arXiv:2605.00222v2 Announce Type: replace
Abstract: Chemical reaction datasets such as USPTO suffer from substantial incompleteness, frequently missing byproducts, co-reactants, and stoichiometric coefficients. This limits their applicability and reliability in downstream applications. Here, we introduce CompleteRXN, a large-scale supervised benchmark for reaction completion under realistic missing-data conditions. We construct a dataset of aligned incomplete and atom-balanced reactions by mapping USPTO records to curated mechanistic reactions. We evaluate representative baselines, including a novel encoder-decoder reaction completion model with constrained decoding, the Constrained Reaction Balancer (CRB), and a recent algorithmic method, SynRBL. On our CompleteRXN benchmark, the CRB achieves high performance across splits of increasing difficulty, reaching 99.20% equivalence accuracy on the random split and 91.12% on the extreme out-of-distribution split. SynRBL produces many balanced and chemically plausible completions, but with lower accuracy on the benchmark test splits. Across all methods, performance degrades with increasing incompleteness. We observe a substantial drop when evaluating on reactions outside the benchmark (full uncurated USPTO), highlighting the gap between benchmark performance and practical robustness and motivating future work.
Science Journals
arXiv:2605.04916v2 Announce Type: replace
Abstract: Inductive Logic Programming (ILP) learns interpretable logical rules from data. Existing methods are transductive: their learned parameters are bound to specific predicates and require retraining for each new task. We introduce Neural Rule Inducer (NRI), a pretrained model for zero-shot rule induction. Rather than encoding literal identities, NRI represents literals using domain-agnostic statistical properties such as class-conditional rates, entropy, and co-occurrence, which generalize across variable identities and counts without retraining. The model consists of a statistical encoder and a parallel slot-based decoder. Parallel decoding preserves the permutation invariance of logical disjunction; an autoregressive decoder would instead impose an arbitrary clause order. Product T-norm relaxation makes rule execution differentiable, allowing end-to-end training on prediction accuracy alone. We evaluate NRI on rule recovery, robustness to label noise and spurious correlations, and zero-shot transfer to real-world benchmarks, and we believe this work opens up the possibility of foundation models for symbolic reasoning. Code and the reference checkpoint are available at https://github.com/phuayj/neural-rule-inducer.
arXiv:2605.30001v1 Announce Type: new
Abstract: The transition toward Software-Defined Vehicles (SDVs) represents a major paradigm shift in vehicle design, transforming traditional hardware-centric systems into software-centric platforms capable of dynamic adaptation and continuous functional evolution. SDVs enable advanced capabilities such as Over-the-Air (OTA) updates, intelligent automation, and connected services driven by AI. This paper presents a comprehensive survey of the architectures, enabling technologies, and operational frameworks that define modern SDVs. It examines the evolution of vehicle architectures from distributed electronic control unit (ECU) systems to domain-based, zonal, and centralized computing platforms. Key enabling technologies are reviewed, including service-oriented software architectures, middleware, automation pipelines, artificial intelligence mechanisms, and cloud-based infrastructures. A structured taxonomy is introduced to organize SDV technologies into functional hardware, E/E architectures, software frameworks, automation mechanisms, and distributed infrastructure domains. The study also investigates the Software-Defined Internet of Vehicles (SDIoV) paradigm, integrating Software-Defined Networking (SDN) with edge and fog computing to support scalable vehicular communication and data processing. Furthermore, key technical challenges related to cybersecurity, interoperability, data management, and system scalability are discussed, along with emerging research directions and future development trends.
