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It is well known that silicon photomultipliers (SiPMs) are prone to radiation damage. With the increasing popularity of SiPMs among new spaceborne missions, especially on CubeSats, it is of paramount importance to characterize their performance in space environment. In this work, we report the in-orbit ageing of SiPM arrays, so-called multi-pixel photon counters (MPPCs), using measurements acquired by the GRBAlpha and VZLUSAT-2 CubeSats at low Earth orbit (LEO) spanning over three years, which in duration is unique. GRBAlpha is a 1U CubeSat launched on March 22, 2021, to a 550 km altitude sun-synchronous polar orbit (SSO) carrying on board a gamma-ray detector based on CsI(Tl) scintillator readout by eight MPPCs and regularly detecting gamma-ray transients such as gamma-ray bursts and solar flares in the energy range of ~30-900 keV. VZLUSAT-2 is a 3U CubeSat launched on January 13, 2022 also to a 550 km altitude SSO carrying on board, among other payloads, two gamma-ray detectors similar to the one on GRBAlpha. We have flight-proven the Hamamatsu MPPCs S13360-3050 PE and demonstrated that MPPCs, shielded by 2.5 mm of PbSb alloy, can be used in an LEO environment on a scientific mission lasting beyond three years. This manifests the potential of MPPCs being employed in future satellites., Comment: Submitted to Nuclear Instruments and Methods in Physics Research Section A, 13 pages, 14 figures

The world is abundant with diverse materials, each possessing unique surface appearances that play a crucial role in our daily perception and understanding of their properties. Despite advancements in technology enabling the capture and realistic reproduction of material appearances for visualization and quality control, the interoperability of material property information across various measurement representations and software platforms remains a complex challenge. A key to overcoming this challenge lies in the automatic identification of materials' perceptual features, enabling intuitive differentiation of properties stored in disparate material data representations. We reasoned that for many practical purposes, a compact representation of the perceptual appearance is more useful than an exhaustive physical description.This paper introduces a novel approach to material identification by encoding perceptual features obtained from dynamic visual stimuli. We conducted a psychophysical experiment to select and validate 16 particularly significant perceptual attributes obtained from videos of 347 materials. We then gathered attribute ratings from over twenty participants for each material, creating a 'material fingerprint' that encodes the unique perceptual properties of each material. Finally, we trained a multi-layer perceptron model to predict the relationship between statistical and deep learning image features and their corresponding perceptual properties. We demonstrate the model's performance in material retrieval and filtering according to individual attributes. This model represents a significant step towards simplifying the sharing and understanding of material properties in diverse digital environments regardless of their digital representation, enhancing both the accuracy and efficiency of material identification., Comment: 14 pages, 12 figures, 3 tables

Though large language models (LLMs) have demonstrated exceptional performance across numerous problems, their application to predictive tasks in relational databases remains largely unexplored. In this work, we address the notion that LLMs cannot yield satisfactory results on relational databases due to their interconnected tables, complex relationships, and heterogeneous data types. Using the recently introduced RelBench benchmark, we demonstrate that even a straightforward application of LLMs achieves competitive performance on these tasks. These findings establish LLMs as a promising new baseline for ML on relational databases and encourage further research in this direction.

For the almost complete intersection ideals $(x_1^2, \dots, x_n^2, (x_1 + \cdots + x_n)^k)$, we compute their reduced Gr\"obner basis for any term ordering, revealing a combinatorial structure linked to lattice paths, elementary symmetric polynomials, and Catalan numbers. Using this structure, we classify the weak Lefschetz property for these ideals. Additionally, we provide a new proof of the well-known result that the squarefree algebra satisfies the strong Lefschetz property. Finally, we compute the Betti numbers of the initial ideals and construct a minimal free resolution using a Mayer-Vietoris tree approach., Comment: 33 pages, 3 figures

Conformally invariant scalar equation permits the Robin boundary condition at infinity of asymptotically-AdS spacetimes. We show how the dynamics of conformal cubic scalar field on the Reissner-Nordstr\"om-anti-de Sitter background depend on the black hole size, charge and the choice of the boundary condition. We study the whole range of admissible charges, including the extremal case. In particular, we observe the transition in stability of the field for large black holes at the specific critical value of the charge. Similarities between Reissner-Nordstr\"om and Kerr black hole let us suspect that similar effect may also occur in rotating black holes., Comment: 13 pages, 8 figures

We investigate the temperature, photon and shadow radii, quasinormal modes (QNMs), greybody factors, and emission rates of deformed AdS black holes, focusing on the effects of the deformation parameter $ \alpha $ and control parameter $ \beta $. Increasing $ \alpha $ enhances the oscillation frequency and damping rate of gravitational waves, while $ \beta $ shows non-linear behaviour. Electromagnetic perturbations exhibit similar trends, though with lower frequencies and damping rates. Greybody factors are mainly influenced by multipole moment $ l $ and $ \alpha $, with $ \beta $ having a more subtle effect. These findings provide insights into black hole dynamics, mergers, and gravitational wave emissions., Comment: 17 pages, 16 figures

Safety fine-tuning algorithms are commonly used to fine-tune language models to reduce harmful outputs, but the exact internal mechanisms of how those models achieve this remain unclear. In studying direct preference optimisation (DPO) for toxicity reduction, current explanations claim that DPO works by dampening the most toxic MLP neurons to learn an offset to avert toxic regions in the residual stream. However, by ablating the most toxic neurons and applying activation patching, we find this explanation incomplete. By projecting neuron activation changes onto a toxicity probe, we find that only 31.8\% of toxicity reduction comes from dampened toxic neurons. Instead, DPO reduces toxicity by accumulating effects across multiple neuron groups, both reducing writing in the toxic direction and promoting anti-toxicity in the residual stream. Moreover, DPO gives noisy adjustments to neuron activations, with many neurons actually increasing toxicity. This indicates that DPO is a balancing process between opposing neuron effects to achieve toxicity reduction.

