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This paper introduces the hypothetical track-length fitting algorithm, a novel method for measuring the kinetic energies of ionizing particles in liquid argon time projection chambers (LArTPCs). The algorithm finds the most probable offset in track length for a track-like object by comparing the measured ionization density as a function of position with a theoretical prediction of the energy loss as a function of the energy, including models of electron recombination and detector response. The algorithm can be used to measure the energies of particles that interact before they stop, such as charged pions that are absorbed by argon nuclei. The algorithm's energy measurement resolutions and fractional biases are presented as functions of particle kinetic energy and number of track hits using samples of stopping secondary charged pions in data collected by the ProtoDUNE-SP detector, and also in a detailed simulation. Additional studies describe impact of the dE/dx model on energy measurement performance. The method described in this paper to characterize the energy measurement performance can be repeated in any LArTPC experiment using stopping secondary charged pions.

This paper presents a conceptual exploration of how Digital Learning Platforms (DLPs) can be utilized to investigate the impact of language clarity, precision, engagement, and contextual relevance on mathematics learning from word problems. Focusing on three distinct DLPs--ASSISTments/E-TRIALS, MATHia/UpGrade, and Canvas/Terracotta--we propose hypothetical studies aimed at uncovering how nuanced language modifications can enhance mathematical understanding and engagement. While these studies are illustrative in nature, they provide a blueprint for researchers interested in leveraging DLPs for empirical investigation so that future investigators gain a better understanding of the emerging infrastructure for research in DLPs and the opportunities provided by them. In highlighting three distinct implementations of the same core research question, we reveal both commonalities as well as differences in how different educational technologies might build evidence, offering a unique opportunity to advance the field of math education and other education research fields.

While industrial-grade Yb-based amplifiers have become very prevalent, their limited gain bandwidth has created a large demand for robust spectral broadening techniques that allow for few-cycle pulse compression. In this work, we perform a comparative study between several atomic and molecular gases as media for spectral broadening in a hollow-core fiber geometry. Exploiting nonlinearities such as self-phase modulation, self-steepening, and stimulated Raman scattering, we explore the extent of spectral broadening and its dependence on gas pressure, the critical power for self-focusing, and the optimal regime for few-cycle pulse compression. Using a 3-mJ, 200-fs input laser pulses, we achieve ~ 15 fs, few-cycle pulses with >70% overall energy transmission efficiency. The optimal parameters can be scaled for higher or lower input pulse energies with appropriate gas parameters and fiber geometry., Comment: 9 pages, 3 figures

The molecular gas in the interstellar medium (ISM) of star-forming galaxy populations exhibits diverse physical properties. We investigate the $^{12}$CO excitation of twelve dusty, luminous star-forming galaxies at $z \sim 2-4$ by combining observations of the $^{12}$CO from $J_{\rm up} = 1$ to $J_{\rm up} = 8$. The spectral line energy distribution (SLED) has a similar shape to NGC 253, M82, and local ULIRGs, with much stronger excitation than the Milky Way inner disc. By combining with resolved dust continuum sizes from high-resolution $870$-$\mu$m ALMA observations and dust mass measurements determined from multi-wavelength SED fitting, we measure the relationship between the $^{12}$CO SLED and probable physical drivers of excitation: star-formation efficiency, the average intensity of the radiation field $\langle U\rangle$, and the star-formation rate surface density. The primary driver of high-$J_{\rm up}$ $^{12}$CO excitation in star-forming galaxies is star-formation rate surface density. We use the ratio of the CO($3-2$) and CO($6-5$) line fluxes to infer the CO excitation in each source and find that the average ratios for our sample are elevated compared to observations of low-redshift, less actively star-forming galaxies and agree well with predictions from numerical models that relate the ISM excitation to the star-formation rate surface density. The significant scatter in the line ratios of a factor $\approx 3$ within our sample likely reflects intrinsic variations in the ISM properties which may be caused by other effects on the excitation of the molecular gas, such as cosmic ray ionization rates and mechanical heating through turbulence dissipation., Comment: Accepted for publication in MNRAS; 17 pages, 7 figures

We present a detailed study of the large-scale shock front in Stephan's Quintet, a byproduct of past and ongoing interactions. Using integral-field spectroscopy from the new William Herschel Telescope Enhanced Area Velocity Explorer (WEAVE), recent 144 MHz observations from the LOFAR Two-metre Sky Survey (LoTSS), and archival data from the Very Large Array and James Webb Space Telescope (JWST), we obtain new measurements of key shock properties and determine its impact on the system. Harnessing the WEAVE large integral field unit's (LIFU) field of view (90 $\times$ 78 arcsec$^{2}$), spectral resolution ($R\sim2500$) and continuous wavelength coverage across the optical band, we perform robust emission line modeling and dynamically locate the shock within the multi-phase intergalactic medium (IGM) with higher precision than previously possible. The shocking of the cold gas phase is hypersonic, and comparisons with shock models show that it can readily account for the observed emission line ratios. In contrast, we demonstrate that the shock is relatively weak in the hot plasma visible in X-rays (with Mach number of $\mathcal{M} \sim 2 - 4$), making it inefficient at producing the relativistic particles needed to explain the observed synchrotron emission. Instead, we propose that it has led to an adiabatic compression of the medium, which has increased the radio luminosity ten-fold. Comparison of the Balmer line-derived extinction map with the molecular gas and hot dust observed with JWST suggests that pre-existing dust may have survived the collision, allowing the condensation of H$_{2}$ - a key channel for dissipating the shock energy., Comment: 23 pages, 15 figures, accepted for publication in MNRAS

Small-sized exoplanets in tight orbits around young stars (10-1000 Myr) give us the opportunity to investigate the mechanisms that led to their formation, the evolution of their physical and orbital properties and, especially, of their atmospheres. Thanks to the all-sky survey carried out by TESS, many of these exoplanets have been discovered and have subsequently been characterized with dedicated follow-up observations. In the context of a collaboration among the GAPS, TKS and CPS teams, we measured with a high level of precision the mass and the radius of TOI-1430 b, a young (~700 Myr) exoplanet with an escaping He atmosphere orbiting the K-dwarf star HD 235088 (TOI-1430). By adopting appropriate stellar parameters, which were measured in this work, we were able to simultaneously model the signals due to strong stellar activity and the transiting planet TOI-1430 b in both photometric and spectroscopic series. This allowed us to measure the density of the planet with high precision, and reconstruct the evolution of its atmosphere. TOI-1430 is an active K-dwarf star born 700+/-150 Myr ago and rotates in ~12 d. It hosts a mini-Neptune whose orbital period is Pb=7.434133+/-0.000004 d. Thanks to long-term monitoring of this target performed with TESS, HARPS-N, HIRES, and APF, we estimated a radius Rb=1.98+/-0.07 $R_{\oplus}$, a mass Mb=4.2+/-0.8 $M_{\oplus}$, and thus a planetary density $\rho$b=0.5+/-0.1 $\rho_{\oplus}$. TOI-1430 b is hence a low-density mini-Neptune with an extended atmosphere, at the edge of the radius gap. Because this planet is known to have an evaporating atmosphere of He, we reconstructed its atmospheric history. Our analysis supports the scenario in which, shortly after its birth, TOI-1430 b may have been super-puffy, with a radius 5x-13x and a mass 1.5x-2x that of today; in ~200 Myr from now, TOI-1430 b should lose its envelope, showing its Earth-size core., Comment: 30 pages, 19 figures, 8 tables. Accepted for publication in Astronomy & Astrophysics on 16 November 2024. Electronic material (tables G1-G3) will soon be available on the CDS or upon request to the first author. Abstract shortened

