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There is substantial variation in the returns to a college degree. One determinant is whether a worker's employment is "matched" with their education. With a novel education-industry crosswalk and panel data on 295,000 graduates, we provide the first estimates of an education-industry match premium leveraging within-person variation in earnings. We document which majors have the most and least matching, how earnings premia vary across fields and gender, and how premia evolve over time. With robust estimators, we show that workers in industries "matched" with their degree experience an average earnings premium of 7-11%, with variation by degree level and major.

As the demand for healthcare professionals increases, Latinos are increasing their representation in healthcare positions. Despite their increased representation, Latinos are more likely to be in healthcare support occupations rather than in healthcare management or professional occupation that offer higher salaries. "Excelencia" has analyzed national datasets to connect healthcare employers with institutions that are graduating and preparing Latinos for the workforce. This brief identifies the top institutions graduating Latinos (2019-2020) from certificates to the doctoral level in healthcare. These top 25 institutions are meeting the nation's economic needs by intentionally serving Latino students and producing Latino talent. This brief also highlights institutions and their intentional efforts to support post-completion success by providing educational credentials needed to support the current and future healthcare workforce.

Although more Latinos are entering the STEM workforce, they continue to be underrepresented in high-salary STEM occupations. To ensure America's future, institutions and STEM employers must both play an active role in preparing, selecting, and sourcing Latino talent for a global economy. Excelencia's research demonstrates that select institutions are investing in the recruitment, development, and completion of Latino talent and STEM employers have opportunities to strengthen their workforce. To bridge this gap, Excelencia has analyzed national datasets to connect STEM employers with institutions that are graduating and preparing Latinos for the workforce. This brief identifies the top institutions graduating Latinos (2019-20) in STEM from certificates to the doctorate level. These top 25 institutions are meeting the nation's economic needs by intentionally serving Latino students and producing Latino talent. This brief also highlights institutions and their intentional efforts to support post-completion success by providing educational credentials needed to support the current and future STEM workforce.

The nation is facing a shortage of educators prepared to meet the needs of an increasingly diverse student population. Latinos are the nation's fastest growing population but are underrepresented in higher-skilled occupations in the classroom and overrepresented in supportive roles with lower wages. This shows the need for employers and institutions to create pathways into the field of education as Latinos see the value to educate others and the community. To ensure America's future, institutions and employers in education must both play an active role in preparing, selecting, and sourcing Latino talent for a global economy. To bridge this gap, "Excelencia" has analyzed national datasets to connect employers in education with institutions that are graduating and preparing Latinos for the workforce. This brief identifies the top institutions graduating Latinos (2019-20) from certificates to the doctoral level in education. These top 25 institutions are meeting the nation's economic needs by intentionally serving Latino students and producing Latino talent. This brief also highlights institutions and their intentional efforts to support post-completion success by providing education credentials needed to support the current and future education workforce.

Bitumen extraction for the production of synthetic crude oil in Canada's Athabasca Oil Sands industry has recently come under spotlight for being a significant source of greenhouse gas emission. A major cause of concern is methane, a greenhouse gas produced by the anaerobic biodegradation of hydrocarbons in oil sands residues, or tailings, stored in settle basins commonly known as oil sands tailing ponds. In order to determine the methane emitting potential of these tailing ponds and have future methane projections, we use real-time weather data, mechanistic models developed from laboratory controlled experiments, and industrial reports to train a physics constrained machine learning model. Our trained model can successfully identify the directions of active ponds and estimate their emission levels, which are generally hard to obtain due to data sampling restrictions. We found that each active oil sands tailing pond could emit between 950 to 1500 tonnes of methane per year, whose environmental impact is equivalent to carbon dioxide emissions from at least 6000 gasoline powered vehicles. Although abandoned ponds are often presumed to have insignificant emissions, our findings indicate that these ponds could become active over time and potentially emit up to 1000 tonnes of methane each year. Taking an average over all datasets that was used in model training, we estimate that emissions around major oil sands regions would need to be reduced by approximately 12% over a year, to reduce the average methane concentrations to 2005 levels., Comment: 19 pages, 8 figures, 2 tables

This paper presents a scheduling algorithm that divides a manufacturing/warehouse floor into zones that an Autonomous Mobile Robot (AMR) will occupy and complete part pick-up and drop-off tasks. Each zone is balanced so that each AMR will share each task equally. These zones change over time to accommodate fluctuations in production and to avoid overloading an AMR with tasks. A decentralized dynamic zoning (DDZ) algorithm is introduced to find the optimal zone design, eliminating the possibility of single-point failure from a centralized unit. Then a simulation is built comparing the adaptability of DDZ and other dynamic zoning algorithms from previous works. Initial results show that DDZ has a much lower throughput than other dynamic zoning algorithms but DDZ can achieve a better distribution of tasks. Initial results show that DDZ had a lower standard deviation of AMR total travel distance which was 2874.7 feet less than previous works. This 68.7\% decrease in standard deviation suggests that AMRs under DDZ travel a similar distance during production. This could be useful for real-world applications by making it easier to design charging and maintenance schedules without much downtime. Video demonstration of the system working can be seen here: \url{https://youtu.be/yVi026oVD7U}, Comment: 11 pages, 5 figures, submitted to Scientific Reports special edition Robotics and Automation in Advanced Manufacturing

Axions are a well-motivated candidate for dark matter. The preeminent method to search for axion dark matter is known as the axion haloscope, which makes use of the conversion of axions to photons in a large magnetic field. Due to the weak coupling of axions to photons however, the expected signal strength is exceptionally small. To increase signal strength, many haloscopes make use of resonant enhancement and high gain amplifiers, while also taking measures to keep receiver noise as low as possible such as the use of dilution refrigerators and ultra low-noise electronics. In this paper we derive the theoretical noise model based on the sources of noise found within a typical axion haloscope receiver chain, using the Axion Dark Matter eXperiment (ADMX) as a case study. We present examples of different noise calibration measurements at 1280~MHz using a variable temperature stage with ADMX during its most recent data taking run. The consistency between the measurements and the detailed model provide suggestions for future improvements within ADMX and other axion haloscopes to reach a lower noise temperature and to simplify the receiver chain design.

