arXiv preprints from January 1, 2026 through September 23, 2026 — 14:59:38 EST
In this work we introduce new generalised quantifiers which allow us to express the Rabin-Mostowski index of automata. Our main results study expressive power and decidability of the monadic second-order (MSO) logic extended with these quantifiers. We study these problems in the realm of both $ω$-words and infinite trees. As it turns...
In this paper, we provide the strong rate of convergence for the Euler--Maruyama scheme for multi-dimensional stochastic differential equations with uniformly locally (unbounded) Hölder continuous drift and multiplicative noise. Our technique is based on Itô--Tanaka trick (Zvonkin transformation) for unbounded drift. Moreover, in...
We exploit the controlled drying of millimeter-sized gel beads to investigate isotropic compression of colloidal fractal gels. Using a custom dynamic light scattering setup, we demonstrate that stresses imposed by drying on the bead surface propagate homogeneously throughout the gel volume, inducing plastic rearrangements. We find...
Federated Learning (FL) enables collaborative model training while keeping training data localized, allowing us to preserve privacy in various domains including remote sensing. However, recent studies show that FL models may still leak sensitive information through their outputs, motivating the need for rigorous privacy evaluation. In...
Multi-modal Large Language Models (MLLMs) are increasingly deployed in interactive applications. However, their safety vulnerabilities become pronounced in multi-turn multi-modal scenarios, where harmful intent can be gradually reconstructed across turns, and security protocols fade into oblivion as the conversation progresses....
Self-interacting dark matter (SIDM) is a well-motivated extension of cold dark matter that can modify halo structure on galactic and group scales while remaining consistent with large-scale structure. However, practical SIDM work often requires bridging several layers, including microphysical scattering models, velocity-dependent...
Multimodal large language models (MLLMs) demonstrate exceptional capabilities in semantic understanding and visual reasoning, yet they still face challenges in precise object localization and resource-constrained edge-cloud deployment. To address this, this paper proposes the AIVD framework, which achieves unified precise localization...
The efficient interfacing of quantum emitters and photons is fundamental to quantum networking. Quantum defects embedded in integrated nanophotonic circuits are promising for such applications due to the deterministic light-matter interactions of high-cooperativity ($C>1$) cavity quantum electrodynamics and potential for scalable...
Quantum machine learning has emerged as a promising application domain for near-term quantum hardware, particularly through hybrid quantum-classical models that leverage both classical and quantum processing. Although numerous hybrid architectures have been proposed and demonstrated successfully on benchmark tasks, a significant open...
Current critic-free RL methods for large reasoning models suffer from severe inefficiency when training on positive homogeneous prompts (where all rollouts are correct), resulting in waste of rollouts due to zero advantage estimates. We introduce a radically simple yet powerful solution to \uline{M}ine \uline{in}trinsic...
Humans can finely perceive material textures, yet articulating such somatic impressions in words is a cognitive bottleneck in design ideation. We present OnomaCompass, a web-based exploration system that links sound-symbolic onomatopoeia and visual texture representations to support early-stage material discovery. Instead of requiring...
We study approximation limits of single-hidden-layer neural networks with analytic activation functions under global coefficient constraints. Under uniform $\ell^1$ bounds, or more generally sub-exponential growth of the coefficients, we show that such networks generate model classes with strong quantitative regularity, leading to...
We show how to construct the implied copula process of response values from a Bayesian additive regression tree (BART) model with prior on the leaf node variances. This copula process, defined on the covariate space, can be paired with any marginal distribution for the dependent variable to construct a flexible distributional BART...
Collaborative training of a machine learning model comes with a risk of sharing sensitive or private data. Federated learning offers a way of collectively training a single global model without the need to share client data, by sharing only the updated parameters from each client's local model. A central server is then used to...
The primary objective of a diverse planning approach is to generate a set of plans that are distinct from one another. Such an approach is applied in a variety of real-world domains, including risk management, automated stream data analysis, and malware detection. More recently, a novel diverse planning paradigm, referred to as...
We apply the conformal compensating technique for constructing matter couplings to conformal scalars on a $D$-dimensional foliated conformal Carroll manifold dividing the tangent space into $(p+1)$-dimensional longitudinal and $(D-p-1)$-dimensional transversal directions corresponding to $p$-branes. We show that the conformal Carroll...
Synchronized rhythmic oscillatory activity in the beta frequency band in the basal ganglia (BG) is a hallmark of Parkinson's disease (PD). Recent experiments and theoretical studies have demonstrated the crucial roles of T-type and L-type calcium currents in shaping the activity patterns of subthalamic nucleus (STN) neurons. However,...
Sawtooth chain magnets have been a subject of historical interest in the field of frustrated magnetism, with classical olivine family $M_2TX_4$, ($M$ - 3d, $T$ - 4p, $X$ - chalcogen elements) typically realizing simple $\mathbf{k} = (000)$ states. The magnetism of the Mn$_2$GeO$_4$ olivine is surprisingly complex, proceeding from...
We investigate distributed online convex optimization with compressed communication, where $n$ learners connected by a network collaboratively minimize a sequence of global loss functions using only local information and compressed data from neighbors. Prior work has established regret bounds of...
We propose an estimator of a concave cumulative distribution function under the measurement error model, where the non-negative variables of interest are perturbed by additive independent random noise. The estimator is defined as the least concave majorant on the positive half-line of the deconvolution estimator of the distribution...
We analyze the role of virtual temperatures for passive quantum states through the lens of majorization theory. A mean temperature over the virtual temperatures of adjacent energy levels is defined to compare the passive states of the system resulting from isoenergetic and isoentropic transformations. The role of the minimum and the...
Driven by Moore's Law, the dimensions of transistors have been pushed down to the nanometer scale. Advanced quantum transport (QT) solvers are required to accurately simulate such nano-devices. The non-equilibrium Green's function (NEGF) formalism lends itself optimally to these tasks, but it is computationally very intensive,...
We respond to a critique 2512.23460[hep-ph] of our paper 2505.05634[hep-ph]
Plasma wakefield accelerators (PWFA) represent one of the promising new accelerator concepts that are now being developed intensively for future applications in high-energy physics and industry. Among the unresolved problems of practical implementation of PWFA there are maintaining the required quality (emittance, size, energy spread...
We study a generalisation of Büchi-Landweber games to the timed setting. The winning condition is specified by a non-deterministic timed automaton, and one of the players can elapse time. We perform a systematic study of synthesis problems in all variants of timed games, depending on which player's winning condition is specified, and...