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A correlational study was conducted using ACT (American College Test) sub-test scores and actual grades earned in corresponding subject areas in order to determine if the ACT was an effective predictor for student's grades at Southern West Virginia Community College (SWVCC). Study subjects were all students (n= 241) who had ACT test scores on file and who were enrolled at SWVCC. Correlational analyses yielded coefficients of such low magnitude as to conclude that there is no significant relationship between ACT test scores and academic performance at SWVCC. Based on these findings, it was recommended that use of the ACT be discontinued and that a more suitable instrument such as the College Qualifying Test or the Comparative Guidance and Placement Test, both of which have been developed for and normed on community college students, be used. (Author/JDS)

Dual Active Galactic Nuclei (dual AGNs), a phase in some galaxy mergers during which both central supermassive black holes (SMBHs) are active, are expected to be a key observable stage leading up to SMBH mergers. Constraining the population of dual AGNs in both the nearby and high-z universe has proven to be elusive until very recently. We present a multi-wavelength follow-up campaign to confirm the nature of a sample of 20 candidate dual AGNs at cosmic noon (z~2) from the VODKA sample. Through a combination of Hubble Space Telescope (HST) and Very Large Array (VLA) imaging, we refute the possibility of gravitational lensing in all but one target. We find evidence of dual AGNs in four systems, while seven exhibit single AGN in galaxy pairs, either through strong radio emission or ancillary emission line data. The remaining systems are either confirmed as quasar-star superpositions (six) or non-lensed pairs (two) that require further investigations to establish AGN activity. Among the systems with radio detections, we find a variety of radio spectral slopes and UV/optical colors suggesting that our sample contains a range of AGN properties, from obscured radio-quiet objects to those with powerful synchrotron-emitting jets. This study presents one of the largest dedicated multi-wavelength follow-up campaigns to date searching for dual AGNs at high redshift. We confirm several of the highest-z systems at small physical separations, thus representing some of the most evolved dual AGN systems at the epoch of peak quasar activity known to date., Comment: 26 pages, 8 figures, submitted to ApJ, includes appendix of 9 additional figures and 18 tables

Diffusion models have quickly risen in popularity for their ability to model complex distributions and perform effective posterior sampling. Unfortunately, the iterative nature of these generative models makes them computationally expensive and unsuitable for real-time sequential inverse problems such as ultrasound imaging. Considering the strong temporal structure across sequences of frames, we propose a novel approach that models the transition dynamics to improve the efficiency of sequential diffusion posterior sampling in conditional image synthesis. Through modeling sequence data using a video vision transformer (ViViT) transition model based on previous diffusion outputs, we can initialize the reverse diffusion trajectory at a lower noise scale, greatly reducing the number of iterations required for convergence. We demonstrate the effectiveness of our approach on a real-world dataset of high frame rate cardiac ultrasound images and show that it achieves the same performance as a full diffusion trajectory while accelerating inference 25$\times$, enabling real-time posterior sampling. Furthermore, we show that the addition of a transition model improves the PSNR up to 8\% in cases with severe motion. Our method opens up new possibilities for real-time applications of diffusion models in imaging and other domains requiring real-time inference., Comment: 5 pages, 4 figures, preprint

In modern deep learning, the models are learned by applying gradient updates using an optimizer, which transforms the updates based on various statistics. Optimizers are often hand-designed and tuning their hyperparameters is a big part of the training process. Learned optimizers have shown some initial promise, but are generally unsuccessful as a general optimization mechanism applicable to every problem. In this work we explore a different direction: instead of learning general optimizers, we instead specialize them to a specific training environment. We propose a novel optimizer technique that learns a layer-specific linear combination of update directions provided by a set of base optimizers, effectively adapting its strategy to the specific model and dataset. When evaluated on image classification tasks, this specialized optimizer significantly outperforms both traditional off-the-shelf methods such as Adam, as well as existing general learned optimizers. Moreover, it demonstrates robust generalization with respect to model initialization, evaluating on unseen datasets, and training durations beyond its meta-training horizon.

