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Mastery learning -- the process by which students must demonstrate proficiency with a single topic before moving on -- is well recognized as one of the best ways to learn, yet many teachers struggle or remain unsure about how to implement it into a classroom setting. This study leverages two field experiments to test the efficacy of a program designed to encourage greater mastery learning through technology and proactive continuous teacher support. Focusing on elementary and middle school mathematics, teachers receive weekly coaching in how to use Computer Assisted Learning (CAL) for students to follow a customized roadmap of incremental progress. Results indicate significant intent-to-treat effects on math performance of 0.12-0.22 standard deviations. Further analysis shows that these gains are concentrated among students in classrooms with at least an average of 35 minutes of practice per week. Teachers able to achieve high-dosage practice have a high degree of initial buy-in, a clear implementation strategy for when practice occurs, and a willingness to closely monitor progress and follow-up with struggling students. [The Wilson Sheehan Lab for Economic Opportunities (LEO) at Notre Dame provided additional support for this research.]

Functionalities in crystalline materials are determined by 3-dimensional collective interactions of atoms. The confinement of dimensionality in condensed matter provides an exotic research direction to understand the interaction of atoms, thus can be used to tailor or create new functionalities in material systems. In this study, a 2-dimensional transition metal oxide monolayer is constructed inside complex oxide heterostructures based on the theoretical predictions. The electrostatic boundary conditions of oxide monolayer in the heterostructure is carefully designed to tune the chemical, electronic, and magnetic states of oxide monolayer. The challenge of characterizing such an oxide monolayer is overcome by a combination of transmission electron microscopy, x-ray absorption spectroscopy, cross-sectional scanning tunneling microscopy, and electrical transport measurements. An intriguing metal-insulator transition associated with a magnetic transition is discovered in the MnO2 monolayer. This study paves a new route to understand the confinement of dimensionality and explore new intriguing phenomena in condensed matters., Comment: 23 pages, 6 figures

The Great Outdoors (GO) dataset is a multi-modal annotated data resource aimed at advancing ground robotics research in unstructured environments. This dataset provides the most comprehensive set of data modalities and annotations compared to existing off-road datasets. In total, the GO dataset includes six unique sensor types with high-quality semantic annotations and GPS traces to support tasks such as semantic segmentation, object detection, and SLAM. The diverse environmental conditions represented in the dataset present significant real-world challenges that provide opportunities to develop more robust solutions to support the continued advancement of field robotics, autonomous exploration, and perception systems in natural environments. The dataset can be downloaded at: https://www.unmannedlab.org/the-great-outdoors-dataset/, Comment: 5 pages, 5 figures

Recent advances in AI-generated steganography highlight its potential for safeguarding the privacy of vulnerable democratic actors, including aid workers, journalists, and whistleblowers operating in oppressive regimes. In this work, we address current limitations and establish the foundations for large-throughput generative steganography. We introduce a novel approach that enables secure and efficient steganography within latent diffusion models. We show empirically that our methods perform well across a variety of open-source latent diffusion models, particularly in generative image and video tasks.

We discuss our transient search for directed energy systems in local galaxies, with calculations indicating the ability of modest searches to detect optical Search for Extraterrestrial Intelligence (SETI) sources in the closest galaxies. Our analysis follows Lubin (2016) where a messenger civilization follows a beacon strategy we call "intelligent targeting." We plot the required laser time to achieve an SNR of 10 and find the time for a blind transmission to target all stars in the Milky Way to be achievable for local galactic civilizations. As high cadence and sky coverage is the pathway to enable such a detection, we operate the Local Galactic Transient Survey (LGTS) targeting M31 (the Andromeda Galaxy), the Large Magellanic Cloud (LMC), and the Small Magellanic Cloud (SMC) via Las Cumbres Observatory's (LCO) network of 0.4 m telescopes. We explore the ability of modest searches like the LGTS to detect directed pulses in optical and near-infrared wavelengths from Extraterrestrial Intelligence (ETI) at these distances and conclude a civilization utilizing less powerful laser technology than we can construct in this century is readily detectable with the LGTS's observational capabilities. Data processing of 30,000 LGTS images spanning 5 years is in progress with the TRansient Image Processing Pipeline (TRIPP; Thomas et al. (2025))., Comment: 8 pages, 7 figures, 1 table

The open Tavis-Cummings model consists of $N$ quantum emitters interacting with a common cavity mode, accounts for losses and decoherence, and is frequently explored for quantum information processing and designing quantum devices. As $N$ increases, it becomes harder to simulate the open Tavis-Cummings model using traditional methods. To address this problem, we implement two quantum algorithms for simulating the dynamics of this model in the inhomogenous, non-resonant regime, with up to three excitations in the cavity. We show that the implemented algorithms have gate complexities that scale polynomially, as $O(N^2)$ and $O(N^3)$. One of these algorithms is the sampling-based wave matrix Lindbladization algorithm, for which we propose two protocols to implement its system-independent fixed interaction, resolving key open questions of [Patel and Wilde, Open Sys. & Info. Dyn., 30:2350014 (2023)]. Furthermore, we benchmark our results against a classical differential equation solver and demonstrate the ability to simulate classically intractable systems., Comment: 34 pages, 11 figures

Dense action detection involves detecting multiple co-occurring actions in an untrimmed video while action classes are often ambiguous and represent overlapping concepts. To address this challenge task, we introduce a novel perspective inspired by how humans tackle complex tasks by breaking them into manageable sub-tasks. Instead of relying on a single network to address the entire problem, as in current approaches, we propose decomposing the problem into detecting key concepts present in action classes, specifically, detecting dense static concepts and detecting dense dynamic concepts, and assigning them to distinct, specialized networks. Furthermore, simultaneous actions in a video often exhibit interrelationships, and exploiting these relationships can improve performance. However, we argue that current networks fail to effectively learn these relationships due to their reliance on binary cross-entropy optimization, which treats each class independently. To address this limitation, we propose providing explicit supervision on co-occurring concepts during network optimization through a novel language-guided contrastive learning loss. Our extensive experiments demonstrate the superiority of our approach over state-of-the-art methods, achieving substantial relative improvements of 23.4% and 2.5% mAP on the challenging benchmark datasets, Charades and MultiTHUMOS., Comment: Computer Vision

