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This study examines the usage and effectiveness of Lexercise, a hybrid reading intervention program combining weekly literacy intervention with structured independent practice, in supporting reading skill development among struggling readers. Analysis of 198 students in grades 2-6 during the 2023-2024 school year revealed strong adherence to the prescribed intervention model of weekly 45-minute structured literacy lessons supplemented by four 15-minute independent practice sessions. Results showed a significant positive correlation between total program time and decoding mastery (r = 0.64, p < 0.001), with optimal outcomes observed at 24 and 33 weeks of consistent usage. A random forest analysis identified total hours spent, months of program use, and levels completed as the strongest predictors of decoding mastery. Cluster analysis revealed three distinct learner profiles: Early Learners (younger students with moderate usage), Consistent Progressors (older students with high adherence and strong mastery), and Complex Learners (students with high-level completion but limited mastery). While younger students progressed more quickly through program levels, older students required additional time for mastery, particularly at higher levels. These findings suggest that structured, technology-enhanced reading intervention can effectively support reading skill development when implemented with fidelity, though implementation strategies may need adjustment based on learner profiles.

The ongoing literacy crisis in the U.S. highlights an urgent need for effective, scalable literacy instruction. REED Charitable Foundation (RCF) is a non-profit organization that provides structured literacy training informed by Orton-Gillingham, along with ongoing professional coaching and comprehensive implementation support to help all students learn to read. RCF Model Schools are whole schools that receive this comprehensive training and implementation support designed to provide all aspects of structured literacy instruction. The current correlational study evaluates the RCF Model School approach in 3 schools with over 1500 students across two years of implementation, 2022-2024. Results demonstrate substantial literacy growth for students in RCF Model Schools, with gains that increase as implementation progresses. Results indicate that first and second graders in Year 2 of implementation achieved literacy growth that significantly exceeded national growth norms, and there was a cumulative impact of multi-year implementation across grades. In addition, more students met or exceeded the proficiency benchmark by the end of the year, with students who started in the lowest percentile ranks making the greatest gains. By fostering an environment of ongoing professional learning and job-embedded support, RCF Model Schools appear to provide the robust support system that teachers need to enhance student achievement year after year. [This report was supported by REED Charitable Foundation (RCF).]

This review article discusses some common enhanced sampling methods in relation to the process of self-assembly of biomolecules. An introduction to self-assembly and its challenges is covered followed by a brief overview of the methods and analysis for replica-exchange molecular dynamics, umbrella sampling, metadynamics, and machine learning based techniques. Applications of select methods towards peptides, proteins, polymers, nucleic acids, and supramolecules are discussed. Finally, a short discussion of the future directions of some of these methods is provided.

We present the timing and spectral analyses of the NICER, NuSTAR, and IXPE observations of the magnetar 1E 1841-045 covering 82 days following its August 2024 bursting activity as well as radio observations utilizing MeerKAT and Effelsberg. We supplement our study with a historical NuSTAR and all 2024 pre-outburst NICER observations. The outburst is marked by an X-ray flux enhancement of a factor 1.6 compared to the historical level, predominantly driven by a newly-formed non-thermal emitting component with a photon index $\Gamma=1.5$. This flux showed a 20% decay at the end of our monitoring campaign. The radio monitoring did not reveal any pulsed radio emission with an upper-limit of 20 mJy and 50 mJy ms on the mean flux density and single pulse fluence, respectively. We detect a spin-up glitch at outburst onset with a $\Delta\nu=6.1\times10^{-8}$ Hz and a $\Delta\dot{\nu}=-1.4\times10^{-14}$ Hz s$^{-1}$, consistent with the near-universality of this behavior among the continuously-monitored magnetars. Most intriguingly, the 1E 1841-045 2-10 keV pulse profile is markedly different compared to pre-outburst; it shows a new, narrow (0.1 cycles) peak that appears to shift towards merging with the main, persistently-present, pulse. This is the second case of pulse-peak migration observed in magnetars after SGR 1830$-$0645, and the two sources exhibit a similar rate of phase shift. This implies that this phenomenon is not unique and might present itself in the broader population. The newly-formed peak for 1E 1841-045 is non-thermal, with emission extending to $\gtrsim20$ keV, in contrast to the case of SGR 1830$-$0645. Our results are consistent with an untwisting magnetic field bundle with migration towards the magnetic pole, perhaps accompanied by plastic motion of the crust., Comment: 16 pages, 6 figures, 4 tables, submitted to ApJ, comments welcome

