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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.

This paper presents a rationale for using professional and continuing education (PaCE) units at post-secondary institutions throughout California to design and implement talent-pipelines, research and development collaborations, and other knowledge ecosystems where emerging and returning professionals can acquire the knowledge, skills, and abilities (KSAs), as well as the experience, they need to address the challenges of the Fourth Industrial Revolution (4IR). The paper provides an analysis of the reasons why PaCE units are uniquely positioned to address the needs of industry and job seekers, and on a timetable that keeps pace with 4IR velocity.

Research for Action (RFA) in partnership with the University of Texas at Austin is engaged in a five-year mixed-methods study of the reforms associated with California AB 705. Over the course of the study, our team will assess the implementation, impact, and cost effectiveness of reforms associated with the law. This second interim report, presented at the conclusion of year three of the study, focuses on gaining a deeper understanding of on-campus implementation through a faculty survey administered to math and English departments across our study sample, an Interrupted Time Series analysis with nine cohorts of FTIC student data, and preliminary data collection for our cost effectiveness study. Collectively, these data highlight significant changes that colleges have made on campus regarding shifting enrollments from developmental education into transfer-level coursework in both English and math, and providing additional supports to students to promote retention and completion. We find that AB 705 has demonstrated notable successes in improving enrollment and completion rates in transfer-level courses, particularly in math, among FTIC students in California's community colleges. While our survey results suggest that faculty believe additional resources and supports would be helpful, most faculty report that implementation supports have been adequate.

Attracting and retaining high-quality teachers is a pressing policy concern. Increasing teacher salaries and creating more attractive compensation packages are often proposed as a potential solution. Signed into law in March 2023, the LEARNS Act increased Arkansas's minimum teacher salary from $36,000 to $50,000, guaranteed all teachers a minimum raise of $2,000, and added flexibility allowing school districts to deviate from seniority-based traditional salary schedules. To study school districts' adjustments to the new legislation, we collected information about districts' teacher compensation policies one year before and the first year of implementation. We also integrated this data with teachers' administrative records to study patterns of teacher retention and mobility. Our results reveal a more equitable distribution of starting teacher salaries across districts, with minimal variation. The LEARNS Act notably increased funding for rural and high-poverty districts, mitigating the negative association between starting salaries and district poverty rates. However, the initial effects on teacher retention and mobility were modest. While some positive trends emerged, such as reduced probabilities of teachers transitioning to non-instructional roles and increased new teacher placement in geographic areas of shortage, broader impacts on retention and mobility were limited in the first year of implementation.

Pulsar timing array observations have found evidence for an isotropic gravitational wave background with the Hellings-Downs angular correlations, expected from general relativity. This interpretation hinges on the measured shape of the angular correlations, which is predominately quadrupolar under general relativity. Here we explore a more flexible parameterization: we expand the angular correlations into a sum of Legendre polynomials and use a Bayesian analysis to constrain their coefficients with the 15-year pulsar timing data set collected by the North American Nanohertz Observatory for Gravitational Waves (NANOGrav). When including Legendre polynomials with multipoles $\ell \geq 2$, we only find a significant signal in the quadrupole with an amplitude consistent with general relativity and non-zero at the $\sim 95\%$ confidence level and a Bayes factor of 200. When we include multipoles $\ell \leq 1$, the Bayes factor evidence for quadrupole correlations decreases by more than an order of magnitude due to evidence for a monopolar signal at approximately 4 nHz which has also been noted in previous analyses of the NANOGrav 15-year data. Further work needs to be done in order to better characterize the properties of this monopolar signal and its effect on the evidence for quadrupolar angular correlations., Comment: 15 pages, 6 figures

The detection of a stochastic gravitational wave background by pulsar timing arrays suggests the presence of a supermassive black hole binary population. Although the observed spectrum generally aligns with predictions from orbital evolution driven by gravitational wave emission in circular orbits, there is a discernible preference for a turnover at the lowest observed frequencies. This turnover could indicate a significant hardening phase, transitioning from early environmental influences to later stages predominantly influenced by gravitational wave emission. In the vicinity of these binaries, the ejection of stars or dark matter particles through gravitational three-body slingshots efficiently extracts orbital energy, leading to a low-frequency turnover in the spectrum. By analyzing the NANOGrav 15-year data, we assess how the gravitational wave spectrum depends on the initial inner galactic profile prior to disruption by binary ejections, accounting for a range of initial binary eccentricities. Our findings suggest a parsec-scale galactic center density around $10^6\,M_\odot/\textrm{pc}^3$ across most of the parameter space, offering insights into the environmental effects on black hole evolution and combined matter density near galaxy centers., Comment: 15 pages, 5 figures

