Search

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 3,000 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: 26 pages, 6 figures

Treatment of cancer has rapidly evolved over time in quite dramatic ways, for example from chemotherapies, targeted therapies to immunotherapies and chimeric antigen receptor T-cells. Nonetheless, the basic design of early phase I trials in oncology still follows pre-dominantly a dose-escalation design. These trials monitor safety over the first treatment cycle in order to escalate the dose of the investigated drug. However, over time studying additional factors such as drug combinations and/or variation in the timing of dosing became important as well. Existing designs were continuously enhanced and expanded to account for increased trial complexity. With toxicities occurring at later stages beyond the first cycle and the need to treat patients over multiple cycles, the focus on the first treatment cycle only is becoming a limitation in nowadays multi-cycle treatment therapies. Here we introduce a multi-cycle time-to-event model (TITE-CLRM: Time-Interval-To-Event Complementary-Loglog Regression Model) allowing guidance of dose-escalation trials studying multi-cycle therapies. The challenge lies in balancing the need to monitor safety of longer treatment periods with the need to continuously enroll patients safely. The proposed multi-cycle time to event model is formulated as an extension to established concepts like the escalation with over dose control principle. The model is motivated from a current drug development project and evaluated in a simulation study., Comment: 18 pages, 8 figures, 1 table, 4 supplementary figures

We present Fillerbuster, a method that completes unknown regions of a 3D scene by utilizing a novel large-scale multi-view latent diffusion transformer. Casual captures are often sparse and miss surrounding content behind objects or above the scene. Existing methods are not suitable for handling this challenge as they focus on making the known pixels look good with sparse-view priors, or on creating the missing sides of objects from just one or two photos. In reality, we often have hundreds of input frames and want to complete areas that are missing and unobserved from the input frames. Additionally, the images often do not have known camera parameters. Our solution is to train a generative model that can consume a large context of input frames while generating unknown target views and recovering image poses when desired. We show results where we complete partial captures on two existing datasets. We also present an uncalibrated scene completion task where our unified model predicts both poses and creates new content. Our model is the first to predict many images and poses together for scene completion., Comment: Project page at https://ethanweber.me/fillerbuster/

Quantum Key Distribution (QKD) is currently being discussed as a technology to safeguard communication in a future where quantum computers compromise traditional public-key cryptosystems. In this paper, we conduct a comprehensive security evaluation of QKD-based solutions, focusing on real-world use cases sourced from academic literature and industry reports. We analyze these use cases, assess their security and identify the possible advantages of deploying QKD-based solutions. We further compare QKD-based solutions with Post-Quantum Cryptography (PQC), the alternative approach to achieving security when quantum computers compromise traditional public-key cryptosystems, evaluating their respective suitability for each scenario. Based on this comparative analysis, we critically discuss and comment on which use cases QKD is suited for, considering factors such as implementation complexity, scalability, and long-term security. Our findings contribute to a better understanding of the role QKD could play in future cryptographic infrastructures and offer guidance to decision-makers considering the deployment of QKD., Comment: Submitted to EPJ Quantum Technology

To model the structure of neutron stars (NSs) theoretically,it is common to consider layers with different density regimes. Matching the equation of state (EoS) for the crust and core and obtaining a suitable description of these extreme conditions are crucial for understanding the properties of these compact objects. In this work, we construct ten different NS EoSs incorporating three distinct crust models, which are connected to the core using a thermodynamically and causally consistent formalism. For cold NSs, we propose a linear relationship between pressure and energy density in a narrow region between the crust and core, effectively establishing an interpolation function in the pressure-baryonic chemical potential plane. We then compare this EoS matching method with the classical approach, which neglects causal and thermodynamic consistency. We solve the Tolman-Oppenheimer-Volkoff equation to obtain the mass-radius relationship and compare our results with observational constraints on NSs. Furthermore, we investigate the influence of the new matching formalism on non-radial oscillation frequencies and damping times. Our findings suggest that the method used to glue the crust and core EoS impacts NS observables, such as the radius, oscillation frequencies, and damping times of non-radial modes, which may be crucial for interpreting future gravitational wave observations from neutron star mergers or isolated pulsars. The effects are particularly noticeable for low-mass NSs, regardless of the specific EoS model chosen. In particular, we find that the $p_1$ oscillation mode exhibits significant differences in frequencies among alternative matching methods, whereas the fundamental $f$-mode remains unaffected by changes in crust models or interpolation schemes., Comment: 9 pages, 5 figures, IWARA2024 proceedings published in Astronomische Nachrichten

There is strong evidence for the conjecture that the $\lambda \phi^4$ QFT- model on 4-dimensional non-commutative Moyal space can be non-perturbatively constructed. As preparation, in this paper we construct the 2-dimensional case with the method of stochastic quantization. We show the local well-posedness and global well-posedness of the stochastic quantization equation, leading to a construction of the Moyal $\lambda \phi^4_2$ measure for any non-negative coupling constant $\lambda$., Comment: 68 pages, 45 pictures

Sparse regularization techniques are well-established in machine learning, yet their application in neural networks remains challenging due to the non-differentiability of penalties like the $L_1$ norm, which is incompatible with stochastic gradient descent. A promising alternative is shallow weight factorization, where weights are decomposed into two factors, allowing for smooth optimization of $L_1$-penalized neural networks by adding differentiable $L_2$ regularization to the factors. In this work, we introduce deep weight factorization, extending previous shallow approaches to more than two factors. We theoretically establish equivalence of our deep factorization with non-convex sparse regularization and analyze its impact on training dynamics and optimization. Due to the limitations posed by standard training practices, we propose a tailored initialization scheme and identify important learning rate requirements necessary for training factorized networks. We demonstrate the effectiveness of our deep weight factorization through experiments on various architectures and datasets, consistently outperforming its shallow counterpart and widely used pruning methods., Comment: accepted at ICLR 2025

