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This brief is a continuation of NWEA's research series examining the impact of the COVID-19 pandemic on student achievement and progress toward academic recovery. Initiated in the early phase of the pandemic, this series has leveraged NWEA's large national sample of longitudinal MAP® Growth™ data to track student performance and compare it to historical trends. The authors have tracked two critical aspects of student performance in the wake of the pandemic: achievement and growth. Achievement data reveals the extent of unfinished learning. The authors refer to the distance between current test scores and prepandemic trends as "achievement gaps." Growth data estimate how much test scores increase over time. They use these data to indicate whether students are making gains that keep pace with prepandemic trends. Understanding and tracking both achievement and growth is important. Achievement gaps quantify how much unfinished learning remains, while growth patterns help gauge the rate at which gaps will close. The system needs accelerated or above-average growth for students to catch up. The data provide insights into how effectively this is happening. The cumulative research shows that the harmful effects of the pandemic on student achievement steadily accumulated over the course of the 2020-21 school year (Lewis, Kuhfeld, Ruzek, & McEachin, 2021). Growth generally returned to, or slightly exceeded, prepandemic trends in the 2021-22 school year (Kuhfeld & Lewis, 2022). Progress stalled in 2022-23 when growth in nearly all grades fell short of prepandemic trends (Lewis & Kuhfeld, 2023). In this analysis, the authors use 2023-24 school year data to examine the current progress toward recovery. They examine test scores from approximately 7.7 million students currently in grades 3-8 in 22,400 public schools who have taken MAP Growth reading and math assessments since the onset of the pandemic. The "COVID sample" consists of six separate cohorts of students followed longitudinally across the last three school years. For instance, current fifth-graders are part of the grade 3-5 cohort; they measure this cohort's achievement across third grade in 2021-22, fourth grade in 2022-23, and fifth grade in 2023-24. The authors compared this COVID sample to a comparable group of 10 million students who tested in grades 3-8 in the pre-COVID school years of 2016-17 through 2018-19.

This study investigates the role of college major choices in labor market outcomes, with a focus on racial minorities and immigrants. Drawing upon research on school-to-work linkages, we examine two measures, linkage, the connection between college majors and specific occupations in the labor market, and match, the alignment of workers' occupations with their college majors. Analyzing data from the American Community Survey, 2013-2017, we show that linkage positively predicts earnings, particularly for workers in matched occupations, and negatively predicts unemployment. Notably, Black, Hispanic, and foreign-born workers in matched occupations benefit more from linkage strength than their White and U.S.-born counterparts. This advantage is more pronounced in states that are popular destinations for immigrants. Our findings suggest that earnings and unemployment disparities experienced among racial minorities and immigrants may diminish if they pursue majors closely tied to jobs in the labor market and secure jobs related to their college majors.

Computational fluid dynamics (CFD) is a specialised branch of fluid mechanics that utilises numerical methods and algorithms to solve and analyze fluid-flow problems. One promising avenue to enhance CFD is the use of quantum computing, which has the potential to resolve nonlinear differential equations more efficiently than classical computers. Here, we try to answer the question of which regimes of nonlinear partial differential equations (PDEs) for fluid dynamics can have an efficient quantum algorithm. We propose a connection between the numerical parameter, $R$, that guarantees efficiency in the truncation of the Carleman linearisation, and the physical parameters that describe the fluid flow. This link can be made thanks to the Kolmogorov scale, which determines the minimum size of the grid needed to properly resolve the energy cascade induced by the nonlinear term. Additionally, we introduce the formalism for vector field simulation in different spatial dimensions, providing the discretisation of the operators and the boundary conditions.

This study evaluates the generalisation capabilities of state-of-the-art histopathology foundation models on out-of-distribution multi-stain autoimmune Immunohistochemistry datasets. We compare 13 feature extractor models, including ImageNet-pretrained networks, and histopathology foundation models trained on both public and proprietary data, on Rheumatoid Arthritis subtyping and Sjogren's Disease detection tasks. Using a simple Attention-Based Multiple Instance Learning classifier, we assess the transferability of learned representations from cancer H&E images to autoimmune IHC images. Contrary to expectations, histopathology-pretrained models did not significantly outperform ImageNet-pretrained models. Furthermore, there was evidence of both autoimmune feature misinterpretation and biased feature importance. Our findings highlight the challenges in transferring knowledge from cancer to autoimmune histopathology and emphasise the need for careful evaluation of AI models across diverse histopathological tasks. The code to run this benchmark is available at https://github.com/AmayaGS/ImmunoHistoBench., Comment: Accepted at Workshop on Advancements In Medical Foundation Models (NeurIPS 2024)

We show by an explicit example that the Garsia--Remmel $q$-rook numbers of Ferrers boards do not all have unimodal sequences of coefficients. This resolves in the negative a question from 1986 by the aforementioned authors., Comment: 6 pages

Detached eclipsing binaries (DEBs) allow for the possibility of precise characterization of its stellar components. They offer a unique opportunity to derive their physical parameters in a near-model-independent way for a number of systems consisting of late-type giant stars. Here we aim to expand the sample of low-metallicity late-type giant stars with precise parameters determined. We aim to determine the fundamental parameters like the mass, radius, or effective temperature for three long-period late-type eclipsing binaries from the Large Magellanic Cloud: OGLE-LMC-ECL-25304, OGLE-LMC-ECL-28283, and OGLE-IV LMC554.19.81. Subsequently we aim to determine the evolutionary stages of the systems. We fit the light curves from the OGLE project and radial velocity curves from high resolution spectrographs using the Wilson-Devinney code. The spectral analysis was performed with the GSSP code and resulted in the determination of atmospheric parameters such as effective temperatures and metallicities. We used isochrones provided by the MIST models based on the MESA code to derive evolutionary status of the stars. We present the first analysis of three DEBs composed of similar He-burning late-type stars passing through the blue loop. Estimated masses for OGLE-LMC-ECL-29293 (G4III + G4III) are $M_1=2.898\pm0.031$ and $M_2=3.153\pm0.038$ $M_\odot$, stellar radii are $R_1=19.43\pm0.31$ and $R_2=19.30\pm0.31$ $R_\odot$. OGLE-LMC-ECL-25304 (G4III + G5III) has stellar masses of $M_1=3.267\pm0.028$ and $M_2=3.229\pm0.029$ $M_\odot$, radii of $R_1=23.62\pm0.42$ and $R_2=25.10\pm0.43$ $R_\odot$. OGLE-IV LMC554.19.81 (G2III + G2III) have masses of $M_1=3.165\pm0.020$ and $M_2=3.184\pm0.020$ $M_\odot$, radii of $R_1=18.86\pm0.26$ and $R_2=19.64\pm0.26$ $R_\odot$. All masses were determined with a precision better than 2\% and radii better than 1.5\%. The ages of the stars are in the range of 270-341 Myr., Comment: 15 pages, 15 figures. Accepted for publication in A&A

