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This paper presents a game master AI for single-player role-playing games. The AI is designed to deliver interactive text-based narratives and experiences typically associated with multiplayer tabletop games like Dungeons & Dragons. We report on the design process and the series of experiments to improve the functionality and experience design, resulting in two functional versions of the system. While v1 of our system uses simplified prompt engineering, v2 leverages a multi-agent architecture and the ReAct framework to include reasoning and action. A comparative evaluation demonstrates that v2 as an agentic system maintains play while significantly improving modularity and game experience, including immersion and curiosity. Our findings contribute to the evolution of AI-driven interactive fiction, highlighting new avenues for enhancing solo role-playing experiences., Comment: 17 pages, 10 figures, 1 table, submitted for review

Transmission electron microscopes (TEMs) enable atomic-scale imaging and characterisation, driving advances across fields from materials science to biology. Quantum correlations, specifically entanglement, may provide a basis for novel hybrid sensing techniques to make TEMs compatible with sensitive samples prone to radiation damage. We present a protocol to certify entanglement between electrons and photons naturally arising from certain coherent cathodoluminescence processes. Using mutually unbiased bases in position and momentum, our method allows robust, state-agnostic entanglement verification and provides a lower bound on the entanglement of formation, enabling quantitative comparisons across platforms. Simulations under experiment-inspired conditions and preliminary experimental data highlight the feasibility of implementing this approach in modern TEM systems with optical specimen access. Our work integrates photonic quantum information techniques with electron microscopy. It establishes a foundation for entanglement-based imaging at the atomic scale, offering a potential pathway to reduce radiation exposure., Comment: 13 pages, 7 figures

Chat-based prompts respond with verbose linear-sequential texts, making it difficult to explore and refine ambiguous intents, back up and reinterpret, or shift directions in creative AI-assisted design work. AI-Instruments instead embody "prompts" as interface objects via three key principles: (1) Reification of user-intent as reusable direct-manipulation instruments; (2) Reflection of multiple interpretations of ambiguous user-intents (Reflection-in-intent) as well as the range of AI-model responses (Reflection-in-response) to inform design "moves" towards a desired result; and (3) Grounding to instantiate an instrument from an example, result, or extrapolation directly from another instrument. Further, AI-Instruments leverage LLM's to suggest, vary, and refine new instruments, enabling a system that goes beyond hard-coded functionality by generating its own instrumental controls from content. We demonstrate four technology probes, applied to image generation, and qualitative insights from twelve participants, showing how AI-Instruments address challenges of intent formulation, steering via direct manipulation, and non-linear iterative workflows to reflect and resolve ambiguous intents., Comment: 18 pages, 10 figures. To appear in the Proceedings of the 2025 ACM CHI Conference on Human Factors in Computing Systems, Yokohama, Japan. https://hugoromat.github.io/ai_instruments/

Despite Generative AI (GenAI) systems' potential for enhancing content creation, users often struggle to effectively integrate GenAI into their creative workflows. Core challenges include misalignment of AI-generated content with user intentions (intent elicitation and alignment), user uncertainty around how to best communicate their intents to the AI system (prompt formulation), and insufficient flexibility of AI systems to support diverse creative workflows (workflow flexibility). Motivated by these challenges, we created IntentTagger: a system for slide creation based on the notion of Intent Tags - small, atomic conceptual units that encapsulate user intent - for exploring granular and non-linear micro-prompting interactions for Human-GenAI co-creation workflows. Our user study with 12 participants provides insights into the value of flexibly expressing intent across varying levels of ambiguity, meta-intent elicitation, and the benefits and challenges of intent tag-driven workflows. We conclude by discussing the broader implications of our findings and design considerations for GenAI-supported content creation workflows., Comment: 31 pages, 30 figures, 3 tables. To appear in the Proceedings of the 2025 ACM CHI Conference on Human Factors in Computing Systems, Yokohama, Japan

Time-domain interferometry is an important principle in Fourier transform (FT) and nonlinear femto- to attosecond spectroscopy. To optimize the resolution and sensitivity of this approach, various interferometer stabilization schemes have been developed. Among them, acousto-optical phase modulation (AOPM) of the interferometer arms combined with phase-synchronous lock-in detection has proven as a particular sensitive technique. However, the acousto-optical modulators (AOMs), required for this technique, introduce several disadvantages. Here, we demonstrate an alternative phase modulation scheme which omits AOMs, termed PM scheme here. As a benchmark, we directly compare the performance between the PM and the AOPM scheme in a linear FT spectroscopy experiment and find comparable sensitivity in both approaches.

