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In August 2017, Hurricane Harvey ravaged the Houston area, causing the homelessness of nearly 24,000 students in the Houston Independent School District (Houston ISD) alone. Additionally, nearly all Houston ISD schools sustained damage of some kind, resulting in school closures, campus relocations, and even the delaying of the start of classes for some students. In the first brief of this two-part series, the authors examine the characteristics of students who became homeless due to Harvey. They found that students who became temporarily homeless for a year or less due to Harvey tended to fare as well as or better on educational outcomes than even their never-homeless peers.

In August 2017, the Houston area was ravaged by one of the costliest natural disasters in history--Hurricane Harvey. In this brief, the first in a two-part series, the authors examine the effects of Harvey on student homelessness in the Houston Independent School District (Houston ISD). The authors find that student homelessness in Houston ISD quadrupled due to Harvey, and most students experiencing homelessness lived, at least temporarily, in unsheltered contexts, such as sleeping in a car or on the street. Unlike other high-profile storms such as Hurricane Katrina, students who became homeless due to Harvey tended to be broadly representative of the district in terms of their demographic characteristics. However, they differed systematically from students who experienced homelessness for conventional, economic reasons such as job loss and medical debt, who were particularly likely to be Black. The authors conclude with implications of these findings for educational stakeholders in preparation for both generational and "everyday" homelessness crises.

We investigate orbifolds of lattice conformal field theories with the goal of constructing theories with large gap. We consider Barnes-Wall lattices, which are a family of lattices with no short vectors, and orbifold by an extraspecial 2-group of lattice automorphisms. To construct the orbifold CFT, we investigate the orbifold vertex operator algebra and its twisted modules. To obtain a holomorphic CFT, a certain anomaly 3-cocycle $\omega$ needs to vanish; based on evidence we provide, we conjecture that it indeed does. Granting this conjecture, we construct a holomorphic CFT of central charge 128 with gap 4., Comment: 27 pages. Ancillary Mathematica and Magma files

This paper leverages stochastic geometry to model, analyze, and optimize multi-band unmanned aerial vehicle (UAV) communication networks operating across low-frequency and millimeter-wave (mmWave) bands. We introduce a novel approach to modeling mmWave antenna gain in such networks, which allows us to better capture and account for interference in our analysis and optimization. We then propose a simple yet effective user-UAV association policy, which strategically biases users towards mmWave UAVs to take advantage of lower interference and wider bandwidths compared to low-frequency UAVs. Under this scheme, we analytically derive the corresponding association probability, coverage probability, and spectral efficiency. We conclude by assessing our proposed association policy through simulation and analysis, demonstrating its effectiveness based on coverage probability and per-user data rates, as well as the alignment between analytical and simulation results.

With the advancement of large language models (LLMs), the biomedical domain has seen significant progress and improvement in multiple tasks such as biomedical question answering, lay language summarization of the biomedical literature, clinical note summarization, etc. However, hallucinations or confabulations remain one of the key challenges when using LLMs in the biomedical and other domains. Inaccuracies may be particularly harmful in high-risk situations, such as making clinical decisions or appraising biomedical research. Studies on the evaluation of the LLMs' abilities to ground generated statements in verifiable sources have shown that models perform significantly worse on lay-user generated questions, and often fail to reference relevant sources. This can be problematic when those seeking information want evidence from studies to back up the claims from LLMs[3]. Unsupported statements are a major barrier to using LLMs in any applications that may affect health. Methods for grounding generated statements in reliable sources along with practical evaluation approaches are needed to overcome this barrier. Towards this, in our pilot task organized at TREC 2024, we introduced the task of reference attribution as a means to mitigate the generation of false statements by LLMs answering biomedical questions.

We report the first instance of an M dwarf/brown dwarf obliquity measurement for the TOI-2119 system using the Rossiter-McLaughlin effect. TOI-2119 b is a transiting brown dwarf orbiting a young, active early M dwarf ($T_{\rm{eff}}$ = 3553 K). It has a mass of 64.4 M$_{\rm{J}}$ and radius of 1.08 R$_{\rm{J}}$, with an eccentric orbit ($e$ = 0.3) at a period of 7.2 days. For this analysis, we utilise NEID spectroscopic transit observations and ground based simultaneous transit photometry from the Astrophysical Research Consortium (ARC) and the Las Campanas Remote Observatory (LCRO). We fit all available data of TOI-2119 b to refine the brown dwarf parameters and update the ephemeris. The classical Rossiter-McLaughlin technique yields a projected star-planet obliquity of $\lambda=-0.8\pm1.1^\circ$ and a three-dimensional obliquity of $\psi=15.7\pm5.5^\circ$. Additionally, we spatially resolve the stellar surface of TOI-2119 utilising the Reloaded Rossiter-McLaughlin technique to determine the projected star-planet obliquity as $\lambda=1.26 \pm 1.2^{\circ}$. Both of these results agree within $2\sigma$ and confirm the system is aligned, where TOI-2119 b joins an emerging group of aligned brown dwarf obliquities. We also probe stellar surface activity on the surface of TOI-2119 in the form of centre-to-limb variations as well as the potential for differential rotation. Overall, we find tentative evidence for centre-to-limb variations on the star but do not detect evidence of differential rotation., Comment: Accepted to MNRAS pending minor corrections which have been implemented in this version. 12 pages, 9 figures, 4 tables

Predicting crash likelihood in complex driving environments is essential for improving traffic safety and advancing autonomous driving. Previous studies have used statistical models and deep learning to predict crashes based on semantic, contextual, or driving features, but none have examined the combined influence of these factors, termed roadway complexity in this study. This paper introduces a two-stage framework that integrates roadway complexity features for crash prediction. In the first stage, an encoder extracts hidden contextual information from these features, generating complexity-infused features. The second stage uses both original and complexity-infused features to predict crash likelihood, achieving an accuracy of 87.98% with original features alone and 90.15% with the added complexity-infused features. Ablation studies confirm that a combination of semantic, driving, and contextual features yields the best results, which emphasize their role in capturing roadway complexity. Additionally, complexity index annotations generated by Large Language Models outperform those by Amazon Mechanical Turk, highlighting the potential of automated tools for accurate, scalable crash prediction systems.

