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In order to assist higher education institutions and their students during the pandemic, the federal government established the Higher Education Emergency Relief (HEER) Fund, which directed over $75 billion to institutions of higher education--including nearly $25 billion to community colleges--over a three-year period. The U.S. Department of Education worked on a rapid timeline to distribute these funds to institutions, which they could use to provide direct aid to students facing financial challenges and cover institutional costs related to the pandemic. Drawing on a survey of community colleges in six states--California, Michigan, New York (State University of New York [SUNY] colleges), Ohio, Tennessee, and Texas--this report provides insight into the specific pandemic recovery activities colleges implemented, colleges' perceptions of how successful HEER funds were in addressing student and institutional needs during the pandemic, and colleges' views of unmet needs. The institutional survey was completed by 170 out of a total of 265 community colleges in the six states. Key findings from the report: (1) Colleges spent nearly all the HEER funds they received. Given the large amount of HEER funding and the fact that colleges did not need to submit a proposal and budget for how they would use the funds, it should not be assumed that colleges would have spent all the money they received. Yet colleges spent nearly all the funds they received by the time the HEER program ended in June 2023; (2) HEER funds met a variety of student and institutional needs during the pandemic. Colleges had relatively few problems using the funds and felt that the aid was successful in mitigating student and institutional hardships; (3) Colleges focused on retaining existing students; they employed a variety of methods to support students in need. Colleges used HEER funds to support and retain existing (pre-pandemic) students rather than to recruit new students. They focused on supporting students with financial exigencies, including those experiencing food and housing insecurity. They used institutional aid to forgive debt owed to the college and to provide food, housing, and childcare assistance; (4) Spending patterns suggest that colleges experienced similar challenges during the pandemic and often prioritized the same objectives. Despite differences in state contexts and institutional settings, colleges tended to allocate funds in similar ways. For example, most colleges used aid for campus safety and technology hardware. Expenditure patterns also shifted over time in similar ways, indicating that colleges were responsive to evolving needs; (5) Expenditures related to campus safety and technology remained strong but decreased in frequency over time; expenditures to support students' mental health increased in frequency. Mental health services was the only expenditure category that increased in frequency in each of the three years of funding, likely reflecting the toll the pandemic took on students' mental health; (6) Comparing pre- and post-pandemic spending, HEER funds had the most impact on increasing support for technology hardware, high-speed internet, and housing assistance. Colleges used HEER funds both to fund existing services and to begin offering new ones. Fewer than a third of colleges had services in place to provide technology hardware, high-speed internet, and housing assistance before the pandemic; many more did so afterward; (7) Concerns about the end of HEER funding and priorities for future funding expose a need for continued flexible resources to address students' financial needs. Colleges' main concern about the end of HEER funding was that it would limit their ability to support students during an emergency. Their top priority for using future funding was additional student aid; and (8) Rural and vocational/technical colleges (as defined by the Carnegie Classification) may have had fewer resources prior to the pandemic and may be in greater need of additional support. Colleges in towns and rural areas and colleges focused on technical training were less likely to offer a number of supports both pre- and post-pandemic. Rural colleges were also less likely to report having received additional funding for pandemic recovery from sources other than HEER funds. Overall, while the survey findings suggest that HEER largely met the goals for which it was intended, they also point to the importance of addressing systemic challenges facing community college students and the institutions that serve them. Now that the immediate crisis of the pandemic has passed and HEER funding has ended, there is an opportunity to think strategically about the investments that are needed to promote student success over the long term, particularly for underserved and financially vulnerable students who are the most at risk of stopping out or not enrolling in the first place.

In its 2023 decision in "Students for Fair Admissions v. Harvard," the Supreme Court effectively ended its approval of affirmative action in higher education admissions, holding that practices at Harvard and the University of North Carolina (UNC) were unlawful. The Court concluded that UNC's practices violated the guarantee of equal protection in the Fourteenth Amendment to the Constitution, which generally prohibits governmental racial discrimination. Additionally, while the Fourteenth Amendment's Equal Protection Clause applies directly only to state-run educational institutions, its nondiscrimination requirements apply equally to private colleges and universities that receive federal funds under Title VI of the Civil Rights Act of 1964 (Title VI), which prohibits recipients of federal dollars from discriminating on the basis of race. In "Students for Fair Admissions," the Court concluded that Harvard's affirmative action program violated this statutory provision for the same reasons that UNC's violated the Constitution. This report describes the evolution of the Supreme Court's voluntary affirmative action jurisprudence and the "Students for Fair Admissions" decision's implications. The report concludes by discussing the role that Title VI plays in ensuring equal protection in higher education, including several avenues for congressional action under the Act.

To help the nation respond to the pandemic, Congress injected about $4.6 trillion into the U.S. economy through the Coronavirus Aid, Relief, and Economic Security (CARES) Act and subsequent legislation. Of this amount, over $75 billion was directed to institutions of higher education through the Higher Education Emergency Relief (HEER) Fund, including nearly $25 billion to community colleges. Using data from the U.S. Department of Education's Education Stabilization Fund Transparency Portal and the Integrated Postsecondary Education Data System, this ARCC Network report examines how HEER funds were distributed to community colleges and the extent to which the colleges spent those funds. It also explores how HEER funding and spending patterns differed by institutional and student characteristics. HEER funding was based mostly on particular kinds of enrollments: States and community colleges with higher enrollments received more funds, and those with higher percentages of Pell recipients and full-time students tended to receive larger awards. The majority of HEER funds were intended to aid in the transition to distance learning, support faculty and staff training, and maintain core instruction and services at colleges. A substantial portion of HEER funding also went to students in the form of emergency aid, and some colleges received "other" HEER funding based on institutional characteristics to address additional unmet needs. Community colleges spent nearly all the HEER funding they received: Collectively, 976 community colleges spent 95% of funds, and nearly half the colleges (484) spent virtually all of their HEER funds. Colleges that spent less of their HEER funding tended to have received larger per-student awards. [This report was written with the Accelerating Recovery in Community Colleges (ARCC) Network.]

Recent advancements in artificial intelligence have enabled generative models to produce synthetic scientific images that are indistinguishable from pristine ones, posing a challenge even for expert scientists habituated to working with such content. When exploited by organizations known as paper mills, which systematically generate fraudulent articles, these technologies can significantly contribute to the spread of misinformation about ungrounded science, potentially undermining trust in scientific research. While previous studies have explored black-box solutions, such as Convolutional Neural Networks, for identifying synthetic content, only some have addressed the challenge of generalizing across different models and providing insight into the artifacts in synthetic images that inform the detection process. This study aims to identify explainable artifacts generated by state-of-the-art generative models (e.g., Generative Adversarial Networks and Diffusion Models) and leverage them for open-set identification and source attribution (i.e., pointing to the model that created the image)., Comment: Accepted in IEEE International Workshop on Information Forensics and Security - WIFS 2024, Rome, Italy

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

A series of longstanding questions in harmonic analysis ask if the intersection of all prime ``$p$-adic versions" of an object, such as a doubling measure, or a Muckenhoupt or reverse H\"older weight, recovers the full object. Investigation into these questions was reinvigorated in 2019 by work of Boylan-Mills-Ward, culminating in showing that this recovery fails for a finite intersection in work of Anderson-Bellah-Markman-Pollard-Zeitlin. Via generalizing a new number-theoretic construction therein, we answer these questions.

