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We present a comprehensive study of the flaring mode of the transitional millisecond pulsar (tMSP) PSR J1023+0038 during its X-ray sub-luminous state, using strictly simultaneous X-ray, UV, optical, and radio observations. The X-ray flares exhibit UV and optical counterparts and coincide with the brightest radio flare observed in the past decade, reaching 1.2 mJy at 6 GHz and lasting ~1 hour. During the flare, the optical polarization drops from ~1.4% to ~0.5%, indicating the emergence of an unpolarized component. We propose that the thickening of the disc, which enlarges the shock region between the pulsar wind and the accretion flow and may drive the X-ray flaring observed in tMSPs, enhances the ionization level of the disc, thereby generating an increased number of free electrons. These electrons could then be channelled by magnetic field lines into the jet. This increased jet mass-loading could drive the associated radio and optical variability. The radio spectral evolution during flares is consistent with synchrotron self-absorption in jet ejecta or internal shocks within the compact jet. We infer radio polarization upper limits (<8.7%, <2.3%, and <8.2%, before, during, and after the radio flare) that further support a compact jet origin but do not rule out discrete ejections. Our findings suggest that tMSPs could serve as essential laboratories for investigating jet-launching mechanisms, mainly because they operate under very low mass accretion rates. This accretion regime has not been explored before in the context of the accretion-ejection coupling., Comment: Accepted for publication in A&AL

We compare measurements of star formation efficiency to cloud-scale gas properties across PHANGS-ALMA. Dividing 67 galaxies into 1.5 kpc scale regions, we calculate the molecular gas depletion time, tau_dep= Sigma_mol/Sigma_SFR, and the star formation efficiency per free-fall time, eff=tau_ff/tau_dep, for each region. Then we test how tau_dep and eff vary as functions of the regional mass-weighted mean molecular gas properties on cloud scales (60-150pc): gas surface density, , velocity dispersion, , virial parameter, , and gravitational free-fall time, . and tau_dep correlate positively, consistent with the expectation that gas density plays a key role in setting the rate of star formation. Our fiducial measurements suggest tau_dep \propto ^0.5 and eff \approx 0.34%, though the exact numbers depend on the adopted fitting methods. We also observe anti-correlations between tau_dep and and between tau_dep^mol and . All three correlations may reflect the same underlying link between density and star formation efficiency combined with systematic variations in the degree to which self-gravity binds molecular gas in galaxies. We highlight the tau_dep- relation because of the lower degree of correlation between the axes. Contrary to theoretical expectations, we observe an anti-correlation between tau_dep^mol and and no significant correlation between eff and . Our results depend sensitively on the adopted CO-to-H2 conversion factor, with corrections for excitation and emissivity effects in inner galaxies playing an important role. We emphasize that our simple methodology and clean selection allow easy comparison to numerical simulations and highlight this as a logical next direction., Comment: Accepted for publication in the Astrophysical Journal. 35 pages, 16 figures, 9 tables. Full data tables available here: https://www.canfar.net/storage/vault/list/phangs/RELEASES/Leroy_etal_2025

In vaccine trials with long-term participant follow-up, it is of great importance to identify surrogate markers that accurately infer long-term immune responses. These markers offer practical advantages such as providing early, indirect evidence of vaccine efficacy, and can accelerate vaccine development while identifying potential biomarkers. High-throughput technologies like RNA-sequencing have emerged as promising tools for understanding complex biological systems and informing new treatment strategies. However, these data are high-dimensional, presenting unique statistical challenges for existing surrogate marker identification methods. We introduce Rank-based Identification of high-dimensional SurrogatE Markers (RISE), a novel approach designed for small sample, high-dimensional settings typical in modern vaccine experiments. RISE employs a non-parametric univariate test to screen variables for promising candidates, followed by surrogate evaluation on independent data. Our simulation studies demonstrate RISE's desirable properties, including type one error rate control and empirical power under various conditions. Applying RISE to a clinical trial for inactivated influenza vaccination, we sought to identify genes whose post-vaccination expression could serve as a surrogate for the induced immune response. This analysis revealed a signature of genes whose combined expression at 1 day post-injection appears to be a reasonable surrogate for the neutralising antibody titres at 28 days after vaccination. Pathways related to innate antiviral signalling and interferon stimulation were strongly represented in this derived surrogate, providing a clear immunological interpretation.

Nanophotonic devices in color center-containing hosts provide efficient readout, control, and entanglement of the embedded emitters. Yet control over color center formation - in number, position, and coherence - in nanophotonic devices remains a challenge to scalability. Here, we report a controlled creation of highly coherent diamond nitrogen-vacancy (NV) centers with nanoscale three-dimensional localization in prefabricated nanostructures with high yield. Combining nitrogen $\delta$-doping during chemical vapor deposition diamond growth and localized electron irradiation, we form shallow NVs registered to the center of diamond nanopillars with wide tunability over NV number. We report positioning precision of ~ 4 nm in depth and 46(1) nm laterally in pillars (102(2) nm in bulk diamond). We reliably form single NV centers with long spin coherence times (average $T_2^{Hahn}$ = 98 $\mu s$) and 1.8x higher average photoluminescence compared to NV centers randomly positioned in pillars. We achieve a 3x improved yield of NV centers with single electron-spin sensitivity over conventional implantation-based methods. Our high-yield defect creation method will enable scalable production of solid-state defect sensors and processors.

