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The San Andreas Fault system, known for its frequent seismic activity, provides an extensive dataset for earthquake studies. The region's well-instrumented seismic networks have been crucial in advancing research on earthquake statistics, physics, and subsurface Earth structures. In recent years, earthquake data from California has become increasingly valuable for deep learning applications, such as Generalized Phase Detection (GPD) for phase detection and polarity determination, and PhaseNet for phase arrival-time picking. The continuous accumulation of data, particularly those manually labeled by human analysts, serves as an essential resource for advancing both regional and global deep learning models. To support the continued development of machine learning and data mining studies, we have compiled a unified California Earthquake Event Dataset (CEED) that integrates seismic records from the Northern California Earthquake Data Center (NCEDC) and the Southern California Earthquake Data Center (SCEDC). The dataset includes both automatically and manually determined parameters such as earthquake origin time, source location, P/S phase arrivals, first-motion polarities, and ground motion intensity measurements. The dataset is organized in an event-based format organized by year spanning from 2000 to 2024, facilitating cross-referencing with event catalogs and enabling continuous updates in future years. This comprehensive open-access dataset is designed to support diverse applications including developing deep learning models, creating enhanced catalog products, and research into earthquake processes, fault zone structures, and seismic risks.

Graphene placed in a perpendicular magnetic field supports optical modes known as magnetoplasmons which are transversally confined to the graphene layer. Unlike ordinary graphene plasmons, these magnetoplasmonic surface waves are characterized by a band gap corresponding to the cyclotron frequency. In addition, these magnetoplasmon bands are topological, characterized by a non-zero Chern number. This leads to the existence of topologically protected edge states at domain edges where the Chern number changes. Since the Chern number is dependent on the direction of the magnetic field, edge states exist at domain edges across which the magnetic field flips direction. Physically, the magnetic field can only flip direction at gradual domain edges with finite width creating topological heterojunctions. These topological heterojunctions support extra edge states known as Volkov-Pankratov edge states which can enter the band gap and support propagation in both directions. The number of Volkov-Pankratov states at a heterojunction varies as a function of the width of the gradual domain edge.

Machine-learned interatomic potentials are revolutionising atomistic materials simulations by providing accurate and scalable predictions within the scope covered by the training data. However, generation of an accurate and robust training data set remains a challenge, often requiring thousands of first-principles calculations to achieve high accuracy. Foundation models have started to emerge with the ambition to create universally applicable potentials across a wide range of materials. While foundation models can be robust and transferable, they do not yet achieve the accuracy required to predict reaction barriers, phase transitions, and material stability. This work demonstrates that foundation model potentials can reach chemical accuracy when fine-tuned using transfer learning with partially frozen weights and biases. For two challenging datasets on reactive chemistry at surfaces and stability and elastic properties of tertiary alloys, we show that frozen transfer learning with 10-20% of the data (hundreds of datapoints) achieves similar accuracies to models trained from scratch (on thousands of datapoints). Moreover, we show that an equally accurate, but significantly more efficient surrogate model can be built using the transfer learned potential as the ground truth. In combination, we present a simulation workflow for machine learning potentials that improves data efficiency and computational efficiency.

Corotating Interaction Regions (CIRs) are recurring structures in the solar wind, characterized by interactions between fast and slow solar wind streams that compress and heat plasma. This study investigates the polytropic behavior of distinct regions in and around CIRs: uncompressed slow solar wind, compressed slow solar wind, compressed fast solar wind, and uncompressed fast solar wind. Using Wind spacecraft data and an established methodology for calculating the polytropic index ({\gamma}), we analyze 117 CIR events. Results indicate varying {\gamma} values across regions, with heating observed in compressed regions driven by Alfv\'en wave dissipation originating from fast streams. In the uncompressed fast solar wind, {\gamma} exceeds adiabatic values the most and correlates well with strong Alfv\'enic wave activity.

Colour light curves of resident space objects (RSOs) encapsulate distinctive features that can offer insights into an object's structure and design, making them an invaluable tool for classification and characterisation. We present the results of the first large systematic colour survey of the GEO belt in which we obtain full-night multi-colour light curves for 112 active geostationary objects between April and May 2023. Colour light curve maps were created to compare and contrast the colours between different satellites and bus configurations. We find that satellites with BSS-702 and STAR-2 buses can be effectively distinguished from the colour measurements on these maps, but comparing the average colour of individual satellites within given solar equatorial phase angle ranges shows that it is difficult to distinguish between bus configurations based on colour alone. We also find tentative evidence to suggest that there is a relationship between colour and time spent on orbit for the Eurostar-3000 class satellites, which is unseen behaviour within other bus configuration classes. The satellites in our sample exhibit `redder' colours than the Sun, which is in agreement with previous findings. We found common light curve features such as symmetrical colour changes as well as unique regions of short timescale glinting which are `bluer' than other regimes within the colour light curves. If these features are indeed seasonal, this would be a powerful characterisation tool. We are able to detect and resolve features in the light curve of the LDPE-3A satellite related to manoeuvres being performed. Finally, we measured the solar panel offsets of 54 satellites in our sample and found variation in the type of colour response. The majority of which did not exhibit any colour change across the solar panel glints compared to them shifting towards 'redder' or 'bluer' colours., Comment: 24 pages, 27 figures. Accepted for publication in Advances in Space Research (ASR)