arXiv:2605.28700v2 Announce Type: replace
Abstract: The GSM-Symbolic benchmark (Mirzadeh et al., 2025) reported consistent performance drops across 25 Large Language Models (LLMs) when tested on template-generated variants of GSM8K problems, concluding that the models lack genuine reasoning capabilities. We argue that this conclusion rests on shaky statistical ground. Re-evaluating 20 open-weight models using Generalised Linear Mixed Models with per-question random effects, we find that only half exhibit statistically significant performance changes under the original prompt format. Moreover, we identify a previously unacknowledged factor: the main GSM-Symbolic dataset contains a systematically shifted distribution of larger integers in problem texts relative to GSM-Base (K-S statistic = 0.12, p < 0.001), contradicting the original authors' claims. Controlling for this large number effect accounts for significance in roughly half the remaining cases. Among models with statistically significant performance deltas, we identify distinct, model-specific failure profiles - including fragility of variable binding, arithmetic limitations, and dual-task interference - underscoring that blanket claims about LLM reasoning are both statistically premature and mechanistically misleading.
arXiv:2508.11311v2 Announce Type: replace-cross
Abstract: The squeezed photons, as a quantum-correlated light with reduced noise, have emerged as a great resource for sensing the structures of matter. Here we study the transient absorption (TA) scheme using the squeezed photons whose spectral correlation of amplitudes can be tailored. A microscopic theory is developed, revealing a highly time-energy-resolved nature of the signal that is not attainable by conventional TA scheme. Such a capability is elaborated by applying to monolayer transition metal dichalcogenide materials (TMDs), achieving a real-time monitoring of valley excitons and their dynamics. Moreover, we show the intermediate squeezing regime-not the strong squeezing-which the time-resolved spectroscopy is in favor of. Our work offers a new paradigm for studying nonequilibrium dynamics of matter, in light of the photocatalysis and optoelectronics.
arXiv:2510.06796v3 Announce Type: replace-cross
Abstract: Understanding the entanglement structure of local Hamiltonian ground spaces is a physically motivated problem, with applications ranging from tensor network design to quantum error-correcting codes. To this end, we study the complexity of estimating ground state entanglement, and more generally entropy estimation for low energy states and Gibbs states. We find, in particular, that the classes qq-QAM [Kobayashi, le Gall, Nishimura, SICOMP 2019] (a quantum analogue of public-coin AM) and QMA(2) (QMA with unentangled proofs) play a crucial role for such problems, showing: (1) Detecting a high-entanglement ground state is qq-QAM-complete, (2) computing an additive error approximation to the Helmholtz free energy (equivalently, a multiplicative error approximation to the partition function) is in qq-QAM, (3) detecting a low-entanglement ground state is QMA(2)-hard, and (4) detecting low energy states which are close to product states can range from QMA-complete to QMA(2)-complete. Our results make progress on an open question of [Bravyi, Chowdhury, Gosset and Wocjan, Nature Physics 2022] on free energy, and yield the first QMA(2)-complete Hamiltonian problem using local Hamiltonians (cf. the sparse QMA(2)-complete Hamiltonian problem of [Chailloux, Sattath, CCC 2012]).
arXiv:2510.08535v2 Announce Type: replace-cross
Abstract: Diffusion models are central to generative modeling and have been adapted to graphs by diffusing adjacency matrix representations. The challenge of having up to $n!$ such representations for graphs with $n$ nodes is only partially mitigated by using permutation-equivariant learning architectures. Despite their computational efficiency, existing graph diffusion models struggle to distinguish certain graph families and their spectra, unless graph data are augmented with ad hoc features. This shortcoming stems from enforcing the inductive bias within the learning architecture. In this work, we leverage random matrix theory to analytically extract the spectral properties of the diffusion process, allowing us to push most of the inductive bias from the architecture into the dynamics. Building on this, we introduce the Dyson Diffusion Model, which employs Dyson's Brownian motion to capture the spectral dynamics of an Ornstein-Uhlenbeck process on the adjacency matrix. Furthermore, conditioned on the spectral dynamics, we formulate a Lie group diffusion, appropriately modeling the remaining degrees of freedom. Strikingly, the resulting learning problem becomes permutation invariant at the Lie algebra level. We demonstrate that the Dyson Diffusion Model learns graph spectra accurately and outperforms existing graph diffusion models.
