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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.

The observation of neutrino oscillations has established that neutrinos have non-zero masses. This phenomenon is not explained by the Standard Model of particle physics, but one viable explanation to this dilemma involves the existence of heavy neutral leptons in the form of right-handed neutrinos. This work presents the first search for heavy neutral leptons with the IceCube Neutrino Observatory. The standard three flavor neutrino model is extended by adding a fourth GeV-scale mass state allowing mixing with the $\tau$ sector through the parameter $|U_{\tau4}|^2$. The analysis is performed by searching for signatures of heavy neutral leptons that are directly produced via up-scattering of atmospheric $\nu_\tau$'s inside the IceCube detection volume. Three heavy neutral lepton mass values, $m_4$, of 0.3 GeV, 0.6 GeV, and 1.0 GeV are tested using ten years of data, collected between 2011 and 2021. No significant signal of heavy neutral leptons is observed for any of the tested masses. The resulting constraints for the mixing parameter are $|U_{\tau4}|^2 < 0.19$ ($m_4 = 0.3$ GeV), $|U_{\tau4}|^2 < 0.36$ ($m_4 = 0.6$ GeV), and $|U_{\tau4}|^2 < 0.40$ ($m_4 = 1.0$ GeV) at the 90% confidence level. This analysis serves as proof-of-concept for heavy neutral lepton searches in IceCube. The heavy neutral lepton event generator, developed in this work, and the analysis of the expected signatures lay the fundamental groundwork for future searches thereof.

This work explores the impacts of magnetogram projection effects on machine learning-based solar flare forecasting models. Utilizing a methodology proposed by Falconer et al. (2016), we correct for projection effects present in Georgia State University's Space Weather Analytics for Solar Flares (SWAN-SF) benchmark data set. We then train and run a support vector machine classifier on the corrected and uncorrected data, comparing differences in performance. Additionally, we provide insight into several other methodologies that mitigate projection effects, such as stacking ensemble classifiers and active region location-informed models. Our analysis shows that data corrections slightly increase both the true positive (correctly predicted flaring samples) and false positive (non-flaring samples predicted as flaring) prediction rates, averaging a few percent. Similarly, changes in performance metrics are minimal for the stacking ensemble and location-based model. This suggests that a more complicated correction methodology may be needed to see improvements. It may also indicate inherent limitations when using magnetogram data for flare forecasting., Comment: Accepted for publication in ApJ; 22 pages, 9 figures, 4 tables

Large language models (LLMs) show promise for health applications when combined with behavioral sensing data. Traditional approaches convert sensor data into text prompts, but this process is prone to errors, computationally expensive, and requires domain expertise. These challenges are particularly acute when processing extended time series data. While time series foundation models (TFMs) have recently emerged as powerful tools for learning representations from temporal data, bridging TFMs and LLMs remains challenging. Here, we present Time2Lang, a framework that directly maps TFM outputs to LLM representations without intermediate text conversion. Our approach first trains on synthetic data using periodicity prediction as a pretext task, followed by evaluation on mental health classification tasks. We validate Time2Lang on two longitudinal wearable and mobile sensing datasets: daily depression prediction using step count data (17,251 days from 256 participants) and flourishing classification based on conversation duration (46 participants over 10 weeks). Time2Lang maintains near constant inference times regardless of input length, unlike traditional prompting methods. The generated embeddings preserve essential time-series characteristics such as auto-correlation. Our results demonstrate that TFMs and LLMs can be effectively integrated while minimizing information loss and enabling performance transfer across these distinct modeling paradigms. To our knowledge, we are the first to integrate a TFM and an LLM for health, thus establishing a foundation for future research combining general-purpose large models for complex healthcare tasks., Comment: 17 pages, 7 figures

While the sources of the diffuse astrophysical neutrino flux detected by the IceCube Neutrino Observatory are still largely unknown, one of the promising methods used towards understanding this is investigating the potential temporal and spatial correlations between neutrino alerts and the electromagnetic radiation from blazars. We report on the multiwavelength target-of-opportunity observations of the blazar B3 2247+381, taken in response to an IceCube multiplet alert for a cluster of muon neutrino events compatible with the source location between May 20, 2022 and November 10, 2022. B3 2247+381 was not detected with VERITAS during this time period. The source was found to be in a low-flux state in the optical, ultraviolet and gamma-ray bands for the time interval corresponding to the neutrino event, but was detected in the hard X-ray band with NuSTAR during this period. We find the multiwavelength spectral energy distribution is well described using a simple one-zone leptonic synchrotron self-Compton radiation model. Moreover, assuming the neutrinos originate from hadronic processes within the jet, the neutrino flux would be accompanied by a photon flux from the cascade emission, and the integrated photon flux required in such a case would significantly exceed the total multiwavelength fluxes and the VERITAS upper limits presented here. The lack of flaring activity observed with VERITAS, combined with the low multiwavelength flux levels, and given the significance of the neutrino excess is at 3$\sigma$ level (uncorrected for trials), makes B3 2247+381 an unlikely source of the IceCube multiplet. We conclude that the neutrino excess is likely a background fluctuation., Comment: 26 pages, 5 figures. Accepted for publication in the Astrophysical Journal (ApJ)

We revisit and elucidate the $\widehat{A}$-genus, Hirzebruch's $L$-genus and Witten's $W$-genus, cobordism invariants of special classes of manifolds. After slight modification, involving Hecke's trick, we find that the $\widehat{A}$-genus and $L$-genus arise directly from Jacobi's theta function. In this way, for every $k\geq 0,$ we obtain exact formulas for the quasimodular expressions of $\widehat{A}_k$ and $L_k$ as ``traces'' of partition Eisenstein series \[\widehat{\mathcal{A}}_k(\tau)= \operatorname{Tr}_k(\phi_{\widehat{A}};\tau)\ \ \ \ \ \ {\text {\rm and}}\ \ \ \ \ \ \mathcal{L}_k(\tau)= \operatorname{Tr}_k(\phi_L;\tau). \] Surprisingly, Ramanujan defined twists of the $\widehat{\mathcal{A}}_k(\tau)$ in his ``lost notebook'' in his study of derivatives of theta functions, decades before Borel and Hirzebruch rediscovered them in the context of spin manifolds. In addition, we show that the nonholomorphic $G_2^{\star}$-completion of the characteristic series of the Witten genus is the Jacobi theta function avatar of the $\widehat{A}$-genus.