Learning complex distributions is a fundamental challenge in contemporary applications. Generative models, such as diffusion models, have demonstrated remarkable success in overcoming many limitations of traditional statistical methods. Shen and Meinshausen (2024) introduced engression, a generative approach based on scoring rules that maps noise (and covariates, if available) directly to data. While effective, engression struggles with highly complex distributions, such as those encountered in image data. In this work, we extend engression to improve its capability in learning complex distributions. We propose a framework that defines a general forward process transitioning from the target distribution to a known distribution (e.g., Gaussian) and then learns a reverse Markov process using multiple engression models. This reverse process reconstructs the target distribution step by step. Our approach supports general forward processes, allows for dimension reduction, and naturally discretizes the generative process. As a special case, when using a diffusion-based forward process, our framework offers a method to discretize the training and inference of diffusion models efficiently. Empirical evaluations on simulated and climate data validate our theoretical insights, demonstrating the effectiveness of our approach in capturing complex distributions.

Femtosecond interferometry combined with acousto-optical phase modulation is an effective approach to implement various types of coherent nonlinear and multidimensional spectroscopy schemes. The high sensitivity of this method has recently enabled the study of highly dilute gaseous and ultracold quantum systems for which the attainable spectral resolution is of particular interest. Here, we directly compare the performance and spectral resolution between two experimental implementations, that are step-wise and continuous rapid scanning of the underlying Fourier transform interferometers. We show the performance advantage of the rapid-scanning approach and demonstrate a spectral resolution of 250 MHz in the spectroscopy of laser-cooled Li atoms. This is a 10-fold resolution improvement compared to previous experiments., Comment: 4 pages, 3 figures

The instrumental variable (IV) approach is commonly used to infer causal effects in the presence of unmeasured confounding. Existing methods typically aim to estimate the mean causal effects, whereas a few other methods focus on quantile treatment effects. The aim of this work is to estimate the entire interventional distribution. We propose a method called Distributional Instrumental Variable (DIV), which uses generative modelling in a nonlinear IV setting. We establish identifiability of the interventional distribution under general assumptions and demonstrate an 'under-identified' case, where DIV can identify the causal effects while two-step least squares fails to. Our empirical results show that the DIV method performs well for a broad range of simulated data, exhibiting advantages over existing IV approaches in terms of the identifiability and estimation error of the mean or quantile treatment effects. Furthermore, we apply DIV to an economic data set to examine the causal relation between institutional quality and economic development and our results align well with the original study. We also apply DIV to a single-cell data set, where we study the generalizability and stability in predicting gene expression under unseen interventions. The software implementations of DIV are available in R and Python.

We introduce Lifting By Gaussians (LBG), a novel approach for open-world instance segmentation of 3D Gaussian Splatted Radiance Fields (3DGS). Recently, 3DGS Fields have emerged as a highly efficient and explicit alternative to Neural Field-based methods for high-quality Novel View Synthesis. Our 3D instance segmentation method directly lifts 2D segmentation masks from SAM (alternately FastSAM, etc.), together with features from CLIP and DINOv2, directly fusing them onto 3DGS (or similar Gaussian radiance fields such as 2DGS). Unlike previous approaches, LBG requires no per-scene training, allowing it to operate seamlessly on any existing 3DGS reconstruction. Our approach is not only an order of magnitude faster and simpler than existing approaches; it is also highly modular, enabling 3D semantic segmentation of existing 3DGS fields without requiring a specific parametrization of the 3D Gaussians. Furthermore, our technique achieves superior semantic segmentation for 2D semantic novel view synthesis and 3D asset extraction results while maintaining flexibility and efficiency. We further introduce a novel approach to evaluate individually segmented 3D assets from 3D radiance field segmentation methods., Comment: Accepted to WACV 2025

This paper presents an elementary proof of quantitative uniform-in-time propagation of chaos for the Cucker--Smale model under sufficiently strong interaction. The idea is to combine existing finite-time propagation of chaos estimates with existing uniform-in-time stability estimates for the interacting particle system, in order to obtain a uniform-in-time propagation of chaos estimate with an explicit rate of convergence in the number of particles. This is achieved via a method that is similar in spirit to the classical 'stability + consistency implies convergence' approach in numerical analysis.