arXiv:2605.29999v1 Announce Type: new
Abstract: The accelerating growth of global data generation demands data storage platforms that offer high capacity, long lifespan, and low energy consumption beyond the limits of electronic memory technologies. Optical storage provides an attractive alternative. However, its density is fundamentally constrained by the optical diffraction limit and the limited scalability from the point-by-point laser writing, as well as thermal accumulation during high-speed writing. Here, we introduce a large-scale optical data storage scheme that is compatible with the progress in chip fabrication by combining electron-beam lithography (EBL) and ion implantation to deterministically encode high-density data. The approach achieves precise control of ion number and spatial distribution, enabling multi-bit grayscale encoding and wavelength division multiplexing with chip-scale patterning over millimeter areas. Wavelength-selective readout is performed using downconversion and upconversion fluorescence detection, allowing crosstalk-free retrieval of multiplexed data channels. We further develop a neural network-based super-resolution algorithm that reconstructs data beyond the diffraction limit, further increasing the effective storage density. Using this integrated framework, we achieve an optical data density of 10 Gbit/cm$^2$ with high fidelity. Our results establish a micro/nano-fabrication-compatible route to large-scale, high-density optical memory and provide a foundation for next-generation cold data optical storage technologies.
arXiv:2510.11350v4 Announce Type: replace-cross
Abstract: Magnetic materials are crucial for manipulating electron spin and magnetic fields, enabling applications in data storage, spintronics, charge transport, and energy conversion, while also providing insight into fundamental quantum phenomena. In numerous applications, the interaction between electrons and lattice vibrations, known as electron-phonon coupling, can be of significant importance. In that regard, we extend the EPW package to be able to interpolate the electron-phonon matrix elements combining perturbation theory and maximally localized Wannier functions. This allows to use dense momentum grids at a reasonable computational cost when computing electron-phonon-related quantities and physical properties. We validate our implementation considering ferromagnetic iron and nickel, where we explore the phonon induced mass enhancement and Eliashberg spectral function finding different importance of each spin channel for both compounds. Furthermore, we evaluate the carrier resistivity at finite temperatures for both systems, considering the role of the magnetic phase in carrier transport. Our findings indicate that in the case of Fe, the primary contributor to resistivity is electron-phonon scattering. In contrast, for Ni, electron-phonon scattering constitutes less than one-third of the resistivity, underscoring a fundamental difference in the transport properties of the two systems.
arXiv:2605.29995v1 Announce Type: new
Abstract: Superimposed pilot (SIP) transmission improves spectral efficiency by eliminating the dedicated pilot overhead required in orthogonal pilot (OP)-based schemes. However, SIP suffers from severe pilot-data coupling, which leads to a critical performance-complexity bottleneck at the receiver. To address this issue, this paper proposes a low-overhead transmission framework that revitalizes data-dependent superimposed training (DDST) with enhanced interference mitigation strategies. First, for quasi-static block-fading channels, an enhanced DDST receiver is developed to achieve non-iterative pilot-data decoupling by exploiting data-dependent algebraic structures. Second, to overcome the sensitivity of conventional DDST to channel variations and symbol misidentification in fast time-varying environments, a mix transmission scheme is developed. By strategically applying DDST to a subset of resource elements, the proposed scheme combines the interference-free transmission property of OP with the zero-pilot-overhead advantage of SIP, thereby improving demapping reliability and interference suppression. Furthermore, under the proposed mix scheme, a Vision Transformer-based neural receiver is designed to capture the orthogonal structure between pilots and perturbation-bearing data, as well as the underlying channel correlations, thereby relaxing the stringent quasi-static assumption required for interference disentanglement. Simulation results demonstrate that the proposed framework achieves significant performance gains in the low-to-medium SNR regime under time-varying channels while providing superior computational efficiency compared with state-of-the-art SIP receivers.