Neutral hydrogen (HI) bubbles and shells are common in the interstellar medium (ISM). Studying their properties provides insight into the characteristics of the local ISM as well as the galaxy in which the bubbles reside. We report the detection of magnetic fields associated with superbubbles in the nearby irregular galaxy, the Small Magellanic Cloud (SMC). Using the Polarisation Sky Survey of the Universe's Magnetism (POSSUM) pilot survey, we obtain a high-density grid ($\approx 25 \,\rm sources\,deg^{-2}$) of Faraday rotation measure (RM) from polarized sources behind the SMC. This provides a sufficiently large number of RM measurements to study the magnetic properties of three of the largest HI shells previously identified in the SMC. The RM profiles as a function of distance from the shell centre show characteristic patterns at angular scales comparable to the shell size. We demonstrate that this can be explained by magneto-hydrodynamic simulation models of bubbles expanding in magnetised environments. From the observations, we estimate the line-of-sight magnetic field strength at the edges of the shells is enhanced by $\sim1\,\rm \mu G$ with respect to their centres. This is an order of magnitude larger than the field strength in the ambient medium ($\sim 0.1\,\rm \mu G$) estimated based on the expansion velocity of the shells. This paper highlights the power of densely mapped RM grids in studying the magnetic properties of galactic substructures beyond the Milky Way., Comment: 15 pages, 7 figures, Accepted for publication in MNRAS

We present photometric and spectroscopic observations of SN2020xga and SN2022xgc, two hydrogen-poor superluminous supernovae (SLSNe-I) at $z = 0.4296$ and $z = 0.3103$ respectively, that show an additional set of broad Mg II absorption lines, blueshifted by a few thousand km s$^{-1}$ with respect to the host galaxy absorption system. Previous work interpreted this as due to resonance line scattering of the SLSN continuum by rapidly expanding CSM expelled shortly before the explosion. The peak rest-frame $g$-band magnitude of SN2020xga is $-22.30 \pm 0.04$ mag and of SN2022xgc is $-21.97 \pm 0.05$ mag, placing them among the brightest SLSNe-I. We use high-quality spectra from ultraviolet to near-infrared wavelengths to model the Mg II line profiles and infer the properties of the CSM shells. We find that the CSM shell of SN2020xga resides at $\sim 1.3 \times 10^{16} \rm cm$ moving with a maximum velocity of $4275~\rm km~s^{-1}$, and the shell of SN2022xgc is located at $\sim 0.8 \times 10^{16} \rm cm$ reaching up to $4400~\rm km~s^{-1}$. These shells were expelled $\sim 11$ and $\sim 5$ months before explosion for SN2020xga and SN2022xgc respectively, possibly as a result of Luminous Blue Variable-like eruptions or pulsational pair instability (PPI) mass loss. We also analyze optical photometric data and model the light curves considering powering from the magnetar spin-down mechanism. The results support very energetic magnetars, approaching the mass-shedding limit, powering these SNe with ejecta masses of $\sim 7-9 \rm~M_\odot$. The ejecta masses inferred from the magnetar modeling are not consistent with the PPI scenario pointing towards stars $> 50~\rm M_\odot$ He-core, hence alternative scenarios such as fallback accretion are discussed., Comment: 25 pages text, 8 pages appendix, 21 figures. Submitted to A&A

Today's most advanced multimodal models remain proprietary. The strongest open-weight models rely heavily on synthetic data from proprietary VLMs to achieve good performance, effectively distilling these closed models into open ones. As a result, the community is still missing foundational knowledge about how to build performant VLMs from scratch. We present Molmo, a new family of VLMs that are state-of-the-art in their class of openness. Our key innovation is a novel, highly detailed image caption dataset collected entirely from human annotators using speech-based descriptions. To enable a wide array of user interactions, we also introduce a diverse dataset mixture for fine-tuning that includes in-the-wild Q&A and innovative 2D pointing data. The success of our approach relies on careful choices for the model architecture details, a well-tuned training pipeline, and, most critically, the quality of our newly collected datasets, all of which will be released. The best-in-class 72B model within the Molmo family not only outperforms others in the class of open weight and data models but also compares favorably against proprietary systems like GPT-4o, Claude 3.5, and Gemini 1.5 on both academic benchmarks and human evaluation. We will be releasing all of our model weights, captioning and fine-tuning data, and source code in the near future. Select model weights, inference code, and demo are available at https://molmo.allenai.org.

Estimating a soft robot's pose and applied forces, also called proprioception, is crucial for safe interaction of the robot with its environment. However, most solutions for soft robot proprioception use dedicated sensors, particularly for external forces, which introduce design trade-offs, rigidity, and risk of failure. This work presents an approach for pose estimation and contact detection for soft robots actuated by shape memory alloy (SMA) artificial muscles, using no dedicated force sensors. Our framework uses the unique material properties of SMAs to self-sense their internal stress, via offboard measurements of their electrical resistance and in-situ temperature readings, in an existing fully-soft limb design. We demonstrate that a simple polynomial regression model on these measurements is sufficient to predict the robot's pose, under no-contact conditions. Then, we show that if an additional measurement of the true pose is available (e.g. from an already-in-place bending sensor), it is possible to predict a binary contact/no-contact using multiple combinations of self-sensing signals. Our hardware tests verify our hypothesis via a contact detection test with a human operator. This proof-of-concept validates that self-sensing signals in soft SMA-actuated soft robots can be used for proprioception and contact detection, and suggests a direction for integrating proprioception into soft robots without design compromises. Future work could employ machine learning for enhanced accuracy., Comment: 6 pages, 7 figures