When training large flexible models, practitioners often rely on grid search to select hyperparameters that control over-fitting. This grid search has several disadvantages: the search is computationally expensive, requires carving out a validation set that reduces the available data for training, and requires users to specify candidate values. In this paper, we propose an alternative: directly learning regularization hyperparameters on the full training set via the evidence lower bound ("ELBo") objective from variational methods. For deep neural networks with millions of parameters, we recommend a modified ELBo that upweights the influence of the data likelihood relative to the prior. Our proposed technique overcomes all three disadvantages of grid search. In a case study on transfer learning of image classifiers, we show how our method reduces the 88+ hour grid search of past work to under 3 hours while delivering comparable accuracy. We further demonstrate how our approach enables efficient yet accurate approximations of Gaussian processes with learnable length-scale kernels., Comment: arXiv admin note: text overlap with arXiv:2410.19675

We show that for a list-arithmetic pretopos $\mathcal{E}$ with pullback stable coequalisers, the $2$-category $\mathbf{Cat}(\mathcal{E})$ of internal categories, functors and natural transformations has finite $2$-colimits., Comment: 21 pages, comments welcome

Semantic segmentation plays a crucial role in enabling machines to understand and interpret visual scenes at a pixel level. While traditional segmentation methods have achieved remarkable success, their generalization to diverse scenes and unseen object categories remains limited. Recent advancements in large language models (LLMs) offer a promising avenue for bridging visual and textual modalities, providing a deeper understanding of semantic relationships. In this paper, we propose LangSeg, a novel LLM-guided semantic segmentation method that leverages context-sensitive, fine-grained subclass descriptors generated by LLMs. Our framework integrates these descriptors with a pre-trained Vision Transformer (ViT) to achieve superior segmentation performance without extensive model retraining. We evaluate LangSeg on two challenging datasets, ADE20K and COCO-Stuff, where it outperforms state-of-the-art models, achieving up to a 6.1% improvement in mean Intersection over Union (mIoU). Additionally, we conduct a comprehensive ablation study and human evaluation to validate the effectiveness of our method in real-world scenarios. The results demonstrate that LangSeg not only excels in semantic understanding and contextual alignment but also provides a flexible and efficient framework for language-guided segmentation tasks. This approach opens up new possibilities for interactive and domain-specific segmentation applications.

In topological phases of matter for which the bulk and boundary support distinct electronic gaps, there exists the possibility of decoupled mobility gaps in the presence of disorder. This is in analogy with the well-studied problem of realizing separate or concomitant bulk-boundary criticality in conventional Landau theory. Using a three-dimensional axion insulator having clean, gapped surfaces with $e^2/2h$ quantized Hall conductance, we show the bulk and surface mobility gap evolve differently in the presence of disorder. The decoupling of the bulk and surface topology yields a regime that realizes a two-dimensional, unquantized anomalous Hall metal in the Gaussian unitary ensemble (GUE), which shares some spectral and response properties akin to the surface states of a conventional three-dimensional (3D) topological insulator. The generality of these results as well as extensions to other insulators and superconductors is discussed., Comment: 6 + 10 pages, 3 + 11 Figures

The work of Emergency Management (EM) agencies requires timely collection of relevant data to inform decision-making for operations and public communication before, during, and after a disaster. However, the limited human resources available to deploy for field data collection is a persistent problem for EM agencies. Thus, many of these agencies have started leveraging social media as a supplemental data source and a new venue to engage with the public. While prior research has analyzed the potential benefits and attitudes of practitioners and the public when leveraging social media during disasters, a gap exists in the critical analysis of the actual practices and uses of social media among EM agencies, across both geographical regions and phases of the EM lifecycle - typically mitigation, preparedness, response, and recovery. In this paper, we conduct a mixed-method analysis to update and fill this gap on how EM practitioners in the U.S. and Europe use social media, building on a survey study of about 150 professionals and a follow-up interview study with 11 participants. The results indicate that using social media is no longer a non-traditional practice in operational and informational processes for the decision-making of EM agencies working at both the local level (e.g., county or town) and non-local level (e.g., state/province, federal/national) for emergency management. Especially, the practitioners affiliated with agencies working at the local level have a very high perceived value of social media for situational awareness (e.g., analyzing disaster extent and impact) and public communication (e.g., disseminating timely information and correcting errors in crisis coverage). We conclude with the policy, technological, and socio-technical needs to design future social media analytics systems to support the work of EM agencies in such communication including the applications of AI., Comment: To appear at CSCW 2025 (PACMHCI - Proceedings of the ACM on Human Computer Interaction)

This paper establishes strong profinite rigidity results for K\"ahler groups, showing that certain groups are determined within the class of residually finite K\"ahler groups by their profinite completion. Examples include products of surface groups and certain groups with exotic finiteness properties studied earlier by Dimca-Papadima-Suciu and Llosa Isenrich. Consequently, there are aspherical smooth projective varieties that are determined up to homeomorphism by their algebraic fundamental group. The main tool is the following: the holomorphic fibrations of a closed K\"ahler manifold over hyperbolic 2-orbifolds can be recovered from the profinite completion of its fundamental group. We also prove profinite invariance of the BNS invariant.