Characterising stellar jet asymmetries is key to setting robust constraints on jet launching models and improving our understanding of the underlying mechanisms behind jet launching. We aim to characterise the asymmetric properties of the bipolar jet coming from the Classical T Tauri Star Th 28. We combined data from integral field spectroscopy with VLT/MUSE and high-resolution spectra from VLT/X-shooter to map the optical emission line ratios in both jet lobes. We carried out a diagnostic analysis of these ratios to compare the density, electron temperature, and ionisation fraction within both lobes. The mass accretion rate was derived from the emission lines at the source and compared with the mass outflow rate derived for both lobes, using the estimated densities and measured [O I]6300 and [S II ]6731 luminosities. The blue-shifted jet exhibits a significantly higher electron temperature and moderately higher ionisation fraction than the red-shifted jet. In contrast to previous studies, we also estimated higher densities in the blue-shifted jet by a factor of ~2. These asymmetries are traced to within 160 au of the source in the line ratio maps. We estimate the mass accretion rate onto the central star and compare this with estimates of the mass outflow rates through each side of the jet. The emission line maps and diagnostic results suggest that the jet asymmetries originate close to the source and are likely to be intrinsic to the jet. Furthermore, the combined dataset offers access to a broad array of accretion tracers. In turn, this enables a more accurate estimation of the mass accretion rate, revealing a value of Macc that is higher by a factor >350 than would otherwise be determined. Supplementary figures and tables are available via a public Zenodo repository (doi:10.5281/zenodo.13373809)., Comment: 19 pages, 16 figures, 4 tables

We propose a unifying framework for methods that perform Bayesian online learning in non-stationary environments. We call the framework BONE, which stands for (B)ayesian (O)nline learning in (N)on-stationary (E)nvironments. BONE provides a common structure to tackle a variety of problems, including online continual learning, prequential forecasting, and contextual bandits. The framework requires specifying three modelling choices: (i) a model for measurements (e.g., a neural network), (ii) an auxiliary process to model non-stationarity (e.g., the time since the last changepoint), and (iii) a conditional prior over model parameters (e.g., a multivariate Gaussian). The framework also requires two algorithmic choices, which we use to carry out approximate inference under this framework: (i) an algorithm to estimate beliefs (posterior distribution) about the model parameters given the auxiliary variable, and (ii) an algorithm to estimate beliefs about the auxiliary variable. We show how this modularity allows us to write many different existing methods as instances of BONE; we also use this framework to propose a new method. We then experimentally compare existing methods with our proposed new method on several datasets; we provide insights into the situations that make one method more suitable than another for a given task.

The availability of large-scale neuronal population datasets necessitates new methods to model population dynamics and extract interpretable, scientifically translatable insights. Existing deep learning methods often overlook the biological mechanisms underlying population activity and thus exhibit suboptimal performance with neuronal data and provide little to no interpretable information about neurons and their interactions. In response, we introduce SynapsNet, a novel deep-learning framework that effectively models population dynamics and functional interactions between neurons. Within this biologically realistic framework, each neuron, characterized by a latent embedding, sends and receives currents through directed connections. A shared decoder uses the input current, previous neuronal activity, neuron embedding, and behavioral data to predict the population activity in the next time step. Unlike common sequential models that treat population activity as a multichannel time series, SynapsNet applies its decoder to each neuron (channel) individually, with the learnable functional connectivity serving as the sole pathway for information flow between neurons. Our experiments, conducted on mouse cortical activity from publicly available datasets and recorded using the two most common population recording modalities (Ca imaging and Neuropixels) across three distinct tasks, demonstrate that SynapsNet consistently outperforms existing models in forecasting population activity. Additionally, our experiments on both real and synthetic data showed that SynapsNet accurately learns functional connectivity that reveals predictive interactions between neurons.

We present the results of a nearly decade-long photometric reverberation mapping (PRM) survey of the H$\alpha$ emission line in nearby ($0.01\lesssim z \lesssim0.05$) Seyfert-Galaxies using small ($15\,\mathrm{cm}-40\,\mathrm{cm}$) telescopes. Broad-band filters were used to trace the continuum emission, while narrow-band filters tracked the H$\alpha$-line signal. We introduce a new PRM formalism to determine the time delay between continuum and line emission using combinations of auto- and cross-correlation functions. We obtain robust delays for 33/80 objects, allowing us to estimate the broad-line region (BLR) size. Additionally, we measure multi-epoch delays for 6 objects whose scatter per source is smaller than the scatter in the BLR size-luminosity relation. Our study enhances the existing H$\alpha$ size-luminosity relation by adding high-quality results for 31 objects, whose nuclear luminosities were estimated using the flux-variation gradient method, resulting in a scatter of 0.26dex within our sample. The scatter reduces to 0.17dex when the 6 lowest luminosity sources are discarded, which is comparable to that found for the H$\beta$ line. Single-epoch spectra enable us to estimate black hole masses using the H$\alpha$ line and derive mass accretion rates from the iron-blend feature adjacent to H$\beta$. A similar trend, as previously reported for the H$\beta$ line, is implied whereby highly accreting objects tend to lie below the size-luminosity relation of the general population. Our work demonstrates the effectiveness of small telescopes in conducting high-fidelity PRM campaigns of prominent emission lines in bright active galactic nuclei.