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

When employees expect evaluation and performance incentives will continue (or begin) in the future, the potential future rewards create an incentive to invest in relevant skills today. Because skills benefit job performance, the effects of evaluation can persist after the rewards end or even anticipate the start of rewards. I provide empirical evidence of these dynamics from a quasi-experiment in Tennessee schools. New performance measures improve teachers' value-added contributions to student achievement. But improvements are twice as large when the teacher also expects future rewards linked to future scores. Value-added remains at the now higher level after performance incentives end.

Research for Action (RFA) in partnership with the University of Texas at Austin is engaged in a five-year mixed-methods study of the reforms associated with California AB 705. Over the course of the study, our team will assess the implementation, impact, and cost effectiveness of reforms associated with the law. This report first offers a descriptive quantitative analysis of short-term outcome (enrollment and completion) trends in math and English at the state level. This descriptive analysis examines the relationship between AB 705 and course enrollment and completion, which will serve as the basis for the quasi-experimental study in subsequent project years. The second part of the report presents findings from institutional site visits aimed at understanding how institutions have implemented the reforms, who is involved in implementation, and how implementation is experienced by students.

To develop a deeper understanding of who Child Care and Early Education (CCEE) educators are--and what influences impact the entry, retention, turnover, and advancement (that is, the workforce dynamics) in this industry--the Building and Sustaining the Child Care and Early Education Workforce (BASE) research team identified and documented existing literature about the CCEE workforce and strategies that are intended to strengthen it. The team conducted a literature review (among several other research activities) to identify, review, and synthesize relevant literature and the evidence base on both multilevel influences that may shape CCEE workforce dynamics and the effectiveness of strategies that aim to build a sustained, qualified CCEE workforce. The team also conducted an environmental scan--a review to identify strategies currently being implemented to build, advance, and sustain the CCEE workforce. This annotated bibliography contains a comprehensive list of the publications that were formally included in the literature review to provide researchers, practitioners, and policymakers with a quick guide to understanding the recent research landscape of the CCEE workforce. This annotated bibliography details the methods that were used to review the published literature and summarizes elements of the publications.

Child care and early education (CCEE) are essential resources for families and children across the country. Yet qualified educators are choosing to leave their positions, and young professionals are choosing not to enter the field. This instability has been a long-lasting issue and is thought to be driven largely by structural and systemic forces that shape the everyday experiences of the CCEE workforce. These issues have been exacerbated by the COVID-19 pandemic. This report lays out a conceptual framework to serve as a guide for researchers, policymakers, and administrators who are conducting research on the CCEE workforce and who are designing and implementing strategies to improve entry, retention, turnover, and advancement of the workforce. This report is also intended to inform how the field can work to build and sustain this critical workforce. The conceptual framework consists of three main components that are intended to provide a clear understanding of factors that can disrupt the stability of the CCEE workforce pipeline. By articulating the main components, the conceptual framework encourages researchers, policymakers, and administrators to consider a range of multilevel influences on workforce dynamics when designing strategies and when evaluating whether strategies are working as intended. This framework also aims to underscore that the individuals who make up the workforce--many of whom have been marginalized due to race, ethnicity, social class, and gender--interact with broader social, political, and cultural contexts that shape economic and educational policies, structures, and opportunities.

Evidence has emerged for a stochastic signal correlated among 67 pulsars within the 15-year pulsar-timing data set compiled by the NANOGrav collaboration. Similar signals have been found in data from the European, Indian, Parkes, and Chinese PTAs. This signal has been interpreted as indicative of the presence of a nanohertz stochastic gravitational wave background. To explore the internal consistency of this result we investigate how the recovered signal strength changes as we remove the pulsars one by one from the data set. We calculate the signal strength using the (noise-marginalized) optimal statistic, a frequentist metric designed to measure correlated excess power in the residuals of the arrival times of the radio pulses. We identify several features emerging from this analysis that were initially unexpected. The significance of these features, however, can only be assessed by comparing the real data to synthetic data sets. After conducting identical analyses on simulated data sets, we do not find anything inconsistent with the presence of a stochastic gravitational wave background in the NANOGrav 15-year data. The methodologies developed here can offer additional tools for application to future, more sensitive data sets. While this analysis provides an internal consistency check of the NANOGrav results, it does not eliminate the necessity for additional investigations that could identify potential systematics or uncover unmodeled physical phenomena in the data., Comment: 21 pages, 11 figures, 2 tables