We present a search for the diffuse extremely-high-energy neutrino flux using $12.6$ years of IceCube data. The non-observation of neutrinos with energies well above $10 \, \mathrm{PeV}$ constrains the all-flavor neutrino flux at $10^{18} \, \mathrm{eV}$ to a level of $E^2 \Phi_{\nu_e + \nu_\mu + \nu_\tau} \simeq 10^{-8} \, \mathrm{GeV} \, \mathrm{cm}^{-2} \, \mathrm{s}^{-1} \, \mathrm{sr}^{-1}$, the most stringent limit to date. Using this data, we constrain the proton fraction of ultra-high-energy cosmic rays (UHECRs) above $\simeq 30 \, \mathrm{EeV}$ to be $\lesssim 70\,$% (at $90\,$% CL) if the cosmological evolution of the sources is comparable to or stronger than the star formation rate. This result complements direct air-shower measurements by being insensitive to uncertainties associated with hadronic interaction models. It is the first such result to disfavor the ``proton-only" hypothesis for UHECRs using neutrino data.

We introduce a family of algebraic structures as a higher-dimensional generalization of vertex algebras, which we term cohomological vertex algebras. The formal punctured 1-disk underlying a vertex algebra is replaced by a ring modeling the cohomology of certain schemes such as the formal punctured $n$-disk and the formal 1-disk with doubled origin. The latter scheme leads to the raviolo vertex algebras of Garner & Williams. We prove several structural theorems for cohomological vertex algebras, as well as examine an appropriate Witt-like algebra and its central extensions, giving a definition of a cohomological vertex operator algebra. Using a reconstruction theorem for cohomological vertex algebras, we provide basic examples analogous to their vertex algebra counterparts.

As children increasingly consume media on devices, parents look for ways this usage can support learning and growth, especially in domains like social-emotional learning. We introduce eaSEL, a system that (a) integrates social-emotional learning (SEL) curricula into children's video consumption by generating reflection activities and (b) facilitates parent-child discussions around digital media without requiring co-consumption of videos. We present a technical evaluation of our system's ability to detect social-emotional moments within a transcript and to generate high-quality SEL-based activities for both children and parents. Through a user study with N=20 parent-child dyads, we find that after completing an eaSEL activity, children reflect more on the emotional content of videos. Furthermore, parents find that the tool promotes meaningful active engagement and could scaffold deeper conversations around content. Our work paves directions in how AI can support children's social-emotional reflection of media and family connections in the digital age., Comment: ACM Conference on Human Factors in Computing Systems (CHI) 2025

We report the results of a rapid follow-up campaign on the Type IIb Supernova (SN) 2022hnt. We present a daily, multi-band, photometric follow-up using the Las Cumbres Observatory, the Zwicky Transient Facility, the orbiting \textit{Swift} observatory, and the Asteroid Terrestrial-impact Last Alert System (ATLAS). A distinctive feature in the light curve of SN 2022hnt and other IIb SNe is an early narrow peak prior to the ${}^{56}$Ni peak caused by rapid shock cooling of the hydrogen envelope, which can serve as an important probe of the properties of the massive progenitor star in the moments before explosion. Using SN 2022hnt as a case study, we demonstrate a framework of considerations for the application of shock cooling models to type IIb SNe, outlining a consistent procedure for future surveys of Type IIb SNe progenitor and explosion properties. \hll{We fit several recent models of shock-cooling emission and obtain progenitor radii between $\sim50$ and $\sim100$ $R_\odot$, as well as hydrogen-enriched envelope masses between $\sim0.01$ and $\sim0.1$ $M_\odot$, both consistent with values for other IIb SNe. One of these models is the model of \cite{Morag2023}, marking the first time this model has been applied to a Type IIb SN.} We evaluate contrasting predictions between shock-cooling models to construct a fiducial parameter set which can be used for comparison to other SNe. Finally, we investigate the possibility of extended wind breakout or precursor emission captured in the earliest detections., Comment: 19 pages, 11 figures; accepted for publication in ApJ

In the IceCube Neutrino Observatory, a signal of astrophysical neutrinos is obscured by backgrounds from atmospheric neutrinos and muons produced in cosmic-ray interactions. IceCube event selections used to isolate the astrophysical neutrino signal often focus on t/he morphology of the light patterns recorded by the detector. The analyses presented here use the new IceCube Enhanced Starting Track Event Selection (ESTES), which identifies events likely generated by muon neutrino interactions within the detector geometry, focusing on neutrino energies of 1-500 TeV with a median angular resolution of 1.4{\deg}. Selecting for starting track events filters out not only the atmospheric-muon background, but also the atmospheric-neutrino background in the southern sky. This improves IceCube's muon neutrino sensitivity to southern-sky neutrino sources, especially for Galactic sources that are not expected to produce a substantial flux of neutrinos above 100 TeV. In this work, the ESTES sample was applied for the first time to searches for astrophysical sources of neutrinos, including a search for diffuse neutrino emission from the Galactic plane. No significant excesses were identified from any of the analyses; however, constraining limits are set on the hadronic emission from TeV gamma-ray Galactic plane objects and models of the diffuse Galactic plane neutrino flux., Comment: 23 pages, 8 figures, 4 tables. Submitted to ApJ

Recovery rate prediction plays a pivotal role in bond investment strategies, enhancing risk assessment, optimizing portfolio allocation, improving pricing accuracy, and supporting effective credit risk management. However, forecasting faces challenges like high-dimensional features, small sample sizes, and overfitting. We propose a hybrid Quantum Machine Learning model incorporating Parameterized Quantum Circuits (PQC) within a neural network framework. PQCs inherently preserve unitarity, avoiding computationally costly orthogonality constraints, while amplitude encoding enables exponential data compression, reducing qubit requirements logarithmically. Applied to a global dataset of 1,725 observations (1996-2023), our method achieved superior accuracy (RMSE 0.228) compared to classical neural networks (0.246) and quantum models with angle encoding (0.242), with efficient computation times. This work highlights the potential of hybrid quantum-classical architectures in advancing recovery rate forecasting.