We present the TRansient Image Processing Pipeline (TRIPP), a transient and variable source detection pipeline that employs both difference imaging and light curve analysis techniques for astronomical data. Additionally, we demonstrate TRIPP's rapid analysis capability by detecting transient candidates in near-real time. TRIPP was tested using image data of the supernova SN2023ixf and from the Local Galactic Transient Survey (LGTS) collected by the Las Cumbres Observatory's (LCO) network of 0.4 m telescopes. To verify the methods employed by TRIPP, we compare our results to published findings on the photometry of SN2023ixf. Additionally, we report the ability of TRIPP to detect transient signals from optical Search for Extra Terrestrial Intelligence (SETI) sources., Comment: 11 pages, 10 figures, 2 tables

Reproduction numbers are widely used for the estimation and prediction of epidemic spreading processes over networks. However, conventional reproduction numbers of an overall network do not indicate where an epidemic is spreading. Therefore, we propose a novel notion of local distributed reproduction numbers to capture the spreading behaviors of each node in a network. We first show how to compute them and then use them to derive new conditions under which an outbreak can occur. These conditions are then used to derive new conditions for the existence, uniqueness, and stability of equilibrium states of the underlying epidemic model. Building upon these local distributed reproduction numbers, we define cluster distributed reproduction numbers to model the spread between clusters composed of nodes. Furthermore, we demonstrate that the local distributed reproduction numbers can be aggregated into cluster distributed reproduction numbers at different scales. However, both local and cluster distributed reproduction numbers can reveal the frequency of interactions between nodes in a network, which raises privacy concerns. Thus, we next develop a privacy framework that implements a differential privacy mechanism to provably protect the frequency of interactions between nodes when computing distributed reproduction numbers. Numerical experiments show that, even under differential privacy, the distributed reproduction numbers provide accurate estimates of the epidemic spread while also providing more insights than conventional reproduction numbers.

Observations have argued that Milky Way (MW), Andromeda, and lower-mass galaxies exhibit extended soft X-ray diffuse halos to radii $R\gtrsim100\,$kpc in the circumgalactic medium (CGM). If interpreted as thermal emission, the shallow surface brightness profiles $S_{X}\propto R^{-1}$ are difficult to explain and contradict other observations. We show that such halos instead arise from inverse Compton (IC) scattering of CMB photons with GeV cosmic ray (CR) electrons. GeV electrons have ~Gyr lifetimes and escape the galaxy, forming a shallow extended profile out to $\gtrsim100\,$kpc, where IC off the CMB should produce soft, thermal-like X-ray spectra peaked at ~keV. The observed keV halo luminosities and brightness profiles agree well with those expected for CRs observed in the local interstellar medium (LISM) escaping the galaxy, with energetics consistent with known CRs from SNe and/or AGN, around galaxies with stellar masses $M_{\ast}\lesssim2\times 10^{11}\,M_{\odot}$. At higher masses observed X-ray luminosities are larger than predicted from IC and should be dominated by hot gas. In the MW+M31, the same models of escaping CRs reproduce gamma-ray observations if we assume an LISM-like proton-to-electron ratio and CR-pressure-dominated halo. In all other halos, the radio and $\gamma$-ray brightness is below detectable limits. If true, the observations provide qualitatively new constraints on CGM and CR physics: X-ray brightness directly traces the CR lepton energy density in the CGM. This agrees with LISM values within 10 kpc, which following the profile expected for escaping CRs in the CGM. The inferred CR pressure is a major part of the MW CGM pressure budget. X-ray surface brightness and luminosity allows one to further determine the CGM diffusivity at radii $\sim10-1000\,$kpc. These also agree with LISM values at small radii but increase in the CGM., Comment: Submitted to the Open Journal of Astrophysics. 13 pages, 7 figures, comments welcome

Imaging inverse problems can be solved in an unsupervised manner using pre-trained diffusion models. In most cases, that involves approximating the gradient of the measurement-conditional score function in the reverse process. Since the approximations produced by existing methods are quite poor, especially early in the reverse process, we propose a new approach that re-estimates and renoises the image several times per diffusion step. Renoising adds carefully shaped colored noise that ensures the pre-trained diffusion model sees white-Gaussian error, in accordance with how it was trained. We demonstrate the effectiveness of our "DDfire" method at 20, 100, and 1000 neural function evaluations on linear inverse problems and phase retrieval.

We study light-front physics and conformal symmetry, and their interplay both on and off the light cone. The full symmetry of the light cone is conformal symmetry not just Lorentz symmetry. Spontaneously breaking conformal symmetry gives masses to particles and takes them off the light cone. Canonical quantization specifies equal-time commutators on the light cone. Equal instant-time and equal light-front-time commutators look very different, but can be shown to be equivalent by looking at unequal-time commutators. We discuss the connection of the light-front approach to the infinite momentum frame approach, and show that vacuum graphs are outside this framework. We show that there is a light-front structure to both AdS/CFT and the eikonal approximation. While mass generation involves scale breaking mass scales, we show that such mass scales can arise via dynamical symmetry breaking in the presence of scale invariant interactions at a renormalization group fixed point., Comment: 31 pages. Prepared for European Physics Journal - Special Topics on the theme Physics at the Light-Front

The first International AI Safety Report comprehensively synthesizes the current evidence on the capabilities, risks, and safety of advanced AI systems. The report was mandated by the nations attending the AI Safety Summit in Bletchley, UK. Thirty nations, the UN, the OECD, and the EU each nominated a representative to the report's Expert Advisory Panel. A total of 100 AI experts contributed, representing diverse perspectives and disciplines. Led by the report's Chair, these independent experts collectively had full discretion over the report's content.