In this Technical Design Report (TDR) we describe the SABRE South detector to be built at the Stawell Underground Physics Laboratory (SUPL). The SABRE South detector is designed to test the long-standing DAMA/LIBRA signal of an annually modulating rate consistent with dark matter by using the same target material. SABRE South uses seven ultra-high purity NaI(Tl) crystals (with a total target mass of either 35 kg or 50 kg), hermetically sealed in copper enclosures that are suspended within a liquid scintillator active veto. High quantum efficiency and low background Hamamatsu R11065 photomultiplier tubes are directly coupled to both ends of the crystal, and enclosed with the crystal in an oxygen free high thermal conductivity copper enclosure. The active veto system consists of 11.6 kL of linear alkylbenzene (LAB) doped with a mixture of fluorophores and contained in a steel vessel, which is instrumented with at least 18 Hamamatsu R5912 photomultipliers. The active veto tags key radiogenic backgrounds intrinsic to the crystals, such as ${^{40}}$K, and is expected to suppress the total background by 27% in the 1-6 keV region of interest. In addition to the liquid scintillator veto, a muon veto is positioned above the detector shielding. This muon veto consists of eight EJ-200 scintillator modules, with Hamamatsu R13089 photomultipliers coupled to both ends. With an expected total background of 0.72 cpd/kg/keV, SABRE South can test the DAMA/LIBRA signal with 5$\sigma$ discovery or 3$\sigma$ exclusion after two years of data taking.

Solar trajectory monitoring is a pivotal challenge in solar energy systems, underpinning applications such as autonomous energy harvesting and environmental sensing. A prevalent failure mode in sustained solar tracking arises when the predictive algorithm erroneously diverges from the solar locus, erroneously anchoring to extraneous celestial or terrestrial features. This phenomenon is attributable to an inadequate assimilation of solar-specific objectness attributes within the tracking paradigm. To mitigate this deficiency inherent in extant methodologies, we introduce an innovative objectness regularization framework that compels tracking points to remain confined within the delineated boundaries of the solar entity. By encapsulating solar objectness indicators during the training phase, our approach obviates the necessity for explicit solar mask computation during operational deployment. Furthermore, we leverage the high-DoF robot arm to integrate our method to improve its robustness and flexibility in different outdoor environments.

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

Behavior Trees (BTs) are high-level controllers that are useful in a variety of planning tasks and are gaining traction in robotic mission planning. As they gain popularity in safety-critical domains, it is important to formalize their syntax and semantics, as well as verify properties for them. In this paper, we formalize a class of BTs we call Stateful Behavior Trees (SBTs) that have auxiliary variables and operate in an environment that can change over time. SBTs have access to persistent shared memory (often known as a blackboard) that keeps track of these auxiliary variables. We demonstrate that SBTs are equivalent in computational power to Turing Machines when the blackboard can store mathematical (i.e., unbounded) integers. We further identify syntactic assumptions where SBTs have computational power equivalent to finite state automata, specifically where the auxiliary variables are of finitary types. We present a domain specific language (DSL) for writing SBTs and adapt the tool BehaVerify for use with this DSL. This new DSL in BehaVerify supports interfacing with popular BT libraries in Python, and also provides generation of Haskell code and nuXmv models, the latter of which is used for model checking temporal logic specifications for the SBTs. We include examples and scalability results where BehaVerify outperforms another verification tool by a factor of 100., Comment: In Proceedings FMAS2024, arXiv:2411.13215

Behavior Trees (BTs) are high level controllers that have found use in a wide range of robotics tasks. As they grow in popularity and usage, it is crucial to ensure that the appropriate tools and methods are available for ensuring they work as intended. To that end, we created a new methodology by which to create Runtime Monitors for BTs. These monitors can be used by the BT to correct when undesirable behavior is detected and are capable of handling LTL specifications. We demonstrate that in terms of runtime, the generated monitors are on par with monitors generated by existing tools and highlight certain features that make our method more desirable in various situations. We note that our method allows for our monitors to be swapped out with alternate monitors with fairly minimal user effort. Finally, our method ties in with our existing tool, BehaVerify, allowing for the verification of BTs with monitors., Comment: In Proceedings FMAS2024, arXiv:2411.13215