While ordinals have traditionally been studied mostly in classical frameworks, constructive ordinal theory has seen significant progress in recent years. However, a general constructive treatment of ordinal exponentiation has thus far been missing. We present two seemingly different definitions of constructive ordinal exponentiation in the setting of homotopy type theory. The first is abstract, uses suprema of ordinals, and is solely motivated by the expected equations. The second is more concrete, based on decreasing lists, and can be seen as a constructive version of a classical construction by Sierpi\'{n}ski based on functions with finite support. We show that our two approaches are equivalent (whenever it makes sense to ask the question), and use this equivalence to prove algebraic laws and decidability properties of the exponential. All our results are formalized in the proof assistant Agda.

Along the optimal trajectory of an optimal control problem constrained by a semilinear parabolic partial differential equation, we prove the differentiability of the value function with respect to the initial condition and, under additional assumptions on the solution of the state equation, the differentiability of the value function with respect to the time variable. In our proof, we rely on local growth assumptions commonly associated with the study of second-order sufficient conditions. These assumptions are generally applicable to a wide range of problems, including, for instance, certain tracking-type problems. Finally, we discuss the differentiability of the value function in a neighborhood of the optimal trajectory when a growth condition for optimal controls is used.

Quantum correlations in the form of entanglement, quantum steering or Bell nonlocality are resources for various information-processing tasks, but their detailed quantification and characterization remains complicated. One counter-intuitive effect is the phenomenon of superactivation, meaning that two copies of a quantum state may exhibit forms of correlations which are absent on the single-copy level. We develop a systematic approach towards a full understanding of this phenomenon using the paradigm of genuine multipartite entanglement. We introduce systematic methods for studying superactivation of entanglement based on symmetries and generalized notions of multipartite distillability. With this, we present novel criteria for superactivation as well as a quantitative theory of it. Moreover, we present evidence for incompressible entanglement on multi-copy systems, which cannot be reduced to the single-copy level., Comment: 20 pages, 7 figures

In this paper we derive new types of KZ equations for conformal blocks with one irregular singularity at infinity. To this end, we construct irregular representations of Kac-Moody algebras where we restrict ourselves to the $sl(2,\mathbb{C})$ case. We show how such irregular representations correspond to irregular Gaiotto-Teschner representations of the Virasoro algebra. By connecting to 2d Liouville theory, we show how the conformal blocks governed by our irregular KZ equation correspond to 4d Argyres-Douglas theories with surface operator insertions. The corresponding flat connections describe braiding between such operators on the Gaiotto curve., Comment: 38 pages, 1 figure

We introduce the Velocity Jumps approach, denoted as JUMP, a new class of Molecular dynamics integrators, replacing the Langevin dynamics by a hybrid model combining a classical Langevin diffusion and a piecewise deterministic Markov process, where the expensive computation of long-range pairwise interactions is replaced by a resampling of the velocities at random times. This framework allows for an acceleration in the simulation speed while preserving sampling and dynamical properties such as the diffusion constant. It can also be integrated in classical multi-timestep methods, pushing further the computational speedup, while avoiding some of the resonance issues of the latter thanks to the random nature of jumps. The JUMP, JUMP-RESPA and JUMP-RESPA1 integrators have been implemented in the GPU-accelerated version of the Tinker-HP package and are shown to provide significantly enhanced performances compared to their BAOAB, BAOAB-RESPA and BAOAB-RESPA1 counterparts respectively.

We generalize the seminal polynomial partitioning theorems of Guth and Katz to a set of semi-Pfaffian sets. Specifically, given a set $\Gamma \subseteq \mathbb{R}^n$ of $k$-dimensional semi-Pfaffian sets, where each $\gamma \in \Gamma$ is defined by a fixed number of Pfaffian functions, and each Pfaffian function is in turn defined with respect to a Pfaffian chain $\vec{q}$ of length $r$, for any $D \ge 1$, we prove the existence of a polynomial $P \in \mathbb{R}[X_1, \ldots, X_n]$ of degree at most $D$ such that each connected component of $\mathbb{R}^n \setminus Z(P)$ intersects at most $\sim \frac{|\Gamma|}{D^{n - k - r}}$ elements of $\Gamma$. Also, under some mild conditions on $\vec{q}$, for any $D \ge 1$, we prove the existence of a Pfaffian function $P'$ of degree at most $D$ defined with respect to $\vec{q}$, such that each connected component of $\mathbb{R}^n \setminus Z(P')$ intersects at most $\sim \frac{|\Gamma|}{D^{n-k}}$ elements of $\Gamma$. To do so, given a $k$-dimensional semi-Pfaffian set $\mathcal{X} \subseteq \mathbb{R}^n$, and a polynomial $P \in \mathbb{R}[X_1, \ldots, X_n]$ of degree at most $D$, we establish a uniform bound on the number of connected components of $\mathbb{R}^n \setminus Z(P)$ that $\mathcal{X}$ intersects; that is, we prove that the number of connected components of $(\mathbb{R}^n \setminus Z(P)) \cap \mathcal{X}$ is at most $\sim D^{k+r}$. Finally as applications, we derive Pfaffian versions of Szemer\'edi-Trotter type theorems, and also prove bounds on the number of joints between Pfaffian curves., Comment: 29 pages, 0 figures Added hypothesis of algebraic independence of Pfaffian chain to avoid obtaining a zero Pfaffian partitioning function