The increasing technological advancements towards miniaturized physiological measuring devices have enabled continuous monitoring of epileptic patients outside of specialized environments. The large amounts of data that can be recorded with such devices holds significant potential for developing automated seizure detection frameworks. In this work, we present SeizeIT2, the first open dataset of wearable data recorded in patients with focal epilepsy. The dataset comprises more than 11,000 hours of multimodal data, including behind-the-ear electroencephalography, electrocardiography, electromyography and movement (accelerometer and gyroscope) data. The dataset contains 886 focal seizures recorded from 125 patients across five different European Epileptic Monitoring Centers. We present a suggestive training/validation split to propel the development of AI methodologies for seizure detection, as well as two benchmark approaches and evaluation metrics. The dataset can be accessed on OpenNeuro and is stored in Brain Imaging Data Structure (BIDS) format., Comment: 9 pages, 5 figures, 2 tables, paper in submission on Scientific Data Journal from Nature

The strong coupling $\alpha_\mathrm{s}$ can be obtained from the static energy as shown in previous lattices studies. For short distances, the static energy can be calculated both on the lattice with the use of Wilson line correlators, and with the perturbation theory up to three loop accuracy with leading ultrasoft log resummation. Comparing the perturbative expression and lattice data allows for precise determination of $\alpha_\mathrm{s}(m_Z)$. We will present preliminary results for the determination of $\alpha_\mathrm{s} {(M_Z)}$ in (2+1+1)-flavor QCD using the configurations made availableby the MILC-collaboration with smallest lattice spacing reaching 0.0321fm., Comment: 10 pages, 4 figures, Contribution to the 41st International Symposium on Lattice Field Theory (LATTICE2024), 28 July - 3 August 2024, Liverpool, UK

Monoclonal antibodies (mAbs) represent one of the most prevalent FDA-approved modalities for treating autoimmune diseases, infectious diseases, and cancers. However, discovery and development of therapeutic antibodies remains a time-consuming and expensive process. Recent advancements in machine learning (ML) and artificial intelligence (AI) have shown significant promise in revolutionizing antibody discovery and optimization. In particular, models that predict antibody biological activity enable in-silico evaluation of binding and functional properties; such models can prioritize antibodies with the highest likelihoods of success in costly and time-intensive laboratory testing procedures. We here explore an AI model for predicting the binding and receptor blocking activity of antibodies against influenza A hemagglutinin (HA) antigens. Our present model is developed with the MAMMAL framework for biologics discovery to predict antibody-antigen interactions using only sequence information. To evaluate the model's performance, we tested it under various data split conditions to mimic real-world scenarios. Our models achieved an AUROC $\geq$ 0.91 for predicting the activity of existing antibodies against seen HAs and an AUROC of 0.9 for unseen HAs. For novel antibody activity prediction, the AUROC was 0.73, which further declined to 0.63-0.66 under stringent constraints on similarity to existing antibodies. These results demonstrate the potential of AI foundation models to transform antibody design by reducing dependence on extensive laboratory testing and enabling more efficient prioritization of antibody candidates. Moreover, our findings emphasize the critical importance of diverse and comprehensive antibody datasets to improve the generalization of prediction models, particularly for novel antibody development.

In this article we introduce the notion of Floer function which has the property that the Hessian is a Fredholm operator of index zero in a scale of Hilbert spaces. Since the Hessian has a complicated transformation under chart transition, in general this is not an intrinsic condition. Therefore we introduce the concept of Floerfolds for which we show that the notion of Floer function is intrinsic., Comment: 19 pages

The circumgalactic medium (CGM) plays a critical role in galaxy evolution, influencing gas flows, feedback processes, and galactic dynamics. Observations show a substantial cold gas reservoir in the CGM, but the mechanisms driving its formation and evolution remain unclear. Cosmic rays (CRs), as a source of non-thermal pressure, are increasingly recognized as key regulators of cold gas dynamics. This study explores how CRs affect cold clouds that condense from the hot CGM via thermal instability (TI). Using 3D CR-magnetohydrodynamic (CRMHD) simulations with AREPO, we assess the impact of various CR transport models on cold gas evolution. Under purely advective CR transport, CR pressure significantly suppresses the collapse of thermally unstable regions, altering the CGM's structure. In contrast, realistic CR transport models reveal that CRs escape collapsing regions via streaming and diffusion along magnetic fields, diminishing their influence on the thermal and dynamic structure of the cold CGM. The ratio of the CR escape time to the cloud collapse time emerges as a critical factor in determining the impact of CRs on TI. CRs remain confined within cold clouds when effective CR diffusion is slow which maximizes their pressure support and inhibits collapse. Fast effective CR diffusion, as realized in our 2-moment CRMHD model, facilitates rapid CR escape, reducing their stabilizing effect. This realistic CR transport model shows a wide dynamic range of the effective CR diffusion coefficient, ranging from $10^{29}$ to $10^{30}\,\mathrm{cm^{2}\,s^{-1}}$ for thermally- to CR-dominated atmospheres, respectively. In addition to these CR transport-related effects, we demonstrate that high numerical resolution is crucial to avoid spuriously large clouds formed in low-resolution simulations, which would result in overly long CR escape times and artificially amplified CR pressure support., Comment: 20 pages and 17 figures in main text; submitted to A&A

The contributions of this short technical note are two-fold. Firstly, we introduce a modified version of a generalized Bellman-Ford algorithm calculating the value function of optimal control problems defined on hyper-graphs. Those Bellman-Ford algorithms can be used in particular for the synthesis of near-optimal controllers by the principle of symbolic control. Our modification causes less nodes of the hyper-graph being iterated during the execution compared to our initial version of the algorithm published in 2020. Our second contribution lies in the field of Plan recognition applied to drone missions driven by symbolic controllers. We address and resolve the Plan and Goal Recognition monitor's dependence on a pre-defined initial guess for a drone's task allocation and mission execution. To validate the enhanced implementation, we use a more challenging scenario for UAV-based aerial firefighting, demonstrating the practical applicability and robustness of the system architecture.