Lyman $\alpha$ (Ly$\alpha$) emission is one of few observable features of galaxies that can trace the neutral hydrogen content in the Universe during the Epoch of Reionization (EoR). To accomplish this we need an efficient way to survey for Ly$\alpha$ emitters (LAEs) at redshifts beyond 7, requiring unbiased emission-line observations that are both sufficiently deep and wide to cover enough volume to detect them. Here we present results from PASSAGE -- a pure-parallel JWST/NIRISS slitless spectroscopic survey to detect Ly$\alpha$ emitters deep into the EoR, without the bias of photometric preselection. We identify four LAEs at $7.5\leq z\leq9.5$ in four surveyed pointings, and estimate the luminosity function (LF). We find that the LF does show a marked decrease compared to post-reionization measurements, but the change is a factor of $\lesssim 10$, which is less than expected from theoretical calculations and simulations, as well as observational expectations from the pre-JWST literature. Modeling of the IGM and expected \lya\ profiles implies these galaxies reside in ionized bubbles of $\gtrapprox 2$ physical Mpc. We also report that in the four fields we detect {3,1,0,0} LAEs, which could indicate strong field-to-field variation in the LAE distribution, consistent with a patchy HI distribution at $z\sim8$. We compare the recovered LAE number counts with expectations from simulations and discuss the potential implications for reionization and its morphology., Comment: Resubmitted to the ApJ after addressing referee comments

Ultrashort XUV pulses of the Free-Electron-LASer in Hamburg (FLASH) were used to investigate laser-induced fragmentation patterns of the prototypical chiral molecule 1-iodo-2-methyl-butane (C$_5$H$_{11}$I) in a pump-probe scheme. Ion velocity-map images and mass spectra of optical-laser-induced fragmentation were obtained for subsequent FEL exposure with photon energies of 63 eV and 75 eV. These energies specifically address the iodine 4d edge of neutral and singly charged iodine, respectively. The presented ion spectra for two optical pump-laser wavelengths, i.e., 800 nm and 267 nm, reveal substantially different cationic fragment yields in dependence on the wavelength and intensity. For the case of 800-nm-initiated fragmentation, the molecule dissociates notably slower than for the 267-nm pump. The results underscore the importance of considering optical-laser wavelength and intensity in the dissociation dynamics of this prototypical chiral molecule that is a promising candidate for future studies of its asymmetric nature., Comment: 12 pages, 9 figures

We describe a concrete device roadmap towards a fault-tolerant quantum computing architecture based on noise-resilient, topologically protected Majorana-based qubits. Our roadmap encompasses four generations of devices: a single-qubit device that enables a measurement-based qubit benchmarking protocol; a two-qubit device that uses measurement-based braiding to perform single-qubit Clifford operations; an eight-qubit device that can be used to show an improvement of a two-qubit operation when performed on logical qubits rather than directly on physical qubits; and a topological qubit array supporting lattice surgery demonstrations on two logical qubits. Devices that enable this path require a superconductor-semiconductor heterostructure that supports a topological phase, quantum dots and coupling between those quantum dots that can create the appropriate loops for interferometric measurements, and a microwave readout system that can perform fast, low-error single-shot measurements. We describe the key design components of these qubit devices, along with the associated protocols for demonstrations of single-qubit benchmarking, Clifford gate execution, quantum error detection, and quantum error correction, which differ greatly from those in more conventional qubits. Finally, we comment on implications and advantages of this architecture for utility-scale quantum computation., Comment: 11+6 pages, 8+5 figures

This paper tests the ability of a machine vision system with a monochrome camera to differentiate colored objects. The system is designed to autonomously and continuously sort colored objects of which the user specifies the desired color(s). The system uses camera color light filters and red-green-blue (RGB) color light emitting diode (LED) lights to aid the machine vision system in recognizing part contrast. Additionally the system is controlled by a Programmable Logic Controller (PLC) which is integrated with a Robot that is used to remove parts. The parts are fed into the workcell via a conveyor belt that is controlled by the PLC. The user has the ability to select the desired acceptable colors on both the HMI and the physical pushbuttons., Comment: 12 pages, 26 figures, 23rd Annual Hawaii International Conference on Education, Ja n. 4-7, 2025, Honolulu, Hawa ii

