entropy and disequilibrium measures, separately or in combination. Chaotic signal was given prime importance in such studies while no such measure was proposed so far, how complex were the extreme events when compared to non-extreme chaos. We address here this question of complexity in extreme events and investigate if we can distinguish them from non-extreme chaotic signal. The normalized Shannon entropy in combination with disequlibrium is used for our study and it is able to distinguish between extreme chaos and non-extreme chaos and moreover, it depicts the transition points from periodic to extremes via Pomeau-Manneville intermittency and, from small amplitude to large amplitude chaos and its transition to extremes via interior crisis. We report a general trend of complexity against a system parameter that increases during a transition to extreme events, reaches a maximum, and then starts decreasing. We employ three models, a nonautonomous Lienard system, 2-dimensional Ikeda map and a 6-dimensional coupled Hindmarh-Rose system to validate our proposition., Comment: 9 pages, 6 figures, Accepted for publication in Chaos: An Interdisciplinary Journal of Nonlinear Science (2024)

We present the mission concept "Mission to Analyze the UltraViolet universE" (MAUVE), a wide-field spectrometer and imager conceived during the inaugural NASA Astrophysics Mission Design School. MAUVE responds to the 2023 Announcement of Opportunity for Probe-class missions, with a budget cap of \$1 billion, and would hypothetically launch in 2031. However, the formulation of MAUVE was an educational exercise and the mission is not being developed further. The Principal Investigator-led science of MAUVE aligns with the priorities outlined in the 2020 Astrophysics Decadal Survey, enabling new characterizations of exoplanet atmospheres, the early-time light curves of some of the universe's most explosive transients, and the poorly-understood extragalactic background light. Because the Principal Investigator science occupies 30% of the observing time available during the mission's 5 yr lifespan, we provide an observing plan that would allow for 70% of the observing time to be used for General Observer programs, with community-solicited proposals. The onboard detector (THISTLE) claims significant heritage from the Space Telescope Imaging Spectrograph on Hubble, but extends its wavelength range down to the extreme UV. We note that MAUVE would be the first satellite in decades with the ability to access this regime of the electromagnetic spectrum. MAUVE has a field of view of 900" x 900" a photometric sensitivity extending to $m_{UV}\leq 24$, and a resolving power of $R\sim1000$. This paper provides full science and mission traceability matrices for this concept, and also outlines cost and scheduling timelines aimed at enabling a within-budget mission and an on-time launch., Comment: 21 pages, 15 figures. Published by the Publications of the Astronomical Society of the Pacific

Divisible residuated lattices are algebraic structures corresponding to a more comprehensive logic than Hajek's basic logic with an important significance in the study of fuzzy logic. The purpose of this paper is to investigate commutative rings whose the lattice of ideals can be equipped with a structure of divisible residuated lattice. We show that these rings are multiplication rings. A characterization, more examples and their connections to other classes of rings are established. Furthermore, we analyze the structure of divisible residuated lattices using finite commutative rings. From computational considerations, we present an explicit construction of isomorphism classes of divisible residuated lattices (that are not BL-algebras) of small size n $(2 \le n \le 6)$ and we give summarizing statistics.

We present a novel Automatic Target Recognition (ATR) system using open-vocabulary object detection and classification models. A primary advantage of this approach is that target classes can be defined just before runtime by a non-technical end user, using either a few natural language text descriptions of the target, or a few image exemplars, or both. Nuances in the desired targets can be expressed in natural language, which is useful for unique targets with little or no training data. We also implemented a novel combination of several techniques to improve performance, such as leveraging the additional information in the sequence of overlapping frames to perform tubelet identification (i.e., sequential bounding box matching), bounding box re-scoring, and tubelet linking. Additionally, we developed a technique to visualize the aggregate output of many overlapping frames as a mosaic of the area scanned during the aerial surveillance or reconnaissance, and a kernel density estimate (or heatmap) of the detected targets. We initially applied this ATR system to the use case of detecting and clearing unexploded ordinance on airfield runways and we are currently extending our research to other real-world applications., Comment: Accepted to the Thirty-Seventh Annual Conference on Innovative Applications of Artificial Intelligence (IAAI-25)