arXiv:2603.11539v2 Announce Type: replace-cross
Abstract: The origin of ice's slipperiness has long puzzled scientists. To resolve this question, we simulate ice-glass (amorphous silica) friction at the nanoscale from first principles and upscale to the macroscale using a frictional heating model. We find that nanoscale simulations alone cannot capture the correct velocity dependence of ice friction, resulting in an overestimated coefficient of friction. By properly accounting for frictional heating, we find a strong increase in contact temperature toward the melting point, even under modest motion of 1 millimeter with velocities above 0.1 m/s, yielding excellent agreement with experimental friction data across a wide range of velocities. While the initial formation of a lubricating film on ice may occur without heating, the ultimate slipperiness of ice hinges on frictional heating, as proposed by Bowden and Hughes in 1939, but without incorporating melting.
arXiv:2603.14644v3 Announce Type: replace-cross
Abstract: Publicly available full-field digital mammography (FFDM) datasets remain limited in size, clinical annotations, and vendor diversity, hindering the development of robust models. We introduce LUMINA, a curated, multi-vendor FFDM dataset that explicitly encodes acquisition energy and vendor metadata to capture clinically relevant appearance variations often overlooked in existing benchmarks. This dataset contains 1824 images from 468 patients (960 benign, 864 malignant), with pathology-confirmed labels, BI-RADS assessments, and breast-density annotations. LUMINA spans six acquisition systems and includes both high- and low-energy imaging styles, enabling systematic analysis of vendor- and energy-induced domain shifts. To address these variations, we propose a foreground-only pixel-space alignment method (''energy harmonization'') that maps images to a low-energy reference while preserving lesion morphology. We benchmark CNN and transformer models on three clinically relevant tasks: diagnosis (benign vs. malignant), BI-RADS classification, and density estimation. Two-view models consistently outperform single-view models. EfficientNet-B0 achieves an AUC of 93.54% for diagnosis, while Swin-T achieves the best macro-AUC of 89.43% for density prediction. Harmonization improves performance across architectures and produces more localized Grad-CAM responses. Overall, LUMINA provides (1) a vendor-diverse benchmark and (2) a model-agnostic harmonization framework for reliable and deployable mammography AI.
arXiv:2605.28746v2 Announce Type: replace-cross
Abstract: This paper studies preference-shaped expected improvement criteria for Bayesian multiobjective optimization. We consider two indicator families which are often used for similar algorithmic purposes, but which are geometrically different. The hypervolume indicator is based on a dystopian reference point and measures dominated volume in objective space. The R2 indicator is based on a utopian point and evaluates approximation sets through weighted Tchebycheff scalarization envelopes. The purpose of the paper is to make precise which preference transformations preserve exact computation, Pareto compatibility, and monotonicity properties, and which transformations change the underlying geometry. On the hypervolume side, we revisit canonical EHVI through the Deng representation, formulate product-density weighted EHVI in desirability coordinates, discuss cone-based EHVI as ordinary EHVI after a linear cone transformation, and separate these cases from truncated EHVI, where variance monotonicity may fail. On the R2 side, we prove that exact integral R2 improvement is not, in general, an ordinary objective-space weighted hypervolume. The obstruction is lower-dimensional: Lebesgue-density hypervolume cannot see certain boundary contributions that Tchebycheff scalarizations still detect. We then show that exact integral R2 improvement is exactly a scalarization-space volume, namely the measure of the Tchebycheff shadow between the incumbent scalarization envelope and the reference envelope. This representation yields finite-sum ER2I algorithms for discrete R2, quadrature methods for exact integral R2, and an achievement-space Gaussian surrogate formulation in which ER2I is an integral of scalar Gaussian expected improvements.