Alkali superoxides differ from conventional transition metal magnets, exhibit magnetism from partially occupied oxygen molecular $\pi^*$-orbitals. Among them, CsO$_2$ stands out for its potential to exhibit novel quantum collective phenomena, such as an orbital order induced Tomonaga-Luttinger liquid state. Using ab-initio Hubbard models, superexchange theory, and experimental spin wave measurements, we propose that CsO$_2$ exhibits unconventional magnetoelectric characteristics at low temperature. Our analysis confirms a canted antiferromagnetic ground state and a spin-flop transition, with ferroelectricity is induced by breaking inversion and time-reversal symmetry in the spin-flop phase. Consequently, our analysis reveals a strong interplay not only between exchange interactions but also among magnetically-induced polarization and orbital order. The magnetic structure, stabilized by orbital order, induces magnetically-induced polarization through an antisymmetric mechanism. Overall, our results reveal the coexistence of three highly entangled orders in CsO$_2$, namely, orbital, spin and ferroelectricity., Comment: 13 pages, 9 figures

The goal of this study is to develop a computational model of the progression of changes in mitochondrial phenotype resulting from infection with pathogenic mycobacteria. This ultimately will enable a large-scale virulence screen of mutant bacterial libraries. Mycobacterium tuberculosis (Mtb) is an intracellular pathogen, but only a small number of its genes have been studied for roles in intracellular host cell survival and replication. Mitochondria are the powerhouse of the host cell and play critical roles in cell survival when attacked by certain pathogens. When Mtb bacteria invade host cells, they induce changes in mitochondrial morphology, making mitochondria a novel target for image processing and machine learning to determine virulence associations of genes in Mtb and potentially other related intracellular pathogens. By hypothesizing mitochondria as an instance of a dynamic and interconnected graph, we demonstrate a statistical approach for quantitatively recognizing novel mitochondrial phenotypes induced by invading pathogens.

Transitional millisecond pulsars (tMSPs) are neutron-star systems that alternate between a rotation-powered radio millisecond pulsar state and an accretion-disk-dominated low-mass X-ray binary (LMXB)-like state on multi-year timescales. During the LMXB-like state, the X-ray emission from tMSPs switches between "low" and "high" X-ray brightness modes on a timescale of seconds to minutes (or longer), while the radio emission shows variability on timescales of roughly minutes. Coordinated VLA and Chandra observations of the nearby tMSP PSR J1023+0038 uncovered a clear anti-correlation between radio and X-ray luminosities such that the radio emission consistently peaks during the X-ray low modes. In addition, there are sometimes also radio/X-ray flares that show no obvious correlation. In this paper, we present simultaneous radio and X-ray observations of a promising tMSP candidate system, 3FGL J1544.6-1125, which shows optical, gamma-ray, and X-ray phenomena similar to PSR J1023+0038, but which is challenging to study because of its greater distance. Using simultaneous VLA and Chandra observations we find that the radio and X-ray emission are consistent with being anti-correlated in a manner similar to PSR J1023+0038. We discuss how our results help in understanding the origin of bright radio emission from tMSPs. The greater sensitivity of upcoming telescopes like the Square Kilometre Array will be crucial for studying the correlated radio/X-ray phenomena of tMSP systems., Comment: 21 pages, 14 figures, 2 tables. Submitted to MNRAS

Galaxy mergers are a key driver of galaxy formation and evolution, including the triggering of AGN and star formation to a still unknown degree. We thus investigate the impact of galaxy mergers on star formation and AGN activity using a sample of 3,330 galaxies at $z = [4.5, 8.5]$ from eight JWST fields (CEERS, JADES GOODS-S, NEP-TDF, NGDEEP, GLASS, El-Gordo, SMACS-0723, and MACS-0416), collectively covering an unmasked area of 189 arcmin$^2$. We focuses on star formation rate (SFR) enhancement, AGN fraction, and AGN excess in major merger ($\mu > 1/4$) close-pair samples, defined by $\Delta z < 0.3$ and projected separations $r_p < 100$ kpc, compared to non-merger samples. We find that SFR enhancement occurs only at $r_p < 20$ kpc, with values of $0.25 \pm 0.10$ dex and $0.26 \pm 0.11$ dex above the non-merger medians for $z = [4.5, 6.5]$ and $z = [6.5, 8.5]$, respectively. No other statistically significant enhancements in galaxy sSFR or stellar mass are observed at any projected separation or redshift bin. We also compare our observational results with predictions from the SC-SAM simulation and find no evidence of star formation enhancement in the simulations at any separation range. Finally, we examine the AGN fraction and AGN excess, finding that the fraction of AGNs in AGN-galaxy pairs, relative to the total AGN population, is $3.25^{+1.50}_{-1.06}$ times greater than the fraction of galaxy pairs relative to the overall galaxy population at the same redshift. We find that nearly all AGNs have a companion within 100 kpc and observe an excess AGN fraction in close-pair samples compared to non-merger samples. This excess is found to be $1.26 \pm 0.06$ and $1.34 \pm 0.06$ for AGNs identified via the inferred BPT diagram and photometric SED selection, respectively., Comment: 17 Pages, 7 Figures, Submitted to MNRAS