The rampdown in tokamak operations is a difficult to simulate phase during which the plasma is often pushed towards multiple instability limits. To address this challenge, and reduce the risk of disrupting operations, we leverage recent advances in Scientific Machine Learning (SciML) to develop a neural state-space model (NSSM) that predicts plasma dynamics during Tokamak \`a Configuration Variable (TCV) rampdowns. By integrating simple physics structure and data-driven models, the NSSM efficiently learns plasma dynamics during the rampdown from a modest dataset of 311 pulses with only five pulses in the reactor relevant high performance regime. The NSSM is parallelized across uncertainties, and reinforcement learning (RL) is applied to design trajectories that avoid multiple instability limits with high probability. Experiments at TCV ramping down high performance plasmas show statistically significant improvements in current and energy at plasma termination, with improvements in speed through continuous re-training. A predict-first experiment, increasing plasma current by 20\% from baseline, demonstrates the NSSM's ability to make small extrapolations with sufficient accuracy to design trajectories that successfully terminate the pulse. The developed approach paves the way for designing tokamak controls with robustness to considerable uncertainty, and demonstrates the relevance of the SciML approach to learning plasma dynamics for rapidly developing robust trajectories and controls during the incremental campaigns of upcoming burning plasma tokamaks.

In this work, Bernoulli's Law of Large Numbers, also known as the Golden theorem, has been extended to study the relations between empirical probability and empirical randomness of an otherwise random experiment. Using the example of a coin toss and a dice role, some interesting results are drawn. Analytically and using numerical computations, empirical randomness of each outcome has been shown to increase by \textit{ chance}, which itself depends on the growth rate of empirical probabilities. The analyses presented in this work, apart form depicting the nature of flow of random experiments in repetitions, also present dynamical behaviours of the random experiment, and experimental and simulation-based verifications of the mathematical analyses. It also presents an appreciation of the beauty of Bernoulli's Golden theorem and its applications by extension.

Recent years have seen a surge of interest in the community studying the effect of ultraviolet radiation environment, predominantly set by OB stars, on protoplanetary disc evolution and planet formation. This is important because a significant fraction of planetary systems, potentially including our own, formed in close proximity to OB stars. This is a rapidly developing field, with a broad range of observations across many regions recently obtained or recently scheduled. In this paper, stimulated by a series of workshops on the topic, we take stock of the current and upcoming observations. We discuss how the community can build on this recent success with future observations to make progress in answering the big questions of the field, with the broad goal of disentangling how external photoevaporation contributes to shaping the observed (exo)planet population. Both existing and future instruments offer numerous opportunities to make progress towards this goal., Comment: Submitted to the Open Journal of Astrophysics. Corresponding author Thomas Haworth

Compositionally graded alloys (CGAs) are often proposed for use in structural components where the combination of two or more alloys within a single part can yield substantial enhancement in performance and functionality. For these applications, numerous design methodologies have been developed, one of the most sophisticated being the application of path planning algorithms originally designed for robotics to solve CGA design problems. In addition to the traditional application to structural components, this work proposes and demonstrates the application of this CGA design framework to rapid alloy design, synthesis, and characterization. A composition gradient in the CoCrFeNi alloy space was planned between the maximum and minimum stacking fault energy (SFE) as predicted by a previously developed model in a face-centered cubic (FCC) high entropy alloy (HEA) space. The path was designed to be monotonic in SFE and avoid regions that did not meet FCC phase fraction and solidification range constraints predicted by CALculation of PHAse Diagrams (CALPHAD). Compositions from the path were selected to produce a linear gradient in SFE, and the CGA was built using laser directed energy deposition (L-DED). The resulting gradient was characterized for microstructure and mechanical properties, including hardness, elastic modulus, and strain rate sensitivity. Despite being predicted to contain a single FCC phase throughout the gradient, part of the CGA underwent a martensitic transformation, thereby demonstrating a limitation of using equilibrium CALPHAD calculations for phase stability predictions. More broadly, this demonstrates the ability of the methods employed to bring attention to blind spots in alloy models.

In the centers of many galaxy clusters, the hot ($\sim$10$^7$ K) intracluster medium (ICM) can become dense enough that it should cool on short timescales. However, the low measured star formation rates in massive central galaxies and absence of soft X-ray lines from cooling gas suggest that most of this gas never cools - this is known as the "cooling flow problem." The latest observations suggest that black hole jets are maintaining the vast majority of gas at high temperatures. A cooling flow has yet to be fully mapped through all gas phases in any galaxy cluster. Here, we present new observations of the Phoenix cluster using the James Webb Space Telescope to map the [Ne VI] $\lambda$7.652$\mu$m emission line, allowing us to probe gas at 10$^{5.5}$ K on large scales. These data show extended [Ne VI] emission cospatial with (i) the cooling peak in the ICM, (ii) the coolest gas phases, and (iii) sites of active star formation. Taken together, these imply a recent episode of rapid cooling, causing a short-lived spike in the cooling rate which we estimate to be 5,000-23,000 M$_\odot$ yr$^{-1}$. These data provide the first large-scale map of gas at temperatures between 10$^5$-10$^6$ K in a cluster core, and highlight the critical role that black hole feedback plays in not only regulating but also promoting cooling., Comment: 42 pages, 16 figures, 3 tables. Published in Nature