The reuse of historical clinical trial data has significant potential to accelerate medical research and drug development. However, interoperability challenges, particularly with missing medical codes, hinders effective data integration across studies. While Large Language Models (LLMs) offer a promising solution for automated coding without labeled data, current approaches face challenges on complex coding tasks. We introduce ALIGN, a novel compositional LLM-based system for automated, zero-shot medical coding. ALIGN follows a three-step process: (1) diverse candidate code generation; (2) self-evaluation of codes and (3) confidence scoring and uncertainty estimation enabling human deferral to ensure reliability. We evaluate ALIGN on harmonizing medication terms into Anatomical Therapeutic Chemical (ATC) and medical history terms into Medical Dictionary for Regulatory Activities (MedDRA) codes extracted from 22 immunology trials. ALIGN outperformed the LLM baselines, while also providing capabilities for trustworthy deployment. For MedDRA coding, ALIGN achieved high accuracy across all levels, matching RAG and excelling at the most specific levels (87-90% for HLGT). For ATC coding, ALIGN demonstrated superior performance, particularly at lower hierarchy levels (ATC Level 4), with 72-73% overall accuracy and 86-89% accuracy for common medications, outperforming baselines by 7-22%. ALIGN's uncertainty-based deferral improved accuracy by 17% to 90% accuracy with 30% deferral, notably enhancing performance on uncommon medications. ALIGN achieves this cost-efficiently at \$0.0007 and \$0.02 per code for GPT-4o-mini and GPT-4o, reducing barriers to clinical adoption. ALIGN advances automated medical coding for clinical trial data, contributing to enhanced data interoperability and reusability, positioning it as a promising tool to improve clinical research and accelerate drug development.

We introduce the Situated Corpus Of Understanding Transactions (SCOUT), a multi-modal collection of human-robot dialogue in the task domain of collaborative exploration. The corpus was constructed from multiple Wizard-of-Oz experiments where human participants gave verbal instructions to a remotely-located robot to move and gather information about its surroundings. SCOUT contains 89,056 utterances and 310,095 words from 278 dialogues averaging 320 utterances per dialogue. The dialogues are aligned with the multi-modal data streams available during the experiments: 5,785 images and 30 maps. The corpus has been annotated with Abstract Meaning Representation and Dialogue-AMR to identify the speaker's intent and meaning within an utterance, and with Transactional Units and Relations to track relationships between utterances to reveal patterns of the Dialogue Structure. We describe how the corpus and its annotations have been used to develop autonomous human-robot systems and enable research in open questions of how humans speak to robots. We release this corpus to accelerate progress in autonomous, situated, human-robot dialogue, especially in the context of navigation tasks where details about the environment need to be discovered., Comment: 14 pages, 7 figures

In this paper, we describe the development of symbolic representations annotated on human-robot dialogue data to make dimensions of meaning accessible to autonomous systems participating in collaborative, natural language dialogue, and to enable common ground with human partners. A particular challenge for establishing common ground arises in remote dialogue (occurring in disaster relief or search-and-rescue tasks), where a human and robot are engaged in a joint navigation and exploration task of an unfamiliar environment, but where the robot cannot immediately share high quality visual information due to limited communication constraints. Engaging in a dialogue provides an effective way to communicate, while on-demand or lower-quality visual information can be supplemented for establishing common ground. Within this paradigm, we capture propositional semantics and the illocutionary force of a single utterance within the dialogue through our Dialogue-AMR annotation, an augmentation of Abstract Meaning Representation. We then capture patterns in how different utterances within and across speaker floors relate to one another in our development of a multi-floor Dialogue Structure annotation schema. Finally, we begin to annotate and analyze the ways in which the visual modalities provide contextual information to the dialogue for overcoming disparities in the collaborators' understanding of the environment. We conclude by discussing the use-cases, architectures, and systems we have implemented from our annotations that enable physical robots to autonomously engage with humans in bi-directional dialogue and navigation., Comment: 52 pages, 14 figures