We present spectroscopy of the accreting X-ray binary and millisecond pulsar SAX J1808.4-3658. These observations are the first to be obtained during a reflaring phase. We collected spectroscopic data during the beginning of reflaring of the 2019 outburst and we compare them to previous datasets, taken at different epochs both of the same outburst and across the years. In order to do so, we also present spectra of the source taken during quiescence in 2007, one year before the next outburst. We made use of data taken by the Very Large Telescope (VLT) X-shooter spectrograph on August 31, 2019, three weeks after the outburst peak. For flux calibration, we used photometric data taken during the same night by the 1m telescopes from the Las Cumbres Observatory network that are located in Chile. We compare our spectra to the quiescent data taken by the VLT-FORS1 spectrograph in September 2007. We inspected the spectral energy distribution by fitting our data with a multi-colour accretion disk model and sampled the posterior probability density function for the model parameters with a Markov-Chain Monte Carlo algorithm. We find the optical spectra of the 2019 outburst to be unusually featureless, with no emission lines present despite the high resolution of the instrument. Fitting the UV-optical spectral energy distribution with a disk plus irradiated star model results in a very large value for the inner disk radius of $\sim 5130 \pm 240$ km, which could suggest that the disk has been emptied of material during the outburst, possibly accounting for the emission-less spectra. Alternatively, the absence of emission lines could be due to a significant contribution of the jet emission at optical wavelengths., Comment: Accepted for publication by A&A, 7 pages, 8 figures, 2 tables

Predictive maintenance has been used to optimize system repairs in the industrial, medical, and financial domains. This technique relies on the consistent ability to detect and predict anomalies in critical systems. AI models have been trained to detect system faults, improving predictive maintenance efficiency. Typically there is a lack of fault data to train these models, due to organizations working to keep fault occurrences and down time to a minimum. For newly installed systems, no fault data exists since they have yet to fail. By using diffusion models for synthetic data generation, the complex training datasets for these predictive models can be supplemented with high level synthetic fault data to improve their performance in anomaly detection. By learning the relationship between healthy and faulty data in similar systems, a diffusion model can attempt to apply that relationship to healthy data of a newly installed system that has no fault data. The diffusion model would then be able to generate useful fault data for the new system, and enable predictive models to be trained for predictive maintenance. The following paper demonstrates a system for generating useful, multivariate synthetic data for predictive maintenance, and how it can be applied to systems that have yet to fail.

Extreme ultraviolet (XUV) light sources allow for the probing of bound electron dynamics on attosecond scales, interrogation of high-energy-density matter, and access to novel regimes of strong-field quantum electrodynamics. Despite the importance of these applications, coherent XUV sources remain relatively rare, and those that do exist are limited in their peak intensity and spatio-polarization structure. Here, we demonstrate that photon acceleration of an optical vector vortex pulse in the moving density gradient of an electron beam-driven plasma wave can produce a high-intensity, tunable-wavelength XUV pulse with the same vector vortex structure as the original pulse. Quasi-3D, boosted-frame particle-in-cell simulations show the transition of optical vector vortex pulses with 800-nm wavelengths and intensities below $10^{18}$ W/cm$^2$ to XUV vector vortex pulses with 36-nm wavelengths and intensities exceeding $10^{20}$ W/cm$^2$ over a distance of 1.2 cm. The XUV pulses have sub-femtosecond durations and nearly flat phase fronts. The production of such high-quality, high-intensity XUV vector vortex pulses could expand the utility of XUV light as a diagnostic and driver of novel light-matter interactions., Comment: 25 pages, 5 figures, planned submission to scientific journal

Efficient learning from demonstration for long-horizon tasks remains an open challenge in robotics. While significant effort has been directed toward learning trajectories, a recent resurgence of object-centric approaches has demonstrated improved sample efficiency, enabling transferable robotic skills. Such approaches model tasks as a sequence of object poses over time. In this work, we propose a scheme for transferring observed object arrangements to novel object instances by learning these arrangements on canonical class frames. We then employ this scheme to enable a simple yet effective approach for training models from as few as five demonstrations to predict arrangements of a wide range of objects including tableware, cutlery, furniture, and desk spaces. We propose a method for optimizing the learned models to enables efficient learning of tasks such as setting a table or tidying up an office with intra-category transfer, even in the presence of distractors. We present extensive experimental results in simulation and on a real robotic system for table setting which, based on human evaluations, scored 73.3% compared to a human baseline. We make the code and trained models publicly available at http://oplict.cs.uni-freiburg.de., Comment: 8 pages, 7 figures, 2 tables, submitted to RA-L

PSR B1259-63 is a gamma-ray binary system with a 48 ms radio pulsar orbiting around an O9.5Ve star, LS 2883, in a highly eccentric ~3.4 yr long orbit. Close to the periastron the system is detected from radio up to the TeV energies due to the interaction of LS 2883 and pulsar's outflows. The observations of last 4 periastra passages taken in 2010-2021 demonstrate periastron to periastron variability at all wavelength, probably linked to the state of the decretion disk. In this paper we present the results of our optical, radio and X-ray observational campaigns on PSR B1259-63 performed in 2024 accompanied with the analysis of the publicly available GeV FERMI/LAT data. We show that this periastron passage was characterised by the early flaring of X-rays before the periastron passage and GeV emission after the periastron passage, which can be explained by a larger size of the decretion disk as supported by the optical observations. The structure of the GeV flare is also in agreement with the disruption of the large dense disk. The possible X-ray/radio correlation was observed only during the post-periastron rise of X-ray and radio emission., Comment: submitted to MNRAS