arXiv:2605.29872v1 Announce Type: cross
Abstract: QEM is widely regarded as a plausible bridge from NISQ devices to FTQC. Yet the empirical studies used to assess the effectiveness of QEM techniques on concrete problems have received comparatively little scrutiny with respect to the validity of their conclusions. We systematically review 81 recent QEM papers using an eight-criterion framework covering statistical rigour, reproducibility, and reporting quality. Among the applicable papers, only 15 (25%) use inferential methods, while 25 (42%) report uncertainty only descriptively, without testing whether the claimed effects are statistically supported. To demonstrate the consequences of these omissions, we use ZNE as a representative and widely used case study and identify two compounding sources of artefacts in current QEM benchmarks. First, we observe parameter sensitivity: in a 132-configuration sweep, implicitly assumed choices such as scale factors, extrapolation method, and hardware calibration are not merely incidental but active, with variations changing conclusions from statistically significant improvement to statistically significant degradation. Second, we identify a drift-induced effectiveness illusion: in a 72-hour longitudinal study on real hardware, temporal drift alone can make the same ZNE configuration exhibit an effect size more than three times as large, depending solely on when it is executed, and also drastically reduces the effective number of independent observations. These findings do not imply that QEM methods are intrinsically unsound; rather, they show that current evaluation practice can make mitigation performance appear more robust than the evidence warrants. We therefore propose minimum reporting standards for QEM evaluations, including explicit parameter documentation, robustness checks, longitudinal drift assessment, and inferential statistical testing with effect-size reporting.
arXiv:2605.29926v1 Announce Type: new
Abstract: Accurate prediction of drug-target interactions (DTI) is critical for drug discovery. Existing methods often rely on single-modal representations (e.g., sequences or graphs) or combine only two modalities, overlooking 3D structural features. To address this challenge, we propose TriMod-DTI, a triple-modal contrastive learning framework that incorporates 1D sequences, 2D graphs, and 3D structures of drugs and proteins, obtaining the universal and complementary feature representations for DTI prediction. We design a Feature Extractor to capture drug and target features across the three modalities, thereby enriching their representations. We further propose a triple-modal contrastive learning strategy to align different modal representations of the same drug or protein in the latent space. By constructing cross-modal positive and negative sample pairs, this approach enhances the model's discriminative ability. Experiments on three benchmark datasets demonstrate that TriMod-DTI outperforms state-of-the-art methods. The ablation studies validate the contributions of each modality. Moreover, case studies highlight its practical potential for DTI prediction and drug discovery.
arXiv:2512.03109v2 Announce Type: replace
Abstract: Agentic AI systems execute a sequence of actions, such as reasoning steps or tool calls, in response to a user prompt. To evaluate the success of their trajectories, researchers have developed verifiers, such as LLM judges and process-reward models, to score the quality of each action in an agent's trajectory. Although these heuristic scores can be informative, there are no guarantees of correctness when used to decide whether an agent will yield a successful output. Here, we introduce e-valuator, a method to convert any black-box verifier score into a decision rule with provable control of false alarm rates. We frame the problem of distinguishing successful trajectories (that is, a sequence of actions that will lead to a correct response to the user's prompt) and unsuccessful trajectories as a sequential hypothesis testing problem. E-valuator builds on tools from e-processes to develop a sequential hypothesis test that remains statistically valid at every step of an agent's trajectory, enabling online monitoring of agents over arbitrarily long sequences of actions. Empirically, we demonstrate that e-valuator provides greater statistical power and better false alarm rate control than other strategies across six datasets and three agents. We additionally show that e-valuator can be used for to quickly terminate problematic trajectories and save tokens. Together, e-valuator provides a lightweight, model-agnostic framework that converts verifier heuristics into decisions rules with statistical guarantees, enabling the deployment of more reliable agentic systems.
arXiv:2605.29841v1 Announce Type: new
Abstract: This paper presents a distributed nonlinear model predictive control that uses alternating direction method of mul tipliers for district heating networks. Exploiting a graph-based modeling of the thermal dynamics, our controller optimizes the mass flow absorption of buildings in a distributed cooperative scheme that mediates between the superior performance of the centralized control and the privacy preservation of the decentralized schemes. A benchmark three-building network simulation is used to compare the performance of the proposed solution with a decentralized model predictive control scheme.