The Neural Contextual Reinforcement Framework introduces an innovative approach to enhancing the logical coherence and structural consistency of text generated by large language models. Leveraging reinforcement learning principles, the framework integrates custom reward functions and dynamic context alignment mechanisms to address challenges inherent in maintaining long-range dependencies across extended sequences. The architecture incorporates multi-head attention layers and hierarchical encoding modules, enabling the model to produce outputs that align closely with human expectations of logical structure and semantic flow. Quantitative evaluations across diverse datasets demonstrate substantial improvements in coherence metrics, perplexity reduction, and semantic alignment, showcasing the framework's ability to outperform baseline models in both general and domain-specific tasks. Qualitative analyses further highlight the framework's capacity to generate text with improved narrative clarity and reduced redundancy, reflecting its effectiveness in balancing fluency with structural precision. In addition to its performance gains, the framework exhibits robustness in handling noisy input data and scalability across varying model sizes, reinforcing its versatility in practical applications. Experimental results reveal that optimal context window sizes significantly influence coherence outcomes, showing the importance of architectural flexibility in adapting to diverse linguistic structures. Cross-lingual performance evaluations affirm the framework's adaptability to multiple languages, extending its utility beyond monolingual contexts. Resource efficiency analyses indicate a reduction in computational overhead compared to traditional approaches, emphasizing the practicality of the framework for large-scale deployment.

Restricting the chain-antichain principle CAC to partially ordered sets which respect the natural ordering of the integers is a trivial distinction in the sense of classical reverse mathematics. We utilize computability-theoretic reductions to formally establish distinguishing characteristics of CAC and the aforementioned restriction to elaborate on the apparent differences obfuscated over RCA0. Stable versions of both principles are also analyzed in this way.

Planetesimal belts are ubiquitous around nearby stars, and their spatial properties hold crucial information for planetesimal and planet formation models. We present resolved dust observations of 74 planetary systems as part of the REsolved ALMA and SMA Observations of Nearby Stars (REASONS) survey and archival reanalysis. We uniformly modelled interferometric visibilities for the entire sample to obtain the basic spatial properties of each belt, and combined these with constraints from multi-wavelength photometry. We report key findings from a first exploration of this legacy dataset: (1) Belt dust masses are depleted over time in a radially dependent way, with dust being depleted faster in smaller belts, as predicted by collisional evolution. (2) Most belts are broad discs rather than narrow rings, with much broader fractional widths than rings in protoplanetary discs. We link broad belts to either unresolved substructure or broad planetesimal discs produced if protoplanetary rings migrate. (3) The vertical aspect ratios (h = H/R) of 24 belts indicate orbital inclinations of 1-20 degrees, implying relative particle velocities of 0.1-4 km/s, and no clear evolution of heights with system age. This could be explained by early stirring within the belt by large bodies (with sizes of at least 140 km to the size of the Moon), by inheritance of inclinations from the protoplanetary disc stage, or by a diversity in evolutionary pathways and gravitational stirring mechanisms. We release the REASONS legacy multidimensional sample of millimetre-resolved belts to the community as a valuable tool for follow-up multi-wavelength observations and population modelling studies., Comment: Published in A&A, 693, A151. 27 pages, 15 figures, 3 tables

Many active galaxies harbor powerful relativistic jets, however, the detailed mechanisms of their formation and acceleration remain poorly understood. To investigate the area of jet acceleration and collimation with the highest available angular resolution, we study the innermost region of the bipolar jet in the nearby low-ionization nuclear emission-line region (LINER) galaxy NGC 1052. We combined observations of NGC 1052 taken with VLBA, GMVA, and EHT over one week in the spring of 2017. For the first time, NGC 1052 was detected with the EHT, providing a size of the central region in-between both jet bases of 250 RS (Schwarzschild radii) perpendicular to the jet axes. This size estimate supports previous studies of the jets expansion profile which suggest two breaks of the profile at around 300 RS and 10000 RS distances to the core. Furthermore, we estimated the magnetic field to be 1.25 Gauss at a distance of 22 {\mu}as from the central engine by fitting a synchrotron-self absorption spectrum to the innermost emission feature, which shows a spectral turn-over at about 130 GHz. Assuming a purely poloidal magnetic field, this implies an upper limit on the magnetic field strength at the event horizon of 26000 Gauss, which is consistent with previous measurements. The complex, low-brightness, double-sided jet structure in NGC 1052 makes it a challenge to detect the source at millimeter (mm) wavelengths. However, our first EHT observations have demonstrated that detection is possible up to at least 230 GHz. This study offers a glimpse through the dense surrounding torus and into the innermost central region, where the jets are formed. This has enabled us to finally resolve this region and provide improved constraints on its expansion and magnetic field strength., Comment: 22 pages, 10 figures, published in A&A

The 2017 observing campaign of the Event Horizon Telescope (EHT) delivered the first very long baseline interferometry (VLBI) images at the observing frequency of 230 GHz, leading to a number of unique studies on black holes and relativistic jets from active galactic nuclei (AGN). In total, eighteen sources were observed: the main science targets, Sgr A* and M87 along with various calibrators. We investigated the morphology of the sixteen AGN in the EHT 2017 data set, focusing on the properties of the VLBI cores: size, flux density, and brightness temperature. We studied their dependence on the observing frequency in order to compare it with the Blandford-K\"onigl (BK) jet model. We modeled the source structure of seven AGN in the EHT 2017 data set using linearly polarized circular Gaussian components and collected results for the other nine AGN from dedicated EHT publications, complemented by lower frequency data in the 2-86 GHz range. Then, we studied the dependences of the VLBI core flux density, size, and brightness temperature on the frequency measured in the AGN host frame. We compared the observations with the BK jet model and estimated the magnetic field strength dependence on the distance from the central black hole. Our results indicate a deviation from the standard BK model, particularly in the decrease of the brightness temperature with the observing frequency. Either bulk acceleration of the jet material, energy transfer from the magnetic field to the particles, or both are required to explain the observations.