We present an extensive study of 4H-SiC nanomechanical resonators electrochemically etched out of a monocrystalline wafer. Combining piezo-driven interferometric determination of the mechanical spectra with scanning-laser-Doppler vibrometry, an unambiguous assignment of resonance peaks to flexural and torsional modes is achieved. The investigation of multiple harmonic eigenmodes of singly and doubly clamped resonators with varying geometry allows for a comprehensive characterization. Excellent intrinsic mechanical quality factors up to $2\times10^5$ are found at room temperature, approaching the thermoelastic limit at eigenfrquencies exceeding 10 MHz. Mechanical stress is essentially absent. Young's modulus in agreement with literature. These findings are robust under post-processing treatments, in particular atomic layer etching and high-temperature thermal annealing. The resulting on-chip high-quality mechanical resonators represent a valuable technological element for a broad range of applications. In particular, the monolithic architecture meets the requirements of spin-based photonic quantum technologies on the upcoming SiC platform.

This article provides a systematic up-to-date survey of automatic summarization techniques, datasets, models, and evaluation methods in the legal domain. Through specific source selection criteria, we thoroughly review over 120 papers spanning the modern `transformer' era of natural language processing (NLP), thus filling a gap in existing systematic surveys on the matter. We present existing research along several axes and discuss trends, challenges, and opportunities for future research.

This study documents the relationship between computer skills/digital literacy and influenza vaccination take-up among older adults in Europe during and after the COVID-19 pandemic. Using data from the Survey of Health, Aging and Retirement in Europe, we find a positive partial association between influenza vaccination take-up and two indicators of computer skills/digital literacy, self-assessed pre-pandemic computer skills and having used a computer at work in any pre-pandemic job. We do not estimate significant behavioural changes for individuals with better computer skills that may have been driven by spillover effects from the pandemic experience.

The fundamental understanding of the root causes of failure requires information on atomic-scale processes. Molecular dynamics (MD) simulations are widely used to provide these insights while maintaining chemical specificity. However, the boundary conditions that can be applied in MD fracture simulations are limited to the linear-elastic fracture mechanics (LEFM) far-field solution, posing restrictions in simulating bending or shearing deformations commonly applied in classical experimental setups. This has to date prevented the accurate and precise prediction of key fracture quantities such as critical stress intensity factors. To overcome these limitations, we here apply the domain-decomposition Capriccio method to couple atomistic MD samples representing silica glass synthesized at various quenching rates with the finite element (FE) method and perform both mode I (tension, three- and four-point bending) and mode III simulations at different loading rates. We investigate multiple criteria to best identify the onset of crack propagation based on the virial stress, the number of pair interactions, the kinetic energy/temperature, the crack velocity, and the crack opening displacement. Based on this, we propose a novel protocol that allows us to determine the fracture toughness of silica glass under mode I and III conditions from atomistic data with increased fidelity, with our results being in good agreement with experimental data and predictions from LEFM. Overall, our contribution demonstrates how coupled FE-MD simulations enable chemically specific quantitative predictions of the fracture behavior of amorphous materials under arbitrary mechanical loading conditions.

We present a dynamic theory for the droplet size distribution in chemically active emulsions, considering a simple ternary mixture with a conserved density. We show that the collective behavior of many droplets, such as monodispersity, emerges through a coupling of the conserved density in the far field. Using our theory, we determine the stationary state of such emulsions and characterize the relaxation of the droplet size distributions at late times. A key finding is a universal scaling behavior, leading to the collapse of the rescaled size distributions at late times. Our results suggest that the key features of our dynamic theory are generic and apply to the broader class of multi-component systems with conservation laws and active chemical reactions.

We introduce Docling, an easy-to-use, self-contained, MIT-licensed, open-source toolkit for document conversion, that can parse several types of popular document formats into a unified, richly structured representation. It is powered by state-of-the-art specialized AI models for layout analysis (DocLayNet) and table structure recognition (TableFormer), and runs efficiently on commodity hardware in a small resource budget. Docling is released as a Python package and can be used as a Python API or as a CLI tool. Docling's modular architecture and efficient document representation make it easy to implement extensions, new features, models, and customizations. Docling has been already integrated in other popular open-source frameworks (e.g., LangChain, LlamaIndex, spaCy), making it a natural fit for the processing of documents and the development of high-end applications. The open-source community has fully engaged in using, promoting, and developing for Docling, which gathered 10k stars on GitHub in less than a month and was reported as the No. 1 trending repository in GitHub worldwide in November 2024., Comment: Accepted to AAAI 25: Workshop on Open-Source AI for Mainstream Use

Deep learning algorithms -- typically consisting of a class of deep artificial neural networks (ANNs) trained by a stochastic gradient descent (SGD) optimization method -- are nowadays an integral part in many areas of science, industry, and also our day to day life. Roughly speaking, in their most basic form, ANNs can be regarded as functions that consist of a series of compositions of affine-linear functions with multidimensional versions of so-called activation functions. One of the most popular of such activation functions is the rectified linear unit (ReLU) function $\mathbb{R} \ni x \mapsto \max\{ x, 0 \} \in \mathbb{R}$. The ReLU function is, however, not differentiable and, typically, this lack of regularity transfers to the cost function of the supervised learning problem under consideration. Regardless of this lack of differentiability issue, deep learning practioners apply SGD methods based on suitably generalized gradients in standard deep learning libraries like {\sc TensorFlow} or {\sc Pytorch}. In this work we reveal an accurate and concise mathematical description of such generalized gradients in the training of deep fully-connected feedforward ANNs and we also study the resulting generalized gradient function analytically. Specifically, we provide an appropriate approximation procedure that uniquely describes the generalized gradient function, we prove that the generalized gradients are limiting Fr\'echet subgradients of the cost functional, and we conclude that the generalized gradients must coincide with the standard gradient of the cost functional on every open sets on which the cost functional is continuously differentiable., Comment: 38 pages