We investigated the effect of 0.6 MeV proton irradiation on the superconducting and normal state properties of thin-film $\text{YBa}_{2}\text{Cu}_{3}\text{O}_{7-\delta}$ superconductors. A thin-film YBCO superconductor ($\approx$ 567 nm thick) was subject to a series of proton irradiations with a total fluence of $7.6\times10^{16}$ $\text{p/cm}^2$. Upon irradiation, $T_c$ was drastically decreased from 89.3 K towards zero with a corresponding increase in its normal state resistivity above $T_c$. This increase in resistivity which indicates the increase of defects inside the thin-film sample can be converted to the dimensionless scattering rate. We found that the relation between $T_c$ and dimensionless scattering rate obtained during proton irradiation approximates the generalized d-wave Abrikosov-Gor'kov theory better than the previous results obtained from electron irradiations. This is an unexpected result since the electron irradiation is known to be most effective to suppress superconductivity over other heavier ion irradiations such as proton irradiation. It suggests that the type of defects created by proton irradiation evolves from cascade defects (in bulk single crystals) to point-like defects (in thin-film single crystals) as the thickness decreases.

The SNO+ detector operated initially as a water Cherenkov detector. The implementation of a sealed covergas system midway through water data taking resulted in a significant reduction in the activity of $^{222}$Rn daughters in the detector and allowed the lowest background to the solar electron scattering signal above 5 MeV achieved to date. This paper reports an updated SNO+ water phase $^8$B solar neutrino analysis with a total livetime of 282.4 days and an analysis threshold of 3.5 MeV. The $^8$B solar neutrino flux is found to be $\left(2.32^{+0.18}_{-0.17}\text{(stat.)}^{+0.07}_{-0.05}\text{(syst.)}\right)\times10^{6}$ cm$^{-2}$s$^{-1}$ assuming no neutrino oscillations, or $\left(5.36^{+0.41}_{-0.39}\text{(stat.)}^{+0.17}_{-0.16}\text{(syst.)} \right)\times10^{6}$ cm$^{-2}$s$^{-1}$ assuming standard neutrino oscillation parameters, in good agreement with both previous measurements and Standard Solar Model Calculations. The electron recoil spectrum is presented above 3.5 MeV.

Transition metal dichalcogenides (TMDs) are layered two-dimensional semiconductors explored for various optoelectronic applications, ranging from light-emitting diodes to single-photon emitters. To interact strongly with light, such devices require monolayer TMDs, which exhibit a direct bandgap. These atomically thin sheets are typically obtained through mechanical exfoliation followed by manual identification with a brightfield optical microscope. While this traditional procedure provides high-quality crystals, the identification step is time-intensive, low-throughput, and prone to human error, creating a significant bottleneck for TMD research. Here, we report a simple and fully automated approach for high-throughput identification of TMD monolayers using photoluminescence microscopy. Compared to a manual search and verification, our methodology offers a four-orders-of-magnitude decrease in the time a researcher must invest per identified monolayer. This ability enables us to measure geometric and photoluminescence-intensity features of more than 2,400 monolayers and bilayers of WSe$_2$, MoSe$_2$, and MoS$_2$. Due to these large numbers, we can study and quantify material properties previously inaccessible. For example, we show that the mean photoluminescence intensity from a monolayer correlates with its size due to reduced emission from its edges. Further, we observe large variations in brightness (up to 10$\times$) from WSe$_2$ monolayers of different batches produced by the same supplier. Therefore, our automated approach not only increases fabrication efficiency but also enhances sample quality for optoelectronic devices of atomically thin semiconductors.

More than 3000 spectroscopically confirmed Type Ia supernovae (SNe Ia) are presented in the Zwicky Transient Facility SN Ia Data Release 2 (ZTF DR2). In this paper, we detail the spectral properties of 482 SNe Ia near maximum light, up to a redshift limit of $z$ $\leq$ 0.06. We measure the velocities and pseudo-equivalent widths (pEW) of key spectral features (Si II $\lambda$5972 and Si II $\lambda$6355) and investigate the relation between the properties of the spectral features and the photometric properties from the SALT2 light-curve parameters as a function of spectroscopic sub-class. We discuss the non-negligible impact of host galaxy contamination on SN Ia spectral classifications, as well as investigate the accuracy of spectral template matching of the ZTF DR2 sample. We define a new subclass of underluminous SNe Ia (`04gs-like') that lie spectroscopically between normal SNe Ia and transitional 86G-like SNe Ia (stronger Si II $\lambda$5972 than normal SNe Ia but significantly weaker Ti II features than `86G-like' SNe). We model these `04gs-like' SN Ia spectra using the radiative-transfer spectral synthesis code tardis and show that cooler temperatures alone are unable to explain their spectra; some changes in elemental abundances are also required. However, the broad continuity in spectral properties seen from bright (`91T-like') to faint normal SN Ia, including the transitional and 91bg-like SNe Ia, suggests that variations within a single explosion model may be able to explain their behaviour.