Hierarchical sorting is a fundamental task for programmable matter, inspired by the spontaneous formation of interfaces and membranes in nature. The task entails particles of different types, present in fixed densities, sorting into corresponding regions of a space that are themselves organized. By analyzing the Gibbs distribution of a general fixed-magnetization model of equilibrium statistical mechanics, we prove that particles moving stochastically according to local affinities solve the hierarchical sorting task. The analysis of fixed-magnetization models is notoriously difficult, and approaches that have led to recent breakthroughs in sampling the low-temperature regime only work in the variable-magnetization setting by default. To overcome this barrier, we introduce a new approach for comparing the partition functions of fixed- and variable-magnetization models. The core technique identifies a special class of configurations that contribute comparably to the two partition functions, which then serves as a bridge between the fixed- and variable-magnetization settings. Our main result is an estimate of the Gibbs distribution that unifies existing and new results for models at fixed magnetization, including the Ising, Potts, and Blume--Capel models, and leads to stochastic distributed algorithms for hierarchical sorting and other self-organizing tasks, like compression and separation.

The Quantum Gravity Mediated Entanglement (QGEM) protocol offers a novel method to probe the quantumness of gravitational interactions at non-relativistic scales. This protocol leverages the Stern-Gerlach effect to create $\mathcal{O}(\sim \mu m)$ spatial superpositions of two nanodiamonds (mass $\sim 10^{-15}$ kg) with NV spins, which are then allowed to interact and become entangled solely through the gravitational interaction. Since electromagnetic interactions such as Casimir-Polder and dipole-dipole interactions dominate at this scale, screening them to ensure the masses interact exclusively via gravity is crucial. In this paper, we propose using magnetic traps based on micro-fabricated wires, which provide strong gradients with relatively modest magnetic fields to trap nanoparticles for interferometric entanglement experiments. The design consists of a small trap to cool the center-of-mass motion of the nanodiamonds and a long trap with a weak direction suitable for creating macroscopic superpositions. In contrast to permanent-magnet-based long traps, the micro-fabricated wire-based approach allows fast switching of the magnetic trapping and state manipulation potentials and permits integrated superconducting shielding, which can screen both electrostatic and magnetic interactions between nanodiamonds in a gravitational entanglement experiment. The setup also provides a possible platform for other tests of quantum coherence in macroscopic systems and searches for novel short-range forces., Comment: 11 pages, 8 figures

Generative modeling typically concerns the transport of a single source distribution to a single target distribution by learning (i.e., regressing onto) simple probability flows. However, in modern data-driven fields such as computer graphics and single-cell genomics, samples (say, point-clouds) from datasets can themselves be viewed as distributions (as, say, discrete measures). In these settings, the standard generative modeling paradigm of flow matching would ignore the relevant geometry of the samples. To remedy this, we propose \emph{Wasserstein flow matching} (WFM), which appropriately lifts flow matching onto families of distributions by appealing to the Riemannian nature of the Wasserstein geometry. Our algorithm leverages theoretical and computational advances in (entropic) optimal transport, as well as the attention mechanism in our neural network architecture. We present two novel algorithmic contributions. First, we demonstrate how to perform generative modeling over Gaussian distributions, where we generate representations of granular cell states from single-cell genomics data. Secondly, we show that WFM can learn flows between high-dimensional and variable sized point-clouds and synthesize cellular microenvironments from spatial transcriptomics datasets. Code is available at [WassersteinFlowMatching](https://github.com/DoronHav/WassersteinFlowMatching)., Comment: 24 pages, 10 figures

We use the Ladder Technique to establish bijections between the ideals of related Fell bundles., Comment: 11 pages

We study how social image concerns affect information sharing patterns between peers. An individual receives a signal ("news") about the state of the world and can either share it with a peer or not. This signal has two attributes: a headline -- e.g., arguing for or against human-induced climate change -- and a veracity status, indicating if the signal is based on facts or made-up. The headline is observable at no cost by everyone, while observing the veracity status is costly and the cost depends on an individual's type. We study the sharing patterns induced by two different types of social image concern: wanting to be perceived as talented, which implies being able to distinguish proper from fake news, and wanting to signal one's worldview. Our model can rationalize the empirical finding that fake news may be shared with a higher propensity than proper news (e.g., Vosoughi et al., 2018). We show that both a veracity and a worldview concern may rationalize this finding, though sharing patterns are empirically distinguishable and welfare implications differ.