We apply reinforcement learning (RL) to establish whether at a given position in the Coulomb branch of the moduli space of a 4d $\mathcal{N} = 2$ quantum field theory (QFT) the BPS spectrum is finite. If it is, we furthermore determine the full BPS spectrum at such point in moduli space. We demonstrate that using a RL model one can efficiently determine the suitable sequence of quiver mutations of the BPS quiver that will generate the full BPS spectrum. We analyse the performance of the RL model on random BPS quivers and show that it converges to a solution various orders of magnitude faster than a systematic brute-force scan. As a result, we show that our algorithm can be used to identify all minimal chambers of a given $\mathcal{N}=2$ QFT, a task previously intractable with computer scanning. As an example, we recover all minimal chambers of the $\text{SU}(2)$ $N_f = 4$ gauge theory, and discover new minimal chambers for theories that can be realized by IIB geometric engineering., Comment: 8 pages, 6 figures, code available at Github link

Tree canopy height is one of the most important indicators of forest biomass, productivity, and ecosystem structure, but it is challenging to measure accurately from the ground and from space. Here, we used a U-Net model adapted for regression to map the mean tree canopy height in the Amazon forest from Planet NICFI images at ~4.78 m spatial resolution for the period 2020-2024. The U-Net model was trained using canopy height models computed from aerial LiDAR data as a reference, along with their corresponding Planet NICFI images. Predictions of tree heights on the validation sample exhibited a mean error of 3.68 m and showed relatively low systematic bias across the entire range of tree heights present in the Amazon forest. Our model successfully estimated canopy heights up to 40-50 m without much saturation, outperforming existing canopy height products from global models in this region. We determined that the Amazon forest has an average canopy height of ~22 m. Events such as logging or deforestation could be detected from changes in tree height, and encouraging results were obtained to monitor the height of regenerating forests. These findings demonstrate the potential for large-scale mapping and monitoring of tree height for old and regenerating Amazon forests using Planet NICFI imagery., Comment: will be submitted to the journal Remote Sensing of Environment in February 2025

We report a search for high-energy astrophysical neutrino multiplets, detections of multiple neutrino clusters in the same direction within 30 days, based on an analysis of 11.4 years of IceCube data. A new search method optimized for transient neutrino emission with a monthly time scale is employed, providing a higher sensitivity to neutrino fluxes. This result is sensitive to neutrino transient emission, reaching per-flavor flux of approximately $10^{-10}\ {\rm erg}\ {\rm cm}^{-2}\ {\rm sec}^{-1}$ from the Northern sky in the energy range $E\gtrsim 50$~TeV. The number of doublets and triplets identified in this search is compatible with the atmospheric background hypothesis, which leads us to set limits on the nature of neutrino transient sources with emission timescales of one month.

Radar is a low-cost and ubiquitous automotive sensor, but is limited by array resolution and sensitivity when performing direction of arrival analysis. Synthetic Aperture Radar (SAR) is a class of techniques to improve azimuth resolution and sensitivity for radar. Interferometric SAR (InSAR) can be used to extract elevation from the variations in phase measurements in SAR images. Utilizing InSAR we show that a typical, low-resolution radar array mounted on a vehicle can be used to accurately localize detections in 3D space for both urban and agricultural environments. We generate point clouds in each environment by combining InSAR with a signal processing scheme tailored to automotive driving. This low-compute approach allows radar to be used as a primary sensor to map fine details in complex driving environments, and be used to make autonomous perception decisions., Comment: 9 pages, 6 figures

Stable $^{205}$Tl ions have the lowest known energy threshold for capturing electron neutrinos ($\nu_e$) of ${ E}_{\nu_e}\ge50.6$\,keV. The Lorandite Experiment (LOREX), proposed in the 1980s, aims at obtaining the longtime averaged solar neutrino flux by utilizing natural deposits of Tl-bearing lorandite ores. To determine the $\nu_e$ capture cross section, it is required to know the strength of the weak transition connecting the ground state of $^{205}$Tl and the 2.3 keV first excited state in $^{205}$Pb. The only way to experimentally address this transition is to measure the bound-state beta decay ($\beta_{b}$) of fully ionized $\mathrm{^{205}Tl^{81+}}$ ions. After three decades of meticulous preparation, the half-life of the $\beta_{b}$ decay of $\mathrm{^{205}Tl^{81+}}$ has been measured to be $291_{-27}^{+33}$ days using the Experimental Storage Ring (ESR) at GSI, Darmstadt. The longer measured half-life compared to theoretical estimates reduces the expected signal-to-noise ratio in the LOREX, thus challenging its feasibility.

Micromobility vehicles, such as e-scooters, are increasingly popular in urban communities but present significant challenges in terms of road safety, user privacy, infrastructure planning, and civil engineering. Addressing these critical issues requires a large-scale and easily accessible research infrastructure to collect diverse mobility and contextual data from micromobility users in realistic settings. To this end, we present ScooterLab, a community research testbed comprising a fleet of customizable battery-powered micromobility vehicles retrofitted with advanced sensing, communication, and control capabilities. ScooterLab enables interdisciplinary research at the intersection of computing, mobility, and urban planning by providing researchers with tools to design and deploy customized sensing experiments and access curated datasets. The testbed will enable advances in machine learning, privacy, and urban transportation research while promoting sustainable mobility.