For data-constrained, complex and dynamic industrial environments, there is a critical need for transferable and multimodal methodologies to enhance anomaly detection and therefore, prevent costs associated with system failures. Typically, traditional PdM approaches are not transferable or multimodal. This work examines the use of Large Language Models (LLMs) for anomaly detection in complex and dynamic manufacturing systems. The research aims to improve the transferability of anomaly detection models by leveraging Large Language Models (LLMs) and seeks to validate the enhanced effectiveness of the proposed approach in data-sparse industrial applications. The research also seeks to enable more collaborative decision-making between the model and plant operators by allowing for the enriching of input series data with semantics. Additionally, the research aims to address the issue of concept drift in dynamic industrial settings by integrating an adaptability mechanism. The literature review examines the latest developments in LLM time series tasks alongside associated adaptive anomaly detection methods to establish a robust theoretical framework for the proposed architecture. This paper presents a novel model framework (AAD-LLM) that doesn't require any training or finetuning on the dataset it is applied to and is multimodal. Results suggest that anomaly detection can be converted into a "language" task to deliver effective, context-aware detection in data-constrained industrial applications. This work, therefore, contributes significantly to advancements in anomaly detection methodologies.

We consider rational maps $f$ on the Riemann sphere $\widehat {\mathbb{C}}$ with an $f$-invariant set $P\subset \widehat {\mathbb{C}}$ of four marked points containing the postcritical set of $f$. We show that the dynamics of the corresponding Thurston pullback map $\sigma_f$ on the completion $\overline{\mathcal{T}_P}$ of the associated Teichm\"uller space $\mathcal{T}_P$ with respect to the Weil-Petersson metric is easy to understand when $\overline{\mathcal{T}_P}$ admits a cover by sets with good combinatorial and dynamical properties. In particular, the map $f$ has a finite global curve attractor in this case. Using a result by Eremenko and Gabrielov, we also show that if $P$ contains all critical points of $f$ and each point in $P$ is periodic, then such a cover of $\overline{\mathcal{T}_P}$ can be obtained from a $\sigma_f$-invariant tessellation by ideal hyperbolic triangles., Comment: 12 pages

We present the first X-ray polarization measurement of the neutron star low-mass X-ray binary and Z-source, GX 340$+$0, in the normal branch (NB) using a 200 ks observation with the Imaging X-ray Polarimetric Explorer (IXPE). This observation was performed in 2024 August. Along with IXPE, we also conducted simultaneous observations with NICER, AstroSat, Insight-HXMT, ATCA, and GMRT to investigate the broadband spectral and timing properties in the X-ray and radio wavelengths. During the campaign, the source traced a complete Z-track during the IXPE observation but spent most of the time in the NB. We measure X-ray polarization degree (PD) of $1.22\pm0.25\%$ in the 2-8 keV energy band with a polarization angle (PA) of $38\pm6^\circ$. The PD in the NB is observed to be weaker than in the horizontal branch (HB) but aligned in the same direction. The PD of the source exhibits a marginal increase with energy while the PA shows no energy dependence. The joint spectro-polarimetric modeling is consistent with the observed X-ray polarization originating from a single spectral component from the blackbody, the Comptonized emission, or reflection feature, while the disk emission does not contribute towards the X-ray polarization. GMRT observations at 1.26 GHz during HB had a tentative detection at 4.5$\pm$0.7 mJy while ATCA observations a day later during the NB detected the source at 0.70$\pm$0.05 mJy and 0.59$\pm$0.05 mJy in the 5.5 & 9 GHz bands, respectively, suggesting an evolving jet structure depending on the Z-track position., Comment: 17 pages, 5 figures, 4 tables; Submitted to ApJ

The reported discovery of a cold ($\sim$10$^4~\text{K}$) disc-like structure around the super-massive black hole at the centre of the Milk Way, Sagittarius A* (Sgr A*), has challenged our understanding of the gas dynamics and thermodynamic state of the plasma in its immediate vicinity. State-of-the-art simulations do not agree on whether or not such a disc can indeed be a product of the multiple stellar wind interactions of the mass-losing stars in the region. This study aims to constrain the conditions for the formation of a cold disc as a natural outcome of the system of the mass-losing stars orbiting around Sgr A*, to investigate if the disc is a transient or long-lasting structure, and to assess the validity of the model through direct comparisons with observations. We conduct a set of hydrodynamic simulations of the observed Wolf-Rayet (WR) stars feeding Sgr A* using the finite-volume adaptive mesh-refinement code Ramses. We focus, for the first time, on the impact of the chemical composition of the plasma emanating from the WR stars. The simulations show that the chemical composition of the plasma affects the radiative cooling enough to impact the properties of the medium such as density and temperature and, as a consequence, the rate at which the material inflows onto Sgr A*. We demonstrated that the formation of a cold disc from the stellar winds is possible for certain chemical compositions that are consistent with the current observational constraints. However, even in such a case, it is not possible to reproduce the reported properties of the observed disc-like structure, namely its inclination and hydrogen recombination line fluxes. We conclude that the stellar winds on their own cannot form the cold disc around Sgr A* inferred from the observations. Either relevant ingredients are still missing in the model, or the interpretation of the observed data needs to be revised., Comment: Submitted to A&A, 17 pages, 11 figures (+2 pages, +2 figures in Appendix)

