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This study investigates the impact of readiness programs on Early Admission (EA) students at a 2-year public college. EA programs allow high school seniors students to enroll full-time in college courses while earning both high school and college credits. However, these students often face challenges in academic success, social-emotional support, and connectivity. The purpose of this mixed-methods study was to examine how different types of readiness programs (full-semester, half-semester, or none) influence EA students' experiences in these areas. The research was guided by the central question: "What type of readiness program has the most impact on EA students?" Four sub-questions explored the differences in academic success, social-emotional support, and connectivity across the three readiness program conditions. The methodology used an explanatory sequential mixed-methods design, beginning with quantitative data collection through pre- and post-surveys, followed by qualitative data from open-ended questions to explain the results further. Thirty EA students from Olivia State College participated, with each assigned to one of three groups: full-semester program, half-semester program, or no program. The findings revealed that the full-semester readiness program had the most significant positive impact on students' academic performance, emotional well-being, and peer connectivity. The half-semester program showed moderate benefits, while students with no readiness program struggled the most across all measured outcomes. The study concluded that continuous and structured support over a full semester is essential for promoting EA students' success in both academic and social-emotional domains. These findings suggest that 2-year public colleges should prioritize implementing comprehensive readiness programs to enhance the academic and personal development of EA students. Future research should explore the long-term effects of such programs and their impact on diverse student populations.

Differences in effect sizes between researcher developed (RD) and independently developed (ID) outcome measures are widely documented but poorly understood in education research. We conduct a meta-analysis using item-level outcome data to test potential mechanisms that explain differences in effects by RD or ID outcome type. Our analysis of 45 effect sizes from 30 studies shows that both greater standard deviations of item-specific treatment effects and lower correlations between item-specific effects and item easiness predict larger effect sizes and reduce the observed difference between RD and ID measures from 0.24 SDs to 0.15 SDs. The findings advance our understanding of how item properties predict educational intervention outcomes and underscore the affordances of analyzing item-level data for building theory in education research.

We measure radial stellar age gradients in the relatively isolated gas-rich dwarf irregular WLM, combining JWST NIRCam and NIRISS imaging with six archival Hubble fields over semi-major axis equivalent distances of 0$\lesssim$R$_{SMA}$$\lesssim$4 kpc ($\lesssim$3R$_{hl}$). Fitting lifetime star formation histories (SFHs) to resolved color-magnitude diagrams (CMDs), radial age gradients are quantified using $\tau_{90}$ and $\tau_{50}$, the lookback times to form 90\% and 50\% of the cumulative stellar mass. We find that globally, the outskirts of WLM are older on average, with ($\delta$$\tau_{90}$, $\delta$$\tau_{50}$)/$\delta$R$_{SMA}=$(0.82$^{+0.10}_{-0.10}$, 1.60$^{+0.23}_{-0.22}$) Gyr/kpc (stat.), in good agreement with simulations. However, we also detect an azimuthal dependence of radial stellar age gradients, finding that stars on the leading edge of WLM (relative to its proper motion) are both younger and have a flatter age gradient compared to the trailing edge. This difference persists over 0.6$\lesssim$R$_{SMA}$$\lesssim$3.2 kpc ($\sim$0.5$-$2.5R$_{hl}$) and lookback times up to $\sim$8 Gyr, and is robust to assumed stellar evolutionary model. Our results are consistent with star formation triggered by ram pressure stripping from a circumgalactic and/or intergalactic medium, suggested by recent HI observations. If confirmed, processes typifying dense environments, such as ram pressure stripping, may be more relevant to the evolution of isolated galaxies than previously thought., Comment: ApJ in press. 23 pages, 8 figures, 3 tables

Goal-Conditioned Reinforcement Learning (GCRL) provides a versatile framework for developing unified controllers capable of handling wide ranges of tasks, exploring environments, and adapting behaviors. However, its reliance on trial-and-error poses challenges for real-world applications, as errors can result in costly and potentially damaging consequences. To address the need for safer learning, we propose a method that enables agents to learn goal-conditioned behaviors that explore without the risk of making harmful mistakes. Exploration without risks can seem paradoxical, but environment dynamics are often uniform in space, therefore a policy trained for safety without exploration purposes can still be exploited globally. Our proposed approach involves two distinct phases. First, during a pretraining phase, we employ safe reinforcement learning and distributional techniques to train a safety policy that actively tries to avoid failures in various situations. In the subsequent safe exploration phase, a goal-conditioned (GC) policy is learned while ensuring safety. To achieve this, we implement an action-selection mechanism leveraging the previously learned distributional safety critics to arbitrate between the safety policy and the GC policy, ensuring safe exploration by switching to the safety policy when needed. We evaluate our method in simulated environments and demonstrate that it not only provides substantial coverage of the goal space but also reduces the occurrence of mistakes to a minimum, in stark contrast to traditional GCRL approaches. Additionally, we conduct an ablation study and analyze failure modes, offering insights for future research directions., Comment: 12 pages, 13 figures, Published as an extended abstract at AAMAS 2025