We present a new method to combine multimass equilibrium dynamical models and pulsar timing data to constrain the mass distribution and remnant populations of Milky Way globular clusters (GCs). We first apply this method to 47 Tuc, a cluster for which there exists an abundance of stellar kinematic data and which is also host to a large population of millisecond pulsars. We demonstrate that the pulsar timing data allow us to place strong constraints on the overall mass distribution and remnant populations even without fitting on stellar kinematics. Our models favor a small population of stellar-mass BHs in this cluster (with a total mass of $446^{+75}_{-72} \mathrm{M_\odot}$), arguing against the need for a large ($ > 2000 \ \mathrm{M_\odot}$) central intermediate-mass black hole. We then apply the method to Terzan 5, a heavily obscured bulge cluster which hosts the largest population of millisecond pulsars of any Milky Way GC and for which the collection of conventional stellar kinematic data is very limited. We improve existing constraints on the mass distribution and structural parameters of this cluster and place stringent constraints on its black hole content, finding an upper limit on the mass in BHs of $\sim 4000 \ \mathrm{M_\odot}$. This method allows us to probe the central dynamics of GCs even in the absence of stellar kinematic data and can be easily applied to other GCs with pulsar timing data, for which datasets will continue to grow with the next generation of radio telescopes., Comment: 33 pages, 19 figures. Accepted for publication in ApJ

Ultrasound images formed by delay-and-sum beamforming are plagued by artifacts that only clear up after compounding many transmissions. Some prior works pose imaging as an inverse problem. This approach can yield high image quality with few transmits, but requires a very fine image grid and is not robust to changes in measurement model parameters. We present INverse grid-Free Estimation of Reflectivities (INFER), an off-grid and stochastic algorithm that solves the inverse scattering problem in ultrasound imaging. Our method jointly optimizes for the locations of the gridpoints, their reflectivities, and the measurement model parameters such as the speed of sound. This approach allows us to use significantly fewer gridpoints, while obtaining better contrast and resolution and being more robust to changes in the imaging target and the hardware. The use of stochastic optimization enables solving for multiple transmissions simultaneously without increasing the required memory or computational load per iteration. We show that our method works across different imaging targets and across different transmit schemes and compares favorably against other beamforming and inverse solvers. The source code and the dataset to reproduce the results in this paper are available at www.github.com/vincentvdschaft/off-grid-ultrasound.

Neutrinoless double beta decay is one of the most sensitive probes for new physics beyond the Standard Model of particle physics. One of the isotopes under investigation is $^{136}$Xe, which would double beta decay into $^{136}$Ba. Detecting the single $^{136}$Ba daughter provides a sort of ultimate tool in the discrimination against backgrounds. Previous work demonstrated the ability to perform single atom imaging of Ba atoms in a single-vacancy site of a solid xenon matrix. In this paper, the effort to identify signal from individual barium atoms is extended to Ba atoms in a hexa-vacancy site in the matrix and is achieved despite increased photobleaching in this site. Abrupt fluorescence turn-off of a single Ba atom is also observed. Significant recovery of fluorescence signal lost through photobleaching is demonstrated upon annealing of Ba deposits in the Xe ice. Following annealing, it is observed that Ba atoms in the hexa-vacancy site exhibit antibleaching while Ba atoms in the tetra-vacancy site exhibit bleaching. This may be evidence for a matrix site transfer upon laser excitation. Our findings offer a path of continued research toward tagging of Ba daughters in all significant sites in solid xenon., Comment: 9 pages, 8 figures

We investigate the absolute calibration of the Tip of the Red Giant Branch (TRGB) in the Small Magellanic Cloud (SMC) using small amplitude red giant stars (SARGs) classified by the Optical Gravitational Lensing Experiment (OGLE). We show that all stars near the SMC's TRGB are SARGs. Distinguishing older and younger RGs near the Tip according to two period-luminosity sequences labeled A and B, we show many similarities among SARG populations of the LMC and the SMC, along with notable differences. Specifically, SMC SARGs have shorter periods due to lower metallicity and lower amplitudes due to younger ages than LMC SARGs. We discover two period-color relations near the TRGB that span all A-sequence and B-sequence stars in the OGLE-III footprints of the SMC and LMC, and we investigate using periods instead of color for TRGB standardization. Using variability derived information only, we trace the SMC's age and metallicity gradients and show the core to be populated by younger, more metal rich RGs. B-sequence SARGs yield both the most precise and the brightest tip magnitude, and they are best suited for distance determination and Hubble constant measurements because they correspond to the oldest stars near TRGB. Assuming the geometric distance measured by detached eclipsing binaries, the B-sequence yields the SMC's most accurate TRGB calibration to date: M_{F814W,syn} = -4.057 \pm 0.019(stat.) \pm 0.029(syst.) mag (1.5% in distance). Further study of SARGs will unravel the impact of population diversity on TRGB distances and further improve TRGB standardization., Comment: 20 pages, 13 figures, submitted for peer-review. Comments welcome!

