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The actions of a teacher are one of the strongest factors influencing students' academic achievement and future life success. However, some of the most common frameworks that are used to evaluate teachers and guide their development or improvement are not based on or informed by research about effective teaching methods. Other frameworks are difficult for schools, districts, teachers, and other relevant parties to access due to barriers such as cost. Considering the value of effective teaching, it is worthwhile for a teacher development and evaluation framework informed by research to be created and made easily available. This paper attempts to outline such a framework. This framework has never been researched in practice, but it is based on the available research regarding which teaching practices are generally most effective for students' learning and what sort of evaluation elements are most predictive and useful in assessing teachers. Given this, the framework may be a valuable tool to be used and/or adapted by schools, districts, teachers, and others involved in evaluating teacher effectiveness and guiding professional improvement for teachers.

With growing demand for online courses, it is important to assess the efficacy of quality online virtual instruction. This study investigates the impact of Edmentum's Courseware Algebra I on student achievement using data on 1,205 ninth-grade students from a large urban school district in the Midwest during the 2020-2021 school year. Utilizing a quasi-experimental design, the study compares the NWEA Measures of Academic Progress (MAP) Mathematics scores of students participating in online virtual instruction to those receiving traditional in-person instruction. The study findings reveal that students in the online Algebra I course demonstrated significantly higher mathematics achievement than their peers in in-person classes, with an effect size of 0.13. The positive outcomes associated with Courseware Algebra I highlight its potential as an effective option for high school students.

Causal discovery with time series data remains a challenging yet increasingly important task across many scientific domains. Convergent cross mapping (CCM) and related methods have been proposed to study time series that are generated by dynamical systems, where traditional approaches like Granger causality are unreliable. However, CCM often yields inaccurate results depending upon the quality of the data. We propose the Tangent Space Causal Inference (TSCI) method for detecting causalities in dynamical systems. TSCI works by considering vector fields as explicit representations of the systems' dynamics and checks for the degree of synchronization between the learned vector fields. The TSCI approach is model-agnostic and can be used as a drop-in replacement for CCM and its generalizations. We first present a basic version of the TSCI algorithm, which is shown to be more effective than the basic CCM algorithm with very little additional computation. We additionally present augmented versions of TSCI that leverage the expressive power of latent variable models and deep learning. We validate our theory on standard systems, and we demonstrate improved causal inference performance across a number of benchmark tasks., Comment: 18 pages, 7 figures. Accepted to NeurIPS 2024

Prime-Cam, one of the primary instruments for the Fred Young Submillimeter Telescope (FYST) developed by the CCAT Collaboration, will house up to seven instrument modules, with the first operating at 280 GHz. Each module will include three arrays of superconducting microwave kinetic inductance detectors (KIDs). The first KID array fabricated for the 280 GHz module uses titanium-nitride (TiN) as the superconducting material and has 3,456 individual detectors, while the other two arrays use aluminum. This paper presents the design and laboratory characterization of the 280 GHz TiN array, which is cooled below its critical temperature to ~0.1 K and read out over six RF feedlines. LED mapping, a technique for matching the measured resonant frequency of a detector to its physical position, was performed on the array so that the results can be used to lithographically trim the KID capacitors and increase the yield of the array by reducing frequency collisions. We present the methods and results of LED mapping the 280 GHz TiN KID array before deployment on FYST., Comment: 4 pages, 4 figures, submitted to IEEE Transactions on Applied Superconductivity (IEEE TAS)

Software development is a dynamic activity that requires engineers to work effectively with tools, processes, and collaborative teams. As a result, the presence of friction can significantly hinder productivity, increase frustration, and contribute to low morale among developers. By contrast, higher satisfaction levels are positively correlated with higher levels of perceived productivity. Hence, understanding the factors that cause bad experiences for developers is critical for fostering a positive and productive engineering environment. In this research, we employed a mixed-method approach, including interviews, surveys, diary studies, and analysis of developer telemetry data to uncover and triangulate common factors that cause "bad days" for developers. The interviews involved 22 developers across different levels and roles. The survey captured the perception of 214 developers about factors that cause them to have "bad days," their frequency, and their impact on job satisfaction. The daily diary study engaged 79 developers for 30 days to document factors that caused "bad days" in the moment. We examined the telemetry signals of 131 consenting participants to validate the impact of bad developer experience using system data. Findings from our research revealed factors that cause "bad days" for developers and significantly impact their work and well-being. We discuss the implications of these findings and suggest future work.

Generative AI coding tools are relatively new, and their impact on developers extends beyond traditional coding metrics, influencing beliefs about work and developers' roles in the workplace. This study aims to illuminate developers' preexisting beliefs about generative AI tools, their self perceptions, and how regular use of these tools may alter these beliefs. Using a mixed methods approach, including surveys, a randomized controlled trial, and a three week diary study, we explored the real world application of generative AI tools within a large multinational software company. Our findings reveal that the introduction and sustained use of generative AI coding tools significantly increases developers' perceptions of these tools as both useful and enjoyable. However, developers' views on the trustworthiness of AI generated code remained unchanged. We also discovered unexpected uses of these tools, such as replacing web searches and fostering creative ideation. Additionally, 84 percent of participants reported positive changes in their daily work practices, and 66 percent noted shifts in their feelings about their work, ranging from increased enthusiasm to heightened awareness of the need to stay current with technological advances. This research provides both qualitative and quantitative insights into the evolving role of generative AI in software development and offers practical recommendations for maximizing the benefits of this emerging technology, particularly in balancing the productivity gains from AI-generated code with the need for increased scrutiny and critical evaluation of its outputs., Comment: 11 pages, 2 tables, 1 figure

While DNA origami is a powerful bottom-up fabrication technique, the physical and chemical stability of DNA nanostructures is generally limited to aqueous buffer conditions. Wet chemical silicification can stabilise these structures but does not add further functionality. Here, we demonstrate a versatile 3D nanofabrication technique to conformally coat micrometre-sized DNA origami crystals with functional metal oxides via atomic layer deposition (ALD). In addition to depositing homogenous and conformal nanometre-thin ZnO, TiO2, and IrO2 (multi)layers inside SiO2-stablised crystals, we establish a method to directly coat bare DNA crystals with ALD layers while maintaining the crystal integrity, enabled by critical point drying and low ALD process temperatures. As a proof-of-concept application, we demonstrate electrocatalytic water oxidation using ALD IrO2-coated DNA origami crystals, resulting in improved performance relative to planar films. Overall, our coating strategy establishes a tool set for designing custom-made 3D nanomaterials with precisely defined topologies and material compositions, combining the unique advantages of DNA origami and atomically controlled deposition of functional inorganic materials.