arXiv:2512.03010v2 Announce Type: replace
Abstract: LiDAR-captured point clouds are often considered the gold standard in active 3D reconstruction. While their accuracy is exceptional in flat regions, the capturing is susceptible to miss small geometric structures and may fail with dark, absorbent materials. Alternatively, capturing multiple photos of the scene and applying 3D photogrammetry can infer these details as they often represent feature-rich regions. However, the accuracy of LiDAR for featureless regions is rarely reached. Therefore, we suggest combining the strengths of LiDAR and camera-based capture by introducing SurfFill: a Gaussian surfel-based LiDAR completion scheme. We analyze LiDAR capturings and attribute LiDAR beam divergence as a main factor for artifacts, manifesting mostly at thin structures and edges. We use this insight to introduce an ambiguity heuristic for completed scans by evaluating the change in density in the point cloud. This allows us to identify points close to missed areas, which we can then use to grow additional points from to complete the scan. For this point growing, we constrain Gaussian surfel reconstruction to focus optimization and densification on these ambiguous areas. Finally, Gaussian primitives of the reconstruction in ambiguous areas are extracted and sampled for points to complete the point cloud. To address the challenges of large-scale reconstruction, we extend this pipeline with a divide-and-conquer scheme for building-sized point cloud completion. We evaluate on the task of LiDAR point cloud completion of synthetic and real-world scenes and find that our method outperforms previous reconstruction methods.
arXiv:2605.29621v1 Announce Type: new
Abstract: We examine the information security practices of Ugandan climate activists protesting the development of the East African Crude Oil Pipeline (EACOP). We conducted five-week fieldwork in Kampala, Uganda, which included interviews with 13 anti-EACOP activists. Through an inductive analysis, we report on the complexities faced by small groups of predominantly student protesters as they covertly organise small-scale anti-EACOP protests within a context marked by state surveillance and repression. Our study points to a multi-layered adversarial landscape, where participants' experiences of direct threats, including arrests and information compromise, and their fears of abduction, shaped their security practices. These practices were rooted in autonomous decision-making within groups. We present a grounded understanding of how participants' need to protect information for their own security, as well as that of others, permeated their lives, leading them to adjust day-to-day aspects of their device management, communication, accommodation, transport and social relations as deliberate tactics to mitigate surveillance.
arXiv:2511.14426v2 Announce Type: replace
Abstract: In recent years, diffusion-based models have demonstrated exceptional performance in searching for simultaneously stable, unique, and novel (S.U.N.) crystalline materials. However, most of these models don't have the ability to change the number of atoms in the crystal during the generation process, which limits the variability of model sampling trajectories. In this paper, we demonstrate the severity of this restriction and introduce a simple yet powerful technique, mirage infusion, which enables diffusion models to change the state of the atoms that make up the crystal from existent to non-existent (mirage) and vice versa. We show that this technique improves model quality by up to x2.5 compared to the same model without this modification. The resulting model, Mirage Atom Diffusion (MiAD), is an equivariant joint diffusion model for de novo crystal generation that is capable of altering the number of atoms during the generation process. MiAD achieves an 8.2% S.U.N. rate on the MP-20 dataset, which substantially exceeds existing state-of-the-art approaches. Code: https://github.com/andrey-okhotin/miad.git
arXiv:2511.10861v3 Announce Type: replace
Abstract: Convolutional Neural Networks (CNNs) pre-trained on large-scale datasets such as ImageNet are widely used as feature extractors to construct high-accuracy classification models from scarce data for specific tasks. In such scenarios, fine-tuning the pre-trained CNN is difficult due to data scarcity, necessitating the use of fixed weights. However, when the weights are kept fixed, many filters that do not contribute to the target task remain in the model, leading to unnecessary redundancy and reduced efficiency. Therefore, effective methods are needed to reduce model size by pruning filters that are unnecessary for inference. To address this, approaches utilizing Layer-wise Relevance Propagation (LRP) have been proposed. LRP quantifies the contribution of each filter to the inference result, enabling the pruning of filters with low relevance. However, existing LRP-based pruning methods have been observed to cause cascading accuracy degradation. In this study, we introduce an accuracy-aware pruning control mechanism for existing LRP-based filter pruning methods, which suppresses cascading accuracy degradation by dynamically adjusting the pruning rate and the pruning order using the harmonic mean of class accuracy, and compresses the pre-trained model while preserving task-specific performance in a small-data environment. We demonstrate that this control mechanism effectively mitigates cascading accuracy degradation and achieves higher classification accuracy compared to existing LRP-based pruning methods, improving the class-averaged area under the accuracy-pruning-rate curve (AUC) of VGG16 by approximately 15\% over conventional LRP-based approaches.