Aerosols are a ubiquitous feature of planetary atmospheres and leave clear spectral imprints in exoplanet spectra. Pre-JWST, exoplanet retrieval frameworks mostly adopted simple parametric approximations. With JWST, we now have access to mid-infrared wavelengths where aerosols have detectable composition-specific resonance features. Here, we implement new features into the open-source atmospheric retrieval code POSEIDON to account for the complex scattering, reflection, and absorption properties of Mie scattering aerosols. We provide an open-source database of these Mie scattering cross sections and optical properties. We also extend the radiative transfer and retrieval functionality in POSEIDON to include multiple scattering reflection and emission spectroscopy. We demonstrate these new retrieval capabilities on archival Hubble and Spitzer transmission and secondary eclipse spectra of the hot Jupiter HD 189733 b. We find that a high-altitude, low-density, thin slab composed of sub-micron particles is necessary to fit HD 189733 b's transmission spectrum, with multiple aerosol species providing a good fit. We additionally retrieve a sub-solar H$_2$O abundance, a sub-solar K abundance, and do not detect CO$_2$. Our joint thermal and reflection retrievals of HD 189733 b's secondary eclipse spectrum, however, finds no evidence of dayside aerosols, a sub-solar dayside H$_2$O abundance, enhanced CO$_2$, and slighty sub-solar alkali abundances. We additionally explore how retrieval model choices, such as cloud parameterization, aerosol species and properties, and thermal structure parameterization affect retrieved atmospheric properties. Upcoming JWST data for hot Jupiters like HD 189733 b will be well suited to enable deeper exploration of aerosol properties, allowing the formulation of a self-consistent, multi-dimensional picture of cloud formation processes., Comment: 51 pages, 17 figures, 13 tables. Accepted for publication in ApJ (October 2024)

Let $D: \mathbb{Z}_m^n \to \mathbb{Z}_m^n$ be defined so \[D(x_1, x_2, ..., x_n)=(x_1+x_2 \; \text{mod} \; m, x_2+x_3 \; \text{mod} \; m, ..., x_n+x_1 \; \text{mod} \; m).\] $D$ is known as the Ducci function and for $\mathbf{u} \in \mathbb{Z}_m^n$, $\{D^{\alpha}(\mathbf{u})\}_{\alpha=0}^{\infty}$ is the Ducci sequence of $\mathbf{u}$. Every Ducci sequence enters a cycle because $\mathbb{Z}_m^n$ is finite. In this paper, we aim to establish an upper bound for how long it will take for a Ducci sequence in $\mathbb{Z}_m^n$ to enter its cycle when $n$ is even.

We introduce a new class of billiard-like system, ``bouncing outer billiards" which are 3-dimensional cousins of outer billiards of Neumann and Moser. We prove that bouncing outer billiard on a smooth convex body has at least four 1-parameter families of fixed points. We also fully describe dynamics of bouncing outer billiard on a line segment. Finally we carry out numerical experiments suggesting very complicated (non-ergodic) behavior for several shapes including the square and an ellipse.

Warm Saturn type exoplanets orbiting M-dwarfs are particularly suitable for in-depth cloud characterisation through transmission spectroscopy due to their favourable stellar to planetary radius contrast. However, modelling cloud formation consistently within the 3D atmosphere remains computationally challenging. The aim is to explore the combined atmospheric and micro-physical cloud structure, and the kinetic gas-phase chemistry for the warm Saturn HATS0-6b orbiting an M-dwarf. A combined 3D cloudy atmosphere model is constructed by iteratively executing the 3D General Circulation Model (GCM) expeRT/MITgcm and a kinetic cloud formation model, each in its full complexity. Resulting cloud particle number densities, sizes, and compositions are used to derive the local cloud opacity which is then utilised in the next GCM iteration. The disequilibrium H/C/O/N gas-phase chemistry is calculated for each iteration to assess the resulting transmission spectrum in post-processing. The cloud opacity feedback causes a temperature inversion at the sub-stellar point and at the evening terminator at gas pressures higher than 0.01 bar. Furthermore, clouds cool the atmosphere between 0.01 bar and 10 bar, and narrow the equatorial wind jet. The transmission spectrum shows muted gas-phase absorption and a cloud particle silicate feature at approximately 10 micron. The combined atmosphere-cloud model retains the full physical complexity of each component and therefore enables a detailed physical interpretation with JWST NIRSpec and MIRI LRS observational accuracy. The model shows that warm Saturn type exoplanets around M-dwarfs are ideal candidates to search for limb asymmetries in clouds and chemistry, identify cloud particle composition by observing their spectral features, and identify the cloud-induced strong thermal inversion that arises on these planets specifically., Comment: 21 pages, 15 figures, Accepted by A&A

Ultraviolet wavelengths offer unique insights into aerosols in exoplanetary atmospheres. However, only a handful of exoplanets have been observed in the ultraviolet to date. Here, we present the ultraviolet-visible transmission spectrum of the inflated hot Jupiter WASP-127b. We observed one transit of WASP-127b with WFC3/UVIS G280 as part of the Hubble Ultraviolet-optical Survey of Transiting Legacy Exoplanets (HUSTLE), obtaining a transmission spectrum from 200-800 nm. Our reductions yielded a broad-band transit depth precision of 91 ppm and a median precision of 240 ppm across 59 spectral channels. Our observations are suggestive of a high-altitude cloud layer with forward modeling showing they are composed of sub-micron particles and retrievals indicating a high opacity patchy cloud. While our UVIS/G280 data only offers weak evidence for Na, adding archival HST WFC3/IR and STIS observations raises the overall Na detection significance to 4.1-sigma. Our work demonstrates the capabilities of HST WFC3/UVIS G280 observations to probe the aerosols and atmospheric composition of transiting hot Jupiters with comparable precision to HST STIS., Comment: 25 pages, 8 figures, 6 tables

