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This paper investigates the representation theory of the algebra of partially transposed permutation operators, $\mathcal{A}^d_{p,p}$, which provides a matrix representation for the abstract walled Brauer algebra. This algebra has recently gained significant attention due to its relevance in quantum information theory, particularly in the efficient quantum circuit implementation of the mixed Schur-Weyl transform. In contrast to previous Gelfand-Tsetlin type approaches, our main technical contribution is the explicit construction of irreducible matrix units in the second-highest ideal that are group-adapted to the action of $\mathbb{C}[\mathcal{S}_p]\times \mathbb{C}[\mathcal{S}_p]$ subalgebra, where $\mathcal{S}_p$ is the symmetric group. This approach suggests a recursive method for constructing irreducible matrix units in the remaining ideals of the algebra. The framework is general and applies to systems with arbitrary numbers of components and local dimensions. The obtained results are applied to a special class of operators motivated by the mathematical formalism appearing in all variants of the port-based teleportation protocols through the mixed Schur-Weyl duality. We demonstrate that the given irreducible matrix units are, in fact, eigenoperators for the considered class., Comment: 48 pages, 6 figures

We propose an approach to integrated optical spectral filtering that allows arbitrary programmability, can compensate automatically for imperfections in filter fabrication, allows multiple simultaneous and separately programmable filter functions on the same input, and can configure itself automatically to the problem of interest, for example to filter or reject multiple arbitrarily chosen frequencies. The approach exploits splitting the input light into an array of multiple waveguides of different lengths that then feed a programmable interferometer array that can also self-configure. It can give spectral response similar to arrayed waveguide gratings but offers many other filtering functions, as well as supporting other structures based on non-redundant arrays for precise spectral filtering. Simultaneous filtering also allows, for the first time to our knowledge, an automatic measurement of the temporal coherency matrix and physical separation into the Karhunen-Lo\`eve expansion of temporally partially coherent light fields.

This paper presents LiFT, a lightweight, fully quantized 3D object detection algorithm for LiDAR data, optimized for real-time inference on FPGA platforms. Through an in-depth analysis of FPGA-specific limitations, we identify a set of FPGA-induced constraints that shape the algorithm's design. These include a computational complexity limit of 30 GMACs (billion multiply-accumulate operations), INT8 quantization for weights and activations, 2D cell-based processing instead of 3D voxels, and minimal use of skip connections. To meet these constraints while maximizing performance, LiFT combines novel mechanisms with state-of-the-art techniques such as reparameterizable convolutions and fully sparse architecture. Key innovations include the Dual-bound Pillar Feature Net, which boosts performance without increasing complexity, and an efficient scheme for INT8 quantization of input features. With a computational cost of just 20.73 GMACs, LiFT stands out as one of the few algorithms targeting minimal-complexity 3D object detection. Among comparable methods, LiFT ranks first, achieving an mAP of 51.84% and an NDS of 61.01% on the challenging NuScenes validation dataset. The code will be available at https://github.com/vision-agh/lift., Comment: The paper has been accepted for the DASIP 2025 workshop in conjunction with the HiPEAC 2025 conference in Barcelona

The detection of high-energy astrophysical neutrinos by IceCube has opened new windows for neutrino astronomy, but their sources remains largely unresolved. We study a methodology to address this - deep-stacking - that exploits correlations between observed neutrinos and comprehensive catalogs of potential source populations, including faint, high-redshift sources. By stacking signals from numerous weak sources and optimizing source weighting, significant gains in sensitivity can be achieved, particularly in the low-background regime where individual high-energy neutrinos dominate. We provide a semi-analytic framework to estimate sensitivity improvements for populations of sources under various background scenarios and redshift evolutions. Our analysis demonstrates that deep-stacking can increase detection sensitivity by a factor of 3 to 5, enabling detailed population studies. Furthermore, we discuss the potential to resolve the diffuse neutrino flux and investigate the redshift evolution of source populations. This approach offers a direct path toward identifying the primary sites of cosmic-ray acceleration and the mechanisms responsible for high-energy neutrino production., Comment: 12 pages

We prove the stronger version of Harnack's inequality for positive harmonic functions defined on the unit disc.

The objective of BioCreative8 Track 3 is to extract phenotypic key medical findings embedded within EHR texts and subsequently normalize these findings to their Human Phenotype Ontology (HPO) terms. However, the presence of diverse surface forms in phenotypic findings makes it challenging to accurately normalize them to the correct HPO terms. To address this challenge, we explored various models for named entity recognition and implemented data augmentation techniques such as synonym marginalization to enhance the normalization step. Our pipeline resulted in an exact extraction and normalization F1 score 2.6\% higher than the mean score of all submissions received in response to the challenge. Furthermore, in terms of the normalization F1 score, our approach surpassed the average performance by 1.9\%. These findings contribute to the advancement of automated medical data extraction and normalization techniques, showcasing potential pathways for future research and application in the biomedical domain., Comment: This article is part of the Proceedings of the BioCreative VIII Challenge and Workshop: Curation and Evaluation in the era of Generative Models

Ion bombardment is currently an active area of research for patterning rare earth/transition metal ferrimagnetic thin films because the magnetic properties are extremely sensitive to changes in the constituent sublattices. It has previously been shown that ion bombardment can be used to deliberately reduce the contribution of the rare earth sublattice in rare earth/transition metal ferrimagnets by selective oxidation. However, the exact mechanism by which oxidation occurs remains an outstanding question. We show that the defects introduced by ion bombardment of Tb/Co multilayers using different ion species with projected range (i.e., 10 keV He + , 15 keV O + , and 30 keV Ga +) create easy diffusion paths for oxygen to penetrate the system. The choice of ion species and fluence enables the effective composition of the films to be tailored by reducing the amount of magnetically-active Tb.