Organic based semiconductor materials offer emerging and sustainable solutions for solar energy conversion technologies and electronics. However, knowledge of their intrinsic (photo)physical properties and light-matter interactions is often limited, especially with respect to the frequency dependent dielectric properties on the relevant timescales of exciton separation and charge generation (fs-ps). By using terahertz-time-domain spectroscopy (THz-TDS), we show that the complex permittivity and THz conductivity of different polymer materials and graphitic carbon nitrides - melon and poly(heptazine imides) (K-PHI) - can be measured directly and accurately in different environmental humidities. Its effects are most strongly observed in the ionic and 2D carbon nitride, K-PHI, where both real permittivity and THz conductivity double from dry to humid conditions (~4-8 and 75-150 S/m, respectively) surpassing the intrinsic dielectric response from water or K-PHI through synergistic effects. Our findings are backed by fs-ps transient absorption spectroscopy (TAS), confirming the impact of humidity on light conversion behaviour on the ps-timescale in K-PHI. We highlight the importance of dielectric property characterization at THz frequencies as critical for understanding photophysical behaviour at exciton or charge separation time scales, especially in the presence of water and hydrated ions, which may also be beneficial for computation, exploring next generation photocatalysts, electronics or ionotronics.

This study explores how the Large Language Models (LLMs) adjust linguistic features to create personalized persuasive outputs. While research showed that LLMs personalize outputs, a gap remains in understanding the linguistic features of their persuasive capabilities. We identified 13 linguistic features crucial for influencing personalities across different levels of the Big Five model of personality. We analyzed how prompts with personality trait information influenced the output of 19 LLMs across five model families. The findings show that models use more anxiety-related words for neuroticism, increase achievement-related words for conscientiousness, and employ fewer cognitive processes words for openness to experience. Some model families excel at adapting language for openness to experience, others for conscientiousness, while only one model adapts language for neuroticism. Our findings show how LLMs tailor responses based on personality cues in prompts, indicating their potential to create persuasive content affecting the mind and well-being of the recipients., Comment: 31 pages

The increasing volume and complexity of IoT systems demand a transition from the cloud-centric model to a decentralized IoT architecture in the so-called Computing Continuum, with no or minimal reliance on central servers. This paradigm shift, however, raises novel research concerns for decentralized coordination, calling for accurate policies. However, building such strategies is not trivial. Our work aims to relieve the DevOps engineers from this concern and propose a solution for autonomous, decentralized task allocation at runtime for IoT systems. To this end, we present a semantic communication approach and an ad-hoc lightweight coordination strategy based on Ant Colony Optimization (ACO). We compare the ACO strategy with Random Search and Gossip protocol-based algorithms. We conduct accurate experiments with up to a hundred nodes in both a static and a dynamic environment, i.e., with device outages. We show that ACO finds a matching node with the smallest hops and messages sent. While the Gossip strategy can allocate the most tasks successfully, ACO scales better, thus being a promising candidate for decentralized task coordination in IoT clusters.

Among various topological insulators, Sn-doped Bi$_{1.1}$Sb$_{0.9}$Te$_{2}$S stands out for its exceptional properties. It has a wide energy gap and typically exhibits a well-isolated Dirac point and a Fermi level positioned within the gap. The samples we present display metallic-like low-temperature resistivity attributed to surface states, pronounced quantum oscillations observable even at 40 K, and a Fermi level located approximately 100 meV above the Dirac point. In this work, we report an unusual effect: a strong temperature dependence of the quantum oscillation frequency, which decreases by around 10\% between 2 and 40 K. This reduction significantly exceeds the expected effects of the Sommerfeld and topological corrections for Dirac quasi-particles, which could account for only one-eighth of the observed change. We attribute this change to the temperature-induced renormalization of the bulk band gap size due to electron-phonon interactions, which in turn affect the position of the surface Dirac point within the gap. Furthermore, we propose that in this compound, surface quantum oscillations can serve as a precise tool for investigating the low-temperature evolution of the bulk band gap size., Comment: 7 pages, 3 figures

Recent literature proposes combining short-term experimental and long-term observational data to provide credible alternatives to conventional observational studies for identification of long-term average treatment effects (LTEs). I show that experimental data have an auxiliary role in this context. They bring no identifying power without additional modeling assumptions. When modeling assumptions are imposed, experimental data serve to amplify their identifying power. If the assumptions fail, adding experimental data may only yield results that are farther from the truth. Motivated by this, I introduce two assumptions on treatment response that may be defensible based on economic theory or intuition. To utilize them, I develop a novel two-step identification approach that centers on bounding temporal link functions -- the relationship between short-term and mean long-term potential outcomes. The approach provides sharp bounds on LTEs for a general class of assumptions, and allows for imperfect experimental compliance -- extending existing results.