Chemical abundances of stellar streams can be used to determine the nature of a stream's progenitor. Here we study the progenitor of the recently discovered Leiptr stellar stream, which was previously suggested to be a tidally disrupted halo globular cluster. We obtain high-resolution spectra of five red giant branch stars selected from the Gaia DR2 STREAMFINDER catalog with Magellan/MIKE. One star is a clear non-member. The remaining four stars display chemical abundances consistent with those of a low-mass dwarf galaxy: they have a low mean metallicity, $\langle{\rm[Fe/H]}\rangle = -2.2$; they do not all have identical metallicities; and they display low [$\alpha$/Fe] $\sim 0$ and [Sr/Fe] and [Ba/Fe] $\sim -1$. This pattern of low $\alpha$ and neutron-capture element abundances is only found in intact dwarf galaxies with stellar mass $\lesssim 10^5 M_\odot$. Although more data are needed to be certain, Leiptr's chemistry is consistent with being the lowest-mass dwarf galaxy stream without a known intact progenitor, possibly in the mass range of ultra-faint dwarf galaxies. Leiptr thus preserves a record of one of the lowest-mass early accretion events into the Milky Way., Comment: 18 pages, 12 figures, 8 tables, to be submitted to The Open Journal of Astrophysics

This study investigates the risks of exposing confidential chemical structures when machine learning models trained on these structures are made publicly available. We use membership inference attacks, a common method to assess privacy that is largely unexplored in the context of drug discovery, to examine neural networks for molecular property prediction in a black-box setting. Our results reveal significant privacy risks across all evaluated datasets and neural network architectures. Combining multiple attacks increases these risks. Molecules from minority classes, often the most valuable in drug discovery, are particularly vulnerable. We also found that representing molecules as graphs and using message-passing neural networks may mitigate these risks. We provide a framework to assess privacy risks of classification models and molecular representations. Our findings highlight the need for careful consideration when sharing neural networks trained on proprietary chemical structures, informing organisations and researchers about the trade-offs between data confidentiality and model openness.

One approach to reconciling local measurements of a high expansion rate with observations of acoustic oscillations in the CMB and galaxy clustering (the "Hubble tension") is to introduce additional contributions to the $\Lambda$CDM model that are relevant before recombination. While numerous possibilities exist, none are currently well-motivated or preferred by data. However, future CMB experiments, which will measure acoustic peaks to much smaller scales and resolve polarization signals with higher signal-to-noise over large sky areas, should detect almost any such modification at high significance. We propose a model-agnostic method to capture most relevant possible deviations from $\Lambda$CDM due to additional non-interacting components, while remaining sufficiently constraining to enable detection across various scenarios. The phenomenological model uses a fluid model with four parameters governing additional density contributions that peak at different redshifts, and two sound speed parameters. We forecast possible constraints with Simons Observatory, explore parameter degeneracies that arise in $\Lambda$CDM, and demonstrate that this method could detect a range of specific models. Which of the new parameters gets excited can indicate the nature of any new physics, while the generality of the model allows for testing with future data in a way that should not be plagued by a posteriori choices or publication bias. When testing our model with Planck data, we find good consistency with the $\Lambda$CDM model, but the data also allows for large Hubble parameter, especially if the sound speed of an additional component is not too different to that of radiation., Comment: 29 pages, 9 figures

Recent advances have demonstrated that $\mathcal{O}(\log M)$ measurements suffice to predict $M$ properties of arbitrarily large quantum many-body systems. However, these remarkable findings assume that the properties to be predicted are chosen independently of the data. This assumption can be violated in practice, where scientists adaptively select properties after looking at previous predictions. This work investigates the adaptive setting for three classes of observables: local, Pauli, and bounded-Frobenius-norm observables. We prove that $\Omega(\sqrt{M})$ samples of an arbitrarily large unknown quantum state are necessary to predict expectation values of $M$ adaptively chosen local and Pauli observables. We also present computationally-efficient algorithms that achieve this information-theoretic lower bound. In contrast, for bounded-Frobenius-norm observables, we devise an algorithm requiring only $\mathcal{O}(\log M)$ samples, independent of system size. Our results highlight the potential pitfalls of adaptivity in analyzing data from quantum experiments and provide new algorithmic tools to safeguard against erroneous predictions in quantum experiments., Comment: 10 pages, 4 figures + 39-page appendix

We develop a robust Machine Learning classifier model utilizing the eXtreme-Gradient Boosting (XGB) algorithm for improved classification of Galactic Wolf-Rayet (WR) stars based on Infrared (IR) colors and positional attributes. For our study, we choose an extensive dataset of 6555 stellar objects (from 2MASS and AllWISE data releases) lying in the Milky Way (MW) with available photometric magnitudes of different types including WR stars. Our XGB classifier model can accurately (with an 86\% detection rate) identify a sufficient number of WR stars against a large sample of non-WR sources. The XGB model outperforms other ensemble classifier models such as the Random Forest. Also, using the XGB algorithm, we develop a WR sub-type classifier model that can differentiate the WR subtypes from the non-WR sources with a high model accuracy ($>60\%$). Further, we apply both XGB-based models to a selection of 6457 stellar objects with unknown object types, detecting 58 new WR star candidates and predicting sub-types for 10 of them. The identified WR sources are mainly located in the Local spiral arm of the MW and mostly lie in the solar neighborhood., Comment: 19 pages, Accepted to APJ

AI agents will be predictable in certain ways that traditional agents are not. Where and how can we leverage this predictability in order to improve social welfare? We study this question in a game-theoretic setting where one agent can pay a fixed cost to simulate the other in order to learn its mixed strategy. As a negative result, we prove that, in contrast to prior work on pure-strategy simulation, enabling mixed-strategy simulation may no longer lead to improved outcomes for both players in all so-called "generalised trust games". In fact, mixed-strategy simulation does not help in any game where the simulatee's action can depend on that of the simulator. We also show that, in general, deciding whether simulation introduces Pareto-improving Nash equilibria in a given game is NP-hard. As positive results, we establish that mixed-strategy simulation can improve social welfare if the simulator has the option to scale their level of trust, if the players face challenges with both trust and coordination, or if maintaining some level of privacy is essential for enabling cooperation.