Opaque algorithms disseminate and mediate the content that users consume on online social media platforms. This algorithmic mediation serves users with contents of their liking, on the other hand, it may cause several inadvertent risks to society at scale. While some of these risks, e.g., filter bubbles or dissemination of hateful content, are well studied in the community, behavioral addiction, designated by the Digital Services Act (DSA) as a potential systemic risk, has been understudied. In this work, we aim to study if one can effectively diagnose behavioral addiction using digital data traces from social media platforms. Focusing on the TikTok short-format video platform as a case study, we employ a novel mixed methodology of combining survey responses with data donations of behavioral traces. We survey 1590 TikTok users and stratify them into three addiction groups (i.e., less/moderately/highly likely addicted). Then, we obtain data donations from 107 surveyed participants. By analyzing users' data we find that, among others, highly likely addicted users spend more time watching TikTok videos and keep coming back to TikTok throughout the day, indicating a compulsion to use the platform. Finally, by using basic user engagement features, we train classifier models to identify highly likely addicted users with $F_1 \geq 0.55$. The performance of the classifier models suggests predicting addictive users solely based on their usage is rather difficult., Comment: Accepted at ICWSM 2025, to appear

Over the past decade, contrary to the early popular expectation that large-scale discourse in online communities would foster greater consensus, the large-scale structure of online discourse has been measured to be strongly polarized. Though it was posited that this effect was driven mainly by the algorithmic curation of content by social platforms (the "Filter Bubble") it appears that polarization is mainly driven by the tendency of interactions among users to be homophilic; users strongly prefer to endorse content and other users that are similar to them. Users organize into homophilic clusters or "echo chambers" where interactions within clusters are more likely to be positive and interactions between clusters are more likely to be negative. This motivates studying polarization in online discourse through modeling as an example of self organized dynamics. Models of opinion formation on networks have tended to only encode positive interactions (attraction) between users - as a consequence consensus or non-interacting clusters of users are ubiquitous behaviors. In this research a model of network-based opinion formation is introduced that includes a novel dynamic of negative interaction; instead of modeling negative interaction as repulsion in the opinion space it is modeled as an attraction to a "Doppelg\"anger" user. It is demonstrated that this model exhibits rich behavior. Four distinct regimes of local interaction correspond to different population-wide dynamics - Neutral Consensus, Extreme Consensus, Polarization and Undecided. In the Polarization regime, local interactions are modeled to be homophilic and the structure of discourse that emerges replicates what is seen in empirical data; clustered users where interactions within clusters is positive and interaction between clusters is negative., Comment: 28 pages, 6 figures

Active metasurfaces, compact platforms for nanoscale light manipulation, are transforming technologies like holography, quantum cryptography, and optical computing. Despite their versatility, tunability in metasurfaces has mainly relied on shifting the resonance wavelength or increasing material losses to spectrally detune or quench resonant modes, respectively. However, both methods face fundamental limitations, such as limited Q-factor and near-field enhancement control and the inability to achieve resonance on/off switching by completely coupling and decoupling the mode from the far-field. Here, we demonstrate temporal symmetry-breaking in metasurfaces via ultrafast optical pumping, marking the first experimental realization of radiative loss-driven resonance creation, annihilation, broadening, and sharpening. We introduce restored symmetry-protected bound states in the continuum as a new concept which are central to the realization of temporal symmetry-breaking. These states arise in metasurfaces with geometrically asymmetric unit cells, where the total dipole moment, composed of two antisymmetric dipoles, cancels out. Mie-resonant optical absorption within specific regions of the unit cell locally modifies the refractive index, disrupting the balance between the two dipole moments. A total dipole moment is thereby created or annihilated, and consequently, the radiative loss is tuned. This enables full control over coupling to incoming light, allowing precise adjustment of the resonance linewidth, near-field enhancement, and resonance amplitude. Our work establishes radiative loss-based active metasurfaces with potential applications ranging from high-speed optical and quantum communications to time-crystals and photonic circuits.

Chromatic calorimetry introduces a novel approach to calorimeter design in High Energy Physics (HEP) by integrating Quantum Dot (QD) technology into traditional homogeneous calorimeters. The tunable emission spectra of QDs provide new possibilities for energy reconstruction and potentially improved particle identification in homogeneous devices. This paper presents the initial conceptual design of a chromatic calorimeter aimed at validating the feasibility of QD-based chromatic calorimetry. The study evaluates its expected performance through detailed simulations, comparing the results to the existing calorimetric technologies. By embedding QDs with distinct emission properties within scintillators, we aim to improve both timing resolution and longitudinal segmentation, thus opening new avenues for precision measurements in future HEP experiments. While further development and validation are required, QD-enhanced detectors may represent a viable option for future HEP experiments, providing an additional tool for addressing the evolving demands of particle detection.

Modern telecommunication networks face an increasing complexity due to the rapidly growing number of networked devices and rising amounts of data. The literature advocates for self-managing networks as a means to tackle the resulting challenges. While self-managing networks provide potential solutions to these challenges, current research solely focuses on the perspective of network operators. However, modern telecommunication networks involve various stakeholders, such as service providers and end users, and necessitate interactions between them. By transitioning from a single-stakeholder to a multi-stakeholder perspective, we address the preferences of all involved parties, acknowledging potential conflicts of interest and constraints like information asymmetries. This broader perspective facilitates the development of more effective self-managing networks, significantly enhancing their performance metrics compared to approaches that solely prioritize the concerns of network operators.