Continual learning (CL) has emerged as a critical area in machine learning, enabling neural networks to learn from evolving data distributions while mitigating catastrophic forgetting. However, recent research has identified the stability gap -- a phenomenon where models initially lose performance on previously learned tasks before partially recovering during training. Such learning dynamics are contradictory to the intuitive understanding of stability in continual learning where one would expect the performance to degrade gradually instead of rapidly decreasing and then partially recovering later. To better understand and alleviate the stability gap, we investigate it at different levels of the neural network architecture, particularly focusing on the role of the classification head. We introduce the nearest-mean classifier (NMC) as a tool to attribute the influence of the backbone and the classification head on the stability gap. Our experiments demonstrate that NMC not only improves final performance, but also significantly enhances training stability across various continual learning benchmarks, including CIFAR100, ImageNet100, CUB-200, and FGVC Aircrafts. Moreover, we find that NMC also reduces task-recency bias. Our analysis provides new insights into the stability gap and suggests that the primary contributor to this phenomenon is the linear head, rather than the insufficient representation learning., Comment: Accepted at WACV 2025

CNNs exhibit inherent equivariance to image translation, leading to efficient parameter and data usage, faster learning, and improved robustness. The concept of translation equivariant networks has been successfully extended to rotation transformation using group convolution for discrete rotation groups and harmonic functions for the continuous rotation group encompassing $360^\circ$. We explore the compatibility of the SA mechanism with full rotation equivariance, in contrast to previous studies that focused on discrete rotation. We introduce the Harmformer, a harmonic transformer with a convolutional stem that achieves equivariance for both translation and continuous rotation. Accompanied by an end-to-end equivariance proof, the Harmformer not only outperforms previous equivariant transformers, but also demonstrates inherent stability under any continuous rotation, even without seeing rotated samples during training., Comment: Appears in NeurIPS 2024 Workshop on Symmetry and Geometry in Neural Representations

Energy is a critical driver of modern economic systems. Accurate energy price forecasting plays an important role in supporting decision-making at various levels, from operational purchasing decisions at individual business organizations to policy-making. A significant body of literature has looked into energy price forecasting, investigating a wide range of methods to improve accuracy and inform these critical decisions. Given the evolving landscape of forecasting techniques, the literature lacks a thorough empirical comparison that systematically contrasts these methods. This paper provides an in-depth review of the evolution of forecasting modeling frameworks, from well-established econometric models to machine learning methods, early sequence learners such LSTMs, and more recent advancements in deep learning with transformer networks, which represent the cutting edge in forecasting. We offer a detailed review of the related literature and categorize forecasting methodologies into four model families. We also explore emerging concepts like pre-training and transfer learning, which have transformed the analysis of unstructured data and hold significant promise for time series forecasting. We address a gap in the literature by performing a comprehensive empirical analysis on these four family models, using data from the EU energy markets, we conduct a large-scale empirical study, which contrasts the forecasting accuracy of different approaches, focusing especially on alternative propositions for time series transformers.

We explore the applicability of the transcorrelated method to the elements in the second row of the periodic table. We use transcorrelated Hamiltonians in conjunction with full configuration interaction quantum Monte Carlo and coupled cluster techniques to obtain total energies and ionisation potentials, investigating their dependence on the nature and size of the basis sets used. Transcorrelation accelerates convergence to the complete basis set limit relative to conventional approaches, and chemically accurate results can generally be obtained with the cc-pVTZ basis, even with a frozen Ne core in the post-Hartree--Fock treatment., Comment: 11 pages, 5 figures

We demonstrate that epitaxial thin film antiferromagnet Mn2As exhibits the quench-switching effect, which was previously reported only in crystallographically similar antiferromagnetic CuMnAs thin films. Quench switching in Mn2As shows stronger increase in resistivity, reaching hundreds of percent at 5K, and significantly longer retention time of the metastable high-resistive state before relaxation towards the low-resistive uniform magnetic state. Qualitatively, Mn2As and CuMnAs show analogous parametric dependence of the magnitude and relaxation of the quench-switching signal. Quantitatively, relaxation dynamics in both materials show direct proportionality to the N\'eel temperature. This confirms that the quench switching has magnetic origin in both materials. The presented results suggest that the antiferromagnets crystalizing in the Cu2Sb structure are well suited for exploring and exploiting the intriguing physics of highly non-uniform magnetic states associated with the quench switching., Comment: 7pages, 4 figures and supplemental material

Quantum resistance is vital for emerging cryptographic systems as quantum technologies continue to advance towards large-scale, fault-tolerant quantum computers. Resistance may be offered by quantum key distribution (QKD), which provides information-theoretic security using quantum states of photons, but may be limited by transmission loss at long distances. An alternative approach uses classical means and is conjectured to be resistant to quantum attacks, so-called post-quantum cryptography (PQC), but it is yet to be rigorously proven, and its current implementations are computationally expensive. To overcome the security and performance challenges present in each, here we develop hybrid protocols by which QKD and PQC inter-operate within a joint quantum-classical network. In particular, we consider different hybrid designs that may offer enhanced speed and/or security over the individual performance of either approach. Furthermore, we present a method for analyzing the security of hybrid protocols in key distribution networks. Our hybrid approach paves the way for joint quantum-classical communication networks, which leverage the advantages of both QKD and PQC and can be tailored to the requirements of various practical networks., Comment: 6 pages, 3 figures, including extra supplementary materials

Space has emerged as an exciting new application area for machine learning, with several missions equipping deep learning capabilities on-board spacecraft. Pre-processing satellite data through on-board training is necessary to address the satellite downlink deficit, as not enough transmission opportunities are available to match the high rates of data generation. To scale this effort across entire constellations, collaborated training in orbit has been enabled through federated learning (FL). While current explorations of FL in this context have successfully adapted FL algorithms for scenario-specific constraints, these theoretical FL implementations face several limitations that prevent progress towards real-world deployment. To address this gap, we provide a holistic exploration of the FL in space domain on several fronts. 1) We develop a method for space-ification of existing FL algorithms, evaluated on 2) FLySTacK, our novel satellite constellation design and hardware aware testing platform where we perform rigorous algorithm evaluations. Finally we introduce 3) AutoFLSat, a generalized, hierarchical, autonomous FL algorithm for space that provides a 12.5% to 37.5% reduction in model training time than leading alternatives.