Measurements of the EMC effect in the tritium and helium-3 mirror nuclei are reported. The data were obtained by the MARATHON Jefferson Lab experiment, which performed deep inelastic electron scattering from deuterium and the three-body nuclei, using a cryogenic gas target system and the High Resolution Spectrometers of the Hall A Facility of the Lab. The data cover the Bjorken $x$ range from 0.20 to 0.83, corresponding to a squared four-momentum transfer $Q^2$ range from 2.7 to $11.9\gevsq$, and to an invariant mass $W$ of the final hadronic state greater than 1.84 GeV/${\it c}^2$. The tritium EMC effect measurement is the first of its kind. The MARATHON experimental results are compared to results from previous measurements by DESY-HERMES and JLab-Hall C experiments, as well as with few-body theoretical predictions., Comment: arXiv admin note: text overlap with arXiv:2104.05850

Large Language Models (LLMs) are increasingly embedded into software products across diverse industries, enhancing user experiences, but at the same time introducing numerous challenges for developers. Unique characteristics of LLMs force developers, who are accustomed to traditional software development and evaluation, out of their comfort zones as the LLM components shatter standard assumptions about software systems. This study explores the emerging solutions that software developers are adopting to navigate the encountered challenges. Leveraging a mixed-method research, including 26 interviews and a survey with 332 responses, the study identifies 19 emerging solutions regarding quality assurance that practitioners across several product teams at Microsoft are exploring. The findings provide valuable insights that can guide the development and evaluation of LLM-based products more broadly in the face of these challenges., Comment: 10 pages, 2 tables

We present the discovery of TOI-7041 b (TIC 201175570 b), a hot Saturn transiting a red giant star with measurable stellar oscillations. We observe solar-like oscillations in TOI-7041 with a frequency of maximum power of $\nu_{\rm max} = 218.50\pm2.23$ $\mu$Hz and a large frequency separation of $\Delta\nu = 16.5282\pm0.0186$ $\mu$Hz. Our asteroseismic analysis indicates that TOI-7041 has a radius of $4.10 \pm 0.06$(stat) $\pm$ 0.05(sys) $R_\odot$, making it one of the largest stars around which a transiting planet has been discovered with the Transiting Exoplanet Survey Satellite (TESS), and the mission's first oscillating red giant with a transiting planet. TOI-7041 b has an orbital period of $9.691 \pm 0.006$ days and a low eccentricity of $e = 0.04 \pm 0.04$. We measure a planet radius of $1.02 \pm 0.03$ $R_J$ with photometry from TESS, and a planet mass of $0.36 \pm 0.16$ $M_J$ ($114 \pm 51$ $M_\oplus$) with ground-based radial velocity measurements. TOI-7041 b appears less inflated than similar systems receiving equivalent incident flux, and its circular orbit indicates that it is not undergoing tidal heating due to circularization. The asteroseismic analysis of the host star provides some of the tightest constraints on stellar properties for a TESS planet host and enables precise characterization of the hot Saturn. This system joins a small number of TESS-discovered exoplanets orbiting stars that exhibit clear stellar oscillations and indicates that extended TESS observations of evolved stars will similarly provide a path to improved exoplanet characterization., Comment: 16 pages, 9 figures, 3 tables

In this study, we propose a novel approach to uncover subgroup-specific and subgroup-common latent factors addressing the challenges posed by the heterogeneity of neurological and mental disorders, which hinder disease understanding, treatment development, and outcome prediction. The proposed approach, sparse Group Factor Analysis (GFA) with regularised horseshoe priors, was implemented with probabilistic programming and can uncover associations (or latent factors) among multiple data modalities differentially expressed in sample subgroups. Synthetic data experiments showed the robustness of our sparse GFA by correctly inferring latent factors and model parameters. When applied to the Genetic Frontotemporal Dementia Initiative (GENFI) dataset, which comprises patients with frontotemporal dementia (FTD) with genetically defined subgroups, the sparse GFA identified latent disease factors differentially expressed across the subgroups, distinguishing between "subgroup-specific" latent factors within homogeneous groups and "subgroup common" latent factors shared across subgroups. The latent disease factors captured associations between brain structure and non-imaging variables (i.e., questionnaires assessing behaviour and disease severity) across the different genetic subgroups, offering insights into disease profiles. Importantly, two latent factors were more pronounced in the two more homogeneous FTD patient subgroups (progranulin (GRN) and microtubule-associated protein tau (MAPT) mutation), showcasing the method's ability to reveal subgroup-specific characteristics. These findings underscore the potential of sparse GFA for integrating multiple data modalities and identifying interpretable latent disease factors that can improve the characterization and stratification of patients with neurological and mental health disorders., Comment: 38 pages, 14 figures

We study random integer-valued Lipschitz functions on regular trees. It was shown by Peled, Samotij and Yehudayoff that such functions are localized, however, finer questions about the structure of Gibbs measures remain unanswered. Our main result is that the weak limit of a uniformly chosen 1-Lipschitz function with 0 boundary condition on a $d$-ary tree of height $n$ exists as $n \to \infty$ if $2 \le d \le 7$, but not if $d \ge 8$, thereby partially answering a question posed by Peled, Samotij and Yehudayoff. For large $d$, the value at the root alternates between being almost entirely concentrated on 0 for even $n$ and being roughly uniform on $\{-1,0,1\}$ for odd $n$, leading to different limits as $n$ approaches infinity along evens or odds. For $d \ge 8$, the essence of this phenomenon is preserved, which obstructs the convergence. For $d \le 7$, this phenomenon ceases to exist, and the law of the value at the root loses its connection with the parity of $n$. Along the way, we also obtain an alternative proof of localization. The key idea is a fixed point convergence result for a related operator on $\ell^\infty$, and a procedure to show that the iterations get into a `basin of attraction' of the fixed point. We also prove some accompanying analogous `even-odd phenomenon' type results about $M$-lipschitz functions on general non-amenable graphs with high enough expansion (this includes for example the large $d$ case for regular trees). We also prove a convergence result for 1-Lipschitz functions with $\{0,1\}$ boundary condition. This last result relies on an absolute value FKG for uniform 1-Lipschitz functions when shifted by $1/2$., Comment: 36 pages

This letter explores the implementation of a safe control law for systems of dynamically coupled cooperating agents. Under a CBF-based collaborative safety framework, we examine how the maximum safety capability for a given agent, which is computed using a collaborative safety condition, influences safety requests made to neighbors. We provide conditions under which neighbors may be resilient to non-compliance of neighbors to safety requests, and compute an upper bound for the total amount of non-compliance an agent is resilient to, given its 1-hop neighborhood state and knowledge of the network dynamics. We then illustrate our results via simulation on a networked susceptible-infected-susceptible (SIS) epidemic model., Comment: This work is under review for publication in IEEE Control Systems Letters (L-CSS)