The blue compact dwarf galaxy I Zw 18 is one of the most metal-poor ($Z \sim 3% Z_{\sun}$) star-forming galaxies in the local Universe. Its evolutionary status has sparked debate within the astronomical community. We aim to investigate the stellar populations of I Zw 18 in the near-IR using JWST/NIRCam's high spatial resolution and sensitivity. Additionally, we aim to derive the galaxy's spatially resolved star formation history (SFH) over the last 1 Gyr and provide constraints for older epochs. We used DOLPHOT to measure positions and fluxes of point sources in the F115W and F200W filters' images of I Zw 18. To derive I Zw 18's SFH, we applied the color-magnitude diagram (CMD) fitting technique SFERA 2.0, using two independent sets of stellar models. Our analysis reveals three main stellar populations: one younger than $\sim30$ Myr, mainly in the northwest star-forming (SF) region; an intermediate-age population ($\sim 100 - 800$ Myr) in the southeast SF region; and a red and faint population linked to the underlying halo, older than 1 Gyr and possibly as old as 13.8 Gyr. The main body of the galaxy shows a very low star formation rate (SFR) of $\sim 10^{-4} M_{\odot} \text{yr}^{-1}$ between 1 and 13.8 Gyr ago. In the last billion years, I Zw 18 shows increasing SF, with strong bursts around $\sim10$ and $\sim100$ Myr ago. Component C mirrors the main body's evolution but with lower SFRs. Our findings confirm that I Zw 18 contains stars of all ages, indicating it is not a young galaxy but has an old stellar halo, similar to other BCDs. The low SF activity over the past billion years supports the "slow cooking" dwarf scenario, explaining its low metal content. Currently, the galaxy is undergoing its strongest SF episode ($\sim 0.6 M_{\odot} \text{yr}^{-1}$) mainly in the northwest region, likely due to a recent gravitational interaction with Component C., Comment: 14 pages, 11 figures, accepted for publications in Astronomy & Astrophysics (section "4. Extragalactic astronomy")

Subsampling is commonly used to mitigate costs associated with data acquisition, such as time or energy requirements, motivating the development of algorithms for estimating the fully-sampled signal of interest $x$ from partially observed measurements $y$. In maximum-entropy sampling, one selects measurement locations that are expected to have the highest entropy, so as to minimize uncertainty about $x$. This approach relies on an accurate model of the posterior distribution over future measurements, given the measurements observed so far. Recently, diffusion models have been shown to produce high-quality posterior samples of high-dimensional signals using guided diffusion. In this work, we propose Active Diffusion Subsampling (ADS), a method for performing active subsampling using guided diffusion in which the model tracks a distribution of beliefs over the true state of $x$ throughout the reverse diffusion process, progressively decreasing its uncertainty by choosing to acquire measurements with maximum expected entropy, and ultimately generating the posterior distribution $p(x | y)$. ADS can be applied using pre-trained diffusion models for any subsampling rate, and does not require task-specific retraining - just the specification of a measurement model. Furthermore, the maximum entropy sampling policy employed by ADS is interpretable, enhancing transparency relative to existing methods using black-box policies. Experimentally, we show that ADS outperforms fixed sampling strategies, and study an application of ADS in Magnetic Resonance Imaging acceleration using the fastMRI dataset, finding that ADS performs competitively with supervised methods. Code available at https://active-diffusion-subsampling.github.io/., Comment: 17 pages, 12 figures, preprint