This paper argues that market governance mechanisms should be considered a key approach in the governance of artificial intelligence (AI), alongside traditional regulatory frameworks. While current governance approaches have predominantly focused on regulation, we contend that market-based mechanisms offer effective incentives for responsible AI development. We examine four emerging vectors of market governance: insurance, auditing, procurement, and due diligence, demonstrating how these mechanisms can affirm the relationship between AI risk and financial risk while addressing capital allocation inefficiencies. While we do not claim that market forces alone can adequately protect societal interests, we maintain that standardised AI disclosures and market mechanisms can create powerful incentives for safe and responsible AI development. This paper urges regulators, economists, and machine learning researchers to investigate and implement market-based approaches to AI governance.

We consider multi-agent systems with general information networks where an agent may only observe a subset of other agents. A system designer assigns local utility functions to the agents guiding their actions towards an outcome which determines the value of a given system objective. The aim is to design these local utility functions such that the Price of Anarchy (PoA), which equals the ratio of system objective at worst possible outcome to that at the optimal, is maximized. Towards this, we first develop a linear program (LP) that characterizes the PoA for any utility design and any information network. This leads to another LP that optimizes the PoA and derives the optimal utility design. Our work substantially generalizes existing approaches to the utility design problem. We also numerically show the robustness of proposed framework against unanticipated communication failures.

Amorphous oxide semiconductors are gaining interest for logic and memory transistors compatible with low-temperature fabrication. However, their low thermal conductivity and heterogeneous interfaces suggest that their performance may be severely limited by self-heating, especially at higher power and device densities. Here, we investigate the high-field breakdown of amorphous indium tin oxide (ITO) transistors with scanning thermal microscopy (SThM) and multiphysics simulations. The ITO devices break irreversibly at channel temperatures of ~180 {\deg}C and ~340 {\deg}C on SiO${_2}$ and HfO${_2}$ substrates, respectively, but failure appears primarily caused by thermally-induced compressive strain near the device contacts. Combining SThM measurements with simulations allows us to estimate a thermal boundary conductance (TBC) of 35 ${\pm}$ 12 MWm${^-}$${^2}$K${^-}$${^1}$ for ITO on SiO${_2}$, and 51 ${\pm}$ 14 MWm${^-}$${^2}$K${^-}$${^1}$ for ITO on HfO${_2}$. The latter also enables significantly higher breakdown power due to better heat dissipation and closer thermal expansion matching. These findings provide valuable insights into the thermo-mechanical limitations of ITO devices, paving the way for more reliable and high-performance amorphous oxide transistors.

Watermarking, the practice of embedding imperceptible information into media such as images, videos, audio, and text, is essential for intellectual property protection, content provenance and attribution. The growing complexity of digital ecosystems necessitates watermarks for different uses to be embedded in the same media. However, to detect and decode all watermarks, they need to coexist well with one another. We perform the first study of coexistence of deep image watermarking methods and, contrary to intuition, we find that various open-source watermarks can coexist with only minor impacts on image quality and decoding robustness. The coexistence of watermarks also opens the avenue for ensembling watermarking methods. We show how ensembling can increase the overall message capacity and enable new trade-offs between capacity, accuracy, robustness and image quality, without needing to retrain the base models.

The state-of-the-art online learning models generally conduct a single online gradient descent when a new sample arrives and thus suffer from suboptimal model weights. To this end, we introduce an online broad learning system framework with closed-form solutions for each online update. Different from employing existing incremental broad learning algorithms for online learning tasks, which tend to incur degraded accuracy and expensive online update overhead, we design an effective weight estimation algorithm and an efficient online updating strategy to remedy the above two deficiencies, respectively. Specifically, an effective weight estimation algorithm is first developed by replacing notorious matrix inverse operations with Cholesky decomposition and forward-backward substitution to improve model accuracy. Second, we devise an efficient online updating strategy that dramatically reduces online update time. Theoretical analysis exhibits the splendid error bound and low time complexity of our model. The most popular test-then-training evaluation experiments on various real-world datasets prove its superiority and efficiency. Furthermore, our framework is naturally extended to data stream scenarios with concept drift and exceeds state-of-the-art baselines.

Methods to ensure factual accuracy of text generated by large language models (LLM) in clinical medicine are lacking. VeriFact is an artificial intelligence system that combines retrieval-augmented generation and LLM-as-a-Judge to verify whether LLM-generated text is factually supported by a patient's medical history based on their electronic health record (EHR). To evaluate this system, we introduce VeriFact-BHC, a new dataset that decomposes Brief Hospital Course narratives from discharge summaries into a set of simple statements with clinician annotations for whether each statement is supported by the patient's EHR clinical notes. Whereas highest agreement between clinicians was 88.5%, VeriFact achieves up to 92.7% agreement when compared to a denoised and adjudicated average human clinican ground truth, suggesting that VeriFact exceeds the average clinician's ability to fact-check text against a patient's medical record. VeriFact may accelerate the development of LLM-based EHR applications by removing current evaluation bottlenecks., Comment: 62 pages, 5 figures, 1 table, pre-print manuscript

We investigate the central density structure of dark matter halos in cold dark matter (CDM) and self-interacting dark matter (SIDM) models using simulations that are part of the Feedback In Realistic Environments (FIRE) project. For simulated halos of dwarf galaxy scale ($M_{\rm halo}(z=0)\approx 10^{10}\,M_\odot$), we study the central structure in both dissipationless simulations and simulations with full FIRE-2 galaxy formation physics. As has been demonstrated extensively in recent years, both baryonic feedback and self-interactions can convert central cusps into cores, with the former process doing so in a manner that depends sensitively on stellar mass at fixed $M_{\rm halo}$. Whether the two processes (baryonic feedback and self-interactions) are distinguishable, however, remains an open question. Here we demonstrate that, compared to feedback-induced cores, SIDM-induced cores transition more quickly from the central region of constant density to the falling density at larger radial scales. This result holds true even when including identical galaxy formation modeling in SIDM simulations as is used in CDM simulations, since self-interactions dominate over galaxy formation physics in establishing the central structure of SIDM halos in this mass regime. The change in density profile slope as a function of radius therefore holds the potential to discriminate between self-interactions and galaxy formation physics as the driver of core formation in dwarf galaxies., Comment: 11 pages, 7 figures

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.