Correlated behaviours between the radio emission and the X-ray emission in Galactic black hole X-ray binaries (BH XRBs) in the X-ray hard state are crucial to the understanding of disc-jet coupling of accreting black holes. The BH transient 4U 1543-47 went into outburst in 2021 following ~19 years of quiescence. We followed it up with ~weekly cadence with MeerKAT for about one year and a half until it faded into quiescence. Multi-epoch quasi-simultaneous MeerKAT and X-ray observations allowed us to trace the compact jet emission and its X-ray emission. In its hard spectral state across three orders of magnitude of X-ray luminosities above ~10$^{34}$ ergs/s, we found the correlation between radio and X-ray emission had a power-law index of 0.82$\pm$0.09, steeper than the canonical value of ~0.6 for BH XRBs. In addition, the radio vs. X-ray correlation shows a large range of the power-law normalization, with the maximum significantly larger than that obtained for most BH XRBs, indicating it can be particularly radio-bright and variable in the X-ray binary sample. The radio emission is unlikely diluted by discrete jet components. The observed peculiar radio-bright and variable behaviours provide the evidence for the relativistic effects of a variable Lorentz factor in the range between 1 and ~2 of the compact jet., Comment: 13 pages, 2 figures, accepted for publication in MNRAS Letters

For a graph with colored vertices, a rainbow subgraph is one where all vertices have different colors. For graph $G$, let $c_k(G)$ denote the maximum number of different colors in a coloring without a rainbow path on $k$ vertices, and $cp_k(G)$ the maximum number of colors if the coloring is required to be proper. The parameter $c_3$ has been studied by multiple authors. We investigate these parameters for trees and $k \ge 4$. We first calculate them when $G$ is a path, and determine when the optimal coloring is unique. Then for trees $T$ of order $n$, we show that the minimum value of $c_4(T)$ and $cp_4(T)$ is $(n+2)/2$, and the trees with the minimum value of $cp_4(T)$ are the coronas. Further, the minimum value of $c_5(T)$ and $cp_5(T)$ is $(n+3)/2$ , and the trees with the minimum value of either parameter are octopuses.

Using the Event Horizon Telescope (EHT), the gravitationally lensed rings around the supermassive black holes (SMBHs) in Messier 87 (M87) and Sagittarius A* (Sgr A*) have now been successfully imaged at a resolution under 10 gravitational radii (R$_{\rm g}$ $ = \rm{GM/c^2}$). To expand studies beyond M87 and Sgr A*, we have constructed the Event Horizon and Environs (ETHER) sample, a comprehensive database encompassing approximately 3.15 million SMBH mass estimates, $\sim$ 20,000 Very-Long Baseline Interferometry (VLBI) radio flux densities, and $\sim$ 36,000 hard X-ray flux densities. This database is designed to identify and optimize target selection for the EHT and its upgrades on the ground and in space. We have identified a Gold Sample (GS) of nearby low-luminosity Active Galactic Nuclei (AGNs) within it that are ideal for studying jet bases and potentially imaging black hole shadows. We observed 27 of these AGNs using the EHT from 2022 to 2024, providing an opportunity to resolve and image accretion flows and jets at resolutions of $\leq$ 100 R$_{\rm g}$. Only a few SMBHs have sufficiently high enough flux density to be imaged at scales of $\leq$ 50 R$_{\rm g}$ with the present EHT. Among these are M87, Sgr A*, NGC4594 (Sombrero/M104), NGC4261, and NGC4374 (Messier 84/M84). Of these, NGC4261, Sombrero, and M84 have been observed and/or are scheduled for deep imaging with EHT+ALMA from 2023 to 2025. Sombrero, NGC4261, M84, NGC4278, and NGC5232 are clearly detected in our EHT+ALMA observations in 2022, indicating that the 230 GHz flux density from the accretion flows is significantly high. Ongoing imaging of the ETHER GS will enable measurements of black hole mass and spin, help constrain General Relativity, and enrich our understanding of jet launching and accretion inflows across a broad multi-parameter space, including black hole mass, spin, accretion rate, and orientation., Comment: 9 pages, 6 figures, 1 table, published in Proceedings of the 16th EVN Symposium, Ed. E. Ros, P. Benke, S.A. Dzib, I. Rottmann, & J.A. Zensus, Bonn: Max-Planck-Institut f\"ur Radioastronomie, 2024, pages 75-84, https://cloud.mpifr-bonn.mpg.de/index.php/s/BkX2CC2Xjn2aKR4

In this paper, we consider the inverse scattering problem associated with an anisotropic medium with a conductive boundary condition. We will assume that the corresponding far--field pattern or Cauchy data is either known or measured. The conductive boundary condition models a thin coating around the boundary of the scatterer. We will develop two direct sampling methods to solve the inverse shape problem by numerically recovering the scatterer. To this end, we study direct sampling methods by deriving that the corresponding imaging functionals decay as the sampling point moves away from the scatterer. These methods have been applied to other inverse shape problems, but this is the first time they will be applied to an anisotropic scatterer with a conductive boundary condition. These methods allow one to recover the scatterer by considering an inner--product of the far--field data or the Cauchy data. Here, we will assume that the Cauchy data is known on the boundary of a region $\Omega$ that completely encloses the scatterer $D$. We present numerical reconstructions in two dimensions to validate our theoretical results for both circular and non-circular scatterers.