We present cosmological results from the measurement of clustering of galaxy, quasar and Lyman-$\alpha$ forest tracers from the first year of observations with the Dark Energy Spectroscopic Instrument (DESI Data Release 1). We adopt the full-shape (FS) modeling of the power spectrum, including the effects of redshift-space distortions, in an analysis which has been validated in a series of supporting papers. In the flat $\Lambda$CDM cosmological model, DESI (FS+BAO), combined with a baryon density prior from Big Bang Nucleosynthesis and a weak prior on the scalar spectral index, determines matter density to $\Omega_\mathrm{m}=0.2962\pm 0.0095$, and the amplitude of mass fluctuations to $\sigma_8=0.842\pm 0.034$. The addition of the cosmic microwave background (CMB) data tightens these constraints to $\Omega_\mathrm{m}=0.3056\pm 0.0049$ and $\sigma_8=0.8121\pm 0.0053$, while further addition of the the joint clustering and lensing analysis from the Dark Energy Survey Year-3 (DESY3) data leads to a 0.4% determination of the Hubble constant, $H_0 = (68.40\pm 0.27)\,{\rm km\,s^{-1}\,Mpc^{-1}}$. In models with a time-varying dark energy equation of state, combinations of DESI (FS+BAO) with CMB and type Ia supernovae continue to show the preference, previously found in the DESI DR1 BAO analysis, for $w_0>-1$ and $w_a<0$ with similar levels of significance. DESI data, in combination with the CMB, impose the upper limits on the sum of the neutrino masses of $\sum m_\nu < 0.071\,{\rm eV}$ at 95% confidence. DESI data alone measure the modified-gravity parameter that controls the clustering of massive particles, $\mu_0=0.11^{+0.45}_{-0.54}$, while the combination of DESI with the CMB and the clustering and lensing analysis from DESY3 constrains both modified-gravity parameters, giving $\mu_0 = 0.04\pm 0.22$ and $\Sigma_0 = 0.044\pm 0.047$, in agreement with general relativity. [Abridged.], Comment: This DESI Collaboration Key Publication is part of the 2024 publication series using the first year of observations (see https://data.desi.lbl.gov/doc/papers/). 55 pages, 10 figures

JWST has revealed a large population of compact, red galaxies at $z>4$ known as Little Red Dots (LRDs). We analyze the spectral energy distributions (SEDs) of 95 LRDs from the JWST PRIMER survey with complete photometric coverage from $1-18\ \mu$m using NIRCam and MIRI imaging, representing the most extensive SED analysis on a large LRD sample with long-wavelength MIRI data. We examine SED models in which either galaxy or active galactic nucleus (AGN) emission dominates the rest-frame UV or optical continuum, extracting physical properties to explore each scenario's implications. In the galaxy-only model, we find massive, dusty stellar populations alongside unobscured, low-mass components, hinting at inhomogeneous obscuration. The AGN-only model indicates dusty, luminous AGNs with low hot dust fractions compared to typical quasars. A hybrid AGN and galaxy model suggests low-mass, unobscured galaxies in the UV, with stellar mass estimates spanning $\sim$2 dex across the different models, underscoring the need for caution in interpreting LRD stellar masses. With MIRI photometry, the galaxy-only model produces stellar masses within cosmological limits, but extremely high stellar mass densities are inferred. The hybrid model infers highly overmassive black holes exceeding those in recently reported high-redshift AGNs, hinting at a partial AGN contribution to the rest-optical continuum or widespread super-Eddington accretion. Our findings highlight the extreme conditions required for both AGN or galaxy dominated scenarios in LRDs, supporting a mixed contribution to the red continuum, or novel scenarios to explain the observed emission., Comment: 22 pages, 10 figures, submitted to ApJ. Machine-readable form of Table 2 available at: https://github.com/geneckleung/lrd_primer_miri

There is growing interest in applying AI to radiology report generation, particularly for chest X-rays (CXRs). This paper investigates whether incorporating pixel-level information through segmentation masks can improve fine-grained image interpretation of multimodal large language models (MLLMs) for radiology report generation. We introduce MAIRA-Seg, a segmentation-aware MLLM framework designed to utilize semantic segmentation masks alongside CXRs for generating radiology reports. We train expert segmentation models to obtain mask pseudolabels for radiology-specific structures in CXRs. Subsequently, building on the architectures of MAIRA, a CXR-specialised model for report generation, we integrate a trainable segmentation tokens extractor that leverages these mask pseudolabels, and employ mask-aware prompting to generate draft radiology reports. Our experiments on the publicly available MIMIC-CXR dataset show that MAIRA-Seg outperforms non-segmentation baselines. We also investigate set-of-marks prompting with MAIRA and find that MAIRA-Seg consistently demonstrates comparable or superior performance. The results confirm that using segmentation masks enhances the nuanced reasoning of MLLMs, potentially contributing to better clinical outcomes., Comment: Accepted as Proceedings Paper at ML4H 2024

