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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.