Suppressing of an ordered state that competes with superconductivity is one route to enhance superconducting transition temperatures. Whereas the effect of suppressing magnetic states is still not fully understood, materials featuring charge-density waves and superconductivity offer a clearer scenario as both states can be associated with electron-phonon coupling. Metallic transition-metal dichalcogenides are prime examples for such intertwined electron-phonon-driven phases, yet, various compounds do not show the expected interrelation or feature additional mechanisms which makes an unambiguous interpretation difficult. Here, we report high-pressure X-ray diffraction and inelastic X-ray scattering measurements of the prototypical transition-metal dichalcogenide 2$H$-TaSe$_2$ and determine the evolution of the charge-density-wave state and its lattice dynamics up to and beyond its suppression at the critical pressure $p_c = 19.9(1)\,\rm{GPa}$ and at low temperatures. The high quality of our data allows the full refinement of the commensurate charge-density-wave superstructure at low pressure and we find the quantum critical point of the charge-density-wave to be in close vicinity to the reported maximum superconducting transition temperature $T_{sc} = 8.2\,\rm{K}$. $Ab-initio$ calculations corroborate that 2$H$-TaSe$_2$ is a reference example of order-suppressed enhanced superconductivity and can serve as a textbook case to investigate superconductivity near a charge-density-wave quantum critical point.

While learning personalization offers great potential for learners, modern practices in higher education require a deeper consideration of domain models and learning contexts, to develop effective personalization algorithms. This paper introduces an innovative approach to higher education curriculum modelling that utilizes large language models (LLMs) for knowledge graph (KG) completion, with the goal of creating personalized learning-path recommendations. Our research focuses on modelling university subjects and linking their topics to corresponding domain models, enabling the integration of learning modules from different faculties and institutions in the student's learning path. Central to our approach is a collaborative process, where LLMs assist human experts in extracting high-quality, fine-grained topics from lecture materials. We develop a domain, curriculum, and user models for university modules and stakeholders. We implement this model to create the KG from two study modules: Embedded Systems and Development of Embedded Systems Using FPGA. The resulting KG structures the curriculum and links it to the domain models. We evaluate our approach through qualitative expert feedback and quantitative graph quality metrics. Domain experts validated the relevance and accuracy of the model, while the graph quality metrics measured the structural properties of our KG. Our results show that the LLM-assisted graph completion approach enhances the ability to connect related courses across disciplines to personalize the learning experience. Expert feedback also showed high acceptance of the proposed collaborative approach for concept extraction and classification., Comment: Accepted in the IEEE Global Engineering Education Conference (EDUCON2025), London, UK, 22-25 April, 2025

The concept of a Tomonaga-Luttinger liquid (TLL) has been established as a fundamental theory for the understanding of one-dimensional quantum systems. Originally formulated as a replacement for Landau's Fermi-liquid theory, which accurately predicts the behaviour of most 3D metals but fails dramatically in 1D, the TLL description applies to a even broader class of 1D systems,including bosons and anyons. After a certain number of theoretical breakthroughs, its descriptive power has now been confirmed experimentally in different experimental platforms. They extend from organic conductors, carbon nanotubes, quantum wires, topological edge states of quantum spin Hall insulators to cold atoms, Josephson junctions, Bose liquids confined within 1D nanocapillaries and spin chains. In the ground state of such systems, quantum fluctuations become correlated on all length scales, but, counter-intuitively, no long-range order exists. In this respect, this review will illustrate the validity of conformal field theory for describing real-world systems, establishing the boundaries for its application and, on the other side will discuss the spectacular demonstration of how the quantum-critical TLL state governs the properties of many-body systems in one dimension., Comment: Brief review of Tomonaga-Luttinger liquid physics and its experimental realizations. Comments and suggestions welcome. (RevTeX 4.2, 21 pages, 6 figures)

During the past decade, state-of-the-art planet-finder instruments like SPHERE@VLT, coupling coronagraphic devices and extreme adaptive optics systems, unveiled, thanks to large surveys, around 20 planetary mass companions at semi-major axis greater than 10 astronomical units. Direct imaging being the only detection technique to be able to probe this outer region of planetary systems, the SPHERE infrared survey for exoplanets (SHINE) was designed and conducted from 2015 to 2021 to study the demographics of such young gas giant planets around 400 young nearby solar-type stars. In this paper, we present the observing strategy, the data quality, and the point sources analysis of the full SHINE statistical sample as well as snapSHINE. Both surveys used the SPHERE@VLT instrument with the IRDIS dual band imager in conjunction with the integral field spectrograph IFS and the angular differential imaging observing technique. All SHINE data (650 datasets), corresponding to 400 stars, including the targets of the F150 survey, are processed in a uniform manner with an advanced post-processing algorithm called PACO ASDI. An emphasis is put on the classification and identification of the most promising candidate companions. Compared to the previous early analysis SHINE F150, the use of advanced post-processing techniques significantly improved by one or 2 magnitudes (x3-x6) the contrast detection limits, which will allow us to put even tighter constraints on the radial distribution of young gas giants. This increased sensitivity directly places SHINE as the largest and deepest direct imaging survey ever conducted. We detected and classified more than 3500 physical sources. One additional substellar companion has been confirmed during the second phase of the survey (HIP 74865 B), and several new promising candidate companions are awaiting second epoch confirmations., Comment: Accepted for publication in A&A. 19 pages, 19 figures. Abstract shortened to comply with ArxiV standards. Data ingestion at the CDS is ongoing

3D printing has progressed significantly, allowing objects to be produced using a wide variety of materials. Recent advances have employed focused ultrasound in 3D printing, to allow printing inside acoustically transparent materials. Here we introduce a Selective Ultrasonic Melting (SUM) method for 3D printing of poly ({\epsilon}-caprolactone) (PCL) powder mixed with water. The printing was done by mechanically moving a focused ultrasound transducer. The microstructure and porosity of the prints were analyzed with micro-computed tomography ({\mu}CT). The open porosity of the printed samples was determined using the water intrusion method and by passing fluorescent microspheres through the structure. The cytocompatibility of the printed structures was confirmed by seeding NIH-3T3 fibroblast cells on the scaffolds, followed by analysis using live/dead fluorescent assay. and visualization using scanning electron microscopy (SEM). We demonstrated that SUM is a viable technique to print structures with active control of their porosity This method provides an alternative to methods such as fused deposition modelling (FDM) and material jetting.