The UK has a long-established reputation for excellence in research across a broad range of fields, but in recent years, there has been greater emphasis on STEM investment and greater recognition of the UK's success in STEM. This paper examines the relative strengths of SHAPE disciplines and demonstrates that the UK's SHAPE research portfolio outperforms the UK's STEM research, for each international benchmark considered in this work. It is argued that SHAPE research is becoming increasingly important as a partner to STEM as the widespread use of technology creates societal challenges. It is also argued that the strength of UK SHAPE is the basis of a strategic advantage for UK research., Comment: 13 pages, 9 figures

Directed growth, anisotropic cell shapes, and confinement drive self-organization in multicellular systems. We investigate the influence of particle shape on the distribution and dynamics of nematic microdomains in a minimal in-silico model of proliferating, sterically interacting particles, akin to colonies of rod-shaped bacteria. By introducing continuously tuneable tip variations around a common rod shape with spherical caps, we find that subtle changes significantly impact the emergent dynamics, leading to distinct patterns of microdomain formation and stability. Our analysis reveals separate effects of particle shape and aspect ratio, as well as a transition from exponential to scale-free size distributions, which we recapitulate using an effective master equation model. This allows us to relate differences in microdomain size distributions to different physical mechanisms of microdomain breakup. Our results thereby contribute to the characterization of the effective dynamics in growing aggregates at large and intermediate length scales and the microscopic properties that control it. This could be relevant both for biological self-organization and design strategies for future artificial systems., Comment: 7 pages, 6 figures, minor/formatting updates

A promising path to realizing higher superconducting transition temperatures $T_c$ is the strategic engineering of artificial heterostructures. For example, quantum materials exhibiting some but not all of the characteristics necessary for a robust superconducting state could, in principle, be coupled with other materials in a way that alleviates their intrinsic shortcomings. In this work, we add numerical support to the hypothesis that a strongly interacting superconductor weakened by phase fluctuations can boost its $T_c$ by hybridizing the system with a metal. Using determinant quantum Monte Carlo (DQMC), we simulate a two-dimensional bilayer composed of an attractive Hubbard model and a metallic layer in two regimes of the interaction strength $-|U|$. In the strongly interacting regime, we find that increasing the interlayer hybridization $t_\perp$ results in a nonmonotonic enhancement of $T_c$, with an optimal value comparable to the maximum $T_c$ observed in the single-layer attractive Hubbard model, confirming trends inferred from other approaches. In the intermediate coupling regime, when $-|U|$ is close to the value associated with the maximum $T_c$ of the single-layer model, increasing $t_\perp$ tends to decrease $T_c$, implying that the correlated layer was already optimally tuned. Importantly, we demonstrate that the mechanism behind these trends is related to enhancement in the superfluid stiffness, as was initially proposed by Kivelson [Physica B: Condensed Matter 318, 61 (2002)].

Ultrasound imaging is widely used in medical diagnosis, especially for fetal health assessment. However, the availability of high-quality annotated ultrasound images is limited, which restricts the training of machine learning models. In this paper, we investigate the use of diffusion models to generate synthetic ultrasound images to improve the performance on fetal plane classification. We train different classifiers first on synthetic images and then fine-tune them with real images. Extensive experimental results demonstrate that incorporating generated images into training pipelines leads to better classification accuracy than training with real images alone. The findings suggest that generating synthetic data using diffusion models can be a valuable tool in overcoming the challenges of data scarcity in ultrasound medical imaging.

Community detection is a critical task in graph theory, social network analysis, and bioinformatics, where communities are defined as clusters of densely interconnected nodes. However, detecting communities in large-scale networks with millions of nodes and billions of edges remains challenging due to the inefficiency and unreliability of existing methods. Moreover, many current approaches are limited to specific graph types, such as unweighted or undirected graphs, reducing their broader applicability. To address these issues, we propose a novel heuristic community detection algorithm, termed CoDeSEG, which identifies communities by minimizing the two-dimensional (2D) structural entropy of the network within a potential game framework. In the game, nodes decide to stay in current community or move to another based on a strategy that maximizes the 2D structural entropy utility function. Additionally, we introduce a structural entropy-based node overlapping heuristic for detecting overlapping communities, with a near-linear time complexity.Experimental results on real-world networks demonstrate that CoDeSEG is the fastest method available and achieves state-of-the-art performance in overlapping normalized mutual information (ONMI) and F1 score., Comment: Accepted by The Web Conference 2025 (WWW2025)

The proposed construction of new particle accelerator-based facilities in the coming decades -- and upgrades to existing facilities -- provides the unique opportunity to embed innovative environmental impact reduction techniques into their design. This living document provides high-level guidelines to improve environmental sustainability in the planning, construction, operational and decommissioning stages of large accelerator facilities. A collection of various resources is provided, with examples of some existing and suggested practices., Comment: 24 pages, 1 figure. Living document, version 1

