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Water must condense into ice clouds in the coldest brown dwarfs and exoplanets. When they form, these icy clouds change the emergent spectra, temperature structure, and albedo of the substellar atmosphere. The properties of clouds are governed by complex microphysics but these complexities are often not captured by the simpler parameterized cloud models used in climate models or retrieval models. Here, we combine microphysical cloud modeling and 1D climate modeling to incorporate insights from microphysical models into a self-consistent, parameterized cloud model. Using the 1D Community Aerosol and Radiation Model for Atmospheres (CARMA), we generate microphysical water clouds and compare their properties with those from the widely-used EddySed cloud model (Ackerman & Marley 2001) for a grid of Y dwarfs. We find that the mass of water condensate in our CARMA water clouds is significantly limited by available condensation nuclei; in models without additional seed particles for clouds added, the atmosphere becomes supersaturated. We incorporate water latent heat release in the convective and radiative parts of the atmosphere and find no significant impact on water-ice cloud formation for typical gas giant compositions. Our analysis reveals the CARMA cloud profiles have a gradual decrease in opacity of approximately 4% per bar below the cloud base. Incorporating this gradual cloud base falloff and a variable $f_{sed}$ parameter allows spectra generated from the parameterized Eddysed model to better match those of the microphysical CARMA model. This work provides recommendations for efficiently generating microphysically-informed water clouds for future models of cold substellar objects with H/He atmospheres., Comment: 15 pages, 19 figures. Published in ApJ

Hyperspectral sensing is a valuable tool for detecting anomalies and distinguishing between materials in a scene. Hyperspectral anomaly detection (HS-AD) helps characterize the captured scenes and separates them into anomaly and background classes. It is vital in agriculture, environment, and military applications such as RSTA (reconnaissance, surveillance, and target acquisition) missions. We previously designed an equal voting ensemble of hyperspectral unmixing and three unsupervised HS-AD algorithms. We later utilized a supervised classifier to determine the weights of a voting ensemble, creating a hybrid of heterogeneous unsupervised HS-AD algorithms with a supervised classifier in a model stacking, which improved detection accuracy. However, supervised classification methods usually fail to detect novel or unknown patterns that substantially deviate from those seen previously. In this work, we evaluate our technique and other supervised and unsupervised methods using general hyperspectral data to provide new insights.

KM3NeT/ORCA is a water Cherenkov neutrino detector under construction and anchored at the bottom of the Mediterranean Sea. The detector is designed to study oscillations of atmospheric neutrinos and determine the neutrino mass ordering. This paper focuses on an initial configuration of ORCA, referred to as ORCA6, which comprises six out of the foreseen 115 detection units of photo-sensors. A high-purity neutrino sample was extracted, corresponding to an exposure of 433 kton-years. The sample of 5828 neutrino candidates is analysed following a binned log-likelihood method in the reconstructed energy and cosine of the zenith angle. The atmospheric oscillation parameters are measured to be $\sin^2\theta_{23}= 0.51^{+0.04}_{-0.05}$, and $ \Delta m^2_{31} = 2.18^{+0.25}_{-0.35}\times 10^{-3}~\mathrm{eV^2} \cup \{-2.25,-1.76\}\times 10^{-3}~\mathrm{eV^2}$ at 68\% CL. The inverted neutrino mass ordering hypothesis is disfavoured with a p-value of 0.25., Comment: 29 pages, 12 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.

The electrical properties and performance characteristics of niobium dioxide (NbO$_\mathrm{2}$)-based memristive devices are examined at cryogenic temperatures. Sub-stoichiometric Nb$_\mathrm{2}$O$_\mathrm{5}$ was deposited via magnetron sputtering and patterned in microscale (2$\times$2 - 15$\times$15 $\mu$m$^2$) cross-bar Au/Ru/NbO$_\mathrm{x}$/Pt devices and electroformed at 3-5 V to make NbO$_\mathrm{2}$ filaments. At cryogenic temperatures, the threshold voltage ($V_\mathrm{th}$) increased by more than a factor of 3. The hold voltage ($V_\mathrm{h}$) was significantly lower than the threshold voltage for fast voltage sweeps (200 ms per measurement). If the sample is allowed to cool between voltage measurements, the hold voltage increases, but never reaches the threshold voltage, indicating the presence of non-volatile Nb$_\mathrm{2}$O$_\mathrm{5}$ in the filament. The devices have an activation energy of $E_a \approx 1.4$ eV, lower than other NbO$_\mathrm{2}$ devices reported. Our works shows that even nominally ``bad" memristive devices can be improved by reducing the leakage current and increases the sample resistance at cryogenic temperatures., Comment: 16 pages, 5 figures

Classical T Tauri Stars (CTTSs) are highly variable stars that possess gas- and dust-rich disks from which planets form. Much of their variability is driven by mass accretion from the surrounding disk, a process that is still not entirely understood. A multi-epoch optical spectral monitoring campaign of four CTTSs (TW Hya, RU Lup, BP Tau, and GM Aur) was conducted along with contemporaneous HST UV spectra and ground-based photometry in an effort to determine accretion characteristics and gauge variability in this sample. Using an accretion flow model, we find that the magnetospheric truncation radius varies between 2.5-5 R* across all of our observations. There is also significant variability in all emission lines studied, particularly Halpha, Hbeta, and Hgamma. Using previously established relationships between line luminosity and accretion, we find that, on average, most lines reproduce accretion rates consistent with accretion shock modeling of HST spectra to within 0.5 dex. Looking at individual contemporaneous observations, however, these relationships are less accurate, suggesting that variability trends differ from the trends of the population and that these empirical relationships should be used with caution in studies of variability., Comment: 39 pages, 10 figures, 12 tables