Strong lensing systems, expected to be abundantly discovered by next-generation surveys, offer a powerful tool for studying cosmology and galaxy evolution. The connection between galaxy structure and cosmology through distance ratios highlights the need to examine the evolution of lensing galaxy mass density profiles. We propose a novel, dark energy-model-independent method to investigate the mass density slopes of lensing galaxies and their redshift evolution using an extended power-law (EPL) model. We employ a non-parametric approach based on Artificial Neural Networks (ANNs) trained on Type Ia Supernovae (SNIa) data to reconstruct distance ratios of strong lensing systems. These ratios are compared with theoretical predictions to estimate the evolution of EPL model parameters. Analyses conducted at three levels, including the combined sample, individual lenses, and binned groups, ensure robust and reliable estimates. A negative trend in the mass density slope with redshift is observed, quantified as $\partial\gamma/\partial z = -0.20 \pm 0.12$ under a triangular prior for anisotropy. This study demonstrates that the redshift evolution of density slopes in lensing galaxies can be determined independently of dark energy models. Simulations based on LSST Rubin Observatory forecasts, which anticipate 100,000 strong lenses, show that spectroscopic follow-up of just 10 percent of these systems can constrain the redshift evolution coefficient with uncertainty ($\Delta\partial\gamma/\partial z$) to 0.021. This precision distinguishes evolving and non-evolving density slopes, providing new insights into galaxy evolution and cosmology., Comment: 16 pages, 16 figures

This article introduces the first comprehensive benchmark for the Polish language at this scale: LLMzSz{\L} (LLMs Behind the School Desk). It is based on a coherent collection of Polish national exams, including both academic and professional tests extracted from the archives of the Polish Central Examination Board. It covers 4 types of exams, coming from 154 domains. Altogether, it consists of almost 19k closed-ended questions. We investigate the performance of open-source multilingual, English, and Polish LLMs to verify LLMs' abilities to transfer knowledge between languages. Also, the correlation between LLMs and humans at model accuracy and exam pass rate levels is examined. We show that multilingual LLMs can obtain superior results over monolingual ones; however, monolingual models may be beneficial when model size matters. Our analysis highlights the potential of LLMs in assisting with exam validation, particularly in identifying anomalies or errors in examination tasks.

Domain structures play a crucial role in the electric, mechanical and other properties of ferroelectric materials. In this study, we uncover the physical origins of the enigmatic zigzag domain structure in the prototypical multiferroic material BiFeO$_3$. Using phase-field simulations within the Landau-Ginzburg-Devonshire framework, we demonstrate that spatially-homogeneous defect charges result in domain structures that closely resemble those observed experimentally. The acquired understanding of the underlying physics of pyramidal-domain formation may enable the engineering of new materials with self-assembled domain structures exhibiting defined domain periodicity at the nanometre scale, opening avenues for advanced applications.

Neural network models implemented in embedded devices have been shown to be susceptible to side-channel attacks (SCAs), allowing recovery of proprietary model parameters, such as weights and biases. There are already available countermeasure methods currently used for protecting cryptographic implementations that can be tailored to protect embedded neural network models. Shuffling, a hiding-based countermeasure that randomly shuffles the order of computations, was shown to be vulnerable to SCA when the Fisher-Yates algorithm is used. In this paper, we propose a design of an SCA-secure version of the Fisher-Yates algorithm. By integrating the masking technique for modular reduction and Blakely's method for modular multiplication, we effectively remove the vulnerability in the division operation that led to side-channel leakage in the original version of the algorithm. We experimentally evaluate that the countermeasure is effective against SCA by implementing a correlation power analysis attack on an embedded neural network model implemented on ARM Cortex-M4. Compared to the original proposal, the memory overhead is $2\times$ the biggest layer of the network, while the time overhead varies from $4\%$ to $0.49\%$ for a layer with $100$ and $1000$ neurons, respectively.

This paper analyzes how the ferroelectric properties of cubic-like BiFeO$_3$ nanoparticles are affected by different terminations and charge distributions at the surface using ab-initio-based atomistic computational experiments. Our findings unveil multiple multidomain configurations and illustrate how the different order parameters evolve towards the surface. Interestingly, for neutral terminations, a non-rhombohedral phase of BiFeO$_3$ with a stripe-like polarization arrangement was stabilized. We evaluate the polarization, oxygen octahedra rotation, and volume variation for all the configurations obtained, taking advantage of the atomic-scale details provided by the methods used in this study., Comment: 11 pages, 5 figures

Although deep learning models have had great success in natural language processing and computer vision, we do not observe comparable improvements in the case of tabular data, which is still the most common data type used in biological, industrial and financial applications. In particular, it is challenging to transfer large-scale pre-trained models to downstream tasks defined on small tabular datasets. To address this, we propose VisTabNet -- a cross-modal transfer learning method, which allows for adapting Vision Transformer (ViT) with pre-trained weights to process tabular data. By projecting tabular inputs to patch embeddings acceptable by ViT, we can directly apply a pre-trained Transformer Encoder to tabular inputs. This approach eliminates the conceptual cost of designing a suitable architecture for processing tabular data, while reducing the computational cost of training the model from scratch. Experimental results on multiple small tabular datasets (less than 1k samples) demonstrate VisTabNet's superiority, outperforming both traditional ensemble methods and recent deep learning models. The proposed method goes beyond conventional transfer learning practice and shows that pre-trained image models can be transferred to solve tabular problems, extending the boundaries of transfer learning.