Aligning large language models (LLMs) with human values is essential for their safe deployment and widespread adoption. Current LLM safety benchmarks often focus solely on the refusal of individual problematic queries, which overlooks the importance of the context where the query occurs and may cause undesired refusal of queries under safe contexts that diminish user experience. Addressing this gap, we introduce CASE-Bench, a Context-Aware Safety Evaluation Benchmark that integrates context into safety assessments of LLMs. CASE-Bench assigns distinct, formally described contexts to categorized queries based on Contextual Integrity theory. Additionally, in contrast to previous studies which mainly rely on majority voting from just a few annotators, we recruited a sufficient number of annotators necessary to ensure the detection of statistically significant differences among the experimental conditions based on power analysis. Our extensive analysis using CASE-Bench on various open-source and commercial LLMs reveals a substantial and significant influence of context on human judgments (p<0.0001 from a z-test), underscoring the necessity of context in safety evaluations. We also identify notable mismatches between human judgments and LLM responses, particularly in commercial models within safe contexts., Comment: 24 pages

A machine learning method to predict steady external fluid flows using elliptic input features is introduced. Using data from as few as one high-fidelity simulation, the proposed method produces models generalizable under changes to boundary geometry by using solutions to elliptic boundary value problems over the flow domain as the model input, instead of Cartesian coordinates of the domain. Training data is generated through pointwise evaluation of flow features at points selected through a quad-tree adaptive sampling method to concentrate training points in areas with large field gradients. Models are trained within a training window around the body, while predictions are smoothly extended to freestream conditions using a Partition-of-Unity extension. Predictive capabilities of the machine learning model are demonstrated in steady-state flow of incompressible fluid around a cylinder and a Joukowski airfoil. The predicted flow field is used to warm-start CFD simulations to achieve acceleration in solver convergence.

Use of appropriate initialization to warm-start Reynolds-averaged Navier-Stokes (RANS) simulations of turbulent flow can facilitate convergence and lead to efficient use of computational resources. In this work, a method to model downstream wake development in external turbulent flow is proposed and used for RANS solver convergence acceleration. To balance the model accuracy and cost, the proposed method divides the analysis domain into three regions: near-body, wake and off-body. An approach based on a convolutional neural network is introduced as an efficient method to predict the downstream wake development. The model training only requires data from a single simulation, and its use is demonstrated to be effective in accelerating the RANS simulation when combined with an accurate flow prediction in the near-body region. The simulation using the proposed method took 26.3x fewer iterations, achieving 16.4x speedup in wall-clock time, compared to a baseline run using freestream initialization.

Determining a beam's full trajectory requires tracking both its position and momentum (angular) information. However, the product of position and momentum uncertainty in a simultaneous measurement of the two parameters is bound by the Heisenberg uncertainty limit (HUL). In this work, we present a proof-of-principle demonstration of a quantum-enhanced beam tracking technique, leveraging the inherent position and momentum entanglement between photons produced via spontaneous parametric down-conversion (SPDC). We show that quantum entanglement can be exploited to achieve a beam tracking accuracy beyond the HUL in a simultaneous measurement. Moreover, with existing detection technologies, it is already possible to achieve near real-time beam tracking capabilities at the single-photon level. The technique also exhibits high resilience to background influences, with negligible reduction in tracking accuracy even when subjected to a disruptive beam that is significantly brighter than SPDC., Comment: 7 pages, 5 figures

Graph Convolution Networks (GCNs) are widely considered state-of-the-art for recommendation systems. Several studies in the field of recommendation systems have attempted to apply collaborative filtering (CF) into the Neural ODE framework. These studies follow the same idea as LightGCN, which removes the weight matrix or with a discrete weight matrix. However, we argue that weight control is critical for neural ODE-based methods. The importance of weight in creating tailored graph convolution for each node is crucial, and employing a fixed/discrete weight means it cannot adjust over time within the ODE function. This rigidity in the graph convolution reduces its adaptability, consequently hindering the performance of recommendations. In this study, to create an optimal control for Neural ODE-based recommendation, we introduce a new method called Graph Neural Controlled Differential Equations for Collaborative Filtering (CDE-CF). Our method improves the performance of the Graph ODE-based method by incorporating weight control in a continuous manner. To evaluate our approach, we conducted experiments on various datasets. The results show that our method surpasses competing baselines, including GCNs-based models and state-of-the-art Graph ODE-based methods., Comment: Accepted in WWW 2025 short paper

Summary: VTX is a molecular visualization software capable to handle most molecular structures and dynamics trajectories file formats. It features a real-time high-performance molecular graphics engine, based on modern OpenGL, optimized for the visualization of massive molecular systems and molecular dynamics trajectories. VTX includes multiple interactive camera and user interaction features, notably free-fly navigation and a fully modular graphical user interface designed for increased usability. It allows the production of high-resolution images for presentations and posters with custom background. VTX design is focused on performance and usability for research, teaching and educative purposes. Availability and implementation: VTX is open source and free for non commercial use. Builds for Windows and Ubuntu Linux are available at http://vtx.drugdesign.fr. The source code is available at https://github.com/VTX-Molecular-Visualization, Comment: 5 pages, 1 figure, download VTX at http://vtx.drugdesign.fr, follow development at https://github.com/VTX-Molecular-Visualization

We introduce pseudoalgebras for relative pseudomonads and develop their theory. For each relative pseudomonad $T$, we construct a free--forgetful relative pseudoadjunction that exhibits the bicategory of $T$-pseudoalgebras as terminal among resolutions of $T$. The Kleisli bicategory for $T$ thus embeds into the bicategory of pseudoalgebras as the sub-bicategory of free pseudoalgebras. We consequently obtain a coherence theorem that implies, for instance, that the bicategory of distributors is biequivalent to the 2-category of presheaf categories. In doing so, we extend several aspects of the theory of pseudomonads to relative pseudomonads, including doctrinal adjunction, transport of structure, and lax-idempotence. As an application of our general theory, we prove that, for each class of colimits $\Phi$, there is a correspondence between monads relative to free $\Phi$-cocompletions, and $\Phi$-cocontinuous monads on free $\Phi$-cocompletions., Comment: 56 pages