Recent studies have suggested the existence of a `signal-to-noise paradox' (SNP) in ensemble forecasts that manifests as situations where the correlation between the forecast ensemble mean and the observed truth is larger than the correlation between the forecast ensemble mean and individual forecast members. A perfectly reliable ensemble, in which forecast members and observations are drawn from the same underlying probability distribution, will not exhibit an SNP if sample statistics can be evaluated using a sufficiently large ensemble size ($N$) over a sufficiently large number of independent cases ($M$). However, when $M$ is finite, an apparent SNP will sometimes occur as a natural consequence of sampling uncertainty, even in a perfectly reliable ensemble with many members. In this study, we evaluate the forecast skill, reliability characteristics, and signal-to-noise properties of three large-scale atmospheric circulation indices in 100-member subseasonal reforecasts. Consistent with recent studies, this reforecast dataset exhibits an apparent SNP in the North Atlantic Oscillation (NAO) at subseasonal lead times. However, based on several lines of evidence, we conclude that the apparent paradox in this dataset is a consequence of large observational sampling uncertainties that are insensitive to ensemble size and common to all model comparisons over the same period. Furthermore, we demonstrate that this apparent SNP can be eliminated by application of an unbiased reliability calibration. However, this is achieved through overfitting such that sample statistics from calibrated forecasts inherit the large sampling uncertainties present in the observations and thus exhibit unphysical variations with lead time. Finally, we make several recommendations for the robust and unbiased evaluation of reliability and signal-to-noise properties in the presence of large sampling uncertainties.

Loop-based boson samplers interfere photons in the time degree of freedom using a sequence of delay lines. Since they require few hardware components while also allowing for long-range entanglement, they are strong candidates for demonstrating quantum advantage beyond the reach of classical emulation. We propose a method to exploit this loop-based structure to more efficiently simulate such systems. Our algorithm exploits a causal-cone argument to decompose the circuit into smaller effective components that can each be simulated sequentially by calling a state vector simulator as a subroutine. To quantify the complexity of our approach, we develop a new lattice path formalism that allows us to efficiently characterize the state space that must be tracked during the simulation. In addition, we develop a heuristic method that allows us to predict the expected average and worst-case memory requirements of running these simulations. We use these methods to compare the simulation complexity of different families of loop-based interferometers, allowing us to quantify the potential for quantum advantage of single-photon Boson Sampling in loop-based architectures., Comment: 27 pages, 15 figures

The X-ray polarization observations made possible with the Imaging X-ray Polarimetry Explorer (IXPE) offer new ways of probing high-energy emission processes in astrophysical jets from blazars. Here we report on the first X-ray polarization observation of the blazar S4 0954+65 in a high optical and X-ray state. During our multi-wavelength campaign on the source, we detected an optical flare whose peak coincided with the peak of an X-ray flare. This optical-X-ray flare most likely took place in a feature moving along the parsec-scale jet, imaged at 43 GHz by the Very Long Baseline Array. The 43 GHz polarization angle of the moving component underwent a rotation near the time of the flare. In the optical band, prior to the IXPE observation, we measured the polarization angle to be aligned with the jet axis. In contrast, during the optical flare the optical polarization angle was perpendicular to the jet axis; after the flare, it reverted to being parallel to the jet axis. Due to the smooth behavior of the optical polarization angle during the flare, we favor shocks as the main acceleration mechanism. We also infer that the ambient magnetic field lines in the jet were parallel to the jet position angle. The average degree of optical polarization during the IXPE observation was (14.3$\pm$4.1)%. Despite the flare, we only detected an upper limit of 14% (at 3$\sigma$ level) on the X-ray polarization degree; although a reasonable assumption on the X-ray polarization angle results in an upper limit of 8.8% ($3\sigma$). We model the spectral energy distribution (SED) and spectral polarization distribution (SPD) of S4 0954+65 with leptonic (synchrotron self-Compton) and hadronic (proton and pair synchrotron) models. The constraints we obtain with our combined multi-wavelength polarization observations and SED modeling tentatively disfavor hadronic models for the X-ray emission in S4 0954+65., Comment: Submitted to A&A, 16 pages, 5 figures, and 7 tables

Using available information from Drell-Yan data on pion and kaon structure functions, an approach is described which enables the development of pointwise profiles for all pion and kaon parton distribution functions (DFs) without reference to theories of hadron structure. The key steps are construction of structure-function-constrained probability-weighted ensembles of valence DF replicas and use of an evolution scheme for parton DFs that is all-orders exact. The DFs obtained express qualitatively sound features of light-meson structure, e.g., the effects of Higgs boson couplings into QCD and the size of heavy-quark momentum fractions in light hadrons. In order to improve the results, additional and more precise data on the $u$-quark-in-kaon, $u^K$, to $u$-quark-in-pion, $u^\pi$, DF ratio would be necessary. Of greater value would be extraction of $u^K$ alone, thereby avoiding inference from the ratio: currently, the data-based form of $u^K$ is materially influenced by results for $u^\pi$., Comment: 8 pages, 5 figures, 3 tables