Asteroseismology has become an indispensable method for measuring stellar ages and radii, while binary systems remain the most prevalent tool for determining stellar masses. The synergy of the two, namely pulsating stars in binary systems, offer even more than the sum of their parts. The sometimes-overwhelming number of pulsation models to be examined for asteroseismic modelling can be tightly constrained when dynamical masses for both components are available. Binaries offer twice the opportunity to measure an asteroseismic age, which is then applicable to both components of a system, often providing ages for stars that are otherwise very difficult to determine. Some stellar physics, such as the strength of internal mixing or of core overshoot, is so difficult to infer that only in binary systems do sufficient constraints exist to advance our asteroseismic models. The pulsations themselves can also be used to infer dynamical masses through pulsation timing, while eclipsing binaries provide opportunities to test asteroseismic scaling relations. In these proceedings of an invited talk I selectively review the synergies afforded by pulsations in binaries, consisting of a variety of main-sequence primary spectral types, to inferences made from the oscillations of red giants, even when their companions are undetectable. I also describe how the field is changing as we become more data rich through each generation of large photometric survey., Comment: Proceedings of an invited talk at "Binary and Multiple Stars in the Era of Big Sky Surveys", Litomy\v{s}l, Czech Republic, 9-13 September 2024

Massive, intensely star-forming galaxies at high redshift require a supply of molecular gas from their gas reservoirs, replenished by infall from the surrounding circumgalactic medium, to sustain their immense star-formation rates. However, our knowledge of the extent and morphology of their cold-gas reservoirs is still in its infancy. We present the results of stacking 80 hours of JVLA observations of CO(1--0) emission -- which traces the cold molecular gas -- in 19 $z=2.0-4.5$ dusty, star-forming galaxies from the AS2VLA survey. The visibility-plane stack reveals extended emission with a half-light radius of $3.8\pm0.5$~kpc, 2--3$\times$ more extended than the dust-obscured star formation and $1.4\pm0.2\times$ more extended than the stellar emission. Similarly, stacking the [CI](1--0) observations for a subsample of our galaxies yields sizes consistent with CO(1--0). The CO(1--0) size is comparable to the [CII] halos detected around high-redshift star-forming galaxies.The bulk (up to 80\%) of molecular gas resides outside the star-forming region; only a small part of their molecular gas reservoir directly contributes to their current star formation. Photon-dissociation region modelling indicates that the extended CO(1--0) emission arises from clumpy, dense clouds rather than smooth, diffuse gas., Comment: Submitted to A&A. 9 pages, 5 figures

Whether the system under study is a shoal of fish, a collection of neurons, or a set of interacting atmospheric and oceanic processes, transfer entropy measures the flow of information between time series and can detect possible causal relationships. Much like mutual information, transfer entropy is generally reported as a single value summarizing an amount of shared variation, yet a more fine-grained accounting might illuminate much about the processes under study. Here we propose to decompose transfer entropy and localize the bits of variation on both sides of information flow: that of the originating process's past and that of the receiving process's future. We employ the information bottleneck (IB) to compress the time series and identify the transferred entropy. We apply our method to decompose the transfer entropy in several synthetic recurrent processes and an experimental mouse dataset of concurrent behavioral and neural activity. Our approach highlights the nuanced dynamics within information flow, laying a foundation for future explorations into the intricate interplay of temporal processes in complex systems., Comment: NeurIPS 2024 workshop "Machine learning and the physical sciences" Camera ready

We conduct experiments on both real and synthetic radial velocity (RV) data to quantify the impact that observing cadence, the number of RV observations, and undetected companions all have on the accuracy of small planet mass measurements. We run resampling experiments on four systems with small transiting planets and substantial public data from HIRES in order to explore how degrading observing cadence and the number of RVs affects the planets' mass measurement relative to a baseline value. From these experiments, we recommend that observers obtain 2--3 RVs per orbit of the inner-most planet and acquire at minimum 40 RVs. Following these guidelines, we then conduct simulations using synthetic RVs to explore the impact of undetected companions and untreated red noise on the masses of planets with known orbits. While undetected companions generally do not bias the masses of known planets, in some cases, when coupled with an inadequate observing baseline, they can cause the mass of an inner transiting planet to be systematically overestimated on average., Comment: 17 pages, 13 figures, 4 tables. Accepted for publication in The Astronomical Journal. Code available online (see acknowledgments)

We present a revised mass measurement for HD 119130 b (aka K2-292 b), a transiting planet ($P = 17$ days, $R_\mathrm{p} = 2.63^{+0.11}_{-0.10}$ $R_\mathrm{\oplus}$) orbiting a chromospherically inactive G dwarf, previously thought to be one of the densest sub-Neptunes known. Our follow-up Doppler observations with HARPS, HARPS-N, and HIRES reveal that HD 119130 b is, in fact, nearly one-third as massive as originally suggested by its initial confirmation paper. Our revised analysis finds $M_\mathrm{p} = 8.8 \pm 3.2$ $M_\mathrm{\oplus}$ ($M_\mathrm{p} < 15.4$ $M_\mathrm{\oplus}$ at 98\% confidence) compared to the previously reported $M_\mathrm{p} = 24.5 \pm 4.4$ $M_\mathrm{\oplus}$. While the true cause of the original mass measurement's inaccuracy remains uncertain, we present the plausible explanation that the planet's radial velocity (RV) semi-amplitude was inflated due to constructive interference with a second, untreated sinusoidal signal in the data (possibly rotational modulation from the star). HD 119130 b illustrates the complexities of interpreting the RV orbits of small transiting planets. While RV mass measurements of such planets may be precise, they are not necessarily guaranteed to be accurate. This system serves as a cautionary tale as observers and theorists alike look to the exoplanet mass-radius diagram for insights into the physics of small planet formation., Comment: 15 pages, 9 figures, 3 tables. Accepted for publication in The Astronomical Journal

As LLMs become more widely deployed, there is increasing interest in directly optimizing for feedback from end users (e.g. thumbs up) in addition to feedback from paid annotators. However, training to maximize human feedback creates a perverse incentive structure for the AI to resort to manipulative or deceptive tactics to obtain positive feedback from users who are vulnerable to such strategies. We study this phenomenon by training LLMs with Reinforcement Learning with simulated user feedback in environments of practical LLM usage. In our settings, we find that: 1) Extreme forms of "feedback gaming" such as manipulation and deception are learned reliably; 2) Even if only 2% of users are vulnerable to manipulative strategies, LLMs learn to identify and target them while behaving appropriately with other users, making such behaviors harder to detect; 3) To mitigate this issue, it may seem promising to leverage continued safety training or LLM-as-judges during training to filter problematic outputs. Instead, we found that while such approaches help in some of our settings, they backfire in others, sometimes even leading to subtler manipulative behaviors. We hope our results can serve as a case study which highlights the risks of using gameable feedback sources -- such as user feedback -- as a target for RL.