Progress in AI has relied on human-generated data, from annotator marketplaces to the wider Internet. However, the widespread use of large language models now threatens the quality and integrity of human-generated data on these very platforms. We argue that this issue goes beyond the immediate challenge of filtering AI-generated content--it reveals deeper flaws in how data collection systems are designed. Existing systems often prioritize speed, scale, and efficiency at the cost of intrinsic human motivation, leading to declining engagement and data quality. We propose that rethinking data collection systems to align with contributors' intrinsic motivations--rather than relying solely on external incentives--can help sustain high-quality data sourcing at scale while maintaining contributor trust and long-term participation.

Feature importance measures are widely studied and are essential for understanding model behavior, guiding feature selection, and enhancing interpretability. However, many machine learning fitted models involve complex, higher-order interactions between features. Existing feature importance metrics fail to capture these higher-order effects while existing interaction metrics often suffer from limited applicability or excessive computation; no methods exist to conduct statistical inference for feature interactions. To bridge this gap, we first propose a new model-agnostic metric, interaction Leave-One-Covariate-Out iLOCO, for measuring the importance of higher-order feature interactions. Next, we leverage recent advances in LOCO inference to develop distribution-free and assumption-light confidence intervals for our iLOCO metric. To address computational challenges, we also introduce an ensemble learning method for calculating the iLOCO metric and confidence intervals that we show is both computationally and statistically efficient. We validate our iLOCO metric and our confidence intervals on both synthetic and real data sets, showing that our approach outperforms existing methods and provides the first inferential approach to detecting feature interactions.

Machine learning inter-atomic potentials (MLIPs) are revolutionizing the field of molecular dynamics (MD) simulations. Recent MLIPs have tended towards more complex architectures trained on larger datasets. The resulting increase in computational and memory costs may prohibit the application of these MLIPs to perform large-scale MD simulations. Here, we present a teacher-student training framework in which the latent knowledge from the teacher (atomic energies) is used to augment the students' training. We show that the light-weight student MLIPs have faster MD speeds at a fraction of the memory footprint compared to the teacher models. Remarkably, the student models can even surpass the accuracy of the teachers, even though both are trained on the same quantum chemistry dataset. Our work highlights a practical method for MLIPs to reduce the resources required for large-scale MD simulations.

Evaluating large language models (LLMs) for tasks like fact extraction in support of knowledge graph construction frequently involves computing accuracy metrics using a ground truth benchmark based on a knowledge graph (KG). These evaluations assume that errors represent factual disagreements. However, human discourse frequently features metalinguistic disagreement, where agents differ not on facts but on the meaning of the language used to express them. Given the complexity of natural language processing and generation using LLMs, we ask: do metalinguistic disagreements occur between LLMs and KGs? Based on an investigation using the T-REx knowledge alignment dataset, we hypothesize that metalinguistic disagreement does in fact occur between LLMs and KGs, with potential relevance for the practice of knowledge graph engineering. We propose a benchmark for evaluating the detection of factual and metalinguistic disagreements between LLMs and KGs. An initial proof of concept of such a benchmark is available on Github., Comment: 6 pages, 2 tables, to appear in Reham Alharbi, Jacopo de Berardinis, Paul Groth, Albert Mero\~no-Pe\~nuela, Elena Simperl, Valentina Tamma (eds.), ISWC 2024 Special Session on Harmonising Generative AI and Semantic Web Technologies. CEUR-WS.org (forthcoming), for associated code and data see https://github.com/bradleypallen/trex-metalinguistic-disagreement

Consider, in the moduli space of Riemann surfaces of a fixed genus, the subset of surfaces with non-trivial automorphisms. Of special interest are the numerous subsets of surfaces admitting an action of a given finite group, $G$, acting with a specific signature. In a previous study we declared two Riemann surfaces to be \emph{modular companions} if they have topologically equivalent $G$ actions, and that their $G$ quotients are conformally equivalent orbifolds. In this article we present a geometrically-inspired measure to decide whether two modular companions are conformally equivalent (or how different), respecting the $G$ action. Along the way, we construct a moduli space for surfaces with the specified $G$ action and associated equivariant tilings on these surfaces. We specifically apply the ideas to planar, finite group actions whose quotient orbifold is a sphere with four cone points.