In response to numerous programs seeking to advance quantum education and workforce development in the United States, experts from academia, industry, government, and professional societies convened for a National Science Foundation-sponsored workshop in February 2024 to explore the benefits and challenges of establishing a national center for quantum education. Broadly, such a center would foster collaboration and build the infrastructure required to develop a diverse and quantum-ready workforce. The workshop discussions centered around how a center could uniquely address gaps in public, K-12, and undergraduate quantum information science and engineering (QISE) education. Specifically, the community identified activities that, through a center, could lead to an increase in student awareness of quantum careers, boost the number of educators trained in quantum-related subjects, strengthen pathways into quantum careers, enhance the understanding of the U.S. quantum workforce, and elevate public engagement with QISE. Core proposed activities for the center include professional development for educators, coordinated curriculum development and curation, expanded access to educational laboratory equipment, robust evaluation and assessment practices, network building, and enhanced public engagement with quantum science.

The discovery and identification of molecules in biological and environmental samples is crucial for advancing biomedical and chemical sciences. Tandem mass spectrometry (MS/MS) is the leading technique for high-throughput elucidation of molecular structures. However, decoding a molecular structure from its mass spectrum is exceptionally challenging, even when performed by human experts. As a result, the vast majority of acquired MS/MS spectra remain uninterpreted, thereby limiting our understanding of the underlying (bio)chemical processes. Despite decades of progress in machine learning applications for predicting molecular structures from MS/MS spectra, the development of new methods is severely hindered by the lack of standard datasets and evaluation protocols. To address this problem, we propose MassSpecGym -- the first comprehensive benchmark for the discovery and identification of molecules from MS/MS data. Our benchmark comprises the largest publicly available collection of high-quality labeled MS/MS spectra and defines three MS/MS annotation challenges: \textit{de novo} molecular structure generation, molecule retrieval, and spectrum simulation. It includes new evaluation metrics and a generalization-demanding data split, therefore standardizing the MS/MS annotation tasks and rendering the problem accessible to the broad machine learning community. MassSpecGym is publicly available at \url{https://github.com/pluskal-lab/MassSpecGym}.

Single photon detectors (SPDs) are essential technology in quantum science, quantum network, biology, and advanced imaging. To detect the small quantum of energy carried in a photon, conventional SPDs rely on energy excitation across either a semiconductor bandgap or superconducting gap. While the energy gap suppresses the false-positive error, it also sets an energy scale that can limit the detection efficiency of lower energy photons and spectral bandwidth of the SPD. Here, we demonstrate an orthogonal approach to detect single near-infrared photons using graphene calorimeters. By exploiting the extremely low heat capacity of the pseudo-relativistic electrons in graphene near its charge neutrality point, we observe an electron temperature rise up to ~2 K using a hybrid Josephson junction. In this proof-of-principle experiment, we achieve an intrinsic quantum efficiency of 87% (75%) with dark count < 1 per second (per hour) at operation temperatures as high as 1.2 K. Our results highlight the potential of electron calorimetric SPDs for detecting lower-energy photons from the mid-IR to microwave regimes, opening pathways to study space science in far-infrared regime, to search for dark matter axions, and to advance quantum technologies across a broader electromagnetic spectrum.

Harmful cyanobacterial blooms (CBs) have a growing global prevalence, emerging as a significant environmental concern due to their potential toxicity. Understanding how the different mechanisms affect CBs is crucial to develop actionable management strategies. For this, we derive a stoichiometric dynamical system that describes the qualitative population dynamics of cyanobacteria and their toxicity in north-temperate freshwater ecosystems. Our model quantifies the hypoxic effects of CBs on fish mortality and the effect of microcystin-LR (MC-LR), a potent toxin produced by cyanobacteria, on aquatic macro-invertebrates, phytoplankton, and fish species. By fitting the model to lakes with varying physical characteristics, eutrophic conditions, and water temperature, we can delineate and understand the driving components of CBs. We show that decreases in water exchange rate, depth of epilimnion, or light attenuation increases bloom intensity and duration. Furthermore, our models concur that eutrophication and increasing water temperatures exacerbate the intensity of CBs. We observe a severe bioaccumulative effect of MC-LR in aquatic species, stressing the potential impact on humans and other terrestrial animals. We validate our model with field measurements demonstrating its applicability to several realistic lake conditions. These insights are essential for informing targeted interventions to reduce CBs and their ecological impacts., Comment: Main Document: 11 pages

Within the Milky Way (MW), younger stellar populations exhibit steeper (more negative) metallicity radial gradients; the origin of this trend remains debated. The FIRE-2 cosmological simulations of MW-mass galaxies show the same trend as the MW, which in FIRE-2 arises because the metallicity gradient of the interstellar medium (ISM), and thus of stars at birth, became steeper over time. We seek to understand this evolution in the context of inside-out radial growth of galaxies. Most FIRE-2 galaxies grew radially inside-out in both gas and stars; specifically, their surface density profiles, $\Sigma(R)$, became shallower over time. Combined with a realized superlinear (Kennicutt-Schmidt-like) relation between star formation and total gas density, the profile of the ratio $\Sigma_{\rm star}(R)/\Sigma_{\rm gas}(R)$ became shallower (flatter) over time. Thus, if metals stayed where they were injected into the ISM from stars, the metallicity gradient would become shallower over time, as some models predict. However, metallicity gradients in FIRE-2 became steeper over time, because of the additional effects of (radial) mixing of metals in the ISM. Specifically, the velocity dispersion and net radial advection of gas declined over time, as ISM turbulence decreased and the disk settled, leading to upside-down vertical growth. In FIRE-2, this evolution in metal mixing of gas associated with upside-down growth dominates over inside-out radial growth, causing the metallicity radial gradient of the ISM and of stars at birth to become steeper over time. We argue that this reflects the ISM history of the MW and of typical MW-mass galaxies., Comment: 22 pages, submitted to ApJ