The feasibility study of a new technique for thermal neutron detection using a Timepix3 camera (TPX3Cam) with custom-made optical add-ons operated in event-mode data acquisition is presented. The camera has a spatial resolution of ~ 16 um and a temporal resolution of 1.56 ns. Thermal neutrons react with 6 Lithium to produce a pair of 2.73 MeV tritium and 2.05 MeV alpha particles, which in turn interact with a thin layer of LYSO crystal to produce localized scintillation photons. These photons are directed by a pair of lenses to an image intensifier, before being recorded by the TPX3Cam. The results were reconstructed through a custom clustering algorithm utilizing the Time-of-Arrival (ToA) and geometric centre of gravity of the hits. Filtering parameters were found through data analysis to reduce the background of gamma and other charged particles. The efficiency of the converter is 4%, and the overall detection efficiency of the system including the lead shielding and polythene moderator is ~ 0.34%, all converted thermal neutrons can be seen by the TPX3Cam. The experiment used a weak thermal neutron source against a large background, the measured signal-to-noise ratio is 1/67.5. Under such high noise, thermal neutrons were successfully detected and predicted the reduced neutron rate, and matched the simulated rate of the thermal neutrons converted from the source. This result demonstrated the excellent sensitivity of the system.

We performed a search for the $K_L \to \pi^{0} \nu \bar{\nu}$ decay using the data taken in 2021 at the J-PARC KOTO experiment. With newly installed counters and new analysis method, the expected background was suppressed to $0.252\pm0.055_{\mathrm{stat}}$$^{+0.052}_{-0.067}$$_{\mathrm{syst}}$. With a single event sensitivity of $(9.33 \pm 0.06_{\rm stat} \pm 0.84_{\rm syst})\times 10^{-10}$, no events were observed in the signal region. An upper limit on the branching fraction for the decay was set to be $2.2\times10^{-9}$ at the 90% confidence level (C.L.), which improved the previous upper limit from KOTO by a factor of 1.4. With the same data, a search for $K_L \to \pi^{0} X^{0}$ was also performed, where $X^{0}$ is an invisible boson with a mass ranging from 1 MeV/$c^{2}$ to 260 MeV/$c^{2}$. For $X^{0}$ with a mass of 135 MeV/$c^{2}$, an upper limit on the branching fraction of $K_L \to \pi^{0} X^{0}$ was set to be $1.6\times10^{-9}$ at the 90% C.L., Comment: 6 pages, 4 figures; submitted for publication

Kin selection theory has proven to be a popular and widely accepted account of how altruistic behaviour can evolve under natural selection. Hamilton's rule, first published in 1964, has since been experimentally validated across a range of different species and social behaviours. In contrast to this large body of work in natural populations, however, there has been relatively little study of kin selection \emph{in silico}. In the current work, we offer what is to our knowledge the first demonstration of kin selection emerging naturally within a population of agents undergoing continuous neuroevolution. Specifically, we find that zero-sum transfer of resources from parents to their infant offspring evolves through kin selection in environments where it is hard for offspring to survive alone. In an additional experiment, we show that kin selection in our simulations relies on a combination of kin recognition and population viscosity. We believe that our work may contribute to the understanding of kin selection in minimal evolutionary systems, without explicit notions of genes and fitness maximisation., Comment: 13 pages, 5 figures

While supermassive black-hole (SMBH)-binaries are not the only viable source for the low-frequency gravitational wave background (GWB) signal evidenced by the most recent pulsar timing array (PTA) data sets, they are expected to be the most likely. Thus, connecting the measured PTA GWB spectrum and the underlying physics governing the demographics and dynamics of SMBH-binaries is extremely important. Previously, Gaussian processes (GPs) and dense neural networks have been used to make such a connection by being built as conditional emulators; their input is some selected evolution or environmental SMBH-binary parameters and their output is the emulated mean and standard deviation of the GWB strain ensemble distribution over many Universes. In this paper, we use a normalizing flow (NF) emulator that is trained on the entirety of the GWB strain ensemble distribution, rather than only mean and standard deviation. As a result, we can predict strain distributions that mirror underlying simulations very closely while also capturing frequency covariances in the strain distributions as well as statistical complexities such as tails, non-Gaussianities, and multimodalities that are otherwise not learnable by existing techniques. In particular, we feature various comparisons between the NF-based emulator and the GP approach used extensively in past efforts. Our analyses conclude that the NF-based emulator not only outperforms GPs in the ease and computational cost of training but also outperforms in the fidelity of the emulated GWB strain ensemble distributions.