Deep conditional generative models are excellent tools for creating high-quality images and editing their attributes. However, training modern generative models from scratch is very expensive and requires large computational resources. In this paper, we introduce StyleAutoEncoder (StyleAE), a lightweight AutoEncoder module, which works as a plugin for pre-trained generative models and allows for manipulating the requested attributes of images. The proposed method offers a cost-effective solution for training deep generative models with limited computational resources, making it a promising technique for a wide range of applications. We evaluate StyleAutoEncoder by combining it with StyleGAN, which is currently one of the top generative models. Our experiments demonstrate that StyleAutoEncoder is at least as effective in manipulating image attributes as the state-of-the-art algorithms based on invertible normalizing flows. However, it is simpler, faster, and gives more freedom in designing neural

This paper presents our submission to the COOOL competition, a novel benchmark for detecting and classifying out-of-label hazards in autonomous driving. Our approach integrates diverse methods across three core tasks: (i) driver reaction detection, (ii) hazard object identification, and (iii) hazard captioning. We propose kernel-based change point detection on bounding boxes and optical flow dynamics for driver reaction detection to analyze motion patterns. For hazard identification, we combined a naive proximity-based strategy with object classification using a pre-trained ViT model. At last, for hazard captioning, we used the MOLMO vision-language model with tailored prompts to generate precise and context-aware descriptions of rare and low-resolution hazards. The proposed pipeline outperformed the baseline methods by a large margin, reducing the relative error by 33%, and scored 2nd on the final leaderboard consisting of 32 teams.

Aougab and Gaster [Math. Proc. Cambridge Philos. Soc. 174 (2023), 569--584] proved that any set of simple closed curves on the torus, where any two are non-homotopic and intersect at most $k$ times, has a maximum size of $k+O(\sqrt{k}\log k)$. We prove the maximum size of such a set is at most $k+O(1)$ and determine the exact maximum size for all sufficiently large $k$. In particular, we show that the maximum does not exceed $k+4$ when $k$ is large., Comment: 22 pages

Since the majority of audio DeepFake (DF) detection methods are trained on English-centric datasets, their applicability to non-English languages remains largely unexplored. In this work, we present a benchmark for the multilingual audio DF detection challenge by evaluating various adaptation strategies. Our experiments focus on analyzing models trained on English benchmark datasets, as well as intra-linguistic (same-language) and cross-linguistic adaptation approaches. Our results indicate considerable variations in detection efficacy, highlighting the difficulties of multilingual settings. We show that limiting the dataset to English negatively impacts the efficacy, while stressing the importance of the data in the target language., Comment: Keywords: Audio DeepFakes, DeepFake detection, multilingual audio DeepFakes

Recently, $B(E2, 0^+_1 \to 2^+_1)$ transition strength have been measured in $^{36}$Ca and $^{38}$Ca. Surprisingly, the measured value in $^{36}$Ca: $B(E2\ \uparrow) = 131(20)$ e$^2$fm$^4$, is significantly larger than in $^{38}$Ca, where $B(E2\ \uparrow) = 101(11)$ e$^2$fm$^4$, whereas an opposite tendency of $B(E2)$ values is seen in the mirror nuclei $^{36}$S and $^{38}$Ar. The resonance $2^+_1$ in $^{36}$Ca lies 465 keV above the proton emission threshold and its description requires inclusion of the coupling to the continuum. In this work, we are analyzing $B(E2\ \uparrow)$ values in $^{36}$Ca, $^{36}$S, $^{38}$Ca, and $^{38}$Ar using the real-energy continuum shell model, the so-called shell model embedded in the continuum, in the $(1s_{1/2}\, 0d_{3/2}\, 0f_{7/2}\, 1p_{3/2})$ model space with the monopole adjusted effective interaction ZBM-IO., Comment: 12 pages, 5 figures

We study mechanism design when agents hold private information about both their preferences and a common payoff-relevant state. We show that standard message-driven mechanisms cannot implement socially efficient allocations when agents have multidimensional types, even under favorable conditions. To overcome this limitation, we propose data-driven mechanisms that leverage additional post-allocation information, modeled as an estimator of the payoff-relevant state. Our data-driven mechanisms extend the classic Vickrey-Clarke-Groves class. We show that they achieve exact implementation in posterior equilibrium when the state is either fully revealed or the utility is linear in an unbiased estimator. We also show that they achieve approximate implementation with a consistent estimator, converging to exact implementation as the estimator converges, and present bounds on the convergence rate. We demonstrate applications to digital advertising auctions and large language model (LLM)-based mechanisms, where user engagement naturally reveals relevant information.