Current machine learning (ML)-based algorithms for filtering electroencephalography (EEG) time series data face challenges related to cumbersome training times, regularization, and accurate reconstruction. To address these shortcomings, we present an ML filtration algorithm driven by a logistic covariance-targeted adversarial denoising autoencoder (TADA). We hypothesize that the expressivity of a targeted, correlation-driven convolutional autoencoder will enable effective time series filtration while minimizing compute requirements (e.g., runtime, model size). Furthermore, we expect that adversarial training with covariance rescaling will minimize signal degradation. To test this hypothesis, a TADA system prototype was trained and evaluated on the task of removing electromyographic (EMG) noise from EEG data in the EEGdenoiseNet dataset, which includes EMG and EEG data from 67 subjects. The TADA filter surpasses conventional signal filtration algorithms across quantitative metrics (Correlation Coefficient, Temporal RRMSE, Spectral RRMSE), and performs competitively against other deep learning architectures at a reduced model size of less than 400,000 trainable parameters. Further experimentation will be necessary to assess the viability of TADA on a wider range of deployment cases., Comment: [Accepted] Artificial Intelligence for Time Series Analysis (AI4TS): Theory, Algorithms, and Applications @ AAAI 2025, Philadelphia, PA, USA

Humanoid robots have great potential to perform various human-level skills. These skills involve locomotion, manipulation, and cognitive capabilities. Driven by advances in machine learning and the strength of existing model-based approaches, these capabilities have progressed rapidly, but often separately. Therefore, a timely overview of current progress and future trends in this fast-evolving field is essential. This survey first summarizes the model-based planning and control that have been the backbone of humanoid robotics for the past three decades. We then explore emerging learning-based methods, with a focus on reinforcement learning and imitation learning that enhance the versatility of loco-manipulation skills. We examine the potential of integrating foundation models with humanoid embodiments, assessing the prospects for developing generalist humanoid agents. In addition, this survey covers emerging research for whole-body tactile sensing that unlocks new humanoid skills that involve physical interactions. The survey concludes with a discussion of the challenges and future trends.

We introduce a variety $Y_{n,k}$, which we call the \textit{affine $\Delta$-Springer fiber}, generalizing the affine Springer fiber studied by Hikita, whose Borel-Moore homology has an $S_n$ action and a bigrading that corresponds to the Delta Conjecture symmetric function $\mathrm{rev}_q\,\omega \Delta'_{e_{k-1}}e_n$ under the Frobenius character map. We similarly provide a geometric interpretation for the Rational Shuffle Theorem in the integer slope case $(km,k)$. The variety $Y_{n,k}$ has a map to the affine Grassmannian whose fibers are the $\Delta$-Springer fibers introduced by Levinson, Woo, and the third author. Part of our proof of our geometric realization relies on our previous work on a Schur skewing operator formula relating the Rational Shuffle Theorem to the Delta Conjecture., Comment: 39 pages

As part of the BISTRO survey, we present JCMT 850 $\mu$m polarimetric observations towards the Orion Integral-Shaped Filament (ISF) that covers three portions known as OMC-1, OMC-2, and OMC-3. The magnetic field threading the ISF seen in the JCMT POL-2 map appears as a tale of three: pinched for OMC-1, twisted for OMC-2, and nearly uniform for OMC-3. A multi-scale analysis shows that the magnetic field structure in OMC-3 is very consistent at all the scales, whereas the field structure in OMC-2 shows no correlation across different scales. In OMC-1, the field retains its mean orientation from large to small scales, but shows some deviations at small scales. Histograms of relative orientations between the magnetic field and filaments reveal a bimodal distribution for OMC-1, a relatively random distribution for OMC-2, and a distribution with a predominant peak at 90$^\circ$ for OMC-3. Furthermore, the magnetic fields in OMC-1 and OMC-3 both appear to be aligned perpendicular to the fibers, which are denser structures within the filament, but the field in OMC-2 is aligned along with the fibers. All these suggest that gravity, turbulence, and magnetic field are each playing a leading role in OMC-1, 2, and 3, respectively. While OMC-2 and 3 have almost the same gas mass, density, and non-thermal velocity dispersion, there are on average younger and fewer young stellar objects in OMC-3, providing evidence that a stronger magnetic field will induce slower and less efficient star formation in molecular clouds., Comment: published in the ApJ Letters

In a seminal paper now a decade old, it was shown that dark matter detectors geared at probing interactions with nucleons could also be used to probe dark matter interactions with electrons. In this work, we show that new detector concepts designed to probe dark matter-electron interactions at low masses can similarly be used to probe new parameter space for dark matter-nucleon interactions. We demonstrate the power of this approach by using existing data from superconducting detectors to place new limits on the interactions of nuclei with MeV-scale dark matter. Further, we show that advances in detector technology that have been anticipated for electronic interactions will automatically extend sensitivity deep into uncharted territory for nuclear interactions. This doubles the effective science output of future low-threshold experiments., Comment: 5 pages + references, 2 figures

This summary of the second Terrestrial Very-Long-Baseline Atom Interferometry (TVLBAI) Workshop provides a comprehensive overview of our meeting held in London in April 2024, building on the initial discussions during the inaugural workshop held at CERN in March 2023. Like the summary of the first workshop, this document records a critical milestone for the international atom interferometry community. It documents our concerted efforts to evaluate progress, address emerging challenges, and refine strategic directions for future large-scale atom interferometry projects. Our commitment to collaboration is manifested by the integration of diverse expertise and the coordination of international resources, all aimed at advancing the frontiers of atom interferometry physics and technology, as set out in a Memorandum of Understanding signed by over 50 institutions., Comment: Summary of the second Terrestrial Very-Long-Baseline Atom Interferometry Workshop held at Imperial College London: https://indico.cern.ch/event/1369392/