Piecewise deterministic Markov processes provide scalable methods for sampling from the posterior distributions in big data settings by admitting principled sub-sampling strategies that do not bias the output. An important example is the Zig-Zag process of [Ann. Stats. 47 (2019) 1288 - 1320] where clever sub-sampling has been shown to produce an essentially independent sample at a cost that does not scale with the size of the data. However, sub-sampling also leads to slower convergence and poor mixing of the process, a behaviour which questions the promised scalability of the algorithm. We provide a large sample scaling analysis of the Zig-Zag process and its sub-sampling versions in settings of parametric Bayesian inference. In the transient phase of the algorithm, we show that the Zig-Zag trajectories are well approximated by the solution to a system of ODEs. These ODEs possess a drift in the direction of decreasing KL-divergence between the assumed model and the true distribution and are explicitly characterized in the paper. In the stationary phase, we give weak convergence results for different versions of the Zig-Zag process. Based on our results, we estimate that for large data sets of size n, using suitable control variates with sub-sampling in Zig-Zag, the algorithm costs O(1) to obtain an essentially independent sample; a computational speed-up of O(n) over the canonical version of Zig-Zag and other traditional MCMC methods, Comment: 47 pages, 7 figues, 1 table

Silicon-based ion trap chips can benefit from existing advanced fabrication technologies, such as multi-metal layer techniques for two-dimensional architectures and silicon photonics for the integration of on-chip optical components. However, the scalability of these technologies may be compromised by semiconductor charging, where photogenerated charge carriers produce electric potentials that disrupt ion motion. Inspired by recent studies on charge distribution mechanisms in semiconductors, we developed a silicon-based chip with gold coated on all exposed silicon surfaces. This modification significantly stabilized ion motion compared to a chip without such metallic shielding, a result that underscores the detrimental effects of exposed silicon. With the mitigation of background silicon-induced fields to negligible levels, quantum operations such as sideband cooling and two-ion entangling gates, which were previously infeasible with the unshielded chip, can now be implemented.

This study undertakes a detailed intercomparison of four open-source electricity system capacity expansion models--Temoa, Switch, GenX, and USENSYS--to examine their suitability for guiding U.S. power sector decarbonization policies. We isolate the effects of model-specific differences on policy outcomes and investment decisions by harmonizing empirical inputs via PowerGenome and systematically defining "scenarios" (policy conditions) and "configurations" (model setup choices). Our framework allows each model to be tested on identical assumptions for policy, technology costs, and operational constraints, thus distinguishing results that arise from data inputs or configuration versus inherent model structure. Key findings highlight that, when harmonized, models produce very similar capacity portfolios under each current policies and net-zero configuration, with less than 1 percent difference in system costs for most configurations. This agreement across models allows us to examine the impact of configuration choices. For example, configurations that assume unit commitment constraints or economic retirement of generators reveal the difference in investment decisions and system costs that arise from these modeling choices, underscoring the need for clear scenario and configuration definitions in policy guidance. Through this study, we identify critical structural assumptions that influence model outcomes and demonstrate the advantages of a standardized approach when using capacity expansion models. This work offers a valuable benchmark and identifies a few key modeling choices for policymakers, which ultimately will enhance transparency and reliability in modeling efforts to inform the clean energy transition for clean energy planning.

The breaking and enforcing of symmetries is a crucial ingredient in designing topologically robust materials. While magnetic fields can break time-reversal symmetry to create Chern insulators in electronic and microwave systems, at optical frequencies natural materials cannot respond to magnetic fields, which presents a challenge for the scalable exploitation of topologically enhanced devices. Here, we leverage the natural geometry of fibre to build a scalable photonic Chern insulator by twisting the fibre during fabrication. The twist inside optical fibre breaks an effective time-reversal symmetry and induces a pseudo-magnetic field, which we observe via photonic Landau levels. Unavoidably, this twist introduces a competing topology-destroying effect through a parabolic profile in the effective refractive index. Using simulations to guide experimental materials design, we discover the Goldilocks regime where the real-space Chern invariant survives, guaranteeing topological protection against fabrication-induced disorder of any symmetry class., Comment: 23 pages, 16 figures

Early and accurate melanoma detection is crucial for improving patient outcomes. Recent advancements in artificial intelligence AI have shown promise in this area, but the technologys effectiveness across diverse skin tones remains a critical challenge. This study conducts a systematic review and preliminary analysis of AI based melanoma detection research published between 2013 and 2024, focusing on deep learning methodologies, datasets, and skin tone representation. Our findings indicate that while AI can enhance melanoma detection, there is a significant bias towards lighter skin tones. To address this, we propose including skin hue in addition to skin tone as represented by the LOreal Color Chart Map for a more comprehensive skin tone assessment technique. This research highlights the need for diverse datasets and robust evaluation metrics to develop AI models that are equitable and effective for all patients. By adopting best practices outlined in a PRISMA Equity framework tailored for healthcare and melanoma detection, we can work towards reducing disparities in melanoma outcomes., Comment: 22 pages, 4 figures, 7 tables,accepted for publication in Future of Information and Communication Conference (FICC) 2025, whose proceedings will be published in 'Lecture Notes in Networks and Systems' by Springer Nature

Objectives: Despite the recent adoption of large language models (LLMs) for biomedical information extraction, challenges in prompt engineering and algorithms persist, with no dedicated software available. To address this, we developed LLM-IE: a Python package for building complete information extraction pipelines. Our key innovation is an interactive LLM agent to support schema definition and prompt design. Materials and Methods: The LLM-IE supports named entity recognition, entity attribute extraction, and relation extraction tasks. We benchmarked on the i2b2 datasets and conducted a system evaluation. Results: The sentence-based prompting algorithm resulted in the best performance while requiring a longer inference time. System evaluation provided intuitive visualization. Discussion: LLM-IE was designed from practical NLP experience in healthcare and has been adopted in internal projects. It should hold great value to the biomedical NLP community. Conclusion: We developed a Python package, LLM-IE, that provides building blocks for robust information extraction pipeline construction.