Survival prediction often involves estimating the time-to-event distribution from censored datasets. Previous approaches have focused on enhancing discrimination and marginal calibration. In this paper, we highlight the significance of conditional calibration for real-world applications -- especially its role in individual decision-making. We propose a method based on conformal prediction that uses the model's predicted individual survival probability at that instance's observed time. This method effectively improves the model's marginal and conditional calibration, without compromising discrimination. We provide asymptotic theoretical guarantees for both marginal and conditional calibration and test it extensively across 15 diverse real-world datasets, demonstrating the method's practical effectiveness and versatility in various settings., Comment: Accepted to NeurIPS 2024. 41 pages, 23 figures

We study the dynamics and interactions of the solitonic domain walls that occur in realistic axion electrodynamics models including the Chern-Simons interaction, $a\epsilon_{\mu\nu\lambda\sigma}F^{\mu\nu} F^{\lambda\sigma}$, between an axion $a(x)$ of mass $m_a$, and a massless U(1) gauge field, e.g. EM, interacting with strength $\alpha=e^2/4\pi$ with charged matter, e.g. electron-positron pairs. In particular, in the presence of a U(1) gauge-and-matter relativistic thermal plasma we study the friction experienced by the walls due to the Chern-Simons term. Utilizing the linear response method we include the collective effects of the plasma, as opposed to purely particle scattering across the wall (as is done in previous treatments) which is valid only in the thin wall regime that is rarely applicable in realistic cases. We show that the friction depends on the Lorentz-$\gamma$-factor-dependent inverse thickness of the wall in the plasma frame, $\ell^{-1} \sim \gamma m_a$, compared to the three different plasma scales, the temperature $T$, the Debye mass $m_D\sim\sqrt{\alpha} T$, and the damping rate $\Gamma \sim \alpha^2 T$, and elucidate the underlying physical intuition for this behavior. (For friction in the thin-wall-limit we correct previous expressions in the literature.) We further consider the effects of long-range coherent magnetic fields that are possibly present in the early universe and compare their effect with that of thermal magnetic fields. Finally, we briefly discuss the possible early universe consequences of our results for domain wall motion and network decay, stochastic gravitational wave production from domain wall networks, and possible primordial black hole production from domain wall collapse, though a more complete discussion of these topics is reserved for a companion paper., Comment: 56 Pages, 8 Figures. Added references

Efficient encoding and representation of large 3D molecular structures with high fidelity is critical for biomolecular design applications. Despite this, many representation learning approaches restrict themselves to modeling smaller systems or use coarse-grained approximations of the systems, for example modeling proteins at the resolution of amino acid residues rather than at the level of individual atoms. To address this, we develop quantized auto-encoders that learn atom-level tokenizations of complete proteins, RNA and small molecule structures with reconstruction accuracies below and around 1 Angstrom. We demonstrate that the Mamba state space model architecture employed is comparatively efficient, requiring a fraction of the training data, parameters and compute needed to reach competitive accuracies and can scale to systems with almost 100,000 atoms. The learned structure tokens of bio2token may serve as the input for all-atom language models in the future.

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

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

Fine-tuning language models has become increasingly popular following the proliferation of open models and improvements in cost-effective parameter efficient fine-tuning. However, fine-tuning can influence model properties such as safety. We assess how fine-tuning can impact different open models' propensity to output toxic content. We assess the impacts of fine-tuning Gemma, Llama, and Phi models on toxicity through three experiments. We compare how toxicity is reduced by model developers during instruction-tuning. We show that small amounts of parameter-efficient fine-tuning on developer-tuned models via low-rank adaptation on a non-adversarial dataset can significantly alter these results across models. Finally, we highlight the impact of this in the wild, demonstrating how toxicity rates of models fine-tuned by community contributors can deviate in hard-to-predict ways., Comment: To be presented at NeurIPS 2024 Safe Generative AI Workshop

Thermal tides are atmospheric tides caused by variations in day-night insolation, similar to gravitational tides but with key differences. While both result in delayed mass redistribution, energy dissipation, and angular momentum exchanges between the planet and its host star, thermal tides can drive a planet's dynamics away from the rotational equilibrium states predicted by classical tidal theory. In this work, we present a novel closed-form solution for the thermotidal response of rocky planets. This general solution is derived from first principles, assuming either dry or moist adiabatic temperature profiles for the planet's atmosphere, and can be readily applied to study the long-term evolution of exoplanets in the habitable zones of their host stars. Despite relying on a small number of parameters, the model successfully captures the key features of the thermotidal torque predicted by General Circulation Models (GCMs). It also accurately predicts Earth's current semidiurnal thermotidal response and provides new insights into the evolution of the length of day during the Precambrian era., Comment: 5 pages, 2 figures, proceedings of the SF2A annual meeting

The presence of two chlorine atoms in 1,2-dichloroethene allows for isomerisation around the double bond. This isomerisation can lead to rich photochemistry. We present a time-resolved pump-probe photoelectron spectroscopy measurement on both the cis- and trans- isomers of 1,2-dichloroethene. A universal XUV probe of 22.3 eV is used allowing observation of photoelectrons formed anywhere on the potential energy surface, even from the ground-state or dissociation products. Following excitation with a 200 nm probe both ultrafast excited state dynamics and product formation are observed within the time resolution of the experiment. Excited state population begins to return to the ground state on an ultrafast time scale (< 70 fs) and population of products channels is observed on the same timescale. With the aid of ab initio calculations it is found that population transfer from the excited state is facilitated by vibrational modes involving C-C-H bends.