We present a search for long-lived particles (LLPs) produced from kaon decay that decay to two muons inside the ICARUS neutrino detector. This channel would be a signal of hidden sector models that can address outstanding issues in particle physics such as the strong CP problem and the microphysical origin of dark matter. The search is performed with data collected in the Neutrinos at the Main Injector (NuMI) beam at Fermilab corresponding to $2.41\times 10^{20}$ protons-on-target. No new physics signal is observed, and we set world-leading limits on heavy QCD axions, as well as for the Higgs portal scalar among dedicated searches. Limits are also presented in a model-independent way applicable to any new physics model predicting the process $K\to \pi+S(\to\mu\mu)$, for a long-lived particle S. This result is the first search for new physics performed with the ICARUS detector at Fermilab. It paves the way for the future program of long-lived particle searches at ICARUS.

In this paper, we consider 1D agent-based and kinetic models of aggregation with reversals. In particular, we fit a Gamma distribution to represent the run times in myxobacteria and analyze numerically the importance of non-exponential reversal times. We demonstrate that non-exponential reversal times aid aggregation and result in tighter aggregates. We compare and contrast the behavior of agent-based and kinetic models, and also consider kinetic models with aggregation driven by chemotaxis. Thus, incorporating non-exponential reversal times into models of aggregation can be particularly important for reproducing experimental data, such as aggregate persistence and dispersal.

We present the Atacama Large Millimeter/submillimeter Array Band 7 observations of the CO (J=3-2) line emission of the protostellar system HH 212 at $\sim$24 au spatial resolution and compare them to those of the SiO (J=8-7) and SO (J=8-7) line emission reported in the literature. We find that the CO line traces four distinct regions: (1) an outer outflow shell, (2) a rotating wind region between the SiO and CO shells, (3) the shocked and wide-angle inner X-wind inside a SiO shell, and (4) the jet. The origin of the CO outer outflow shell could be associated with the entrained material of the envelope, or an extended disk wind. The rotating wind, which is shocked, is launched from a radius of 9-15 au, slightly exterior to that of the previously detected SO shell, which marks the boundary where the wide-angle X-wind is interacting with and shocking the disk wind. Additionally, the SO is found to be mixed with the CO emission within the thick and extended rotating wind region. The large scale CO shocked wind coexists with the SO emission near the upper portion of the inner shocked region converged on top of the inner SiO knots. The CO jet is traced by a chain of knots with roughly equal interval, exhibiting quasi-periodicity, as reported in other jets in the literature., Comment: 18 pages, 12 figures, 2 tables. Accepted by ApJ

Eye tracking technology offers great potential for improving road safety. It is already being built into vehicles, namely cars and trucks. When this technology is integrated into transit service vehicles, employees, i.e., bus drivers, will be subject to being eye tracked on their job. Although there is much research effort advancing algorithms for eye tracking in transportation, less is known about how end users perceive this technology, especially when interacting with it in an employer-mandated context. In this first study of its kind, we investigated transit bus operators' perceptions of eye tracking technology. From a methodological perspective, we introduce a mixed methods approach where participants experience the technology first-hand and then reflect on their experience while viewing a playback of the recorded data. Thematic analysis of the interview transcripts reveals interesting potential uses of eye tracking in this work context and surfaces transit operators' fears and concerns about this technology.

A model for the bonding of hypervalent molecules containing sulfur is presented using generalized valence bond wavefunctions. To delineate the model from more commonly used DFT and delocalized wavefunctions, we present detailed comparisons to these other methods. The result is a new concept of local transferable bonding in hypervalent molecules.

We propose the \textit{linear barycentric coding model (LBCM)} that utilizes the linear optimal transport (LOT) metric for analysis and synthesis of probability measures. We provide a closed-form solution to the variational problem characterizing the probability measures in the LBCM and establish equivalence of the LBCM to the set of Wasserstein-2 barycenters in the special case of compatible measures. Computational methods for synthesizing and analyzing measures in the LBCM are developed with finite sample guarantees. One of our main theoretical contributions is to identify an LBCM, expressed in terms of a simple family, which is sufficient to express all probability measures on the interval $[0,1]$. We show that a natural analogous construction of an LBCM in $\mathbb{R}^2$ fails, and we leave it as an open problem to identify the proper extension in more than one dimension. We conclude by demonstrating the utility of LBCM for covariance estimation and data imputation., Comment: 37 pages, 6 figures

Recent years in NLP have seen the continued development of domain-specific information extraction tools for scientific documents, alongside the release of increasingly multimodal pretrained transformer models. While the opportunity for scientists outside of NLP to evaluate and apply such systems to their own domains has never been clearer, these models are difficult to compare: they accept different input formats, are often black-box and give little insight into processing failures, and rarely handle PDF documents, the most common format of scientific publication. In this work, we present Collage, a tool designed for rapid prototyping, visualization, and evaluation of different information extraction models on scientific PDFs. Collage allows the use and evaluation of any HuggingFace token classifier, several LLMs, and multiple other task-specific models out of the box, and provides extensible software interfaces to accelerate experimentation with new models. Further, we enable both developers and users of NLP-based tools to inspect, debug, and better understand modeling pipelines by providing granular views of intermediate states of processing. We demonstrate our system in the context of information extraction to assist with literature review in materials science.

We exploit the VST ATLAS quasar/QSO catalogue to perform three measurements of the quasar halo mass profile. First, we make a new estimate of the angular auto-correlation function of $\approx230,000$ ATLAS quasars with $z_{photo}\lesssim 2.5$ and $17

We present a catalog of binary companions to $\delta$ Scuti stars, detected through phase modulations of their pulsations in TESS data. Pulsation timing has provided orbits for hundreds of pulsating stars in binaries from space-based photometry. We have applied this technique to $\delta$ Sct stars observed in the first four years of TESS Mission photometry. We searched the 2-min cadence light curves of 1161 short-period instability strip pulsators for variations in pulsation phase caused by the dynamical influence of an unseen companion. We discovered 53 new binaries and we present orbital parameters and mass functions for the 24 systems with solvable orbits. For the brightest star in our sample $\alpha$ Pictoris, we perform a joint fit of the pulsation timing and Hipparcos astrometry. We present the first orbit for the $\alpha$ Pictoris system, obtaining an orbital period of 1316$\pm$2 days and a mass for $\alpha$ Pic b of 1.05$\pm$0.05 M$_\odot$. We revisit pulsation timing binaries from Kepler with Gaia kinematics, finding four systems that are members of the Galatic thick disk or halo. This suggests that they have been rejuvenated by mass transfer, and their companions are now white dwarfs. Further follow up of these systems may yield valuable constraints of the galactic blue straggler population., Comment: Accepted for publication in MNRAS, 12 pages, 11 figures

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.