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

Backgrounds: Information extraction (IE) is critical in clinical natural language processing (NLP). While large language models (LLMs) excel on generative tasks, their performance on extractive tasks remains debated. Methods: We investigated Named Entity Recognition (NER) and Relation Extraction (RE) using 1,588 clinical notes from four sources (UT Physicians, MTSamples, MIMIC-III, and i2b2). We developed an annotated corpus covering 4 clinical entities and 16 modifiers, and compared instruction-tuned LLaMA-2 and LLaMA-3 against BiomedBERT in terms of performance, generalizability, computational resources, and throughput to BiomedBERT. Results: LLaMA models outperformed BiomedBERT across datasets. With sufficient training data, LLaMA showed modest improvements (1% on NER, 1.5-3.7% on RE); improvements were larger with limited training data. On unseen i2b2 data, LLaMA-3-70B outperformed BiomedBERT by 7% (F1) on NER and 4% on RE. However, LLaMA models required more computing resources and ran up to 28 times slower. We implemented "Kiwi," a clinical IE package featuring both models, available at https://kiwi.clinicalnlp.org/. Conclusion: This study is among the first to develop and evaluate a comprehensive clinical IE system using open-source LLMs. Results indicate that LLaMA models outperform BiomedBERT for clinical NER and RE but with higher computational costs and lower throughputs. These findings highlight that choosing between LLMs and traditional deep learning methods for clinical IE applications should remain task-specific, taking into account both performance metrics and practical considerations such as available computing resources and the intended use case scenarios.

The Moskowitz-Lombardi rule gives a simple relation between the magnetic moment of an atomic nucleus and the effect of its radial distribution on the hyperfine structure - the magnetic hyperfine anomaly or "Bohr-Weisskopf" effect. It was originally formulated for mercury, for which experimental data for nuclear magnetic moments and hyperfine constants were available for a number of isotopes. While the relation for the differential effect between isotopes may be completely determined experimentally, the value for the additive constant that is needed to give the Bohr-Weisskopf (BW) effect for a single isotope has remained untested. In this work, we determine the BW effect in singly-ionized and neutral mercury from experimental muonic Hg-199 data together with our atomic calculations. We check this result by directly extracting the BW effect from the hyperfine constant for singly-ionized Hg-199 using state-of-the-art atomic many-body calculations. From this we deduce an empirical value for the additive constant in the Moskowitz-Lombardi rule, which differs significantly from the values advocated previously.

We present the discovery of TOI-6303b and TOI-6330b, two massive transiting super-Jupiters orbiting a M0 and a M2 star respectively, as part of the Searching for GEMS survey. These were detected by TESS and then confirmed via ground-based photometry and radial velocity observations with the Habitable-zone Planet Finder (HPF). TOI-6303b has a mass of 7.84 +/- 0.31 MJ, a radius of 1.03 +/- 0.06 RJ , and an orbital period of 9.485 days. TOI-6330b has a mass of 10.00 +/- 0.31 MJ , a radius of 0.97 +/- 0.03 RJ , and an orbital period of 6.850 days. We put these planets in context of super-Jupiters around M-dwarfs discovered from radial-velocity surveys, as well as recent discoveries from astrometry. These planets have masses that can be attributed to two dominant planet formation mechanisms - gravitational instability and core-accretion. Their masses necessitate massive protoplanetary disks that should either be gravitationally unstable, i.e. forming through gravitational instability, or be amongst some of the most massive protoplanetary disks to form objects through core-accretion. We also discuss the eccentricity distribution of these objects, as a potential indicator of their formation and evolutionary mechanisms., Comment: arXiv admin note: text overlap with arXiv:2408.14694

We study kite central configurations in the Newtonian four-body problem. We present a new proof that there exists a unique convex kite central configuration for a given choice of positive masses and a particular ordering of the bodies. Our proof uses tools from differential topology (e.g., the Poincar\'{e}-Hopf Index Theorem) and computational algebraic geometry (e.g., Gr\"{o}bner bases). We also discuss concave kite central configurations, including degenerate examples and bifurcations. Finally, we numerically explore the linear stability of the corresponding kite relative equilibria, finding that the heaviest body must be at least 25 times larger than the combined masses of the other three bodies in order to be linearly stable., Comment: 33 pages, 10 figures

We present preliminary results of a Chandra Large Program to monitor the ultraluminous X-ray source (ULX) populations of three nearby, ULX-rich galaxies over the course of a year, finding the ULX population to show a variety of long-term variability behaviours. Of a sample of 36 ULXs, some show persistent or moderately variable flux, often with a significant relationship between hardness and luminosity, consistent with a supercritically accreting source with varying accretion rates. Six show very high-amplitude variability with no strong relationship between luminosity and hardness, though not all of them show evidence of any long-term periodicity, nor of the bimodal distribution indicative of the propeller effect. We find evidence of additional eclipses for two previously-identified eclipsing ULXs. Additionally, many sources that were previously identified as ULXs in previous studies were not detected at ULX luminosities during our monitoring campaign, indicating a large number of transient ULXs., Comment: 7 pages, 4 figures, to be published in Proceedings of the XMM-Newton Workshop 2024 "The X-ray Mysteries of Neutron Stars and White Dwarfs", Astronomische Nachrichten, in press

Let $F$ be an arbitrary totally real field. Under weak conditions we prove the existence of certain Eisenstein congruences between parallel weight $k \geq 3$ Hilbert eigenforms of level $\mathfrak{mp}$ and Hilbert Eisenstein series of level $\mathfrak{m}$, for arbitrary ideal $\mathfrak{m}$ and prime ideal $\mathfrak{p}\nmid \mathfrak{m}$ of $\mathcal{O}_F$. Such congruences have their moduli coming from special values of Hecke $L$-functions and their Euler factors, and our results allow for the eigenforms to have non-trivial Hecke character. After this, we consider the question of when such congruences can be satisfied by newforms, proving a general result about this., Comment: 23 pages