While cycling offers an attractive option for sustainable transportation, many potential cyclists are discouraged from taking up cycling due to the lack of suitable and safe infrastructure. Efficiently mapping cycling infrastructure across entire cities is necessary to advance our understanding of how to provide connected networks of high-quality infrastructure. Therefore we propose a system capable of classifying available cycling infrastructure from on-bike smartphone camera data. The system receives an image sequence as input, temporally analyzing the sequence to account for sparsity of signage. The model outputs cycling infrastructure class labels defined by a hierarchical classification system. Data is collected via participant cyclists covering 7,006Km across the Greater Melbourne region that is automatically labeled via a GPS and OpenStreetMap database matching algorithm. The proposed model achieved an accuracy of 95.38%, an increase in performance of 7.55% compared to the non-temporal model. The model demonstrated robustness to extreme absence of image features where the model lost only 6.6% in accuracy after 90% of images being replaced with blank images. This work is the first to classify cycling infrastructure using only street-level imagery collected from bike-mounted mobile phone cameras, while demonstrating robustness to feature sparsity via long temporal sequence analysis., Comment: 8 pages, 6 figures, presented at ITSC 2024

We present 850 {\mu}m thermal dust polarization observations with a resolution of 14.4"(~ 0.13 pc) towards an infrared dark cloud G16.96+0.27 using JCMT/POL-2. The average magnetic field orientation, which roughly agrees with the larger-scale magnetic field orientation traced by the Planck 353 GHz data, is approximately perpendicular to the filament structure. The estimated plane-of-sky magnetic field strength is ~ 96 {\mu}G and ~ 60 {\mu}G using two variants of the Davis-Chandrasekhar-Fermi methods. We calculate the virial and magnetic critical parameters to evaluate the relative importance of gravity, the magnetic field, and turbulence. The magnetic field and turbulence are both weaker than gravity, but magnetic fields and turbulence together are equal to gravity, suggesting that G16.96+0.27 is in a quasi-equilibrium state. The cloud-magnetic-field alignment is found to have a trend moving away from perpendicularity in the dense regions, which may serve as a tracer of potential fragmentation in such quiescent filaments., Comment: Accepted by ApJ. 13 pages, 5 figures

In this study, we investigate the contribution of millisecond pulsars (MSPs) to the gamma-ray excess observed in the Galactic Center by analyzing data from high-resolution direct N-body simulations of six globular clusters (GCs) that experience close encounters with the nuclear star cluster. Using the {\phi}-GPU code, we tracked the orbits of individual neutron stars (NSs) formed during the simulations, assuming a fraction of these NSs evolve into MSPs. Our model includes state-of-the-art single stellar evolution code including prescription for neutron star formation. We estimated the gamma-ray flux from these MSPs, considering known values for their gamma-ray emission. Our results show that MSPs originating from the six modeled GCs contribute a small but non-negligible fraction of the observed gamma-ray flux. This finding suggests that the actual gamma-ray flux from MSPs could be much higher when considering the entire population of GCs, potentially significantly contributing to the gamma-ray excess. This study highlights the importance of considering MSPs in the Galactic Center, originating from nearby globular clusters, as a potential source of the observed gamma-ray excess. Future work will involve more sophisticated simulations incorporating binary stellar evolution and comparing the fraction of MSPs in observed GCs to refine our models and improve the accuracy of our estimates., Comment: in Russian language

Manipulation tasks often consist of subtasks, each representing a distinct skill. Mastering these skills is essential for robots, as it enhances their autonomy, efficiency, adaptability, and ability to work in their environment. Learning from demonstrations allows robots to rapidly acquire new skills without starting from scratch, with demonstrations typically sequencing skills to achieve tasks. Behaviour cloning approaches to learning from demonstration commonly rely on mixture density network output heads to predict robot actions. In this work, we first reinterpret the mixture density network as a library of feedback controllers (or skills) conditioned on latent states. This arises from the observation that a one-layer linear network is functionally equivalent to a classical feedback controller, with network weights corresponding to controller gains. We use this insight to derive a probabilistic graphical model that combines these elements, describing the skill acquisition process as segmentation in a latent space, where each skill policy functions as a feedback control law in this latent space. Our approach significantly improves not only task success rate, but also robustness to observation noise when trained with human demonstrations. Our physical robot experiments further show that the induced robustness improves model deployment on robots.