We present an analysis of the photometric and spectroscopic dataset of the Type II supernova (SN) 2018ivc in the nearby (10 Mpc) galaxy Messier 77. Thanks to the high cadence of the CHASE survey, we observed the SN rising very rapidly by nearly three magnitudes in five hours (or 18 mag d$^{-1}$). The $r$-band light curve presents four distinct phases: the maximum light is reached in just one day, then a first, rapid linear decline precedes a short-duration plateau. Finally, a long, slower linear decline lasted for one year. Following a radio rebrightening, we detected SN 2018ivc four years after the explosion. The early spectra show a blue, nearly featureless continuum, but the spectra evolve rapidly: after about 10 days a prominent H$\alpha$ line starts to emerge, with a peculiar profile, but the spectra are heavily contaminated by emission lines from the host galaxy. He I lines, namely $\lambda\lambda$5876,7065, are also strong. On top of the former, a strong absorption from the Na I doublet is visible, indicative of a non-negligible internal reddening. From its equivalent width, we derive a lower limit on the host reddening of $A_V\simeq1.5$ mag, while from the Balmer decrement and a match of the $B-V$ colour curve of SN 2018ivc to that of the comparison objects, a host reddening of $A_V\simeq3.0$ mag is obtained. The spectra are similar to those of SNe II, but with strong He lines. Given the peculiar light curve and spectral features, we suggest SN 2018ivc could be a transitional object between the Type IIL and Type IIb SNe classes. In addition, we found signs of interaction with circumstellar medium in the light curve, making SN 2018ivc also an interacting event. Finally, we modelled the early multi-band light curves and photospheric velocity of SN 2018ivc to estimate the explosion and CSM physical parameters., Comment: 19 pages, 22 figures, 3 tables, 3 appendices, accepted for publication on A&A

Context. Sensitive radio continuum data could remove the difference between the number of known supernova remnants (SNRs) in the Galaxy compared to that expected, but due to confusion in the Galactic plane, faint SNRs can be challenging to distinguish from brighter HII regions and filamentary radio emission. Aims. We wish to exploit new SARAO MeerKAT 1.3 GHz Galactic Plane Survey (SMGPS) radio continuum data, which covers $251\deg \le l \le 358\deg$ and $2\deg \le l \le 61\deg$ at $|b|\le 1.5\deg$, to search for SNR candidates in the Milky Way disk. Methods. We also use MIR data from the Spitzer GLIMPSE, Spitzer MIPSGAL, and WISE surveys to help identify SNR candidates. The identified SNR candidate are sources of extended radio continuum emission that lack MIR counterparts, are not known as HII regions in the WISE Catalog of Galactic HII Regions, and are not known previously as SNRs Results. We locate 237 new Galactic SNR candidates in the SMGPS data. We also identify and confirm the expected radio morphology for 201 objects listed in the literature as being SNRs and 130 previously-identified SNR candidates. The known and candidate SNRs have similar spatial distributions and angular sizes. Conclusions. The SMGPS data allowed us to identify a large population of SNR candidates that can be confirmed as true SNRs using radio polarization measurements or by deriving radio spectral indices. If the 237 candidates are confirmed as true SNRs, it would approximately double the number of known Galactic SNRs in the survey area, alleviating much of the difference between the known and expected populations., Comment: Accepted by A&A. Images and FITS files of each source can be found here: https://doi.org/10.48479/0n8c-5q84

Human speech conveys prosody, linguistic content, and speaker identity. This article investigates a novel speaker anonymization approach using an end-to-end network based on a Vector-Quantized Variational Auto-Encoder (VQ-VAE) to deal with these speech components. This approach is designed to disentangle these components to specifically target and modify the speaker identity while preserving the linguistic and emotionalcontent. To do so, three separate branches compute embeddings for content, prosody, and speaker identity respectively. During synthesis, taking these embeddings, the decoder of the proposed architecture is conditioned on both speaker and prosody information, allowing for capturing more nuanced emotional states and precise adjustments to speaker identification. Findings indicate that this method outperforms most baseline techniques in preserving emotional information. However, it exhibits more limited performance on other voice privacy tasks, emphasizing the need for further improvements.

This paper presents chemical abundances of twelve elements (C, Na, Mg, Al, Si, K, Ca, Ti, V, Cr, Mn, and Fe) for 80 FGK dwarfs in the Pleiades open cluster, which span a temperature range of $\sim$2000 K in T$_{\rm eff}$, using the high-resolution (R$\sim$22,500) near-infrared SDSS-IV/APOGEE spectra ($\lambda$1.51--1.69 \micron). Using a 1D LTE abundance analysis, we determine an overall metallicity of [Fe/H]=+0.03$\pm$0.04 dex, with the elemental ratios [$\alpha$/Fe]=+0.01$\pm$0.05, [odd-z/Fe]=-0.04$\pm$0.08, and [iron peak/Fe]=-0.02$\pm$0.08. These abundances for the Pleiades are in line with the abundances of other open clusters at similar galactocentric distances as presented in the literature. Examination of the abundances derived from each individual spectral line revealed that several of the stronger lines displayed trends of decreasing abundance with decreasing $T_{\rm eff}$. The list of spectral lines that yield abundances that are independent of $T_{\rm eff}$ are presented and used for deriving the final abundances. An investigation into possible causes of the temperature-dependent abundances derived from the stronger lines suggests that the radiative codes and the APOGEE line list we employ may inadequately model van der Waals broadening, in particular in the cooler K dwarfs., Comment: 16 pages, 7 figures, and 4 tables. Accepted for publication in MNRAS

This paper presents surface acoustic wave (SAW) acoustic delay lines (ADL) for studying propagation loss mechanisms in Lithium Niobate (LN). Devices were fabricated by depositing 50 nm aluminum patterns on 600 nm X-Cut LN on amorphous silicon on silicon carbide, where longitudinally dominant SAW was targeted. Upon fabrication, higher-order thickness-based cross-sectional Lam\'e modes and Rayleigh modes were studied for their Q factors using acoustic delay lines. Utilizing bi-directional electrodes, ADL with lateral lambda values ranging from 0.4 um to 0.6 um were measured. Higher order Lame modes were found to have consistently higher Q factors than their Rayleigh mode counterpart, on the order of 1000-3000, showing high-frequency SAW devices as still viable candidates for frequency scaling without a substantial increase in loss., Comment: 4 pages, 7 figures, accepted by IEEE UFFC-JS

SN 2023tsz is a Type Ibn supernova (SNe Ibn) discovered in an extremely low-mass host. SNe Ibn are an uncommon subtype of stripped-envelope core-collapse SNe. They are characterised by narrow helium emission lines in their spectra and are believed to originate from the collapse of massive Wolf-Rayet (WR) stars, though their progenitor systems still remain poorly understood. In terms of energetics and spectrophotometric evolution, SN 2023tsz is largely a typical example of the class, although line profile asymmetries in the nebular phase are seen, which may indicate the presence of dust formation or unshocked circumstellar material. Intriguingly, SN 2023tsz is located in an extraordinarily low-mass host galaxy that is in the 2nd percentile for SESN host masses and star formation rates (SFR). The host has a radius of 1.0 kpc, a $g$-band absolute magnitude of $-12.73$, and an estimated metallicity of $\log(Z_{*}/Z_{\odot}$) = $-1.56$. The SFR and metallicity of the host galaxy raise questions about the progenitor of SN 2023tsz. The low SFR suggests that a star with sufficient mass to evolve into a WR would be uncommon in this galaxy. Further, the very low-metallicity is a challenge for single stellar evolution to enable H and He stripping of the progenitor and produce a SN Ibn explosion. The host galaxy of SN 2023tsz adds another piece to the ongoing puzzle of SNe Ibn progenitors, and demonstrates that they can occur in hosts too faint to be observed in contemporary sky surveys at a more typical SN Ibn redshift., Comment: 14 pages, 10 figures, submitted to MNRAS