Quantum computing applications in the noisy intermediate-scale quantum (NISQ) era demand algorithms capable of generating shallower circuits that are feasible to run on today's quantum systems. This is a challenge, particularly for quantum chemistry applications, considering the inherent complexity of molecular systems. In this paper, we demonstrate advancements that expand the size of chemistry problems that can be run on today's quantum systems by applying hardware-efficient approaches, such as Quantum-Centric Data-Driven Research and Development (QDDRD), optimized algorithms with reduced circuit depth, and execute the experiments with middleware-supported quantum error mitigation. We report up to a 29-fold reduction in circuit depth for covalent drug molecules, enabling Hamiltonian dynamics for reactivity predictions, assuming all-to-all connectivity of quantum hardware. When employed on IBMQ's Heron architecture, we see up to a 16-fold reduction. The overarching impact of this work is that it highlights promising methods that allow researchers to explore the dynamics of commercially relevant chemistry on real quantum hardware via Hamiltonian simulation., Comment: 48 pages, 25 figures

Superfluid helium, the inviscid low-temperature phase of liquid \4He, enables investigation of flows with reduced dimensionality since, due to the vanishing viscosity, sub-micron flow channels can be constructed. In such strongly confined volumes filled with superfluid, the longitudinal acoustic wave is a coupled fluctuation of pressure and entropy density called fourth sound. In this work, we use multiple 4th sound acoustic modes inside a nano-superfluidic acoustic resonator in a pump-probe arrangement to observe localized clusters of quantized vortices leading to two-dimensional turbulence. The localised turbulence enables controllable and asymmetric dissipative coupling between acoustic modes. Furthermore, we derive a general procedure for analytically estimating the superfluid acoustic resonance frequencies inside a volume with mechanically compliant walls. Our work confirms earlier assumptions that turbulence in similar nanofluidic systems initially develops in localized areas of high shear. The multimode pump-probe methods presented here will allow future experiments to study the dynamics of two-dimensional quantum turbulence, e.g., the free decay., Comment: 19 pages, 9 figures

This summary of the second Terrestrial Very-Long-Baseline Atom Interferometry (TVLBAI) Workshop provides a comprehensive overview of our meeting held in London in April 2024, building on the initial discussions during the inaugural workshop held at CERN in March 2023. Like the summary of the first workshop, this document records a critical milestone for the international atom interferometry community. It documents our concerted efforts to evaluate progress, address emerging challenges, and refine strategic directions for future large-scale atom interferometry projects. Our commitment to collaboration is manifested by the integration of diverse expertise and the coordination of international resources, all aimed at advancing the frontiers of atom interferometry physics and technology, as set out in a Memorandum of Understanding signed by over 50 institutions., Comment: Summary of the second Terrestrial Very-Long-Baseline Atom Interferometry Workshop held at Imperial College London: https://indico.cern.ch/event/1369392/

In this Letter, we use the latest results from the Dark Energy Spectroscopic Instrument (DESI) survey to measure the Hubble constant. Baryon acoustic oscillation (BAO) observations released by the DESI survey, allow us to determine $H_0$ from the first principles. Our method is purely data-driven and relies on unanchored luminosity distances reconstructed from SN Ia data and $H(z)$ reconstruction from cosmic chronometers. Thus it circumvents calibrations related to the value of the sound horizon size at the baryon drag epoch or intrinsic luminosity of SN Ia. We find $H_0=68.4^{+1.0}_{-0.8}~{\rm km~s^{-1}~Mpc^{-1}}$ at 68% C.L., which provides the Hubble constant at an accuracy of 1.3% with minimal assumptions. Our assessments of this fundamental cosmological quantity using the BAO data spanning the redshift range $z=0.51-2.33$ agree very well with Planck's results and TRGB results within $1\sigma$. This result is still in a $4.3\sigma$ tension with the results of the Supernova H0 for the Equation of State (SH0ES).

We prove a theorem on the relationships between the lengths of sides of a spherical quadrilateral with three right angles. They are analogous to the relationships in the Lambert quadrilateral in the hyperbolic plane. We apply this theorem in the proof of our second theorem that if $C$ is a two-dimensional spherical convex body of diameter $\delta \in (\frac{1}{2}\pi,\pi)$, then the diameter of the set of extreme points of $C$ is at least $2 \arccos \big(\frac{1}{4}(\cos \delta + \sqrt {\cos^2 \delta +8})\big)$. This estimate cannot be improved., Comment: 11 pages, 2 figures

Limitations of electronics have stimulated the search for novel unconventional computing platforms that enable energy-efficient and ultra-fast information processing. Among various systems, exciton-polaritons stand out as promising candidates for the realization of optical neuromorphic devices. This is due to their unique hybrid light-matter properties, resulting in strong optical nonlinearity and excellent transport capabilities. However, previous implementations of polariton neural networks have been restricted to cryogenic temperatures, limiting their practical applications. In this work, using non-equillibrium Bose-Einstein condensation in a monocrystalline perovskite waveguide, we demonstrate the first room-temperature exciton-polariton neural network. Its performance is verified in various machine learning tasks, including binary classification, and object detection. Our result is a crucial milestone in the development of practical applications of polariton neural networks and provides new perspectives for optical computing accelerators based on perovskites.

This study addresses the challenge of predicting electric vehicle (EV) charging profiles in urban locations with limited data. Utilizing a neural network architecture, we aim to uncover latent charging profiles influenced by spatio-temporal factors. Our model focuses on peak power demand and daily load shapes, providing insights into charging behavior. Our results indicate significant impacts from the type of Basic Administrative Units on predicted load curves, which contributes to the understanding and optimization of EV charging infrastructure in urban settings and allows Distribution System Operators (DSO) to more efficiently plan EV charging infrastructure expansion., Comment: Accepted to Tackling Climate Change with Machine Learning: workshop at NeurIPS 2024

We study realistic models predicting primordial black hole (PBH) formation from density fluctuations generated in a first-order phase transition. We show that the second-order correction in the expansion of the bubble nucleation rate is necessary for accurate predictions and quantify its impact on the abundance of PBHs and gravitational waves (GWs). We find that the distribution of the fluctuations becomes more Gaussian as the second-order term increases. Consequently, models that predict the same PBH abundances can produce different GW spectra., Comment: 7 pages, 6 figures