Long-term time series forecasting (LTSF) involves predicting a large number of future values of a time series based on the past values and is an essential task in a wide range of domains including weather forecasting, stock market analysis, disease outbreak prediction. Over the decades LTSF algorithms have transitioned from statistical models to deep learning models like transformer models. Despite the complex architecture of transformer based LTSF models `Are Transformers Effective for Time Series Forecasting? (Zeng et al., 2023)' showed that simple linear models can outperform the state-of-the-art transformer based LTSF models. Recently, quantum machine learning (QML) is evolving as a domain to enhance the capabilities of classical machine learning models. In this paper we initiate the application of QML to LTSF problems by proposing QuLTSF, a simple hybrid QML model for multivariate LTSF. Through extensive experiments on a widely used weather dataset we show the advantages of QuLTSF over the state-of-the-art classical linear models, in terms of reduced mean squared error and mean absolute error., Comment: submitted for conference publication

Encoder-only transformer models such as BERT offer a great performance-size tradeoff for retrieval and classification tasks with respect to larger decoder-only models. Despite being the workhorse of numerous production pipelines, there have been limited Pareto improvements to BERT since its release. In this paper, we introduce ModernBERT, bringing modern model optimizations to encoder-only models and representing a major Pareto improvement over older encoders. Trained on 2 trillion tokens with a native 8192 sequence length, ModernBERT models exhibit state-of-the-art results on a large pool of evaluations encompassing diverse classification tasks and both single and multi-vector retrieval on different domains (including code). In addition to strong downstream performance, ModernBERT is also the most speed and memory efficient encoder and is designed for inference on common GPUs.

We present spectra of the supernova (SN) impostor AT 2016blu spanning over a decade. This transient exhibits quasiperiodic outbursts with a $\sim$113 d period, likely triggered by periastron encounters in an eccentric binary system where the primary star is a luminous blue variable (LBV). The overall spectrum remains fairly consistent during quiescence and eruptions, with subtle changes in line-profile shapes and other details. Some narrow emission features indicate contamination from a nearby H II region in the host galaxy, NGC 4559. Broader H$\alpha$ profiles exhibit Lorentzian shapes with full width at half-maximum intensity (FWHM) values that vary significantly, showing no correlation with photometric outbursts or the 113 d phase. At some epochs, H$\alpha$ exhibits asymmetric profiles with a stronger redshifted wing, while broad and sometimes multicomponent P Cygni absorption features occasionally appear, but are again uncorrelated with brightness or phase. These P Cygni absorptions have high velocities compared to the FWHM of the H$\alpha$ emission line, perhaps suggesting that the absorption component is not in the LBV's wind, but is instead associated with a companion. The lack of phase dependence in line-profile changes may point to interaction between a companion and a variable or inhomogeneous primary wind, in an orbit with only mild eccentricity. Recent photometric data indicate that AT 2016blu experienced its 20th outburst around May/June 2023, as predicted based on its period. This type of quasiperiodic LBV remains poorly understood, but its spectra and erratic light curve resemble some pre-SN outbursts like those of SN 2009ip., Comment: 21 pages, 17 figures, and 1 table, Submitted to MNRAS

AI control protocols are plans for usefully deploying AI systems in a way that is safe, even if the AI intends to subvert the protocol. Previous work evaluated protocols by subverting them with a human-AI red team, where an AI follows the human-written strategy. This paper investigates how well AI systems can generate and act on strategies for subverting control protocols whilst operating without private memory. Usually, decision-makers only need the ability to generate optimal plans. However, to subvert a protocol, an AI system must be able to reliably generate optimal plans in each context; coordinate plans with other instances of itself without communicating; and take actions with well-calibrated probabilities. We develop evaluations for a model's ability to generate subversion strategies and develop affordance experiments that help isolate component capabilities. Our initial evaluations with Claude-3.5-sonnet highlight reasons why statelessly strategizing to subvert control protocols might be hard. We hope that our work can serve as a starting point for evaluations aimed at assessing whether a model is capable of strategizing to subvert control protocols., Comment: Accepted at AAAI datasafe workshop

Understanding and enhancing student engagement through digital platforms is critical in higher education. This study introduces a methodology for quantifying engagement across an entire module using virtual learning environment (VLE) activity log data. Using study session frequency, immediacy, and diversity, we create a cumulative engagement metric and model it against weekly VLE interactions with resources to identify critical periods and resources predictive of student engagement. In a case study of a computing module at University College London's Department of Statistical Science, we further examine how delivery methods (online, hybrid, in-person) impact student behaviour. Across nine regression models, we validate the consistency of the random forest model and highlight the interpretive strengths of generalised additive models for analysing engagement patterns. Results show weekly VLE clicks as reliable engagement predictors, with early weeks and the first assessment period being key. However, the impact of delivery methods on engagement is inconclusive due to inconsistencies across models. These findings support early intervention strategies to assist students at risk of disengagement. This work contributes to learning analytics research by proposing a refined VLE-based engagement metric and advancing data-driven teaching strategies in higher education., Comment: 20 pages, 6 figures

We present a systematic framework for Floquet prethermalization under strong resonant driving, emphasizing the pivotal role of dynamical space-time symmetries. Our approach demonstrates how dynamical space-time symmetries map onto the projective static symmetry group of the prethermal Hamiltonian governing the prethermal regime. We introduce techniques for detecting dynamical symmetries through the time evolution of local observables, facilitating a detailed analysis of micromotion within each period and surpassing the limitations of conventional stroboscopic Floquet prethermal dynamics. To implement this framework, we present a prethermal protocol that preserves order-two dynamical symmetry in a spin-ladder model, confirming the predicted relationships between the expectation values of local observables at distinct temporal points in the Floquet cycle, linked by this symmetry., Comment: 8+7 pages, 2+3 figures