Fuel-lean hydrogen combustion systems hold significant potential for low pollutant emissions, but are also susceptible to intrinsic combustion instabilities. While most research on these instabilities has focused on flames without wall confinement, practical combustors are typically enclosed by walls that strongly influence the combustion dynamics. In part I of this work, the flame-wall interaction of intrinsically unstable hydrogen/air flames has been studied for a single operating condition through detailed numerical simulations in a two-dimensional head-on quenching configuration. This study extends the previous investigation to a wide range of gas turbine and engine-relevant operating conditions, including variations in equivalence ratio (0.4 - 1.0), unburnt gas temperature (298 K - 700 K), and pressure (1.01325 bar - 20 bar). These parametric variations allow for a detailed analysis and establish a baseline for modeling the effects of varying instability intensities on the quenching process, as the relative influence of thermodiffusive and hydrodynamic instabilities depends on the operating conditions. While the quenching characteristics remain largely unaffected by hydrodynamic instabilities, the presence of thermodiffusive instabilities significantly increases the mean wall-heat flux and reduces the mean quenching distance. Furthermore, the impact of thermodiffusive instabilities on the quenching process intensifies as their intensity increases, driven by an increase in pressures and a decrease in equivalence ratio and unburnt gas temperature., Comment: submitted to Combustion and Flame

We propose a new approach to studying hyperbolic Kac-Moody algebras, focussing on the rank-3 algebra $\mathfrak{F}$ first investigated by Feingold and Frenkel. Our approach is based on the concrete realization of this Lie algebra in terms of a Hilbert space of transverse and longitudinal physical string states, which are expressed in a basis using DDF operators. When decomposed under its affine subalgebra $A_1^{(1)}$, the algebra $\mathfrak{F}$ decomposes into an infinite sum of affine representation spaces of $A_1^{(1)}$ for all levels $\ell\in\mathbb{Z}$. For $|\ell| >1$ there appear in addition coset Virasoro representations for all minimal models of central charge $c<1$, but the different level-$\ell$ sectors of $\mathfrak{F}$ do not form proper representations of these because they are incompletely realized in $\mathfrak{F}$. To get around this problem we propose to nevertheless exploit the coset Virasoro algebra for each level by identifying for each level a (for $|\ell|\geq 3$ infinite) set of `Virasoro ground states' that are not necessarily elements of $\mathfrak{F}$ (in which case we refer to them as `virtual'), but from which the level-$\ell$ sectors of $\mathfrak{F}$ can be fully generated by the joint action of affine and coset Virasoro raising operators. We conjecture (and present partial evidence) that the Virasoro ground states for $|\ell|\geq 3$ in turn can be generated from a finite set of `maximal ground states' by the additional action of the `spectator' coset Virasoro raising operators present for all levels $|\ell| > 2$. Our results hint at an intriguing but so far elusive secret behind Einstein's theory of gravity, with possibly important implications for quantum cosmology., Comment: 48 pages