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/

Transitional millisecond pulsars (tMSPs) bridge the evolutionary gap between accreting neutron stars in low-mass X-ray binaries and millisecond radio pulsars. These systems exhibit a unique subluminous X-ray state characterized by the presence of an accretion disk and rapid switches between high and low X-ray emission modes. The high mode features coherent millisecond pulsations spanning from the X-ray to the optical band. We present multiwavelength polarimetric observations of the tMSP PSR J1023+0038 aimed at conclusively identifying the physical mechanism powering its emission in the subluminous X-ray state. During the high mode, we detect polarized emission in the 2-6 keV energy range, with a polarization degree of 12% +/- 3% and a polarization angle of -2deg +/- 9deg (1sigma) measured counterclockwise from the North celestial pole towards East. At optical wavelengths, we find a polarization degree of 1.41% +/- 0.04% and a polarization angle aligned with that in the soft X-rays, suggesting a common physical mechanism operating across these bands. Remarkably, the polarized flux spectrum matches the pulsed emission spectrum from optical to X-rays. The polarization properties differ markedly from those observed in other accreting neutron stars and isolated rotation-powered pulsars and are also inconsistent with an origin in a compact jet. Our results provide direct evidence that the polarized and pulsed emissions both originate from synchrotron radiation at the shock formed where the pulsar wind interacts with the inner regions of the accretion disk., Comment: Submitted

The separatrix operational space (SepOS) model [Eich \& Manz, \emph{Nuclear Fusion} (2021)] is shown to predict the L-H transition, the L-mode density limit, and the ideal MHD ballooning limit in terms of separatrix parameters for a wide range of Alcator C-Mod plasmas. The model is tested using Thomson scattering measurements across a wide range of operating conditions on C-Mod, spanning $\overline{n}_{e} = 0.3 - 5.5 \times 10^{20}$m$^{-3}$, $B_{t} = 2.5 - 8.0$ T, and $B_{p} = 0.1 - 1.2$ T. An empirical regression for the electron pressure gradient scale length, $\lambda_{p_{e}}$, against a turbulence control parameter, $\alpha_{t}$, and the poloidal fluid gyroradius, $\rho_{s,p}$, for H-modes is constructed and found to require positive exponents for both regression parameters, indicating turbulence widening of near-SOL widths at high $\alpha_{t}$ and an inverse scaling with $B_{p}$, consistent with results on AUG. The SepOS model is also tested in the unfavorable drift direction and found to apply well to all three boundaries, including the L-H transition as long as a correction to the Reynolds energy transfer term, $\alpha_\mathrm{RS} < 1$ is applied. I-modes typically exist in the unfavorable drift direction for values of $\alpha_{t} \lesssim 0.35$. Finally, an experiment studying the transition between the type-I ELMy and EDA H-mode is analyzed using the same framework. It is found that a recently identified boundary at $\alpha_{t} = 0.55$ excludes most EDA H-modes but that the balance of wavenumbers responsible for the L-mode density limit, namely $k_\mathrm{EM} = k_\mathrm{RBM}$, may better describe the transition on C-Mod. The ensemble of boundaries validated and explored is then applied to project regime access and limit avoidance for the SPARC primary reference discharge parameters.

Each application developer desires to provide its users with consistent results and an always-available system despite failures. Boldly, the CALM theorem disagrees. It states that it is hard to design a system that is both consistent and available under network partitions; select at most two out of these three properties. One possible solution is to design coordination-free monotonic applications. However, a majority of real-world applications require coordination. We resolve this dilemma by conjecturing that partial progress is possible under network partitions. This partial progress ensures the system appears responsive to a subset of clients and achieves non-zero throughput during failures. To this extent, we present the design of our CASSANDRA consensus protocol that allows partitioned replicas to order client requests., Comment: 7

Language models (LMs) have shown outstanding performance in text summarization including sensitive domains such as medicine and law. In these settings, it is important that personally identifying information (PII) included in the source document should not leak in the summary. Prior efforts have mostly focused on studying how LMs may inadvertently elicit PII from training data. However, to what extent LMs can provide privacy-preserving summaries given a non-private source document remains under-explored. In this paper, we perform a comprehensive study across two closed- and three open-weight LMs of different sizes and families. We experiment with prompting and fine-tuning strategies for privacy-preservation across a range of summarization datasets across three domains. Our extensive quantitative and qualitative analysis including human evaluation shows that LMs often cannot prevent PII leakage on their summaries and that current widely-used metrics cannot capture context dependent privacy risks.