While the subclass of interacting supernovae with narrow hydrogen emission lines (SNe IIn) consists of some of the longest-lasting and brightest SNe ever discovered, their progenitors are still not well understood. Investigating SNe IIn as they emit across the electromagnetic spectrum is the most robust way to understand the progenitor evolution before the explosion. This work presents X-Ray, optical, infrared, and radio observations of the strongly interacting Type IIn SN 2020ywx covering a period $>1200$ days after discovery. Through multiwavelength modeling, we find that the progenitor of 2020ywx was losing mass at $\sim10^{-2}$--$10^{-3} \mathrm{\,M_{\odot}\,yr^{-1}}$ for at least 100 yr pre-explosion using the circumstellar medium (CSM) speed of $120$ km/s measured from our optical and NIR spectra. Despite the similar magnitude of mass-loss measured in different wavelength ranges, we find discrepancies between the X-ray and optical/radio-derived mass-loss evolution, which suggest asymmetries in the CSM. Furthermore, we find evidence for dust formation due to the combination of a growing blueshift in optical emission lines and near-infrared continuum emission which we fit with blackbodies at $\sim$ 1000 K. Based on the observed elevated mass loss over more than 100 years and the configuration of the CSM inferred from the multiwavelength observations, we invoke binary interaction as the most plausible mechanism to explain the overall mass-loss evolution. SN 2020ywx is thus a case that may support the growing observational consensus that SNe IIn mass loss is explained by binary interaction., Comment: Submitted to ApJ, 31 pages, 19 figures

We analyzed the 7.92$\times 10^{11}$ cosmic-ray-induced muon events collected by the IceCube Neutrino Observatory from May 13, 2011, when the fully constructed experiment started to take data, to May 12, 2023. This dataset provides an up-to-date cosmic-ray arrival direction distribution in the Southern Hemisphere with unprecedented statistical accuracy covering more than a full period length of a solar cycle. Improvements in Monte Carlo event simulation and better handling of year-to-year differences in data processing significantly reduce systematic uncertainties below the level of statistical fluctuations compared to the previously published results. We confirm the observation of a change in the angular structure of the cosmic-ray anisotropy between 10 TeV and 1 PeV, more specifically in the 100-300 TeV energy range.

Control Flow Graphs and Function Call Graphs have become pivotal in providing a detailed understanding of program execution and effectively characterizing the behavior of malware. These graph-based representations, when combined with Graph Neural Networks (GNN), have shown promise in developing high-performance malware detectors. However, challenges remain due to the large size of these graphs and the inherent opacity in the decision-making process of GNNs. This paper addresses these issues by developing several graph reduction techniques to reduce graph size and applying the state-of-the-art GNNExplainer to enhance the interpretability of GNN outputs. The analysis demonstrates that integrating our proposed graph reduction technique along with GNNExplainer in the malware detection framework significantly reduces graph size while preserving high performance, providing an effective balance between efficiency and transparency in malware detection.

We introduce a new framework for analyzing classification datasets based on the ratios of reconstruction errors between autoencoders trained on individual classes. This analysis framework enables efficient characterization of datasets on the sample, class, and entire dataset levels. We define reconstruction error ratios (RERs) that probe classification difficulty and allow its decomposition into (1) finite sample size and (2) Bayes error and decision-boundary complexity. Through systematic study across 19 popular visual datasets, we find that our RER-based dataset difficulty probe strongly correlates with error rate for state-of-the-art (SOTA) classification models. By interpreting sample-level classification difficulty as a label mistakenness score, we further find that RERs achieve SOTA performance on mislabel detection tasks on hard datasets under symmetric and asymmetric label noise. Our code is publicly available at https://github.com/voxel51/reconstruction-error-ratios., Comment: 30 pages, 18 figures

Expanding Fitts' Law into a 3D context, we analyze PointARs, a mixed reality system that teaches pointer skills through an object manipulation task. Nine distinct configurations, varying in object sizes and distances, were explored to evaluate task complexity using metrics such as completion time, error rate, and throughput. Our results support Fitts' Law, showing that increased distances generally increase task difficulty. However, contrary to its predictions, larger objects also led to higher complexity, possibly due to the system's limitations in tracking them. Based on these findings, we suggest using tangible cubes between 1.5" and 2" in size and limiting the distance between objects to 2" for optimal interaction in the system's 3D space. Future research should explore additional configurations and shapes to further validate Fitts' Law in the context of 3D object manipulation in systems like PointARs. This could help refine guidelines for designing mixed reality interfaces., Comment: 9 pages, 8 figures, 4 equations, 7 references

Altermagnets are distinguished by their unique spin group symmetries, where spin and spatial symmetries are fully decoupled, resulting in nonrelativistic spin splitting (NRSS) of electronic bands. This phenomenon, unlike conventional spin splitting driven by relativistic spin-orbit coupling, has transformative potential in fields such as spintronics, superconductivity and energy-efficient electronics. However, direct observation of NRSS is challenging due to presence of competing phases, low N\'eel temperatures, and the limitations of existing experimental probes to unambiguously capture the associated properties. Here, we integrate theoretical and experimental approaches to uncover NRSS in the intercalated transition metal dichalcogenide CoNb$_4$Se$_8$. Symmetry analysis, density functional theory (DFT), a novel Symmetry-Constrained Adaptive Basis (SCAB), and tight-binding modeling predict the presence of symmetry-enforced spin splitting, which we directly confirm using spin-ARPES for the occupied band structure and a newly developed technique, spin- and angle-resolved electron reflection spectroscopy (spin-ARRES), for the unoccupied states. Together, these complementary tools reveal alternating spin textures consistent with our predicted g-wave altermagnetic order and demonstrate the persistence of NRSS across a broad energy range. Crucially, temperature-dependent measurements show the suppression of NRSS at the N\'eel temperature ($T_N$), providing the first direct evidence of an altermagnetic phase transition. Residual spin splitting above $T_N$ suggests the coexistence of altermagnetic fluctuations and spin-orbit coupling effects, underscoring a complex interplay of mechanisms. By establishing CoNb$_4$Se$_8$ as a prototypical g-wave altermagnet, this work offers a robust framework for understanding NRSS, and lays the foundation for designing energy-efficient spin-based technologies., Comment: 26 pages, 15 figures (5 main, 10 supplementary)