arXiv:2506.11483v4 Announce Type: replace
Abstract: We introduce Capsule, a mechanism for seamlessly sharing datacenter resources across multiple players. It decouples player-local and global states to achieve isolation and to maximize cross-player sharing. Our evaluations show that Capsule increases datacenter resource utilization by accommodating up to 2.25x more players without degrading the user experience. This improvement stems from Capsule consuming up to 1.43x less GPU, 3.11x less VRAM, 3.7x less CPU, and 3.87x less RAM compared to the baseline. We evaluated Capsule across four applications and various hardware configurations, including three distinct servers and a multi-server cluster. These results demonstrate that the Capsule design is portable to other game engines.
arXiv:2506.04602v4 Announce Type: replace
Abstract: The burgeoning growth of the esports and multiplayer online gaming community has highlighted the critical importance of evaluating the Most Valuable Player (MVP). The establishment of an explainable and practical MVP evaluation method is very challenging. In our study, we specifically focus on play-by-play data, which records related events during the game, such as assists and points. We aim to address the challenges by introducing a new MVP evaluation framework, denoted as \oursys, which leverages Shapley values. This approach encompasses feature processing, win-loss model training, Shapley value allocation, and MVP ranking determination based on players' contributions. Additionally, we optimize our algorithm to align with expert voting results from the perspective of causality. Finally, we substantiated the efficacy of our method through validation using the NBA dataset and the Dunk City Dynasty dataset and implemented online deployment in the industry.
arXiv:2604.25876v2 Announce Type: replace-cross
Abstract: We introduce $\texttt{cuSkyrmion}$, a 3-dimensional Skyrme model computation and visualization software, that is written in $\texttt{CUDA C}$ for rapid computation and visualization of especially the arrested Newton flow algorithm. The programme is interactive and lets the user construct Skyrmions either with configuration files, specifying coordinates, or simply in run-time using the keyboard and mouse. Rational map ansatz constituent Skyrmions can be inserted at any time and a random generator can produce a stochastic initial configuration. The software is composed into three main modules being a computational module, a rendering module and a main programme. The rendering/visualization module can readily be used by other computational modules and a $\texttt{Python}$-fork, $\texttt{skyrmion_solver}$, has been developed demonstrating the re-usability of the code.
arXiv:2605.30212v1 Announce Type: new
Abstract: Electronic identities (eIDs) are crucial in an increasingly digitalized environment. Pseudonyms, as offered by Austria's governmental sector-specific personal identifiers (bPks), can significantly improve privacy by ensuring that personal data is not universally traceable across public services and private companies. However, the current architecture comes with several challenges regarding availability, privacy, and authenticity, due to a fully centralized design.
This paper proposes bPk#, a distributed architecture to address these issues, reducing reliance on the central authority, while still providing all functional requirements to the existing bPk system. In particular, users are delegated the rights to compute their own pseudonyms, thereby minimizing metadata revealed to the central authority, while (subsets of) service providers may receive the right to compute pseudonyms only within their own domain, thereby reducing the availability needs of the central authority.
To the best of our knowledge, we provide the first formal framework for such delegatable pseudonym systems, together with a generic construction for which we provide formal security proofs. Furthermore, we propose a concrete instantiation of our construction, together with a reference implementation demonstrating the practical efficiency.