Rare-earth ions are characterised by transitions with very narrow linewidths even in solid state crystals. Exceedingly long coherence times have been shown on both spin and optical transitions of rare-earth-ion doped crystals. A key factor, and generally the limitation, for such coherence times, is the effects of electronic and nuclear spins in the host crystal. Despite the attractive prospect, a low-strain, spin-free host crystal for rare-earth-ion dopants has not yet been demonstrated. The dopants experience the lowest strain when they substitute for another rare earth (including yttrium). However every stable isotope of the trivalent rare earth ions has either an electron spin, an nuclear spin, or both. The long optical coherence times reported here with erbium dopants in antiferromagnetically ordered gadolinium vanandate suggest an alternative method to achieve the quiet magnetic environment needed for long coherence times: use a magnetic host fully concentrated in electron spins and operate at temperatures low enough for these spins to be ordered. We also observe avoided crossings in the optical spectra, caused by strong coupling between the erbium ions and gadolinium magnons in the host crystal. This suggests the exciting prospect of microwave to optical quantum transduction using the rare-earth ions in these materials mediated by magnons of the host spins.

As the context limits of Large Language Models (LLMs) increase, the range of possible applications and downstream functions broadens. In many real-world tasks, decisions depend on details scattered across collections of often disparate documents containing mostly irrelevant information. Long-context LLMs appear well-suited to this form of complex information retrieval and reasoning, which has traditionally proven costly and time-consuming. However, although the development of longer context models has seen rapid gains in recent years, our understanding of how effectively LLMs use their context has not kept pace. To address this, we conduct a set of retrieval experiments designed to evaluate the capabilities of 17 leading LLMs, such as their ability to follow threads of information through the context window. Strikingly, we find that many models are remarkably threadsafe: capable of simultaneously following multiple threads without significant loss in performance. Still, for many models, we find the effective context limit is significantly shorter than the supported context length, with accuracy decreasing as the context window grows. Our study also highlights the important point that token counts from different tokenizers should not be directly compared -- they often correspond to substantially different numbers of written characters. We release our code and long-context experimental data.

In this paper, we present a new space-time Petrov-Galerkin-like method. This method utilizes a mixed formulation of Tensor Train (TT) and Quantized Tensor Train (QTT), designed for the spectral element discretization (Q1-SEM) of the time-dependent convection-diffusion-reaction (CDR) equation. We reformulate the assembly process of the spectral element discretized CDR to enhance its compatibility with tensor operations and introduce a low-rank tensor structure for the spectral element operators. Recognizing the banded structure inherent in the spectral element framework's discrete operators, we further exploit the QTT format of the CDR to achieve greater speed and compression. Additionally, we present a comprehensive approach for integrating variable coefficients of CDR into the global discrete operators within the TT/QTT framework. The effectiveness of the proposed method, in terms of memory efficiency and computational complexity, is demonstrated through a series of numerical experiments, including a semi-linear example.

Generating discharge summaries is a crucial yet time-consuming task in clinical practice, essential for conveying pertinent patient information and facilitating continuity of care. Recent advancements in large language models (LLMs) have significantly enhanced their capability in understanding and summarizing complex medical texts. This research aims to explore how LLMs can alleviate the burden of manual summarization, streamline workflow efficiencies, and support informed decision-making in healthcare settings. Clinical notes from a cohort of 1,099 lung cancer patients were utilized, with a subset of 50 patients for testing purposes, and 102 patients used for model fine-tuning. This study evaluates the performance of multiple LLMs, including GPT-3.5, GPT-4, GPT-4o, and LLaMA 3 8b, in generating discharge summaries. Evaluation metrics included token-level analysis (BLEU, ROUGE-1, ROUGE-2, ROUGE-L) and semantic similarity scores between model-generated summaries and physician-written gold standards. LLaMA 3 8b was further tested on clinical notes of varying lengths to examine the stability of its performance. The study found notable variations in summarization capabilities among LLMs. GPT-4o and fine-tuned LLaMA 3 demonstrated superior token-level evaluation metrics, while LLaMA 3 consistently produced concise summaries across different input lengths. Semantic similarity scores indicated GPT-4o and LLaMA 3 as leading models in capturing clinical relevance. This study contributes insights into the efficacy of LLMs for generating discharge summaries, highlighting LLaMA 3's robust performance in maintaining clarity and relevance across varying clinical contexts. These findings underscore the potential of automated summarization tools to enhance documentation precision and efficiency, ultimately improving patient care and operational capability in healthcare settings.

In full-duplex millimeter-wave (mmWave) systems, the effects of beam squint and the frequency-selectivity of self-interference exacerbate over wide bandwidths. This complicates the use of beamforming to cancel self-interference when communicating over bandwidths on the order of gigahertz. In this work, we present the first analog beamforming codebooks tailored to wideband full-duplex mmWave systems, designed to both combat beam squint and cancel frequency-selective self-interference. Our proposed design constructs such codebooks by minimizing self-interference across the entire band of interest while constraining the coverage provided by these codebooks across that same band. Simulation results using computational electromagnetics to model self-interference suggest that a full-duplex 60 GHz system with our design enjoys lower self-interference and delivers better coverage across bandwidths as wide as 6 GHz, when compared to similar codebook designs that ignore beam squint and/or frequency-selectivity. This allows our design to sustain higher SINRs and spectral efficiencies across wide bandwidths, unlocking the potentials of wideband full-duplex mmWave systems.