Recent developments in retrieval-augmented generation (RAG) for selecting relevant tools from a tool knowledge base enable LLM agents to scale their complex tool calling capabilities to hundreds or thousands of external tools, APIs, or agents-as-tools. However, traditional RAG-based tool retrieval fails to capture structured dependencies between tools, limiting the retrieval accuracy of a retrieved tool's dependencies. For example, among a vector database of tools, a "get stock price" API requires a "stock ticker" parameter from a "get stock ticker" API, and both depend on OS-level internet connectivity tools. In this paper, we address this limitation by introducing Graph RAG-Tool Fusion, a novel plug-and-play approach that combines the strengths of vector-based retrieval with efficient graph traversal to capture all relevant tools (nodes) along with any nested dependencies (edges) within the predefined tool knowledge graph. We also present ToolLinkOS, a new tool selection benchmark of 573 fictional tools, spanning over 15 industries, each with an average of 6.3 tool dependencies. We demonstrate that Graph RAG-Tool Fusion achieves absolute improvements of 71.7% and 22.1% over na\"ive RAG on ToolLinkOS and ToolSandbox benchmarks, respectively (mAP@10). ToolLinkOS dataset is available at https://github.com/EliasLumer/Graph-RAG-Tool-Fusion-ToolLinkOS, Comment: 25 pages, 14 figures, 2 tables

We report ALMA and ACA observations of atomic carbon ([\ion{C}{1}]$(1-0)$) and dust continuum in 10 Enormous Ly$\alpha$ Nebulae hosting ultra-luminous Type-I QSOs at $z=2.2-2.5$, as part of the SUrvey of Protocluster ELANe Revealing CO/CI in the Ly$\alpha$ Detected CGM (SUPERCOLD-CGM). We detect [\ion{C}{1}]$(1-0)$ and dust in all ten QSOs and five companion galaxies. We find that the QSOs and companions have higher gas densities and more intense radiation fields than Luminous Infrared galaxies and high-$z$ main sequence galaxies, with the highest values found in the QSOs. By comparing molecular gas masses derived from [\ion{C}{1}]$(1-0)$, CO(4$-$3) and dust continuum, we find that the QSOs and companions display a similar low CO conversion factor of $\alpha_{\rm CO}$\,$\sim$\,0.8 $\rm M_{\sun}$${[\rm K\,km/s\,pc^2]}^{-1}$. After tapering our data to low resolution, the [\ion{C}{1}]$(1-0)$ flux increases for nine QSOs, hinting at the possibility of [\ion{C}{1}]$(1-0)$ in the circum-galactic medium (CGM) on a scale of 16$-$40 kpc. However, the [\ion{C}{1}]$(1-0)$ sensitivity is too low to confirm this for individual targets, except for a tentative (2.7$\sigma$) CGM detection in Q0050+0051{} with M$_{\rm H_2}$\,=\, ($1.0 - 2.8$)$\times 10^{10}$ $\rm M_{\sun}$. The 3$\sigma$ mass limits of molecular CGM for the remaining QSO fields are ($0.2-1.4$)\,$\times$\,10$^{10}$ $\rm M_{\sun}$. This translates into a baryon fraction of $<$0.4-3$\% $ in the molecular CGM relative to the total baryonic halo mass. Our sample also includes a radio-detected AGN, Q1416+2649{}, which shows [\ion{C}{1}]$(1-0)$ and CO(4$-$3) luminosities an order of magnitude fainter for its far-infrared luminosity than other QSOs in our sample, possibly due to a lower molecular gas mass., Comment: 27 pages, 9 figures

We present a modular DNA origami design approach to address the challenges of assembling geometrically complex nanoscale structures, including those with nonuniform Gaussian curvature. This approach features a core structure that completely conserves the scaffold routing across different designs and preserves more than 70% of the DNA staples between designs, dramatically reducing both cost and effort, while enabling precise and independent programming of subunit interactions and binding angles through adjustable overhang lengths and sequences. Using cryogenic electron microscopy, gel electrophoresis, and coarse-grained molecular dynamics simulations, we validate a set of robust design rules. We demonstrate the method's utility by assembling a variety of self-limiting structures, including anisotropic shells with controlled inter-subunit interactions and curvature, and a toroid with globally varying curvature. Our strategy is both cost-effective and versatile, providing a promising and efficient solution for the synthetic fabrication of complex nanostructures., Comment: Main text 9 pages, 4 figures, SI 33 pages, 25 figures and 3 tables