Understanding a material's dissipative response is important for their use in many applications, such as adhesion. In dispersions the interplay between matrix and inclusions complicates any description. Fractional rheology is conveniently used to fit the storage and loss moduli of complex materials, although the physical picture is often elusive. We study the rheology of soft solid/liquid composites of liquid poly(ethylene glycol) (PEG) droplets in a soft poly(dimethylsiloxane) (PDMS) matrix. We analyze the influence of the droplets through fractional rheology and a time-concentration superposition. Viscous dissipation increases proportionally with volume fraction, and we show that a simple, well-fitting model can induce misinterpretation of the physics in a complex material., Comment: 10 pages, 5 figures, submitted to PNAS

We present ExoMiner++, an enhanced deep learning model that builds on the success of ExoMiner to improve transit signal classification in 2-minute TESS data. ExoMiner++ incorporates additional diagnostic inputs, including periodogram, flux trend, difference image, unfolded flux, and spacecraft attitude control data, all of which are crucial for effectively distinguishing transit signals from more challenging sources of false positives. To further enhance performance, we leverage transfer learning from high-quality labeled data from the Kepler space telescope, mitigating the impact of TESS's noisier and more ambiguous labels. ExoMiner++ achieves high accuracy across various classification and ranking metrics, significantly narrowing the search space for follow-up investigations to confirm new planets. To serve the exoplanet community, we introduce new TESS catalogs containing ExoMiner++ classifications and confidence scores for each transit signal. Among the 147,568 unlabeled TCEs, ExoMiner++ identifies 7,330 as planet candidates, with the remainder classified as false positives. These 7,330 planet candidates correspond to 1,868 existing TESS Objects of Interest (TOIs), 69 Community TESS Objects of Interest (CTOIs), and 50 newly introduced CTOIs. 1,797 out of the 2,506 TOIs previously labeled as planet candidates in ExoFOP are classified as planet candidates by ExoMiner++. This reduction in plausible candidates combined with the excellent ranking quality of ExoMiner++ allows the follow-up efforts to be focused on the most likely candidates, increasing the overall planet yield.

The Pandora SmallSat is a NASA flight project aimed at studying the atmospheres of exoplanets -- planets orbiting stars outside our Solar System. Pandora will provide the first dataset of simultaneous, multiband (visible and NIR), long-baseline observations of exoplanets and their host stars. Pandora is an ambitious project that will fly a 0.44 m telescope in a small form factor. To achieve the scientific goals, the mission requires a departure from the traditional cost-schedule paradigm of half-meter-class observatories. Pandora achieves this by leveraging existing capabilities that necessitate minimal engineering development, disruptive and agile management, trusted partnerships with vendors, and strong support from the lead institutions. The Pandora team has developed a suite of high-fidelity parameterized simulation and modeling tools to estimate the performance of both imaging channels. This has enabled a unique bottom-up approach to deriving trades and system requirements. Pandora is a partnership between NASA and Lawrence Livermore National Laboratory. The project completed its Critical Design Review in October 2023 and is slated for launch into Sun-synchronous, low-Earth orbit in Fall 2025., Comment: Paper accepted to the IEEE Aerospace Conference 2025

We compare the cosmological constraints that can be obtained from halo clustering on non-linear scales ($2 h^{-1}$ Mpc < $r$ < $50 h^{-1}$ Mpc) using Betti curves, a topological summary statistic, and $k$-th nearest neighbor ($k$NN) distributions. We quantify the information content of each summary statistic through Fisher matrices computed from the Quijote simulations. Due to the use of simulation-based Fisher forecasts, we pay careful attention to the convergence of the Fisher matrices by looking at their eigendecompositions. We find that, in general, only two directions in the parameter space have constraints that are well converged given the number of Quijote simulations available. We then compare the information content of each summary statistic in the reduced parameter space $\{\Omega_m, \sigma_8\}$. We find that almost all of the information present in the Betti curves comes from the first two, $\beta_0$ and $\beta_1$, which track the number of connected components and one-dimensional loops respectively, and almost no constraining power comes from $\beta_2$ which tracks the number of topological voids. In comparison, we find that the $k$NNs provide very competitive constraints along with several potential advantages in regards to real data. Finally, we find that while the $k$NNs and Betti curves provide some complementary constraints, they are not fully independent, potentially indicating a connection between the two statistics., Comment: 9 pages, 12 figures

Token representations in high-dimensional latent spaces often exhibit redundancy, limiting computational efficiency and reducing structural coherence across model layers. Hierarchical latent space folding introduces a structured transformation mechanism that enforces a multi-scale organization within learned embeddings, refining representational compactness while preserving essential contextual distinctions. The proposed approach incorporates dynamic folding operations that iteratively adjust token embeddings through structured transformations, influencing both short-range and long-range dependencies in sequential processing tasks. Empirical evaluation demonstrates a reduction in representational variance across layers, contributing to more stable perplexity distributions and enhancing predictive confidence in text generation. The structured redistribution of attention head utilization leads to more efficient allocation of computational resources, particularly in deeper layers, where hierarchical refinements improve contextual abstraction. Comparative analysis of activation sparsity patterns suggests that hierarchical adjustments selectively reinforce critical pathways while reducing computational overhead in non-essential regions of the model. Statistical assessments of token reordering frequencies reveal that hierarchical modifications introduce subtle shifts in sequential dependencies, improving contextual alignment while maintaining syntactic correctness. Computational trade-offs associated with hierarchical folding introduce marginal increases in training time per epoch, yet empirical findings indicate that inference efficiency benefits from the structured representation adjustments. The results highlight the impact of hierarchical latent space folding on optimizing model performance through improved representation structuring and computational efficiency.