The coloring problem is a well-research topic and its complexity is known for several classes of graphs. However, the question of its complexity remains open for the class of antiprismatic graphs, which are the complement of prismatic graphs and one of the four remaining cases highlighted by Lozin and Malishev. In this article we focus on the equivalent question of the complexity of the clique cover problem in prismatic graphs. A graph $G$ is prismatic if for every triangle $T$ of $G$, every vertex of $G$ not in $T$ has a unique neighbor in $T$. A graph is co-bridge-free if it has no $C_4+2K_1$ as induced subgraph. We give a polynomial time algorithm that solves the clique cover problem in co-bridge-free prismatic graphs. It relies on the structural description given by Chudnovsky and Seymour, and on later work of Preissmann, Robin and Trotignon. We show that co-bridge-free prismatic graphs have a bounded number of disjoint triangles and that implies that the algorithm presented by Preissmann et al. applies., Comment: 24 pages, 6 figures

This paper investigates the utility of Neural Radiance Fields (NeRF) models in extending the regions of operation of a mobile robot, controlled by Image-Based Visual Servoing (IBVS) via static CCTV cameras. Using NeRF as a 3D-representation prior, the robot's footprint may be extrapolated geometrically and used to train a CNN-based network to extract it online from the robot's appearance alone. The resulting footprint results in a tighter bound than a robot-wide bounding box, allowing the robot's controller to prescribe more optimal trajectories and expand its safe operational floor area., Comment: Accepted as extended abstract for ICRA@40

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.

Elasmobranchs (shark sand rays) represent a critical component of marine ecosystems. Yet, they are experiencing global population declines and effective monitoring of populations is essential to their protection. Underwater stationary videos, such as those from Baited Remote Underwater Video Stations (BRUVS), are critical for understanding elasmobranch spatial ecology and abundance. However, processing these videos requires time-consuming manual analysis that can delay conservation. To address this challenge, we developed SharkTrack, a semi-automatic underwater video analysis software. SharkTrack uses Convolutional Neural Networks (CNN) and Multi-Object Tracking to automatically detect and track elasmobranchs and provides an annotation pipeline to manually classify elasmobranch species and compute species-specific MaxN (ssMaxN), the standard metric of relative abundance. When tested on BRUVS footage from locations unseen by the CNN model during training, SharkTrack computed ssMaxN with 89% accuracy over 207 hours of footage. The semi-automatic SharkTrack pipeline required two minutes of manual classification per hour of video, an estimated 95% reduction of manual analysis time compared to traditional methods. Furthermore, we demonstrate SharkTrack accuracy across diverse marine ecosystems and elasmobranch species, an advancement compared to previous models, which were limited to specific species or locations. SharkTrack applications extend beyond BRUVS, facilitating the analysis of any underwater stationary video. By making video analysis faster and more accessible, SharkTrack enables research and conservation organisations to monitor elasmobranch populations more efficiently, thereby improving conservation efforts. To further support these goals, we provide public access to the SharkTrack software.

We present RelBench, a public benchmark for solving predictive tasks over relational databases with graph neural networks. RelBench provides databases and tasks spanning diverse domains and scales, and is intended to be a foundational infrastructure for future research. We use RelBench to conduct the first comprehensive study of Relational Deep Learning (RDL) (Fey et al., 2024), which combines graph neural network predictive models with (deep) tabular models that extract initial entity-level representations from raw tables. End-to-end learned RDL models fully exploit the predictive signal encoded in primary-foreign key links, marking a significant shift away from the dominant paradigm of manual feature engineering combined with tabular models. To thoroughly evaluate RDL against this prior gold-standard, we conduct an in-depth user study where an experienced data scientist manually engineers features for each task. In this study, RDL learns better models whilst reducing human work needed by more than an order of magnitude. This demonstrates the power of deep learning for solving predictive tasks over relational databases, opening up many new research opportunities enabled by RelBench.

The surge in scientific publications challenges the use of publication counts as a measure of scientific progress, requiring alternative metrics that emphasize the quality and novelty of scientific contributions rather than sheer quantity. This paper proposes the use of Relaxed Word Mover's Distance (RWMD), a semantic text similarity measure, to evaluate the novelty of scientific papers. We hypothesize that RWMD can more effectively gauge the growth of scientific knowledge. To test such an assumption, we apply RWMD to evaluate seminal papers, with Hirsch's H-Index paper as a primary case study. We compare RWMD results across three groups: 1) H-Index-related papers, 2) scientometric studies, and 3) unrelated papers, aiming to discern redundant literature and hype from genuine innovations. Findings suggest that emphasizing knowledge claims offers a deeper insight into scientific contributions, marking RWMD as a promising alternative method to traditional citation metrics, thus better tracking significant scientific breakthroughs.