Recently, diffusion models have gained popularity due to their impressive generative abilities. These models learn the implicit distribution given by the training dataset, and sample new data by transforming random noise through the reverse process, which can be thought of as gradual denoising. In this work, we examine the generation process as a ``correlation machine'', where random noise is repeatedly enhanced in correlation with the implicit given distribution. To this end, we explore the linear case, where the optimal denoiser in the MSE sense is known to be the PCA projection. This enables us to connect the theory of diffusion models to the spiked covariance model, where the dependence of the denoiser on the noise level and the amount of training data can be expressed analytically, in the rank-1 case. In a series of numerical experiments, we extend this result to general low rank data, and show that low frequencies emerge earlier in the generation process, where the denoising basis vectors are more aligned to the true data with a rate depending on their eigenvalues. This model allows us to show that the linear diffusion model converges in mean to the leading eigenvector of the underlying data, similarly to the prevalent power iteration method. Finally, we empirically demonstrate the applicability of our findings beyond the linear case, in the Jacobians of a deep, non-linear denoiser, used in general image generation tasks.

Continuous prompts, or "soft prompts", are a widely-adopted parameter-efficient tuning strategy for large language models, but are often less favorable due to their opaque nature. Prior attempts to interpret continuous prompts relied on projecting individual prompt tokens onto the vocabulary space. However, this approach is problematic as performant prompts can yield arbitrary or contradictory text, and it interprets prompt tokens individually. In this work, we propose a new approach to interpret continuous prompts that elicits textual descriptions from their representations during model inference. Using a Patchscopes variant (Ghandeharioun et al., 2024) called InSPEcT over various tasks, we show our method often yields accurate task descriptions which become more faithful as task performance increases. Moreover, an elaborated version of InSPEcT reveals biased features in continuous prompts, whose presence correlates with biased model predictions. Providing an effective interpretability solution, InSPEcT can be leveraged to debug unwanted properties in continuous prompts and inform developers on ways to mitigate them.

Facial expression recognition (FER) has emerged as a promising approach to the development of emotion-aware intelligent systems. The performance of FER in multiple domains is continuously being improved, especially through advancements in data-driven learning approaches. However, a key challenge remains in utilizing FER in real-world contexts, namely ensuring user understanding of these systems and establishing a suitable level of user trust towards this technology. We conducted an empirical user study to investigate how explanations of FER can improve trust, understanding and performance in a human-computer interaction task that uses FER to trigger helpful hints during a navigation game. Our results showed that users provided with explanations of the FER system demonstrated improved control in using the system to their advantage, leading to a significant improvement in their understanding of the system, reduced collisions in the navigation game, as well as increased trust towards the system., Comment: 8 pages, Submitted and Accepted to the ACII2024 conference

In this work we present TRFD, a derivative-free trust-region method based on finite differences for minimizing composite functions of the form $f(x)=h(F(x))$, where $F$ is a black-box function assumed to have a Lipschitz continuous Jacobian, and $h$ is a known convex Lipschitz function, possibly nonsmooth. The method approximates the Jacobian of $F$ via forward finite differences. We establish an upper bound for the number of evaluations of $F$ that TRFD requires to find an $\epsilon$-approximate stationary point. For L1 and Minimax problems, we show that our complexity bound reduces to $\mathcal{O}(n\epsilon^{-2})$ for specific instances of TRFD, where $n$ is the number of variables of the problem. Assuming that $h$ is monotone and that the components of $F$ are convex, we also establish a worst-case complexity bound, which reduces to $\mathcal{O}(n\epsilon^{-1})$ for Minimax problems. Numerical results are provided to illustrate the relative efficiency of TRFD in comparison with existing derivative-free solvers for composite nonsmooth optimization.