In out-of-distribution (OOD) detection, one is asked to classify whether a test sample comes from a known inlier distribution or not. We focus on the case where the inlier distribution is defined by a training dataset and there exists no additional knowledge about the novelties that one is likely to encounter. This problem is also referred to as novelty detection, one-class classification, and unsupervised anomaly detection. The current literature suggests that contrastive learning techniques are state-of-the-art for OOD detection. We aim to improve on those techniques by combining/ensembling their scores using the framework of null hypothesis testing and, in particular, a novel generalized likelihood ratio test (GLRT). We demonstrate that our proposed GLRT-based technique outperforms the state-of-the-art CSI and SupCSI techniques from Tack et al. 2020 in dataset-vs-dataset experiments with CIFAR-10, SVHN, LSUN, ImageNet, and CIFAR-100, as well as leave-one-class-out experiments with CIFAR-10. We also demonstrate that our GLRT outperforms the score-combining methods of Fisher, Bonferroni, Simes, Benjamini-Hochwald, and Stouffer in our application.

As more and more automatic vehicles, power consumption prediction becomes a vital issue for task scheduling and energy management. Most research focuses on automatic vehicles in transportation, but few focus on automatic ground vehicles (AGVs) in smart factories, which face complex environments and generate large amounts of data. There is an inevitable trade-off between feature diversity and interference. In this paper, we propose Distributed Multi-Head learning (DMH) systems for power consumption prediction in smart factories. Multi-head learning mechanisms are proposed in DMH to reduce noise interference and improve accuracy. Additionally, DMH systems are designed as distributed and split learning, reducing the client-to-server transmission cost, sharing knowledge without sharing local data and models, and enhancing the privacy and security levels. Experimental results show that the proposed DMH systems rank in the top-2 on most datasets and scenarios. DMH-E system reduces the error of the state-of-the-art systems by 14.5% to 24.0%. Effectiveness studies demonstrate the effectiveness of Pearson correlation-based feature engineering, and feature grouping with the proposed multi-head learning further enhances prediction performance.

In real-world scientific discovery, human beings always make use of the accumulated prior knowledge with imagination pick select one or a few most promising hypotheses from large and noisy data analysis results. In this study, we introduce a new type of graph structure, the text-numeric graph (TNG), which is defined as graph entities and associations have both text-attributed information and numeric information. The TNG is an ideal data structure model for novel scientific discovery via graph reasoning because it integrates human-understandable textual annotations or prior knowledge, with numeric values that represent the observed or activation levels of graph entities or associations in different samples. Together both the textual information and numeric values determine the importance of graph entities and associations in graph reasoning for novel scientific knowledge discovery. We further propose integrating large language models (LLMs) and graph neural networks (GNNs) to analyze the TNGs for graph understanding and reasoning. To demonstrate the utility, we generated the text-omic(numeric) signaling graphs (TOSG), as one type of TNGs, in which all graphs have the same entities, associations and annotations, but have sample-specific entity numeric (omic) values using single cell RNAseq (scRNAseq) datasets of different diseases. We proposed joint LLM-GNN models for key entity mining and signaling pathway mining on the TOSGs. The evaluation results showed the LLM-GNN and TNGs models significantly improve classification accuracy and network inference. In conclusion, the TNGs and joint LLM-GNN models are important approaches for scientific discovery., Comment: 29 pages, 6 figures

We assess evidence for changes in tail characteristics of wind, solar irradiance and temperature variables output from CMIP6 global climate models (GCMs) due to climate forcing. We estimate global and climate zone annual maximum and annual means for period (2015, 2100) from daily output of seven GCMs for daily wind speed, maximum wind speed, solar irradiance and near-surface temperature. We calculate corresponding annualised data for individual locations within neighbourhoods of the North Atlantic and Celtic Sea region. We consider output for three climate scenarios and multiple climate ensembles. We estimate non-stationary extreme value models for annual extremes, and non-homogeneous Gaussian regressions for annual means, using Bayesian inference. We use estimated statistical models to quantify the distribution of (i) the change in 100-year return value for annual extremes, and (2) the change in annual mean, over the period (2025, 2125). To summarise results, we estimate linear mixed effects models for observed variation of (i) and (ii). Evidence for changes in the 100-year return value for annual maxima of solar irradiance and temperature is much stronger than for wind variables over time and with climate scenario.

As the era of big data arrives, traditional artificial intelligence algorithms have difficulty processing the demands of massive and diverse data. Mixture of experts (MoE) has shown excellent performance and broad application prospects. This paper provides an in-depth review and analysis of the latest progress in this field from multiple perspectives, including the basic principles, algorithmic models, key technical challenges, and application practices of MoE. First, we introduce the basic concept of MoE and its core idea and elaborate on its advantages over traditional single models. Then, we discuss the basic architecture of MoE and its main components, including the gating network, expert networks, and learning algorithms. Next, we review the applications of MoE in addressing key technical issues in big data. For each challenge, we provide specific MoE solutions and their innovations. Furthermore, we summarize the typical use cases of MoE in various application domains. This fully demonstrates the powerful capability of MoE in big data processing. We also analyze the advantages of MoE in big data environments. Finally, we explore the future development trends of MoE. We believe that MoE will become an important paradigm of artificial intelligence in the era of big data. In summary, this paper systematically elaborates on the principles, techniques, and applications of MoE in big data processing, providing theoretical and practical references to further promote the application of MoE in real scenarios., Comment: Preprint. 5 figures, 3 tables