The relation between magnetic field strength B and gas density n in the interstellar medium is of fundamental importance to many areas of astrophysics, from protostellar disks to galaxy evolution. We present and compare Bayesian analyses of the B - n relation for a comprehensive observational data set, as well as a large body of numerical MHD simulations. We extend the original Zeeman relation of Crutcher et al. (2010) with a large body of magnetic data that includes 700 observations with the Davis-Chandrasekhar-Fermi method. By using a new multiparameter Bayesian analysis we present a new, more general, time-averaged observational relation: B \propto n^{0.27 \pm 0.017} for n \leq n_0 and B \propto n^{0.54 \pm 0.18} for n \geq n_0, with n_0 = 924^(+145-144) cm^-3. We perform a separate analysis on 19 numerical magnetohydrodynamics simulations that cover a wide range of scales, resolutions, initial conditions, and completed with a variety of codes: arepo, flash, pencil, and ramses. The power law exponents derived from the simulations depend on several physical factors including: dynamo effects, time scales, turbulence, and the initial seed field strength. In particular, early-time simulations where the density, velocity and magnetic fields are unevolved do not match the observational scalings. The simulations that trace the observed density range best, the evolved dwarf galaxy and Milky Way like galaxy simulations, settle into a near consistent exponent of = 0.5 in the dense gas, with variability in the diffuse gas exponent., Comment: 23 figures, 28 pages, submitted to MNRAS, Comments welcome

Technology mapping involves mapping logical circuits to a library of cells. Traditionally, the full technology library is used, leading to a large search space and potential overhead. Motivated by randomly sampled technology mapping case studies, we propose MapTune framework that addresses this challenge by utilizing reinforcement learning to make design-specific choices during cell selection. By learning from the environment, MapTune refines the cell selection process, resulting in a reduced search space and potentially improved mapping quality. The effectiveness of MapTune is evaluated on a wide range of benchmarks, different technology libraries and technology mappers. The experimental results demonstrate that MapTune achieves higher mapping accuracy and reducing delay/area across diverse circuit designs, technology libraries and mappers. The paper also discusses the Pareto-Optimal exploration and confirms the perpetual delay-area trade-off. Conducted on benchmark suites ISCAS 85/89, ITC/ISCAS 99, VTR8.0 and EPFL benchmarks, the post-technology mapping and post-sizing quality-of-results (QoR) have been significantly improved, with average Area-Delay Product (ADP) improvement of 22.54\% among all different exploration settings in MapTune. The improvements are consistently remained for four different technologies (7nm, 45nm, 130nm, and 180 nm) and two different mappers., Comment: IEEE/ACM International Conference on Computer-Aided Design (ICCAD '24), October 27--31, 2024

We study the anomalies of two-dimensional BPS defects in six-dimensional $\mathcal{N}=(1,0)$ superconformal field theories. Using a holographic description of these defects furnished by probe D4-branes in AdS${}_7$ solutions of ten-dimensional type IIA supergravity, we compute the two independent defect Weyl anomalies from the on-shell action for a spherical defect and defect sphere entanglement entropy. We find agreement between the holographic prediction for the defect A-type anomaly coming from the defect sphere free energy and the leading large $N$ contribution to the defect `t Hooft anomaly found using anomaly inflow. We also find agreement between the holographic computation of the expectation value of a surface operator wrapping a torus and the supersymmetric localization computation for a circular Wilson loop in $\mathcal{N}=1$ super Yang-Mills theory on $S^5$. Lastly, we holographically compute the defect gravitational anomaly from the Wess-Zumino action of the probe D4-brane, which provides a subleading large $N$ correction to the defect A-type anomaly., Comment: 35 pages, 3 figures

We prove that if $G$ is a finite simple group and $x, y \in G$ are involutions, then $|x^G \cap C_G(y)| \rightarrow \infty$ as $|G| \rightarrow \infty$. This extends results of Guralnick-Robinson and Skresanov. We also prove a related result about $C_{G}(t)/O(C_G(t))$ that does not require the classification of finite simple groups.

In this paper we investigate convergence for the Variational Iteration Method (VIM) which was introduced and described in \cite{He0},\cite{He1}, \cite{He2}, and \cite{He3}. We prove the convergence of the iteration scheme for a linear Klein-Gorden equation with a variable coefficient whose unique solution is known. The iteration scheme depends on a {\em Lagrange multiplier}, $\lambda(r,s)$, which is represented as a power series. We show that the VIM iteration scheme converges uniformly on compact intervals to the unique solution. We also prove convergence when $\lambda(r,s)$ is replaced by any of its partial sums. The first proof follows a familiar pattern, but the second requires a new approach. The second approach also provides some detail regarding the structure of the iterates.

Heat management in devices is a key to their efficiency and longevity. Here, thermal switches (TS) are of great importance because of their ability to transition between different thermal conductivity $\kappa$ states. While traditional TS are bulky and slow, recent experiments have suggested "smart" responsive (bio--inspired) polymers as their fast alternatives. One example is poly(N--isopropylacrylamide) (PNIPAM) in water, where $\kappa$ drops suddenly around a temperature $T_{\ell} \simeq 305$ K when a PNIPAM undergoes a coil--to--globule transition. At a first glance, this may suggest that the change in polymer conformation has a direct influence on TS. However, it may be presumptuous to trivially "only" link conformations with TS, especially because many complex microscopic details control macroscopic conformational transition. Motivated by this, we study TS in "smart" polymers using generic simulations. As the test cases, we investigate two different modes of polymer collapse using external stimuli, i.e., changing $T$ and cosolvent mole fraction $x_{\rm c}$. Collapse upon increasing $T$ shows a direct correlation between the conformation and $\kappa$ switching, while no correlation is observed in the latter case. These results suggest that the (co--)solvent--monomer interactions play a greater important role than the exact conformation in dictating TS. While some results are compared with the available experiments, possible future directions are also highlighted.