Large language models (LLMs) provide a compelling foundation for building generally-capable AI agents. These agents may soon be deployed at scale in the real world, representing the interests of individual humans (e.g., AI assistants) or groups of humans (e.g., AI-accelerated corporations). At present, relatively little is known about the dynamics of multiple LLM agents interacting over many generations of iterative deployment. In this paper, we examine whether a "society" of LLM agents can learn mutually beneficial social norms in the face of incentives to defect, a distinctive feature of human sociality that is arguably crucial to the success of civilization. In particular, we study the evolution of indirect reciprocity across generations of LLM agents playing a classic iterated Donor Game in which agents can observe the recent behavior of their peers. We find that the evolution of cooperation differs markedly across base models, with societies of Claude 3.5 Sonnet agents achieving significantly higher average scores than Gemini 1.5 Flash, which, in turn, outperforms GPT-4o. Further, Claude 3.5 Sonnet can make use of an additional mechanism for costly punishment to achieve yet higher scores, while Gemini 1.5 Flash and GPT-4o fail to do so. For each model class, we also observe variation in emergent behavior across random seeds, suggesting an understudied sensitive dependence on initial conditions. We suggest that our evaluation regime could inspire an inexpensive and informative new class of LLM benchmarks, focussed on the implications of LLM agent deployment for the cooperative infrastructure of society., Comment: 15 pages, 6 figures

Multimodal AI has the potential to significantly enhance document-understanding tasks, such as processing receipts, understanding workflows, extracting data from documents, and summarizing reports. Code generation tasks that require long-structured outputs can also be enhanced by multimodality. Despite this, their use in commercial applications is often limited due to limited access to training data and restrictive licensing, which hinders open access. To address these limitations, we introduce BigDocs-7.5M, a high-quality, open-access dataset comprising 7.5 million multimodal documents across 30 tasks. We use an efficient data curation process to ensure our data is high-quality and license-permissive. Our process emphasizes accountability, responsibility, and transparency through filtering rules, traceable metadata, and careful content analysis. Additionally, we introduce BigDocs-Bench, a benchmark suite with 10 novel tasks where we create datasets that reflect real-world use cases involving reasoning over Graphical User Interfaces (GUI) and code generation from images. Our experiments show that training with BigDocs-Bench improves average performance up to 25.8% over closed-source GPT-4o in document reasoning and structured output tasks such as Screenshot2HTML or Image2Latex generation. Finally, human evaluations showed a preference for outputs from models trained on BigDocs over GPT-4o. This suggests that BigDocs can help both academics and the open-source community utilize and improve AI tools to enhance multimodal capabilities and document reasoning. The project is hosted at https://bigdocs.github.io ., Comment: The project is hosted at https://bigdocs.github.io

We present ALMA observations of the [CI] 492 and 806$\,$GHz fine-structure lines in 25 dusty star-forming galaxies (DSFGs) at $z\,{=}\,4.3$ in the core of the SPT2349$-$56 protocluster. The protocluster galaxies exhibit a median $L^\prime_{[\text{CI}](2-1)}/L^\prime_{[\text{CI}](1-0)}$ ratio of 0.94 with an interquartile range of 0.81-1.24. These ratios are markedly different to those observed in DSFGs in the field (across a comparable redshift and 850$\,\mu$m flux density range), where the median is 0.55 with an interquartile range of 0.50-0.76, and we show that this difference is driven by an excess of [CI](2-1) in the protocluster galaxies for a given 850$\,\mu$m flux density. We estimate gas excitation temperatures of $T_{\rm ex}\,{=}\,59.1^{+8.1}_{-6.8}\,$K for our protocluster sample and $T_{\rm ex}\,{=}\,33.9^{+2.4}_{-2.2}\,$K for the field sample. Our main interpretation of this result is that the protocluster galaxies have had their cold gas driven to their cores via close-by interactions within the dense environment, leading to an overall increase in the average gas density and excitation temperature, and an elevated [CI](2-1) luminosity-to-far-infrared luminosity ratio., Comment: Submitted to ApJL

We introduce Best-of-N (BoN) Jailbreaking, a simple black-box algorithm that jailbreaks frontier AI systems across modalities. BoN Jailbreaking works by repeatedly sampling variations of a prompt with a combination of augmentations - such as random shuffling or capitalization for textual prompts - until a harmful response is elicited. We find that BoN Jailbreaking achieves high attack success rates (ASRs) on closed-source language models, such as 89% on GPT-4o and 78% on Claude 3.5 Sonnet when sampling 10,000 augmented prompts. Further, it is similarly effective at circumventing state-of-the-art open-source defenses like circuit breakers. BoN also seamlessly extends to other modalities: it jailbreaks vision language models (VLMs) such as GPT-4o and audio language models (ALMs) like Gemini 1.5 Pro, using modality-specific augmentations. BoN reliably improves when we sample more augmented prompts. Across all modalities, ASR, as a function of the number of samples (N), empirically follows power-law-like behavior for many orders of magnitude. BoN Jailbreaking can also be composed with other black-box algorithms for even more effective attacks - combining BoN with an optimized prefix attack achieves up to a 35% increase in ASR. Overall, our work indicates that, despite their capability, language models are sensitive to seemingly innocuous changes to inputs, which attackers can exploit across modalities.

We report on the coherent creation, control and read-out of a single photonic qutrit in a Rydberg ensemble. In each measurement, an optical photon is stored as a Rydberg polariton through electromagnetically induced transparency. Employing two microwave fields, the polariton is driven into an arbitrary superposition of three collective states, each encoded in a Rydberg state. The collective state is mapped into a photonic time-bin qutrit with the microwave field and read out sequentially. The complete sequence, including preparation, control, and read-out, is less than 1.8~$\mu$s, which mitigates decoherence significantly. We measure the coherence of the qutrit with non-destructive Ramsey interferometry, which is preferable for quantum information processing, and find good quantitative agreement with the theoretical model. The ability to write, process and read out the single photonic qutrit on microsecond time scales with microwave coupled Rydberg states demonstrates the coherent connectivity among the high Hilbert space of the qutrit.Our study is an important step in exploring qutrit based quantum information processes and quantum simulation of topological physics with microwave coupled Rydberg atom ensembles., Comment: 7 pages, 4 figures