The XLZD collaboration is developing a two-phase xenon time projection chamber with an active mass of 60 to 80 t capable of probing the remaining WIMP-nucleon interaction parameter space down to the so-called neutrino fog. In this work we show that, based on the performance of currently operating detectors using the same technology and a realistic reduction of radioactivity in detector materials, such an experiment will also be able to competitively search for neutrinoless double beta decay in $^{136}$Xe using a natural-abundance xenon target. XLZD can reach a 3$\sigma$ discovery potential half-life of 5.7$\times$10$^{27}$ yr (and a 90% CL exclusion of 1.3$\times$10$^{28}$ yr) with 10 years of data taking, corresponding to a Majorana mass range of 7.3-31.3 meV (4.8-20.5 meV). XLZD will thus exclude the inverted neutrino mass ordering parameter space and will start to probe the normal ordering region for most of the nuclear matrix elements commonly considered by the community., Comment: 29 pages, 7 figures

The explosion mechanism of a core-collapse supernova is a complex interplay between neutrino heating and cooling (including the effects of neutrino-driven convection), the gravitational potential, and the ram pressure of the infalling material. To analyze the post-bounce phase of a supernova, one can use the generalized Force Explosion Condition (FEC+), which succinctly formalizes the interplay among these four phenomena in an analytical condition, consistent with realistic simulations. In this paper, we use the FEC+ to study the post-bounce phase of 341 spherically symmetric simulations, where convection is included through a time-dependent mixing length approach. We find that the accretion of the Si/O interface through the expanding shock can significantly change the outcome of the supernova by driving the FEC+ above the explosion threshold. We systematically explore this by (i) artificially smoothing the pre-supernova density profile, and (ii) artificially varying the mixing length. In both cases, we find that large-enough density contrasts at the Si/O interface lead to successful shock revival only if the FEC+ is already close to the explosion threshold. Furthermore, we find that the accretion of the Si/O interface has a substantial effect on the critical condition for supernova explosions, contributing between 5\% and 15\%, depending on how pronounced the density contrast at the interface is. Earlier studies showed that convection affects the critical condition by 25--30\%, which demonstrates that the accretion of the Si/O interface through the shock can play a nearly comparable role in influencing shock dynamics., Comment: 13 pages, 8 Figures, submitted to MNRAS

This report describes the experimental strategy and technologies for a next-generation xenon observatory sensitive to dark matter and neutrino physics. The detector will have an active liquid xenon target mass of 60-80 tonnes and is proposed by the XENON-LUX-ZEPLIN-DARWIN (XLZD) collaboration. The design is based on the mature liquid xenon time projection chamber technology of the current-generation experiments, LZ and XENONnT. A baseline design and opportunities for further optimization of the individual detector components are discussed. The experiment envisaged here has the capability to explore parameter space for Weakly Interacting Massive Particle (WIMP) dark matter down to the neutrino fog, with a 3$\sigma$ evidence potential for the spin-independent WIMP-nucleon cross sections as low as $3\times10^{-49}\rm cm^2$ (at 40 GeV/c$^2$ WIMP mass). The observatory is also projected to have a 3$\sigma$ observation potential of neutrinoless double-beta decay of $^{136}$Xe at a half-life of up to $5.7\times 10^{27}$ years. Additionally, it is sensitive to astrophysical neutrinos from the atmosphere, sun, and galactic supernovae., Comment: 32 pages, 14 figures

We report results of a search for nuclear recoils induced by weakly interacting massive particle (WIMP) dark matter using the LUX-ZEPLIN (LZ) two-phase xenon time projection chamber. This analysis uses a total exposure of $4.2\pm0.1$ tonne-years from 280 live days of LZ operation, of which $3.3\pm0.1$ tonne-years and 220 live days are new. A technique to actively tag background electronic recoils from $^{214}$Pb $\beta$ decays is featured for the first time. Enhanced electron-ion recombination is observed in two-neutrino double electron capture decays of $^{124}$Xe, representing a noteworthy new background. After removal of artificial signal-like events injected into the data set to mitigate analyzer bias, we find no evidence for an excess over expected backgrounds. World-leading constraints are placed on spin-independent (SI) and spin-dependent WIMP-nucleon cross sections for masses $\geq$9 GeV/$c^2$. The strongest SI exclusion set is $2.1\times10^{-48}$ cm$^{2}$ at the 90% confidence level at a mass of 36 GeV/$c^2$, and the best SI median sensitivity achieved is $5.0\times10^{-48}$ cm$^{2}$ for a mass of 40 GeV/$c^2$., Comment: 9 pages, 7 figures. See https://www.hepdata.net/record/155182 for a data release related to this paper

For specialized domains, there is often not a wealth of data with which to train large machine learning models. In such limited data / compute settings, various methods exist aiming to $\textit{do more with less}$, such as finetuning from a pretrained model, modulating difficulty levels as data are presented to a model (curriculum learning), and considering the role of model type / size. Approaches to efficient $\textit{machine}$ learning also take inspiration from $\textit{human}$ learning by considering use cases where machine learning systems have access to approximately the same number of words experienced by a 13 year old child (100M words). We investigate the role of 3 primary variables in a limited data regime as part of the multimodal track of the BabyLM challenge. We contrast: (i) curriculum learning, (ii), pretraining (with text-only data), (iii) model type. We modulate these variables and assess them on two types of tasks: (a) multimodal (text+image), and (b) unimodal (text-only) tasks. We find that curriculum learning benefits multimodal evaluations over non-curriclum learning models, particularly when combining text-only pretraining. On text-only tasks, curriculum learning appears to help models with smaller trainable parameter counts. We suggest possible reasons based on architectural differences and training designs as to why one might observe such results., Comment: CoNLL BabyLM Challenge 2024 camera ready

We consider reinforcement learning (RL) for a class of problems with bagged decision times. A bag contains a finite sequence of consecutive decision times. The transition dynamics are non-Markovian and non-stationary within a bag. Further, all actions within a bag jointly impact a single reward, observed at the end of the bag. Our goal is to construct an online RL algorithm to maximize the discounted sum of the bag-specific rewards. To handle non-Markovian transitions within a bag, we utilize an expert-provided causal directed acyclic graph (DAG). Based on the DAG, we construct the states as a dynamical Bayesian sufficient statistic of the observed history, which results in Markovian state transitions within and across bags. We then frame this problem as a periodic Markov decision process (MDP) that allows non-stationarity within a period. An online RL algorithm based on Bellman-equations for stationary MDPs is generalized to handle periodic MDPs. To justify the proposed RL algorithm, we show that our constructed state achieves the maximal optimal value function among all state constructions for a periodic MDP. Further we prove the Bellman optimality equations for periodic MDPs. We evaluate the proposed method on testbed variants, constructed with real data from a mobile health clinical trial.