Electron backscatter diffraction (EBSD) has developed over the last few decades into a valuable crystallographic characterisation method for a wide range of sample types. Despite these advances, issues such as the complexity of sample preparation, relatively slow acquisition, and damage in beam-sensitive samples, still limit the quantity and quality of interpretable data that can be obtained. To mitigate these issues, here we propose a method based on the subsampling of probe positions and subsequent reconstruction of an incomplete dataset. The missing probe locations (or pixels in the image) are recovered via an inpainting process using a dictionary-learning based method called beta-process factor analysis (BPFA). To investigate the robustness of both our inpainting method and Hough-based indexing, we simulate subsampled and noisy EBSD datasets from a real fully sampled Ni-superalloy dataset for different subsampling ratios of probe positions using both Gaussian and Poisson noise models. We find that zero solution pixel detection (inpainting un-indexed pixels) enables higher quality reconstructions to be obtained. Numerical tests confirm high quality reconstruction of band contrast and inverse pole figure maps from only 10% of the probe positions, with the potential to reduce this to 5% if only inverse pole figure maps are needed. These results show the potential application of this method in EBSD, allowing for faster analysis and extending the use of this technique to beam sensitive materials.

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

The ability of Language Models (LMs) to understand natural language makes them a powerful tool for parsing human instructions into task plans for autonomous robots. Unlike traditional planning methods that rely on domain-specific knowledge and handcrafted rules, LMs generalize from diverse data and adapt to various tasks with minimal tuning, acting as a compressed knowledge base. However, LMs in their standard form face challenges with long-horizon tasks, particularly in partially observable multi-agent settings. We propose an LM-based Long-Horizon Planner for Multi-Agent Robotics (LLaMAR), a cognitive architecture for planning that achieves state-of-the-art results in long-horizon tasks within partially observable environments. LLaMAR employs a plan-act-correct-verify framework, allowing self-correction from action execution feedback without relying on oracles or simulators. Additionally, we present MAP-THOR, a comprehensive test suite encompassing household tasks of varying complexity within the AI2-THOR environment. Experiments show that LLaMAR achieves a 30% higher success rate compared to other state-of-the-art LM-based multi-agent planners., Comment: 27 pages, 4 figures, 5 tables

This article presents constraints on dark-matter-electron interactions obtained from the first underground data-taking campaign with multiple SuperCDMS HVeV detectors operated in the same housing. An exposure of 7.63 g-days is used to set upper limits on the dark-matter-electron scattering cross section for dark matter masses between 0.5 and 1000 MeV/$c^2$, as well as upper limits on dark photon kinetic mixing and axion-like particle axioelectric coupling for masses between 1.2 and 23.3 eV/$c^2$. Compared to an earlier HVeV search, sensitivity was improved as a result of an increased overburden of 225 meters of water equivalent, an anticoincidence event selection, and better pile-up rejection. In the case of dark-matter-electron scattering via a heavy mediator, an improvement by up to a factor of 25 in cross-section sensitivity was achieved., Comment: 7 pages + title and references, 4 figures, and 1 table

Defect engineering in two-dimensional semiconductors has been exploited to tune the optoelectronic properties and introduce new quantum states in the band gap. Chalcogen vacancies in transition metal dichalcogenides in particular have been found to strongly impact charge carrier concentration and mobility in 2D transistors as well as feature sub-gap emission and single-photon response. In this letter, we investigate the layer-dependent charge state lifetime of Se vacancies in WSe$_2$. In one monolayer WSe$_2$, we observe ultrafast charge transfer from the lowest unoccupied orbital of the top Se vacancy to the graphene substrate within (1.0 $\pm$ 0.2) ps measured via the current saturation in scanning tunneling approach curves. For Se vacancies decoupled by TMD multilayers, we find a sub-exponential increase of the charge lifetime from (62 $\pm$ 14) ps in bilayer to few nanoseconds in four-layer WSe$_2$, alongside a reduction of the defect state binding energy. Additionally, we attribute the continuous suppression and energy shift of the dI/dV in-gap defect state resonances at very close tip--sample distances to a current saturation effect. Our results provide a key measure of the layer-dependent charge transfer rate of chalcogen vacancies in TMDs.

In this note, we give a group-theoretic condition which is equivalent to the fact that the trivial character is the only complex irreducible character of a finite group G which is contained in the principal p-block for each prime p in a specified set of prime divisors of the order of G. This character-theoretic condition has been studied previously by a number of authors.