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

Haptic sensations that align with virtual reality (VR) experiences have a profound impact on presence and enjoyment. There is potential to explore the dynamic capabilities of pneumatic inflatables to offer immersive sensations in virtual environments, including variations in shape, size, and stiffness. We introduce Pneutouch, an ungrounded and untethered wrist-worn device designed as a pneumatic haptic interface for VR interactions. Pneutouch's dynamic inflation ability enables programmable stiffness and shape change of haptic proxies. Additionally, multiple haptic proxies can be delivered into and out of the user's hand grasp. We describe the implementation of the Pneutouch device. We conducted user studies to demonstrate the affordances of pneumatic inflatables and assessed the device's efficacy in providing haptic feedback. With Pneutouch, our goal is to expand what can be touched in the virtual space and bring more immersion into virtual reality., Comment: 12 pages

ArchNEMESIS is an open-source Python package developed for the analysis of remote sensing spectroscopic observations of planetary atmospheres. It is based on the widely used NEMESIS radiative transfer and retrieval tool, which has been extensively used for the investigation of a wide variety of planetary environments. The main goal of archNEMESIS is to provide the capabilities of its Fortran-based predecessor, keeping or exceeding the efficiency in the calculations, and benefitting from the advantages Python tools provide in terms of usability and portability. The code, stored in a public GitHub repository under a GPL-v3.0 license, is accompanied by detailed documentation available at https://archnemesis.readthedocs.io/., Comment: Submitted 27 January 2025

The bursty, time-variable nature of star formation in the first billion years, as revealed by JWST, drives phases of temporary quiescence in low-mass galaxies that quench after starbursts. These galaxies provide unique probes of the burstiness of early star formation and its underlying physical processes. Using the SERRA cosmological zoom-in simulations, we analyze over 200 galaxies with $M_\star<10^{9.5}\rm M_\odot$ at $z\sim 6-8$, finding that most experience quiescent phases driven by stellar feedback, with minimal influence from environmental effects. The fraction of temporarily quiescent galaxies increases with decreasing mass and luminosity, representing the dominant population at $M_\star<10^8\rm M_\odot$ and $M_{UV}>-17$. By forward modeling their spectral energy distributions, we show that they are faint ($\langle M_{UV}\rangle = -15.6$ for $M_\star=10^{8}\rm M_\odot$), have strong Balmer breaks ($> 0.5$) and no emission lines. Comparing our predicted fractions with JWST results, we find similar luminosity-dependent trends; however, the observed fractions of temporarily quiescent galaxies at $M_{UV}\sim-20$ to $-19$ are higher, suggesting that stronger feedback or additional mechanisms beyond supernovae may be at play. We propose searching for F200W drop-outs and satellites in the proximity ($<5^{\prime\prime}$) of massive ($>10^{10}\rm M_\odot$) galaxies as effective strategies to uncover the hidden majority of faint ($M_{UV}>-17$), temporarily quiescent systems, crucial for constraining early feedback processes in low-mass galaxies., Comment: submitted to ApJ, comments welcome

Understanding the mathematical reasoning capabilities of Large Language Models (LLMs) is a central topic in the study of artificial intelligence. This new domain necessitates the creation of datasets of reasoning tasks for both training and benchmarking the performance of LLMs. To this end, we introduce the Karp dataset: The first dataset composed of detailed proofs of NP-completeness reductions. The reductions vary in difficulty, ranging from simple exercises of undergraduate courses to more challenging reductions from academic papers. We compare the performance of state-of-the-art models on this task and demonstrate the effect of fine-tuning with the Karp dataset on reasoning capacity., Comment: Accepted to the 4th workshop on mathematical reasoning and AI at NeurIPS 2024

The Lyman Alpha damping wing is seen in absorption against the spectra of high-redshift galaxies. Modeling of this wing is a way to measure the volume neutral hydrogen fraction of the Universe, as well as to measure the strength of HI absorption in the form of a damped Ly{\alpha} absorber (DLA). We use very high-quality ultraviolet spectra of low-z galaxies to create mock high-redshift, low-resolution spectra complete with a damping wing resulting from the IGM and HI absorption from local, dense gas to mimic current James Webb Space Telescope (JWST) observations of the early universe. The simulated spectra are used to test the ability to recover the "true" values of the column density of the DLA (N_DLA) and the volume neutral hydrogen fraction (x_HI) of the IGM through modified stellar continuum fitting. We find that the ability to recover the true values of N_DLA and x_HI simultaneously is compromised by degeneracies between these two parameters, and the spectral shape itself at low-resolution (R~100). The root mean squared error between the recovered and true column density of the DLAs is on the order of 1 dex. This error somewhat decreases with improved resolution (R~1000), but systematically underestimates N_DLA when Ly{\alpha} emission is present in the mock spectra. The recovered values of x_HI are poorly constrained and do not improve substantially with the higher resolution. We recommend accounting for these sources of uncertainty and biases when using galaxies' Ly{\alpha} damping wings to measure the intergalactic neutral hydrogen fraction., Comment: Submitted to ApJ, all comments most welcome