The rapid evolution of encryption-based threats has rendered conventional detection mechanisms increasingly ineffective against sophisticated attack strategies. Monitoring entropy variations across hierarchical system levels offers an alternative approach to identifying unauthorized data modifications without relying on static signatures. A framework leveraging hierarchical entropy disruption was introduced to analyze deviations in entropy distributions, capturing behavioral anomalies indicative of malicious encryption operations. Evaluating the framework across multiple ransomware variants demonstrated its capability to achieve high detection accuracy while maintaining minimal computational overhead. Entropy distributions across different system directories revealed that encryption activities predominantly targeted user-accessible files, aligning with observed attacker strategies. Detection latency analysis indicated that early-stage identification was feasible, mitigating potential data loss before critical system impact occurred. The framework's ability to operate efficiently in real-time environments was validated through an assessment of resource utilization, confirming a balanced trade-off between detection precision and computational efficiency. Comparative benchmarking against established detection methods highlighted the limitations of conventional approaches in identifying novel ransomware variants, whereas entropy-based anomaly detection provided resilience against obfuscation techniques.

In this paper, we propose a centralized secondary control for the real-time steady-state optimization of multi-terminal HVdc grids under voltage and current limits. First, we present the dynamic models of the grid components, including the modular multilevel converter (MMC) stations and their different control layers. We also derive the quasi-static input-output model of the system, which is suitable for the steady-state control design. Second, we formulate a general optimization problem using this quasi-static model and find the Karush-Kuhn-Tucker optimality conditions of its solutions. Third, we propose a secondary control based on primal-dual dynamics to adjust the voltage setpoints of the dispatchable MMCs, with which the system asymptotically converges to a steady state that satisfies these optimality conditions. Fourth, we provide a communication triggering mechanism to reduce the communication traffic between the secondary control unit and the MMC stations. Finally, we verify our proposal for different case studies by adapting it to an offshore multi-terminal HVdc grid composed of heterogeneous MMC stations simulated in the MATLAB/Simulink environment. The problems of proportional current minimization and loss reduction are two special case studies.

Multi-class segmentation of the aorta in computed tomography angiography (CTA) scans is essential for diagnosing and planning complex endovascular treatments for patients with aortic dissections. However, existing methods reduce aortic segmentation to a binary problem, limiting their ability to measure diameters across different branches and zones. Furthermore, no open-source dataset is currently available to support the development of multi-class aortic segmentation methods. To address this gap, we organized the AortaSeg24 MICCAI Challenge, introducing the first dataset of 100 CTA volumes annotated for 23 clinically relevant aortic branches and zones. This dataset was designed to facilitate both model development and validation. The challenge attracted 121 teams worldwide, with participants leveraging state-of-the-art frameworks such as nnU-Net and exploring novel techniques, including cascaded models, data augmentation strategies, and custom loss functions. We evaluated the submitted algorithms using the Dice Similarity Coefficient (DSC) and Normalized Surface Distance (NSD), highlighting the approaches adopted by the top five performing teams. This paper presents the challenge design, dataset details, evaluation metrics, and an in-depth analysis of the top-performing algorithms. The annotated dataset, evaluation code, and implementations of the leading methods are publicly available to support further research. All resources can be accessed at https://aortaseg24.grand-challenge.org.

We introduce the first generative model capable of simultaneous multi-object compositing, guided by both text and layout. Our model allows for the addition of multiple objects within a scene, capturing a range of interactions from simple positional relations (e.g., next to, in front of) to complex actions requiring reposing (e.g., hugging, playing guitar). When an interaction implies additional props, like `taking a selfie', our model autonomously generates these supporting objects. By jointly training for compositing and subject-driven generation, also known as customization, we achieve a more balanced integration of textual and visual inputs for text-driven object compositing. As a result, we obtain a versatile model with state-of-the-art performance in both tasks. We further present a data generation pipeline leveraging visual and language models to effortlessly synthesize multimodal, aligned training data.

Art, as a universal language, can be interpreted in diverse ways, with artworks embodying profound meanings and nuances. The advent of Large Language Models (LLMs) and the availability of Multimodal Large Language Models (MLLMs) raise the question of how these transformative models can be used to assess and interpret the artistic elements of artworks. While research has been conducted in this domain, to the best of our knowledge, a deep and detailed understanding of the technical and expressive features of artworks using LLMs has not been explored. In this study, we investigate the automation of a formal art analysis framework to analyze a high-throughput number of artworks rapidly and examine how their patterns evolve over time. We explore how LLMs can decode artistic expressions, visual elements, composition, and techniques, revealing emerging patterns that develop across periods. Finally, we discuss the strengths and limitations of LLMs in this context, emphasizing their ability to process vast quantities of art-related data and generate insightful interpretations. Due to the exhaustive and granular nature of the results, we have developed interactive data visualizations, available online https://cognartive.github.io/, to enhance understanding and accessibility.