In this paper, we develop a framework for the automatic taxiing of aircraft between hangar and take-off given a graph-based model of an airport. We implement a high-level path-planning algorithm that models taxiway intersections as nodes in an undirected graph, algorithmically constructs a directed graph according to the physical limitations of the aircraft, and finds the shortest valid taxi path through the directed graph using Dijkstra's algorithm. We then use this shortest path to construct a reference trajectory for the aircraft to follow that considers the turning capabilities of a given aircraft. Using high-order control barrier functions (HOCBFs), we construct safety conditions for multi-obstacle avoidance and safe reference tracking for simple 2D unicycle dynamics with acceleration control inputs. We then use these safety conditions to design an MPC-CBF framework that tracks the reference trajectory while adhering to the safety constraints. We compare the performance of our MPC-CBF controller with a PID-CBF control method via simulations., Comment: This work is under review to be presented at the 2025 American Control Conference

This work explores a collaborative method for ensuring safety in multi-agent formation control problems. We formulate a control barrier function (CBF) based safety filter control law for a generic distributed formation controller and extend our previously developed collaborative safety framework to an obstacle avoidance problem for agents with acceleration control inputs. We then incorporate multi-obstacle collision avoidance into the collaborative safety framework. This framework includes a method for computing the maximum capability of agents to satisfy their individual safety requirements. We analyze the convergence rate of our collaborative safety algorithm, and prove the linear-time convergence of cooperating agents to a jointly feasible safe action for all agents under the special case of a tree-structured communication network with a single obstacle for each agent. We illustrate the analytical results via simulation on a mass-spring kinematics-based formation controller and demonstrate the finite-time convergence of the collaborative safety algorithm in the simple proven case, the more general case of a fully-connected system with multiple static obstacles, and with dynamic obstacles., Comment: This work is under review for publication in Automatica. arXiv admin note: text overlap with arXiv:2311.11156

We show that the Faraday effect can be derived from a simple two-state model. This approach uses a quantized electromagnetic field and does not make reference to differences in refractive indices of left- and right-circularly polarized light. Instead it treats the effect as a forward Rayleigh scattering process between two orthogonal modes of the quantized field, and thus emphasises the quantum-mechanical aspects of the phenomenon.

Actively participating in body movement such as dance, sports, and fitness activities is challenging for people who are blind or have low vision (BLV). Teachers primarily rely on verbal instructions and physical demonstrations with limited accessibility. Recent work shows that technology can support body movement education for BLV people. However, there is limited involvement with the BLV community and their teachers to understand their needs. By conducting a series of two surveys, 23 interviews and four focus groups, we gather the voices and perspectives of BLV people and their teachers. This provides a rich understanding of the challenges of body movement education. We identify ten major themes, four key design challenges, and propose potential solutions. We encourage the assistive technologies community to co-design potential solutions to these identified design challenges promoting the quality of life of BLV people and supporting the teachers in the provision of inclusive education.

Infinite nuclear matter provides valuable insights into the behavior of nuclear systems and aids our understanding of atomic nuclei and large-scale stellar objects such as neutron stars. However, partly due to the large basis needed to converge the system's binding energy, size-extensive methods such as coupled-cluster theory struggle with long computational run times, even using the nation's largest high-performance computing facilities. This research introduces a novel approach to the problem. We propose using a machine learning method to predict the coupled-cluster energies of infinite matter systems in the complete basis limit, leveraging only data collected using smaller basis sets. This method promises to deliver high-accuracy results with significantly reduced run times. The sequential regression extrapolation (SRE) algorithm, based on Gaussian processes, was created to perform these extrapolations. By combining Bayesian machine learning with a unique method of formatting the training data, we can create a powerful extrapolator that can make accurate predictions given very little data. The SRE algorithm successfully predicted the CCD(T) energies for pure neutron matter across six densities near nuclear saturation density, with an average error of 0.0083 MeV/N. The algorithm achieved an average error of 0.038 MeV/A for symmetric nuclear matter. These predictions were made with a time savings of 83.8 node hours for pure neutron matter and 284 node hours for symmetric nuclear matter. Additionally, the symmetry energy at these six densities was predicted with an average error of 0.031 MeV/A and a total time savings of 368 node hours compared to the traditional converged coupled-cluster calculations performed without the SRE algorithm.

This work presents a proof-of-concept solution designed to improve the accuracy of radiographic feature characterisation in pre-surgical CT/MR volumes. The solution involves 3D co-location of 2D digital histology slides within ex-vivo, tumour tissue CT volumes. In the initial step, laboratory measurements obtained during histology dissection were used to seed the placement of the individual histology slices in corresponding tumour tissue CT volumes. The process was completed by aligning corresponding bone in histology images to bone in the CT using in-plane point-based registration. Six bisected canine humerus datasets of ex-vivo CT and corresponding measurements were used to validate dissection placements. Digital seeding exhibited a plane misalignment of 0.19 +- 1.8 mm. User input sensitivity caused 0.08 +- 0.2 mm in plane translation and between 0 and 1.6 degrees deviation. These are of similar magnitude to reported misalignment of 0.9-1.3 mm and 1.1-1.9 degrees in related prostate histology co-location [1]. Although this work only reported on animal femur sarcoma CT images and histology slide images, the solution can be generalised to various sarcoma geometries and presentation sites. Furthermore, the solution co-locates high-fidelity data to advance tissue characterisation with minimal disruption to existing clinical workflows. Improved tissue characterisation accuracy, supported by the resolution of histology images, can enhance surgical planning accuracy and patient outcomes by bringing the insights offered by histology closer to the start of the presentation-diagnosis-planning-surgery-recovery loop., Comment: 17 pages, 6 figures

We present a novel way to synthesise the radio emission from extensive air showers. It is a hybrid approach which uses a single microscopic Monte-Carlo simulation to generate the radio emission from a shower with a different longitudinal evolution, primary particle type and energy. The method employs semi-analytical relations which only depend on the shower parameters to transform the radio signal in the simulated antennas. We apply this method to vertical air showers with energies ranging from $10^{17}$ eV to $10^{19}$ eV and compare the results with CoREAS using two different metrics. In order to gauge the performance over our simulation set, we subsequently use every shower in the set as a template to synthesise the emission from the other showers. Depending on the scoring metric, template synthesis reconstructs the radio emission with an accuracy of 5 to 10%., Comment: Proceedings of the 9th ARENA Workshop 2022