We present the first emission spectrum of the hot Jupiter WASP-17 b using one eclipse observation from the JWST Near Infrared Imager and Slitless Spectrograph (NIRISS) Single Object Slitless Spectroscopy (SOSS) mode. Covering a wavelength range of 0.6 to 2.8 microns, our retrieval analysis reveals a strong detection of H2O in WASP-17b dayside atmosphere (6.4sigma). Our retrievals consistently favor a super-solar dayside H2O abundance and a non-inverted temperature-pressure profile over a large pressure range. Additionally, our examination of the brightness temperature reveals excess emission below 1 microns, suggesting the possibility of a high internal temperature (600 to 700 K) and/or contributions from reflected light. We highlight that JWST emission spectroscopy retrieval results can be sensitive to whether negative eclipse depths are allowed at optical wavelengths during light curve fitting. Our findings deepen our understanding of WASP-17b atmospheric composition while also highlighting the sensitivity of our results to pressure-temperature profile parameterizations. This work is part of a series of studies by our JWST Telescope Scientist Team (JWST-TST), in which we will use Guaranteed Time Observations to perform Deep Reconnaissance of Exoplanet Atmospheres through Multi-instrument Spectroscopy (DREAMS)., Comment: Accepted for Publication in the Astronomical Journal (AJ) October 4, 2024

We present the first spectroscopic characterisation of the dayside atmosphere of WASP-17b in the mid-infrared using a single JWST MIRI/LRS eclipse observation. From forward-model fits to the 5-12 $\mu$m emission spectrum, we tightly constrain the heat redistribution factor of WASP-17b to be 0.92$\pm$0.02 at the pressures probed by this data, indicative of inefficient global heat redistribution. We also marginally detect a supersolar abundance of water, consistent with previous findings for WASP-17b, but note our weak constraints on this parameter. These results reflect the thermodynamically rich but chemically poor information content of MIRI/LRS emission data for high-temperature hot Jupiters. Using the eclipse mapping method, which utilises the signals that the spatial emission profile of an exoplanet imprints on the eclipse light curve during ingress/egress due to its partial occultation by the host star, we also construct the first eclipse map of WASP-17b, allowing us to diagnose its multidimensional atmospheric dynamics for the first time. We find a day-night temperature contrast of order 1000 K at the pressures probed by this data, consistent with our derived heat redistribution factor, along with an eastward longitudinal hotspot offset of $18.7^{+11.1\deg}_{-3.8}$, indicative of the presence of an equatorial jet induced by day-night thermal forcing being the dominant redistributor of heat from the substellar point. These dynamics are consistent with general circulation model predictions for WASP-17b. This work is part of a series of studies by the JWST Telescope Scientist Team (JWST-TST), in which we use Guaranteed Time Observations to perform Deep Reconnaissance of Exoplanet Atmospheres through Multi-instrument Spectroscopy (DREAMS)., Comment: 28 pages, 8 figures. Published in The Astronomical Journal

Learning from Demonstration (LfD) is a powerful type of machine learning that can allow novices to teach and program robots to complete various tasks. However, the learning process for these systems may still be difficult for novices to interpret and understand, making effective teaching challenging. Explainable artificial intelligence (XAI) aims to address this challenge by explaining a system to the user. In this work, we investigate XAI within LfD by implementing an adaptive explanatory feedback system on an inverse reinforcement learning (IRL) algorithm. The feedback is implemented by demonstrating selected learnt trajectories to users. The system adapts to user teaching by categorizing and then selectively sampling trajectories shown to a user, to show a representative sample of both successful and unsuccessful trajectories. The system was evaluated through a user study with 26 participants teaching a robot a navigation task. The results of the user study demonstrated that the proposed explanatory feedback system can improve robot performance, teaching efficiency and user understanding of the robot., Comment: 8 Pages, 9 Figures, 2 Tables

We introduce a model of randomly connected neural populations and study its dynamics by means of the dynamical mean-field theory and simulations. Our analysis uncovers a rich phase diagram, featuring high- and low-dimensional chaotic phases, separated by a crossover region characterized by low values of the maximal Lyapunov exponent and participation ratio dimension, but with high and rapidly changing values of the Lyapunov dimension. Counterintuitively, chaos can be attenuated by either adding noise to strongly modular connectivity or by introducing modularity into random connectivity. Extending the model to include a multilevel, hierarchical connectivity reveals that a loose balance between activities across levels drives the system towards the edge of chaos.