Fixed target experiments where beam electrons are focused upon a thin target have shown great potential for probing new physics, including the sub-GeV dark matter (DM) paradigm. However, a signal in future experiments such as the light dark matter experiment (LDMX) would require an independent validation to assert its DM origin. To this end, we propose to combine LDMX and next generation DM direct detection (DD) data in a four-step analysis strategy, which we here illustrate with Monte Carlo simulations. In the first step, the hypothetical LDMX signal (i.e. an excess in the final state electron energy and transverse momentum distributions) is $\textit{recorded}$. In the second step, a DM DD experiment operates with increasing exposure to test the DM origin of the LDMX signal. Here, LDMX and DD data are simulated. In the third step, a posterior probability density function (pdf) for the DM model parameters is extracted from the DD data, and used to $\textit{predict}$ the electron recoil energy and transverse momentum distributions at LDMX. In the last step, $\textit{predicted}$ and $\textit{recorded}$ electron recoil energy and transverse momentum distributions are compared in a chi-square test. We present the results of this comparison in terms of a threshold exposure that a DD experiment has to operate with to assert whether $\textit{predicted}$ and $\textit{recorded}$ distributions $\textit{can}$ be statistically dependent. We find that this threshold exposure grows with the DM particle mass, $m_\chi$. It varies from 0.012 kg-year for a DM mass of $m_\chi=4$ MeV to 1 kg-year for $m_\chi=25$ MeV, which is or will soon be within reach.

Effective Hamiltonian calculations for large quantum systems can be both analytically intractable and numerically expensive using standard techniques. In this manuscript, we present numerical techniques inspired by Nonperturbative Analytical Diagonalization (NPAD) and the Magnus expansion for the efficient calculation of effective Hamiltonians. While these tools are appropriate for a wide array of applications, we here demonstrate their utility for models that can be realized in circuit-QED settings. Our numerical techniques are available as an open-source Python package, ${\rm qCH_{eff}}$ (https://github.com/NVlabs/qCHeff), which uses the CuPy library for GPU-acceleration. We report up to 15x speedup on GPU over CPU for NPAD, and up to 42x speedup for the Magnus expansion (compared to QuTiP), for large system sizes., Comment: 19 pages, 8 figures. The source code is available at https://github.com/NVlabs/qCHeff

In this short paper, we introduce work that is aiming to purposefully venture into this mesh of questions from a different starting point. Interjecting into the conversation, we want to ask: 'What is it to be skilled at work?' Building on work from scholars like Tim Ingold, and strands of longstanding research in workplace studies and CSCW, our interest is in turning the attention to the active work of 'being good', or 'being skilled', at what we as workers do. As we see it, skill provides a counterpoint to the version of intelligence that appears to be easily blackboxed in systems like Slack, and that ultimately reduces much of what people do to work well together. To put it slightly differently, skill - as we will argue below - gives us a way into thinking about work as a much more entangled endeavour, unfolding through multiple and interweaving sets of practices, places, tools and collaborations. In this vein, designing for the future of work seems to be about much more than where work is done or how we might bolt on discrete containers of intelligence. More fruitful would be attending to how we succeed in threading so many entities together to do our jobs well - in 'coming to be skilled'.

Artificial intelligence (AI) advancements over the past few years have had an unprecedented and revolutionary impact across everyday application areas. Its significance also extends to technical challenges within science and engineering, including the nascent field of quantum computing (QC). The counterintuitive nature and high-dimensional mathematics of QC make it a prime candidate for AI's data-driven learning capabilities, and in fact, many of QC's biggest scaling challenges may ultimately rest on developments in AI. However, bringing leading techniques from AI to QC requires drawing on disparate expertise from arguably two of the most advanced and esoteric areas of computer science. Here we aim to encourage this cross-pollination by reviewing how state-of-the-art AI techniques are already advancing challenges across the hardware and software stack needed to develop useful QC - from device design to applications. We then close by examining its future opportunities and obstacles in this space., Comment: 42 pages, 7 figures, 2 tables

Quantum Neuromorphic Computing (QNC) merges quantum computation with neural computation to create scalable, noise-resilient algorithms for quantum machine learning (QML). At the core of QNC is the quantum perceptron (QP), which leverages the analog dynamics of interacting qubits to enable universal quantum computation. Canonically, a QP features $N$ input qubits and one output qubit, and is used to determine whether an input state belongs to a specific class. Rydberg atoms, with their extended coherence times and scalable spatial configurations, provide an ideal platform for implementing QPs. In this work, we explore the implementation of QPs on Rydberg atom arrays, assessing their performance in tasks such as phase classification between Z2, Z3, Z4 and disordered phases, achieving high accuracy, including in the presence of noise. We also perform multi-class entanglement classification by extending the QP model to include multiple output qubits, achieving 95\% accuracy in distinguishing noisy, high-fidelity states based on separability. Additionally, we discuss the experimental realization of QPs on Rydberg platforms using both single-species and dual-species arrays, and examine the error bounds associated with approximating continuous functions.