The half-life of $^{39}$Ar is measured using the DEAP-3600 detector located 2 km underground at SNOLAB. In 2016-2020, DEAP-3600 used a target mass of (3269 $\pm$ 24) kg of liquid argon distilled from the atmosphere in a direct-detection dark matter search. Such an argon mass also enables direct measurements of argon isotope properties. The decay of $^{39}$Ar in DEAP-3600 is the dominant source of triggers by two orders of magnitude, ensuring high statistics and making DEAP-3600 well-suited for measuring this isotope's half-life. Use of the pulse-shape discrimination technique in DEAP-3600 allows for powerful discrimination between nuclear recoils and electron recoils, resulting in the selection of a clean sample of $^{39}$Ar decays. Observing over a period of 3.4 years, the $^{39}$Ar half-life is measured to be $(302 \pm 8_{\rm stat} \pm 6_{\rm sys})$ years. This new direct measurement suggests that the half-life of $^{39}$Ar may be significantly longer than the accepted value, with potential implications for measurements using this isotope's half-life as input., Comment: 11 pages, 8 figures

This paper proposes a cutting mechanics-based machine learning (CMML) modeling method to discover governing equations of machining dynamics. The main idea of CMML design is to integrate existing physics in cutting mechanics and unknown physics in data to achieve automated model discovery, with the potential to advance machining modeling. Based on existing physics in cutting mechanics, CMML first establishes a general modeling structure governing machining dynamics, that is represented by a set of unknown differential algebraic equations. CMML can therefore achieve data-driven discovery of these unknown equations through effective cutting mechanics-based nonlinear learning function space design and discrete optimization-based learning algorithm. Experimentally verified time domain simulation of milling is used to validate the proposed modeling method. Numerical results show CMML can discover the exact milling dynamics models with process damping and edge force from noisy data. This indicates that CMML has the potential to be used for advancing machining modeling in practice with the development of effective metrology systems.

JWST provides a unique dataset to study reionization's earliest stages, promising insights into the first galaxies. Many JWST/NIRSpec prism spectra reveal smooth Lyman-alpha breaks in z>5 galaxies, implying damping wing scattering by neutral hydrogen. We investigate what current prism spectra imply about the intergalactic medium (IGM), and how best to use NIRSpec spectra to recover IGM properties. We use a sample of 99 z~5.5-13 galaxies with high S/N prism spectra in the public archive, including 12 at z>10. We analyse these spectra using damping wing sightlines from inhomogeneous reionizing IGM simulations, mapping between the distance of a source from neutral IGM and the mean IGM neutral fraction. We marginalise over absorption by local HI around galaxies, and Lyman-alpha emission. We observe a decline in the median and variance of flux around the Lyman-alpha break with increasing redshift, consistent with an increasingly neutral IGM, as ionized regions become smaller and rarer. At $z\gtrsim9$ the spectra become consistent with an almost fully neutral IGM. We find S/N>15 per pixel is required to robustly estimate IGM properties from prism spectra. We fit a sub-sample of high S/N spectra and infer mean neutral fractions $\overline{x}_\mathrm{HI}=0.33^{+0.18}_{-0.27}, 0.64^{+0.17}_{-0.23}$ ($>0.70$ excluding GNz11) at $z \approx 6.5, 9.3$. We also investigate local HI absorption, finding median column density $\log_{10}N_\mathrm{HI}\approx10^{20.8}$ cm$^{-2}$, comparable to $z\sim3$ Lyman-break galaxies, with no significant redshift evolution. We find galaxies showing the strongest absorption are more likely to be in close associations (<500 pkpc), implying enhanced absorption in massive dark matter halos. Future deep prism and grating spectroscopy of z>9 sources will provide tighter constraints on the earliest stages of reionization, key for understanding the onset of star formation., Comment: 15 pages, 13 figures, 11 pages in appendices. Submitted to A&A