The focused helium ion beam microscope is a versatile imaging and nanofabrication instrument enabling direct-write lithography with sub-10-nm resolution. Subsurface damage and swelling of substrates due to helium ion implantation is generally unwanted. However, these effects can also be leveraged for specific nanofabrication tasks. To explore this, we investigate focused helium ion beam induced swelling of bulk crystalline silicon and free-standing amorphous silicon nitride membranes using various irradiation strategies. We show that the creation of near-surface voids due to helium ion implantation can be used to induce surface nanostructure and create subsurface nanochannels. By tailoring the ion dose and beam energy, the size and depth of the swollen features can be controlled. Swelling heights of several hundred nanometers are demonstrated and for the embedded nanochannels, void internal diameters down to 30nm are shown. Potential applications include the engineering of texturized substrates and the prototyping of on-chip nanofluidic transport devices.

Watermarking has recently emerged as an effective strategy for detecting the outputs of large language models (LLMs). Most existing schemes require \emph{white-box} access to the model's next-token probability distribution, which is typically not accessible to downstream users of an LLM API. In this work, we propose a principled watermarking scheme that requires only the ability to sample sequences from the LLM (i.e. \emph{black-box} access), boasts a \emph{distortion-free} property, and can be chained or nested using multiple secret keys. We provide performance guarantees, demonstrate how it can be leveraged when white-box access is available, and show when it can outperform existing white-box schemes via comprehensive experiments.

Interactions between active individuals in animal collectives lead to emergent responses that remain elusive in synthetic soft matter. Here, shape-morphing polymers are used to create bio-inspired transient solids that self-assemble with controlled mechanical properties and disassemble on demand. Dilute-suspensions of magnetic, heat-responsive liquid crystal elastomer ribbons mechanically interlock, inducing reversible aggregation. A mathematical model is developed that sheds light on the role of topological mechanisms in aggregation. Aggregation was favored for ribbons with moderate curvature at 25C above crosslinking temperature as compared to flat ribbons or higher curvature ribbons at higher temperatures. The ribbon suspensions reversibly transition between fluid- and solid-like states, exhibiting up to 6 orders-of-magnitude increase in the storage moduli of the entangled aggregates compared with the liquid dispersions. Controlled dissociation is induced by imparting kinetic energy to the individual ribbons at high magnetic field rotation speeds (> 200 RPM). Ribbon shape and the medium in which dissociation occurs were shown to govern disassembly. Imparting dynamic collective behaviors into synthetic systems may enable a range of potential applications from bio-inspired soft robotics to injectable biomaterials., Comment: 16 pages, 6 figures

Magnetic colloids can be driven with time-varying fields to form clusters and voids that re-organize over vastly different timescales. However, the driving force behind these non-equilibrium dynamics is not well-understood. Here, we introduce a topological framework that predicts protected edge flows that drive reorganization, despite strong thermal motion. We verify this theory experimentally using micron-sized super-paramagnetic colloids to demonstrate how large-scale flow properties emerge. Our results elucidate fundamental principles that shape and control non-equilibrium colloidal aggregates., Comment: 6 pages, 4 figures in main text; the first two authors contributed equally to this work

Educators are more than workers within educational systems; they are stewards of educational systems. They must analyze student performance data, identify patterns that inform targeted interventions and personalized learning plans, continuously develop the curriculum, set ambitious learning goals and use up-to-date pedagogical theory to adapt instructional strategies, act as advocates for educational policies that promote inclusivity and equity, and much more. Most educators deeply care about the learning and wellbeing of their students and colleagues. Given the chance, they will do whatever they can to make improvements to these ends. In this role as architects of change, educators deal with conflicting definitions of success, multiple stakeholders, complex causal relationships, ambiguous data, and intricate human factors. Amid all this, most educators and the educational systems around them are strained to the capacity of what their time, training, and budgets allow. The problem is not merely that they must perform demanding tasks, but more so that they must constantly implement improvements and interventions amid the complex challenges of the organizations in which they work. These challenges can be especially difficult in implementation of related education technology, which is continuously developing at sometimes rapid pace. Whether the context is an individual classroom, a school district, or a postsecondary institution, implementing beneficial human-technology partnerships requires attending to the needs and constraints of these classrooms, districts, institutions, and so forth as organizations and engaging in this work as a partnership with educators. This chapter lays out the principles and processes of developing successful educator-technology partnerships including key considerations for each step and an example protocol for engaging in this endeavor., Comment: Book Chapter, 24 pages, 6 tables, 1 figure