Hydrogen combustion systems operated under fuel-lean conditions offer great potential for low emissions. However, these operating conditions are also susceptible to intrinsic thermodiffusive combustion instabilities. Even though technical combustors are enclosed by walls that significantly influence the combustion process, intrinsic flame instabilities have mostly been investigated in canonical freely-propagating flame configurations unconfined by walls. This study aims to close this gap by investigating the flame-wall interaction of thermodiffusive unstable hydrogen/air flame through detailed numerical simulations in a two-dimensional head-on quenching configuration. It presents an in-depth qualitative and quantitative analysis of the quenching process, revealing the major impact factors of the instabilities on the quenching characteristics. The thermodiffusive instabilities result in lower quenching distances and increased wall heat fluxes compared to one-dimensional head-on quenching flames under similar operation conditions. The change in quenching characteristics seems not to be driven by kinematic effects. Instead, the increased wall heat fluxes are caused by the enhanced flame reactivity of the unstable flame approaching the wall, which results from mixture variations associated with the instabilities. Overall, the study highlights the importance of studying flame-wall interaction in more complex domains than simple one-dimensional configurations, where such instabilities are inherently suppressed. Further, it emphasizes the need to incorporate local mixture variations induced by intrinsic combustion instabilities in combustion models for flame-wall interactions. In part II of this study, the scope is expanded to gas turbine and internal combustion engine relevant conditions through a parametric study, varying the equivalence ratio, pressure, and unburnt temperature., Comment: submitted to Combustion and Flame

Modern reconstruction techniques can effectively model complex 3D scenes from sparse 2D views. However, automatically assessing the quality of novel views and identifying artifacts is challenging due to the lack of ground truth images and the limitations of no-reference image metrics in predicting detailed artifact maps. The absence of such quality metrics hinders accurate predictions of the quality of generated views and limits the adoption of post-processing techniques, such as inpainting, to enhance reconstruction quality. In this work, we propose a new no-reference metric, Puzzle Similarity, which is designed to localize artifacts in novel views. Our approach utilizes image patch statistics from the input views to establish a scene-specific distribution that is later used to identify poorly reconstructed regions in the novel views. We test and evaluate our method in the context of 3D reconstruction; to this end, we collected a novel dataset of human quality assessment in unseen reconstructed views. Through this dataset, we demonstrate that our method can not only successfully localize artifacts in novel views, correlating with human assessment, but do so without direct references. Surprisingly, our metric outperforms both no-reference metrics and popular full-reference image metrics. We can leverage our new metric to enhance applications like automatic image restoration, guided acquisition, or 3D reconstruction from sparse inputs.

We extend the graphical calculus developed in the first part of this paper to the parametrising spaces of quantum vertex operators. This involves a graphical implementation of the dynamical twist functor, which is a strict monoidal functor that describes how a morphism acting on the spin space of a quantum vertex operator $\Phi$ is transported to a morphism on the parametrising space of $\Phi$. The monoidal structure of the underlying nonstrict monoidal functor, considered before by Etingof and Varchenko in 1999, is given in terms of dynamical fusion operators, which are operators that describe the fusion of quantum vertex operators on the level of parametrising spaces. In the second part of the paper we use the extended graphical calculus to give intuitive, graphical derivations of various systems of difference equations for universal multipoint weighted trace functions. This includes the dual $q$-Knizhnik-Zamolodchikov-Bernard (KZB) and the dual Macdonald-Ruijsenaars (MR) equations, earlier obtained by Etingof and Varchenko in 2000, as well as an extension of the dual MR equations called dual coordinate MR equations. We use a known symmetry property of the universal weighted trace function, involving the exchange of its geometric and spectral parameter, to derive non-dual versions of these equations., Comment: 89 pages

Driving the nuclear fusion reaction p+11B -> 3 alpha + 8.7 MeV in laboratory conditions, by interaction between high-power laser pulses and matter, has become a popular field of research, due to numerous applications that it can potentially allow: an alternative to deuterium-tritium (DT) for fusion energy production, astrophysics studies and alpha-particle generation for medical treatments. A possible scheme for laser-driven p-11B reactions is to direct a beam of laser-accelerated protons onto a boron sample (the so-called 'pitcher-catcher' scheme). This technique was successfully implemented on large, energetic lasers, yielding hundreds of joules per shot at low repetition. We present here a complementary approach, exploiting the high-repetition rate of the VEGA III petawatt laser at CLPU (Spain), aiming at accumulating results from many interactions at much lower energy, for better controlling the parameters and the statistics of the measurements. Despite a moderate energy per pulse, our experiment allowed exploring the laser-driven fusion process with tens (up to hundreds) of laser shots. The experiment provided a clear signature of the produced reactions and of the fusion products, accumulated over many shots, leading to an improved optimization of the diagnostic for these experimental campaigns In this paper we discuss the effectiveness of the laser-driven p-11B fusion in the pitcher-catcher scheme, at high-repetition rate, addressing the challenges of this experimental scheme and highlighting its critical aspects. Our proposed methodologies allow evaluating the performance of this scheme for laser-driven alpha particle production and can be adapted to high-repetition rate laser facilities with higher energy and intensity.