Causal inference across multiple data sources has the potential to improve the generalizability, transportability, and replicability of scientific findings. However, data integration methods for time-to-event outcomes -- common in medical contexts such as clinical trials -- remain underdeveloped. Existing data fusion methods focus on binary or continuous outcomes, neglecting the distinct challenges of survival analysis, including right-censoring and the unification of discrete and continuous time frameworks. To address these gaps, we propose two novel approaches for multi-source causal survival analysis. First, considering a target site-specific causal effect, we introduce a semiparametric efficient estimator for scenarios where data-sharing is feasible. Second, we develop a federated learning framework tailored to privacy-constrained environments. This framework dynamically adjusts source site-specific contributions, downweighting biased sources and upweighting less biased ones relative to the target population. Both approaches incorporate nonparametric machine learning models to enhance robustness and efficiency, with theoretical guarantees applicable to both continuous and discrete time-to-event outcomes. We demonstrate the practical utility of our methods through extensive simulations and an application to two randomized trials of a monoclonal neutralizing antibody for HIV-1 prevention: HVTN 704/HPTN 085 (cisgender men and transgender persons in the Americas and Switzerland) and HVTN 703/HPTN 081 (women in sub-Saharan Africa). The results highlight the potential of our approaches to efficiently estimate causal effects while addressing heterogeneity across data sources and adhering to privacy and robustness constraints.

This paper introduces GLLM, an innovative tool that leverages Large Language Models (LLMs) to automatically generate G-code from natural language instructions for Computer Numerical Control (CNC) machining. GLLM addresses the challenges of manual G-code writing by bridging the gap between human-readable task descriptions and machine-executable code. The system incorporates a fine-tuned StarCoder-3B model, enhanced with domain-specific training data and a Retrieval-Augmented Generation (RAG) mechanism. GLLM employs advanced prompting strategies and a novel self-corrective code generation approach to ensure both syntactic and semantic correctness of the generated G-code. The architecture includes robust validation mechanisms, including syntax checks, G-code-specific verifications, and functional correctness evaluations using Hausdorff distance. By combining these techniques, GLLM aims to democratize CNC programming, making it more accessible to users without extensive programming experience while maintaining high accuracy and reliability in G-code generation.

The integration of generative AI (GenAI) and large language models (LLMs) in healthcare presents both unprecedented opportunities and challenges, necessitating innovative regulatory approaches. GenAI and LLMs offer broad applications, from automating clinical workflows to personalizing diagnostics. However, the non-deterministic outputs, broad functionalities and complex integration of GenAI and LLMs challenge existing medical device regulatory frameworks, including the total product life cycle (TPLC) approach. Here we discuss the constraints of the TPLC approach to GenAI and LLM-based medical device regulation, and advocate for global collaboration in regulatory science research. This serves as the foundation for developing innovative approaches including adaptive policies and regulatory sandboxes, to test and refine governance in real-world settings. International harmonization, as seen with the International Medical Device Regulators Forum, is essential to manage implications of LLM on global health, including risks of widening health inequities driven by inherent model biases. By engaging multidisciplinary expertise, prioritizing iterative, data-driven approaches, and focusing on the needs of diverse populations, global regulatory science research enables the responsible and equitable advancement of LLM innovations in healthcare.

This is the second in a series of two papers to establish the conjectured mass-angular momentum inequality for multiple black holes, modulo the extreme black hole 'no hair theorem'. More precisely it is shown that either there is a counterexample to black hole uniqueness, in the form of a regular axisymmetric stationary vacuum spacetime with an asymptotically flat end and multiple degenerate horizons which is 'ADM minimizing', or the following statement holds. Complete, simply connected, maximal initial data sets for the Einstein equations with multiple ends that are either asymptotically flat or asymptotically cylindrical, admit an ADM mass lower bound given by the square root of total angular momentum, under the assumption of nonnegative energy density and axisymmetry. Moreover, equality is achieved in the mass lower bound only for a constant time slice of an extreme Kerr spacetime. The proof is based on a novel flow of singular harmonic maps with hyperbolic plane target, under which the renormalized harmonic map energy is monotonically nonincreasing. Relevant properties of the flow are achieved through a refined asymptotic analysis of solutions to the harmonic map equations and their linearization., Comment: 64 pages

This paper advances a theoretical argument about the role capital plays in structuring CHI research. We introduce the concept of technological capture to theorize the mechanism by which this happens. Using this concept, we decompose the effect on CHI into four broad forms: technological capture creates market-creating, market-expanding, market-aligned, and externality-reducing CHI research. We place different CHI subcommunities into these forms -- arguing that many of their values are inherited from capital underlying the field. Rather than a disciplinary- or conference-oriented conceptualization of the field, this work theorizes CHI as tightly-coupled with capital via technological capture. The paper concludes by discussing some implications for CHI.