Leveraging the wide area coverage of the COSMOS-Web survey, we quantify the abundance of different morphological types from $z\sim 7$ with unprecedented statistics and establish robust constraints on the epoch of emergence of the Hubble sequence. We measure the global (spheroids, disk-dominated, bulge-dominated, peculiar) and resolved (stellar bars) morphologies for about 400,000 galaxies down to F150W=27 using deep learning, representing a two-orders-of-magnitude increase over previous studies. We then provide reference Stellar Mass Functions (SMFs) of different morphologies between $z\sim 0.2$ and $z\sim 7$ and best-fit parameters to inform models of galaxy formation. All catalogs and data are made publicly available. (a)At redshift z > 4.5, the massive galaxy population ($\log M_*/M_\odot>10$) is dominated by disturbed morphologies (~70%) -- even in the optical rest frame -- and very compact objects (~30%) with effective radii smaller than ~500pc. This confirms that a significant fraction of the star formation at cosmic dawn occurs in very dense regions, although the stellar mass for these systems could be overestimated.(b)Galaxies with Hubble-type morphologies -- including bulge and disk-dominated galaxies -- arose rapidly around $z\sim 4$ and dominate the morphological diversity of massive galaxies as early as $z\sim 3$. (c)Using stellar bars as a proxy, we speculate that stellar disks in massive galaxies might have been common (>50%) among the star-forming population since cosmic noon ($z\sim2$-2.5) and formed as early as $z\sim 7$ (d)Massive quenched galaxies are predominantly bulge-dominated from z~4 onward, suggesting that morphological transformations briefly precede or are simultaneous to quenching mechanisms at the high-mass end. (e) Low-mass ($\log M_*/M_\odot<10$) quenched galaxies are typically disk-dominated, pointing to different quenching routes in the two ends of the stellar mass spectrum from cosmic dawn., Comment: subnmitted to A&A. Comments Welcome

Positive human-perception of robots is critical to achieving sustained use of robots in shared environments. One key factor affecting human-perception of robots are their sounds, especially the consequential sounds which robots (as machines) must produce as they operate. This paper explores qualitative responses from 182 participants to gain insight into human-perception of robot consequential sounds. Participants viewed videos of different robots performing their typical movements, and responded to an online survey regarding their perceptions of robots and the sounds they produce. Topic analysis was used to identify common properties of robot consequential sounds that participants expressed liking, disliking, wanting or wanting to avoid being produced by robots. Alongside expected reports of disliking high pitched and loud sounds, many participants preferred informative and audible sounds (over no sound) to provide predictability of purpose and trajectory of the robot. Rhythmic sounds were preferred over acute or continuous sounds, and many participants wanted more natural sounds (such as wind or cat purrs) in-place of machine-like noise. The results presented in this paper support future research on methods to improve consequential sounds produced by robots by highlighting features of sounds that cause negative perceptions, and providing insights into sound profile changes for improvement of human-perception of robots, thus enhancing human robot interaction., Comment: 9 pages, 6 figures - Accepted to be published in the conference proceedings for HRI'25 - the 20th IEEE/ACM International Conference on Human-Robot Interaction. This paper has a companion paper: arXiv:2406.02938 Copyright 2025 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media

Generating and distributing remote entangled pairs (EPs) is a primary function of quantum networks, as entanglement is the fundamental resource for key quantum network applications. A critical performance metric for quantum networks is the time-to-serve (TTS) for users' EP requests, which is the time to distribute EPs between the requested nodes. Minimizing the TTS is essential given the limited qubit coherence time. In this paper, we study the Adaptive Continuous entanglement generation Protocol (ACP), which enables quantum network nodes to continuously generate EPs with their neighbors, while adaptively selecting the neighbors to optimize TTS. Meanwhile, entanglement purification is used to mitigate decoherence in pre-generated EPs prior to the arrival of user requests. We extend the SeQUeNCe simulator to fully implement ACP and conduct extensive simulations across various network scales. Our results show that ACP reduces TTS by up to 94% and increases entanglement fidelity by up to 0.05., Comment: 8 pages, 10 figures, accepted at QCNC 2025

Inductive biases are what allow learners to make guesses in the absence of conclusive evidence. These biases have often been studied in cognitive science using concepts or categories -- e.g. by testing how humans generalize a new category from a few examples that leave the category boundary ambiguous. We use these approaches to study generalization in foundation models during in-context learning. Modern foundation models can condition on both vision and text, and differences in how they interpret and learn from these different modalities is an emerging area of study. Here, we study how their generalizations vary by the modality in which stimuli are presented, and the way the stimuli are described in text. We study these biases with three different experimental paradigms, across three different vision-language models. We find that the models generally show some bias towards generalizing according to shape over color. This shape bias tends to be amplified when the examples are presented visually. By contrast, when examples are presented in text, the ordering of adjectives affects generalization. However, the extent of these effects vary across models and paradigms. These results help to reveal how vision-language models represent different types of inputs in context, and may have practical implications for the use of vision-language models., Comment: 10 pages

The confinement of plasmas in tokamaks and stellarators depends on magnetic field lines lying in nested toroidal surfaces. The transition near the plasma edge away from the lines lying in magnetic surfaces defines properties of divertors. The transition in time defines properties of disruptions. Divertor design and disruption analysis require a detailed understanding of these transitions. The use of a Gaussian window function allows far more information to be extracted about these transitions using far shorter field line integrations than can be obtained using traditional methods based on Poincar\'e plots. The physics of divertors and disruptions is reviewed to clarify why the type of information that can be gained from more efficient methods of analysis is of central importance to the fusion program based on magnetic confinement.