Collaboration has been shown to enhance creativity, leading to more innovative and effective outcomes. While previous research has explored the abilities of Large Language Models (LLMs) to serve as co-creative partners in tasks like writing poetry or creating narratives, the collaborative potential of LLMs in humor-rich and culturally nuanced domains remains an open question. To address this gap, we conducted a user study to explore the potential of LLMs in co-creating memes - a humor-driven and culturally specific form of creative expression. We conducted a user study with three groups of 50 participants each: a human-only group creating memes without AI assistance, a human-AI collaboration group interacting with a state-of-the-art LLM model, and an AI-only group where the LLM autonomously generated memes. We assessed the quality of the generated memes through crowdsourcing, with each meme rated on creativity, humor, and shareability. Our results showed that LLM assistance increased the number of ideas generated and reduced the effort participants felt. However, it did not improve the quality of the memes when humans collaborated with LLM. Interestingly, memes created entirely by AI performed better than both human-only and human-AI collaborative memes in all areas on average. However, when looking at the top-performing memes, human-created ones were better in humor, while human-AI collaborations stood out in creativity and shareability. These findings highlight the complexities of human-AI collaboration in creative tasks. While AI can boost productivity and create content that appeals to a broad audience, human creativity remains crucial for content that connects on a deeper level., Comment: to appear in: 30th International Conference on Intelligent User Interfaces IUI 25 March 2427 2025 Cagliari Italy

We discuss the inclusion of the process of multiple ionization of atoms in high-intensity electromagnetic fields into particle-in-cell (PIC) codes applied to the simulation of laser-plasma interactions. The approach is based on the application of the Perelomov-Popov-Terent'yev formulas for ionization rates within the paradigm of sequential tunnel ionization. We analyze difficulties and possible inconsistencies of the approach, which may cause problems in the implementation of PIC codes. To test the sensitivity to the ionization model variations, we perform simulations of an argon target ionization by an incident high-intensity laser pulse. For this, we implement an original procedure in PIC code SMILEI, which allows us to account for the dependence on the magnetic quantum number in ionization rates consistently, and also include barrier suppression at high fields., Comment: 14 pages, 3 figures, 3 tables

We present the strongest limits to date on the mixing angle, $\theta$, with which a new scalar particle, $S$, mixes with the Higgs field in the mass range $100$ $MeV

Many AI companies are training their large language models (LLMs) on data without the permission of the copyright owners. The permissibility of doing so varies by jurisdiction: in countries like the EU and Japan, this is allowed under certain restrictions, while in the United States, the legal landscape is more ambiguous. Regardless of the legal status, concerns from creative producers have led to several high-profile copyright lawsuits, and the threat of litigation is commonly cited as a reason for the recent trend towards minimizing the information shared about training datasets by both corporate and public interest actors. This trend in limiting data information causes harm by hindering transparency, accountability, and innovation in the broader ecosystem by denying researchers, auditors, and impacted individuals access to the information needed to understand AI models. While this could be mitigated by training language models on open access and public domain data, at the time of writing, there are no such models (trained at a meaningful scale) due to the substantial technical and sociological challenges in assembling the necessary corpus. These challenges include incomplete and unreliable metadata, the cost and complexity of digitizing physical records, and the diverse set of legal and technical skills required to ensure relevance and responsibility in a quickly changing landscape. Building towards a future where AI systems can be trained on openly licensed data that is responsibly curated and governed requires collaboration across legal, technical, and policy domains, along with investments in metadata standards, digitization, and fostering a culture of openness.

Designing culturally relevant questionnaires for ICTD research is challenging, particularly when adapting surveys for populations to non-western contexts. Prior work adapted questionnaires through expert reviews and pilot studies, which are resource-intensive and time-consuming. To address these challenges, we propose using large language models (LLMs) to automate the questionnaire pretesting process in cross-cultural settings. Our study used LLMs to adapt a U.S.-focused climate opinion survey for a South African audience. We then tested the adapted questionnaire with 116 South African participants via Prolific, asking them to provide feedback on both versions. Participants perceived the LLM-adapted questions as slightly more favorable than the traditional version. Our note opens discussions on the potential role of LLMs in adapting surveys and facilitating cross-cultural questionnaire design., Comment: Accepted to ICTD 2024 (Notes)

This paper explores ideas and provides a potential roadmap for the development and evaluation of physics-specific large-scale AI models, which we call Large Physics Models (LPMs). These models, based on foundation models such as Large Language Models (LLMs) - trained on broad data - are tailored to address the demands of physics research. LPMs can function independently or as part of an integrated framework. This framework can incorporate specialized tools, including symbolic reasoning modules for mathematical manipulations, frameworks to analyse specific experimental and simulated data, and mechanisms for synthesizing theories and scientific literature. We begin by examining whether the physics community should actively develop and refine dedicated models, rather than relying solely on commercial LLMs. We then outline how LPMs can be realized through interdisciplinary collaboration among experts in physics, computer science, and philosophy of science. To integrate these models effectively, we identify three key pillars: Development, Evaluation, and Philosophical Reflection. Development focuses on constructing models capable of processing physics texts, mathematical formulations, and diverse physical data. Evaluation assesses accuracy and reliability by testing and benchmarking. Finally, Philosophical Reflection encompasses the analysis of broader implications of LLMs in physics, including their potential to generate new scientific understanding and what novel collaboration dynamics might arise in research. Inspired by the organizational structure of experimental collaborations in particle physics, we propose a similarly interdisciplinary and collaborative approach to building and refining Large Physics Models. This roadmap provides specific objectives, defines pathways to achieve them, and identifies challenges that must be addressed to realise physics-specific large scale AI models.