From $>1000$ orbits of HST imaging, we present deep homogeneous resolved star color-magnitude diagrams that reach the oldest main sequence turnoff and uniformly measured star formation histories (SFHs) of 36 dwarf galaxies ($-6 \ge M_V \ge -17$) associated with the M31 halo, and for 10 additional fields in M31, M33, and the Giant Stellar Stream. From our SFHs we find: i) the median stellar age and quenching epoch of M31 satellites correlate with galaxy luminosity and galactocentric distance. Satellite luminosity and present-day distance from M31 predict the satellite quenching epoch to within $1.8$ Gyr at all epochs. This tight relationship highlights the fundamental connection between satellite halo mass, environmental history, and star formation duration. ii) There is no difference between the median SFH of galaxies on and off the great plane of Andromeda satellites. iii) $\sim50$\% of our M31 satellites show prominent ancient star formation ($>12$ Gyr ago) followed by delayed quenching ($8-10$ Gyr ago), which is not commonly observed among the MW satellites. iv) A comparison with TNG50 and FIRE-2 simulated satellite dwarfs around M31-like hosts show that some of these trends (dependence of SFH on satellite luminosity) are reproduced in the simulations while others (dependence of SFH on galactocentric distance, presence of the delayed-quenching population) are weaker or absent. We provide all photometric catalogs and SFHs as High-Level Science Products on MAST., Comment: Accepted for publication on ApJ. 47 pages, 24 figures, 12 tables. Corresponding HLSP data can be retrieved at: https://archive.stsci.edu/hlsp/m31-satellites

We present the discovery of 11 new transiting brown dwarfs and low-mass M-dwarfs from NASA's TESS mission: TOI-2844, TOI-3122, TOI-3577, TOI-3755, TOI-4462, TOI-4635, TOI-4737, TOI-4759, TOI-5240, TOI-5467, and TOI-5882. They consist of 5 brown dwarf companions and 6 very low mass stellar companions ranging in mass from $25 M_{\rm J}$ to $128 M_{\rm J}$. We used a combination of photometric time-series, spectroscopic, and high resolution imaging follow-up as a part of the TESS Follow-up Observing Program (TFOP) in order to characterize each system. With over 50 transiting brown dwarfs confirmed, we now have a large enough sample to directly test different formation and evolutionary scenarios. We provide a renewed perspective on the transiting brown dwarf desert and its role in differentiating between planetary and stellar formation mechanisms. Our analysis of the eccentricity distribution for the transiting brown dwarf sample does not support previous claims of a transition between planetary and stellar formation at $\sim42$ $M_{\rm J}$. We also contribute a first look into the metallicity distribution of transiting companions in the range $7 - 150$ $M_{\rm J}$, showing that this too does not support a $\sim42$ $M_{\rm J}$ transition. Finally, we also detect a significant lithium absorption feature in one of the brown dwarf hosts (TOI-5882) but determine that the host star is likely old based on rotation, kinematic, and photometric measurements. We therefore claim that TOI-5882 may be a candidate for planetary engulfment., Comment: Submitted, 32 pages, 16 figures

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 report the discovery of BD+05$\,$4868$\,$Ab, a transiting exoplanet orbiting a bright ($V=10.16$) K-dwarf (TIC 466376085) with a period of 1.27 days. Observations from NASA's Transiting Exoplanet Survey Satellite (TESS) reveal variable transit depths and asymmetric transit profiles that are characteristic of comet-like tails formed by dusty effluents emanating from a disintegrating planet. Unique to BD+05$\,$4868$\,$Ab is the presence of prominent dust tails in both the trailing and leading directions that contribute to the extinction of starlight from the host star. By fitting the observed transit profile and analytically modeling the drift of dust grains within both dust tails, we infer large grain sizes ($\sim1-10\,\mu$m) and a mass loss rate of $10\,M_{\rm \oplus}\,$Gyr$^{-1}$, suggestive of a lunar-mass object with a disintegration timescale of only several Myr. The host star is probably older than the Sun and is accompanied by an M-dwarf companion at a projected physical separation of 130 AU. The brightness of the host star, combined with the planet's relatively deep transits ($0.8-2.0\%$), presents BD+05$\,$4868$\,$Ab as a prime target for compositional studies of rocky exoplanets and investigations into the nature of catastrophically evaporating planets., Comment: 24 pages, 17 figures. Submitted to AAS Journals

Tidal synchronization plays a fundamental role in the evolution of binary star systems. However, key details such as the timescale of synchronization, efficiency of tidal dissipation, final rotational period, and dependence on stellar mass are not well constrained. We present a catalog of rotation periods, orbital periods, and eccentricities from eclipsing binaries (EBs) that can be used to study the role of tides in the rotational evolution of low-mass dwarf (FGKM spectral type) binaries. This study presents the largest catalog of EB orbital and rotational periods (Porb and Prot) measured from the Transiting Exoplanet Satellite Survey (TESS). We first classify 4584 light curves from the TESS Eclipsing Binary Catalog according to out-of-eclipse stellar variability type: starspot modulation, ellipsoidal variability, non-periodic variability, and "other" variability (e.g. pulsations). We then manually validate each light curve classification, resulting in a sample of 1039 candidates with 584 high confidence EBs that exhibit detectable star-spot modulation. From there, we measure and compare the rotation period of each starspot-modulated EB using three methods: Lomb-Scargle periodograms, autocorrelation function, and phase dispersion minimization. We find that our period distributions are consistent with previous work that used a sample of 816 starspot EBs from Kepler to identify two populations: a synchronous population (with Porb \approx Prot) and a subsynchronous population (with 8Porb \approx 7Prot). Using Bayesian model comparison, we find that a bimodal distribution is a significantly better fit than a unimodal distribution for Kepler and TESS samples, both individually or combined, confirming that the subsynchronous population is statistically significant., Comment: 27 pages with 21 figures