In contrast to classes of neural networks where the learned representations become increasingly expressive with network depth, the learned representations in graph neural networks (GNNs), tend to become increasingly similar. This phenomena, known as oversmoothing, is characterized by learned representations that cannot be reliably differentiated leading to reduced predictive performance. In this paper, we propose an analogy between oversmoothing in GNNs and consensus or agreement in opinion dynamics. Through this analogy, we show that the message passing structure of recent continuous-depth GNNs is equivalent to a special case of opinion dynamics (i.e., linear consensus models) which has been theoretically proven to converge to consensus (i.e., oversmoothing) for all inputs. Using the understanding developed through this analogy, we design a new continuous-depth GNN model based on nonlinear opinion dynamics and prove that our model, which we call behavior-inspired message passing neural network (BIMP) circumvents oversmoothing for general inputs. Through extensive experiments, we show that BIMP is robust to oversmoothing and adversarial attack, and consistently outperforms competitive baselines on numerous benchmarks., Comment: 23 pages, 3 figures

We apply topological methods to better understand how the magnetic field in the stellarator edge can be diverted away from the confined region. Our primary method is calculating the winding numbers of closed contours, which gives information on the number and nature of fixed points within a bounded region. We first apply this to the non-resonant divertor (NRD) Hamiltonian system, and present a simple explanation for the system's diversion: trajectories are guided away from the confined region by X-points which are "unpaired" i.e. do not have corresponding O-points and therefore do not resemble an island chain. We show how similar phenomena can occur in a similar, axisymmetric Hamiltonian system. Secondly, we find examples of neoclassically optimised stellarators in the QUASR database which divert the magnetic field via unpaired X-points. We present and discuss three examples, each containing novel phenomena which might be desirable for stellarator divertors. These findings broaden the horizons of how magnetic fields can be diverted in realistic stellarators, and may be attractive for future experiments and stellarator reactor design., Comment: 24 pages, 10 figures. Submitted to Nuclear Fusion

Research question answering requires accurate retrieval and contextual understanding of scientific literature. However, current Retrieval-Augmented Generation (RAG) methods often struggle to balance complex document relationships with precise information retrieval. In this paper, we introduce Contextualized Graph Retrieval-Augmented Generation (CG-RAG), a novel framework that integrates sparse and dense retrieval signals within graph structures to enhance retrieval efficiency and subsequently improve generation quality for research question answering. First, we propose a contextual graph representation for citation graphs, effectively capturing both explicit and implicit connections within and across documents. Next, we introduce Lexical-Semantic Graph Retrieval (LeSeGR), which seamlessly integrates sparse and dense retrieval signals with graph encoding. It bridges the gap between lexical precision and semantic understanding in citation graph retrieval, demonstrating generalizability to existing graph retrieval and hybrid retrieval methods. Finally, we present a context-aware generation strategy that utilizes the retrieved graph-structured information to generate precise and contextually enriched responses using large language models (LLMs). Extensive experiments on research question answering benchmarks across multiple domains demonstrate that our CG-RAG framework significantly outperforms RAG methods combined with various state-of-the-art retrieval approaches, delivering superior retrieval accuracy and generation quality., Comment: 10 pages, 2 figures

Benchmarks are important tools for tracking the rapid advancements in large language model (LLM) capabilities. However, benchmarks are not keeping pace in difficulty: LLMs now achieve over 90\% accuracy on popular benchmarks like MMLU, limiting informed measurement of state-of-the-art LLM capabilities. In response, we introduce Humanity's Last Exam (HLE), a multi-modal benchmark at the frontier of human knowledge, designed to be the final closed-ended academic benchmark of its kind with broad subject coverage. HLE consists of 2,700 questions across dozens of subjects, including mathematics, humanities, and the natural sciences. HLE is developed globally by subject-matter experts and consists of multiple-choice and short-answer questions suitable for automated grading. Each question has a known solution that is unambiguous and easily verifiable, but cannot be quickly answered via internet retrieval. State-of-the-art LLMs demonstrate low accuracy and calibration on HLE, highlighting a significant gap between current LLM capabilities and the expert human frontier on closed-ended academic questions. To inform research and policymaking upon a clear understanding of model capabilities, we publicly release HLE at https://lastexam.ai., Comment: 27 pages, 6 figures

Temporal graphs arise when modeling interactions that evolve over time. They usually come in several flavors, depending on the number of parameters used to describe the temporal aspects of the interactions: time of appearance, duration, delay of transmission. In the point model, edges appear at specific points in time, while in the more general interval model, edges can be present over multiple time intervals. In both models, the delay for traversing an edge can change with each edge appearance. When time is discrete, the two models are equivalent in the sense that the presence of an edge during an interval is equivalent to a sequence of point-in-time occurrences of the edge. However, this transformation can drastically change the size of the input and has complexity issues. Indeed, we show a gap between the two models with respect to the complexity of the classical problem of computing a fastest temporal path from a source vertex to a target vertex, i.e. a path where edges can be traversed one after another in time and such that the total duration from source to target is minimized. It can be solved in near-linear time in the point model, while we show that the interval model requires quadratic time under classical assumptions of fine-grained complexity. With respect to linear time, our lower bound implies a factor of the number of vertices, while the best known algorithm has a factor of the number of underlying edges. Interestingly, we show that near-linear time is possible in the interval model when restricted to all delays being zero, i.e. traversing an edge is instantaneous.