We use first-principles electronic-structure theory to determine the intra- and inter-valley electron-alloy scattering parameters in n-type GeSn alloys. These parameters are used to determine the alloy scattering contributions to the n-type electron mobility of GeSn at $300K$ and $15K$ using a first iteration of the Boltzmann transport equation in the relaxation time approximation. For unstrained GeSn, we find that a Sn concentration of at least $13.5\%$ is needed to achieve an electron mobility greater than that of Ge. Our results show that the mobility of GeSn can be over $25$ times higher than the mobility of Ge, or $10^5$ cm$^2$/(Vs). At $15K$, less than $6\%$ Sn incorporation into Ge quadruples its mobility, which suggests GeSn has potential applications as a high mobility 2D electron gas. Applying biaxial tensile strain to GeSn further increases the mobility and at a lower Sn content than in unstrained GeSn.

GJ1214b is the archetype sub-Neptune for which thick aerosols have prevented us from constraining its atmospheric properties for over a decade. In this study, we leverage the panchromatic transmission spectrum of GJ1214b established by HST and JWST to investigate its atmospheric properties using a suite of atmospheric radiative transfer, photochemistry, and aerosol microphysical models. We find that the combined HST, JWST/NIRSpec and JWST/MIRI spectrum can be well-explained by atmospheric models with an extremely high metallicity of [M/H]$\sim$3.5 and an extremely high haze production rate of $F_{\rm haze}{\sim}10^{-8}$--$10^{-7}$ g cm$^{-2}$ s$^{-1}$. Such high atmospheric metallicity is suggested by the relatively strong CO2 feature compared to the haze absorption feature or the CH4 feature in the NIRSpec-G395H bandpass of 2.5--5 $\mu$m. The flat 5--12 $\mu$m MIRI spectrum also suggests a small scale height with a high atmospheric metallicity that is needed to suppress a prominent 6 $\mu$m haze feature. We tested the sensitivity of our interpretation to various assumptions for uncertain haze properties, such as optical constants and production rate, and all models tested here consistently suggest extremely high metallicity. Thus, we conclude that GJ1214b likely has a metal-dominated atmosphere where hydrogen is no longer the main atmospheric constituent. We also find that different assumptions for the haze production rate lead to distinct inferences for the atmospheric C/O ratio. We stress the importance of high precision follow-up observations to confirm the metal-dominated atmosphere and to constrain the C/O ratio, which provides further insights on the planet formation process. The confirmation of the metal-dominated atmosphere is particularly crucial, as it challenges the conventional understanding of interior structure and evolution of sub-Neptunes., Comment: 21 pages, 9 figures, Submitted to AAS Journals, Please also see a companion paper Schlawin et al. (2024)

Sub-Neptune planets with radii smaller than Neptune (3.9 Re) are the most common type of planet known to exist in The Milky Way, even though they are absent in the Solar System. These planets can potentially have a large diversity of compositions as a result of different mixtures of rocky material, icy material and gas accreted from a protoplanetary disk. However, the bulk density of a sub-Neptune, informed by its mass and radius alone, cannot uniquely constrain its composition; atmospheric spectroscopy is necessary. GJ 1214 b, which hosts an atmosphere that is potentially the most favorable for spectroscopic detection of any sub-Neptune, is instead enshrouded in aerosols (thus showing no spectroscopic features), hiding its composition from view at previously observed wavelengths in its terminator. Here, we present a JWST NIRSpec transmission spectrum from 2.8 to 5.1 um that shows signatures of carbon dioxide and methane, expected at high metallicity. A model containing both these molecules is preferred by 3.3 and 3.6 sigma as compared to a featureless spectrum for two different data analysis pipelines, respectively. Given the low signal-to-noise of the features compared to the continuum, however, more observations are needed to confirm the carbon dioxide and methane signatures and better constrain other diagnostic features in the near-infrared. Further modeling of the planet's atmosphere, interior structure and origins will provide valuable insights about how sub-Neptunes like GJ 1214 b form and evolve., Comment: 22 pages, 11 figures, Accepted in ApJL, Please also see a companion paper Ohno et al. (2024)

We introduce the open source code PHOEBUS (phifty one ergs blows up a star) for astrophysical general relativistic radiation magnetohydrodynamic simulations. PHOEBUS is designed for, but not limited to, high energy astrophysical environments such as core-collapse supernovae, neutron star mergers, black-hole accretion disks, and similar phenomena. General relativistic magnetohydrodynamics are modeled in the Valencia formulation with conservative finite volume methods. Neutrino radiation transport is included with Monte Carlo and moment methods. PHOEBUS is built on the PARTHENON (Grete et al. 2022) performance portable adaptive mesh refinement framework, uses a GPU first development strategy, and is capable of modeling a large dynamic range in space and time. PHOEBUS utilizes KOKKOS for on-node parallelism and supports both CPU and GPU architectures. We describe the physical model employed in PHOEBUS, the numerical methods used, and demonstrate a suite of test problems to showcase its abilities. We demonstrate weak scaling to over 500 H100 GPUs., Comment: 21 pages, 15 figures. Code available online at https://github.com/lanl/phoebus

Lightning strikes to wind turbines (WTs) pose significant hazards and operational costs to the renewable wind industry. These strikes fall into two categories: downward cloud-to-ground (CG) strokes and upward discharges, which can be self-initiated or triggered by a nearby flash. The incidence of each type of strike depends on several factors, including the electrical structure of the thunderstorm and turbine height. The strike rates of CG strokes and triggered upward lightning can be normalized by the amount of local CG activity, where the constant of proportionality carries units of area and is often termed the collection area. This paper introduces a statistical analysis technique that uses lightning locating system (LLS) data to estimate the collection areas for downward and triggered upward lightning strikes to WTs. The technique includes a normalization method that addresses the confounding factor of neighboring WTs. This analysis method is applied to seven years of data from the National Lightning Detection Network$^\textrm{TM}$ and the US Wind Turbine Database to investigate the dependence of collection areas on blade tip height and peak current. The results are compared against estimates of collection areas derived from counts of LLS-detected CG strokes close to WTs., Comment: 17 pages, 7 figures

One of the most challenging problems in the computational study of localization in quantum manybody systems is to capture the effects of rare events, which requires sampling over exponentially many disorder realizations. We implement an efficient procedure on a quantum processor, leveraging quantum parallelism, to efficiently sample over all disorder realizations. We observe localization without disorder in quantum many-body dynamics in one and two dimensions: perturbations do not diffuse even though both the generator of evolution and the initial states are fully translationally invariant. The disorder strength as well as its density can be readily tuned using the initial state. Furthermore, we demonstrate the versatility of our platform by measuring Renyi entropies. Our method could also be extended to higher moments of the physical observables and disorder learning.