The Langevin dynamics is a diffusion process extensively used, in particular in molecular dynamics simu-lations, to sample Gibbs measures. Some alternatives based on (piecewise deterministic) kinetic velocity jumpprocesses have gained interest over the last decade. One interest of the latter is the possibility to split forces(at the continuous-time level), reducing the numerical cost for sampling the trajectory. Motivated by this, anumerical scheme based on hybrid dynamics combining velocity jumps and Langevin diffusion, numericallymore efficient than their classical Langevin counterparts, has been introduced for computational chemistry in [42]. The present work is devoted to the numerical analysis of this scheme. Our main results are, first, theexponential ergodicity of the continuous-time velocity jump Langevin process, second, a Talay-Tubaro expan-sion of the invariant measure of the numerical scheme, showing in particular that the scheme is of weak order2 in the step-size and, third, a bound on the quadratic risk of the corresponding practical MCMC estimator(possibly with Richardson extrapolation). With respect to previous works on the Langevin diffusion, newdifficulties arise from the jump operator, which is non-local.

In this paper we study the question of how to reconstruct the state of a power system using Phasor Measurement Units (PMUs). In our previous research we proved that this question has an affirmative answer imposing some rather strict structural assumptions: namely, neglecting the generator rotors saliency and assuming that the stator resistance of the synchronous generator is zero. It was shown in simulations that the performance of the proposed observer was sensitive to these assumptions, observing a transient quality degradation for realistic simulations not imposing these assumptions. Moreover, it was assumed in our previous work that the mechanical power and the field voltage are available for measurement, a scenario that it is not always realistic. In this paper we accomplish two ambitious objectives. First, we propose a new observer that does not impose the simplifying assumptions on the generator model. Secondly, we consider the more realistic scenario where only mechanical power is available for measurement. That is, we solve a problem of state reconstruction of a nonlinear system with partially known input measurements -- that is well-known to be a very challenging task. The design of the first observer relies on two recent developments proposed by the authors, a parameter estimation based approach to the problem of state estimation and the use of the Dynamic Regressor Extension and Mixing (DREM) technique to estimate these parameters. The use of DREM allows us to overcome the problem of lack of persistent excitation that stymies the application of standard parameter estimation designs. On the other hand, the observer for the partial input measurement scenario relies on the clever exploitation of the systems model. Simulation results illustrates the good performance of the proposed observers.

This is mainly a survey on the properties of Strong Metric Regularity (SMR) and Strong Metric subRegularity (SMsR) of mappings representing first order optimality conditions (so-called optimality mappings) of optimization problems in infinite dimensional spaces. The focus is on the optimality mappings associated with optimal control problems for ODE systems or PDEs. We especially emphasize an extension of the concepts of SMR and SMsR which involves two metrics either in the domain or in the image spaces. The paper shows the relevance of this extension in optimal control.

Trajectories can be regarded as time-series of coordinates, typically arising from motile objects. Methods for trajectory classification are particularly important to detect different movement patterns, while methods for regression to compute motility metrics and forecasting. Recent advances in computer vision have facilitated the processing of trajectories rendered as images via artificial neural networks with 2d convolutional layers (CNNs). This approach leverages the capability of CNNs to learn spatial hierarchies of features from images, necessary to recognize complex shapes. Moreover, it overcomes the limitation of other machine learning methods that require input trajectories with a fixed number of points. However, rendering trajectories as images can introduce poorly investigated artifacts such as information loss due to the plotting of coordinates on a discrete grid, and spectral changes due to line thickness and aliasing. In this study, we investigate the effectiveness of CNNs for solving classification and regression problems from synthetic trajectories that have been rendered as images using different modalities. The parameters considered in this study include line thickness, image resolution, usage of motion history (color-coding of the temporal component) and anti-aliasing. Results highlight the importance of choosing an appropriate image resolution according to model depth and motion history in applications where movement direction is critical., Comment: 13 pages, 5 figures

This contribution gives a panoramic overview of the development of N=8 supergravity and its relation to other maximally supersymmetric theories over the past 40 years. It also provides a personal perspective on the future role of this theory in attempts at unification., Comment: Invited contribution to the book "Half a century of supergravity", eds. A. Ceresole and G. Dall'Agata, 22 pages. V2: minor corrections, references added

Existence, uniqueness and stability of the solutions of linear stochastic evolution equations are investigated. The results obtained are used to prove theorems on solvability of linear second order stochastic partial differential equations in $L_{p}$-setting with singular lower order coefficients.