This article investigates equivariant parametrized cellular cohomology, a cohomology theory introduced by Costenoble-Waner for spaces with an action by a compact Lie group $G$. The theory extends the $RO(G)$-graded cohomology of a $G$-space $B$ to a cohomology graded by $RO(\Pi B)$, the representations of the equivariant fundamental groupoid of $B$. This paper is meant to serve as a guide to this theory and contains some new computations. We explain the key ingredients for defining parametrized cellular cohomology when $G$ is a finite group, with particular attention to the case of the cyclic group $G=C_2$. We compute some examples and observe that $RO(\Pi B)$ is not always free. When $G$ is the trivial group, we explain how to identify equivariant parametrized cellular cohomology with cellular cohomology in local coefficients. Finally, we illustrate the theory with some new computations of parametrized cellular cohomology for several spaces with $G = C_2$ and $G=C_4$.

Analysing stellar parameters and abundances from nearly one million Gaia DR3 Radial Velocity Spectrometer (RVS) spectra poses challenges due to the limited spectral coverage (restricted to the infrared Ca II triplet) and variable signal-to-noise ratios of the data. To address this, we use The Cannon, a data-driven method, to transfer stellar parameters and abundances from the GALAH Data Release 4 (DR4; R ~ 28,000) catalogue to the lower resolution Gaia DR3 RVS spectra (R ~ 11,500). Our model, trained on 14,484 common targets, predicts parameters such as Teff, log g, and [Fe/H], along with several other elements across approximately 800,000 Gaia RVS spectra. We utilise stars from open and globular clusters present in the Gaia RVS catalogue to validate our predicted mean [Fe/H] with high precision (~0.02-0.10 dex). Additionally, we recover the bimodal distribution of [Ti/Fe] versus [Fe/H], reflecting the high and low alpha-components of Milky Way disk stars, demonstrating The Cannon's capability for accurate stellar abundance determination from medium-resolution Gaia RVS spectra. The methodologies and resultant catalogue presented in this work highlight the remarkable potential of the RVS dataset, which by the end of the Gaia mission will comprise spectra of over 200 million stars., Comment: Submitted to MNRAS, 16 pages, 15 figures

Accurate characterization of quantum yield is crucial to the reconstruction of energy depositions in silicon at the eV scale. This work presents a new method for experimentally calculating quantum yield using vacuum UV-sensitive silicon photomultipliers (SiPMs), which can be used to determine the probabilities that a UV photon absorbed in a silicon crystal will produce one, two, or three electron-hole pairs. Results are presented which fully constrain the distribution at photon energies up to 7.75eV. This method works by exploiting the saturation of photon detection efficiency which occurs when these devices are biased sufficiently high above their avalanche breakdown voltage. The measured quantum yield values are lower than those that have been previously reported by experimental data and modelling -- this is expected to impact the sensitivity of experiments searching for light dark matter through direct detection in semiconductors, and should also be taken into account when characterizing the performance of UV photodetectors with high quantum efficiency. Measurements have been taken using a Hamamatsu VUV4 and an FBK VUV-HD3 device, showing good agreements between devices, and at a range of temperatures from 163-233K. The validity of the method is assessed using supplementary measurements of absolute photon detection efficiency, and an additional novel method of measuring average quantum yield using DC current-voltage measurements of SiPMs is presented and used for corroboration., Comment: 13 pages, 11 figures

Characterizing a quantum system by learning its state or evolution is a fundamental problem in quantum physics and learning theory with a myriad of applications. Recently, as a new approach to this problem, the task of agnostic state tomography was defined, in which one aims to approximate an arbitrary quantum state by a simpler one in a given class. Generalizing this notion to quantum processes, we initiate the study of agnostic process tomography: given query access to an unknown quantum channel $\Phi$ and a known concept class $\mathcal{C}$ of channels, output a quantum channel that approximates $\Phi$ as well as any channel in the concept class $\mathcal{C}$, up to some error. In this work, we propose several natural applications for this new task in quantum machine learning, quantum metrology, classical simulation, and error mitigation. In addition, we give efficient agnostic process tomography algorithms for a wide variety of concept classes, including Pauli strings, Pauli channels, quantum junta channels, low-degree channels, and a class of channels produced by $\mathsf{QAC}^0$ circuits. The main technical tool we use is Pauli spectrum analysis of operators and superoperators. We also prove that, using ancilla qubits, any agnostic state tomography algorithm can be extended to one solving agnostic process tomography for a compatible concept class of unitaries, immediately giving us efficient agnostic learning algorithms for Clifford circuits, Clifford circuits with few T gates, and circuits consisting of a tensor product of single-qubit gates. Together, our results provide insight into the conditions and new algorithms necessary to extend the learnability of a concept class from the standard tomographic setting to the agnostic one., Comment: 11+52 pages, 2 figures, 1 table

Modern Question Answering (QA) and Reasoning approaches based on Large Language Models (LLMs) commonly use prompting techniques, such as Chain-of-Thought (CoT), assuming the resulting generation will have a more granular exploration and reasoning over the question space and scope. However, such methods struggle with generating outputs that are faithful to the intermediate chain of reasoning produced by the model. On the other end of the spectrum, neuro-symbolic methods such as Faithful CoT (F-CoT) propose to combine LLMs with external symbolic solvers. While such approaches boast a high degree of faithfulness, they usually require a model trained for code generation and struggle with tasks that are ambiguous or hard to formalise strictly. We introduce $\textbf{F}$aithful $\textbf{L}$ogic-$\textbf{A}$ided $\textbf{R}$easoning and $\textbf{E}$xploration ($\textbf{FLARE}$), a novel interpretable approach for traversing the problem space using task decompositions. We use the LLM to plan a solution, soft-formalise the query into facts and predicates using a logic programming code and simulate that code execution using an exhaustive multi-hop search over the defined space. Our method allows us to compute the faithfulness of the reasoning process w.r.t. the generated code and analyse the steps of the multi-hop search without relying on external solvers. Our methods achieve SOTA results on $\mathbf{7}$ out of $\mathbf{9}$ diverse reasoning benchmarks. We also show that model faithfulness positively correlates with overall performance and further demonstrate that $\textbf{FLARE}$ allows pinpointing the decisive factors sufficient for and leading to the correct answer with optimal reasoning during the multi-hop search.