The $\Delta$-Springer fibers $Y_{n,\lambda,s}$, introduced by Levinson, Woo, and the second author, generalize Springer fibers for $\mathrm{GL}_n(\mathbb{C})$ and give a geometric interpretation of the of the Delta Conjecture from algebraic combinatorics (at $t=0$). We prove that all irreducible components of the $\Delta$-Springer fiber $Y_{n,n-1}=Y_{n,(1^{n-1}),n-1}$ are smooth. In fact, we prove that any intersection of irreducible components of $Y_{n,n-1}$ is a smooth Hessenberg variety which has the structure of an iterated Grassmannian fiber bundle. We then give a presentation of the singular cohomology ring of each irreducible component of $Y_{n,n-1}$ and a combinatorial formula for the Poincar\'e polynomial of an arbitrary union of intersections of irreducible components in terms of arm and leg statistics on Dyck paths., Comment: 19 pages

Deep learning increasingly relies on massive data with substantial costs for storage, annotation, and model training. To reduce these costs, coreset selection aims to find a representative subset of data to train models while ideally performing on par with the full data training. State-of-the-art coreset methods use carefully-designed criteria to quantify the importance of each data example via ground truth labels and dataset-specific training, then select examples whose scores lie in a certain range to construct a coreset. These methods work well in their respective settings, however, they cannot select data that are unlabeled, which is the majority of real-world data. To that end, this paper motivates and formalizes the problem of unlabeled coreset selection to enable greater scale and reduce annotation costs for deep learning. As a solution, we develop Zero-Shot Coreset Selection (ZCore), a method that efficiently selects coresets without ground truth labels or training on candidate data. Instead, ZCore uses existing foundation models to generate a zero-shot embedding space for unlabeled data, then quantifies the relative importance of each example based on overall coverage and redundancy within the embedding distribution. We evaluate ZCore on four datasets and outperform several state-of-the-art label-based methods, leading to a strong baseline for future research in unlabeled coreset selection. On ImageNet, ZCore selections achieve a downstream model accuracy of 53.99% with only 10% training data, which outperforms label-based methods while removing annotation requirements for 1.15 million images. Our code is publicly available at https://github.com/voxel51/zcore.

Guessing Codeword Decoding (GCD) is a recently proposed soft-input forward error correction decoder for arbitrary binary linear codes. Inspired by recent proposals that leverage binary linear codebook structure to reduce the number of queries made by Guessing Random Additive Noise Decoding (GRAND), for binary linear codes that include a full-message single parity-check (SPC) bit, we show that it is possible to reduce the number of queries made by GCD by a factor of up to 2 with the greatest guesswork reduction realized at lower SNRs, without impacting decoding precision. Codes without a full-message SPC can be modified to include one by changing a column of the generator matrix to obtain a decoding complexity advantage, and we demonstrate that this can often be done without losing decoding precision. To practically avail of the complexity advantage, a noise effect pattern generator capable of producing sequences for given Hamming weights, such as the landslide algorithm developed for ORBGRAND, is necessary., Comment: 6 pages, 11 figures

Radioactive nuclei with lifetimes on the order of millions of years can reveal the formation history of the Sun and active nucleosynthesis occurring at the time and place of its birth. Among such nuclei whose decay signatures are found in the oldest meteorites, $^{205}$Pb is a powerful example, as it is produced exclusively by slow neutron captures (the s process), with most being synthesized in asymptotic giant branch (AGB) stars. However, making accurate abundance predictions for $^{205}$Pb has so far been impossible because the weak decay rates of $^{205}$Pb and $^{205}$Tl are very uncertain at stellar temperatures. To constrain these decay rates, we measured for the first time the bound-state $\beta^-$ decay of fully ionized $^{205}$Tl$^{81+}$, an exotic decay mode that only occurs in highly charged ions. The measured half-life is 4.7 times longer than the previous theoretical estimate and our 10% experimental uncertainty has eliminated the main nuclear-physics limitation. With new, experimentally backed decay rates, we used AGB stellar models to calculate $^{205}$Pb yields. Propagating those yields with basic galactic chemical evolution (GCE) and comparing with the $^{205}$Pb/$^{204}$Pb ratio from meteorites, we determined the isolation time of solar material inside its parent molecular cloud. We find positive isolation times that are consistent with the other s-process short-lived radioactive nuclei found in the early Solar System. Our results reaffirm the site of the Sun's birth as a long-lived, giant molecular cloud and support the use of the $^{205}$Pb--$^{205}$Tl decay system as a chronometer in the early Solar System., Comment: 21 pages and 11 figures/tables. Published in Nature (2024)

Spin fluctuations have been proposed as a key mechanism for mediating superconductivity, particularly in high-temperature superconducting cuprates, where conventional electron-phonon interactions alone cannot account for the observed critical temperatures. Traditionally, their role has been analyzed through tight-binding based model Hamiltonians. In this work we present a method that combines density functional theory with a momentum- and frequency-dependent pairing interaction derived from the Fluctuation Exchange (FLEX) type Random Phase Approximation (FLEX-RPA) to compute Eliashberg spectral functions $\alpha ^{2}F(\omega )$ which are central to spin fluctuation theory of superconductivity. We apply our numerical procedure to study a series of cuprates where our extracted material specific $\alpha ^{2}F(\omega )$ are found to exhibit remarkable similarities characterized by a sharp peak in the vicinity of 40-60 meV and their rapid decay at higher frequencies. Our exact diagonalization of a linearized BCS gap equation extracts superconducting energy gap functions for realistic Fermi surfaces of the cuprates and predicts their symmetry to be $d_{x^{2}-y^{2}}$ in all studied systems. Via a variation of on-site Coulomb repulsion $U$ for the copper $d$-electrons we show that that the range of the experimental values of $T_{c}$ can be reproduced in this approach but is extremely sensitive to the proximity of the spin density wave instability. These data highlight challenges in building first-principle theories of high temperature superconductivity but offer new insights beyond previous treatments, such as the confirmation of the usability of approximate BCS-like $T_{c}$ equations, together with the evaluations of the material specific coupling constant $\lambda $ without reliance on tight-binding approximations of their electronic structures., Comment: 15 pages, 4 figures, 1 table