The rapid expansion of Internet of Things (IoT), edge, and embedded devices in the past decade has introduced numerous challenges in terms of security and configuration management. Simultaneously, advances in cloud-native development practices have greatly enhanced the development experience and facilitated quicker updates, thereby enhancing application security. However, applying these advances to IoT, edge, and embedded devices remains a complex task, primarily due to the heterogeneous environments and the need to support devices with extended lifespans. WebAssembly and the WebAssembly System Interface (WASI) has emerged as a promising technology to bridge this gap. As WebAssembly becomes more popular on IoT, edge, and embedded devices, there is a growing demand for hardware interface support in WebAssembly programs. This work presents WASI proposals and proof-of-concept implementations to enable hardware interaction with I2C and USB, which are two commonly used protocols in IoT, directly from WebAssembly applications. This is achieved by running the device drivers within WebAssembly as well. A thorough evaluation of the proof of concepts shows that WASI-USB introduces a minimal overhead of at most 8% compared to native operating system USB APIs. However, the results show that runtime initialization overhead can be significant in low-latency applications., Comment: Preprint of article submitted to IEEE/IFIP Network Operations and Management Symposium 2025

Modern computer systems store vast amounts of personal data, enabling advances in AI and ML but risking user privacy and trust. For privacy reasons, it is desired sometimes for an ML model to forget part of the data it was trained on. This paper presents a new approach to machine unlearning using forgetting neural networks (FNN). FNNs are neural networks with specific forgetting layers, that take inspiration from the processes involved when a human brain forgets. While FNNs had been proposed as a theoretical construct, they have not been previously used as a machine unlearning method. We describe four different types of forgetting layers and study their properties. In our experimental evaluation, we report our results on the MNIST handwritten digit recognition and fashion datasets. The effectiveness of the unlearned models was tested using Membership Inference Attacks (MIA). Successful experimental results demonstrate the great potential of our proposed method for dealing with the machine unlearning problem.

Major software failures are reported to be due to misconfiguration. As manual configuration is too error-prone to be deemed a reliable strategy for dynamic and complex systems, automated configuration management has become a standard. Countermeasures against misconfiguration can be focused on prevention or, if failure already occurred, detection. Configuration is often used as a broad term for any set of parameters or system states that dictate how an application will behave, but in this paper, we only focus on parameters consumed on process startup, usually from configuration files. Our objective is to enhance configuration management processes in environments based on the distributed cloud model, a novel cloud model that allows dynamic allocation of strategically located resources. The two mechanisms we propose are configuration validation using schemas and configuration version control with support for detecting differences between configuration versions. Our solution reduces the risk of incorrect configuration as schemas prevent any non-compliant configuration from reaching applications. However, if failure still occurs because the schema was incomplete or a valid configuration revealed existing software bugs, the version control system can precisely locate configuration changes that triggered the failure.

We prove that Floer theory induces a filtration by ideals on equivariant quantum cohomology of symplectic manifolds equipped with a $\mathbb{C}^*$-action. In particular, this gives rise to Hilbert-Poincar\'e polynomials on ordinary cohomology that depend on Floer theory. En route, the paper develops structural properties of filtrations on three versions of equivariant Floer cohomology. We obtain an explicit presentation for equivariant symplectic cohomology in the Calabi-Yau and Fano settings., Comment: 72 pages, 12 figures, fixed Theorem 1.10 and 14.3, new Remark 1.12 about the persistence module, very minor other changes

Solving complex tasks in a modern information-driven society requires novel materials and concepts for energy-efficient hardware. Antiferromagnets offer a promising platform for seeking such approaches due to their exceptional features: low power consumption and possible high integration density are desirable for information storage and processing or applications in unconventional computing. Among antiferromagnets, CuMnAs stands out for atomic-level scalable magnetic textures, analogue multilevel storage capability, and the magnetic state's control by a single electrical or femtosecond laser pulse. Using a pair of excitation laser pulses, this work examines synaptic and neuronal functionalities of CuMnAs for information processing, readily incorporating two principles of distinct characteristic timescales. Laser-induced transient heat dynamics at sub-nanosecond times represents the short-term memory and causes resistance switching due to quenching into a magnetically fragmented state. This quench switching, detectable electrically from ultrashort times to hours after writing, reminisces the long-term memory. The versatility of the principles' combination is demonstrated by operations commonly used in neural networks. Temporal latency coding, fundamental to spiking neural networks, is utilized to encode data from a grayscale image into sub-nanosecond pulse delays. Applying input laser pulses with distinct amplitudes then allows for pulse-pattern recognition. The results open pathways for designing novel computing architectures., Comment: 20 pages, 10 figures