We report the discovery and characterization of two sub-Saturns from the Transiting Exoplanet Survey Satellite (\textit{TESS}) using high-resolution spectroscopic observations from the MaHPS spectrograph at the Wendelstein Observatory and the SOPHIE spectrograph at the Haute-Provence Observatory. Combining photometry from TESS, KeplerCam, LCOGT, and MuSCAT2 with the radial velocity measurements from MaHPS and SOPHIE we measure precise radii and masses for both planets. TOI-5108 b is a sub-Saturn with a radius of $6.6 \pm 0.1$ $R_\oplus$ and a mass of $32 \pm 5$ $M_\oplus$. TOI-5786 b is similar to Saturn with a radius of $8.54 \pm 0.13$ $R_\oplus$ and a mass of $73 \pm 9$ $M_\oplus$. The host star for TOI-5108 b is a moderately bright (Vmag 9.75) G-type star. TOI-5786 is a slightly dimmer (Vmag 10.2) F-type star. Both planets are close to their host stars with periods of 6.75 days and 12.78 days respectively. This puts TOI-5108 b just inside the bounds of the Neptune desert while TOI-5786 b is right above the upper edge. We estimate hydrogen-helium envelope mass fractions of $38 \%$ for TOI-5108 b and $74 \% $ for TOI-5786 b. However, using a model for the interior structure that includes tidal effects the envelope fraction of TOI-5108 b could be much lower ($\sim 20\,\%$) depending on the obliquity. We estimate mass-loss rates between 1.0 * $10^9$ g/s and 9.8 * $10^9$ g/s for TOI-5108 b and between 3.6 * $10^8$ g/s and 3.5 * $10^9$ g/s for TOI-5786 b. Given their masses, this means that both planets are stable against photoevaporation. We also detect a transit signal for a second planet candidate in the TESS data of TOI-5786 with a period of 6.998 days and a radius of $3.83 \pm 0.16$ $R_\oplus$. Using our RV data and photodynamical modeling, we are able to provide a 3-$\sigma$ upper limit of 26.5 $M_\oplus$ for the mass of the potential inner companion to TOI-5786 b., Comment: 25 pages, 13 figures. Accepted for publication in A&A

This bachelor's thesis examines the capabilities of ChatGPT 4 in code generation across 19 programming languages. The study analyzed solution rates across three difficulty levels, types of errors encountered, and code quality in terms of runtime and memory efficiency through a quantitative experiment. A total of 188 programming problems were selected from the LeetCode platform, and ChatGPT 4 was given three attempts to produce a correct solution with feedback. ChatGPT 4 successfully solved 39.67% of all tasks, with success rates decreasing significantly as problem complexity increased. Notably, the model faced considerable challenges with hard problems across all languages. ChatGPT 4 demonstrated higher competence in widely used languages, likely due to a larger volume and higher quality of training data. The solution rates also revealed a preference for languages with low abstraction levels and static typing. For popular languages, the most frequent error was "Wrong Answer," whereas for less popular languages, compiler and runtime errors prevailed, suggesting frequent misunderstandings and confusion regarding the structural characteristics of these languages. The model exhibited above-average runtime efficiency in all programming languages, showing a tendency toward statically typed and low-abstraction languages. Memory efficiency results varied significantly, with above-average performance in 14 languages and below-average performance in five languages. A slight preference for low-abstraction languages and a leaning toward dynamically typed languages in terms of memory efficiency were observed. Future research should include a larger number of tasks, iterations, and less popular languages. Additionally, ChatGPT 4's abilities in code interpretation and summarization, debugging, and the development of complex, practical code could be analyzed further. ---- Diese Bachelorarbeit untersucht die F\"ahigkeiten von ChatGPT 4 zur Code-Generierung in 19 Programmiersprachen. Betrachtet wurden die L\"osungsraten zwischen drei Schwierigkeitsgraden, die aufgetretenen Fehlerarten und die Qualit\"at des Codes hinsichtlich der Laufzeit- und Speichereffizienz in einem quantitativen Experiment. Dabei wurden 188 Programmierprobleme der Plattform LeetCode entnommen, wobei ChatGPT 4 jeweils drei Versuche hatte, mittels Feedback eine korrekte L\"osung zu generieren. ChatGPT 4 l\"oste 39,67 % aller Aufgaben erfolgreich, wobei die Erfolgsrate mit zunehmendem Schwierigkeitsgrad deutlich abnahm und bei komplexen Problemen in allen Sprachen signifikante Schwierigkeiten auftraten. Das Modell zeigte eine h\"ohere Kompetenz in weit verbreiteten Sprachen, was wahrscheinlich auf eine gr\"o{\ss}ere Menge und h\"ohere Qualit\"at der Trainingsdaten zur\"uckzuf\"uhren ist. Bez\"uglich der L\"osungsraten zeigte das Modell zudem eine Pr\"aferenz f\"ur Sprachen mit niedrigem Abstraktionsniveau und statischer Typisierung. Bei Sprachen hoher Popularit\"at trat der Fehler Wrong Answer am h\"aufigsten auf, w\"ahrend bei weniger popul\"aren Sprachen Compiler- und Laufzeitfehler \"uberwogen, was auf h\"aufige Missverst\"andnisse und Verwechslungen bez\"uglich der spezifischen strukturellen Eigenschaften dieser Sprachen zur\"uckzuf\"uhren ist. ChatGPT 4 demonstrierte in allen Programmiersprachen eine \"uberdurchschnittliche Laufzeiteffizienz und tendierte diesbez\"uglich erneut zu statisch typisierten und niedrig abstrahierten Sprachen. Die Werte zur Speichereffizienz variierten erheblich, wobei in 14 Sprachen \"uberdurchschnittliche und in f\"unf Sprachen unterdurchschnittliche Werte erzielt wurden. Es zeigte sich diesbez\"uglich eine leichte Tendenz zugunsten von niedrig abstrahierten sowie eine Pr\"aferenz zu dynamisch typisierten Sprachen. Zuk\"unftige Forschung sollte eine h\"ohere Anzahl an Aufgaben, Iterationen und unpopul\"aren Sprachen einbeziehen. Dar\"uber hinaus k\"onnten die F\"ahigkeiten von ChatGPT 4 in der Code-Interpretation und -Zusammenfassung, im Debugging und in der Entwicklung komplexer, praxisbezogener Codes analysiert werden., Comment: 65 pages, in German, Bachelor's thesis on the evaluation of ChatGPT 4's code generation capabilities in 19 programming languages, University of Potsdam, June 2024