There is increased interest in using generative AI to create 3D spaces for Virtual Reality (VR) applications. However, today's models produce artificial environments, falling short of supporting collaborative tasks that benefit from incorporating the user's physical context. To generate environments that support VR telepresence, we introduce SpaceBlender, a novel pipeline that utilizes generative AI techniques to blend users' physical surroundings into unified virtual spaces. This pipeline transforms user-provided 2D images into context-rich 3D environments through an iterative process consisting of depth estimation, mesh alignment, and diffusion-based space completion guided by geometric priors and adaptive text prompts. In a preliminary within-subjects study, where 20 participants performed a collaborative VR affinity diagramming task in pairs, we compared SpaceBlender with a generic virtual environment and a state-of-the-art scene generation framework, evaluating its ability to create virtual spaces suitable for collaboration. Participants appreciated the enhanced familiarity and context provided by SpaceBlender but also noted complexities in the generative environments that could detract from task focus. Drawing on participant feedback, we propose directions for improving the pipeline and discuss the value and design of blended spaces for different scenarios.

Reinforcement learning from human feedback (RLHF) has become a key method for aligning large language models (LLMs) with human preferences through the use of reward models. However, traditional reward models typically generate point estimates, which oversimplify the diversity and complexity of human values and preferences. In this paper, we introduce Quantile Reward Models (QRMs), a novel approach to reward modeling that learns a distribution over rewards instead of a single scalar value. Our method uses quantile regression to estimate a full, potentially multimodal distribution over preferences, providing a more powerful and nuanced representation of preferences. This distributional approach can better capture the diversity of human values, addresses label noise, and accommodates conflicting preferences by modeling them as distinct modes in the distribution. Our experimental results show that QRM outperforms comparable traditional point-estimate models on RewardBench. Furthermore, we demonstrate that the additional information provided by the distributional estimates can be utilized in downstream applications, such as risk-aware reinforcement learning, resulting in LLM policies that generate fewer extremely negative responses. Our code and model are released at https://github.com/Nicolinho/QRM.

{The paper studies classical, type-free theories of truth and determinateness. Recently, Volker Halbach and Kentaro Fujimoto proposed a novel approach to classical determinate truth, in which determinateness is axiomatized by a primitive predicate. In the paper we propose a different strategy to develop theories of classical determinate truth in Halbach and Fujimoto's sense featuring a \emph{defined} determinateness predicate. This puts our theories of classical determinate truth in continuity with a standard approach to determinateness by authors such as Feferman and Reinhardt. The theories entail all principles of Fujimoto and Halbach's theories, and are proof-theoretically equivalent to Halbach and Fujimoto's CD+. They will be shown to be logically equivalent to a class of natural theories of truth, the \emph{classical closures of Kripke-Feferman truth}. The analysis of the proposed theories will also provide new insights on Fujimoto and Halbach's theories: we show that the latter cannot prove most of the axioms of the classical closures of Kripke-Feferman truth. This entails that, unlike what happens in our theories of truth and determinateness, Fujimoto and Halbach's \emph{inner theories} -- the sentences living under two layers of truth -- cannot be closed under standard logical rules of inference.}

In this work, we analyze the cosmological model in which the expansion is driven by a classical, free Klein-Gordon field on a flat, four-dimensional Friedmann-Lema\^itre-Robertson-Walker spacetime. The model allows for arbitrary mass, non-zero cosmological constant and coupling to curvature. We find that there are strong restrictions to the parameter space, due to the requirement for the reality of the field values. At early cosmological times, we observe Big Bang singularities, solutions where the scale factor asymptotically approaches zero, and Small Bangs. The latter are solutions for which the Hubble parameter diverges at a finite value of the scale factor. They appear generically in our model for certain curvature couplings. An early inflationary era is observed for a specific value of the curvature coupling without further assumptions (unlike in many other inflationary models). A late-time Dark Energy period is present for all solutions with positive cosmological constant, numerically suggesting that a "cosmic no-hair" theorem holds under more general assumptions than the original Wald version which relies on classical energy conditions. The classical fields in consideration can be viewed as resembling one-point functions of a semiclassical model, in which the cosmological expansion is driven by a quantum field., Comment: 48 pages, 13 figures