Assuming Galactic cosmic rays originate in supernovae and the winds of massive stars, starburst galaxies should produce very-high-energy (VHE; E$>$100 GeV) gamma-ray emission via the interaction of their copious quantities of cosmic rays with the large reservoirs of dense gas within the galaxies. Such VHE emission was detected by VERITAS from the starburst galaxy M 82 in 2008-09. An extensive, multi-year campaign followed these initial observations, yielding a total of 254 h of good quality VERITAS data on M 82. Leveraging modern analysis techniques and the larger exposure, these VERITAS data show a more statistically significant VHE signal ($\sim$6.5 standard deviations ($\sigma$)). The corresponding photon spectrum is well fit by a power law ($\Gamma = 2.3 \pm 0.3_{stat} \pm0.2_{sys}$) and the observed integral flux is F($>$450 GeV) = $(3.2 \pm0.6_{stat} \pm 0.6_{sys}) \times 10^{-13}~\mathrm{cm^{-2}~s}^{-1}$, or $\sim$0.4\% of the Crab Nebula flux above the same energy threshold. The improved VERITAS measurements, when combined with various multi-wavelength data, enable modeling of the underlying emission and transport processes. A purely leptonic scenario is found to be a poor representation of the gamma-ray spectral energy distribution (SED). A lepto-hadronic scenario with cosmic rays following a power-law spectrum in momentum (index $s\simeq 2.25$), and with significant bremsstrahlung below $1$~GeV, provides a good match to the observed SED. The synchrotron emission from the secondary electrons indicates that efficient non-radiative losses of cosmic-ray electrons may be related to advective escape from the starburst core., Comment: 15 pages, 7 figures; Accepted for the publication in The Astrophysical Journal (ApJ)

By combining ab initio downfolding with cluster dynamical mean-field theory, we study the degree of correlations in the low-temperature structures of the breathing-mode kagome van der Waals materials Nb$_3$(F,Cl,Br,I)$_8$. We show that the Coulomb correlation strength steadily increases from I to Br, Cl, and F, allowing us to identify Nb$_3$I$_8$ as a weakly correlated (obstructed atomic) insulator whose gap is only mildly affected by the local Coulomb interaction. Nb$_3$Br$_8$ and Nb$_3$Cl$_8$ are strongly correlated insulators, whose gaps are significantly influenced by Coulomb-induced vertex corrections. Nb$_3$F$_8$ is a prototypical bulk Mott-insulator whose gap is initially opened by strong correlation effects. Angle-resolved photoemission spectroscopy measurements comparing Nb$_3$Br$_8$ and Nb$_3$I$_8$ allow us to experimentally confirm these findings by revealing spectroscopic footprints of the degree of correlation. Our calculations further predict that the entire material family can be tuned into correlated charge-transfer or Mott-insulating phases upon electron or hole doping. Our magnetic property analysis additionally shows that inter-layer magnetic frustrations in the high-temperature phase drive the lattice phase transition to the low-temperature structures. The accompanying bilayer hybridization through inter-layer dimerization yields magnetic singlet ground states in the Cl, Br, and I compounds. Our findings establish Nb$_3$X$_8$ as a robust, versatile, and tunable class for van der Waals-based Coulomb and Mott engineering and allow us to speculate on the symmetry-breaking effects necessary for the recently observed Josephson diode effect in NbSe$_2$/Nb$_3$Br$_8$/NbSe$_2$ heterostructures., Comment: 23 pages, 18 figures

Content generation and manipulation approaches based on deep learning methods have seen significant advancements, leading to an increased need for techniques to detect whether an image has been generated or edited. Another area of research focuses on the insertion and harmonization of objects within images. In this study, we explore the potential of using harmonization data in conjunction with a segmentation model to enhance the detection of edited image regions. These edits can be either manually crafted or generated using deep learning methods. Our findings demonstrate that this approach can effectively identify such edits. Existing forensic models often overlook the detection of harmonized objects in relation to the background, but our proposed Disharmony Network addresses this gap. By utilizing an aggregated dataset of harmonization techniques, our model outperforms existing forensic networks in identifying harmonized objects integrated into their backgrounds, and shows potential for detecting various forms of edits, including virtual try-on tasks.

The present work addresses the simulation of pore emptying during the drying of battery electrodes. For this purpose, a model based on the multiphase-field method (MPF) is used, since it is an established approach for modeling and simulating multiphysical problems. A model based on phase fields is introduced that takes into account fluid flow, capillary effects, and wetting behavior, all of which play an important role in drying. In addition, the MPF makes it possible to track the movement of the liquid-air interface without computationally expensive adaptive mesh generation. The presented model is used for the first time to investigate pore emptying in real hard carbon microstructures. For this purpose, the microstructures of real dried electrodes are used as input for the simulations. The simulations performed here demonstrate the importance of considering the resolved microstructural information compared to models that rely only on statistical geometry parameters such as pore size distributions. The influence of various parameters such as different microstructures, fluid viscosity, and the contact angle on pore emptying are investigated. In addition, this work establishes a correlation between the capillary number and the breakthrough time of the solvent as well as the height difference of the solvent front at the time of breakthrough. The results indicate that the drying process can be optimized by doping the particle surface, which changes the contact angle between the fluids and the particles.

We present the successful measurement of the squared visibility of Sirius at a telescope separation of 3.3 m using small 0.25 m Newtonian-style telescopes in an urban backyard setting. The primary science goal for small-scale intensity interferometers has been to measure the angular diameters of stars. Recent advances in low jitter time-tagging equipment and Single Photon Avalanche Detectors have made the detection of second-order photon correlation signals feasible with small low-cost telescopes. Using Sirius as a target star, we observe a photon count rate of $\sim$1.9 Mcps per detector with matched 1.2 nm wide filters at 589.3 nm and measured the spatial squared visibility at a telescope separation of 3.3 m to be $|V_{12}(3.3\text{m})|^2 = 0.94\pm0.16$. The measured detection significance is $\sim7 \sigma$ after 13.55 h of integration. The uncertainty in the measured visibility includes uncertainty in the instrument response function.The squared visibility agrees closely with the expected value of $0.94\pm0.01$. These results demonstrate that using small low-cost telescopes is feasible for intensity interferometry of bright stars. This enables a simple scaling in sensitivity by further realistic improvements in the instrument response jitter as well as increasing both the number of spectral bands and the number of telescopes towards systems capable of resolving objects such as quasars, white dwarfs, and galactic Cepheid variable stars.