Given a square box $\Lambda_n\subseteq\mathbb Z^2$ of side-length $L^n$ with $L,n>1$, we study hierarchical random fields $\{\phi_x\colon x\in\Lambda_n\}$ with law proportional to ${\rm e}^{\frac12\beta(\varphi,\Delta_n\phi)}\prod_{x\in\Lambda_n}\nu({\rm d}\phi_x)$, where $\beta>0$ is the inverse temperature, $\Delta_n$ is a hierarchical Laplacian on $\Lambda_n$, and $\nu$ is a non-degenerate $1$-periodic measure on $\mathbb R$. Our setting includes the integer-valued Gaussian field (a.k.a. DG-model or Villain Coulomb gas) and the sine-Gordon model. Relying on renormalization group analysis we derive sharp asymptotic formulas, in the limit as $n\to\infty$, for the covariance $\langle\phi_x\phi_y\rangle$ and the fractional charge $\langle{\rm e}^{2\pi{\rm i}\alpha(\phi_x-\phi_y)}\rangle$ in the subcritical $\beta<\beta_{\rm c}:=\pi^2/\log L$, critical $\beta=\beta_{\rm c}$ and slightly supercritical $\beta>\beta_{\rm c}$ regimes. The field exhibits logarithmic correlations throughout albeit with a distinct $\beta$-dependence of the variance and fractional-charge exponents in the sub/supercritical regimes. Explicit logarithmic corrections appear at the critical point., Comment: 69 pages

Gaussian Splatting has changed the game for real-time photo-realistic rendering. One of the most popular applications of Gaussian Splatting is to create animatable avatars, known as Gaussian Avatars. Recent works have pushed the boundaries of quality and rendering efficiency but suffer from two main limitations. Either they require expensive multi-camera rigs to produce avatars with free-view rendering, or they can be trained with a single camera but only rendered at high quality from this fixed viewpoint. An ideal model would be trained using a short monocular video or image from available hardware, such as a webcam, and rendered from any view. To this end, we propose GASP: Gaussian Avatars with Synthetic Priors. To overcome the limitations of existing datasets, we exploit the pixel-perfect nature of synthetic data to train a Gaussian Avatar prior. By fitting this prior model to a single photo or video and fine-tuning it, we get a high-quality Gaussian Avatar, which supports 360$^\circ$ rendering. Our prior is only required for fitting, not inference, enabling real-time application. Through our method, we obtain high-quality, animatable Avatars from limited data which can be animated and rendered at 70fps on commercial hardware. See our project page (https://microsoft.github.io/GASP/) for results., Comment: Project page: https://microsoft.github.io/GASP/

Nonlinear magnetic interactions provide access to complex quantum spin dynamics and thus enable the study of intriguing physical phenomena. However, these interactions are often dominated by the linear Zeeman effect, which can complicate system dynamics and make their analysis more challenging. In this article, we theoretically and experimentally introduce a method to induce the quadratic Zeeman effect while effectively compensating for its linear counterpart. By isolating the quadratic Zeeman contributions, we demonstrate and analyze controlled superposition generation between specific magnetic sublevels in room-temperature rubidium-87 atoms. This study opens avenues for controlling any spin system, regardless of its total angular momentum, which we plan to explore further in the context of quantum-state tomography and engineering (e.g., spin squeezing).

This paper addresses critical questions surrounding the security of government-issued identity documents and their potential misuse, with an emphasis on understanding the perspectives of ordinary citizens across Europe and the United States of America. Drawing upon research on technology acceptance and diffusion, the research focuses on understanding the factors that influence users' adoption of novel identity management solutions. Our methodology includes a comprehensive, census-representative survey spanning citizens from France, Germany, Italy, Spain, the United Kingdom, and the USA. The paper's findings underscore a robust confidence in government-issued identity documents, contrasted by a lower trust in private sector services, including social media platforms and email accounts. The adoption of artificial intelligence for identity verification remains contested, with a significant percentage of respondents undecided, indicating a need for explicit explanation and transparency about its implementation and related risks. Public sentiment leans towards acceptance of government data collection for identification purposes; however, the sharing of this data with private entities elicits more apprehension.

This paper deals with the state estimation of stochastic models with continuous dynamics. The aim is to incorporate spectral differentiation methods into the solution to the Fokker-Planck equation in grid-based state estimation routine, while taking into account the specifics of the field, such as probability density function (PDF) features, moving grid, zero boundary conditions, etc. The spectral methods, in general, achieve very fast convergence rate of O(c^N )(O < c < 1) for analytical functions such as the probability density function, where N is the number of grid points. This is significantly better than the standard finite difference method (or midpoint rule used in discrete estimation) typically used in grid-based filter design with convergence rate O( 1 / N^2 ). As consequence, the proposed spectral method based filter provides better state estimation accuracy with lower number of grid points, and thus, with lower computational complexity., Comment: Accepted to FUSION2024

Corrosion presents a major challenge to the longevity and reliability of products across various industries, particularly in the aerospace sector. Corrosion arises from chemical processes occurring on an atomistic scale, which lead to macroscopic degradation. Addressing this issue requires multi-scale modeling approaches, which rely on microscopic parameters that are challenging to measure experimentally or model with conventional quantum chemistry techniques. In this work, we develop and demonstrate a hybrid quantum-classical workflow tailored for atomistic simulations of corrosion processes, with a specific focus on the initial step of the oxygen reduction reaction -- a critical trigger for the corrosion of aluminum alloys widely used in modern aircraft. Using a combination of classical quantum chemistry methods and quantum computing frameworks, we identify reaction geometries characterized by multi-configurational electronic structures that are ideal for exploring with quantum algorithms. For the first time in this context, we explore both noisy intermediate-scale quantum and fault-tolerant quantum algorithms for these multi-configurational system, integrating them within a workflow designed to bridge atomistic simulations with macroscopic modeling approaches, such as finite element methods. Furthermore, we conduct a detailed quantum resource estimation to assess when and how quantum computers may play a meaningful role in tackling these problems. Our results demonstrate that significant advancements in quantum hardware but also in algorithms and error correction techniques are needed to make quantum computation practically viable for this class of problems. Nevertheless, this work establishes a critical foundation for applying quantum computation to corrosion modeling and highlights its potential to address complex, business-relevant challenges in materials science.