Owing to their rapid cooling rate and hence loss-limited propagation distance, cosmic-ray electrons and positrons (CRe) at very high energies probe local cosmic-ray accelerators and provide constraints on exotic production mechanisms such as annihilation of dark matter particles. We present a high-statistics measurement of the spectrum of CRe candidate events from 0.3 to 40 TeV with the High Energy Stereoscopic System (H.E.S.S.), covering two orders of magnitude in energy and reaching a proton rejection power of better than $10^{4}$. The measured spectrum is well described by a broken power law, with a break around 1 TeV, where the spectral index increases from $\Gamma_1 = 3.25$ $\pm$ 0.02 (stat) $\pm$ 0.2 (sys) to $\Gamma_2 = 4.49$ $\pm$ 0.04 (stat) $\pm$ 0.2 (sys). Apart from the break, the spectrum is featureless. The absence of distinct signatures at multi-TeV energies imposes constraints on the presence of nearby CRe accelerators and the local CRe propagation mechanisms., Comment: main paper: 8 pages, 4 figures, supplemental material: 12 pages, 14 figures, accepted for publication in Physical Review Letters https://journals.aps.org/prl/

In this study we report spatially resolved, wideband spectral properties of three giant radio galaxies (GRGs) in the COSMOS field: MGTC J095959.63+024608.6 , MGTC J100016.84+015133.0 and MGTC J100022.85+031520.4. One such galaxy MGTC J100022.85+031520.4 is reported here for the first time with a projected linear size of 1.29 Mpc at a redshift of 0.1034. Unlike the other two, it is associated with a brightest cluster galaxy (BCG), making it one of the few GRGs known to inhabit cluster environments. We examine the spectral age distributions of the three GRGs using new MeerKAT UHF-band (544-1088 MHz) observations, and $L$-band (900-1670 MHz) data from the MeerKAT International GHz Tiered Extragalactic Exploration (MIGHTEE) survey. We test two different models of spectral ageing, the Jaffe-Perola and Tribble models, using the Broadband Radio Astronomy Tools (\textsc{brats}) software which we find agree well with each other. We estimate the Tribble spectral age for MGTC J095959.63+024608.6 as 68 Myr, MGTC J100016.84+015133.0 as 47 Myr and MGTC J100022.85+031520.4 as 67 Myr. We find significant disagreements between these spectral age estimates and the estimates of the dynamical ages of these GRGs, modelled in cluster and group environments. Our results highlight the need for additional processes which are not accounted for in either the dynamic age or spectral age estimations., Comment: 13 pages, 9 figures. Accepted for publication in MNRAS

In this paper we create a definition for prime gaps in the Gaussian integers using a boxcar metric. From this we used numerical methods to derive an asymptotic upper bound for the gaps in this scenario, namely O(log^2|p_{n}|).

Sepsis is a life-threatening condition defined by end-organ dysfunction due to a dysregulated host response to infection. Although the Surviving Sepsis Campaign has launched and has been releasing sepsis treatment guidelines to unify and normalize the care for sepsis patients, it has been reported in numerous studies that disparities in care exist across the trajectory of patient stay in the emergency department and intensive care unit. Here, we apply a number of reinforcement learning techniques including behavioral cloning, imitation learning, and inverse reinforcement learning, to learn the optimal policy in the management of septic patient subgroups using expert demonstrations. Then we estimate the counterfactual optimal policies by applying the model to another subset of unseen medical populations and identify the difference in cure by comparing it to the real policy. Our data comes from the sepsis cohort of MIMIC-IV and the clinical data warehouses of the Mass General Brigham healthcare system. The ultimate objective of this work is to use the optimal learned policy function to estimate the counterfactual treatment policy and identify deviations across sub-populations of interest. We hope this approach would help us identify any disparities in care and also changes in cure in response to the publication of national sepsis treatment guidelines.