Single-cell datasets often lack individual cell labels, making it challenging to identify cells associated with disease. To address this, we introduce Mixture Modeling for Multiple Instance Learning (MMIL), an expectation maximization method that enables the training and calibration of cell-level classifiers using patient-level labels. Our approach can be used to train e.g. lasso logistic regression models, gradient boosted trees, and neural networks. When applied to clinically-annotated, primary patient samples in Acute Myeloid Leukemia (AML) and Acute Lymphoblastic Leukemia (ALL), our method accurately identifies cancer cells, generalizes across tissues and treatment timepoints, and selects biologically relevant features. In addition, MMIL is capable of incorporating cell labels into model training when they are known, providing a powerful framework for leveraging both labeled and unlabeled data simultaneously. Mixture Modeling for MIL offers a novel approach for cell classification, with significant potential to advance disease understanding and management, especially in scenarios with unknown gold-standard labels and high dimensionality., Comment: Erin Craig and Timothy Keyes contributed equally to this work

The Near-InfraRed Planet Searcher or NIRPS is a precision radial velocity spectrograph developed through collaborative efforts among laboratories in Switzerland, Canada, Brazil, France, Portugal and Spain. NIRPS extends to the 0.98-1.8 $\mu$m domain of the pioneering HARPS instrument at the La Silla 3.6-m telescope in Chile and it has achieved unparalleled precision, measuring stellar radial velocities in the infrared with accuracy better than 1 m/s. NIRPS can be used either stand-alone or simultaneously with HARPS. Commissioned in late 2022 and early 2023, NIRPS embarked on a 5-year Guaranteed Time Observation (GTO) program in April 2023, spanning 720 observing nights. This program focuses on planetary systems around M dwarfs, encompassing both the immediate solar vicinity and transit follow-ups, alongside transit and emission spectroscopy observations. We highlight NIRPS's current performances and the insights gained during its deployment at the telescope. The lessons learned and successes achieved contribute to the ongoing advancement of precision radial velocity measurements and high spectral fidelity, further solidifying NIRPS' role in the forefront of the field of exoplanets., Comment: Proceeding at the SPIE Astronomical Telescopes + Instrumentation conference [Yokohama,Japan; June 2024]

The SNO+ collaboration reports its first spectral analysis of long-baseline reactor antineutrino oscillation using 114 tonne-years of data. Fitting the neutrino oscillation probability to the observed energy spectrum yields constraints on the neutrino mass-squared difference $\Delta m^2_{21}$. In the ranges allowed by previous measurements, the best-fit $\Delta m^2_{21}$ is (8.85$^{+1.10}_{-1.33}$) $\times$ 10$^{-5}$ eV$^2$. This measurement is continuing in the next phases of SNO+ and is expected to surpass the present global precision on $\Delta m^2_{21}$ with about three years of data.

Electron-neutrino charged-current interactions with xenon nuclei were modeled in the nEXO neutrinoless double-beta decay detector (~5-tonne, 90% ${}^{136}$Xe, 10% ${}^{134}$Xe) to evaluate its sensitivity to supernova neutrinos. Predictions for event rates and detectable signatures were modeled using the MARLEY event generator. We find good agreement between MARLEY's predictions and existing theoretical calculations of the inclusive cross sections at supernova neutrino energies. The interactions modeled by MARLEY were simulated within the nEXO simulation framework and were run through an example reconstruction algorithm to determine the detector's efficiency for reconstructing these events. The simulated data, incorporating the detector response, were used to study the ability of nEXO to reconstruct the incident electron-neutrino spectrum and these results were extended to a larger xenon detector of the same isotope enrichment. We estimate that nEXO will be able to observe electron-neutrino interactions with xenon from supernovae as far as 5 to 8 kpc from earth, while the ability to reconstruct incident electron-neutrino spectrum parameters from observed interactions in nEXO is limited to closer supernovae., Comment: 17 pages, 16 figures

Pauli matrices and Pauli strings are widely used in quantum computing. These mathematical objects are useful to describe or manipulate the quantum state of qubits. They offer a convenient basis to express operators and observables used in different problem instances such as molecular simulation and combinatorial optimization. Therefore, it is important to have a well-rounded, versatile and efficient tool to handle a large number of Pauli strings and operators expressed in this basis. This is the objective behind the development of the PauliArray library presented in this work. This library introduces data structures to represent arrays of Pauli strings and operators as well as various methods to modify and combine them. Built using NumPy, PauliArray offers fast operations and the ability to use broadcasting to easily carry out otherwise cumbersome manipulations. Applications to the fermion-to-qubit mapping, to the estimation of expectation values and to the computation of commutators are considered to illustrate how PauliArray can simplify some relevant tasks and accomplish them faster than current libraries., Comment: 10 pages, 4 figures, submitted to IEEE Quantum Week 2024, GitHub repository : https://github.com/algolab-quantique/pauliarray, Documentation : https://algolab-quantique.github.io/pauliarray/index.html