This paper deals with the state prediction of nonlinear stochastic dynamic systems. The emphasis is laid on a solution to the integral Chapman-Kolmogorov equation by a deterministic-integration-rule-based point-mass method. A novel concept of reliable data-augmented, i.e., mathematics- and data-informed, integration rule is developed to enhance the point-mass state predictor, where the trained neural network (representing data contribution) is used for the selection of the best integration rule from a set of available rules (representing mathematics contribution). The proposed approach combining the best properties of the standard mathematics-informed and novel data-informed rules is thoroughly discussed., Comment: This work has been accepted to IFAC for publication - IFAC SYSID24

We present a joint analysis of TTVs and Doppler data for the transiting exoplanet system TOI-4504. TOI-4504 c is a warm Jupiter-mass planet that exhibits the largest known transit timing variations (TTVs), with a peak-to-node amplitude of $\sim$ 2 days, the largest value ever observed, and a super-period of $\sim$ 930 d. TOI-4504 b and c were identified in public TESS data, while the TTVs observed in TOI-4504 c, together with radial velocity (RV) data collected with FEROS, allowed us to uncover a third, non-transiting planet in this system, TOI-4504 d. We were able to detect transits of TOI-4504 b in the TESS data with a period of 2.4261$\pm 0.0001$ days and derive a radius of 2.69$\pm 0.19$ R$_{\oplus}$. The RV scatter of TOI-4504 was too large to constrain the mass of TOI-4504 b, but the RV signals of TOI-4504 c \& d were sufficiently large to measure their masses. The TTV+RV dynamical model we apply confirms TOI-4504 c as a warm Jupiter planet with an osculating period of 82.54$\pm 0.02$ d, mass of 3.77$\pm 0.18$ M$_{\rm J}$ and a radius of 0.99$\pm 0.05$ R$_{\rm J}$, while the non-transiting planet TOI-4504 d, has an orbital period of 40.56$\pm 0.04$ days and mass of 1.42$_{-0.06}^{+0.07}$ M$_{\rm J}$. We present the discovery of a system with three exoplanets: a hot sub-Neptune and two warm Jupiter planets. The gas giant pair is stable and likely locked in a first-order 2:1 mean-motion resonance (MMR). The TOI-4504 system is an important addition to MMR pairs, whose increasing occurrence supports a smooth migration into a resonant configuration during the protoplanetary disk phase., Comment: 20 pages, 12 figures, accepted in ApJL

Efficiently preparing approximate ground-states of large, strongly correlated systems on quantum hardware is challenging and yet nature is innately adept at this. This has motivated the study of thermodynamically inspired approaches to ground-state preparation that aim to replicate cooling processes via imaginary-time evolution. However, synthesizing quantum circuits that efficiently implement imaginary-time evolution is itself difficult, with prior proposals generally adopting heuristic variational approaches or using deep block encodings. Here, we use the insight that quantum imaginary-time evolution is a solution of Brockett's double-bracket flow and synthesize circuits that implement double-bracket flows coherently on the quantum computer. We prove that our Double-Bracket Quantum Imaginary-Time Evolution (DB-QITE) algorithm inherits the cooling guarantees of imaginary-time evolution. Concretely, each step is guaranteed to i) decrease the energy of an initial approximate ground-state by an amount proportion to the energy fluctuations of the initial state and ii) increase the fidelity with the ground-state. Thus DB-QITE provides a means to systematically improve the approximation of a ground-state using shallow circuits.

The origin of the large angular momenta observed for fission fragments is still a question under discussion. To address this, we study isomeric yield ratios (IYR), i.e. the relative population of two or more long-lived metastable states with different spins, of fission products. We report on IYR of 17 isotopes produced in the 28 MeV $\alpha$-induced fission of $^{232}$Th at the IGISOL facility of the University of Jyv\"askyl\"a. The fissioning nuclei in this reaction are $^{233,234,235}$U*. We compare our data to IYR from thermal neutron-induced fission of $^{233}$U and $^{235}$U, and we observe statistically significant larger IYR in the $^{232}$Th($\alpha$,f) reaction, where the average compound nucleus (CN) spin is 7.5 $\hbar$, than in $^{233,235}$U(n$_{th}$,f), with average spins 2.5 and 3.5 $\hbar$, respectively. To assess the influence of the excitation energy, we study literature data of IYR from photon-induced fission reactions, and find that the IYR are independent of the CN excitation energy. We conclude that the different IYR must be explained by the different CN spin alone. This implies that the FF angular momentum only partly comes from the fission process itself, and is in addition influenced by the angular momentum present in the CN., Comment: Submitted to Physical Review Letters

Masked Image Modeling (MIM) has emerged as a popular method for Self-Supervised Learning (SSL) of visual representations. However, for high-level perception tasks, MIM-pretrained models offer lower out-of-the-box representation quality than the Joint-Embedding Architectures (JEA) - another prominent SSL paradigm. To understand this performance gap, we analyze the information flow in Vision Transformers (ViT) learned by both approaches. We reveal that whereas JEAs construct their representation on a selected set of relevant image fragments, MIM models aggregate nearly whole image content. Moreover, we demonstrate that MIM-trained ViTs retain valuable information within their patch tokens, which is not effectively captured by the global [cls] token representations. Therefore, selective aggregation of relevant patch tokens, without any fine-tuning, results in consistently higher-quality of MIM representations. To our knowledge, we are the first to highlight the lack of effective representation aggregation as an emergent issue of MIM and propose directions to address it, contributing to future advances in Self-Supervised Learning.