The James Webb Space Telescope (JWST) is discovering an increasing number of galaxies well into the early stages of the Epoch of Reionization (EoR). Many of these galaxies are clustered with strong Lyman alpha (Ly$\alpha$) emission, motivating the presence of surrounding cosmic HII regions that would facilitate Ly$\alpha$ transmission through the intergalactic medium (IGM). Detecting these HII "bubbles" would allow us to connect their growth to the properties of the galaxies inside them. Here we develop a new forward-modeling framework to estimate the local HII region size and location from Ly$\alpha$ spectra of galaxy groups in the early stages of the EoR. Our model takes advantage of the complementary information provided by neighboring sightlines through the IGM. Our forward models sample the main sources of uncertainty, including: (i) the global neutral fraction; (ii) EoR morphology; (iii) emergent Ly$\alpha$ emission; and (iv) NIRSpec instrument noise. Depending on the availability of complementary nebular lines, $\sim$ 0.006 $\unicode{x2013}$ 0.01 galaxies per cMpc$^3$, are required to be $\gtrsim$95\% confident that the HII bubble location and size recovered by our method is accurate to within $\sim$ 1 comoving Mpc. This corresponds roughly to tens of galaxies at $z\sim7\unicode{x2013}8$ in $\sim$2x2 tiled pointing with JWST NIRSpec. Such a sample is achievable with a targeted survey with completeness down to $M_{\rm UV}^{\rm min}\lesssim$ -19 $\unicode{x2013}$ -17, depending on the over-density of the field. We test our method on 3D EoR simulations as well as misspecified equivalent width distributions, in both cases accurately recovering the HII region surrounding targeted galaxy groups., Comment: 14 pages, 15 figures, submitted to A&A

We present the results of the Distant Giants Survey, a three-year radial velocity (RV) campaign to search for wide-separation giant planets orbiting Sun-like stars known to host an inner transiting planet. We defined a distant giant to have $a$ = 1--10 AU and $M_{p} \sin i = 70-4000$ \mearth~ = 0.2-12.5 \mj, and required transiting planets to have $a<1$ AU and $R_{p} = 1-4$ \rearth. We assembled our sample of 47 stars using a single selection function, and observed each star at monthly intervals to obtain $\approx$30 RV observations per target. The final catalog includes a total of twelve distant companions: four giant planets detected during our survey, two previously known giant planets, and six objects of uncertain disposition identified through RV/astrometric accelerations. Statistically, half of the uncertain objects are planets and the remainder are stars/brown dwarfs. We calculated target-by-target completeness maps to account for missed planets. We found evidence for a moderate enhancement of distant giants (DG) in the presence of close-in small planets (CS), P(DG|CS) = $30^{+14}_{-12}\%$, over the field rate of P(DG) = $16^{+2}_{-2}\%$. No enhancement is disfavored ($p \sim$ 8%). In contrast to a previous study, we found no evidence that stellar metallicity enhances P(DG|CS). We found evidence that distant giant companions are preferentially found in systems with multiple transiting planets and have lower eccentricities than randomly selected giant planets. This points toward dynamically cool formation pathways for the giants that do not disturb the inner systems., Comment: 32 pages, 20 figures, 4 tables. Comments welcome

We combine ultraviolet imaging of the 13H survey field, taken with the XMM-Newton Optical Monitor telescope (XMM-OM) and the Neil Gehrels Swift Observatory Ultraviolet and Optical Telescope (UVOT) in the UVM2 band, to measure rest-frame ultraviolet 1500A luminosity functions of star-forming galaxies with redshifts between 0.4 and 0.6. In total the UVM2 imaging covers a sky area of 641 square arcmin, and we detect 273 galaxies in the UVM2 image with 0.4

Determining the electronic phase diagram of a quantum material as a function of temperature (T) and applied magnetic field (H) forms the basis for understanding the microscopic origin of transport properties, such as the anomalous Hall effect (AHE) and topological Hall effect (THE). For many magnetic quantum materials, including EuAl$_4$, a THE arises from a topologically protected magnetic skyrmion lattice with a non-zero scalar spin chirality. We identified a square skyrmion lattice (sSkL) peak in Eu(Ga$_{1-x}$Al$_x$)$_4$ ($x$ = 0.9) identical to the peak previously observed in EuAl$_4$ by performing neutron scattering measurements throughout the phase diagram. Comparing these neutron results with transport measurements, we found that in both compounds the maximal THE does not correspond to the sSkL area. Instead of the maximal THE, the maximal magnetocaloric effect (MCE) boundaries better identify the sSkL lattice phase observed by neutron scattering measurements. The maximal THE therefore arises from interactions of itinerant electrons with frustrated spin fluctuations in a topologically trivial magnetic state.