We propose a novel approach to denoising diffusion magnetic resonance images (dMRI) using convolutional neural networks, that exploits the benefits of data acquired at multiple b-values to offset the need for many redundant observations. Denoising is especially relevant in dMRI since noise can have a deleterious impact on both quantification accuracy and image preprocessing. The most successful methods proposed to date, like Marchenko-Pastur Principal Component Analysis (MPPCA) denoising, are tailored to diffusion-weighting repeated for many encoding directions. They exploit high redundancy of the dataset that oversamples the diffusion-encoding direction space, since many directions have collinear components. However, there are many dMRI techniques that do not entail a large number of encoding directions or repetitions, and are therefore less suited to this approach. For example, clinical dMRI exams may include as few as three encoding directions, with low or negligible data redundancy across directions. Moreover, promising new dMRI approaches, like spherical b-tensor encoding (STE), benefit from high b-values while sensitizing the signal to diffusion along all directions in just a single shot. We introduce a convolutional neural network approach that we call multi-b-value-based denoising (MBD). MBD exploits the similarity in diffusion-weighted images (DWI) across different b-values but along the same diffusion encoding direction. It allows denoising of diffusion images with high noise variance while avoiding blurring, and using just a small number input images., Comment: this is a biomedical engineering work using machine learning to enhance medical images

An accurate approximation of solutions to elliptic problems in infinite domains is challenging from a computational point of view. This is due to the need to replace the infinite domain with a sufficiently large and bounded computational domain, and posing artificial boundary conditions on the boundary of the truncated computational geometry, which will then pollute the solution in an interior region of interest. For elliptic problems with periodically varying coefficients (with a possibly unknown period), a modelling strategy based on exponentially regularized elliptic problem was previously developed and analysed. The main idea was to replace the infinite domain periodic problem with a regularized elliptic problem posed over a finite domain, while retaining an accuracy decaying exponentially with respect to the size of the truncated domain. In this article, we extend the analysis to problems, where no structural assumptions on the coefficient are assumed. Moreover, the analysis here uncovers an interesting property of the right hand side in the Fourier domain for the method to converge fast for problems beyond periodicity., Comment: 20 pages, 6 figures

The NOvA collaboration reports cross-section measurements for $\nu_{\mu}$ charged-current interactions with low hadronic energy (maximum kinetic energy of 250 MeV for protons and 175 MeV for pions) in the NOvA Near Detector. The results are presented as a double-differential cross section as a function of the direct observables of the final-state muon kinematics. Results are also presented as a single-differential cross section as a function of the derived square of the four-momentum transfer, $Q^{2}$, and as a function of the derived neutrino energy. The data correspond to an accumulated 8.09$\times10^{20}$ protons-on-target (POT) in the neutrino mode of the NuMI beam, with a narrow band of neutrino energies peaked at 1.8 GeV. The analysis provides a sample of neutrino-nucleus interactions with an enhanced fraction of quasi-elastic and two-particle-two-hole (2p2h) interactions. This enhancement allows quantitative comparisons with various nuclear models. We find strong disagreement between data and theory-based models in various regions of the muon kinematic phase space, especially in the forward muon direction., Comment: 20 pages, 12 figures. The second version includes an additional citation and adds four previously missing authors

We present results of cosmological zoom-in simulations of a massive protocluster down to redshift $z\approx4$ (when the halo mass is $\approx10^{13}$ M$_\odot$) using the SWIFT code and the EAGLE galaxy formation model, focusing on supermassive black hole (BH) physics. The BH was seeded with a mass of $10^4$ M$_\odot$ at redshift $z\approx17$. We compare the base model that uses an Eddington limit on the BH accretion rate and thermal isotropic feedback by the AGN, with one where super-Eddington accretion is allowed, as well as two other models with BH spin and jets. In the base model, the BH grows at the Eddington limit from $z=9$ to $z=5.5$, when it becomes massive enough to halt its own and its host galaxy's growth through feedback. We find that allowing super-Eddington accretion leads to drastic differences, with the BH going through an intense but short super-Eddington growth burst around $z\approx7.5$, during which it increases its mass by orders of magnitude, before feedback stops further growth (of both the BH and the galaxy). By $z\approx4$ the galaxy is only half as massive in the super-Eddington cases, and an order of magnitude more extended, with the half-mass radius reaching values of a few physical kpc instead of a few hundred pc. The BH masses in our simulations are consistent with the intrinsic BH mass$-$stellar mass relation inferred from high-redshift observations by JWST. This shows that galaxy formation models using the $\Lambda$CDM cosmology are capable of reproducing the observed massive BHs at high redshift. Allowing jets, either at super- or sub-Eddington rates, has little impact on the host galaxy properties, but leads to lower BH masses as a result of stronger self-regulation, which is itself a consequence of higher feedback efficiencies., Comment: Submitted to MNRAS

In response to climate change and urban heat island effects, enhancing human thermal comfort in cities is crucial for sustainable urban development. Traditional methods for investigating the urban thermal environment and corresponding human thermal comfort level are often resource intensive, inefficient, and limited in scope. To address these challenges, we (1) introduce the concept of thermal affordance, which represents the inherent capacity of a streetscape to influence human thermal comfort based on its visual and physical features; and (2) a method to evaluate it (visual assessment of thermal affordance -- VATA), which combines street view imagery (SVI), online and in-filed surveys, and statistical learning algorithms. VATA extracts five categories of image features from SVI data and establishes 19 visual-perceptual indicators for streetscape visual assessment. Using a multi-task neural network and elastic net regression, we model their chained relationship to predict and comprehend thermal affordance for Singapore. VATA predictions are validated with field-investigated OTC data, providing a cost-effective and scalable method to assess the thermal comfort potential of urban streetscape. This framework can inform streetscape design to support sustainable, livable, and resilient urban environments.