Quantum machine learning has gained considerable attention as quantum technology advances, presenting a promising approach for efficiently learning complex data patterns. Despite this promise, most contemporary quantum methods require significant resources for variational parameter optimization and face issues with vanishing gradients, leading to experiments that are either limited in scale or lack potential for quantum advantage. To address this, we develop a general-purpose, gradient-free, and scalable quantum reservoir learning algorithm that harnesses the quantum dynamics of neutral-atom analog quantum computers to process data. We experimentally implement the algorithm, achieving competitive performance across various categories of machine learning tasks, including binary and multi-class classification, as well as timeseries prediction. Effective and improving learning is observed with increasing system sizes of up to 108 qubits, demonstrating the largest quantum machine learning experiment to date. We further observe comparative quantum kernel advantage in learning tasks by constructing synthetic datasets based on the geometric differences between generated quantum and classical data kernels. Our findings demonstrate the potential of utilizing classically intractable quantum correlations for effective machine learning. We expect these results to stimulate further extensions to different quantum hardware and machine learning paradigms, including early fault-tolerant hardware and generative machine learning tasks., Comment: 10 + 14 pages, 4 + 7 figures

Transition metal dichalcogenide (TMD) twisted homobilayers have been established as an ideal platform for studying strong correlation phenomena, as exemplified by the recent discovery of fractional Chern insulator (FCI) states in twisted MoTe2 and Chern insulators (CI) and unconventional superconductivity in twisted WSe2. In these systems, nontrivial topology in the strongly layer-hybridized regime can arise from a spatial patterning of interlayer tunneling amplitudes and layer-dependent potentials that yields a lattice of layer skyrmions. Here we report the direct observation of skyrmion textures in the layer degree of freedom of Rhombohedral-stacked (R-stacked) twisted WSe2 homobilayers. This observation is based on scanning tunneling spectroscopy that separately resolves the {\Gamma}-valley and K-valley moir\'e electronic states. We show that {\Gamma}-valley states are subjected to a moir\'e potential with an amplitude of ~ 120 meV. At ~150 meV above the {\Gamma}-valley, the K-valley states are subjected to a weaker moir\'e potential of ~30 meV. Most significantly, we reveal opposite layer polarization of the K-valley at the MX and XM sites within the moir\'e unit cell, confirming the theoretically predicted skyrmion layer-texture. The dI/dV mappings allow the parameters that enter the continuum model for the description of moir\'e bands in twisted TMD bilayers to be determined experimentally, further establishing a direct correlation between the shape of LDOS profile in real space and topology of topmost moir\'e band.

Fast and accurate approximations of gas cooling and heating functions are needed for hydrodynamic galaxy simulations. We use machine learning to analyze atomic gas cooling and heating functions in the presence of a generalized incident local radiation field computed by Cloudy. We characterize the radiation field through binned radiation field intensities instead of the photoionization rates used in our previous work. We find a set of 6 energy bins whose intensities exhibit relatively low correlation. We use these bins as features to train machine learning models to predict Cloudy cooling and heating functions at fixed metallicity. We compare the relative SHAP importance of the features. From the SHAP analysis, we identify a feature subset of 3 energy bins ($0.5-1, 1-4$, and $13-16 \, \mathrm{Ry}$) with the largest importance and train additional models on this subset. We compare the mean squared errors and distribution of errors on both the entire training data table and a randomly selected 20% test set withheld from model training. The machine learning models trained with 3 and 6 bins, as well as 3 and 4 photoionization rates, have comparable accuracy everywhere. We conclude that 3 energy bins (or 3 analogous photoionization rates: molecular hydrogen photodissociation, neutral hydrogen HI, and fully ionized carbon CVI) are sufficient to characterize the dependence of the gas cooling and heating functions on our assumed incident radiation field model., Comment: 12 + 5 pages, 7 + 4 figures. To be submitted to OJA. Comments welcome!

We observed the low mass X-ray binary Cyg X-2 for a total of 18 nights over two observing runs in July and September of 2006, using the Otto Struve Telescope at McDonald Observatory and the Rossi X-ray Timing Explorer. Using discrete cross correlations, we found peaks occurring at near-zero lags in the flaring branch of the colour-colour diagram, which could signify reprocessing, in addition to an anti-correlation within the normal branch. When comparing optical flux to the system's placement on the Z track, two distinct behaviors were seen: (1) a state with no correlation, and (2) a multi-valued (horizontal and normal branches)/correlated (flaring branch) state. The correlation was the result of direct steps and more gradual falls to and from the flaring branch respectively. Finally, we modeled timed spectra with 64 second bins with an extended accretion disc corona model. We found that correlations occurred between the optical and the various fitted parameters, particularly the blackbody normalization (and blackbody radius by extension) in higher intensity regions. Despite this, the Z track location was found to be a far better predictor of physical parameters than the optical flux, with clean correlations seen in every branch of the Z track. Where optical correlations are found, the location on the Z track was a better predictor of optical flux than any individual physical parameter., Comment: 19 pages, MNRAS in press

Inspired by the ancient spiral constructed by the greek philosopher Theodorus which is based on concatenated right triangles, we have created a spiral. In this spiral, called \emph{Fibonacci--Theodorus}, the sides of the triangles have lengths corresponding to Fibonacci numbers. Towards the end of the paper, we present a generalized method applicable to second-order recurrence relations. Our exploration of the Fibonacci--Theodorus spiral aims to address a variety of questions, showcasing its unique properties and behaviors. For example, we study topics such as area, perimeter, and angles. Notably, we establish a relationship between the ratio of two consecutive areas and the golden ratio, a pattern that extends to angles sharing a common vertex. Furthermore, we present some asymptotic results. For instance, we demonstrate that the sum of the first $n$ areas comprising the spiral approaches a multiple of the sum of the initial $n$ Fibonacci numbers. Moreover, we provide a sequence of open problems related to all spiral worked in this paper. Finally, in his work Hahn, Hahn observed a potential connection between the golden ratio and the ratio of areas between spines of lengths $\sqrt{F_{n+1}}$ and $\sqrt{F_{n+2}-1}$ and the areas between spines of lengths $\sqrt{F_{n}}$ and $\sqrt{F_{n+1}-1}$ in the Theodorus spiral. However, no formal proof has been provided in his work. In this paper, we provide a proof for Hahn's conjecture., Comment: Accepted for publication by Proceedings of Fibonacci Quarterly