Modern AI agents, driven by advances in large foundation models, promise to enhance our productivity and transform our lives by augmenting our knowledge and capabilities. To achieve this vision, AI agents must effectively plan, perform multi-step reasoning and actions, respond to novel observations, and recover from errors, to successfully complete complex tasks across a wide range of scenarios. In this work, we introduce Magentic-One, a high-performing open-source agentic system for solving such tasks. Magentic-One uses a multi-agent architecture where a lead agent, the Orchestrator, plans, tracks progress, and re-plans to recover from errors. Throughout task execution, the Orchestrator directs other specialized agents to perform tasks as needed, such as operating a web browser, navigating local files, or writing and executing Python code. We show that Magentic-One achieves statistically competitive performance to the state-of-the-art on three diverse and challenging agentic benchmarks: GAIA, AssistantBench, and WebArena. Magentic-One achieves these results without modification to core agent capabilities or to how they collaborate, demonstrating progress towards generalist agentic systems. Moreover, Magentic-One's modular design allows agents to be added or removed from the team without additional prompt tuning or training, easing development and making it extensible to future scenarios. We provide an open-source implementation of Magentic-One, and we include AutoGenBench, a standalone tool for agentic evaluation. AutoGenBench provides built-in controls for repetition and isolation to run agentic benchmarks in a rigorous and contained manner -- which is important when agents' actions have side-effects. Magentic-One, AutoGenBench and detailed empirical performance evaluations of Magentic-One, including ablations and error analysis are available at https://aka.ms/magentic-one

The Legacy Survey of Space and Time (LSST) at Vera C. Rubin Observatory is planned to begin in the Fall of 2025. The LSST survey cadence has been designed via a community-driven process regulated by the Survey Cadence Optimization Committee (SCOC), which recommended up to 3% of the observing time to carry out Target of Opportunity (ToO) observations. Experts from the scientific community, Rubin Observatory personnel, and members of the SCOC were brought together to deliver a recommendation for the implementation of the ToO program during a workshop held in March 2024. Four main science cases were identified: gravitational wave multi-messenger astronomy, high energy neutrinos, Galactic supernovae, and small potentially hazardous asteroids possible impactors. Additional science cases were identified and briefly addressed in the documents, including lensed or poorly localized gamma-ray bursts and twilight discoveries. Trigger prioritization, automated response, and detailed strategies were discussed for each science case. This document represents the outcome of the Rubin ToO 2024 workshop, with additional contributions from members of the Rubin Science Collaborations. The implementation of the selection criteria and strategies presented in this document has been endorsed in the SCOC Phase 3 Recommendations document (PSTN-056). Although the ToO program is still to be finalized, this document serves as a baseline plan for ToO observations with the Rubin Observatory.

Towards the role of humanoid robots as squad mates in urban operations and other domains, we identified doors as a major area lacking capability development. In this paper, we focus on the ability of humanoid robots to navigate and deal with doors. Human-sized doors are ubiquitous in many environment domains and the humanoid form factor is uniquely suited to operate and traverse them. We present an architecture which incorporates GPU accelerated perception and a tree based interactive behavior coordination system with a whole body motion and walking controller. Our system is capable of performing door traversals on a variety of door types. It supports rapid authoring of behaviors for unseen door types and techniques to achieve re-usability of those authored behaviors. The behaviors are modelled using trees and feature logical reactivity and action sequences that can be executed with layered concurrency to increase speed. Primitive actions are built on top of our existing whole body controller which supports manipulation while walking. We include a perception system using both neural networks and classical computer vision for door mechanism detection outside of the lab environment. We present operator-robot interdependence analysis charts to explore how human cognition is combined with artificial intelligence to produce complex robot behavior. Finally, we present and discuss real robot performances of fast door traversals on our Nadia humanoid robot. Videos online at https://www.youtube.com/playlist?list=PLXuyT8w3JVgMPaB5nWNRNHtqzRK8i68dy., Comment: 15 pages, 23 figure, for submission to Elsevier RAS

We present a comprehensive multi-epoch photometric and spectroscopic study of SN 2024bch, a nearby (19.9 Mpc) Type II supernova (SN) with prominent early high ionization emission lines. Optical spectra from 2.9 days after the estimated explosion reveal narrow lines of H I, He II, C IV, and N IV that disappear by day 6. High cadence photometry from the ground and TESS show that the SN brightened quickly and reached a peak M$_V \sim$ $-$17.8 mag within a week of explosion, and late-time photometry suggests a $^{56}$Ni mass of 0.050 M$_{\odot}$. High-resolution spectra from day 8 and 43 trace the unshocked circumstellar medium (CSM) and indicate a wind velocity of 30--40 km s$^{-1}$, a value consistent with a red supergiant (RSG) progenitor. Comparisons between models and the early spectra suggest a pre-SN mass-loss rate of $\dot{M} \sim 10^{-3}-10^{-2}\ M_\odot\ \mathrm{yr}^{-1}$, which is too high to be explained by quiescent mass loss from RSGs, but is consistent with some recent measurements of similar SNe. Persistent blueshifted H I and [O I] emission lines seen in the optical and NIR spectra could be produced by asymmetries in the SN ejecta, while the multi-component H$\alpha$ may indicate continued interaction with an asymmetric CSM well into the nebular phase. SN 2024bch provides another clue to the complex environments and mass-loss histories around massive stars., Comment: Accepted in ApJ 2024 Dec 30