Optically interfaced solid-state defects are promising candidates for quantum communication technologies. The ideal defect system would feature bright telecom emission, long-lived spin states, and a scalable material platform, simultaneously. Here, we employ one such system, vanadium (V4+) in silicon carbide (SiC), to establish a potential telecom spin-photon interface within a mature semiconductor host. This demonstration of efficient optical spin polarization and readout facilitates all optical measurements of temperature-dependent spin relaxation times (T1). With this technique, we lower the temperature from about 2K to 100 mK to observe a remarkable four-orders-of-magnitude increase in spin T1 from all measured sites, with site-specific values ranging from 57 ms to above 27 s. Furthermore, we identify the underlying relaxation mechanisms, which involve a two-phonon Orbach process, indicating the opportunity for strain-tuning to enable qubit operation at higher temperatures. These results position V4+ in SiC as a prime candidate for scalable quantum nodes in future quantum networks., Comment: 11 pages, 6 figures

Several challenges make it difficult for sparse neural networks to compete with dense models. First, setting a large fraction of weights to zero impairs forward and gradient signal propagation. Second, sparse studies often need to test multiple sparsity levels, while also introducing new hyperparameters (HPs), leading to prohibitive tuning costs. Indeed, the standard practice is to re-use the learning HPs originally crafted for dense models. Unfortunately, we show sparse and dense networks do not share the same optimal HPs. Without stable dynamics and effective training recipes, it is costly to test sparsity at scale, which is key to surpassing dense networks and making the business case for sparsity acceleration in hardware. A holistic approach is needed to tackle these challenges and we propose S$\mu$Par as one such approach. S$\mu$Par ensures activations, gradients, and weight updates all scale independently of sparsity level. Further, by reparameterizing the HPs, S$\mu$Par enables the same HP values to be optimal as we vary both sparsity level and model width. HPs can be tuned on small dense networks and transferred to large sparse models, greatly reducing tuning costs. On large-scale language modeling, S$\mu$Par training improves loss by up to 8.2% over the common approach of using the dense model standard parameterization., Comment: 9 pages main text, 11 pages reference and appendix, 11 figures

Discovering transiting exoplanets with relatively long orbital periods ($>$10 days) is crucial to facilitate the study of cool exoplanet atmospheres ($T_{\rm eq} < 700 K$) and to understand exoplanet formation and inward migration further out than typical transiting exoplanets. In order to discover these longer period transiting exoplanets, long-term photometric and radial velocity campaigns are required. We report the discovery of TOI-2447 b ($=$ NGTS-29b), a Saturn-mass transiting exoplanet orbiting a bright (T=10.0) Solar-type star (T$_{\rm eff}$=5730 K). TOI-2447 b was identified as a transiting exoplanet candidate from a single transit event of 1.3% depth and 7.29 h duration in $TESS$ Sector 31 and a prior transit event from 2017 in NGTS data. Four further transit events were observed with NGTS photometry which revealed an orbital period of P=69.34 days. The transit events establish a radius for TOI-2447 b of $0.865 \pm 0.010\rm R_{\rm J}$, while radial velocity measurements give a mass of $0.386 \pm 0.025 \rm M_{\rm J}$. The equilibrium temperature of the planet is $414$ K, making it much cooler than the majority of $TESS$ planet discoveries. We also detect a transit signal in NGTS data not caused by TOI-2447 b, along with transit timing variations and evidence for a $\sim$150 day signal in radial velocity measurements. It is likely that the system hosts additional planets, but further photometry and radial velocity campaigns will be needed to determine their parameters with confidence. TOI-2447 b/NGTS-29b joins a small but growing population of cool giants that will provide crucial insights into giant planet composition and formation mechanisms., Comment: 16 pages, 12 figures. Accepted for publication in MNRAS

We recently described the results of an initial search through TESS Sector 61 for free-floating planets. In this short note, we provide important context for our results and clarify the language used in our initial manuscript to ensure that our intended message is appropriately conveyed., Comment: 2 pages; note regarding arXiv:2404.11666

Providing feedback to students in a sustainable way represents a perennial challenge for secondary teachers of writing. Employing artificial intelligence (AI) tools to give students personalized and immediate feedback holds great promise. Project Topeka offered middle school teachers pre-curated teaching materials, foundational texts and videos, essay prompts, and a platform for students to submit and revise essay drafts with AI-generated scores and feedback. We analyze AI-generated writing scores of 3,233 7th- and 8th-grade students in school year 2021-22 and find that students' growth over time generally was not explained by teachers' (n=35) experience or self-reported instructional approaches. We also find that students' growth increased significantly as their baseline score decreased (i.e., a student with the lowest possible baseline grew more than a student with a medium baseline). Lastly, based on an in-person convening of 16 Topeka teachers, we compared their scores and feedback to AI-generated scores and feedback on the same essays, finding that generally the AI tool was more generous, with differences likely driven by teachers' ability to understand the whole essay's success better than the AI tool.