The extreme environments of transiting close-in exoplanets in highly-eccentric orbits are ideal for testing exo-climate physics. Spectroscopically resolved phase curves not only allow for the characterization of their thermal response to irradiation changes but also unveil phase-dependent atmospheric chemistry and dynamics. We observed a partial phase curve of the highly-eccentric close-in giant planet HAT-P-2b ($e=0.51,M=9M_{\rm{Jup}}$) with the Wide Field Camera 3 aboard the Hubble Space Telescope. Using these data, we updated the planet's orbital parameters and radius, and retrieved high-frequency pulsations consistent with the planet-induced pulsations reported in Spitzer data. We found that the peak in planetary flux occurred at $6.7\pm0.6$ hr after periastron, with a heating and cooling timescales of $9.0^{+3.5}_{-2.1}$ hr, and $3.6^{+0.7}_{-0.6}$ hr, respectively. We compare the light-curve to various 1-dimensional and 3-dimensional forward models, varying the planet's chemical composition. The strong contrast in flux increase and decrease timescales before and after periapse indicates an opacity term that emerges during the planet's heating phase, potentially due to more H$^{-}$ than expected from chemical equilibrium models. The phase-resolved spectra are largely featureless, that we interpret as indicative an inhomogeneous dayside. However, we identified an anomalously high flux in the spectroscopic bin coinciding with the hydrogen Paschen $\beta$ line and that is likely connected to the planet's orbit. We interpret this as due to shock heating of the upper atmosphere given the short timescale involved, or evidence for other star-planet interactions., Comment: In review in AJ

Understanding the mountain pine beetle's dispersal patterns is critical for evaluating its threat to Canada's boreal forests. It is generally believed that higher beetle densities lead to increased long-distance dispersal due to aggregation pheromones becoming repellent at high densities, causing beetles to seek areas with less competition. However, using helicopter surveys of infested trees, along with statistical models, we find no evidence supporting a positive relationship. Instead, we observe a weak negative association between population density and dispersal at all spatial scales. A possible explanation is that at low population densities, beetles cannot successfully attack healthy trees and must travel farther to find weakened hosts. Even so, the influence of beetle density on dispersal is minor compared to the spatiotemporal variation in the overall (density-independent) scale of dispersal, as revealed by our models. This variation accounts for the MPB's erratic range expansion across western Alberta, which varied from 20 km to 220 km annually., Comment: 24 pages, 8 figures, 1 table

Significant success has been achieved in fabricating angstrom-scale artificial solid ionic channels aiming to replicate the biological ion channels (BICs).Besides high selectivity, BICs also exhibit sophisticated ion gating and interplay. However, such behavior and functionality are seldomly recreated in the artificial counterparts due to the insufficient understanding of the molecular origin. Here we report cooperative and inhibitory ion transport in angstrom-scale acetate functionalized MoS2 two dimensional channels., Comment: 34 pages for main text and 40 pages for SI

Scalar extensions of the Standard Model are of much interest at the LHC and future colliders. In particular, these models can give rise to resonant di-Higgs production and alter the Higgs trilinear coupling. In this paper, we study di-Higgs production in the Standard Model extended by a real scalar singlet with no additional symmetries. We determine how large the resonant di-Higgs rate and variation in the Higgs trilinear coupling can be in four scenarios: current LHC results and projected results at the HL-LHC, the HL-LHC combined with a circular $e^-e^+$ collider such as the CEPC or FCC-ee, and the HL-LHC combined with a linear $e^-e^+$ collider such as the ILC. While these are updated results from a previous study using current LHC data, we go further and find benchmark points in the multi-TeV resonance regime for future colliders beyond the HL-LHC. Considering current LHC results, the resonant di-Higgs rate can still be an order of magnitude larger than the SM predicted di-Higgs rate. In the HL-LHC scenario, the Higgs trilinear coupling can still be a factor of three larger than the SM prediction for resonance masses in the $1.5-3.5$ TeV range, where resonant searches may have less reach. This enhancement is just at the projected 2$\sigma$ sensitivity of the HL-LHC. We find there are resonance masses for which the change in the Higgs trilinear is maximized while the resonant rate is negligible. We provide an analytical understanding of these effects with a discussion on the interplay of various constraints on the parameter space and the Higgs trilinear coupling., Comment: 25 pages, 7 figures

We present the first emission spectrum of the hot Jupiter WASP-17 b using one eclipse observation from the JWST Near Infrared Imager and Slitless Spectrograph (NIRISS) Single Object Slitless Spectroscopy (SOSS) mode. Covering a wavelength range of 0.6 to 2.8 microns, our retrieval analysis reveals a strong detection of H2O in WASP-17b dayside atmosphere (6.4sigma). Our retrievals consistently favor a super-solar dayside H2O abundance and a non-inverted temperature-pressure profile over a large pressure range. Additionally, our examination of the brightness temperature reveals excess emission below 1 microns, suggesting the possibility of a high internal temperature (600 to 700 K) and/or contributions from reflected light. We highlight that JWST emission spectroscopy retrieval results can be sensitive to whether negative eclipse depths are allowed at optical wavelengths during light curve fitting. Our findings deepen our understanding of WASP-17b atmospheric composition while also highlighting the sensitivity of our results to pressure-temperature profile parameterizations. This work is part of a series of studies by our JWST Telescope Scientist Team (JWST-TST), in which we will use Guaranteed Time Observations to perform Deep Reconnaissance of Exoplanet Atmospheres through Multi-instrument Spectroscopy (DREAMS)., Comment: Accepted for Publication in the Astronomical Journal (AJ) October 4, 2024