We report the first measurement of the azimuthal anisotropy of J$/\psi$ at forward rapidity ($1.2<|\eta|<2.2$) in Au$+$Au collisions at $\sqrt{s_{_{NN}}}=200$ GeV at the Relativistic Heavy Ion Collider. The data were collected by the PHENIX experiment in 2014 and 2016 with integrated luminosity of 14.5~nb$^{-1}$. The second Fourier coefficient ($v_2$) of the azimuthal distribution of $J/\psi$ is determined as a function of the transverse momentum ($p_T$) using the event-plane method. The measurements were performed for several selections of collision centrality: 0\%--50\%, 10\%--60\%, and 10\%-40\%. We find that in all cases the values of $v_2(p_T)$, which quantify the elliptic flow of J$/\psi$, are consistent with zero. The results are consistent with measurements at midrapidity, indicating no significant elliptic flow of the J$/\psi$ within the quark-gluon-plasma medium at collision energies of $\sqrt{s_{_{NN}}}=200$ GeV., Comment: 369 authors from 72 institutions, 12 pages, 7 figures, 5 tables. v1 is version submitted to Physical Review C. HEPdata tables for the points plotted in figures for this and previous PHENIX publications are (or will be) publicly available at http://www.phenix.bnl.gov/papers.html

We present the first forward-rapidity measurements of elliptic anisotropy of open-heavy-flavor muons at the BNL Relativistic Heavy Ion Collider. The measurements are based on data samples of Au$+$Au collisions at $\sqrt{s_{_{NN}}}=200$ GeV collected by the PHENIX experiment in 2014 and 2016 with integrated luminosity of 14.5~nb$^{-1}$. The measurements are performed in the pseudorapidity range $1.2<|\eta|<2$ and cover transverse momenta $1

The abundance and distribution of metal in asteroid surfaces can be constrained from thermal emission measurements at radio wavelengths, informing our understanding of planetesimal differentiation processes. We observed the M-type asteroid (22) Kalliope and its moon Linus in thermal emission at 1.3, 9, and 20 mm with the Atacama Large Millimeter/submillimeter Array (ALMA) and the Karl G. Jansky Very Large Array (VLA) over most of Kalliope's rotation period. The 1.3 mm data provide ~30 km resolution on the surface of Kalliope, while both the 1.3 and 9 mm data resolve Linus from Kalliope. We find a thermal inertia for Kalliope of 116$^{+326}_{-91}$ J m$^{-2}$ s$^{-0.5}$ K$^{-1}$ and emissivities of 0.65$\pm$0.02 at 1.3 mm, 0.56$\pm$0.03 at 9 mm, and 0.77$\pm$0.02 at 20 mm. Kalliope's millimeter wavelength emission is suppressed compared to its centimeter wavelength emission, and is also depolarized. We measure emissivities for Linus of 0.73$\pm$0.04 and 0.85$\pm$0.17 at 1.3 and 9 mm respectively, indicating a less metal-rich surface composition for Linus. Spatial variability in Kalliope's emissivity reveals a region in the northern hemisphere with a high dielectric constant, suggestive of enhanced metal content. These results are together consistent with a scenario in which Linus formed from reaggregated ejecta from an impact onto a differentiated Kalliope, leaving Kalliope with a higher surface metal content than Linus, which is distributed heterogeneously across its surface. The low emissivity and lack of polarization suggest a reduced regolith composition where iron is in the form of metallic grains and constitutes ~25% of the surface composition., Comment: Accepted to PSJ

Giant planets on long period orbits around the nearest stars are among the easiest to directly image. Unfortunately these planets are difficult to fully constrain by indirect methods, e.g., transit and radial velocity (RV). In this study, we present the discovery of a super-Jupiter, HD 222237 b, orbiting a star located $11.445\pm0.002$ pc away. By combining RV data, Hipparcos and multi-epoch Gaia astrometry, we estimate the planetary mass to be ${5.19}_{-0.58}^{+0.58}\,M_{\rm Jup}$, with an eccentricity of ${0.56}_{-0.03}^{+0.03}$ and a period of ${40.8}_{-4.5}^{+5.8}$ yr, making HD 222237 b a promising target for imaging using the Mid-Infrared Instrument (MIRI) of JWST. A comparative analysis suggests that our method can break the inclination degeneracy and thus differentiate between prograde and retrograde orbits of a companion. We further find that the inferred contrast ratio between the planet and the host star in the F1550C filter ($15.50\,\mu \rm m$) is approximately $1.9\times10^{-4}$, which is comparable with the measured limit of the MIRI coronagraphs. The relatively low metallicity of the host star ($\rm-0.32\,dex$) combined with the unique orbital architecture of this system presents an excellent opportunity to probe the planet-metallicity correlation and the formation scenarios of giant planets., Comment: 18 pages, 10 figures, 3 tables, accepted for publication in MNRAS

The use of Large language models (LLMs) to augment clinical decision support systems is a topic with rapidly growing interest, but current shortcomings such as hallucinations and lack of clear source citations make them unreliable for use in the clinical environment. This study evaluates Ask Avo, an LLM-derived software by AvoMD that incorporates a proprietary Language Model Augmented Retrieval (LMAR) system, in-built visual citation cues, and prompt engineering designed for interactions with physicians, against ChatGPT-4 in end-user experience for physicians in a simulated clinical scenario environment. Eight clinical questions derived from medical guideline documents in various specialties were prompted to both models by 62 study participants, with each response rated on trustworthiness, actionability, relevancy, comprehensiveness, and friendly format from 1 to 5. Ask Avo significantly outperformed ChatGPT-4 in all criteria: trustworthiness (4.52 vs. 3.34, p<0.001), actionability (4.41 vs. 3.19, p<0.001), relevancy (4.55 vs. 3.49, p<0.001), comprehensiveness (4.50 vs. 3.37, p<0.001), and friendly format (4.52 vs. 3.60, p<0.001). Our findings suggest that specialized LLMs designed with the needs of clinicians in mind can offer substantial improvements in user experience over general-purpose LLMs. Ask Avo's evidence-based approach tailored to clinician needs shows promise in the adoption of LLM-augmented clinical decision support software., Comment: 8 pages, 1 figure

Endometriosis, affecting about 10% of individuals assigned female at birth, is challenging to diagnose and manage. Diagnosis typically involves the identification of various signs of the disease using either laparoscopic surgery or the analysis of T1/T2 MRI images, with the latter being quicker and cheaper but less accurate. A key diagnostic sign of endometriosis is the obliteration of the Pouch of Douglas (POD). However, even experienced clinicians struggle with accurately classifying POD obliteration from MRI images, which complicates the training of reliable AI models. In this paper, we introduce the Human-AI Collaborative Multi-modal Multi-rater Learning (HAICOMM) methodology to address the challenge above. HAICOMM is the first method that explores three important aspects of this problem: 1) multi-rater learning to extract a cleaner label from the multiple "noisy" labels available per training sample; 2) multi-modal learning to leverage the presence of T1/T2 MRI images for training and testing; and 3) human-AI collaboration to build a system that leverages the predictions from clinicians and the AI model to provide more accurate classification than standalone clinicians and AI models. Presenting results on the multi-rater T1/T2 MRI endometriosis dataset that we collected to validate our methodology, the proposed HAICOMM model outperforms an ensemble of clinicians, noisy-label learning models, and multi-rater learning methods.