We present a JWST imaging survey with NIRCam and MIRI of NGC 346, the brightest star-forming region in the Small Magellanic Cloud (SMC). By combining aperture and point spread function (PSF) photometry of eleven wavelength bands across these two instruments, we have detected more than 200,000 unique sources. Using near-infrared (IR) color analysis, we observe various evolved and young populations, including 196 young stellar objects (YSOs) and pre-main sequence stars suitable for forthcoming spectroscopic studies. We expand upon this work, creating mid-IR color-magnitude diagrams and determining color cuts to identify 833 reddened sources which are YSO candidates. We observe that these candidate sources are spatially associated with regions of dusty, filamentary nebulosity. Furthermore, we fit model YSO spectral energy distributions (SEDs) to a selection of sources with detections across all of our MIRI bands. We classify with a high degree of confidence 23 YSOs in this sample and estimate their radii, bolometric temperatures, luminosities, and masses. We detect YSOs approaching 1 solar mass, the lowest-mass extragalactic YSOs confirmed to date.

Though free-floating planets (FFPs) that have been ejected from their natal star systems may outpopulate their bound counterparts in the terrestrial-mass range, they remain one of the least explored exoplanet demographics. Due to their negligible electromagnetic emission at all wavelengths, the only observational technique able to detect these worlds is gravitational microlensing. Microlensing by terrestrial-mass FFPs induces rare, short-duration magnifications of background stars, requiring high-cadence, wide-field surveys to detect these events. The Transiting Exoplanet Survey Satellite (TESS), though designed to detect close-bound exoplanets via the transit technique, boasts a cadence as short as 200 seconds and has monitored hundreds of millions of stars, making it well-suited to search for short-duration microlensing events as well. We have used existing data products from the TESS Quick-Look Pipeline (QLP) to perform a preliminary search for FFP microlensing candidates in 1.3 million light curves from TESS Sector 61. We find one compelling candidate associated with TIC-107150013, a source star at $d_s = 3.194$ kpc. The event has a duration $t_E = 0.074^{+0.002}_{-0.002}$ days and shows prominent finite-source features ($\rho = 4.55^{+0.08}_{-0.07}$), making it consistent with an FFP in the terrestrial-mass range. This exciting result indicates that our ongoing search through all TESS sectors has the opportunity to shed new light on this enigmatic population of worlds., Comment: 10 pages, 7 figures, submitted to MNRAS

Modern electronic health records (EHRs) hold immense promise in tracking personalized patient health trajectories through sequential deep learning, owing to their extensive breadth, scale, and temporal granularity. Nonetheless, how to effectively leverage multiple modalities from EHRs poses significant challenges, given its complex characteristics such as high dimensionality, multimodality, sparsity, varied recording frequencies, and temporal irregularities. To this end, this paper introduces a novel multimodal contrastive learning framework, specifically focusing on medical time series and clinical notes. To tackle the challenge of sparsity and irregular time intervals in medical time series, the framework integrates temporal cross-attention transformers with a dynamic embedding and tokenization scheme for learning multimodal feature representations. To harness the interconnected relationships between medical time series and clinical notes, the framework equips a global contrastive loss, aligning a patient's multimodal feature representations with the corresponding discharge summaries. Since discharge summaries uniquely pertain to individual patients and represent a holistic view of the patient's hospital stay, machine learning models are led to learn discriminative multimodal features via global contrasting. Extensive experiments with a real-world EHR dataset demonstrated that our framework outperformed state-of-the-art approaches on the exemplar task of predicting the occurrence of nine postoperative complications for more than 120,000 major inpatient surgeries using multimodal data from UF health system split among three hospitals (UF Health Gainesville, UF Health Jacksonville, and UF Health Jacksonville-North)., Comment: 12 pages, 3 figures. arXiv admin note: text overlap with arXiv:2403.04012

We construct singular solutions of a complex elliptic equation of second order, having an isolated singularity of any order. In particular, we extend results obtained for the real partial differential equation in divergence form by Alessandrini in 1990. Our solutions can be applied to the determination of the optical properties of an anisotropic medium in time-harmonic Diffuse Optical Tomography (DOT).