We introduce the construction of polarized intensity cubes $P$(RA, Dec, $\Phi$) and their visualization as movies, as a powerful technique for interpreting Faraday structure. $P$ is constructed from maps of peak polarized intensity P(RA, Dec) with their corresponding Faraday depth maps $\Phi$(RA, Dec). We illustrate the extensive scientific potential of such visualizations with a variety of science use cases from ASKAP and MeerKAT, presenting models that are consistent with the data but not necessarily unique. We demonstrate how one can, in principle, distinguish between cube structures which originate from unrelated foreground screens from those due to magnetized plasmas local to the emitting source. Other science use cases illustrate how variations in the local $n_e$ $B$, and line-of-sight distance to the synchrotron emitting regions can be distinguished using Faraday rotation. We show, for the first time, how the line-of-sight orientation of AGN jets can be determined. We also examine the case of M87 to show how internal jet magnetic field configurations can be identified, and extend earlier results. We recommend using this technique to re-evaluate all previous analyses of polarized sources that are well-resolved both spatially and in Faraday depth. Recognizing the subjective nature of interpretations at this early stage, we also highlight the need and utility for further scientific and technical developments., Comment: MNRAS, accepted. 16 pages, 19 figures, and as ancillary data, two sets of five animations (.mov and .gif formats). Python code for generating $P$ cubes is available at https://github.com/candersoncsiro/rmsynth3d

There is now substantial evidence for traveling waves and other structured spatiotemporal recurrent neural dynamics in cortical structures; but these observations have typically been difficult to reconcile with notions of topographically organized selectivity and feedforward receptive fields. We introduce a new 'spacetime' perspective on neural computation in which structured selectivity and dynamics are not contradictory but instead are complimentary. We show that spatiotemporal dynamics may be a mechanism by which natural neural systems encode approximate visual, temporal, and abstract symmetries of the world as conserved quantities, thereby enabling improved generalization and long-term working memory.

The 21-cm background is a promising probe of early star formation and black hole activity. While a slew of experiments on the ground seek to detect the 21-cm monopole and spatial fluctuations on large $\sim 10$ arcminute scales, little work has been done on the prospects for detecting the 21-cm dipole signal or its utility as a probe of early galaxies. Though an intrinsically weak signal relative to the monopole, its direction is known well from the cosmic microwave background and wide-field surveys, plus as a relative measurement the dipole could help relax instrumental requirements. In order to understand the constraining power of the dipole, in this work we perform parameter inference on mock datasets that include the dipole, monopole, or both signals. We find that while the monopole does provide the best constraints for a given integration time, constraints from a dipole measurement are competitive, and can in principle constrain the cosmic star formation rate density and efficiency of X-ray photon production in early $z \sim 15$ galaxies to better than a factor of $\sim 2$. This result holds for most of the available prior volume, which is set by constraints on galaxy luminosity functions, the reionization history, and upper limits from 21-cm power spectrum experiments. We also find that predictions for the monopole from a dipole measurement are robust to different choices of signal model. As a result, the 21-cm dipole signal is a valuable target for future observations and offers a robust cross-check on monopole measurements., Comment: 17 pages, 10 figures, submitted to ApJ

In this paper, we study the existence of a solution for a class of Dirichlet problems with a singularity and a convection term. Precisely, we consider the existence of a positive solution to the Dirichlet problem $$-\Delta_p u = \frac{\lambda}{u^{\alpha}} + f(x,u,\nabla u)$$ in a bounded, smooth domain $\Omega$. The convection term has exponents with no upper limitations neither in $u$ nor in $\nabla u$. This is somewhat unexpected and rare. So, we address a wide range of problems not yet contained in the literature. The solution of the problem combines the definition of an auxiliary problem, the method of sub- and super-solution and Schauder's fixed point theorem., Comment: 17 pages

We present a velocity-resolved reverberation mapping analysis of the hypervariable quasar RM160 (SDSS J141041.25+531849.0) at z = 0.359 with 153 spectroscopic epochs of data representing a ten-year baseline (2013-2023). We split the baseline into two regimes based on the 3x flux increase in the light curve: a 'low state' phase during the years 2013-2019 and a 'high state' phase during the years 2022-2023. The velocity-resolved lag profiles (VRLP) indicate that gas with different kinematics dominates the line emission in different states. The H\b{eta} VRLP begins with a signature of inflow onto the BLR in the 'low state', while in the 'high state' it is flatter with less signature of inflow. The H{\alpha} VRLP begins consistent with a virialized BLR in the 'low state', while in the 'high state' shows a signature of inflow. The differences in the kinematics between the Balmer lines and between the 'low state' and the 'high state' suggests complex BLR dynamics. We find that the BLR radius and velocity (both FWHM and {\sigma}) do not obey a constant virial product throughout the monitoring period. We find that BLR lags and continuum luminosity are correlated, consistent with rapid response of the BLR gas to the illuminating continuum. The BLR kinematic profile changes in unpredictable ways that are not related to continuum changes and reverberation lag. Our observations indicate that non-virial kinematics can significantly contribute to observed line profiles, suggesting caution for black-hole mass estimation in luminous and highly varying quasars like RM160., Comment: 23 pages, 17 figures

Sequential models such as recurrent neural networks or transformer-based models became \textit{de facto} tools for multivariate time series forecasting in a probabilistic fashion, with applications to a wide range of datasets, such as finance, biology, medicine, etc. Despite their adeptness in capturing dependencies, assessing prediction uncertainty, and efficiency in training, challenges emerge in modeling high-dimensional complex distributions and cross-feature dependencies. To tackle these issues, recent works delve into generative modeling by employing diffusion or flow-based models. Notably, the integration of stochastic differential equations or probability flow successfully extends these methods to probabilistic time series imputation and forecasting. However, scalability issues necessitate a computational-friendly framework for large-scale generative model-based predictions. This work proposes a novel approach by blending the computational efficiency of recurrent neural networks with the high-quality probabilistic modeling of the diffusion model, which addresses challenges and advances generative models' application in time series forecasting. Our method relies on the foundation of stochastic interpolants and the extension to a broader conditional generation framework with additional control features, offering insights for future developments in this dynamic field.