We present 50-fs, single-shot measurements of the x-ray thermal diffuse scattering (TDS) from copper foils that have been shocked via nanosecond laser-ablation up to pressures above 135~GPa. We hence deduce the x-ray Debye-Waller (DW) factor, providing a temperature measurement. The targets were laser-shocked with the DiPOLE 100-X laser at the High Energy Density (HED) endstation of the European X-ray Free-Electron Laser (EuXFEL). Single x-ray pulses, with a photon energy of 18 keV, were scattered from the samples and recorded on Varex detectors. Despite the targets being highly textured (as evinced by large variations in the elastic scattering), and with such texture changing upon compression, the absolute intensity of the azimuthally averaged inelastic TDS between the Bragg peaks is largely insensitive to these changes, and, allowing for both Compton scattering and the low-level scattering from a sacrificial ablator layer, provides a reliable measurement of $T/\Theta_D^2$, where $\Theta_D$ is the Debye temperature. We compare our results with the predictions of the SESAME 3336 and LEOS 290 equations of state for copper, and find good agreement within experimental errors. We thus demonstrate that single-shot temperature measurements of dynamically compressed materials can be made via thermal diffuse scattering of XFEL radation., Comment: 17 pages, 9 figures in main article; 10 pages, 5 figures in supplementary material

Motivated by deep learning regimes with multiple interacting yet distinct model components, we introduce learning diagrams, graphical depictions of training setups that capture parameterized learning as data rather than code. A learning diagram compiles to a unique loss function on which component models are trained. The result of training on this loss is a collection of models whose predictions ``agree" with one another. We show that a number of popular learning setups such as few-shot multi-task learning, knowledge distillation, and multi-modal learning can be depicted as learning diagrams. We further implement learning diagrams in a library that allows users to build diagrams of PyTorch and Flux.jl models. By implementing some classic machine learning use cases, we demonstrate how learning diagrams allow practitioners to build complicated models as compositions of smaller components, identify relationships between workflows, and manipulate models during or after training. Leveraging a category theoretic framework, we introduce a rigorous semantics for learning diagrams that puts such operations on a firm mathematical foundation.

The ability to revise essays in response to feedback is important for students' writing success. An automated writing evaluation (AWE) system that supports students in revising their essays is thus essential. We present eRevise+RF, an enhanced AWE system for assessing student essay revisions (e.g., changes made to an essay to improve its quality in response to essay feedback) and providing revision feedback. We deployed the system with 6 teachers and 406 students across 3 schools in Pennsylvania and Louisiana. The results confirmed its effectiveness in (1) assessing student essays in terms of evidence usage, (2) extracting evidence and reasoning revisions across essays, and (3) determining revision success in responding to feedback. The evaluation also suggested eRevise+RF is a helpful system for young students to improve their argumentative writing skills through revision and formative feedback.

We obtain the first analytic, interpretable and predictive theory of creativity in convolutional diffusion models. Indeed, score-based diffusion models can generate highly creative images that lie far from their training data. But optimal score-matching theory suggests that these models should only be able to produce memorized training examples. To reconcile this theory-experiment gap, we identify two simple inductive biases, locality and equivariance, that: (1) induce a form of combinatorial creativity by preventing optimal score-matching; (2) result in a fully analytic, completely mechanistically interpretable, equivariant local score (ELS) machine that, (3) without any training can quantitatively predict the outputs of trained convolution only diffusion models (like ResNets and UNets) with high accuracy (median $r^2$ of $0.90, 0.91, 0.94$ on CIFAR10, FashionMNIST, and MNIST). Our ELS machine reveals a locally consistent patch mosaic model of creativity, in which diffusion models create exponentially many novel images by mixing and matching different local training set patches in different image locations. Our theory also partially predicts the outputs of pre-trained self-attention enabled UNets (median $r^2 \sim 0.75$ on CIFAR10), revealing an intriguing role for attention in carving out semantic coherence from local patch mosaics.

The o1 model series is trained with large-scale reinforcement learning to reason using chain of thought. These advanced reasoning capabilities provide new avenues for improving the safety and robustness of our models. In particular, our models can reason about our safety policies in context when responding to potentially unsafe prompts, through deliberative alignment. This leads to state-of-the-art performance on certain benchmarks for risks such as generating illicit advice, choosing stereotyped responses, and succumbing to known jailbreaks. Training models to incorporate a chain of thought before answering has the potential to unlock substantial benefits, while also increasing potential risks that stem from heightened intelligence. Our results underscore the need for building robust alignment methods, extensively stress-testing their efficacy, and maintaining meticulous risk management protocols. This report outlines the safety work carried out for the OpenAI o1 and OpenAI o1-mini models, including safety evaluations, external red teaming, and Preparedness Framework evaluations.