Double- and single-differential cross sections for inclusive charged-current neutrino-nucleus scattering are reported for the kinematic domain 0 to 2 GeV/c in three-momentum transfer and 0 to 2 GeV in available energy, at a mean muon-neutrino energy of 1.86 GeV. The measurements are based on an estimated 995,760 muon-neutrino CC interactions in the scintillator medium of the NOvA Near Detector. The subdomain populated by 2-particle-2-hole reactions is identified by the cross-section excess relative to predictions for neutrino-nucleus scattering that are constrained by a data control sample. Models for 2-particle-2- hole processes are rated by chi-square comparisons of the predicted-versus-measured muon-neutrino CC inclusive cross section over the full phase space and in the restricted subdomain. Shortfalls are observed in neutrino generator predictions obtained using the theory-based Val`encia and SuSAv2 2p2h models., Comment: 20 pages, 14 figures

A novel approach for robust state estimation of marine vessels in rough water is proposed in this paper to enable tight collaboration between Unmanned Aerial Vehicles (UAVs) and a marine vessel, such as cooperative landing or object manipulation, regardless of weather conditions. Our study of marine vessel (in our case Unmanned Surface Vehicle (USV)) dynamics influenced by strong wave motion has resulted in a novel nonlinear mathematical USV model with 6 degrees of freedom (DOFs), which is required for precise USV state estimation and motion prediction. The proposed state estimation approach fuses data from multiple sensors onboard the UAV and the USV to enable redundancy and robustness under varying weather conditions of real-world applications. The proposed approach provides estimated states of the USV with 6 DOFs and predicts its future states to enable tight control of both vehicles on a receding control horizon. The proposed approach was extensively tested in the realistic Gazebo simulator and successfully experimentally validated in many real-world experiments representing different application scenarios, including agile landing on an oscillating and moving USV. A comparative study indicates that the proposed approach significantly surpassed the current state-of-the-art.

Starting from a prototypical model of elasto-plasticity in the small-strain and quasi-static setting, where the evolution of the plastic distortion is driven exclusively by the motion of discrete dislocations, this work performs a rigorous homogenization procedure to a model involving continuously-distributed dislocation fields. Our main result shows the existence of rate-independent evolutions driven by the motion of dislocation fields, obtained as limits of discrete dislocation evolutions. For all notions of solutions we employ the recent concepts of space-time integral and normal currents, which is richer than the classical approach using the Kr\"oner dislocation density tensor. The key technical challenge is to find discrete dislocation evolutions approximating a given dislocation field evolution, which requires a careful recovery construction of space-time slip trajectories and associated displacements. These methods enable one to transfer the properties, most importantly the quasi-static stability, from the discrete to the field regime., Comment: 46 pages

Spin polarization measurements were performed in three 2D Electron Gases in GaAs with densities n = 9.1, 7.2 and 6.5 x10^10 cm-2, in the quantum Hall regime. Full spin polarization at v = 1 surrounded by rapid depolarization due to Skyrmion formation was observed in all devices, consistent with past measurements. Depolarization of the v = 4/3, 8/5 states and repolarization of the v = 5/3 state was also measured, in remarkable agreement with a non-interacting, disorder-free Composite Fermion model. Optical power and temperature dependent measurements of the v = 1 state suggest a regime of non-linear optics.

Robot soccer, in its full complexity, poses an unsolved research challenge. Current solutions heavily rely on engineered heuristic strategies, which lack robustness and adaptability. Deep reinforcement learning has gained significant traction in various complex robotics tasks such as locomotion, manipulation, and competitive games (e.g., AlphaZero, OpenAI Five), making it a promising solution to the robot soccer problem. This paper introduces MARLadona. A decentralized multi-agent reinforcement learning (MARL) training pipeline capable of producing agents with sophisticated team play behavior, bridging the shortcomings of heuristic methods. Further, we created an open-source multi-agent soccer environment based on Isaac Gym. Utilizing our MARL framework and a modified a global entity encoder as our core architecture, our approach achieves a 66.8% win rate against HELIOS agent, which employs a state-of-the-art heuristic strategy. Furthermore, we provided an in-depth analysis of the policy behavior and interpreted the agent's intention using the critic network., Comment: Fixed redundant "-" in the title

This work introduces a quantitative model for the Frank-Read source, considered to be one of the most important micro-mechanical mechanisms of dislocation creation in crystalline materials. It has long been known that these sources create dislocations in a repetitive, oscillatory process, which is driven by an external shear force. Unlike the existing explanations in the literature, the model introduced in the present article is based on just a few simple physical principles, namely line tension and dislocation motion due to a single slip plane flow rule, together with a pinning constraint on the ends of the central dislocation line. A complete discretisation, including suitable re-meshing and 'topological cutting' algorithms, is described and simulation results are discussed. Despite its conceptual simplicity, the model and discretisation described in the present work yield remarkably accurate predictions about the shape and properties of the Frank-Read source. In particular, it is shown that only one dimensionless parameter controls the dynamics of the Frank-Read source if one neglects crystal anisotropy. This allows to derive an emergent law about the length of dislocation line generated per shear energy., Comment: 20 pages, 8 figures

This paper presents an algebraic study of a family of logics related to Abelian logic ($Ab$), the logic of Abelian lattice-ordeded groups. $Ab$ is treated as the basic, weakest logic, and its expansions are referred to as superabelian logics. To account for the language differences in Lukasiewicz logics, we introduced the expansion of $Ab$ with an additional constant and call it pointed Abelian logic ($pAb$), with semantics based on pointed Abelian $l$-groups. We take the first steps in the study of infinitary extensions of $Ab$, give a completeness theorem for $pAb$, and obtain axiomatizations of Lukasiewicz unbound logics relative to $pAb$. The paper concludes with an exploration of other significant extensions of $pAb$ and their algebraic characterizations.