As the AI supply chain grows more complex, AI systems and models are increasingly likely to incorporate multiple internally- or externally-sourced components such as datasets and (pre-trained) models. In such cases, determining whether or not the aggregate AI system or model complies with the EU AI Act (AIA) requires a multi-step process in which compliance-related information about both the AI system or model and all its component parts is: (1) gathered, potentially from multiple arms-length sources; (2) harmonized, if necessary; (3) inputted into an analysis that looks across all of it to render a compliance prediction. Because this process is so complex and time-consuming, it threatens to overburden the limited compliance resources of the AI providers (i.e., developers) who bear much of the responsibility for complying with the AIA. It also renders rapid or real-time compliance analyses infeasible in many AI development scenarios where they would be beneficial to providers. To address these shortcomings, we introduce a complete system for automating provider-side AIA compliance analyses amidst a complex AI supply chain. This system has two key elements. First is an interlocking set of computational, multi-stakeholder transparency artifacts that capture AIA-specific metadata about both: (1) the provider's overall AI system or model; and (2) the datasets and pre-trained models it incorporates as components. Second is an algorithm that operates across all those artifacts to render a real-time prediction about whether or not the aggregate AI system or model complies with the AIA. All told, this system promises to dramatically facilitate and democratize provider-side AIA compliance analyses (and, perhaps by extension, provider-side AIA compliance).

Significant research has focused on low-power stochastic devices built from memristive materials. These devices foster neuromorphic approaches to computational efficiency enhancement in merged biomimetic and CMOS architectures due to their ability to phase transition from a dielectric to a metal at an increased temperature. Niobium dioxide has a volatile memristive phase change that occurs $\sim$800$^\circ$C~that makes it an ideal candidate for future neuromorphic electronics. A straightforward optical system has been developed on a horizontal tube furnace for \emph{in situ} spectral measurements as an as-grown \NbtOf\ film is annealed and ultimately crystallizes as \NbOt. The system measures the changing spectral transmissivity of \NbtOf\ as it undergoes both reduction and crystallization processes. We were also able to measure the transition from metallic-to-non-metallic \NbOt\ during the cooldown phase, which is shown to occur about 100$^\circ$C~ lower on a sapphire substrate than fused silica. After annealing, the material properties of the \NbtOf\ and \NbOt\ were assessed via X-ray photoelectron spectroscopy, X-ray diffraction, and 4-point resistivity, confirming that we have made crystalline \NbOt.

Following the recommendations to NASA and ESA, the search for life on exoplanets will be a priority in the next decades. Two direct imaging space mission concepts are being developed: the Habitable Worlds Observatory (HWO) and the Large Interferometer for Exoplanets (LIFE). HWO focuses on reflected light spectra in the ultraviolet/visible/near-infrared (UV/VIS/NIR), while LIFE captures the mid-infrared (MIR) emission of temperate exoplanets. We assess the potential of HWO and LIFE in characterizing a cloud-free Earth twin orbiting a Sun-like star at 10 pc, both separately and synergistically, aiming to quantify the increase in information from joint atmospheric retrievals on a habitable planet. We perform Bayesian retrievals on simulated data from an HWO-like and a LIFE-like mission separately, then jointly, considering the baseline spectral resolutions currently assumed for these concepts and using two increasingly complex noise simulations. HWO would constrain H$_2$O, O$_2$, and O$_3$, in the atmosphere, with ~ 100 K uncertainty on the temperature profile. LIFE would constrain CO$_2$, H$_2$O, O$_3$ and provide constraints on the thermal atmospheric structure and surface temperature (~ 10 K uncertainty). Both missions would provide an upper limit on CH$_4$. Joint retrievals on HWO and LIFE data would accurately define the atmospheric thermal profile and planetary parameters, decisively constrain CO$_2$, H$_2$O, O$_2$, and O$_3$, and weakly constrain CO and CH$_4$. The detection significance is greater or equal to single-instrument retrievals. Both missions provide specific information to characterize a terrestrial habitable exoplanet, but the scientific yield is maximized with synergistic UV/VIS/NIR+MIR observations. Using HWO and LIFE together will provide stronger constraints on biosignatures and life indicators, potentially transforming the search for life in the universe., Comment: 16 pages (main text, incl. 12 figures) + appendix; accepted for publication in A&A (current version: post 1st revision). Thirteenth paper of LIFE telescope series

Historical linguists have long written a kind of incompletely formalized ''program'' that converts reconstructed words in an ancestor language into words in one of its attested descendants that consist of a series of ordered string rewrite functions (called sound laws). They do this by observing pairs of words in the reconstructed language (protoforms) and the descendent language (reflexes) and constructing a program that transforms protoforms into reflexes. However, writing these programs is error-prone and time-consuming. Prior work has successfully scaffolded this process computationally, but fewer researchers have tackled Sound Law Induction (SLI), which we approach in this paper by casting it as Programming by Examples. We propose a language-agnostic solution that utilizes the programming ability of Large Language Models (LLMs) by generating Python sound law programs from sound change examples. We evaluate the effectiveness of our approach for various LLMs, propose effective methods to generate additional language-agnostic synthetic data to fine-tune LLMs for SLI, and compare our method with existing automated SLI methods showing that while LLMs lag behind them they can complement some of their weaknesses.