Short-period gas giant planets have been shown to be significantly rarer for host stars less massive than the Sun. We report the discovery of two transiting giant planets - TOI-2379 b and TOI-2384 b - with low-mass (early M) host stars. Both planets were detected using TESS photometry and for both the transit signal was validated using ground based photometric facilities. We confirm the planetary nature of these companions and measure their masses using radial velocity observations. We find that TOI-2379 b has an orbital period of 5.469 d and a mass and radius of $5.76\pm0.20$ M$_{J}$ and $1.046\pm0.023$ R$_{J}$ and TOI-2384 b has an orbital period of 2.136 d and a mass and radius of $1.966\pm0.059$ M$_{J}$ and $1.025\pm0.021$ R$_{J}$. TOI-2379 b and TOI-2384 b have the highest and third highest planet-to-star mass ratios respectively out of all transiting exoplanets with a low-mass host star, placing them uniquely among the population of known exoplanets and making them highly important pieces of the puzzle for understanding the extremes of giant planet formation., Comment: Accepted for publication in MNRAS. 15 pages, 12 figures

Edmentum offers a personalized learning platform called Exact Path. Over the past five years, about 56 schools and districts in Virginia have implemented the program. This quasiexperimental study, designed to meet ESSA Tier 2 evidence and What Works Clearinghouse standards with reservations, aimed to investigate the efficacy of Exact Path in Norfolk Public Schools. The study estimated the effects of the program for diverse groups of students in order to inform our understanding of the district context and guide instructional decisions. The study found that Exact Path was positively related to Math achievement for students in Grades 3 through 8. The findings suggest that Exact Path could be an effective tool for improving student success in Virginia and similar contexts. These results have implications for educators, policymakers, and researchers interested in improving academic outcomes through personalized learning.

The MIRI Exoplanets Orbiting White dwarfs (MEOW) Survey is a cycle 2 JWST program to search for exoplanets around dozens of nearby white dwarfs via infrared excess and direct imaging. In this paper, we present the detection of mid-infrared excess at 18 and 21 microns towards the bright (V = 11.4) metal-polluted white dwarf WD 0310-688. The source of the IR excess is almost certainly within the system; the probability of background contamination is $<0.1\%$. While the IR excess could be due to an unprecedentedly small and cold debris disk, it is best explained by a $3.0^{+5.5}_{-1.9}$ M$_{\rm Jup}$ cold (248$^{+84}_{-61}$ K) giant planet orbiting the white dwarf within the forbidden zone (the region where planets are expected to be destroyed during the star's red giant phase). We constrain the source of the IR excess to an orbital separation of 0.1-2 AU, marking the first discovery of a white dwarf planet candidate within this range of separations. WD 0310-688 is a young remnant of an A or late B-type star, and at just 10.4 pc it is now the closest white dwarf with a known planet candidate. Future JWST observations could distinguish the two scenarios by either detecting or ruling out spectral features indicative of a planet atmosphere., Comment: Accepted for publication to ApJL

The stellar obliquity distribution of warm-Jupiter systems is crucial for constraining the dynamical history of Jovian exoplanets, as the warm Jupiters' tidal detachment likely preserves their primordial obliquity. However, the sample size of warm-Jupiter systems with measured stellar obliquities has historically been limited compared to that of hot Jupiters, particularly in hot-star systems. In this work, we present newly obtained sky-projected stellar obliquity measurements for warm-Jupiter systems, TOI-559, TOI-2025, TOI-2031, TOI-2485, TOI-2524, and TOI-3972, derived from the Rossiter-McLaughlin effect, and show that all six systems display alignment with a median measurement uncertainty of 13 degrees. Combining these new measurements with the set of previously reported stellar obliquity measurements, our analysis reveals that single-star warm-Jupiter systems tend to be aligned, even around hot stellar hosts. This alignment exhibits a 3.4-$\sigma$ deviation from the $T_{\rm eff}-\lambda$ dependency observed in hot-Jupiter systems, where planets around cool stars tend to be aligned, while those orbiting hot stars show considerable misalignment. The current distribution of spin-orbit measurements for Jovian exoplanets indicates that misalignments are neither universal nor primordial phenomena affecting all types of planets. The absence of misalignments in single-star warm-Jupiter systems further implies that many hot Jupiters, by contrast, have experienced a dynamically violent history., Comment: 18 pages, 2 figures, accepted for publication in ApJL

Development of methods to control the directional and spectral characteristics of thermal radiation from metallic surfaces is a critical factor enabling many important thermal management applications. In this paper, we study the thermal emission properties of functionalized aluminum surfaces produced through femtosecond laser surface processing (FLSP). These types of surfaces have recently been found to exhibit near-unity broadband omnidirectional emissivity. However, their ultrabroadband absorption response includes visible and near-infrared (IR) radiation, in addition to the mid-IR range, which limits their use as daytime passive radiative cooling devices. Here, we present ways to solve this problem by demonstrating a new design that uses a dielectric Bragg visible light reflector to accurately control the thermal emission spectra of the FLSP surface with the goal of achieving high performance daytime radiative cooling operation. In addition, we propose other designs based on dielectric multilayer structures to further tailor and control the spectra and thermal emission angles of the FLSP surfaces leading to narrowband and broadband directional thermal radiation. The presented photonic engineering approach combined with FLSP structures will be beneficial to various emerging applications, such as radiative cooling, thermal sensing, and thermophotovoltaics.

Recent years have seen increased awareness of the potential significant impacts of computing technologies, both positive and negative. This whitepaper explores how to address possible harmful consequences of computing technologies that might be difficult to anticipate, and thereby mitigate or address. It starts from the assumption that very few harms due to technology are intentional or deliberate; rather, the vast majority result from failure to recognize and respond to them prior to deployment. Nonetheless, there are concrete steps that can be taken to address the difficult problem of anticipating and responding to potential harms from new technologies.