Optimization challenges span a wide array of fields, from logistics and scheduling to finance, materials science, and drug discovery. Among these, Quadratic Unconstrained Binary Optimization (QUBO) problems are especially significant due to their computational complexity and their potential as a key application for quantum computing. In this work, we introduce an approach for solving QUBO problems using hybrid qubit-qumode bosonic quantum computers$\unicode{x2014}$devices that manipulate and measure the quantum states of light within microwave cavity resonators. We map problems with soft and hard constraints onto the Hamiltonian of a hybrid quantum system, consisting of a single qubit coupled to multiple qumodes. The optimal solution is encoded in the ground state of the system, which is revealed by photon-number measurements. Trial states are prepared through universal qubit-qumode circuits, employing echoed conditional displacement (ECD) gates in combination with qubit rotations. Our approach demonstrates the immense potential of hybrid quantum systems, showcasing their ability to efficiently tackle complex optimization problems in both academia and industry., Comment: 12 pages, 11 figures

We explore the evolving relationship between galaxies and their dark matter halos from $z \sim 0.1$ to $z \sim 12$ using mass-limited angular clustering measurements in the 0.54 deg$^2$ of the COSMOS-Web survey. This study provides the first measurements of the mass-limited two-point correlation function at $z \ge 10$ and a consistent analysis spanning 13.4 Gyr of cosmic history, setting new benchmarks for future simulations and models. Using a halo occupation distribution (HOD) framework, we derive characteristic halo masses and the stellar-to-halo mass relationship (SHMR) across redshifts and stellar mass bins. Our results first indicate that HOD models fit data at $z \ge 2.5$ best when incorporating a non-linear scale-dependent halo bias, boosting clustering at non-linear scales (r = 10-100 kpc). We find that galaxies at z > 10.5 with $\log(M_\star / M_\odot) \ge 8.85$ are hosted by halos with $M_{\rm h} \sim 10^{10.5}\,M_\odot$, achieving a star formation efficiency (SFE) $M_\star / (f_b M_{\rm h}) $ up to 1 dex higher than at $z \le 1$. The high galaxy bias at $z \ge 8$ suggests that these galaxies reside in massive halos with intrinsic high SFE. Our SHMR evolves significantly with redshift, starting high at $z \ge 10.5$, decreasing until $z \sim 2 - 3$, then increasing again until the present. Current simulations fail to reproduce both massive high-$z$ galaxies and this evolution, while semi-empirical models linking SFE to halo mass, accretion rates, and redshift align with our findings. We propose that $z > 8$ galaxies experience bursty star formation without significant feedback altering their growth, driving the rapid growth of massive galaxies observed by JWST. Over time, increasing feedback efficiency and exponential halo growth suppress star formation. At $z \sim 2 - 3$ and after, halo growth slows down while star formation continues, supported by gas reservoirs in halos., Comment: Submitted to Astronomy & Astrophysics

Galaxy groups with $M_{tot} \lesssim 10^{14}$ $M_\odot$ and up to a few tens of members are the most common galaxy environment, marking the transition between field and massive clusters. Identifying groups plays a crucial role in understanding structure formation and galaxy evolution. Modern deep surveys allow us to build well-characterized samples of groups up to the regime where structures were taking shape. We aimed to build the largest deep catalog of galaxy groups to date over the COSMOS-Web field effective area of 0.45 deg$^2$, leveraging the deep high quality data of the new COSMOS-Web photometric catalog resulted from the James Webb Space Telescope observations of the COSMOS-Web field. We performed the group search with the AMICO algorithm, a linear matched filter based on an analytical model for the group signal. AMICO has already been tested in wide and deep field surveys, including COSMOS data up to $z=2$. In this work, we tested the algorithm performances at even higher redshift and searched for protocluster cores at $z>2$. We compiled a list of known protoclusters in COSMOS at $2 \leq z \leq 3.7$, matched them with our detections and studied the clustering of the detected cores. We estimated purity and completeness of our sample by creating data-driven mocks with the SinFoniA code and linked signal-to-noise to purity. We detected 1678 groups in the COSMOS-Web field up to $z=3.7$, including lists of members extending nearly two magnitudes deeper than the previous AMICO-COSMOS catalog. 756 groups were detected with purity of 80\%. More than 500 groups have their redshift confirmed by assigning spectroscopic counterparts. This group catalog offers a unique opportunity to explore galaxy evolution in different environments spanning $\sim$12 Gyr and to study groups, from the least rich population to the formation of the most massive clusters., Comment: 17 pages, 14 figures, 2 tables (including appendices), submitted to Astronomy & Astrophysics (A&A)