The physics program of the Higgs factory will focus on measurements of the 125 GeV Higgs boson, with the Higgs-strahlung process being the dominant production channel at 250 GeV. However, production of extra light scalars is still not excluded by the existing experimental data, provided their coupling to the gauge bosons is sufficiently suppressed. Fermion couplings of such a scalar could also be very different from the SM predictions leading to non-standard decay paterns. Presented in this contribution are results from the ongoing studies on prospects of direct light scalar observation at future Higgs factory experiments in different decay channels., Comment: Presented at the International Workshop on Future Linear Colliders (LCWS 2024), 8-11 July 2024, contribution submitted to EPJ Web of Conferences. This work was carried out in the framework of the ILD concept group as a contribution to the ECFA e+e- Higgs/EW/top factory study

High density electromagnetic sandwich calorimeters with high readout granularity are considered for many future collider and fix-target experiments. Optimization of the calorimeter structure from the point of view of the electromagnetic shower energy, position and direction measurement is one of the key aspects of the design. However, mostly uniform sampling structures were considered so far. We developed a semi-analytical approach to study calorimeter performance based on the detailed Geant 4 simulation, which also allows to compare the expected performance for different non-uniform longitudinal readout structures. For multi-objective optimization, procedure based on the genetic algorithm is complemented with non dominated sorting algorithm. This methodology opens new prospects for calorimeter design optimization directly addressing specific measurement scenarios or optimization goals., Comment: Talk presented at the International Workshop on Future Linear Colliders (LCWS 2024), 8-11 July 2024, contribution submitted to EPJ Web of Conferences. This study was performed in the framework of the ECAL group of the LUXE collaboration

This paper introduces the hypothetical track-length fitting algorithm, a novel method for measuring the kinetic energies of ionizing particles in liquid argon time projection chambers (LArTPCs). The algorithm finds the most probable offset in track length for a track-like object by comparing the measured ionization density as a function of position with a theoretical prediction of the energy loss as a function of the energy, including models of electron recombination and detector response. The algorithm can be used to measure the energies of particles that interact before they stop, such as charged pions that are absorbed by argon nuclei. The algorithm's energy measurement resolutions and fractional biases are presented as functions of particle kinetic energy and number of track hits using samples of stopping secondary charged pions in data collected by the ProtoDUNE-SP detector, and also in a detailed simulation. Additional studies describe impact of the dE/dx model on energy measurement performance. The method described in this paper to characterize the energy measurement performance can be repeated in any LArTPC experiment using stopping secondary charged pions.

A numerical scheme for approximating the nonlinear filtering density is introduced and its convergence rate is established, theoretically under a parabolic H\"{o}rmander condition, and empirically for two examples. For the prediction step, between the noisy and partial measurements at discrete times, the scheme approximates the Fokker--Planck equation with a deep splitting scheme, and performs an exact update through Bayes' formula. This results in a classical prediction-update filtering algorithm that operates online for new observation sequences post-training. The algorithm employs a sampling-based Feynman--Kac approach, designed to mitigate the curse of dimensionality. Our convergence proof relies on the Malliavin integration-by-parts formula. As a corollary we obtain the convergence rate for the approximation of the Fokker--Planck equation alone, disconnected from the filtering problem., Comment: 30 pages, 2 figures

The concept of digital twins has attracted significant attention across various domains, particularly within the built environment. However, there is a sheer volume of definitions and the terminological consensus remains out of reach. The lack of a universally accepted definition leads to ambiguities in their conceptualization and implementation, and may cause miscommunication for both researchers and practitioners. We employed Natural Language Processing (NLP) techniques to systematically extract and analyze definitions of digital twins from a corpus of 15,000 full-text articles spanning diverse disciplines in the built environment. The study compares these findings with insights from an expert survey that included 52 experts. The study identifies concurrence on the components that comprise a 'Digital Twin' from a practical perspective across various domains, contrasting them with those that do not, to identify deviations. We investigate the evolution of digital twin definitions over time and across different scales, including manufacturing, building, and urban/geospatial perspectives. We extracted the main components of Digital Twins using Text Frequency Analysis and N-gram analysis. Subsequently, we identified components that appeared in the literature and conducted a Chi-square test to assess the significance of each component in different domains. Our findings indicate that definitions differ based on the field of research in which they are conceived, but with many similarities across domains. One significant generalizable differentiation is related to whether a digital twin was used for High-Performance Real-Time (HPRT) or Long-Term Decision Support (LTDS) applications. We synthesized and contrasted the most representative definitions in each domain, culminating in a novel, data-driven definition specifically tailored for each context.