Despite the recent surge of research efforts to make data visualizations accessible to people who are blind or have low vision (BLV), how to support BLV people's data analysis remains an important and challenging question. As refreshable tactile displays (RTDs) become cheaper and conversational agents continue to improve, their combination provides a promising approach to support BLV people's interactive data exploration and analysis. To understand how BLV people would use and react to a system combining an RTD with a conversational agent, we conducted a Wizard-of-Oz study with 11 BLV participants, where they interacted with line charts, bar charts, and isarithmic maps. Our analysis of participants' interactions led to the identification of nine distinct patterns. We also learned that the choice of modalities depended on the type of task and prior experience with tactile graphics, and that participants strongly preferred the combination of RTD and speech to a single modality. In addition, participants with more tactile experience described how tactile images facilitated a deeper engagement with the data and supported independent interpretation. Our findings will inform the design of interfaces for such interactive mixed-modality systems., Comment: Accepted to be presented at IEEE VIS 2024 (Honorable Mention Award) and published in IEEE TVCG

Growing demands in the semiconductor industry necessitate increasingly stringent requirements on throughput and positioning accuracy of lithographic equipment. Meeting these demands involves employing highly aggressive motion profiles, which introduce position-dependent flexible dynamics, thus compromising achievable position tracking performance. This paper introduces a control approach enabling active compensation of position-dependent flexible dynamics by extending the conventional rigid-body control structure to include active control of flexible dynamics. To facilitate real-time implementation of the control algorithm, appropriate position-dependent weighting functions are introduced, ensuring computationally efficient execution of the proposed approach. The efficacy of the proposed control design approach is demonstrated through experiments conducted on a state-of-the-art extreme ultraviolet (EUV) wafer stage., Comment: 7 pages, 7 Figures

Prime-Cam is a first-generation science instrument for the CCAT Observatory's six-meter aperture Fred Young Submillimeter Telescope (FYST). FYST's crossed-Dragone design provides high optical throughput to take advantage of its unique site at 5600 m on Cerro Chajnantor in Chile's Atacama Desert to reach mapping speeds over ten times greater than current and near-term submillimeter experiments. Housing up to seven independent instrument modules in its 1.8-meter diameter cryostat, Prime-Cam will combine broadband polarization-sensitive modules and spectrometer modules designed for observations in several frequency windows between 210 GHz and 850 GHz to study a wide range of astrophysical questions from Big Bang cosmology to the formation of stars and galaxies in the Epoch of Reionization and beyond. In order to cover this range of frequencies and observation modes, each of the modules contains a set of cold reimaging optics that is optimized for the science goals of that module. These optical setups include several filters, three or four anti-reflection-coated silicon lenses, and a Lyot stop to control the field of view and illumination of the primary mirror, satisfy a series of mechanical constraints, and maximize optical performance within each passband. We summarize the design considerations and trade-offs for the optics in these modules and provide a status update on the fabrication of the Prime-Cam receiver and the design of its 1 K and 100 mK thermal BUSs., Comment: 10 pages, 6 figures, presented at SPIE Millimeter, Submillimeter, and Far-Infrared Detectors and Instrumentation for Astronomy XII

Visual icons provide immediate recognition of features on print maps but do not translate well for touch reading by people who are blind or have low vision due to the low fidelity of tactile perception. We explored 3D printed icons as an equivalent to visual icons for tactile maps addressing these problems. We designed over 200 tactile icons (TactIcons) for street and park maps. These were touch tested by blind and sighted people, resulting in a corpus of 33 icons that can be recognised instantly and a further 34 icons that are easily learned. Importantly, this work has informed the creation of detailed guidelines for the design of TactIcons and a practical methodology for touch testing new TactIcons. It is hoped that this work will contribute to the creation of more inclusive, user-friendly tactile maps for people who are blind or have low vision., Comment: Published in Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI '23), April 23-28, 2023, Hamburg, Germany. ACM, New York, NY, USA

We report the spectroscopic confirmation and fundamental properties of TOI-757 b, a mini-Neptune on a 17.5-day orbit transiting a bright star ($V = 9.7$ mag) discovered by the TESS mission. We acquired high-precision radial velocity measurements with the HARPS, ESPRESSO, and PFS spectrographs to confirm the planet detection and determine its mass. We also acquired space-borne transit photometry with the CHEOPS space telescope to place stronger constraints on the planet radius, supported with ground-based LCOGT photometry. WASP and KELT photometry were used to help constrain the stellar rotation period. We also determined the fundamental parameters of the host star. We find that TOI-757 b has a radius of $R_{\mathrm{p}} = 2.5 \pm 0.1 R_{\oplus}$ and a mass of $M_{\mathrm{p}} = 10.5^{+2.2}_{-2.1} M_{\oplus}$, implying a bulk density of $\rho_{\text{p}} = 3.6 \pm 0.8$ g cm$^{-3}$. Our internal composition modeling was unable to constrain the composition of TOI-757 b, highlighting the importance of atmospheric observations for the system. We also find the planet to be highly eccentric with $e$ = 0.39$^{+0.08}_{-0.07}$, making it one of the very few highly eccentric planets among precisely characterized mini-Neptunes. Based on comparisons to other similar eccentric systems, we find a likely scenario for TOI-757 b's formation to be high eccentricity migration due to a distant outer companion. We additionally propose the possibility of a more intrinsic explanation for the high eccentricity due to star-star interactions during the earlier epoch of the Galactic disk formation, given the low metallicity and older age of TOI-757., Comment: Accepted for publication in MNRAS; 26 pages, 14 figures, 6 tables

This study presents estimates of the global expenditure on article processing charges (APCs) paid to six publishers for open access between 2019 and 2023. APCs are fees charged for publishing in some fully open access journals (gold) and in subscription journals to make individual articles open access (hybrid). There is currently no way to systematically track institutional, national or global expenses for open access publishing due to a lack of transparency in APC prices, what articles they are paid for, or who pays them. We therefore curated and used an open dataset of annual APC list prices from Elsevier, Frontiers, MDPI, PLOS, Springer Nature, and Wiley in combination with the number of open access articles from these publishers indexed by OpenAlex to estimate that, globally, a total of \$8.349 billion (\$8.968 billion in 2023 US dollars) were spent on APCs between 2019 and 2023. We estimate that in 2023 MDPI (\$681.6 million), Elsevier (\$582.8 million) and Springer Nature (\$546.6) generated the most revenue with APCs. After adjusting for inflation, we also show that annual spending almost tripled from \$910.3 million in 2019 to \$2.538 billion in 2023, that hybrid exceed gold fees, and that the median APCs paid are higher than the median listed fees for both gold and hybrid. Our approach addresses major limitations in previous efforts to estimate APCs paid and offers much needed insight into an otherwise opaque aspect of the business of scholarly publishing. We call upon publishers to be more transparent about OA fees., Comment: 21 pages, 6 figures, 4 tables