Defending large language models against jailbreaks so that they never engage in a broadly-defined set of forbidden behaviors is an open problem. In this paper, we investigate the difficulty of jailbreak-defense when we only want to forbid a narrowly-defined set of behaviors. As a case study, we focus on preventing an LLM from helping a user make a bomb. We find that popular defenses such as safety training, adversarial training, and input/output classifiers are unable to fully solve this problem. In pursuit of a better solution, we develop a transcript-classifier defense which outperforms the baseline defenses we test. However, our classifier defense still fails in some circumstances, which highlights the difficulty of jailbreak-defense even in a narrow domain., Comment: Accepted to the AdvML-Frontiers and SoLaR workshops at NeurIPS 2024

We analyze the computation of $n$-point correlation functions in de Sitter spacetime, including loop corrections, using the wavefunction of the universe approach. This method consists of two stages employing distinct Feynman rules. First, one must compute the wavefunction coefficients using interactions as vertices. Then, in the second stage, one computes correlation functions using wavefunction coefficients as vertices. For massless fields, loop corrections in the first stage are free of infrared (IR) divergences, which leads to the question of how this matches the well-known IR behavior of correlators obtained via other methods. By considering a scalar field with an arbitrary potential, we compute $n$-point correlation functions to first order in the potential but to all orders in loops. We find that, although loop integrals in the first stage are indeed IR convergent, the second procedure reintroduces the IR divergence. We discuss how this induces renormalization of the interaction potential such that the final result combining both steps exactly matches the form of $n$-point functions previously calculated with other methods., Comment: 7 pages + references. Comments welcome

In recent years, career and technical education (CTE) programs that include quality work-based learning (WBL) opportunities for students have gained significant traction among educators, policymakers, and stakeholders as an effective way to prepare students for the labor market. However, a lack of data on WBL--which many CTE students participate in--prevents the field from knowing what, if any, contribution WBL makes to student outcomes with CTE. To inform and encourage expanded WBL data collection, a cross-Network research team from the CTE Research Network documented promising practices and innovations by interviewing staff from six school districts that have gone beyond typical data collection and use for work-based learning. Such data have great value not only for researchers, but also for practitioners in managing and improving their programs. Findings include the type of data systems implemented across the six districts and how they were developed; what type of data elements are collected and what processes are in place to guide data entry; how the data is used within the district and with partners, such as advisory boards, regional workforce boards, and employers; and how if at all WBL quality is monitored and recorded. District leaders may use this report to examine ways in which local policies and data collection efforts drive local WBL implementation, monitoring, and improvements. The findings also have implications for state leaders who are adding WBL to their longitudinal data systems and accountability models with local policies, practices, and constraints to consider.

Real world planning problems are often too complex to be effectively tackled by a single unaided human. To alleviate this, some recent work has focused on developing a collaborative planning system to assist humans in complex domains, with bridging the gap between the system's problem representation and the real world being a key consideration. Transferring the speed and correctness formal planners provide to real-world planning problems is greatly complicated by the dynamic and online nature of such tasks. Formal specifications of task and environment dynamics frequently lack constraints on some behaviors or goal conditions relevant to the way a human operator prefers a plan to be carried out. While adding constraints to the representation with the objective of increasing its realism risks slowing down the planner, we posit that the same benefits can be realized without sacrificing speed by modeling this problem as an online preference learning task. As part of a broader cooperative planning system, we present a feedback-driven plan critic. This method makes use of reinforcement learning with human feedback in conjunction with a genetic algorithm to directly optimize a plan with respect to natural-language user preferences despite the non-differentiability of traditional planners. Directly optimizing the plan bridges the gap between research into more efficient planners and research into planning with language models by utilizing the convenience of natural language to guide the output of formal planners. We demonstrate the effectiveness of our plan critic at adhering to user preferences on a disaster recovery task, and observe improved performance compared to an llm-only neurosymbolic approach., Comment: 5 pages, 3 figures

Type Ia supernovae, critical for studying cosmic expansion, arise from thermonuclear explosions of white dwarfs, but their precise progenitor pathways remain unclear. Growing evidence supports the ``double-degenerate'' scenario, where two white dwarfs interact. The absence of other companion types capable of explaining the observed Ia rate, along with observations of hyper-velocity white dwarfs interpreted as surviving companions of such systems provide compelling evidence in favor of this scenario. Upcoming millihertz gravitational wave observatories like the Laser Interferometer Space Antenna (LISA) are expected to detect thousands of double-degenerate systems, though the most compact known candidate Ia progenitors produce only marginally detectable gravitational wave signals. Here, we report observations of ATLAS J1138-5139, a binary white dwarf system with an orbital period of 28 minutes. Our analysis reveals a 1 solar mass carbon-oxygen white dwarf accreting from a helium-core white dwarf. Given its mass, the accreting carbon-oxygen white dwarf is poised to trigger a typical-luminosity Type Ia supernova within a few million years, or to evolve into a stably mass-transferring AM CVn system. ATLAS J1138-5139 provides a rare opportunity to calibrate binary evolution models by directly comparing observed orbital parameters and mass transfer rates closer to merger than any previously identified candidate Type Ia progenitor. Its compact orbit ensures detectability by LISA, demonstrating the potential of millihertz gravitational wave observatories to reveal a population of Type Ia progenitors on a Galactic scale, paving the way for multi-messenger studies offering insights into the origins of these cosmologically significant explosions., Comment: 40 pages, 7 figures, 2 tables