This paper introduces a novel beam tracking scheme for full-duplex ground user terminals aiming to transmit uplink and receive downlink from two low Earth orbit (LEO) satellites at the same time and same frequency. Our proposed technique leverages observed phenomena from a recent measurement campaign to strategically select transmit and receive beams which couple low self-interference across the satellites' trajectories, thereby enabling in-band full-duplex operation. Our scheme takes a measurement-driven approach, meaning it does not rely on explicit knowledge of the self-interference channel and can inherently account for hardware impairments or other nonidealities. We show that our proposed scheme reliably selects beams which spatially cancel self-interference to below the noise floor, circumventing the need for digital/analog cancellation. Simulation results using satellite and orbital parameters published in 3GPP and FCC filings show that this substantial reduction in self-interference does not prohibitively compromise beamforming gain, allowing the user terminal to attain near-maximal SINRs, thus unlocking full-duplex operation.

The neutron star low-mass X-ray binary, EXO 0748--676, recently returned to outburst after a $\sim$ 16 year-long quiescence. Since its return, there has been a global effort to capture the previously unseen rise of the source and to understand its somewhat early return to outburst, as it is typical for a source to spend longer in quiescence than in outburst. Here, we report on the simultaneous optical and X-ray detection of a type I X-ray burst, captured by XMM-Newton during a DDT observation on 30th June 2024. The data show 3 X-ray eclipses consistent with the known ephemeris and one type I X-ray burst at 60492.309 MJD. The X-ray burst is reprocessed into the optical band and captured by XMM-Newton's Optical Monitor during a 4399 s exposure with the B filter in image + fast mode. We determine that the optical peak lags the X-ray peak by 4.46 $\pm$ 1.71s. The optical and X-ray rise times are similar, but the optical decay timescale is shorter than the X-ray decay timescale. The reprocessing site is likely within a few light seconds of the X-ray emitting region, so the companion star, accretion disc and ablated material are all plausible., Comment: 6 Pages, 3 Figures, Accepted for Publication in MNRAS Letters

On September 16th, 2023, an anomalous 10.88 mHz seismic signal was observed globally, persisting for 9 days. One month later an identical signal appeared, lasting for another week. Several studies have theorized that these signals were produced by seiches which formed after two landslide generated mega-tsunamis in an East-Greenland fjord. This theory is supported by seismic inversions, and analytical and numerical modeling, but no direct observations have been made -- until now. Using data from the new Surface Water Ocean Topography mission, we present the first observations of this phenomenon. By ruling out other oceanographic processes, we validate the seiche theory of previous authors and independently estimate its initial amplitude at 7.9 m using Bayesian machine learning and seismic data. This study demonstrates the value of satellite altimetry for studying extreme events, while also highlighting the need for specialized methods to address the altimetric data's limitations, namely temporal sparsity. These data and approaches will help in understanding future unseen extremes driven by climate change., Comment: 19 pages, 9 figures

In this paper, we introduce Hunyuan-Large, which is currently the largest open-source Transformer-based mixture of experts model, with a total of 389 billion parameters and 52 billion activation parameters, capable of handling up to 256K tokens. We conduct a thorough evaluation of Hunyuan-Large's superior performance across various benchmarks including language understanding and generation, logical reasoning, mathematical problem-solving, coding, long-context, and aggregated tasks, where it outperforms LLama3.1-70B and exhibits comparable performance when compared to the significantly larger LLama3.1-405B model. Key practice of Hunyuan-Large include large-scale synthetic data that is orders larger than in previous literature, a mixed expert routing strategy, a key-value cache compression technique, and an expert-specific learning rate strategy. Additionally, we also investigate the scaling laws and learning rate schedule of mixture of experts models, providing valuable insights and guidances for future model development and optimization. The code and checkpoints of Hunyuan-Large are released to facilitate future innovations and applications. Codes: https://github.com/Tencent/Hunyuan-Large Models: https://huggingface.co/tencent/Tencent-Hunyuan-Large, Comment: 17 pages, 4 Figures

One-time programs (Goldwasser, Kalai and Rothblum, CRYPTO 2008) are functions that can be run on any single input of a user's choice, but not on a second input. Classically, they are unachievable without trusted hardware, but the destructive nature of quantum measurements seems to provide a quantum path to constructing them. Unfortunately, Broadbent, Gutoski and Stebila showed that even with quantum techniques, a strong notion of one-time programs, similar to ideal obfuscation, cannot be achieved for any non-trivial quantum function. On the positive side, Ben-David and Sattath (Quantum, 2023) showed how to construct a one-time program for a certain (probabilistic) digital signature scheme, under a weaker notion of one-time program security. There is a vast gap between achievable and provably impossible notions of one-time program security, and it is unclear what functionalities are one-time programmable under the achievable notions of security. In this work, we present new, meaningful, yet achievable definitions of one-time program security for probabilistic classical functions. We show how to construct one time programs satisfying these definitions for all functions in the classical oracle model and for constrained pseudorandom functions in the plain model. Finally, we examine the limits of these notions: we show a class of functions which cannot be one-time programmed in the plain model, as well as a class of functions which appears to be highly random given a single query, but whose one-time program form leaks the entire function even in the oracle model.