We report on IXPE and NuSTAR observations that began forty days following the onset of the 2024 outburst of the magnetar 1E 1841-045, marking the first ever IXPE observation of a magnetar in an enhanced state. Our spectropolarimetric analysis indicates that a non-thermal double power-law (PL) spectral model can fit the phase-averaged intensity data well, with the soft and hard components dominating below and above around 5 keV, respectively. We find that the soft PL exhibits a polarization degree (PD) of about 20% while the hard X-ray PL displays a PD of about 50%; both components have a polarization angle (PA) compatible with 0 degree. These results are supported through model-independent polarization analysis which shows an increasing PD from about 15% to 70% in the 2-3 keV and 6-8 keV ranges, respectively, while the PA remains consistent with 0 degree. We find marginal evidence for variability in the polarization properties with pulse phase, namely a higher PD at spin phases coinciding with the peak in the hard X-ray pulse. We compare the hard X-ray PL to the expectation from direct resonant inverse Compton scattering (RICS) and secondary pair cascade synchrotron radiation from primary high-energy RICS photons, finding that both can provide reasonable spectropolarimetric agreement with the data, yet, the latter more naturally. Finally, we suggest that the soft power law X-ray component may be emission emanating from a Comptonized corona in the inner magnetosphere., Comment: Submitted to ApJL together with a companion paper by Rigoselli et al. Comments are welcome

In this paper, we introduce a novel technique for content safety and prompt injection classification for Large Language Models. Our technique, Layer Enhanced Classification (LEC), trains a Penalized Logistic Regression (PLR) classifier on the hidden state of an LLM's optimal intermediate transformer layer. By combining the computational efficiency of a streamlined PLR classifier with the sophisticated language understanding of an LLM, our approach delivers superior performance surpassing GPT-4o and special-purpose models fine-tuned for each task. We find that small general-purpose models (Qwen 2.5 sizes 0.5B, 1.5B, and 3B) and other transformer-based architectures like DeBERTa v3 are robust feature extractors allowing simple classifiers to be effectively trained on fewer than 100 high-quality examples. Importantly, the intermediate transformer layers of these models typically outperform the final layer across both classification tasks. Our results indicate that a single general-purpose LLM can be used to classify content safety, detect prompt injections, and simultaneously generate output tokens. Alternatively, these relatively small LLMs can be pruned to the optimal intermediate layer and used exclusively as robust feature extractors. Since our results are consistent on different transformer architectures, we infer that robust feature extraction is an inherent capability of most, if not all, LLMs.

Magnetic Detection Electrical Impedance Tomography is a novel technique that could enable non-invasive imaging of fast neural activity in the brain. However, commercial magnetometers are not suited to its technical requirements. Computational modelling was used to determine the optimal number, size and orientation of magnetometers, to inform the future development of MDEIT-specific magnetometers. Images were reconstructed using three sensing axes, arrays of 16 to 160 magnetometers, and cell sizes ranging from 1 to 18 mm. Image quality was evaluated visually and with the weighted spatial variance. Single-axis measurements normal to the surface provided the best image quality, and image quality increased with an increase in sensor number and size. This study can inform future OPM design, showing the size of the vapour cell need not be constrained to that of commercially available OPMs, and that a small array of single-axis, highly sensitive sensors is optimal for MDEIT., Comment: 16 pages, 5 figures, 2 tables

The primary objective of this research is to build a Momentum Transformer that is expected to outperform benchmark time-series momentum and mean-reversion trading strategies. We extend the ideas introduced in the paper Trading with the Momentum Transformer: An Intelligent and Interpretable Architecture to equities as the original paper primarily only builds upon futures and equity indices. Unlike conventional Long Short-Term Memory (LSTM) models, which operate sequentially and are optimized for processing local patterns, an attention mechanism equips our architecture with direct access to all prior time steps in the training window. This hybrid design, combining attention with an LSTM, enables the model to capture long-term dependencies, enhance performance in scenarios accounting for transaction costs, and seamlessly adapt to evolving market conditions, such as those witnessed during the Covid Pandemic. We average 4.14% returns which is similar to the original papers results. Our Sharpe is lower at an average of 1.12 due to much higher volatility which may be due to stocks being inherently more volatile than futures and indices., Comment: 7 pages, 5 figures