This work introduces a new solution-transfer process for slab-based space-time finite element methods. The new transfer process is based on Hsieh-Clough-Tocher (HCT) splines and satisfies the following requirements: (i) it maintains high-order accuracy up to 4th order, (ii) it preserves a discrete maximum principle, (iii) it asymptotically enforces mass conservation, and (iv) it constructs a smooth, continuous surrogate solution in between space-time slabs. While many existing transfer methods meet the first three requirements, the fourth requirement is crucial for enabling visualization and boundary condition enforcement for space-time applications. In this paper, we derive an error bound for our HCT spline-based transfer process. Additionally, we conduct numerical experiments quantifying the conservative nature and order of accuracy of the transfer process. Lastly, we present a qualitative evaluation of the visualization properties of the smooth surrogate solution., Comment: 36 pages, 17 figures, 3 tables

We developed a jitted compiler for training Artificial Neural Networks using C++, LLVM and Cuda. It features object-oriented characteristics, strong typing, parallel workers for data pre-processing, pythonic syntax for expressions, PyTorch like model declaration and Automatic Differentiation. We implement the mechanisms of cache and pooling in order to manage VRAM, cuBLAS for high performance matrix multiplication and cuDNN for convolutional layers. Our experiments with Residual Convolutional Neural Networks on ImageNet, we reach similar speed but degraded performance. Also, the GRU network experiments show similar accuracy, but our compiler have degraded speed in that task. However, our compiler demonstrates promising results at the CIFAR-10 benchmark, in which we reach the same performance and about the same speed as PyTorch. We make the code publicly available at: https://github.com/NoSavedDATA/NoSavedKaleidoscope, Comment: 12 pages, 3 figures and 3 tables

This paper demonstrates that a single-layer neural network using Parametric Rectified Linear Unit (PReLU) activation can solve the XOR problem, a simple fact that has been overlooked so far. We compare this solution to the multi-layer perceptron (MLP) and the Growing Cosine Unit (GCU) activation function and explain why PReLU enables this capability. Our results show that the single-layer PReLU network can achieve 100\% success rate in a wider range of learning rates while using only three learnable parameters.

HPC systems used for research run a wide variety of software and workflows. This software is often written or modified by users to meet the needs of their research projects, and rarely is built with security in mind. In this paper we explore several of the key techniques that MIT Lincoln Laboratory Supercomputing Center has deployed on its systems to manage the security implications of these workflows by providing enforced separation for processes, filesystem access, network traffic, and accelerators to make every user feel like they are running on a personal HPC.

Over the Nullarbor Plain in South Australia, the Desert Fireball Network detected a fireball on the night of 1 June 2019 (7:30 pm local time), and six weeks later recovered a single meteorite (42 g) named Arpu Kuilpu. This meteorite was then distributed to a consortium of collaborating institutions to be measured and analyzed by a number of methodologies including: SEM-EDS, EPMA, ICP-MS, gamma-ray spectrometry, ideal gas pycnometry, magnetic susceptibility measurement, {\mu}CT, optical microscopy, and accelerator and noble gas mass spectrometry techniques. These analyses revealed that Arpu Kuilpu is an unbrecciated H5 ordinary chondrite, with minimal weathering (W0-1) and minimal shock (S2). The olivine and pyroxene mineral compositions (in mol%) are Fa: 19.2 +- 0.2, and Fs: 16.8 +- 0.2, further supporting the H5 type and class. The measured oxygen isotopes are also consistent with an H chondrite ({\delta}17O = 2.904 +- 0.177; {\delta}18O = 4.163 +- 0.336; {\Delta}17O = 0.740 +- 0.002). Ideal gas pycnometry measured bulk and grain densities of 3.66 +- 0.02 and 3.77 +- 0.02 g cm-3, respectively, yielding a porosity of 3.0 % +- 0.7. The magnetic susceptibility of this meteorite is log X = 5.16 +- 0.08. The most recent impact-related heating event experienced by Arpu Kuilpu was measured by 40Ar/39Ar chronology to be 4467 +- 16 Ma, while the cosmic ray exposure age is estimated to be between 6-8 Ma. The noble gas isotopes, radionuclides, and fireball observations all indicate that Arpu Kuilpu's meteoroid was quite small (maximum radius of 10 cm, though more likely between 1-5 cm). Although this meteorite is a rather ordinary ordinary chondrite, its prior orbit resembled that of a Jupiter Family Comet (JFC) further lending support to the assertion that many cm- to m-sized objects on JFC orbits are asteroidal rather than cometary in origin.

We present the first results from a new backend on the Australian Square Kilometre Array Pathfinder, the Commensal Realtime ASKAP Fast Transient COherent (CRACO) upgrade. CRACO records millisecond time resolution visibility data, and searches for dispersed fast transient signals including fast radio bursts (FRB), pulsars, and ultra-long period objects (ULPO). With the visibility data, CRACO can localise the transient events to arcsecond-level precision after the detection. Here, we describe the CRACO system and report the result from a sky survey carried out by CRACO at 110ms resolution during its commissioning phase. During the survey, CRACO detected two FRBs (including one discovered solely with CRACO, FRB 20231027A), reported more precise localisations for four pulsars, discovered two new RRATs, and detected one known ULPO, GPM J1839-10, through its sub-pulse structure. We present a sensitivity calibration of CRACO, finding that it achieves the expected sensitivity of 11.6 Jy ms to bursts of 110 ms duration or less. CRACO is currently running at a 13.8 ms time resolution and aims at a 1.7 ms time resolution before the end of 2024. The planned CRACO has an expected sensitivity of 1.5 Jy ms to bursts of 1.7 ms duration or less, and can detect 10x more FRBs than the current CRAFT incoherent sum system (i.e., 0.5-2 localised FRBs per day), enabling us to better constrain the FRB emission mechanism model and use them as cosmological probes., Comment: 26 pages, 19 figures, 9 tables, Submitted for publication in PASA

High-precision pulsar timing is highly dependent on precise and accurate modeling of any effects that impact the data. It was shown that commonly used Solar Wind models do not accurately account for variability in the amplitude of the Solar wind on both short and long time scales. In this study, we test and validate a new, cutting-edge Solar wind modeling method included in the \texttt{enterprise} software suite through extended simulations, and we apply it to investigate temporal variability in LOFAR data. Our model testing scheme in itself provides an invaluable asset for pulsar timing array (PTA) experiments. As improperly accounting for the solar wind signature in pulsar data can induce false-positive signals, it is of fundamental importance to include in any such investigations. We employ a Bayesian approach utilizing a continuously varying Gaussian process to model the solar wind referred to as Solar Wind Gaussian Process (SWGP). We conduct noise analysis on eight pulsars from the LOFAR dataset with most pulsars having a timespan of $\sim 11$ years encompassing one full solar activity cycle. Our analysis reveals a strong correlation between the electron density at 1 AU and the ecliptic latitude (ELAT) of the pulsar. Pulsars with $|ELAT|< 3^{\circ}$ exhibit significantly higher average electron densities. We observe distinct temporal patterns in electron densities in different pulsars. In particular, pulsars within $|ELAT|< 3^{\circ}$ exhibit similar temporal variations, while the electron densities of those outside this range correlate with the solar activity cycle. The continuous variability in electron density offered in this model represents a substantial improvement over previous models, which assume a single value for piece-wise bins of time. This advancement holds promise for solar wind modeling in future International Pulsar Timing Array data combinations., Comment: Accepted in Section 9. Sun and the Heliosphere, Astronomy and Astrophysics