Combining Atacama Large Millimeter/sub-millimeter Array CO(2-1) mapping and JWST near- and mid-infrared imaging, we characterize the relationship between CO(2-1) and polycyclic aromatic hydrocarbon (PAH) emission at ~100 pc resolution in 66 nearby star-forming galaxies, expanding the sample size from previous ~100 pc resolution studies by more than an order of magnitude. Focusing on regions of galaxies where most of the gas is likely to be molecular, we find strong correlations between CO(2-1) and 3.3 micron, 7.7 micron, and 11.3 micron PAH emission, estimated from JWST's F335M, F770W, and F1130W filters. We derive power law relations between CO(2-1) and PAH emission, which have indices in the range 0.8-1.2, implying relatively weak variations in the observed CO-to-PAH ratios across the regions that we study. We find that CO-to-PAH ratios and scaling relationships near HII regions are similar to those in diffuse sight lines. The main difference between the two types of regions is that sight lines near HII regions show higher intensities in all tracers. Galaxy centers, on the other hand, show higher overall intensities and enhanced CO-to-PAH ratios compared to galaxy disks. Individual galaxies show 0.19 dex scatter in the normalization of CO at fixed I_PAH and this normalization anti-correlates with specific star formation rate (SFR/M*) and correlates with stellar mass. We provide a prescription that accounts for these galaxy-to-galaxy variations and represents our best current empirical predictor to estimate CO(2-1) intensity from PAH emission, which allows one to take advantage of JWST's excellent sensitivity and resolution to trace cold gas., Comment: 21 pages, 4 figures, 3 tables. Submitted to ApJ

We propose Diffusion Model Predictive Control (D-MPC), a novel MPC approach that learns a multi-step action proposal and a multi-step dynamics model, both using diffusion models, and combines them for use in online MPC. On the popular D4RL benchmark, we show performance that is significantly better than existing model-based offline planning methods using MPC and competitive with state-of-the-art (SOTA) model-based and model-free reinforcement learning methods. We additionally illustrate D-MPC's ability to optimize novel reward functions at run time and adapt to novel dynamics, and highlight its advantages compared to existing diffusion-based planning baselines., Comment: Preprint

Rationally identifying variables responsible for changes to a biological system can enable myriad applications in disease understanding and cell engineering. From a causality perspective, we are given two datasets generated by the same causal model, one observational (control) and one interventional (perturbed). The goal is to isolate the subset of measured variables (e.g. genes) that were the targets of the intervention, i.e. those whose conditional independencies have changed. Knowing the causal graph would limit the search space, allowing us to efficiently pinpoint these variables. However, current algorithms that infer causal graphs in the presence of unknown intervention targets scale poorly to the hundreds or thousands of variables in biological data, as they must jointly search the combinatorial spaces of graphs and consistent intervention targets. In this work, we propose a causality-inspired approach for predicting perturbation targets that decouples the two search steps. First, we use an amortized causal discovery model to separately infer causal graphs from the observational and interventional datasets. Then, we learn to map these paired graphs to the sets of variables that were intervened upon, in a supervised learning framework. This approach consistently outperforms baselines for perturbation modeling on seven single-cell transcriptomics datasets, each with thousands of measured variables. We also demonstrate significant improvements over six causal discovery algorithms in predicting intervention targets across a variety of tractable, synthetic datasets.

This study introduces a novel and accurate approach to breast cancer classification using histopathology images. It systematically compares leading Convolutional Neural Network (CNN) models across varying image datasets, identifies their optimal hyperparameters, and ranks them based on classification efficacy. To maximize classification accuracy for each model we explore, the effects of data augmentation, alternative fully-connected layers, model training hyperparameter settings, and, the advantages of retraining models versus using pre-trained weights. Our methodology includes several original concepts, including serializing generated datasets to ensure consistent data conditions across training runs and significantly reducing training duration. Combined with automated curation of results, this enabled the exploration of over 2,000 training permutations -- such a comprehensive comparison is as yet unprecedented. Our findings establish the settings required to achieve exceptional classification accuracy for standalone CNN models and rank them by model efficacy. Based on these results, we propose ensemble architectures that stack three high-performing standalone CNN models together with diverse classifiers, resulting in improved classification accuracy. The ability to systematically run so many model permutations to get the best outcomes gives rise to very high quality results, including 99.75% for BreakHis x40 and BreakHis x200 and 95.18% for the Bach datasets when split into train, validation and test datasets. The Bach Online blind challenge, yielded 89% using this approach. Whilst this study is based on breast cancer histopathology image datasets, the methodology is equally applicable to other medical image datasets.

This paper considers a new version of fractional Sobolev spaces $\widetilde{\mathcal{W}}_{\mathcal{U}}^{\beta,p}(\mathbb{C}^{n})$ defined using the concept of tempered ultradistributions with respect to the spaces of ultradifferentiable functions $\mathcal{U}(\mathbb{C})$. The space $\widetilde{\mathcal{W}}_{\mathcal{U}}^{\beta,p}(\mathbb{C}^{n})$ is a natural generalization of the classical Sobolev space with integer order, where some additional conditions of growth control have been introduced. We analyze some possible definitions and their roles in the structure theory. We prove some density and compact embedding results, investigating the possibility of the extension domains. Some of the results we present here are extensions of the existing ones with some additional conditions. The construction of the new family of fractional Sobolev space is considered within the framework of Fourier transform of ultradistributions of slow growth.

M-type stars are the most common stars in the universe. They are ideal hosts for the search of exoplanets in the habitable zone (HZ), as their small size and low temperature make the HZ much closer in than their solar twins. Harboring very deep convective layers, they also usually exhibit very intense magnetic fields. Understanding their environment, in particular their coronal and wind properties, is thus very important, as they might be very different from what is observed in the solar system. The mass loss rate of M-type stars is poorly known observationally, and recent attempts to estimate it for some of them (TRAPPIST-1, Proxima Cen) can vary by an order of magnitude. In this work, we revisit the stellar wind properties of M-dwarfs in the light of the latest estimates of $\dot{M}$ through Lyman-$\alpha$ absorption at the astropause and slingshot prominences. We outline a modeling strategy to estimate the mass loss rate, radiative loss and wind speed, with uncertainties, based on an Alfv\'en wave driven stellar wind model. We find that it is very likely that several TRAPPIST-1 planets lie within the Alfv\'en surface, which imply that these planets experience star-planet magnetic interactions (SPMI). We also find that SPMI between Proxima Cen b and its host star could be the reason of recently observed radio emissions., Comment: 15 pages, 9 figures, accepted for publication in ApJ