Street View Imagery (SVI) has emerged as a valuable data form in urban studies, enabling new ways to map and sense urban environments. However, fundamental concerns regarding the representativeness, quality, and reliability of SVI remain underexplored, e.g.\ to what extent can cities be captured by such data and do data gaps result in bias. This research, positioned at the intersection of spatial data quality and urban analytics, addresses these concerns by proposing a novel workflow to estimate SVI's feature-level coverage on urban environment. The workflow integrates the positional relationships between SVI and target features, as well as the impact of environmental obstructions. Expanding the domain of data quality to SVI, we introduce an indicator system that evaluates the extent of coverage, focusing on the completeness and frequency dimensions. Using London as a case study, three experiments are conducted to identify potential biases in SVI's ability to cover and represent urban features, with a focus on building facades. The research highlights the limitations of traditional spatial data quality metrics in assessing SVI, and variability of SVI coverage under different data acquisition practices. Tailored approaches that consider the unique metadata and horizontal perspective of SVI are also underscored. The findings suggest that while SVI offers valuable insights, it is no panacea -- its application in urban research requires careful consideration of data coverage and feature-level representativeness to ensure reliable results.

We provide a counterexample to the Lagrangian Poincar\'e recurrence conjecture of Ginzburg and Viterbo in all dimensions $6$ and greater., Comment: 5 pages; simplified the argument and corrected a mistake

This paper presents results of the first full simulation study addressing prospects for observation of long-lived particles (LLPs) with the International Large Detector (ILD). Neutral LLP production, resulting in a displaced vertex signature inside the ILD's time projection chamber (TPC), is considered. We focus on scenarios interesting from the experimental perspective and perform a search based on displaced vertex finding inside the TPC volume. Two experimentally very challenging types of scenarios are explored: first, involving very soft final states due to a small mass splitting between heavy LLP and a dark matter particle to which the LLP decays, and the second one, with a light LLP production resulting in almost colinear vertex tracks because of a large boost of the LLP. The expected limits on the signal production cross section are presented for a wide range of the LLP proper lifetimes corresponding to $c\tau$ from 0.1 mm to 10 km., Comment: 18 pages, 9 figures

Random delays between the occurrence of accident events and the corresponding reporting times of insurance claims is a standard feature of insurance data. The time lag between the reporting and the processing of a claim depends on whether the claim can be processed without delay as it arrives or whether it remains unprocessed for some time because of temporarily insufficient processing capacity that is shared between all incoming claims. We aim to explain and analyze the nature of processing delays and build-up of backlogs. We show how to select processing capacity optimally in order to minimize claims costs, taking delay-adjusted costs and fixed costs for claims settlement capacity into account. Theoretical results are combined with a large-scale numerical study that demonstrates practical usefulness of our proposal.

Chiral superconductors are theorized to exhibit spontaneous edge currents. Here, we found magnetic fields at the edges of UTe$_2$, a candidate odd-parity chiral superconductor, that seem to agree with predictions for a chiral order parameter. However, we did not detect the chiral domains that would be expected, and recent polar Kerr and muon spin relaxation data in nominally clean samples argue against chiral superconductivity. Our results show that hidden sources of magnetism must be carefully ruled out when using spontaneous edge currents to identify chiral superconductivity., Comment: 19 pages, 12 figures

While visual question-answering (VQA) benchmarks have catalyzed the development of reasoning techniques, they have focused on vertical thinking. Effective problem-solving also necessitates lateral thinking, which remains understudied in AI and has not been used to test visual perception systems. To bridge this gap, we formulate visual lateral thinking as a multiple-choice question-answering task and describe a three-step taxonomy-driven methodology for instantiating task examples. Then, we develop COLUMBUS, a synthetic benchmark that applies the task pipeline to create QA sets with text and icon rebus puzzles based on publicly available collections of compounds and common phrases. COLUMBUS comprises over 1,000 puzzles, each with four answer candidates. While the SotA vision-language models (VLMs) achieve decent performance, our evaluation demonstrates a substantial gap between humans and models. VLMs benefit from human-curated descriptions but struggle to self-generate such representations at the right level of abstraction., Comment: 18 pages, 10 figures, submitted to AAAI-25

We present a new method to detect anomalies in texts (in general: in sequences of any data), using language models, in a totally unsupervised manner. The method considers probabilities (likelihoods) generated by a language model, but instead of focusing on low-likelihood tokens, it considers a new metric introduced in this paper: oddballness. Oddballness measures how ``strange'' a given token is according to the language model. We demonstrate in grammatical error detection tasks (a specific case of text anomaly detection) that oddballness is better than just considering low-likelihood events, if a totally unsupervised setup is assumed.

Water diffusion gives rise to micrometer-scale sensitivity of diffusion MRI (dMRI) to cellular-level tissue structure. The advent of precision medicine and quantitative imaging hinges on revealing the information content of dMRI, and providing its parsimonious basis- and hardware-independent "fingerprint". Here we reveal the geometry of a multi-dimensional dMRI signal, classify all 21 invariants of diffusion and covariance tensors in terms of irreducible representations of the group of rotations, and relate them to tissue properties. Previously studied dMRI contrasts are expressed via 7 invariants, while the remaining 14 provide novel complementary information. We design acquisitions based on icosahedral vertices guaranteeing minimal number of measurements to determine 3-4 most used invariants in only 1-2 minutes for the whole brain. Representing dMRI signals via scalar invariant maps with definite symmetries will underpin machine learning classifiers of brain pathology, development, and aging, while fast protocols will enable translation of advanced dMRI into clinical practice.