We present integral field unit observations of the Phoenix Cluster with the JWST Mid-infrared Instrument's Medium Resolution Spectrometer (MIRI/MRS). We focus this study on the molecular gas, dust, and star formation in the brightest cluster galaxy (BCG). We use precise spectral modeling to produce maps of the silicate dust, molecular gas, and polycyclic aromatic hydrocarbons (PAHs) in the inner $\sim$50 kpc of the cluster. We have developed a novel method for measuring the optical depth from silicates by comparing the observed H$_2$ line ratios to those predicted by excitation models. We provide updated measurements of the total molecular gas mass of $2.2^{+0.4}_{-0.1} \times 10^{10}$ ${\rm M}_\odot$, which agrees with CO-based estimates, providing an estimate of the CO-to-H$_2$ conversion factor of $\alpha_{\rm CO} = 0.9 \pm 0.2\,{\rm M}_{\odot}\,{\rm pc}^{-2}\,({\rm K}\,{\rm km}\,{\rm s}^{-1})^{-1}$; an updated stellar mass of $M_* = 2.6 \pm 0.5 \times 10^{10}$ ${\rm M}_\odot$; and star formation rates averaged over 10 and 100 Myr of $\langle{\rm SFR}\rangle_{\rm 10} = 1340 \pm 100$ ${\rm M}_\odot\,{\rm yr}^{-1}$ and $\langle{\rm SFR}\rangle_{\rm 100} = 740 \pm 80$ ${\rm M}_\odot\,{\rm yr}^{-1}$, respectively. The H$_2$ emission seems to be powered predominantly by star formation within the central $\sim 20$ kpc, with no need for an extra particle heating component as is seen in other BCGs. Additionally, we find nearly an order of magnitude drop in the star formation rates estimated by PAH fluxes in cool core BCGs compared to field galaxies, suggesting that hot particles from the intracluster medium are destroying PAH grains even in the centralmost 10s of kpc., Comment: 21 pages, 13 figures, 2 tables. Submitted to ApJ. Comments welcome!

Quantum computing and quantum sensing represent two distinct frontiers of quantum information science. In this work, we harness quantum computing to solve a fundamental and practically important sensing problem: the detection of weak oscillating fields with unknown strength and frequency. We present a quantum computing enhanced sensing protocol that outperforms all existing approaches. Furthermore, we prove our approach is optimal by establishing the Grover-Heisenberg limit -- a fundamental lower bound on the minimum sensing time. The key idea is to robustly digitize the continuous, analog signal into a discrete operation, which is then integrated into a quantum algorithm. Our metrological gain originates from quantum computation, distinguishing our protocol from conventional sensing approaches. Indeed, we prove that broad classes of protocols based on quantum Fisher information, finite-lifetime quantum memory, or classical signal processing are strictly less powerful. Our protocol is compatible with multiple experimental platforms. We propose and analyze a proof-of-principle experiment using nitrogen-vacancy centers, where meaningful improvements are achievable using current technology. This work establishes quantum computation as a powerful new resource for advancing sensing capabilities., Comment: 9 pages, 4 figures + 44 pages, 3 figures

Double reinforcement learning (DRL) enables statistically efficient inference on the value of a policy in a nonparametric Markov Decision Process (MDP) given trajectories generated by another policy. However, this approach necessarily requires stringent overlap between the state distributions, which is often violated in practice. To relax this requirement and extend DRL, we study efficient inference on linear functionals of the $Q$-function (of which policy value is a special case) in infinite-horizon, time-invariant MDPs under semiparametric restrictions on the $Q$-function. These restrictions can reduce the overlap requirement and lower the efficiency bound, yielding more precise estimates. As an important example, we study the evaluation of long-term value under domain adaptation, given a few short trajectories from the new domain and restrictions on the difference between the domains. This can be used for long-term causal inference. Our method combines flexible estimates of the $Q$-function and the Riesz representer of the functional of interest (e.g., the stationary state density ratio for policy value) and is automatic in that we do not need to know the form of the latter - only the functional we care about. To address potential model misspecification bias, we extend the adaptive debiased machine learning (ADML) framework of \citet{van2023adaptive} to construct nonparametrically valid and superefficient estimators that adapt to the functional form of the $Q$-function. As a special case, we propose a novel adaptive debiased plug-in estimator that uses isotonic-calibrated fitted $Q$-iteration - a new calibration algorithm for MDPs - to circumvent the computational challenges of estimating debiasing nuisances from min-max objectives.

AI Agent, powered by large language models (LLMs) as its cognitive core, is an intelligent agentic system capable of autonomously controlling and determining the execution paths under user's instructions. With the burst of capabilities of LLMs and various plugins, such as RAG, text-to-image/video/3D, etc., the potential of AI Agents has been vastly expanded, with their capabilities growing stronger by the day. However, at the intersection between AI and web3, there is currently no ideal agentic framework that can seamlessly integrate web3 applications into AI agent functionalities. In this paper, we propose Eliza, the first open-source web3-friendly Agentic framework that makes the deployment of web3 applications effortless. We emphasize that every aspect of Eliza is a regular Typescript program under the full control of its user, and it seamlessly integrates with web3 (i.e., reading and writing blockchain data, interacting with smart contracts, etc.). Furthermore, we show how stable performance is achieved through the pragmatic implementation of the key components of Eliza's runtime. Our code is publicly available at https://github.com/ai16z/eliza., Comment: 20 pages, 5 figures