When developing machine learning models, image quality assessment (IQA) measures are a crucial component for evaluation. However, commonly used IQA measures have been primarily developed and optimized for natural images. In many specialized settings, such as medical images, this poses an often-overlooked problem regarding suitability. In previous studies, the IQA measure HaarPSI showed promising behavior for natural and medical images. HaarPSI is based on Haar wavelet representations and the framework allows optimization of two parameters. So far, these parameters have been aligned for natural images. Here, we optimize these parameters for two annotated medical data sets, a photoacoustic and a chest X-Ray data set. We observe that they are more sensitive to the parameter choices than the employed natural images, and on the other hand both medical data sets lead to similar parameter values when optimized. We denote the optimized setting, which improves the performance for the medical images notably, by HaarPSI$_{MED}$. The results suggest that adapting common IQA measures within their frameworks for medical images can provide a valuable, generalizable addition to the employment of more specific task-based measures., Comment: 5 pages, 3 figures, 2 tables

We introduce Image2Struct, a benchmark to evaluate vision-language models (VLMs) on extracting structure from images. Our benchmark 1) captures real-world use cases, 2) is fully automatic and does not require human judgment, and 3) is based on a renewable stream of fresh data. In Image2Struct, VLMs are prompted to generate the underlying structure (e.g., LaTeX code or HTML) from an input image (e.g., webpage screenshot). The structure is then rendered to produce an output image (e.g., rendered webpage), which is compared against the input image to produce a similarity score. This round-trip evaluation allows us to quantitatively evaluate VLMs on tasks with multiple valid structures. We create a pipeline that downloads fresh data from active online communities upon execution and evaluates the VLMs without human intervention. We introduce three domains (Webpages, LaTeX, and Musical Scores) and use five image metrics (pixel similarity, cosine similarity between the Inception vectors, learned perceptual image patch similarity, structural similarity index measure, and earth mover similarity) that allow efficient and automatic comparison between pairs of images. We evaluate Image2Struct on 14 prominent VLMs and find that scores vary widely, indicating that Image2Struct can differentiate between the performances of different VLMs. Additionally, the best score varies considerably across domains (e.g., 0.402 on sheet music vs. 0.830 on LaTeX equations), indicating that Image2Struct contains tasks of varying difficulty. For transparency, we release the full results at https://crfm.stanford.edu/helm/image2struct/v1.0.1/., Comment: NeurIPS 2024. First three authors contributed equally

Synchrotron X-ray emission has been detected from nearly a dozen young supernova remnants (SNRs). X-rays of synchrotron origin exhibit linear polarization in a regular, non-randomly oriented magnetic field. The significant polarized X-ray emission from four such SNRs has already been reported on the basis of observations with the Imaging X-ray Polarimetry Explorer (IXPE). The magnetic-field structure as derived from IXPE observations is radial for Cassiopeia A, Tycho's SNR, and SN 1006, and tangential for RX J1713.7-3946. The latter together with the recent detection of a tangential magnetic field in SNR 1E 0102.2-7219 by the Australia Telescope Compact Array in the radio band shows that tangential magnetic fields can also be present in young SNRs. Thus, the dichotomy in polarization between young and middle-aged SNRs (radial magnetic fields in young SNRs, but tangential magnetic fields in middle-aged SNRs), previously noticed in the radio band, deserves additional attention. The present analysis of IXPE observations determines, for the first time, a magnetic-field structure in the northwestern rim of Vela Jr, also known as RX J0852.0-4622, and provides a new example of a young SNR with a tangential magnetic field., Comment: Accepted for publication in Astronomy and Astrophysics

Objective: To design and implement VIEWER, a versatile toolkit for visual analytics of clinical data, and to systematically evaluate its effectiveness across various clinical applications while gathering feedback for iterative improvements. Materials and Methods: VIEWER is an open-source and extensible toolkit that employs distributed natural language processing and interactive visualisation techniques to facilitate the rapid design, development, and deployment of clinical information retrieval, analysis, and visualisation at the point of care. Through an iterative and collaborative participatory design approach, VIEWER was designed and implemented in a large mental health institution, where its clinical utility and effectiveness were assessed using both quantitative and qualitative methods. Results: VIEWER provides interactive, problem-focused, and comprehensive views of longitudinal patient data from a combination of structured clinical data and unstructured clinical notes. Despite a relatively short adoption period and users' initial unfamiliarity, VIEWER significantly improved performance and task completion speed compared to the standard clinical information system. Users and stakeholders reported high satisfaction and expressed strong interest in incorporating VIEWER into their daily practice. Discussion: VIEWER provides a cost-effective enhancement to the functionalities of standard clinical information systems, with evaluation offering valuable feedback for future improvements. Conclusion: VIEWER was developed to improve data accessibility and representation across various aspects of healthcare delivery, including population health management and patient monitoring. The deployment of VIEWER highlights the benefits of collaborative refinement in optimizing health informatics solutions for enhanced patient care.

X-ray polarization is a unique new probe of the particle acceleration in astrophysical jets made possible through the Imaging X-ray Polarimetry Explorer. Here we report on the first dense X-ray polarization monitoring campaign on the blazar Mrk 421. Our observations were accompanied by an even denser radio and optical polarization campaign. We find significant short-timescale variability in both X-ray polarization degree and angle, including a $\sim90^\circ$ angle rotation about the jet axis. We attribute this to random variations of the magnetic field, consistent with the presence of turbulence but also unlikely to be explained by turbulence alone. At the same time, the degree of lower-energy polarization is significantly lower and shows no more than mild variability. Our campaign provides further evidence for a scenario in which energy-stratified shock-acceleration of relativistic electrons, combined with a turbulent magnetic field, is responsible for optical to X-ray synchrotron emission in blazar jets., Comment: 23 pages, including 8 pages of appendices. 12 figures, 3 tables. Submitted to ApJ