We propose a novel method to improve estimation of asset returns for portfolio optimization. This approach first performs a monthly directional market forecast using an online decision tree. The decision tree is trained on a novel set of features engineered from portfolio theory: the efficient frontier functional coefficients. Efficient frontiers can be decomposed to their functional form, a square-root second-order polynomial, and the coefficients of this function captures the information of all the constituents that compose the market in the current time period. To make these forecasts actionable, these directional forecasts are integrated to a portfolio optimization framework using expected returns conditional on the market forecast as an estimate for the return vector. This conditional expectation is calculated using the inverse Mills ratio, and the Capital Asset Pricing Model is used to translate the market forecast to individual asset forecasts. This novel method outperforms baseline portfolios, as well as other feature sets including technical indicators and the Fama-French factors. To empirically validate the proposed model, we employ a set of market sector ETFs.

The tidal disruption of old, compact stellar structures provides strong constraints on macroscopic dark matter candidates such as primordial black holes. In view of recent, new observational data on the Eridanus II dwarf galaxy and on its central stellar cluster, we employ, for the first time, $N$-body simulations to assess the impact of compact massive dark matter candidates on the gravitational stability of the cluster. We find evidence that such candidates must be lighter than about one solar mass if they constitute the totality of the dark matter. We additionally derive robust constraints on the fraction of the dark matter in macroscopic objects as a function of mass, by suitably modeling the remainder of the dark matter as standard fluid-like cold dark matter., Comment: 17 pages, 6 figures

We present a JWST imaging survey of I Zw 18, the archetypal extremely metal-poor, star-forming (SF), blue compact dwarf galaxy. With an oxygen abundance of only $\sim$3% $Z_{\odot}$, it is among the lowest-metallicity systems known in the local Universe, and is, therefore, an excellent accessible analog for the galactic building blocks which existed at early epochs of ionization and star formation. These JWST data provide a comprehensive infrared (IR) view of I Zw 18 with eight filters utilizing both Near Infrared Camera (F115W, F200W, F356W, and F444W) and Mid-Infrared Instrument (F770W, F1000W, F1500W, and F1800W) photometry, which we have used to identify key stellar populations that are bright in the near- and mid-IR. These data allow for a better understanding of the origins of dust and dust-production mechanisms in metal-poor environments by characterizing the population of massive, evolved stars in the red supergiant (RSG) and asymptotic giant branch (AGB) phases. In addition, it enables the identification of the brightest dust-enshrouded young stellar objects (YSOs), which provide insight into the formation of massive stars at extremely low metallicities typical of the very early Universe. This paper provides an overview of the observational strategy and data processing, and presents first science results, including identifications of dusty AGB, RSG, and bright YSO candidates. These first results assess the scientific quality of JWST data and provide a guide for obtaining and interpreting future observations of the dusty and evolved stars inhabiting compact dwarf SF galaxies in the local Universe., Comment: Accepted for publication in The Astronomical Journal; 25 pages, 14 figures, 4 tables

The breadth, scale, and temporal granularity of modern electronic health records (EHR) systems offers great potential for estimating personalized and contextual patient health trajectories using sequential deep learning. However, learning useful representations of EHR data is challenging due to its high dimensionality, sparsity, multimodality, irregular and variable-specific recording frequency, and timestamp duplication when multiple measurements are recorded simultaneously. Although recent efforts to fuse structured EHR and unstructured clinical notes suggest the potential for more accurate prediction of clinical outcomes, less focus has been placed on EHR embedding approaches that directly address temporal EHR challenges by learning time-aware representations from multimodal patient time series. In this paper, we introduce a dynamic embedding and tokenization framework for precise representation of multimodal clinical time series that combines novel methods for encoding time and sequential position with temporal cross-attention. Our embedding and tokenization framework, when integrated into a multitask transformer classifier with sliding window attention, outperformed baseline approaches on the exemplar task of predicting the occurrence of nine postoperative complications of more than 120,000 major inpatient surgeries using multimodal data from three hospitals and two academic health centers in the United States., Comment: ICLR 2024 Workshop on Learning From Time Series for Health. 10 pages, 3 figures