Many practical vision-language applications require models that understand negation, e.g., when using natural language to retrieve images which contain certain objects but not others. Despite advancements in vision-language models (VLMs) through large-scale training, their ability to comprehend negation remains underexplored. This study addresses the question: how well do current VLMs understand negation? We introduce NegBench, a new benchmark designed to evaluate negation understanding across 18 task variations and 79k examples spanning image, video, and medical datasets. The benchmark consists of two core tasks designed to evaluate negation understanding in diverse multimodal settings: Retrieval with Negation and Multiple Choice Questions with Negated Captions. Our evaluation reveals that modern VLMs struggle significantly with negation, often performing at chance level. To address these shortcomings, we explore a data-centric approach wherein we finetune CLIP models on large-scale synthetic datasets containing millions of negated captions. We show that this approach can result in a 10% increase in recall on negated queries and a 40% boost in accuracy on multiple-choice questions with negated captions., Comment: Project page: https://negbench.github.io

The James Webb Space Telescope, launched in 2021, is an infrared observatory of novel design: deployable, with active optics, fully open to space for radiative cooling and orbiting the Lagrange point no. 2. This article explains the rationale leading to this specific design and describes the various other architectures that were considered along the way: from a monolithic 10-meter telescope in geosynchronous orbit to a 6-meter one in High Earth Orbit, then a 16-meter observatory on the Moon, a 4- or 6-meter one in an elliptical heliocentric orbit, and a segmented 8-meter one passively cooled to 50 K at L2, which was finally descoped to 6.6 meters. It also addresses the optimization for scientific performance, the challenge of dealing with such an ultra-low operating temperature, cost issues, supporting technology, modifications made during final design and, finally, how the architecture performs on orbit., Comment: 36 pages, 37 figures, submitted to Journal of Astronomical Telescopes, Instruments and Systems (JATIS)

In the current development of large language models (LLMs), it is important to ensure the accuracy and reliability of the underlying data sources. LLMs are critical for various applications, but they often suffer from hallucinations and inaccuracies due to knowledge gaps in the training data. Knowledge graphs (KGs), as a powerful structural tool, could serve as a vital external information source to mitigate the aforementioned issues. By providing a structured and comprehensive understanding of real-world data, KGs enhance the performance and reliability of LLMs. However, it is common that errors exist in KGs while extracting triplets from unstructured data to construct KGs. This could lead to degraded performance in downstream tasks such as question-answering and recommender systems. Therefore, anomaly detection in KGs is essential to identify and correct these errors. This paper presents an anomaly detection algorithm in knowledge graphs with dual-channel learning (ADKGD). ADKGD leverages a dual-channel learning approach to enhance representation learning from both the entity-view and triplet-view perspectives. Furthermore, using a cross-layer approach, our framework integrates internal information aggregation and context information aggregation. We introduce a kullback-leibler (KL)-loss component to improve the accuracy of the scoring function between the dual channels. To evaluate ADKGD's performance, we conduct empirical studies on three real-world KGs: WN18RR, FB15K, and NELL-995. Experimental results demonstrate that ADKGD outperforms the state-of-the-art anomaly detection algorithms. The source code and datasets are publicly available at https://github.com/csjywu1/ADKGD., Comment: Preprint. 11 figures, 6 tables

Superpixel segmentation is a foundation for many higher-level computer vision tasks, such as image segmentation, object recognition, and scene understanding. Existing graph-based superpixel segmentation methods typically concentrate on the relationships between a given pixel and its directly adjacent pixels while overlooking the influence of non-adjacent pixels. These approaches do not fully leverage the global information in the graph, leading to suboptimal segmentation quality. To address this limitation, we present SIT-HSS, a hierarchical superpixel segmentation method based on structural information theory. Specifically, we first design a novel graph construction strategy that incrementally explores the pixel neighborhood to add edges based on 1-dimensional structural entropy (1D SE). This strategy maximizes the retention of graph information while avoiding an overly complex graph structure. Then, we design a new 2D SE-guided hierarchical graph partitioning method, which iteratively merges pixel clusters layer by layer to reduce the graph's 2D SE until a predefined segmentation scale is achieved. Experimental results on three benchmark datasets demonstrate that the SIT-HSS performs better than state-of-the-art unsupervised superpixel segmentation algorithms. The source code is available at \url{https://github.com/SELGroup/SIT-HSS}., Comment: Accepted by SDM 2025

In business analysis, providing effective recommendations is essential for enhancing company profits. The utilization of graph-based structures, such as bipartite graphs, has gained popularity for their ability to analyze complex data relationships. Link prediction is crucial for recommending specific items to users. Traditional methods in this area often involve identifying patterns in the graph structure or using representational techniques like graph neural networks (GNNs). However, these approaches encounter difficulties as the volume of data increases. To address these challenges, we propose a model called Graph Contrastive Learning for Multi-label Classification (MCGCL). MCGCL leverages contrastive learning to enhance recommendation effectiveness. The model incorporates two training stages: a main task and a subtask. The main task is holistic user-item graph learning to capture user-item relationships. The homogeneous user-user (item-item) subgraph is constructed to capture user-user and item-item relationships in the subtask. We assessed the performance using real-world datasets from Amazon Reviews in multi-label classification tasks. Comparative experiments with state-of-the-art methods confirm the effectiveness of MCGCL, highlighting its potential for improving recommendation systems., Comment: Preprint. 10 figures, 5 tables