We present JWST/NIRSpec PRISM spectroscopic characterization of GHZ9 at z= 10.145 $\pm$ 0.010, currently the most distant source detected by the Chandra X-ray Observatory. The spectrum reveals several UV high-ionization lines, including CII, SiIV, [NIV], CIV, HeII, OIII], NIII], and CIII]. The prominent rest-frame equivalent widths (EW(CIV)$\simeq$65A, EW(HeII)$\simeq$18A, EW(CIII])$\simeq$48A) show the presence of a hard radiation field, while the analysis of line ratio diagnostics suggest this galaxy hosts both AGN and star-formation activity. GHZ9 is nitrogen-enriched (6--9.5 times solar), carbon-poor (0.2--0.65 times solar), metal-poor (Z = 0.01--0.1 Z$_{\odot}$), and compact ($<$ 106 pc), similarly to GNz11, GHZ2, and recently discovered N-enhanced high redshift objects. We exploited the newly available JWST/NIRSpec and NIRCam dataset to perform an independent analysis of the Chandra data confirming that GHZ9 is the most likely JWST source associated to X-ray emission at 0.5-7 keV. Assuming a spectral index $\Gamma$ = 2.3 (1.8), we estimate a black hole (BH) mass of 1.60 $\pm$ 0.31 (0.48 $\pm$ 0.09) $\times$ 10$^8$M$_{\odot}$, which is consistent either with Eddington-accretion onto heavy ($\geq$ 10$^6$ M$_{\odot}$) BH seeds formed at z=18, or super-Eddington accretion onto a light seed of $\sim$ 10$^2-10^4$ M$_{\odot}$ at z = 25. The corresponding BH-to-stellar mass ratio M$_{BH}$/M$_{star}$= 0.33$\pm$0.22 (0.10$\pm$0.07), with a stringent limit $>$0.02, implies an accelerated growth of the BH mass with respect to the stellar mass. GHZ9 is the ideal target to constrain the early phases of AGN-galaxy coevolution with future multi-frequency observations., Comment: Submitted to ApJ

Scanning objects with a more tightly focused beam (for example of photons or electrons) can provide higher-resolution images. However the stronger localisation of energy deposition can damage tissues in organic samples or may rearrange the chemical structure or physical properties of inorganic materials. Scanning an object with a broad beam can deliver an equivalent probe energy but spreads it over a much wider footprint. Sharp images can be reconstructed from the diffuse implanted signal when a decoding step can recover a delta-like impulse response. Huffman sequences, by design, have the optimal delta-like autocorrelation for aperiodic (non-cyclic) convolution and are well-conditioned. Here we adapt 1D Huffman sequences to design 2D Huffman-like discrete arrays that have spatially broad, relatively thin and uniform intensity profiles that retain excellent aperiodic autocorrelation metrics. Examples of broad shaped diffuse beams were developed for the case of x-ray imaging. A variety of masks were fabricated by the deposition of finely structured layers of tantalum on a silicon oxide wafer. The layers form a pattern of discrete pixels that modify the shape of an incident uniform beam of low energy x-rays as it passes through the mask. The intensity profiles of the x-ray beams after transmission through these masks were validated, first by acquiring direct-detector x-ray images of the masks, and second by raster scanning a pinhole over each mask pattern, pixel-by-pixel, collecting "bucket" signals as applied in traditional ghost imaging. The masks were then used to raster scan the shaped x-ray beam over several simple binary and "gray" test objects, again producing bucket signals, from which sharp reconstructed object images were obtained by deconvolving their bucket images.

The US opioid overdose epidemic has been a major public health concern in recent decades. There has been increasing recognition that its etiology is rooted in part in the social contexts that mediate substance use and access; however, reliable statistical measures of social influence are lacking in the literature. We use Facebook's social connectedness index (SCI) as a proxy for real-life social networks across diverse spatial regions that help quantify social connectivity across different spatial units. This is a measure of the relative probability of connections between localities that offers a unique lens to understand the effects of social networks on health outcomes. We use SCI to develop a variable, called "deaths in social proximity", to measure the influence of social networks on opioid overdose deaths (OODs) in US counties. Our results show a statistically significant effect size for deaths in social proximity on OODs in counties in the United States, controlling for spatial proximity, as well as demographic and clinical covariates. The effect size of standardized deaths in social proximity in our cluster-robust linear regression model indicates that a one-standard-deviation increase, equal to 11.70 more deaths per 100,000 population in the social proximity of ego counties in the contiguous United States, is associated with thirteen more deaths per 100,000 population in ego counties. To further validate our findings, we performed a series of robustness checks using a network autocorrelation model to account for social network effects, a spatial autocorrelation model to capture spatial dependencies, and a two-way fixed-effect model to control for unobserved spatial and time-invariant characteristics. These checks consistently provide statistically robust evidence of positive social influence on OODs in US counties.

Evidence for high-redshift supermassive black holes challenges standard scenarios for how such objects form in the early universe. Here, we entertain the possibility that a fraction of the cosmological dark matter could be ultra-strongly self interacting. This would imply that gravothermal collapse occur at early times in the cores of dark matter halos, followed by accretion. We study under which conditions on the abundance and interaction strength and structure of such ultra self-interacting dark matter the black holes resulting from the end-point of gravothermal core collapse can seed the observed, early-forming supermassive black holes. We find, depending on the velocity dependence of the self-interaction cross section, a bimodal structure in the favored parameter space, where data points to either a small collapsing dark matter fraction with a large cross section, or a large fraction and a relatively small cross section. While self-interaction cross sections with different velocity dependence can explain observations, we find that the best, self-consistent results correspond to a Rutherford-like self-interaction, typical of long-range dark-sector forces with light mediators. We discuss complementary observational probes if this scenario is realized in nature, focusing especially on the expected intermediate mass black holes predicted to exist in smaller galaxies., Comment: 28 pages, 6 Figures

Intels efforts to build a practical quantum computer are focused on developing a scalable spin-qubit platform leveraging industrial high-volume semiconductor manufacturing expertise and 300 mm fabrication infrastructure. Here, we provide an overview of the design, fabrication, and demonstration of a new customized quantum test chip, which contains 12-quantum-dot spin-qubit linear arrays, code named Tunnel Falls. These devices are fabricated using immersion and extreme ultraviolet lithography (EUV), along with other standard high-volume manufacturing (HVM) processes, as well as production-level process control. We present key device features and fabrication details, as well as qubit characterization results confirming device functionality. These results corroborate our fabrication methods and are a crucial step towards scaling of extensible 2D qubit array schemes.