In a multi-valley electronic band structure, lifting of the valley degeneracy is associated with rotational symmetry breaking in the electronic fluid, and may emerge through spontaneous symmetry breaking order, or through a large response to a small external perturbation such as strain. In this work we use scanning tunneling microscopy to investigate an unexpected rotational symmetry breaking in Landau levels formed in the unusual floating surface band of ZrSiS. We visualize a ubiquitous splitting of Landau levels into valley-polarized sub-levels. We demonstrate methods to measure valley-selective Landau level spectroscopy, to infer unknown Landau level indices, and to precisely measure each valley's Berry phase in a way that is agnostic to the band structure and topology of the system. These techniques allow us to obtain each valley's dispersion curve and infer a rigid valley-dependent contribution to the band energies. Ruling out spontaneous symmetry breaking by establishing the sample-dependence of this valley splitting, we explain the effect in terms of residual strain. A quantitative estimate indicates that uniaxial strain can be measured to a precision of $ \lt 0.025 \% $. The extreme valley-polarization of the Landau levels results from as little as $ \sim 0.1 \% $ strain, and this suggests avenues for manipulation using deliberate strain engineering., Comment: 13 pages, 9 figures (plus 13 pages, 9 figures)

Bayesian inference provides a powerful tool for leveraging observational data to inform model predictions and uncertainties. However, when such data is limited, Bayesian inference may not adequately constrain uncertainty without the use of highly informative priors. Common approaches for constructing informative priors typically rely on either assumptions or knowledge of the underlying physics, which may not be available in all scenarios. In this work, we consider the scenario where data are available on a population of assets/individuals, which occurs in many problem domains such as biomedical or digital twin applications, and leverage this population-level data to systematically constrain the Bayesian prior and subsequently improve individualized inferences. The approach proposed in this paper is based upon a recently developed technique known as data-consistent inversion (DCI) for constructing a pullback probability measure. Succinctly, we utilize DCI to build population-informed priors for subsequent Bayesian inference on individuals. While the approach is general and applies to nonlinear maps and arbitrary priors, we prove that for linear inverse problems with Gaussian priors, the population-informed prior produces an increase in the information gain as measured by the determinant and trace of the inverse posterior covariance. We also demonstrate that the Kullback-Leibler divergence often improves with high probability. Numerical results, including linear-Gaussian examples and one inspired by digital twins for additively manufactured assets, indicate that there is significant value in using these population-informed priors., Comment: Corrected typos

Inference of Large Language Models (LLMs) across computer clusters has become a focal point of research in recent times, with many acceleration techniques taking inspiration from CPU speculative execution. These techniques reduce bottlenecks associated with memory bandwidth, but also increase end-to-end latency per inference run, requiring high speculation acceptance rates to improve performance. Combined with a variable rate of acceptance across tasks, speculative inference techniques can result in reduced performance. Additionally, pipeline-parallel designs require many user requests to maintain maximum utilization. As a remedy, we propose PipeInfer, a pipelined speculative acceleration technique to reduce inter-token latency and improve system utilization for single-request scenarios while also improving tolerance to low speculation acceptance rates and low-bandwidth interconnects. PipeInfer exhibits up to a 2.15$\times$ improvement in generation speed over standard speculative inference. PipeInfer achieves its improvement through Continuous Asynchronous Speculation and Early Inference Cancellation, the former improving latency and generation speed by running single-token inference simultaneously with several speculative runs, while the latter improves speed and latency by skipping the computation of invalidated runs, even in the middle of inference., Comment: 11 pages, submitted to SC24 conference

In perovskite solar cells, photovoltaic action is created by charge transport layers (CTLs) either side of the light-absorbing metal halide perovskite semiconductor. Hence, the rates for desirable charge extraction and unwanted interfacial recombination at the perovskite-CTL interfaces play a critical role for device efficiency. Here, the electrical properties of perovskite-CTL bilayer heterostructures are obtained using ultrafast THz and optical studies of the charge carrier dynamics after pulsed photoexcitation, combined with a physical model of charge carrier transport that includes the prominent Coulombic forces that arise after selective charge extraction into a CTL, and cross-interfacial recombination. The charge extraction velocity at the interface and the ambipolar diffusion coefficient within the perovskite are determined from the experimental decay profiles for heterostructures with three of the highest performing CTLs, namely C$_{60}$, PCBM and Spiro-OMeTAD. Definitive targets for the further improvement of devices are deduced: fullerenes deliver fast electron extraction, but suffer from a large rate constant for cross-interface recombination or hole extraction. Conversely, Spiro-OMeTAD exhibits slow hole extraction but does not increase the perovskite's surface recombination rate, likely contributing to its success in solar cell devices., Comment: 33 pages, 6 figures

We present a quantitative spectral analysis of the extreme nitrogen-enhanced supergiant HD 93840 (BN1 Ib) at an intermediate galactic latitude. Based on an optical high-resolution spectrum and complementary ultraviolet and infrared (spectro-)photometry, in addition to Gaia data, we carried out a full characterisation of the star's properties. We used both hydrostatic and unified (photosphere+wind) model atmospheres that account for deviations from local thermodynamic equilibrium. A highly unusual surface CNO-mixing signature and a marked stellar overluminosity compared to the mass imply a binary channel for the star's past evolution. The kinematics shows that it has reached its current position above the Galactic plane as a runaway star, likely ejected by the supernova explosion of its former companion star. Its current bulk composition, with a notably increased mean molecular weight due to core He- and progressed shell H-burning, suggests an advanced evolutionary stage. It is poised to yield a rare core-collapse supernova of a blue supergiant about ten OB star population scale heights above the Galactic disk relatively soon, contributing to the metal enrichment of the circumgalactic medium., Comment: 19 pages, 14 figures, published in Astronomy & Astrophysics

Submillimeter and millimeter wavelengths provide a unique view of the Universe, from the gas and dust that fills and surrounds galaxies to the chromosphere of our own Sun. Current single-dish facilities have presented a tantalising view of the brightest (sub-)mm sources, and interferometers have provided the exquisite resolution necessary to analyse the details in small fields, but there are still many open questions that cannot be answered with current facilities. In this report we summarise the science that is guiding the design of the Atacama Large Aperture Submillimeter Telescope (AtLAST). We demonstrate how tranformational advances in topics including star formation in high redshift galaxies, the diffuse circumgalactic medium, Galactic ecology, cometary compositions and solar flares motivate the need for a 50m, single-dish telescope with a 1-2 degree field of view and a new generation of highly multiplexed continuum and spectral cameras. AtLAST will have the resolution to drastically lower the confusion limit compared to current single-dish facilities, whilst also being able to rapidly map large areas of the sky and detect extended, diffuse structures. Its high sensitivity and large field of view will open up the field of submillimeter transient science by increasing the probability of serendipitous detections. Finally, the science cases listed here motivate the need for a highly flexible operations model capable of short observations of individual targets, large surveys, monitoring programmes, target of opportunity observations and coordinated observations with other observatories. AtLAST aims to be a sustainable, upgradeable, multipurpose facility that will deliver orders of magnitude increases in sensitivity and mapping speeds over current and planned submillimeter observatories., Comment: 47 pages, 12 figures. For further details on AtLAST see https://atlast.uio.no