We present the first spectroscopic characterisation of the dayside atmosphere of WASP-17b in the mid-infrared using a single JWST MIRI/LRS eclipse observation. From forward-model fits to the 5-12 $\mu$m emission spectrum, we tightly constrain the heat redistribution factor of WASP-17b to be 0.92$\pm$0.02 at the pressures probed by this data, indicative of inefficient global heat redistribution. We also marginally detect a supersolar abundance of water, consistent with previous findings for WASP-17b, but note our weak constraints on this parameter. These results reflect the thermodynamically rich but chemically poor information content of MIRI/LRS emission data for high-temperature hot Jupiters. Using the eclipse mapping method, which utilises the signals that the spatial emission profile of an exoplanet imprints on the eclipse light curve during ingress/egress due to its partial occultation by the host star, we also construct the first eclipse map of WASP-17b, allowing us to diagnose its multidimensional atmospheric dynamics for the first time. We find a day-night temperature contrast of order 1000 K at the pressures probed by this data, consistent with our derived heat redistribution factor, along with an eastward longitudinal hotspot offset of $18.7^{+11.1\deg}_{-3.8}$, indicative of the presence of an equatorial jet induced by day-night thermal forcing being the dominant redistributor of heat from the substellar point. These dynamics are consistent with general circulation model predictions for WASP-17b. This work is part of a series of studies by the JWST Telescope Scientist Team (JWST-TST), in which we use Guaranteed Time Observations to perform Deep Reconnaissance of Exoplanet Atmospheres through Multi-instrument Spectroscopy (DREAMS)., Comment: 28 pages, 8 figures. Published in The Astronomical Journal

Mechanical THz vibrations in nanocrystals have recently been harnessed for quantum sensing and thermal management. The free boundaries of nanocrystals introduce new surface wave solutions, analogous to the seismic waves on Earth, yet the implications of these surface waves on nanocrystals have remained largely unexplored. Here, we use atomistic molecular dynamics simulations and experimental neutron spectroscopy to elucidate these THz-scale features in nanodiamond. Our key insight is that thermally induced Rayleigh surface phonons, which have a low group velocity and an amplitude that decays exponentially away from the surface, are responsible for the previously observed but unexplained linear scaling of the low-energy vibrational density of states in nanocrystals. Large thermal atomic displacements, relative to the nanoparticle radius, induce perpetual surface quakes, even at ambient conditions. Normalised to the radius, the surface displacement ratio in diamond nanocrystals exceeds that of the largest recorded earthquakes by a factor of $10^{5\pm1}$. We explicate how these dramatic Rayleigh waves coexist with other distinctive features including confined lattice phonons, soft surface modes, the acoustic gap, Love waves, and Lamb modes, thereby offering a complete framework for the vibrational dynamics of nanocrystals., Comment: 14 pages

Recently, NIRSpec PRISM/CLEAR observations by JWST have begun providing rest-frame UV continuum measurements of galaxies at $z\gtrsim7$, revealing signatures of Ly$\alpha$ damping-wing (DW) absorption by the intergalactic medium (IGM). We develop a methodology to constrain the global ionization fraction of the IGM $(Q_{\rm HII})$ using low-resolution spectra, employing the random forest classification (RFC) method. We construct mock spectra using the simulated galaxies and the IGM from the Cosmic Dawn II simulation and train RFC models to estimate $Q_{\rm HII}$ at the redshift of the source and to detect the presence of a damped Ly$\alpha$ absorber (DLA). We find that individual galaxy spectra with spectral bins between 1220 and 1270 {\AA} and with signal-to-noise ratios greater than 20 can place tight constraints on $Q_{\rm HII}$, provided the UV continuum is accurately modeled. This method is particularly effective for the early phase of reionization ($Q_{\rm HII}<50\%$), when the IGM opacity is high in the DW. As a demonstration, we apply our model to existing NIRSpec PRISM/CLEAR spectra, placing upper bounds of $Q_{\rm HII}=59.6\%$, $5.6\%$, and $18.5\%$ at $z=7.7,~9.4,$ and $10.6$, respectively, with $68\%$ confidence, though several modeling uncertainties remain to be discussed. These constraints favor late-starting reionization models, where $\gtrsim 80\%$ of the IGM is ionized after $z=8$. We conclude that high SNR observations of carefully selected targets around $z\sim7-9$ can effectively constrain reionization models., Comment: 20 pages, 11 figures, submitted to ApJ

Nongravitational accelerations in the absence of observed activity have recently been identified on NEOs, opening the question of the prevalence of anisotropic mass-loss in the near-Earth environment. Motivated by the necessity of nongravitational accelerations to identify 2010 VL$_{65}$ and 2021 UA$_{12}$ as a single object, we investigate the problem of linking separate apparitions in the presence of nongravitational perturbations. We find that nongravitational accelerations on the order of $10^{-9}$ au/d$^2$ can lead to a change in plane-of-sky positions of $\sim10^3$ arcsec between apparitions. Moreover, we inject synthetic tracklets of hypothetical nongravitationally-accelerating NEOs into the Minor Planet Center orbit identification algorithms. We find that at large nongravitational accelerations ($|A_i|\geq10^{-8}$ au/d$^2$) these algorithms fail to link a significant fraction of these tracklets. We further show that if orbits can be determined for both apparitions, the tracklets will be linked regardless of nongravitational accelerations, although they may be linked to multiple objects. In order to aid in the identification and linkage of nongravitationally accelerating objects, we propose and test a new methodology to search for unlinked pairs. When applied to the current census of NEOs, we recover the previously identified case but identify no new linkages. We conclude that current linking algorithms are generally robust to nongravitational accelerations, but objects with large nongravitational accelerations may potentially be missed. While current algorithms are well-positioned for the anticipated increase in the census population from future survey missions, it may be possible to find objects with large nongravitational accelerations hidden in isolated tracklet pairs., Comment: 16 pages, 5 figures and 5 appendix figures. Accepted to ApJ