The Moving-Target Traveling Salesman Problem (MT-TSP) aims to find a shortest path for an agent that starts at a stationary depot, visits a set of moving targets exactly once, each within one of their respective time windows, and then returns to the depot. In this paper, we introduce a new Mixed-Integer Conic Program (MICP) formulation that finds the optimum for the Multi-Agent Moving-Target Traveling Salesman Problem (MA-MT-TSP), a generalization of the MT-TSP involving multiple agents. We obtain our formulation by first restating the current state-of-the-art MICP formulation for MA-MT-TSP as a Mixed-Integer Nonlinear Nonconvex Program, and then reformulating it as a new MICP. We present computational results to demonstrate the performance of our approach. The results show that our formulation significantly outperforms the state-of-the-art, with up to a two-order-of-magnitude reduction in runtime, and up to over 90% tighter optimality gap., Comment: 7 pages, 3 figures

Titanium and its alloys are technologically important materials that display a rich phase behaviour. In order to enable large-scale, realistic modelling of Ti and its alloys on the atomistic scale, Machine Learning Interatomic Potentials (MLIPs) are crucial, but rely on databases of atomic configurations. We report databases of such configurations that represent the {\alpha}, \b{eta}, {\omega} and liquid phases of Ti and the Ti-6Al-4V alloy, where we provide total energy, force and stress values evaluated by Density Functional Theory (DFT) using the PBE exchange-correlation functional. We have also leveraged and extended a data reduction strategy, via non-diagonal supercells, for the vibrational properties of Ti and sampling of atomic species within bulk crystalline data for Ti-6Al-4V. These configurations may be used to fit MLIP models that can accurately model the phase behaviour of Ti and Ti-6Al-4V across a broad range of thermodynamic conditions. To validate models, we assembled a set of benchmark protocols, which can be used to rapidly develop and evaluate MLIP models. We demonstrated the utility of our databases and validation tools by fitting models based on the Gaussian Approximation Potential (GAP) and Atomic Cluster Expansion (ACE) frameworks.

We report the analysis of ~1Ms of XMM-Newton observations of the rapidly accreting active galactic nucleus REJ1034+396. The 0.3-9 keV EPIC-pn spectra are well described by a model consisting of steep continuum emission from the corona accompanied by relativistically-blurred reflection from a highly ionized accretion disk. The source is known to exhibit strong excess soft X-ray emission, which we show is well represented by thermal disk photons Comptonized by a warm plasma spanning the inner accretion flow. Additionally, the EPIC-pn data provide compelling evidence ($\Delta C\sim60$ for 4 additional parameters) for the presence of an ultrafast outflow (UFO) with a line-of-sight velocity $v/c=0.307^{+0.001}_{-0.005}$, and an emission signature consistent with reflection of the corona from modestly ionized, outflowing gas. The simultaneous $0.5-2.5$\,keV RGS spectra show clear absorption lines. Modelling of these data confirms the presence of the UFO and constrains its equivalent hydrogen column density, log $N_\mathrm{H}/$(atom cm$^{-2}$) = $21.7^{+0.1}_{-0.2}$. The RGS data also reveal at least two warm absorber components with a modest outflow velocity ($1680^{+40}_{-50}$ km/s). The measured properties and time evolution of the UFO in REJ1034+396 suggest that it is formed from collisionally ionized plasma, launched from the disk surface and accelerated by radiation pressure. The high terminal velocity and substantial absorbing column density imply that the outflow carries sufficient momentum and energy to transform its environment, being capable of driving out essentially all dust and gas it interacts with along the line of sight, even if the AGN were initially surrounded by a Compton thick absorber., Comment: 10 pages, 6 figures, 2 tables; accepted for publication in ApJ

The Inflation Reduction Act subsidizes the deployment of clean electricity, hydrogen production, and carbon capture and storage, which could enable additional actions by other federal, state, and local policymakers to reduce emissions. Power plant rules finalized by the Environmental Protection Agency (EPA) in 2024 are one such example of complementary policies. The rules establish emissions intensity standards, not technology mandates, meaning power plant owners can choose from a range of technologies and control options provided that emissions standards are met. This flexibility makes electricity systems modeling important to understand the potential effects of these regulations. We report below a multi-model analysis of the EPA power plant rules that can provide timely information, including for other countries and states, on emissions impacts, policy design for electricity decarbonization, power sector investments and retirements, cost impacts, and load growth. We also discuss related technical, political, and legal uncertainties.

Determining the electronic phase diagram of a quantum material as a function of temperature (T) and applied magnetic field (H) forms the basis for understanding the microscopic origin of transport properties, such as the anomalous Hall effect (AHE) and topological Hall effect (THE). For many magnetic quantum materials, including EuAl$_4$, a THE arises from a topologically protected magnetic skyrmion lattice with a non-zero scalar spin chirality. We identified a square skyrmion lattice (sSkL) peak in Eu(Ga$_{1-x}$Al$_x$)$_4$ ($x$ = 0.9) identical to the peak previously observed in EuAl$_4$ by performing neutron scattering measurements throughout the phase diagram. Comparing these neutron results with transport measurements, we found that in both compounds the maximal THE does not correspond to the sSkL area. Instead of the maximal THE, the maximal magnetocaloric effect (MCE) boundaries better identify the sSkL lattice phase observed by neutron scattering measurements. The maximal THE therefore arises from interactions of itinerant electrons with frustrated spin fluctuations in a topologically trivial magnetic state.