Despite having different astronomical characteristics, the studies of mira variables and ultra-cool dwarfs frequently show similar red colors, which could cause leading to photometric misclassification. This study uses photometric data from the WISE, 2MASS, and Pan-STARRS surveys to construct color-based selection criteria for red dwarfs, brown dwarfs, and Mira variables. On analyzing the color indices, we developed empirical rules that separate these objects with an overall classification accuracy of approximately 91%-92%. While the differentiation between red dwarfs and both Mira variables and brown dwarfs is effective, challenges remain in distinguishing Mira variables from brown dwarfs due to overlapping color indices. The robustness of our classification technique was validated by a bootstrap analysis, highlighting the significance of color indices in large photometric surveys for stellar classification., Comment: Accepted to Peremennye Zvezdy (Variable Stars), comments are welcomed

We study the phenomenological impact of the pre-equilibrium glasma initial stage of heavy-ion collisions on heavy quark azimuthal correlations and spectra. Using our numerical solver, we simulate the transport of heavy quark test particles in an SU(3) glasma background field. The glasma field equations are formulated using classical real-time lattice gauge theory, and the heavy quark dynamics are described by classical transport equations numerically solved using the colored particle-in-cell method. For the first time, the effect of the glasma stage on the azimuthal correlations of $c\overline{c}$ and $b\overline{b}$ pairs is studied. The resulting azimuthal width $\sigma_{\Delta\phi}$ exhibits a large and quick decorrelation due to the strong glasma fields. Further, we evaluate how the $p_T$-broadening in the glasma affects heavy quark $p_T$-spectra, which are initialized according to the FONLL heavy quark production calculation. The nuclear modification factor $R_{AA}$ is extracted for $c$ and $b$ quarks in the glasma and additional nuclear PDF effects accounting for gluon shadowing are included., Comment: 24 pages, 15 figures

We use classical equations of motion for heavy quarks to show that the pre-equilibrium glasma phase of a heavy ion collision has an extremely strong effect on heavy quark angular correlations. At the same time, the effect on the single inclusive spectrum is much more moderate. This suggests that $D\overline{D}$ meson angular correlations in future LHC measurements could provide direct experimental access to the physics of the pre-equilibrium stage., Comment: 7 pages, 4 figures

We measure the absolute proper motion of Andromeda III using ACS/WFC and WFPC2 exposures spanning an unprecedented 22-year time baseline. The WFPC2 exposures have been processed using a deep-learning centering procedure recently developed as well as an improved astrometric calibration of the camera. The absolute proper motion zero point is given by 98 galaxies and 16 Gaia EDR3 stars. The resulting proper motion is $(\mu_{\alpha} , \mu_{\delta}) = (-10.5\pm12.5, 47.5\pm12.5)~\mu$as yr$^{-1}$. We perform an orbit analysis of And III using two estimates of M31's mass and proper motion. We find that And III's orbit is consistent with dynamical membership to the Great Plane of Andromeda system of satellites although with some looser alignment compared to the previous two satellites NGC 147 and NGC 185. And III is bound to M31 if M31's mass is $M_{\mathrm{vir}}\geq 1.5\times10^{12}\,M_{\odot}$., Comment: Accepted for publication in the Astrophysical Journal

We report the serendipitous discovery of a bow-shock nebula around the cataclysmic variable (CV) SY Cancri. In addition, SY Cnc lies near the edge of a faint Halpha-emitting nebula with a diameter of about 15'. The orientation of the bow shock is consistent with the direction of SY Cnc's proper motion. Nebulae are extremely rare around CVs, apart from those known to have undergone classical-nova (CN) outbursts; bow shocks and off-center nebulae are even more unusual. Nevertheless, the properties of SY Cnc and its nebulosity are strikingly similar to those of V341 Ara, another CV that is also associated with a bow shock and is likewise off-center with respect to its faint Halpha nebula. Both stars are binaries with optically thick accretion disks, belonging to the classes of Z Cam CVs or nova-like variables. We discuss three scenarios to explain the properties of the nebulae. They may have resulted from chance encounters with interstellar gas clouds, with the stars leaving in their wakes material that is recombining after being photoionized by UV radiation from the CVs. Alternatively, the large nebulae could be ejecta from unobserved CN outbursts in the recent past, which have been decelerated through collisions with the interstellar medium (ISM), while the stars continue to snowplow through the gas. Or the faint Halpha nebulae may be ambient ISM that was shock-ionized by a CN outburst in the past and is now recombining., Comment: Accepted by The Astronomical Journal