High redshift observations of 10$^9$ M$_\odot$ supermassive black holes (SMBHs) at $z \sim7$ and `Little Red Dots' that may host overmassive black holes at $z>4$ suggests the existence of so-called heavy seeds (>1000 M$_\odot$) in the early Universe. Recent work has suggested that the rapid assembly of halos may be the key to forming heavy seeds early enough in the Universe to match such observations without the need for extreme radiation fields or dark matter streaming velocities. We perform simulations of BH seed formation in 4 distinct idealised halo collapse scenarios; an isolated 10$^6$ M$_\odot$ minihalo, an isolated 10$^7$ M$_\odot$ atomic halo, the direct collision of two 10$^7$ M$_\odot$ halos and a fly-by collision of two 10$^7$ M$_\odot$ halos. We have shown that halo collisions create a central environment of enhanced density, inside which BH seeds can accrete at enhanced rates. For direct collisions, the gas density peaks are disrupted by the interaction, as the collisionless DM peaks pass through each other while the colliding gas is left in the center, removing the sink particle from its accretion source. When the central density peaks instead experience a fly-by interaction, the sink particle remains embedded in the dense gas and maintains enhanced accretion rates throughout the simulated period when compared to the isolated halo cases. Here the final mass of the sink particle achieved a factor of 2 greater in mass than in the isolated atomic halo case, and a factor of 3 greater than the minihalo case, reaching 10$^4$ M$_\odot$ via its 0.03 pc accretion radius. As the maximum halo mass before collapse is determined by the atomic cooling limit of a few times 10$^7$ M$_{\odot}$, the ability of halo-halo mergers to further boost accretion rates onto the central object may play a crucial role in growing SMBH seeds., Comment: Submitted to A&A

Spatial search is the problem of finding a marked vertex in a graph. A continuous-time quantum walk in the single-excitation subspace of an $n$ spin system solves the problem of spatial search by finding the marked vertex in $O(\sqrt{n})$ time. Here, we investigate a natural extension of the spatial search problem, marking multiple vertices of a graph, which are still marked with local fields. We prove that a continuous-time quantum walk in the $k$-excitation subspace of $n$ spins can determine the binary string of $k$ marked vertices with an asymptotic fidelity in time $O(\sqrt{n})$, despite the size of the state space growing as $O(n^k)$. Numerically, we show that this algorithm can be implemented with interactions that decay as $1/r^\alpha$, where $r$ is the distance between spins, and an $\alpha$ that is readily available in current ion trap systems., Comment: 7 + 7 pages, 8 figures

We introduce a novel method that utilizes the longitude-velocity (l-v) envelope to constrain the Milky Way (MW) bar potential. Previous work (Habing 2016) used the l-v diagram to explain the distribution of the observed high-velocity stars. We successfully reproduce their results, but find that their method is limited to only one single type of periodic orbits. In contrast, we propose that the l-v envelope provides much more comprehensive constraints. We compare the properties of test particles in the Portai et al. (2017) MW potential model (P17) with the observed SiO maser stars from the Bulge Asymmetries and Dynamical Evolution (BAaDE) survey. We find that the l-v envelope generated by the bar potential demonstrates reasonable agreement with the observational data, albeit with slight discrepancies near the Galactic center. The inconsistencies suggest that the P17 potential yields a lower central rotation curve, a slightly larger quadrupole strength, or a possibly underestimated pattern speed. We also adopt an updated version of the P17 potential with a modified central mass component (CMC) proposed by Hunter et al. (2024) (H24). The fitting of the l-v envelope suggests that the H24 potential does not completely address the existing challenges and may hint at a possible underestimation of the central bar mass. Our study demonstrates that the l-v envelope can be used as a valuable tool for constraining the Galactic potential and provides insights into the Milky Way bar potential., Comment: 20 pages, 17 figures, ApJ accepted

Many-body quantum chaos has immense potential as a tool to accelerate the preparation of entangled states and overcome challenges due to decoherence and technical noise. Here, we study how chaos in the paradigmatic Dicke model, which describes the uniform coupling of an ensemble of qubits to a common bosonic mode, can enable the rapid generation of non-Gaussian entangled spin-boson states without fine tuning of system parameters or initial conditions. However, the complexity of these states means that unlocking their utility for quantum-enhanced sensing with standard protocols would require the measurement of complex or typically inaccessible observables. To address this challenge, we develop a sensing scheme based on interaction-based readout that enable us to implement near-optimal quantum-enhanced metrology of global spin rotations or bosonic dipslacements using only spin measurements. We show that our approach is robust to technical noise and imperfections and thus opens new opportunities to exploit complex entangled states generated by chaotic dynamics in current quantum science platforms such as trapped-ion and cavity-QED experiments., Comment: 14 pages, 9 figures

The context window of large language models (LLMs) has been extended significantly in recent years. However, while the context length that the LLM can process has grown, the capability of the model to accurately reason over that context degrades noticeably. This occurs because modern LLMs often become overwhelmed by the vast amount of information in the context; when answering questions, the model must identify and reason over relevant evidence sparsely distributed throughout the text. To alleviate the challenge of long-context reasoning, we develop a retrieve-then-reason framework, enabling LLMs to reason over relevant evidence collected during an intermediate retrieval step. We find that modern LLMs struggle to accurately retrieve relevant facts and instead, often hallucinate "retrieved facts", resulting in flawed reasoning and the production of incorrect answers. To address these issues, we introduce ALR$^2$, a method that augments the long-context reasoning capability of LLMs via an explicit two-stage procedure, i.e., aligning LLMs with the objectives of both retrieval and reasoning. We demonstrate the efficacy of ALR$^2$ for mitigating performance degradation in long-context reasoning tasks. Through extensive experiments on long-context QA benchmarks, we find our method to outperform competitive baselines by large margins, achieving at least 8.4 and 7.9 EM gains on the long-context versions of HotpotQA and SQuAD datasets, respectively.