We consider an optimal flow distribution problem in which the goal is to find a radial configuration that minimizes resistance-induced quadratic distribution costs while ensuring delivery of inputs from multiple sources to all sinks to meet their demands. This problem has critical applications in various distribution systems, such as electricity, where efficient energy flow is crucial for both economic and environmental reasons. Due to its complexity, finding an optimal solution is computationally challenging and NP-hard. In this paper, we propose a novel algorithm called FORWARD, which leverages graph theory to efficiently identify feasible configurations in polynomial time. By drawing parallels with random walk processes on electricity networks, our method simplifies the search space, significantly reducing computational effort while maintaining performance. The FORWARD algorithm employs a combination of network preprocessing, intelligent partitioning, and strategic sampling to construct radial configurations that meet flow requirements, finding a feasible solution in polynomial time. Numerical experiments demonstrate the effectiveness of our approach, highlighting its potential for real-world applications in optimizing distribution networks.

In using observed data to make inferences about a population quantity, it is commonly assumed that the sampling distribution from which the data were drawn belongs to a given parametric family of distributions, or at least, a given finite set of such families, i.e. the population space is assumed to be closed. Here, we address the problem of how to determine an appropriate post-data distribution for a given population quantity when such an assumption about the underlying sampling distribution is not made, i.e. when the population space is open. The strategy used to address this problem is based on the fact that even though, due to an open population space being non-measurable, we are not able to place a post-data distribution over all the sampling distributions contained in such a population space, it is possible to partition this type of space into a finite, countable or uncountable number of subsets such that a distribution can be placed over a variable that simply indicates which of these subsets contains the true sampling distribution. Moreover, it is argued that, by using sampling distributions that belong to a number of parametric families, it is possible to adequately and elegantly represent the sampling distributions that belong to each of the subsets of such a partition. Since a statistical model is conceived as being a model of a population space rather than a model of a sampling distribution, it is also argued that neither the type of models that are put forward nor the expression of pre-data knowledge via such models can be directly brought into question by the data. Finally, the case is made that, as well as not being required in the modelling process that is proposed, the standard practice of using P values to measure the absolute compatibility of an individual or family of sampling distributions with observed data is neither meaningful nor useful., Comment: Possibly the final version

Cooling flows are common in galaxy clusters which have cool cores. The soft X-ray emission below 1 keV from the flows is mostly absorbed by cold dusty gas within the central cooling sites. Further evidence for this process is presented here through a more detailed analysis of the nearby Centaurus cluster and some additional clusters. Predictions of JWST near and mid-infrared spectra from cooling gas are presented. [NeVI] emission at 7.65 micron should be an important diagnostic of gas cooling between 6 and 1.5 times 10^5 K. The emerging overall picture of hidden cooling flows is explored. The efficiency of AGN feedback in reducing the total cooling rate in cool cores is shown to be above 50 percent for many clusters but is rarely above 90 per cent. The reduction is mostly in outer gas. Cooling dominates in elliptical galaxies and galaxy groups which have mass flow rates below about 15M/yr and in some massive clusters where rates can exceed 1000M/yr., Comment: 17 pages, 49 figures submitted to MNRAS

Magneto-transport measurements of Pr$_4$Ni$_3$O$_{10}$ single crystals performed under externally applied pressures up to 73 GPa in diamond anvil cells with either KBr or Nujol oil as pressure media yield signatures of superconductivity with a maximum onset temperature of approximately 31 K. True zero resistance was not observed, consistent with a non-percolating superconducting volume fraction. Magnetization measurements provided corroborating evidence of superconductivity, with a pressure-dependent diamagnetic signal occurring below the onset temperature, and an estimate from the absolute value of the susceptibility suggests a superconducting volume fraction on the order of 10%. We observe sample-to-sample variations in the magnitude and pressure dependence of Tc as well as a dependence on the configuration of electrical contacts on a given sample. Possible causes of this behavior may be significant inhomogeneities in the pressure and/or damage to the samples induced by the pressure media as well as inhomogeneities in the crystals themselves. The results imply that our as-grown Pr$_4$Ni$_3$O$_{10}$ single crystals are not bulk superconductors but that there is a minority structure present within the crystals that is indeed superconducting.

The Axion Dark Matter eXperiment is sensitive to narrow axion flows, given axions compose a fraction of the dark matter with a non-negligible local density. Detecting these low-velocity dispersion flows requires a high spectral resolution and careful attention to the expected signal modulation due to Earth's motion. We report an exclusion on the local axion dark matter density in narrow flows of $\rho_a \gtrsim 0.03\,\mathrm{GeV/cm^3}$ and $\rho_a \gtrsim 0.004\,\mathrm{GeV/cm^3}$ for Dine-Fischler-Srednicki-Zhitnitski and Kim-Shifman-Vainshtein-Zakharov axion-photon couplings, respectively, over the mass range $3.3-4.2\,\mu\text{eV}$. Measurements were made at selected resolving powers to allow for a range of possible velocity dispersions., Comment: 7 pages, 3 figures