Existing learning-based cortical surface reconstruction approaches heavily rely on the supervision of pseudo ground truth (pGT) cortical surfaces for training. Such pGT surfaces are generated by traditional neuroimage processing pipelines, which are time consuming and difficult to generalize well to low-resolution brain MRI, e.g., from fetuses and neonates. In this work, we present CoSeg, a learning-based cortical surface reconstruction framework weakly supervised by brain segmentations without the need for pGT surfaces. CoSeg introduces temporal attention networks to learn time-varying velocity fields from brain MRI for diffeomorphic surface deformations, which fit an initial surface to target cortical surfaces within only 0.11 seconds for each brain hemisphere. A weakly supervised loss is designed to reconstruct pial surfaces by inflating the white surface along the normal direction towards the boundary of the cortical gray matter segmentation. This alleviates partial volume effects and encourages the pial surface to deform into deep and challenging cortical sulci. We evaluate CoSeg on 1,113 adult brain MRI at 1mm and 2mm resolution. CoSeg achieves superior geometric and morphological accuracy compared to existing learning-based approaches. We also verify that CoSeg can extract high-quality cortical surfaces from fetal brain MRI on which traditional pipelines fail to produce acceptable results., Comment: Accepted by the 27th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2024)

The Black Hole Explorer (BHEX) is a mission concept that can dramatically improve state-of-the-art astronomical very long baseline interferometry (VLBI) imaging resolution by extending baseline distances to space. To support these scientific goals, a high data rate downlink is required from space to ground. Laser communications is a promising option for realizing these high data rate, long-distance space-to-ground downlinks with smaller space/ground apertures. Here, we present a scalable laser communications downlink design and current lasercom mission results., Comment: Submitted to SPIE Astronomical Telescopes + Instrumentation, 2024

Survey strategies for upcoming exoplanet direct imaging missions have considered varying assumptions of prior knowledge. Precursor radial velocity surveys could have detected nearby exo-Earths and provided prior orbit and mass constraints. Alternatively, a direct imaging mission performing astrometry could yield constraints on orbit and phase angle of target planets. Understanding the impact of prior mass and orbit information on planetary characterization is crucial for efficiently recognizing habitable exoplanets. To address this question, we use a reflected-light retrieval tool to infer the atmospheric and bulk properties of directly imaged Earth-analogs while considering varying levels of prior information and signal-to-noise ratio (SNR). Because of the strong correlation between the orbit-related parameters and the planetary radius, prior information on the orbital distance and planetary phase yield tight constraints on the planetary radius: from $R_{\rm{p}}=2.95^{+2.69}_{-1.95}~R_{\oplus}$ without prior knowledge, to $R_{\rm{p}}=1.01^{+0.33}_{-0.19}~R_{\oplus}$ with prior determination of the orbit for $\rm{SNR}=20$ in the visible/near-infrared spectral range, thus allowing size determination from reflected light observations. However, additional knowledge of planet mass does not notably enhance radius ($R_{\rm{p}}=0.98^{+0.17}_{-0.14}~R_{\oplus}$) or atmospheric characterization. Also, prior knowledge of the mass alone does not yield a tight radius constraint ($R_{\rm{p}}=1.64^{+1.29}_{-0.80}~R_{\oplus}$) nor improves atmospheric composition inference. By contrast, because of its sensitivity to gas column abundance, detecting a Rayleigh scattering slope or bounding Rayleigh opacity helps to refine gas mixing ratio inferences without requiring prior mass knowledge. Overall, apart from radius determination, increasing the SNR is more beneficial than additional prior observations., Comment: Accepted for publication in ApJL

Making sense of complex inhomogeneous systems composed of many interacting species is a grand challenge that pervades basically all natural sciences. Phase separation and pattern formation in reaction-diffusion systems have largely been studied as two separate paradigms. Here we show that in reaction-diffusion systems composed of many species, the presence of a conservation law constrains the evolution of the conserved quantity to be governed by a Cahn-Hilliard-like equation. This establishes a direct link with the paradigm of coexistence and recent "active" field theories. Hence, even for complex many-species systems a dramatically simplified but accurate description emerges over coarse spatio-temporal scales. Using the nullcline (the line of homogeneous steady states) as the central motif, we develop a geometrical framework through endowing chemical space with a basis and suitable coordinates. This framework allows us to capture and understand the effect of eliminating fast non-conserved degrees of freedom, and to explicitly construct coefficients of the coarse field theory. We expect that the theory we develop here will be particularly relevant to advance our understanding of biomolecular condensates., Comment: 28 pages (21 + supplementary and reference), 10 figures