The development and evaluation of Large Language Models (LLMs) have largely focused on individual capabilities. However, this overlooks the intersection of multiple abilities across different types of expertise that are often required for real-world tasks, which we term cross capabilities. To systematically explore this concept, we first define seven core individual capabilities and then pair them to form seven common cross capabilities, each supported by a manually constructed taxonomy. Building on these definitions, we introduce CrossEval, a benchmark comprising 1,400 human-annotated prompts, with 100 prompts for each individual and cross capability. To ensure reliable evaluation, we involve expert annotators to assess 4,200 model responses, gathering 8,400 human ratings with detailed explanations to serve as reference examples. Our findings reveal that, in both static evaluations and attempts to enhance specific abilities, current LLMs consistently exhibit the "Law of the Weakest Link," where cross-capability performance is significantly constrained by the weakest component. Specifically, across 58 cross-capability scores from 17 models, 38 scores are lower than all individual capabilities, while 20 fall between strong and weak, but closer to the weaker ability. These results highlight the under-performance of LLMs in cross-capability tasks, making the identification and improvement of the weakest capabilities a critical priority for future research to optimize performance in complex, multi-dimensional scenarios., Comment: Data, Code, & Benchmark: www.llm-cross-capabilities.org

The stars of the Milky Way carry the chemical history of our Galaxy in their atmospheres as they journey through its vast expanse. Like barcodes, we can extract the chemical fingerprints of stars from high-resolution spectroscopy. The fourth data release (DR4) of the Galactic Archaeology with HERMES (GALAH) Survey, based on a decade of observations, provides the chemical abundances of up to 32 elements for 917 588 stars that also have exquisite astrometric data from the $Gaia$ satellite. For the first time, these elements include life-essential nitrogen to complement carbon, and oxygen as well as more measurements of rare-earth elements critical to modern-life electronics, offering unparalleled insights into the chemical composition of the Milky Way. For this release, we use neural networks to simultaneously fit stellar parameters and abundances across the full spectrum, leveraging synthetic grids computed with Spectroscopy Made Easy. These grids account for atomic line formation in non-local thermodynamic equilibrium for 14 elements. In a two-iteration process, we first fit stellar labels for all 1 085 520 spectra, then co-add repeated observations and refine these labels using astrometric data from $Gaia$ and 2MASS photometry, improving the accuracy and precision of stellar parameters and abundances. Our validation thoroughly assesses the reliability of spectroscopic measurements and highlights key caveats for catalogue users. GALAH DR4 represents yet another milestone in Galactic archaeology, combining detailed chemical compositions from multiple nucleosynthetic channels with kinematic information and age estimates. The resulting dataset, covering nearly a million stars, opens new avenues for understanding not only the chemical and dynamical history of the Milky Way, but also the broader questions of the origin of elements and the evolution of planets, stars, and galaxies., Comment: 43 pages, 38 figures to be submitted to PASA. Accompanying the GALAH Data Release 4, see https://www.galah-survey.org and https://cloud.datacentral.org.au/teamdata/GALAH/public/GALAH_DR4/. All code available on http://github.com/svenbuder/GALAH_DR4/ and https://github.com/svenbuder/galah_dr4_paper. Comments welcome

Understanding and predicting recreational fishing activity is important for sustainable fisheries management. However, traditional methods of measuring fishing pressure, such as surveys, can be costly and limited in both time and spatial extent. Predictive models that relate fishing activity to environmental or economic factors typically rely on historical data, which often restricts their spatial applicability due to data scarcity. In this study, high-resolution angler-generated data from an online platform and easily accessible auxiliary data were tested to predict daily boat presence and aerial counts of boats at almost 200 lakes over five years in Ontario, Canada. Lake-information website visits alone enabled predicting daily angler boat presence with 78% accuracy. While incorporating additional environmental, socio-ecological, weather and angler-generated features into machine learning models did not remarkably improve prediction performance of boat presence, they were substantial for the prediction of boat counts. Models achieved an R2 of up to 0.77 at known lakes included in the model training, but they performed poorly for unknown lakes (R2 = 0.21). The results demonstrate the value of integrating angler-generated data from online platforms into predictive models and highlight the potential of machine learning models to enhance fisheries management., Comment: 31 pages

Multi-Agent Reinforcement Learning (MARL) methods have shown promise in enabling agents to learn a shared communication protocol from scratch and accomplish challenging team tasks. However, the learned language is usually not interpretable to humans or other agents not co-trained together, limiting its applicability in ad-hoc teamwork scenarios. In this work, we propose a novel computational pipeline that aligns the communication space between MARL agents with an embedding space of human natural language by grounding agent communications on synthetic data generated by embodied Large Language Models (LLMs) in interactive teamwork scenarios. Our results demonstrate that introducing language grounding not only maintains task performance but also accelerates the emergence of communication. Furthermore, the learned communication protocols exhibit zero-shot generalization capabilities in ad-hoc teamwork scenarios with unseen teammates and novel task states. This work presents a significant step toward enabling effective communication and collaboration between artificial agents and humans in real-world teamwork settings., Comment: Accepted to Neurips 2024, 16 pages, 3 figures

Cardiovascular Diseases (CVDs) are the leading cause of death worldwide, taking 17.9 million lives annually. Abdominal Aortic Calcification (AAC) is an established marker for CVD, which can be observed in lateral view Vertebral Fracture Assessment (VFA) scans, usually done for vertebral fracture detection. Early detection of AAC may help reduce the risk of developing clinical CVDs by encouraging preventive measures. Manual analysis of VFA scans for AAC measurement is time consuming and requires trained human assessors. Recently, efforts have been made to automate the process, however, the proposed models are either low in accuracy, lack granular level score prediction, or are too heavy in terms of inference time and memory footprint. Considering all these shortcomings of existing algorithms, we propose 'AACLiteNet', a lightweight deep learning model that predicts both cumulative and granular level AAC scores with high accuracy, and also has a low memory footprint, and computation cost (Floating Point Operations (FLOPs)). The AACLiteNet achieves a significantly improved one-vs-rest average accuracy of 85.94% as compared to the previous best 81.98%, with 19.88 times less computational cost and 2.26 times less memory footprint, making it implementable on portable computing devices., Comment: 10 pages including references

Let $G$ be a finite group, and let $\text{Irr}(G)$ denote the set of the irreducible complex characters of $G$. An element $g\in G$ is called a vanishing element of $G$ if there exists $\chi\in\text{Irr}(G)$ such that $\chi(g)=0$ (i.e., $g$ is a zero of $\chi$) and, in this case, the conjugacy class $g^G$ of $g$ in $G$ is called a vanishing conjugacy class. In this paper we consider several problems concerning vanishing elements and vanishing conjugacy classes; in particular, we consider the problem of determining the least number of conjugacy classes of a finite group $G$ such that every non-linear $\chi\in\text{Irr}(G)$ vanishes on one of them. We also consider the related problem of determining the minimum number of non-linear irreducible characters of a group such that two of them have a common zero.