This article present a new, direct and simple formula for constructing Mignotte sequences.

Large Language Models (LLMs) have demonstrated significant potential in handling specialized tasks, including medical problem-solving. However, most studies predominantly focus on English-language contexts. This study introduces a novel benchmark dataset based on Polish medical licensing and specialization exams (LEK, LDEK, PES) taken by medical doctor candidates and practicing doctors pursuing specialization. The dataset was web-scraped from publicly available resources provided by the Medical Examination Center and the Chief Medical Chamber. It comprises over 24,000 exam questions, including a subset of parallel Polish-English corpora, where the English portion was professionally translated by the examination center for foreign candidates. By creating a structured benchmark from these existing exam questions, we systematically evaluate state-of-the-art LLMs, including general-purpose, domain-specific, and Polish-specific models, and compare their performance against human medical students. Our analysis reveals that while models like GPT-4o achieve near-human performance, significant challenges persist in cross-lingual translation and domain-specific understanding. These findings underscore disparities in model performance across languages and medical specialties, highlighting the limitations and ethical considerations of deploying LLMs in clinical practice.

Space debris presents a critical challenge for the sustainability of future space missions, emphasizing the need for robust and standardized identification methods. However, a comprehensive benchmark for rocket body classification remains absent. This paper addresses this gap by introducing the RoBo6 dataset for rocket body classification based on light curves. The dataset, derived from the Mini Mega Tortora database, includes light curves for six rocket body classes: CZ-3B, Atlas 5 Centaur, Falcon 9, H-2A, Ariane 5, and Delta 4. With 5,676 training and 1,404 test samples, it addresses data inconsistencies using resampling, normalization, and filtering techniques. Several machine learning models were evaluated, including CNN and transformer-based approaches, with Astroconformer reporting the best performance. The dataset establishes a common benchmark for future comparisons and advancements in rocket body classification tasks., Comment: 6 pages, 1 figure, 5 tables, Accepted on Machine Learning and the Physical Sciences Workshop, NeurIPS 2024

In this paper we provide the non-existence criterion for the so-called maximizing curves of odd degrees. Furthermore, in the light of our criterion, we define a new class of plane curves that generalizes the notion of maximizing curves which we call as $M$-curves., Comment: 10 pages

Every year, thousands of people suffer from skull damage and require personalized implants to fill the cranial cavity. Unfortunately, the waiting time for reconstruction surgery can extend to several weeks or even months, especially in less developed countries. One factor contributing to the extended waiting period is the intricate process of personalized implant modeling. Currently, the preparation of these implants by experienced biomechanical experts is both costly and time-consuming. Recent advances in artificial intelligence, especially in deep learning, offer promising potential for automating the process. However, deep learning-based cranial reconstruction faces several challenges: (i) the limited size of training datasets, (ii) the high resolution of the volumetric data, and (iii) significant data heterogeneity. In this work, we propose a novel approach to address these challenges by enhancing the reconstruction through learnable symmetry enforcement. We demonstrate that it is possible to train a neural network dedicated to calculating skull symmetry, which can be utilized either as an additional objective function during training or as a post-reconstruction objective during the refinement step. We quantitatively evaluate the proposed method using open SkullBreak and SkullFix datasets, and qualitatively using real clinical cases. The results indicate that the symmetry-preserving reconstruction network achieves considerably better outcomes compared to the baseline (0.94/0.94/1.31 vs 0.84/0.76/2.43 in terms of DSC, bDSC, and HD95). Moreover, the results are comparable to the best-performing methods while requiring significantly fewer computational resources (< 500 vs > 100,000 GPU hours). The proposed method is a considerable contribution to the field of applied artificial intelligence in medicine and is a step toward automatic cranial defect reconstruction in clinical practice.

We use time-resolved thermometry to monitor the decay of nonequilibrium quasiparticles in superconducting Al in the temperature range from $0.3\,$K to $1.2\,$K. The quasiparticle lifetime at higher temperatures ($T>0.7\,$K) agrees well with the calculated energy flow from electrons to phonons, but at lower temperatures it is significantly shorter than the theory predicts. We show well-defined internal equilibrium of quasiparticle system in the studied thermal transients, which implicates that quasiparticle-quasiparticle relaxation is much faster than electron-phonon interaction.

We initiate a systematic study of utilizing predictions to improve over approximation guarantees of classic algorithms, without increasing the running time. We propose a systematic method for a wide class of optimization problems that ask to select a feasible subset of input items of minimal (or maximal) total weight. This gives simple (near-)linear time algorithms for, e.g., Vertex Cover, Steiner Tree, Min-Weight Perfect Matching, Knapsack, and Clique. Our algorithms produce optimal solutions when provided with perfect predictions and their approximation ratios smoothly degrade with increasing prediction error. With small enough prediction error we achieve approximation guarantees that are beyond reach without predictions in the given time bounds, as exemplified by the NP-hardness and APX-hardness of many of the above problems. Although we show our approach to be optimal for this class of problems as a whole, there is a potential for exploiting specific structural properties of individual problems to obtain improved bounds; we demonstrate this on the Steiner Tree problem. We conclude with an empirical evaluation of our approach.