This paper provides a broad, multi-disciplinary overview of key insights, persistent gaps, and future paths in youth digital well-being research from the perspectives of researchers who are conducting this work., Comment: Journal of Adolescent Research

We present the radio afterglow of short gamma-ray burst (GRB) 230217A, which was detected less than 1 day after the gamma-ray prompt emission with the Australia Telescope Compact Array (ATCA) and the Karl G. Jansky Very Large Array (VLA). The ATCA rapid-response system automatically triggered an observation of GRB 230217A following its detection by the Neil Gehrels Swift Observatory and began observing the event just 32 minutes post-burst at 5.5 and 9 GHz for 7 hours. Dividing the 7-hour observation into three time-binned images allowed us to obtain radio detections with logarithmic central times of 1, 2.8 and 5.2 hours post-burst, the first of which represents the earliest radio detection of any GRB to date. The decline of the light curve is consistent with reverse shock emission if the observing bands are below the spectral peak and not affected by synchrotron self-absorption. This makes GRB 230217A the fifth short GRB with radio detections attributed to a reverse shock at early times ($<1$ day post-burst). Following brightness temperature arguments, we have used our early radio detections to place the highest minimum Lorentz factor (${\Gamma}_{min} > 50$ at $\sim1$ hour) constraints on a GRB in the radio band. Our results demonstrate the importance of rapid radio follow-up observations with long integrations and good sensitivity for detecting the fast-evolving radio emission from short GRBs and probing their reverse shocks., Comment: 11 pages, 3 figures, submitted to ApJL

Classical Cepheid variable stars provide a unique probe to binary evolution in intermediate-mass stars over the course of several tens to hundreds of Myr. We studied the binary and multiple properties of Cepheids, assuming that all mid-B stars form in binaries inside star clusters. The binaries were subjected both to stellar evolution and dynamical encounters with other stars in the cluster. The dynamical cluster environment results in a higher binary fraction among the Cepheids that remain in star clusters ($\approx 60$%) than among the Cepheids which have escaped to the field ($\approx 35$%). In clusters, the binary, triple, and multiple fraction decreases with increasing cluster mass. More massive clusters have binaries of shorter orbital periods than lower mass clusters and field Cepheids. Mergers are very common with $\approx 30$% of mid-B stars not evolving to Cepheids because of the interaction with their companion. Approximately $40$ % of Cepheids have merged with their companion, and the merger event impacts stellar evolution; the age of Cepheids expected from their mass can differ from the age of their host cluster. Our models predict that one in five Cepheids is the result of a merger between stars with mass below the lower mass limit for Cepheids; in clusters, these objects occur substantially later than expected from their mass. Approximately $3$ to $5$ % of all Cepheids have a compact companion ($\approx 0.15$ % of all Cepheids are accompanied by a black hole). The binary fraction derived from our simulations (42%) underestimates the observed binary Cepheid fraction by approximately a factor of 2. This suggests that the true multiplicity fraction of B-stars at birth could be substantially larger than unity and, thus, that mid-B stars may typically form in triple and higher order systems., Comment: 23 pages, 14 figures, 3 tables. Accepted for publication in A&A

We present observations of the two closest globular clusters, NGC 6121 and NGC 6397, taken with the NIRISS detector of JWST. The combination of our new JWST data with archival Hubble Space Telescope (HST) images allows us to compute proper motions, disentangle cluster members from field objects, and probe the main sequence (MS) of the clusters down to <0.1 $M_\odot$ as well as the brighter part of the white-dwarf sequence. We show that theoretical isochrones fall short in modeling the low-mass MS and discuss possible explanations for the observed discrepancies. Our analysis suggests that the lowest-mass members of both clusters are significantly more metal-rich and oxygen-poor than their higher-mass counterparts. It is unclear whether the difference is caused by a genuine mass-dependent chemical heterogeneity, low-temperature atmospheric processes altering the observed abundances, or systematic shortcomings in the models. We computed the present-day local luminosity and mass functions of the two clusters; our data reveal a strong flattening of the mass function indicative of a significant preferential loss of low-mass stars in agreement with previous dynamical models for these two clusters. We have made our NIRISS astro-photometric catalogs and stacked images publicly available to the community., Comment: 15 pages, 12 figures, 2 tables. Accepted for publication in A&A on September 04, 2024. Astro-photometric catalogs and stacked images will be available at the CDS after the paper will be published

Context. Supernova remnants (SNRs) are the late stages of supernovae before their merging into the surrounding medium. Oxygen-rich supernova remnants represent a rare subtype with strong visible light oxygen emission. Aims. We present a new method to detect SNRs exploiting the capabilities of modern visible-light integral-field units based on the shapes of the SNR emission lines. Methods. We search for unresolved shocked regions with broadened emission lines using the medium-resolution integral-field spectrograph MUSE on the Very Large Telescope. The spectral resolving power allows shocked emission sources to be differentiated from photoionised sources based on the linewidths. Results. We find 307 supernova remnants, including seven O-rich SNRs. For all O-rich SNRs, we observe the [O III]{\lambda}{\lambda}4959,5007 emission doublet. In addition, we observe emissions from [O I]{\lambda}{\lambda}6300,6364, [O II]{\lambda}{\lambda}7320,7330, H{\alpha}+[N II]{\lambda}6583 and [S II]{\lambda}{\lambda}6717,6731 to varying degrees. The linewidths for the O-rich SNRs are generally broader than the rest of the SNRs in the sample of this article. The oxygen emission complexes are reminiscient of SNR 4449-1 and some long-lasting SNe. For the O-rich SNRs, we also search for counterparts in archival data of other telescopes; we detect X-ray and mid-IR counterparts for a number of remnants. Conclusions. We have shown efficacy of the method to detect SNRs presented in this article. In addition, the method is also effective in detecting the rare O-rich SNRs, doubling the sample size in the literature. The origin of O-rich SNRs and their link to specific SN types or environments is still unclear, but further work into this new sample will unquestionably help us shed light on these rare remnants., Comment: 36 pages, 46 figures

Building type information is crucial for population estimation, traffic planning, urban planning, and emergency response applications. Although essential, such data is often not readily available. To alleviate this problem, this work creates a comprehensive dataset by providing residential/non-residential building classification covering the entire United States. We propose and utilize an unsupervised machine learning method to classify building types based on building footprints and available OpenStreetMap information. The classification result is validated using authoritative ground truth data for select counties in the U.S. The validation shows a high precision for non-residential building classification and a high recall for residential buildings. We identified various approaches to improving the quality of the classification, such as removing sheds and garages from the dataset. Furthermore, analyzing the misclassifications revealed that they are mainly due to missing and scarce metadata in OSM. A major result of this work is the resulting dataset of classifying 67,705,475 buildings. We hope that this data is of value to the scientific community, including urban and transportation planners.