Despite the widespread use of Scanning Transmission Electron Microscopy (STEM) for observing the structure of materials at the atomic scale, a detailed understanding of some relevant electron beam damage mechanisms is limited. Recent reports suggest that certain types of damage can be modeled as a diffusion process and that the accumulation effects of this process must be kept low in order to reduce damage. We therefore develop an explicit mathematical formulation of spatiotemporal diffusion processes in STEM that take into account both instrument and sample parameters. Furthermore, our framework can aid the design of Diffusion Controlled Sampling (DCS) strategies using optimally selected probe positions in STEM, that constrain the cumulative diffusion distribution. Numerical simulations highlight the variability of the cumulative diffusion distribution for different experimental STEM configurations. These analytical and numerical frameworks can subsequently be used for careful design of 2- and 4-dimensional STEM experiments where beam damage is minimised., Comment: Main document: 29 pages, 12 figures; Supplementary document: 18 pages, 5 figures

Research for Action (RFA) studied why Black teachers were leaving their jobs more than White teachers in Allegheny County from 2014 to 2020. RFA analyses found that the number of Black teachers in the area was consistently dropping, more than the decrease in the overall Black population. The data gathered from speaking directly with 38 current and former Black teachers about their perspectives and experiences through interviews and focus groups conducted in the spring of 2023 yielded three key takeaways: (1) Black teachers support Allegheny County students and schools in several unique and important ways; (2) Black teachers face many challenges that impact their experiences as educators in Allegheny County; and (3) Several factors retain and sustain Black teachers in Allegheny County. The report offers insights into the working conditions for Black teachers and provides recommendations for policymakers, school leaders, teacher diversity initiatives, and the wider community for how to improve efforts to recruit, hire, retain, and sustain Black teachers within the region and beyond. [Additional funding provided by the McElhattan Foundation and the Eden Hall Foundation.]

Conducting rigorous impact studies that produce actionable evidence for educators and policymakers is a key goal of the Standards for Excellence in Education Research (SEER). Although studies can be initiated by individual districts or schools, researchers often conduct their own studies to address broadly relevant questions about the effectiveness of an education program, policy, practice, or intervention. This brief offers practical strategies for researchers newer to school-based impact studies to consider during the various phases of a study, with the aim of helping them achieve their recruitment goals. The strategies focus on four topics that, based in part on the authors' knowledge of recent recruitment efforts, may play an increasingly important role in affecting a district's or school's ability and willingness to participate in impact studies: (1) Attention to urgent priorities and needs; (2) Limited staff capacity; (3) Commitment to fairness and collaborative practices; and (4) Protection of student and staff privacy.

The United States has been cultivating STEM talent for decades with great success, but that robust talent pipeline is threatened by a growing STEM teacher shortage. Left unaddressed, that shortage could curtail employment in STEM professions and the many related careers that increasingly require some degree of scientific and technological literacy. While the STEM teacher shortage is a national problem, the effects are likely to be felt more acutely in a state such as Massachusetts with a high concentration of STEM jobs. This paper provides an overview of the causes of the STEM teacher shortage and offers three recommendations to address it.

Career pathways programs enjoy broad, bipartisan support as a way to give more young people the skills they need to thrive in today's workforce. But getting these programs off the ground can be difficult. Quickly scaling them to reach a critical mass of students and sustaining those efforts over nearly 10 years is almost unheard of -- except in Delaware. The Delaware Pathways Program is recognized as an exemplar among career pathways programs across the country. Its programming includes both high school and advanced coursework, work-based learning experiences, and industry-recognized credentials that prepare students for middle-or high-skill careers. The program has grown from serving less than 1% of Delaware high school students in the 2014-15 school year to over 50% in 2021-22. We interviewed 19 stakeholders -- including Delaware Pathways visionaries, researchers, nonprofit intermediaries, higher education leaders, national contributors, and K-12 administrators at the state, district, and school levels -- to explore the evolution of Delaware Pathways. In our new publication, "Scaling Opportunity: A Case Study on Delaware Pathways," we offer the lessons learned from these interviews, as well as policy recommendations for Delaware leaders as they embark on the next phase of Delaware Pathways. In our accompanying playbook, "Scaling Opportunity: A Policy Playbook for Effective Statewide Career Pathways Programs," we use those lessons learned to identify seven key moves that helped make Delaware's program successful and scalable, and that policymakers and advocates in other states can use to grow their own career pathways programs: (1) Align on and Articulate a Vision; (2) Build and Sustain Key Partnerships; (3) Get Creative With Funding; (4) Ensure That Programs Are High Quality and Easy to Implement; (5) Commit to Intentional and Substantive Data Collection to Assess Impact and Improve Practice; (6) Create Accountability Around Equity; and (7) Communicate the Value of Pathways Programs to Employers.

Across the country, career pathways programs are a popular bipartisan tool for addressing state workforce shortages and ensuring students graduate high school with workforce-ready skills. Delaware Pathways is one such example, formally established by executive order in 2016 by then-Delaware Gov. Jack Markell. The program consists of state-approved career pathways programs that include high school and advanced coursework, work-based learning (WBL) experiences, and industry-recognized credentials that prepare students for middle- or high-skill careers, whether they enter the workforce immediately after high school or later in life. Now, the program is at a crucial inflection point as leaders prepare to expand services to middle school students. To inform the continued evolution of Delaware Pathways, Bellwether conducted an independent analysis of the program's evolution and developed a set of policy recommendations for Delaware leaders to consider moving forward. In addition, the authors describe: (1) the factors that led to the program's successful adoption and scale, and (2) key implementation challenges Delaware faced.