We investigate the hydrodynamic solutions for expanding bubbles in cosmological first-order phase transitions going beyond local thermal equilibrium approximation. Under the assumption of a tangenosidal field profile, we supplement the matching conditions with the entropy produced due to the interaction of the bubble wall with ambient plasma. This allows us to analytically compute the corresponding fluid profiles and find bubble-wall velocity. We show that due to the entropy production, two stable solutions corresponding to a deflagration or hybrid and a detonation can coexist. Finally, we use numerical real-time simulations of bubble growth to show that in such cases it is typically the faster detonation solution which is realised. This effect can be explained in terms of the fluid profile not being fully formed into the predicted steady-state solution as the wall accelerates past this slower solution., Comment: 18 pages, 4 figures

Fiber-based long-haul quantum communication would greatly benefit from a robust and deterministically integrated source of quantum state. Here, we report the design, fabrication, and optical characterization of InAs/InP quantum dots in the InP H1 point defect 2D photonic crystal cavity, integrated with the standard single-mode fiber using a micro-transfer printing technique. The device was placed in a compact cryocooler maintaining a cryogenic temperature of 15 K with single-photon emission characterized by $g^{(2)}(0)=0.14(14)$ and reliable and stable emission (intensity fluctuations given by a standard deviation $\sigma = 0.13$), so that an all-fiber based connection between two laboratory nodes through an open area was established and utilized for testing the quantum channel. In this way, we demonstrate a plug-and-play all-fiber single-photon source operating in the third telecom window, where standard telecommunication fiber networks can be used as a low-loss medium.

Optimization problems pose challenges across various fields. In recent years, quantum annealers have emerged as a promising platform for tackling such challenges. To provide a new perspective, we develop a heuristic tensor-network-based algorithm to reveal the low-energy spectrum of Ising spin-glass systems with interaction graphs relevant to present-day quantum annealers. Our deterministic approach combines a branch-and-bound search strategy with an approximate calculation of marginals via tensor-network contractions. Its application to quasi-two-dimensional lattices with large unit cells of up to 24 spins, realized in current quantum annealing processors, requires a dedicated approach that utilizes sparse structures in the tensor network representation and GPU hardware acceleration. We benchmark our approach on random problems defined on Pegasus and Zephyr graphs with up to a few thousand spins, comparing it against the D-Wave Advantage quantum annealer and Simulated Bifurcation algorithm, with the latter representing an emerging class of classical Ising solvers. Apart from the quality of the best solutions, we compare the diversity of low-energy states sampled by all the solvers. For the biggest considered problems with over 5000 spins, the state-of-the-art tensor network approaches lead to solutions that are $0.1\%$ to $1\%$ worse than the best solutions obtained by Ising machines while being two orders of magnitude slower. We attribute those results to approximate contraction failures. While all three methods can output diverse low-energy solutions, e.g., differing by at least a quarter of spins with energy error below $1\%$, our deterministic branch-and-bound approach finds sets of a few such states at most. On the other hand, both Ising machines prove capable of sampling sets of thousands of such solutions., Comment: 15+7 pages, 12+7 figures

We study the evolution of growing vacuum bubbles. The bubble walls interact with the surrounding fluid and may, consequently, reach a terminal velocity. If the mean free path of the particles in the fluid is much shorter than the bubble wall thickness, the fluid is locally in thermal equilibrium and the wall's terminal velocity can be determined by entropy conservation. On the other hand, if local thermal equilibrium inside the wall cannot be maintained, the wall velocity can be estimated from the pressure impacted by ballistic particle dynamics at the wall. We find that the latter case leads to slightly slower bubble walls. Expectedly, we find the largest differences in the terminal velocity when the fluid is entirely ballistic. This observation indicates that the non-equilibrium effects inside walls are relevant. To study bubble evolution, we perform hydrodynamic lattice simulations in the case of local thermal equilibrium and $N$-body simulations in the ballistic case to investigate the dynamical effects during expansion. Both simulations show that even if a stationary solution exists in theory it may not be reached depending on the dynamics of the accelerating bubble walls., Comment: 26 pages, 7 figures

A finite element model consisting of a conducting filament with or without a gap was used to reproduce behavior of TaO$_x$-based resistive switching devices. The specific goal was to explore the range of possible filament parameters such a filament diameter, composition, gap width, and composition to reproduce the conductance and shape of I-V while keeping the maximum temperature within acceptable range allowing for ion motion and preventing melting. The model solving heat and charge transport produced a good agreement with experimental data for the oxygen content in the filament below TaO$_{1.3}$, the filament diameter range between 6 and 22 nm, and the gap oxygen content between TaO$_{1.7}$ and TaO$_{1.85}$. Gap width was not limited on either low or high sides by the criteria considered in this report. The obtained filament composition corresponds to oxygen deficiency an order of magnitude higher than one estimated by other modelling efforts. This was in a large part due to use of recent experimental values of conductivity as a function of composition and temperature. Our modelling results imply that a large fraction of atoms leaves and/or accumulates within the filament to produce large relative concentration change. This, in turn, necessitates inclusion of strain energy in the filament formation modelling. In addition, the results reproduce non-linear I-V without the necessity of assuming the Poole-Frenkel type of electrical conduction or presence of a barrier at the oxide/metal interface., Comment: 31 pages, 7 figures