Teleoperation of humanoid robots has long been a challenging domain, necessitating advances in both hardware and software to achieve seamless and intuitive control. This paper presents an integrated solution based on several elements: calibration-free motion capture and retargeting, low-latency fast whole-body kinematics streaming toolbox and high-bandwidth cycloidal actuators. Our motion retargeting approach stands out for its simplicity, requiring only 7 IMUs to generate full-body references for the robot. The kinematics streaming toolbox, ensures real-time, responsive control of the robot's movements, significantly reducing latency and enhancing operational efficiency. Additionally, the use of cycloidal actuators makes it possible to withstand high speeds and impacts with the environment. Together, these approaches contribute to a teleoperation framework that offers unprecedented performance. Experimental results on the humanoid robot Nadia demonstrate the effectiveness of the integrated system.

We present a study of the white dwarf (WD) cooling sequence (CS) in the globular cluster (GC) Omega Centauri, the primary goal of a dedicated Hubble Space Telescope (HST) programme. Our analysis has revealed that the peak at the termination of the WD CS is located at $m_{\rm F606W}$=30.1$\pm$0.2 (equivalent to $V$$\sim$31). The brighter part of Omega Centauri's WD CS is consistent with the presence of massive He-core WDs, in agreement with previous HST analyses with ultraviolet and blue filters. Comparative analyses of the WD luminosity function (LF) with theoretical counterparts have shown that a single-age population for the cluster is compatible with the data. However, an analysis of just the WD LF cannot entirely exclude the possibility of an age range, due to uncertainties in the present-day WD mass function, with a star formation history potentially spanning up to 5 billion years, predominantly comprising stars about 13 Gyr old, and with just a minority potentially as young as 8 Gyr. This underscores the need for global spectroscopic and photometric investigations that include simultaneously the WD populations together with the previous evolutionary phases to fully understand the cluster's diverse chemical compositions and ages., Comment: 10 pages, 9 figures. Accepted for publication in Astronomy & Astrophysics on Sep 5, 2024

The EXperiment for Cryogenic Large-Aperture Intensity Mapping (EXCLAIM) is a balloon-borne telescope designed to survey star formation over cosmological time scales using intensity mapping in the 420 - 540 GHz frequency range. EXCLAIM uses a fully cryogenic telescope coupled to six on-chip spectrometers featuring kinetic inductance detectors (KIDs) to achieve high sensitivity, allowing for fast integration in dark atmospheric windows. The telescope receiver is cooled to $\approx$ 1.7 K by immersion in a superfluid helium bath and enclosed in a superfluid-tight shell with a meta-material anti-reflection coated silicon window. In addition to the optics and the spectrometer package, the receiver contains the magnetic shielding, the cryogenic segment of the spectrometer readout, and the sub-Kelvin cooling system. A three-stage continuous adiabatic demagnetization refrigerator (CADR) keeps the detectors at 100 mK while a $^4$He sorption cooler provides a 900 mK thermal intercept for mechanical suspensions and coaxial cables. We present the design of the EXCLAIM receiver and report on the flight-like testing of major receiver components, including the superfluid-tight receiver window and the sub-Kelvin coolers., Comment: 13 pages, 7 figures, 1 table

Physics Informed Neural Networks (PINNs) offer several advantages when compared to traditional numerical methods for solving PDEs, such as being a mesh-free approach and being easily extendable to solving inverse problems. One promising approach for allowing PINNs to scale to multi-scale problems is to combine them with domain decomposition; for example, finite basis physics-informed neural networks (FBPINNs) replace the global PINN network with many localised networks which are summed together to approximate the solution. In this work, we significantly accelerate the training of FBPINNs by linearising their underlying optimisation problem. We achieve this by employing extreme learning machines (ELMs) as their subdomain networks and showing that this turns the FBPINN optimisation problem into one of solving a linear system or least-squares problem. We test our workflow in a preliminary fashion by using it to solve an illustrative 1D problem.

LLMs demonstrate an uncanny ability to process unstructured data, and as such, have the potential to go beyond search and run complex, semantic analyses at scale. We describe the design of an unstructured analytics system, Aryn, and the tenets and use cases that motivate its design. With Aryn, users can specify queries in natural language and the system automatically determines a semantic plan and executes it to compute an answer from a large collection of unstructured documents using LLMs. At the core of Aryn is Sycamore, a declarative document processing engine, built using Ray, that provides a reliable distributed abstraction called DocSets. Sycamore allows users to analyze, enrich, and transform complex documents at scale. Aryn also comprises Luna, a query planner that translates natural language queries to Sycamore scripts, and the Aryn Partitioner, which takes raw PDFs and document images, and converts them to DocSets for downstream processing. Using Aryn, we demonstrate a real world use case for analyzing accident reports from the National Transportation Safety Board (NTSB), and discuss some of the major challenges we encountered in deploying Aryn in the wild., Comment: 6 pages, 3 figures, fixed typos

The transport of macromolecules, such as DNA, through the cytoskeleton is critical to wide-ranging cellular processes from cytoplasmic streaming to transcription. The rigidity and steric hindrances imparted by the network of filaments comprising the cytoskeleton often leads to anomalous subdiffusion, while active processes such as motor-driven restructuring can induce athermal superdiffusion. Understanding the interplay between these seemingly antagonistic contributions to intracellular dynamics remains a grand challenge. Here, we use single-molecule tracking to show that the transport of large linear and circular DNA through motor-driven microtubule networks can be non-gaussian and multi-modal, with the degree and spatiotemporal scales over which these features manifest depending non-trivially on the state of activity and DNA topology. For example, active network restructuring increases caging and non-Gaussian transport modes of linear DNA, while dampening these mechanisms for rings. We further discover that circular DNA molecules exhibit either markedly enhanced subdiffusion or superdiffusion compared to their linear counterparts, in the absence or presence of kinesin activity, indicative of microtubules threading circular DNA. This strong coupling leads to both stalling and directed transport, providing a direct route towards parsing distinct contributions to transport and determining the impact of coupling on the transport signatures. More generally, leveraging macromolecular topology as a route to programming molecular interactions and transport dynamics is an elegant yet largely overlooked mechanism that cells may exploit for intracellular trafficking, streaming, and compartmentalization. This mechanism could be harnessed for the design of self-regulating, sensing, and reconfigurable biomimetic matter., Comment: 26 pages, 5 Figures and 5 SI Figures