Driven by advancements in sensing and computing, deep reinforcement learning (DRL)-based methods have demonstrated significant potential in effectively tackling distribution system restoration (DSR) challenges under uncertain operational scenarios. However, the data-intensive nature of DRL poses obstacles in achieving satisfactory DSR solutions for large-scale, complex distribution systems. Inspired by the transformative impact of emerging foundation models, including large language models (LLMs), across various domains, this paper explores an innovative approach harnessing LLMs' powerful computing capabilities to address scalability challenges inherent in conventional DRL methods for solving DSR. To our knowledge, this study represents the first exploration of foundation models, including LLMs, in revolutionizing conventional DRL applications in power system operations. Our contributions are twofold: 1) introducing a novel LLM-powered Physics-Informed Decision Transformer (PIDT) framework that leverages LLMs to transform conventional DRL methods for DSR operations, and 2) conducting comparative studies to assess the performance of the proposed LLM-powered PIDT framework at its initial development stage for solving DSR problems. While our primary focus in this paper is on DSR operations, the proposed PIDT framework can be generalized to optimize sequential decision-making across various power system operations.

Counterfactual reasoning allows us to explore hypothetical scenarios in order to explain the impacts of our decisions. However, addressing such inquires is impossible without establishing the appropriate mathematical framework. In this work, we introduce the problem of counterfactual reasoning in the context of vector autoregressive (VAR) processes. We also formulate the inference of a causal model as a joint regression task where for inference we use both data with and without interventions. After learning the model, we exploit linearity of the VAR model to make exact predictions about the effects of counterfactual interventions. Furthermore, we quantify the total causal effects of past counterfactual interventions. The source code for this project is freely available at https://github.com/KurtButler/counterfactual_interventions.

The large dimensionality of environments is the limiting factor in applying optimal control to open quantum systems beyond Markovian approximations. Multiple methods exist to simulate non-Markovian open systems which effectively reduce the environment to a number of active degrees of freedom. Here we show that several of these methods can be expressed in terms of a process tensor in the form of a matrix-product-operator, which serves as a unifying framework to show how they can be used in optimal control, and to compare their performance. The matrix-product-operator form provides a general scheme for computing gradients using back propagation, and allows the efficiency of the different methods to be compared via the bond dimensions of their respective process tensors., Comment: 14 pages, 9 figures

Non-Markovian dynamics arising from the strong coupling of a system to a structured environment is essential in many applications of quantum mechanics and emerging technologies. Deriving an accurate description of general quantum dynamics including memory effects is however a demanding task, prohibitive to standard analytical or direct numerical approaches. We present a major release of our open source software package, OQuPy (Open Quantum System in Python), which provides several recently developed numerical methods that address this challenging task. It utilizes the process tensor approach to open quantum systems in which a single map, the process tensor, captures all possible effects of an environment on the system. The representation of the process tensor in a tensor network form allows an exact yet highly efficient description of non-Markovian open quantum systems (NM-OQS). The OQuPy package provides methods to (1) compute the dynamics and multi-time correlations of quantum systems coupled to single and multiple environments, (2) optimize control protocols for NM-OQS, (3) simulate interacting chains of NM-OQS, and (4) compute the mean-field dynamics of an ensemble of NM-OQS coupled to a common central system. Our aim is to provide an easily accessible and extensible tool for researchers of open quantum systems in fields such as quantum chemistry, quantum sensing, and quantum information., Comment: 20 pages, 15 figures

Overcoming the challenge of limited data availability within materials science is crucial for the broad-based applicability of machine learning within materials science. One pathway to overcome this limited data availability is to use the framework of transfer learning (TL), where a pre-trained (PT) machine learning model (on a larger dataset) can be fine-tuned (FT) on a target (typically smaller) dataset. Our study systematically explores the effectiveness of various PT/FT strategies to learn and predict material properties with limited data. Specifically, we leverage graph neural networks (GNNs) to PT/FT on seven diverse curated materials datasets, encompassing sizes ranging from 941 to 132,752 datapoints. We consider datasets that cover a spectrum of material properties, ranging from band gaps (electronic) to formation energies (thermodynamic) and shear moduli (mechanical). We study the influence of PT and FT dataset sizes, strategies that can be employed for FT, and other hyperparameters on pair-wise TL among the datasets considered. We find our pair-wise PT-FT models to consistently outperform models trained from scratch on the target datasets. Importantly, we develop a GNN framework that is simultaneously PT on multiple properties (MPT), enabling the construction of generalized GNN models. Our MPT models outperform pair-wise PT-FT models on several datasets considered, and more significantly, on a 2D material band gap dataset that is completely out-of-distribution from the PT datasets. Finally, we expect our PT/FT and MPT frameworks to be generalizable to other GNNs and materials properties, which can accelerate materials design and discovery for various applications.

We report the discovery of two transiting planets detected by the Transiting Exoplanet Survey Satellite (TESS), TOI-2374 b and TOI-3071 b, orbiting a K5V and an F8V star, respectively, with periods of 4.31 and 1.27 days, respectively. We confirm and characterize these two planets with a variety of ground-based and follow-up observations, including photometry, precise radial velocity monitoring and high-resolution imaging. The planetary and orbital parameters were derived from a joint analysis of the radial velocities and photometric data. We found that the two planets have masses of $(57 \pm 4)$ $M_\oplus$ or $(0.18 \pm 0.01)$ $M_J$, and $(68 \pm 4)$ $M_\oplus$ or $(0.21 \pm 0.01)$ $M_J$, respectively, and they have radii of $(6.8 \pm 0.3)$ $R_\oplus$ or $(0.61 \pm 0.03)$ $R_J$ and $(7.2 \pm 0.5)$ $R_\oplus$ or $(0.64 \pm 0.05)$ $R_J$, respectively. These parameters correspond to sub-Saturns within the Neptunian desert, both planets being hot and highly irradiated, with $T_{\rm eq} \approx 745$ $K$ and $T_{\rm eq} \approx 1812$ $K$, respectively, assuming a Bond albedo of 0.5. TOI-3071 b has the hottest equilibrium temperature of all known planets with masses between $10$ and $300$ $M_\oplus$ and radii less than $1.5$ $R_J$. By applying gas giant evolution models we found that both planets, especially TOI-3071 b, are very metal-rich. This challenges standard formation models which generally predict lower heavy-element masses for planets with similar characteristics. We studied the evolution of the planets' atmospheres under photoevaporation and concluded that both are stable against evaporation due to their large masses and likely high metallicities in their gaseous envelopes., Comment: 24 pages, 22 figures, 10 tables, accepted for publication in MNRAS