Optically-levitated dielectric objects are promising for precision force, acceleration, torque, and rotation sensing due to their extreme environmental decoupling. While many levitated opto-mechanics experiments employ spherical objects, for some applications non-spherical geometries offer advantages. For example, rod-shaped or dumbbell shaped particles have been demonstrated for torque and rotation sensing and high aspect ratio plate-like particles can exhibit reduced photon recoil heating and may be useful for high-frequency gravitational wave detection or as high bandwidth accelerometers. To achieve optimal sensitivity, cooling, and quantum control in these systems, it is beneficial to achieve optimal displacement detection using scattered light. We describe and numerically implement a method based on Fisher information that is applicable to suspended particles of arbitrary geometry. We demonstrate the agreement between our method and prior methods employed for spherical particles, both in the Rayleigh and Lorentz-Mie regimes. As practical examples we analyze the optical detection limits of an optically-levitated high-aspect-ratio disc-like dielectric object and a rod-shaped object for configurations recently realized in experimental work., Comment: 8 pages, 6 figures

We introduce Imagen 3, a latent diffusion model that generates high quality images from text prompts. We describe our quality and responsibility evaluations. Imagen 3 is preferred over other state-of-the-art (SOTA) models at the time of evaluation. In addition, we discuss issues around safety and representation, as well as methods we used to minimize the potential harm of our models.

Early identification and instruction aimed at addressing potential reading difficulties is the key to preventing prolonged difficulties in learning to read and ensuring that all students are reading with proficiency by the end of third grade. Tier 1 core reading curricula needs to be: (a) evidence based, (b) systematic and structured, (c) encompass explicit word reading skills and rich language and knowledge development. Tier 2 supports and interventions and Tier 3 intensive interventions need to be: (a) evidence- based, (b) targeted towards the unique needs of individual students and instructional contexts, (c) based on assessment. Universal Screening of students for signs of dyslexia and other reading difficulties beginning in kindergarten is one of the first steps in this prevention process. The use of evidence-based practices and materials in all tiers of instruction is key to the development of skilled readers. Systematic instruction that is coherent across instructional contexts and guided by a clear scope and sequence is essential. The use of reliable and valid screening, as well as diagnostic, progress monitoring, and assessment tools ensures that further assessments, intervention, and instruction are guided by data. The use of a range of explicit evidence-based instructional approaches ensure that all students receive the instruction they need. Educator training in the complexities of reading development and difficulties empowers educators to teach, assess, and progress monitor the development of foundational reading skills, understand student data, and use data to guide instruction. This document has been reviewed and updated by a collaborative of technical content experts, including Wyoming Department of Education (WDE) staff and Wyoming educators.

Vocational rehabilitation (VR) state agency leaders explored successes and challenges in partnering with higher education programs for students with intellectual disability at a listening session held by the Employment Partnerships Workgroup, supported by the Think College Inclusive Higher Education Network. This concise brief summarizes the key issues discussed and offers VR state leaders' recommendations for enhancing partnerships between state VR agencies and higher education programs to improve access and opportunities for paid employment for students with intellectual disability. Also included are policy implications and recommendations to federal and state funders.

At a listening session held by the Employment Partnerships Workgroup, supported by the Think College Inclusive Higher Education Network, five state vocational rehabilitation (VR) agency leaders explored successes and challenges in partnering with higher education programs for students with intellectual disability. This Insight Brief provides an overview of the listening session and discussion of the key issues identified: language barriers, need for better communication, lack of awareness of systems and guidance, funding challenges, and the need for goal alignment and employment-focused programming. The brief includes recommendations for state VR agencies and higher education programs to overcome these challenges, such as improved communication practices, comprehensive training, and policy updates to improve funding and collaboration efforts.