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

Learning from human feedback has gained traction in fields like robotics and natural language processing in recent years. While prior works mostly rely on human feedback in the form of comparisons, language is a preferable modality that provides more informative insights into user preferences. In this work, we aim to incorporate comparative language feedback to iteratively improve robot trajectories and to learn reward functions that encode human preferences. To achieve this goal, we learn a shared latent space that integrates trajectory data and language feedback, and subsequently leverage the learned latent space to improve trajectories and learn human preferences. To the best of our knowledge, we are the first to incorporate comparative language feedback into reward learning. Our simulation experiments demonstrate the effectiveness of the learned latent space and the success of our learning algorithms. We also conduct human subject studies that show our reward learning algorithm achieves a 23.9% higher subjective score on average and is 11.3% more time-efficient compared to preference-based reward learning, underscoring the superior performance of our method. Our website is at https://liralab.usc.edu/comparative-language-feedback/, Comment: 8th Annual Conference of Robot Learning (2024)

In reinforcement learning, if the agent's reward differs from the designers' true utility, even only rarely, the state distribution resulting from the agent's policy can be very bad, in theory and in practice. When RL policies would devolve into undesired behavior, a common countermeasure is KL regularization to a trusted policy ("Don't do anything I wouldn't do"). All current cutting-edge language models are RL agents that are KL-regularized to a "base policy" that is purely predictive. Unfortunately, we demonstrate that when this base policy is a Bayesian predictive model of a trusted policy, the KL constraint is no longer reliable for controlling the behavior of an advanced RL agent. We demonstrate this theoretically using algorithmic information theory, and while systems today are too weak to exhibit this theorized failure precisely, we RL-finetune a language model and find evidence that our formal results are plausibly relevant in practice. We also propose a theoretical alternative that avoids this problem by replacing the "Don't do anything I wouldn't do" principle with "Don't do anything I mightn't do"., Comment: 10 pages, 7 page appendix, 4 figures

The isotopic compositions of samples returned from Cb-type asteroid Ryugu and Ivuna-type (CI) chondrites are distinct from other carbonaceous chondrites, which has led to the suggestion that Ryugu and CI chondrites formed in a different region of the accretion disk, possibly around the orbits of Uranus and Neptune. We show that, like for Fe, Ryugu and CI chondrites also have indistinguishable Ni isotope anomalies, which differ from those of other carbonaceous chondrites. We propose that this unique Fe and Ni isotopic composition reflects different accretion efficiencies of small FeNi metal grains among the carbonaceous chondrite parent bodies. The CI chondrites incorporated these grains more efficiently, possibly because they formed at the end of the disk's lifetime, when planetesimal formation was also triggered by photoevaporation of the disk. Isotopic variations among carbonaceous chondrites may thus reflect fractionation of distinct dust components from a common reservoir, implying CI chondrites and Ryugu may have formed in the same region of the accretion disk as other carbonaceous chondrites., Comment: Published open access in Science Advances

Rare-earth trihydrides ($R$H$_3$) exhibit intriguing coupled electronic and structural properties as a function of doping, hydrogen vacancies, and thermodynamic conditions. Theoretical studies of these materials typically rely on density functional theory (DFT), including the use of small supercells that may underestimate strong correlation effects and structural distortions which in turn may influence their metallicity. Here, we elucidate the roles of lattice distortions and correlation effects on the electronic properties of pristine and doped $R$H$_3$ by adopting DFT+U and Quantum Monte Carlo (QMC) methods. Linear-response constrained DFT (LR-cDFT) methods find Hubbard U $\approx 2$ eV for $R_d$ orbitals and U$\approx 6$ eV for H$_s$/N$_p$ orbitals. The small U on Lu$_d$ orbitals is consistent with QMC calculations on LuH$_3$ and LuH$_{2.875}$N$_{0.125}$. In pure face-centered-cubic (FCC) $R$H$_3$ ($R$=Lu,Y), neither DFT nor DFT+U with the self-consistently determined U is enough to create a band gap, however a supercell with hydrogen distortions creates a small gap whose magnitude increases when performing DFT+U with self-consistently determined U values. Correlation effects, in turn, have a moderate influence on the coupled structural and electronic properties of doped RH$_3$ compounds and may be important when considering the competition between structural distortions and superconductivity., Comment: 5 figures, 7 pages content + 3 pages references + 2 pages of Appendices

Arthroscopy is a minimally invasive surgical procedure used to diagnose and treat joint problems. The clinical workflow of arthroscopy typically involves inserting an arthroscope into the joint through a small incision, during which surgeons navigate and operate largely by relying on their visual assessment through the arthroscope. However, the arthroscope's restricted field of view and lack of depth perception pose challenges in navigating complex articular structures and achieving surgical precision during procedures. Aiming at enhancing intraoperative awareness, we present a robust pipeline that incorporates simultaneous localization and mapping, depth estimation, and 3D Gaussian splatting to realistically reconstruct intra-articular structures solely based on monocular arthroscope video. Extending 3D reconstruction to Augmented Reality (AR) applications, our solution offers AR assistance for articular notch measurement and annotation anchoring in a human-in-the-loop manner. Compared to traditional Structure-from-Motion and Neural Radiance Field-based methods, our pipeline achieves dense 3D reconstruction and competitive rendering fidelity with explicit 3D representation in 7 minutes on average. When evaluated on four phantom datasets, our method achieves RMSE = 2.21mm reconstruction error, PSNR = 32.86 and SSIM = 0.89 on average. Because our pipeline enables AR reconstruction and guidance directly from monocular arthroscopy without any additional data and/or hardware, our solution may hold the potential for enhancing intraoperative awareness and facilitating surgical precision in arthroscopy. Our AR measurement tool achieves accuracy within 1.59 +/- 1.81mm and the AR annotation tool achieves a mIoU of 0.721., Comment: 8 pages, with 2 additional pages as the supplementary. Accepted by AE-CAI 2024