Work-related transportation incidents significantly impact urban mobility and productivity. These incidents include traffic crashes, collisions between vehicles, and falls that occurred during commuting or work-related transportation (e.g., falling while getting off a bus during the morning commute or while riding a bicycle for work). This study analyzes a decade of work-related transportation incident data (2012--2021) in Santiago, Chile, using records from a major worker's insurance company. Using negative binomial regression, we assess the impact of a 2018 urban speed limit reduction law on incident injury severity. We also explore broader temporal, spatial, and demographic patterns in these incidents in urban and rural areas. The urban speed limit reduction is associated with a decrease of 4.26 days in prescribed medical leave for incidents in urban areas, suggesting that lower speed limits contribute to reduced injury severity. Our broader analysis reveals distinct incident patterns across different groups. Workers traveling by motorcycle and bicycle experience more severe injuries when involved in traffic incidents, with marginal effects of 26.94 and 13.06 additional days of medical leave, respectively, compared to motorized vehicles. Women workers tend to have less severe injuries, with an average of 7.57 fewer days of medical leave. Age is also a significant factor, with older workers experiencing more severe injuries -- each additional year of age is associated with 0.57 more days of medical leave. Our results provide insights for urban planning, transportation policy, and workplace safety initiatives., Comment: 18 pages, 9 figures. Second version after reviews

In this paper, we present a geometric description of foams, which are prevalent in topological quantum field theories (TQFTs) based on quantum algebra, and reciprocally explore the geometry of Rozansky-Witten (RW) theory from an algebraic perspective. This approach illuminates various aspects of decorated TQFTs via geometry of the target space $X$ of RW theory. Through the formulation of the Knizhnik-Zamolodchikov (KZ) equation within this geometric framework, we derive the corresponding braiding and associator morphisms. We discuss applications where the target space of RW theory emerges as the Coulomb branch of a compactified 6d SCFT or Little String Theory, with the latter being particularly intriguing as it results in a compact $X$., Comment: 47 pages, 23 figures

High-dimensional encoding of quantum information holds the potential to greatly increase the computational power of existing devices by enlarging the accessible state space for fixed register size and by reducing the number of required entangling gates. However, qudit-based quantum computation remains far less developed than conventional qubit-based approaches, in particular for photons, which represent natural multi-level information carriers that play a crucial role in the development of quantum networks. A major obstacle for realizing quantum gates between two individual photons is the restriction of direct interaction between photons in linear media. In particular, essential logic components for quantum operations such as native qudit-qudit entangling gates are still missing for optical quantum information processing. Here we address this challenge by presenting a protocol for realizing an entangling gate -- the controlled phase-flip (CPF) gate -- for two photonic qudits in arbitrary dimension. We experimentally demonstrate this protocol by realizing a four-dimensional qudit-qudit CPF gate, whose decomposition would require at least 13 two-qubit entangling gates. Our photonic qudits are encoded in orbital angular momentum (OAM) and we have developed a new active high-precision phase-locking technology to construct a high-dimensional OAM beam splitter that increases the stability of the CPF gate, resulting in a process fidelity within a range of $ [0.64 \pm 0.01, 0.82 \pm 0.01]$. Our experiment represents a significant advance for high-dimensional optical quantum information processing and has the potential for wider applications beyond optical system., Comment: 14 pages, 7 figures

In a previous work [K.A. Meissner and H. Nicolai, Eur. Phys. J. C {\bf 84}, 269 (2024)], two of the present authors have suggested possible experimental ways to search for stable supermassive particles with electric charges of $\cO(1)$ in upcoming underground experiments, in particular the new Jiangmen Underground Neutrino Observatory (JUNO) experiment. In the current paper, we present a detailed analysis of the specific signature of such gravitino-induced events for the JUNO detector and for upcoming liquid argon detectors like DUNE (Deep Underground Neutrino Experiment). The proposed method of detection relies on the ``glow'' produced by photons during the passage of such particles through the detector liquid, which would last for about a few to a few hundred microseconds depending on its velocity and the track. The cross sections for electronic excitation of the main component of the scintillator liquid, namely linear alkylbenzene (LAB), by the passing gravitino are evaluated using quantum-chemical methods. The results show that, if such particles exist, the resulting signals would lead to a unique and unmistakable signature, for which we present event simulations as they would be seen by the JUNO or DUNE photomultipliers. Our analysis brings together two very different research areas, namely fundamental particles physics and the search for a fundamental theory on the one hand, and methods of advanced quantum chemistry on the other.