The fragmentation cross sections of carbon ion beams with kinetic energies of $115 - 353~\text{MeV/u}$ impinging on thin targets of graphite (C), polyvinyl-toluene (C$_9$H$_{10}$) and PMMA (C$_2$O$_5$H$_8$) have been measured at 90$^{\text{o}}$ and 60$^{\text{o}}$ at the CNAO particle therapy center (Pavia, Italy). The presented measurements are a complete reanalysis by the FOOT collaboration of already published elemental cross section on composite targets, in order to refine the analysis, improve the systematic uncertainties and show the comparison with the FLUKA Monte Carlo code calculations. In this work, the kinetic energy at production of measured fragments has been completely redefined, together with the efficiencies computation. The new analysis strategy has been successfully validated against the Monte Carlo cross sections. Two detection arms were positioned at two different angles to perform the measurement at 90$^{\text{o}}$ and 60$^{\text{o}}$. The fragment species have been identified in charge (Z$_{id}$ = H) and mass (M$_{id}$ = $^1$H, $^2$H, $^3$H) combining the information of the deposited energy in thin plastic scintillators, of the deposited energy in a thick LYSO crystal and of the fragments Time of Flight (ToF) measurement. The ToF was also used to compute the fragments measured kinetic energy. The cross sections are presented as a function of the fragments kinetic energy at production thanks to an unfolding technique applied to the data., Comment: Corresponding Author: Marco Toppi, marco.toppi@uniroma1.it

The restaurant industry is currently facing a challenging socio-economic situation caused by the rise of delivery services, inflation, and typically low margins. Often, technological opportunities for process optimization or customer retention are not fully utilized. In our design case study, we investigate which technologies are already being used to improve the customer experience in restaurants and explore a novel new approach to this issue. We designed, implemented, and evaluated a platform with customers and restaurateurs to increase visibility and emotional connection to nearby restaurants through their dishes. Some of our key findings include the enormous potential of combining location-based systems and conversational agents, but also the difficulties in creating content for such platforms. We contribute to the field of Human-Food Interaction by (1) identifying promising design spaces as well as customer and restaurateur requirements for technology in this domain, (2) presenting an innovative design case study to improve the user experience, and (3) exploring the broader implications of our design case study findings for approaching a real-world metaverse., Comment: 32 pages, 8 figures

We report evidence of a new set of sneaked references discovered in the scientific literature. Sneaked references are references registered in the metadata of publications without being listed in reference section or in the full text of the actual publications where they ought to be found. We document here 80,205 references sneaked in metadata of the International Journal of Innovative Science and Research Technology (IJISRT). These sneaked references are registered with Crossref and all cite -- thus benefit -- this same journal. Using this dataset, we evaluate three different methods to automatically identify sneaked references. These methods compare reference lists registered with Crossref against the full text or the reference lists extracted from PDF files. In addition, we report attempts to scale the search for sneaked references to the scholarly literature.

In this paper, we investigate the prospects and challenges of sensor suites in achieving autonomous control for flying insect robots (FIRs) weighing less than a gram. FIRs, owing to their minuscule weight and size, offer unparalleled advantages in terms of material cost and scalability. However, their size introduces considerable control challenges, notably high-speed dynamics, restricted power, and limited payload capacity. While there have been notable advancements in developing lightweight sensors, often drawing inspiration from biological systems, no sub-gram aircraft has been able to attain sustained hover without relying on feedback from external sensing such as a motion capture system. The lightest vehicle capable of sustained hover -- the first level of "sensor autonomy" -- is the much larger 28 g Crazyflie. Previous work reported a reduction in size of that vehicle's avionics suite to 187 mg and 21 mW. Here, we report a further reduction in mass and power to only 78.4 mg and 15 mW. We replaced the laser rangefinder with a lighter and more efficient pressure sensor, and built a smaller optic flow sensor around a global-shutter imaging chip. A Kalman Filter (KF) fuses these measurements to estimate the state variables that are needed to control hover: pitch angle, translational velocity, and altitude. Our system achieved performance comparable to that of the Crazyflie's estimator while in flight, with root mean squared errors of 1.573 degrees, 0.186 m/s, and 0.139 m, respectively, relative to motion capture., Comment: Submitted to a robotics conference

This project explores the design and development of a transition edge sensor (TES) spectrometer for resonant soft X- ray scattering (RSXS) measurements developed in collaboration between Argonne National Laboratory (ANL) and the National Institute of Standards and Technology (NIST). Soft X-ray scattering is a powerful technique for studying the electronic and magnetic properties of materials on a microscopic level. However, the lack of high-performance soft X-ray spectrometers has limited the potential of this technique. TES spectrometers have the potential to overcome these limitations due to their high energy resolution, high efficiency, and broad energy range. This project aims to optimize the design of a TES spectrometer for RSXS measurements and more generally soft X-ray spectroscopy at the Advanced Photon Source (APS) 29-ID, leading to improved understanding of advanced materials. We will present a detailed description of the instrument design and implementation. The spectrometer consists of a large array of approximately 250 high-speed and high-resolution pixels. The pixels have saturation energies of approximately 1 keV, sub-ms pulse duration and energy resolution of approximately 1 eV. The array is read out using microwave multiplexing chips with MHz bandwidth per channel, enabling efficient data throughput. To facilitate measurement of samples in situ under ultra-high vacuum conditions at the beamline, the spectrometer is integrated with an approximately 1 m long snout.

In recent years, toxic content and hate speech have become widespread phenomena on the internet. Moderators of online newspapers and forums are now required, partly due to legal regulations, to carefully review and, if necessary, delete reader comments. This is a labor-intensive process. Some providers of large language models already offer solutions for automated hate speech detection or the identification of toxic content. These include GPT-4o from OpenAI, Jigsaw's (Google) Perspective API, and OpenAI's Moderation API. Based on the selected German test dataset HOCON34k, which was specifically created for developing tools to detect hate speech in reader comments of online newspapers, these solutions are compared with each other and against the HOCON34k baseline. The test dataset contains 1,592 annotated text samples. For GPT-4o, three different promptings are used, employing a Zero-Shot, One-Shot, and Few-Shot approach. The results of the experiments demonstrate that GPT-4o outperforms both the Perspective API and the Moderation API, and exceeds the HOCON34k baseline by approximately 5 percentage points, as measured by a combined metric of MCC and F2-score.