We provide a possible interpretation of excesses reported by ATLAS and CMS at around 95GeV, 650GeV and possibly 320GeV, in terms of CP-even scalars. In particular, the combined {\sl global} statistical significances of independent indications for a 650GeV object reach the $4\sigma$ level! While this seems sufficient incentive for a further investigation, this object cannot be fitted in tradional singlet or doublet extensions of the Standard Model. It requires by itself a larger extension with doubly-charged scalars, that naturally fits the two other excesses on top of the SM-like 125~GeV Higgs. We describe the minimal model and give some numerical illustrations., Comment: To appear in the proceedings of the 29th International Symposium on Particles, Strings and Cosmology (PASCOS 2024) in Quy Nhon, Vietnam, 7-13 July, 2024

Learning the Individual Treatment Effect (ITE) is essential for personalized decision-making, yet causal inference has traditionally focused on aggregated treatment effects. While integrating conformal prediction with causal inference can provide valid uncertainty quantification for ITEs, the resulting prediction intervals are often excessively wide, limiting their practical utility. To address this limitation, we introduce \underline{S}urrogate-assisted \underline{C}onformal \underline{I}nference for \underline{E}fficient I\underline{N}dividual \underline{C}ausal \underline{E}ffects (SCIENCE), a framework designed to construct more efficient prediction intervals for ITEs. SCIENCE accommodates the covariate shifts between source data and target data and applies to various data configurations, including semi-supervised and surrogate-assisted semi-supervised learning. Leveraging semi-parametric efficiency theory, SCIENCE produces rate double-robust prediction intervals under mild rate convergence conditions, permitting the use of flexible non-parametric models to estimate nuisance functions. We quantify efficiency gains by comparing semi-parametric efficiency bounds with and without the surrogates. Simulation studies demonstrate that our surrogate-assisted intervals offer substantial efficiency improvements over existing methods while maintaining valid group-conditional coverage. Applied to the phase 3 Moderna COVE COVID-19 vaccine trial, SCIENCE illustrates how multiple surrogate markers can be leveraged to generate more efficient prediction intervals.

After a review of the reproducing kernel Banach space framework and semi-inner products, we apply the techniques to the setting of Hardy spaces $H^p$ and Bergman spaces $A^p$, $1

In randomized trials and observational studies, it is often necessary to evaluate the extent to which an intervention affects a time-to-event outcome, which is only partially observed due to right censoring. For instance, in infectious disease studies, it is frequently of interest to characterize the relationship between risk of acquisition of infection with a pathogen and a biomarker previously measuring for an immune response against that pathogen induced by prior infection and/or vaccination. It is common to conduct inference within a causal framework, wherein we desire to make inferences about the counterfactual probability of survival through a given time point, at any given exposure level. To determine whether a causal effect is present, one can assess if this quantity differs by exposure level. Recent work shows that, under typical causal assumptions, summaries of the counterfactual survival distribution are identifiable. Moreover, when the treatment is multi-level, these summaries are also pathwise differentiable in a nonparametric probability model, making it possible to construct estimators thereof that are unbiased and approximately normal. In cases where the treatment is continuous, the target estimand is no longer pathwise differentiable, rendering it difficult to construct well-behaved estimators without strong parametric assumptions. In this work, we extend beyond the traditional setting with multilevel interventions to develop approaches to nonparametric inference with a continuous exposure. We introduce methods for testing whether the counterfactual probability of survival time by a given time-point remains constant across the range of the continuous exposure levels. The performance of our proposed methods is evaluated via numerical studies, and we apply our method to data from a recent pair of efficacy trials of an HIV monoclonal antibody., Comment: 22 pages, 2 figures