We present a detailed study of the large-scale shock front in Stephan's Quintet, a byproduct of past and ongoing interactions. Using integral-field spectroscopy from the new William Herschel Telescope Enhanced Area Velocity Explorer (WEAVE), recent 144 MHz observations from the LOFAR Two-metre Sky Survey (LoTSS), and archival data from the Very Large Array and James Webb Space Telescope (JWST), we obtain new measurements of key shock properties and determine its impact on the system. Harnessing the WEAVE large integral field unit's (LIFU) field of view (90 $\times$ 78 arcsec$^{2}$), spectral resolution ($R\sim2500$) and continuous wavelength coverage across the optical band, we perform robust emission line modeling and dynamically locate the shock within the multi-phase intergalactic medium (IGM) with higher precision than previously possible. The shocking of the cold gas phase is hypersonic, and comparisons with shock models show that it can readily account for the observed emission line ratios. In contrast, we demonstrate that the shock is relatively weak in the hot plasma visible in X-rays (with Mach number of $\mathcal{M} \sim 2 - 4$), making it inefficient at producing the relativistic particles needed to explain the observed synchrotron emission. Instead, we propose that it has led to an adiabatic compression of the medium, which has increased the radio luminosity ten-fold. Comparison of the Balmer line-derived extinction map with the molecular gas and hot dust observed with JWST suggests that pre-existing dust may have survived the collision, allowing the condensation of H$_{2}$ - a key channel for dissipating the shock energy., Comment: 23 pages, 15 figures, accepted for publication in MNRAS

The Radio Neutrino Observatory in Greenland (RNO-G) is the first in-ice radio array in the northern hemisphere for the detection of ultra-high energy neutrinos via the coherent radio emission from neutrino-induced particle cascades within the ice. The array is currently in phased construction near Summit Station on the Greenland ice sheet, with 7~stations deployed during the first two boreal summer field seasons of 2021 and 2022. In this paper, we describe the installation and system design of these initial RNO-G stations, and discuss the performance of the array as of summer 2024.

We report the discovery of three ultracompact binary white dwarf systems hosting accretion disks, with orbital periods of 7.95, 8.68, and 13.15 minutes. This significantly augments the population of mass-transferring binaries at the shortest periods, and provides the first evidence that accretors in ultracompacts can be dense enough to host accretion disks even below 10 minutes (where previously only direct-impact accretors were known). In the two shortest-period systems, we measured changes in the orbital periods driven by the combined effect of gravitational wave emission and mass transfer; we find $\dot{P}$ is negative in one case, and positive in the other. This is only the second system measured with a positive $\dot{P}$, and it the most compact binary known that has survived a period minimum. Using these systems as examples, we show how the measurement of $\dot{P}$ is a powerful tool in constraining the physical properties of binaries, e.g. the mass and mass-radius relation of the donor stars. We find that the chirp masses of ultracompact binaries at these periods seem to cluster around $\mathcal{M}_c \sim 0.3 M_\odot$, perhaps suggesting a common origin for these systems or a selection bias in electromagnetic discoveries. Our new systems are among the highest-amplitude known gravitational wave sources in the millihertz regime, providing exquisite opportunity for multi-messenger study with future space-based observatories such as \textit{LISA} and TianQin; we discuss how such systems provide fascinating laboratories to study the unique regime where the accretion process is mediated by gravitational waves., Comment: Accepted for publication in ApJ

Important advances have recently been made in the search for materials with complex multi-phase landscapes that host photoinduced metastable collective states with exotic functionalities. In almost all cases so far, the desired phases are accessed by exploiting light-matter interactions via the imaginary part of the dielectric function through above-bandgap or resonant mode excitation. Nonresonant Raman excitation of coherent modes has been experimentally observed and proposed for dynamic material control, but the resulting atomic excursion has been limited to perturbative levels. Here, we demonstrate that it is possible to overcome this challenge by employing nonresonant ultrashort pulses with low photon energies well below the bandgap. Using mid-infrared pulses, we induce ferroelectric reversal in lithium niobate and phase switching in tin selenide and characterize the large-amplitude mode displacements through femtosecond Raman scattering, second harmonic generation, and x-ray diffraction. This approach, validated by first-principle calculations, defines a novel method for synthesizing hidden phases with unique functional properties and manipulating complex energy landscapes at reduced energy consumption and ultrafast speeds., Comment: 5 figures

Climate change poses an urgent global threat, needing the rapid identification and deployment of innovative solutions. We hypothesise that many of these solutions already exist within scientific literature but remain underutilised. To address this gap, this study employs a curated dataset sourced from OpenAlex, a comprehensive repository of scientific papers. Utilising Large Language Models (LLMs), such as GPT4-o from OpenAI, we evaluate title-abstract pairs from scientific papers on seven dimensions, covering climate change mitigation potential, stage of technological development, and readiness for deployment. The outputs of the language models are then compared with human evaluations to assess their effectiveness in identifying promising yet overlooked climate innovations. Our findings suggest that these LLM-based models can effectively augment human expertise, uncovering climate solutions that are potentially impactful but with far greater speed, throughput and consistency. Here, we focused on UK-based solutions, but the workflow is region-agnostic. This work contributes to the discovery of neglected innovations in scientific literature and demonstrates the potential of AI in enhancing climate action strategies., Comment: 10 pages. Accepted in the LatinX in AI workshop at NeurIPS 2024