This paper presents the measurements of the angular differential cross sections for the forward production of He, Li, Be, B, C and N nuclei in the fragmentation process of a 400$\text{MeV/nucleon}$ $^{16}\text{O}$ beam interacting with a graphite target. Due to the limited data available in this energy regime, these measurements of nuclear fragmentation cross sections are relevant to improve nuclear interaction models for Particle Therapy and space radioprotection applications. The data analyzed in this paper were collected during a measurement campaign carried out at the GSI Helmholtz Center for Heavy Ion Research facility in Darmstadt (Germany) in 2021 by the FOOT collaboration. The results are compared with similar results found in the literature and with a previous FOOT measurement of the same process, using the same setup, from a previous pilot run performed at GSI in 2019. The pilot run data, however, had limited statistics and only allowed for the measurement of elemental fragmentation cross sections integrated in the setup acceptance. The 2021 data set, with statistics more than 100 times larger compared to the data collected in 2019, enabled the measurement of angular differential cross sections, fully exploiting the granularity of the FOOT $\Delta \text{E}$-TOF system. Furthermore, a better comprehension of the FOOT apparatus allowed to improve the analysis techniques, leading to a reduction in the final systematic uncertainties.

Molecular Dynamics simulations are essential tools for understanding the dynamic behavior of biomolecules, yet extracting meaningful molecular pathways from these simulations remains challenging due to the vast amount of generated data. In this work, we present Molecular Kinetics via Topology (MoKiTo), a novel approach that combines the ISOKANN algorithm to determine the reaction coordinate of a molecular system with a topological analysis inspired by the Mapper algorithm. Our strategy efficiently identifies and characterizes distinct molecular pathways, enabling the detection and visualization of critical conformational transitions and rare events. This method offers deeper insights into molecular mechanisms, facilitating the design of targeted interventions in drug discovery and protein engineering.

We perform extensive simulations of the two-dimensional cavity-coupled electron gas in a modulating potential as a minimal model for cavity quantum materials. These simulations are enabled by a newly developed quantum-electrodynamical (QED) auxiliary-field quantum Monte Carlo method. We present a procedure to greatly reduce finite-size effects in such calculations. Based on our results, we show that a modified version of weak-coupling perturbation theory is remarkably accurate for a large parameter region. We further provide a simple parameterization of the light-matter correlation energy as a functional of the cavity parameters and the electronic density. These results provide a numerical foundation for the development of the QED density functional theory, which was previously reliant on analytical approximations, to allow quantitative modeling of a wide range of systems with light-matter coupling., Comment: 6 pages, 3 figures

Controlling the domain structure of ferroic materials is key to manipulating their functionality. Typically, quasi-static electric, magnetic, or strain fields are exploited to transform or pole ferroic domains. In contrast, metallurgy makes use of fast thermal quenches across phase transitions to create new functional states and domain structures. This approach employs the rapid temporal evolution of systems far from equilibrium to overcome the constraints imposed by comparably slow interactions. However, guiding the nonequilibrium evolution of domains towards otherwise inaccessible configurations remains largely unexplored in ferroics. Here, we harness thermal quenches to exert control over a ferroic domain pattern. Cooling at variable speed triggers transitions between two ferroic phases in a rare-earth orthoferrite, with transient domain evolution enabling the selection of the final domain pattern. Specifically, by tuning the quench rate, we can either generate the intrinsic domain structure of the low-temperature phase or transfer the original pattern of the high-temperature phase - creating a hidden metastable domain configuration inaccessible at thermal equilibrium. Real-time imaging during rapid quenching reveals two distinct time scales governing domain evolution: a fast fragmentation phase, followed by a slower relaxation towards a new pattern or back to the original one. This dynamic control of domain configurations, alongside external fields, strain engineering, and all-optical switching, offers a novel approach for actively manipulating ferroic order., Comment: 23 pages, 9 figures

To address an important gap in creating children's stories for vocabulary enrichment, we investigate the automatic evaluation of how well stories convey the semantics of target vocabulary words, a task with substantial implications for generating educational content. We motivate this task, which we call measuring contextual informativeness in children's stories, and provide a formal task definition as well as a dataset for the task. We further propose a method for automating the task using a large language model (LLM). Our experiments show that our approach reaches a Spearman correlation of 0.4983 with human judgments of informativeness, while the strongest baseline only obtains a correlation of 0.3534. An additional analysis shows that the LLM-based approach is able to generalize to measuring contextual informativeness in adult-directed text, on which it also outperforms all baselines., Comment: COLING 2025 main conference short paper

Magnetic fields are ubiquitous across different physical systems of current interest; from the early Universe, compact astrophysical objects and heavy-ion collisions to condensed matter systems. A proper treatment of the effects produced by magnetic fields during the dynamical evolution of these systems, can help to understand observables that otherwise show a puzzling behavior. Furthermore, when these fields are comparable to or stronger than \Lambda_QCD, they serve as excellent probes to help elucidate the physics of strongly interacting matter under extreme conditions of temperature and density. In this work we provide a comprehensive review of recent developments on the description of QED and QCD systems where magnetic field driven effects are important. These include the modification of meson static properties such as masses and form factors, the chiral magnetic effect, the description of anomalous transport coefficients, superconductivity in extreme magnetic fields, the properties of neutron stars, the evolution of heavy-ion collisions, as well as effects on the QCD phase diagram. We describe recent theory and phenomenological developments using effective models as well as LQCD methods. The work represents a state-of-the-art review of the field, motivated by presentations and discussions during the "Workshop on Strongly Interacting Matter in Strong Electromagnetic Fields" that took place in the European Centre for Theoretical Studies in Nuclear Physics and Related Areas (ECT*) in the city of Trento, Italy, September 25-29, 2023., Comment: 325 pages-long review of recent topics of interest in the field of magnetic field effects on QED and QCD matter. To be susbmitted to PNPP