M dwarfs have become increasingly important in the detection of exoplanets and the study of Earth-sized planets and their habitability. However, 20-30% of M dwarfs have companions that can impact the formation and evolution of planetary systems. We use high-resolution imaging and Gaia astrometry to detect stellar companions around M dwarf exoplanet hosts discovered by TESS and determine the projected separation and estimated stellar masses for each system. We find 47 companions around 216 M dwarfs and a multiplicity rate of $19.4\pm2.7$% that is consistent with field M dwarfs. The binary projected separation distribution is shifted to larger separations, confirming the lack of close binaries hosting transiting exoplanets seen in previous studies. We correct the radii of planets with nearby companions and examine the properties of planets in M dwarf multi-star systems. We also note three multi-planet systems that occur in close binaries ($\lesssim 50$ au) where planet formation is expected to be suppressed., Comment: 28 pages, 13 figures, 5 tables, accepted for publication in The Astronomical Journal

Direct sum theorems state that the cost of solving $k$ instances of a problem is at least $\Omega(k)$ times the cost of solving a single instance. We prove the first such results in the randomised parity decision tree model. We show that a direct sum theorem holds whenever (1) the lower bound for parity decision trees is proved using the discrepancy method; or (2) the lower bound is proved relative to a product distribution., Comment: 37 pages

Graph machine learning has enjoyed a meteoric rise in popularity since the introduction of deep learning in graph contexts. This is no surprise due to the ubiquity of graph data in large scale industrial settings. Tacitly assumed in all graph learning tasks is the separation of the graph structure and node features: node features strictly encode individual data while the graph structure consists only of pairwise interactions. The driving belief is that node features are (by themselves) insufficient for these tasks, so benchmark performance accurately reflects improvements in graph learning. In our paper, we challenge this orthodoxy by showing that, surprisingly, node features are oftentimes more-than-sufficient for many common graph benchmarks, breaking this critical assumption. When comparing against a well-tuned feature-only MLP baseline on seven of the most commonly used graph learning datasets, one gains little benefit from using graph structure on five datasets. We posit that these datasets do not benefit considerably from graph learning because the features themselves already contain enough graph information to obviate or substantially reduce the need for the graph. To illustrate this point, we perform a feature study on these datasets and show how the features are responsible for closing the gap between MLP and graph-method performance. Further, in service of introducing better empirical measures of progress for graph neural networks, we present a challenging parametric family of principled synthetic datasets that necessitate graph information for nontrivial performance. Lastly, we section out a subset of real-world datasets that are not trivially solved by an MLP and hence serve as reasonable benchmarks for graph neural networks., Comment: Preprint

A metric space $(X,d)$ is said to be $\delta$-hyperbolic if $d(x,y)+d(z,w)$ is at most $\max(d(x,z)+d(y,w), d(x,w)+d(y,z))$ by $2 \delta$. A geodesic space is $\delta$-slim if every geodesic triangle $\Delta(x,y,z)$ is $\delta$-slim. It is well-established that the notions of $\delta$-slimness, $\delta$-hyperbolicity, $\delta$-thinness and similar concepts are equivalent up to a constant factor. In this paper, we investigate these properties under an average-case framework and reveal a surprising discrepancy: while $\mathbb{E}\delta$-slimness implies $\mathbb{E}\delta$-hyperbolicity, the converse does not hold. Furthermore, similar asymmetries emerge for other definitions when comparing average-case and worst-case formulations of hyperbolicity. We exploit these differences to analyze the random Gaussian distribution in Euclidean space, random $d$-regular graph, and the random Erd\H{o}s-R\'enyi graph model, illustrating the implications of these average-case deviations., Comment: 28 pages, 7 figures

Originally motivated for the generation of (Majorana) neutrino masses, the Type 2 Seesaw Model has also a rich extended Higgs sector with, if accessible at the LHC, a distinctive phenomenology of neutral, charged and doubly-charged states. The goal of the work is to present an exhaustive phenomenological study of the most promising production and decay channels of pair or associated scalars, decaying directly or in cascades to Standard Model particles at the LHC. The study is complementary to the literature in that it highlights a previously unnoticed important sensitivity to a mixing angle. The ensuing uncertainty calls for a comprehensive experimental search strategy for the various processes. These processes can be studied within LHC energies reach, by comparing cutflow results for different final states. We carried out prospective search analyses with multi-lepton, jets and missing energy configurations, assuming an ATLAS-like detector at LHC and HL-LHC, for charged, doubly-charged, and for the first time neutral scalar productions. The work is a collaboration between ATLAS experimentalists and theoreticians in continuation of an endeavor that lead to previous published ATLAS analyses for the search of (doubly)charged Higgs bosons, aiming at proposals for future experimental searches., Comment: 6 pages, 2 figures, ICHEP 2024 conference