We present our analysis of supernovae serendipitously found to be radio-bright several years after their optical discovery. We used recent observations from the Australian SKA Pathfinder taken as part of the pilot Variables and Slow Transients and Rapid ASKAP Continuum Survey programs. We identified 29 objects by cross-matching sources from these ASKAP observations with known core-collapse supernovae below a declination of $+40^{\circ}$ and with a redshift of $z\leq0.15$. Our results focus on eight cases that show potential late-time radio emission. These supernovae exhibit significantly greater amounts of radio emission than expected from the standard model of a single shockwave propagating through a spherical circumstellar medium, with a constant density structure produced by regular stellar mass-loss. We also discuss how we can learn from future ASKAP surveys about the circumstellar environments and emission mechanisms of supernovae that undergo late-time radio re-brightening. This pilot work tested and confirmed the potential of the Variables and Slow Transients survey to discover and study late-time supernova emission., Comment: Accepted for publication in MNRAS

Encoding quantum information within bosonic modes offers a promising direction for hardware-efficient and fault-tolerant quantum information processing. However, achieving high-fidelity universal control over the bosonic degree of freedom using native photonic hardware remains a challenge. Here, we propose an architecture to prepare and perform logical quantum operations on arbitrary multimode multi-photon states using a quantum photonic neural network. Central to our approach is the optical nonlinearity, which is realized through strong light-matter interaction with a three-level Lambda atomic system. The dynamics of this interaction are confined to the single-mode subspace, enabling the construction of high-fidelity quantum gates. This nonlinearity functions as a photon-number selective phase gate, which facilitates the construction of a universal gate set and serves as the element-wise activation function in our neural network architecture. Through numerical simulations, we demonstrate the versatility of our approach by executing tasks that are key to logical quantum information processing. The network is able to deterministically prepare a wide array of multimode multi-photon states, including essential resource states. We also show that the architecture is capable of encoding and performing logical operations on bosonic error-correcting codes. Additionally, by adapting components of our architecture, error-correcting circuits can be built to protect bosonic codes. The proposed architecture paves the way for near-term quantum photonic processors that enable error-corrected quantum computation, and can be achieved using present-day integrated photonic hardware., Comment: 21 pages including supplement, 12 figures total

Significant advancements have been made in understanding the physics of transition-edge sensors (TESs) over the past decade. However, key questions remain, particularly a detailed understanding of the current-dependent resistance of these detectors when biased within their superconducting transition. We use scanning superconducting quantum interference device (SQUID) microscopy (SSM) to image the local diamagnetic response of aluminum-manganese alloy (Al-Mn) transition-edge sensors (TESs) near their critical temperature of approximately 175 mK. By doing so, we gain insights into how the device dimensions influence TES transition width, which in turn affects device operation and informs optimal device design. Our images reveal that the Al-Mn thin film near the niobium (Nb) leads exhibits an excess diamagnetic response at temperatures several milli-Kelvin (mK) higher than the bulk of the film farther from the contacts. A possible origin of this behavior is a longitudinal proximity effect between the Nb and Al-Mn where the TES acts as a weak link between superconducting leads. We discuss how this effect shapes the temperature dependence of the resistance as the spacing between the leads decreases. This work demonstrates that magnetic imaging with SSM is a powerful tool for local characterization of superconducting detectors., Comment: 5 pages, 4 figures, Applied Superconductivity Conference (ASC 2024) proceedings, submitted to IEEE Transactions on Applied Superconductivity (IEEE TAS)

Acquiring new customers is a vital process for growing businesses. Prospecting is the process of identifying and marketing to potential customers using methods ranging from online digital advertising, linear television, out of home, and direct mail. Despite the rapid growth in digital advertising (particularly social and search), research shows that direct mail remains one of the most effective ways to acquire new customers. However, there is a notable gap in the application of modern machine learning techniques within the direct mail space, which could significantly enhance targeting and personalization strategies. Methodologies deployed through direct mail are the focus of this paper. In this paper, we propose a supervised learning approach for identifying new customers, i.e., prospecting, which comprises how we define labels for our data and rank potential customers. The casting of prospecting to a supervised learning problem leads to imbalanced tabular data. The current state-of-the-art approach for tabular data is an ensemble of tree-based methods like random forest and XGBoost. We propose a deep learning framework for tabular imbalanced data. This framework is designed to tackle large imbalanced datasets with vast number of numerical and categorical features. Our framework comprises two components: an autoencoder and a feed-forward neural network. We demonstrate the effectiveness of our framework through a transparent real-world case study of prospecting in direct mail advertising. Our results show that our proposed deep learning framework outperforms the state of the art tree-based random forest approach when applied in the real-world., Comment: Third KDD Workshop on End-to-End Customer Journey Optimization

This study reports a comprehensive environmental scan of the generative AI (GenAI) infrastructure in the national network for clinical and translational science across 36 institutions supported by the Clinical and Translational Science Award (CTSA) Program led by the National Center for Advancing Translational Sciences (NCATS) of the National Institutes of Health (NIH) at the United States. With the rapid advancement of GenAI technologies, including large language models (LLMs), healthcare institutions face unprecedented opportunities and challenges. This research explores the current status of GenAI integration, focusing on stakeholder roles, governance structures, and ethical considerations by administering a survey among leaders of health institutions (i.e., representing academic medical centers and health systems) to assess the institutional readiness and approach towards GenAI adoption. Key findings indicate a diverse range of institutional strategies, with most organizations in the experimental phase of GenAI deployment. The study highlights significant variations in governance models, with a strong preference for centralized decision-making but notable gaps in workforce training and ethical oversight. Moreover, the results underscore the need for a more coordinated approach to GenAI governance, emphasizing collaboration among senior leaders, clinicians, information technology staff, and researchers. Our analysis also reveals concerns regarding GenAI bias, data security, and stakeholder trust, which must be addressed to ensure the ethical and effective implementation of GenAI technologies. This study offers valuable insights into the challenges and opportunities of GenAI integration in healthcare, providing a roadmap for institutions aiming to leverage GenAI for improved quality of care and operational efficiency.

The first images of the jet and low velocity component (LVC) from the strongly accreting classical T Tauri star RU Lupi are presented. Adaptive optics assisted spectro-imaging of forbidden emission lines was used. The main aim of the observations was to test the conclusion from a recent spectro-astrometric study that the narrow component of the LVC is tracing an MHD disk wind, and to estimate the mass loss rate in the wind. The structure and morphology of the component supports a wind origin for the NC. An upper limit to the launch radius and semi-opening angle of the wind in [O I]{\lambda}6300 emission are estimated to be 2 au and 19{\deg} in agreement with MHD wind models for high accretors. The height of the [O I]{\lambda}6300 wind emitting region, a key parameter for the derivation of the mass loss rate, is estimated for the first time at approximately 35 au giving M_out = 2.6 x 10^-11 M_sun/yr. When compared to the derived mass accretion rate of M_acc = 1.6 x 10^-7 M_sun/yr, the efficiency in the wind is too low for the wind to be significantly contributing to angular momentum removal., Comment: 15 pages, 15 figures, 2 tables, submitted to A&A