The broad physics reach of the LUX-ZEPLIN (LZ) experiment covers rare phenomena beyond the direct detection of dark matter. We report precise measurements of the extremely rare decay of $^{124}$Xe through the process of two-neutrino double electron capture (2$\nu$2EC), utilizing a $1.39\,\mathrm{kg} \times \mathrm{yr}$ isotopic exposure from the first LZ science run. A half-life of $T_{1/2}^{2\nu2\mathrm{EC}} = (1.09 \pm 0.14_{\text{stat}} \pm 0.05_{\text{sys}}) \times 10^{22}\,\mathrm{yr}$ is observed with a statistical significance of $8.3\,\sigma$, in agreement with literature. First empirical measurements of the KK capture fraction relative to other K-shell modes were conducted, and demonstrate consistency with respect to recent signal models at the $1.4\,\sigma$ level., Comment: 15 pages, 3 figures

We present radio observations of the long-duration gamma-ray burst (GRB) 221009A which has become known to the community as the Brightest Of All Time or the BOAT. Our observations span the first 475 days post-burst and three orders of magnitude in observing frequency, from 0.15 to 230GHz. By combining our new observations with those available in the literature, we have the most detailed radio data set in terms of cadence and spectral coverage of any GRB to date, which we use to explore the spectral and temporal evolution of the afterglow. By testing a series of phenomenological models, we find that three separate synchrotron components best explain the afterglow. The high temporal and spectral resolution allows us to conclude that standard analytical afterglow models are unable to explain the observed evolution of GRB 221009A. We explore where the discrepancies between the observations and the models are most significant and place our findings in the context of the most well-studied GRB radio afterglows to date. Our observations are best explained by three synchrotron emitting regions which we interpret as a forward shock, a reverse shock and an additional shock potentially from a cocoon or wider outflow. Finally, we find that our observations do not show any evidence of any late-time spectral or temporal changes that could result from a jet break but note that any lateral structure could significantly affect a jet break signature., Comment: 15 pages, 8 figures. Accepted to MNRAS

In this work we explore the possible scenario of strange stars admixed with fermionic or bosonic dark matter. For the description of the ``visible'' sector, we use a specific phenomenological quark model that takes into account in-medium effects for the quark masses through a suitable baryonic density dependence, in which the free parameters are chosen from the analysis of the stellar matter stability window (parametrizations presenting lower energy per baryon than iron nuclei). In the case of the dark sector, we investigate the predictions of fermionic and bosonic models. In the former we consider a spin $1/2$ dark particle, and the latter is described by a dark scalar meson. Both models present a repulsive vector interaction, particularly important for the bosonic model since it helps avoiding the collapse of the star due to the lack of the degeneracy pressure. Our results point out to possible stable strange stars configurations in agreement with data from PSR J0030+0451, PSR J0740+6620, NICER and HESS J1731-347. As another feature, we find stars with dark matter halo configurations for lower values of the dark particle mass., Comment: More references added. Some figures updated

Revisiting Kra\'skiewicz and Pragacz's construction of Schubert modules, we provide a new proof that their characters are equal to Schubert polynomials. The main innovation is a representation-theoretic interpretation of a recurrence relation for Schubert polynomials recently discovered by Nadeau, Spink, and Tewari. Along the way, we review several related constructions, and show that the Nadeau-Spink-Tewari recursion determines the characters of flagged Schur modules coming from a broader class of translucent diagrams., Comment: 37 pages, many figures

Foundation models have emerged as robust models with label efficiency in diverse domains. In medical imaging, these models contribute to the advancement of medical diagnoses due to the difficulty in obtaining labeled data. However, it is unclear whether using a large amount of unlabeled data, biased by the presence of sensitive attributes during pre-training, influences the fairness of the model. This research examines the bias in the Foundation model (RetFound) when it is applied to fine-tune the Brazilian Multilabel Ophthalmological Dataset (BRSET), which has a different population than the pre-training dataset. The model evaluation, in comparison with supervised learning, shows that the Foundation Model has the potential to reduce the gap between the maximum AUC and minimum AUC evaluations across gender and age groups. However, in a data-efficient generalization, the model increases the bias when the data amount decreases. These findings suggest that when deploying a Foundation Model in real-life scenarios with limited data, the possibility of fairness issues should be considered., Comment: Preprint of paper to be presented at Fairness and Ethics Towards Transparent AI: Facing the Challenge through Model Debiasing (FAILED) during ECCV 2024

Several systems have been developed to extract information about characters to aid computational analysis of English literature. We propose character similarity grouping as a holistic evaluation task for these pipelines. We present AustenAlike, a benchmark suite of character similarities in Jane Austen's novels. Our benchmark draws on three notions of character similarity: a structurally defined notion of similarity; a socially defined notion of similarity; and an expert defined set extracted from literary criticism. We use AustenAlike to evaluate character features extracted using two pipelines, BookNLP and FanfictionNLP. We build character representations from four kinds of features and compare them to the three AustenAlike benchmarks and to GPT-4 similarity rankings. We find that though computational representations capture some broad similarities based on shared social and narrative roles, the expert pairings in our third benchmark are challenging for all systems, highlighting the subtler aspects of similarity noted by human readers.

Shannon entropy, as a chaos indicator, is used for online Poincar\'e map regularization and dynamic aperture optimization in the National Synchrotron Light Source-II (NSLS-II) ring. Although various chaos indicators are widely used in studying nonlinear dynamical systems, including modern particle accelerators, it is the first time to use a measurable one in a real-world machine for online nonlinear optimization. Poincar\'e maps, constructed with the turn-by-turn beam trajectory readings from beam position monitors, are commonly used to observe the nonlinearity in ring-based accelerators. However, such observations typically only provide a qualitative interpretation. We analyze their entropy to quantify the chaos in measured Poincar\'e maps. After some canonical transformations on the Poincar\'e maps, not only can the commonly used nonlinear characterizations be extracted, but more importantly, the chaos can be quantitatively calibrated with Shannon entropy, and then used as the online optimization objectives., Comment: 6 pages, 6 figures

Recent advances in AI-powered image editing tools have significantly lowered the barrier to image modification, raising pressing security concerns those related to spreading misinformation and disinformation on social platforms. Image provenance analysis is crucial in this context, as it identifies relevant images within a database and constructs a relationship graph by mining hidden manipulation and transformation cues, thereby providing concrete evidence chains. This paper introduces a novel end-to-end deep learning framework designed to explore the structural information of provenance graphs. Our proposed method distinguishes from previous approaches in two main ways. First, unlike earlier methods that rely on prior knowledge and have limited generalizability, our framework relies upon a patch attention mechanism to capture image provenance clues for local manipulations and global transformations, thereby enhancing graph construction performance. Second, while previous methods primarily focus on identifying tampering traces only between image pairs, they often overlook the hidden information embedded in the topology of the provenance graph. Our approach aligns the model training objectives with the final graph construction task, incorporating the overall structural information of the graph into the training process. We integrate graph structure information with the attention mechanism, enabling precise determination of the direction of transformation. Experimental results show the superiority of the proposed method over previous approaches, underscoring its effectiveness in addressing the challenges of image provenance analysis., Comment: 13 pages, 10 figures