arXiv Math @arxiv_math@qoto.org

We prove that the Gieseker moduli space of stable sheaves on a smooth projective threefold $X$ of Picard rank 1 is separated from the moduli space of PT stable objects by a single wall in the space of Bridgeland stability conditions on $X$, thus realizing the higher rank DT/PT correspondence as a wall-crossing phenomenon in the space of Bridgeland stability conditions. In addition, we also show that only finitely many walls pass through the upper $(β,α)$-plane parametrizing geometric Bridgeland stability conditions on $X$ which destabilize Gieseker stable sheaves, PT stable objects or their duals when $α>α_0$.

A ring $R$ is said to be centrally essential if for every its non-zero element $a$, there exist non-zero central elements $x$ and $y$ with $ax = y$. A ring $R$ is said to be completely centrally essential if all its factor rings are centrally essential rings. It is proved that completely centrally essential semiprimary rings are Lie nilpotent; noetherian completely centrally essential rings are strongly Lie nilpotent (in particular, every such a ring is a $PI$-ring). Every completely centrally essential ring has the classical ring of fractions which is a completely centrally essential ring. If $R$ is a commutative domain and $G$ is an arbitrary group, then any completely centrally essential group ring $RG$ is commutative.

In this paper, we consider two linear inverse problems for the time-fractional wave equation, assuming that its right-hand side takes the separable form $f(t)h(x)$, where $t \geq 0$ and $x \in Ω\subset R^N $. The objective is to determine the unknown function $f(t)$ (Inverse Problem 1) and $h(x)$ (Inverse Problem 2), given that the other function is known. For Inverse Problem 1, we impose an overdetermination condition in the form of a spatial integral over the domain $Ω$, involving the solution of the corresponding direct problem an initial-boundary value problem with standard Cauchy conditions and homogeneous Dirichlet boundary conditions. The integral is weighted by the known spatial factor h(x) from the right-hand side of the equation. This choice of an additional condition enables the explicit construction of a solution to the inverse problem and allows us to prove its unique solvability within the class of regular solutions. To study the direct problem, the regular solution approach is used. For Inverse Problem 2, we introduce a novel integral-type additional condition, referred to as the time-averaged velocity, incorporating an appropriate weight function. The time-dependent factor of the right-hand side of the equation serves as this weight function. Depending on its choice, the additional condition reduces to specifying either the final-time offset or the time-averaged offset. Under this formulation, we establish a new uniqueness result.

In this work, we introduce a novel first-order nonlocal partial differential equation with saturated diffusion to describe the macroscopic behavior of traffic dynamics. We show how the proposed model is better in comparison with existing models in explaining the underlying driver behavior in real traffic data. In doing so, we introduce a methodology for adjusting the parameters of the proposed PDE with respect to the distribution of real datasets. In particular, we conceptually and analytically elaborate on how such calibration connects the solution of the PDE to the probability transition kernel proposed by the datasets. The performance of the model is thoroughly investigated with respect to several metrics. More precisely, we study the capability of the model in capturing the probability distribution realized by the datasets in the form of the fundamental diagram. We show that the model is capable of approximating the dynamics of the evolution of the probability distribution. To this end, we evaluate the performance of the model with regard to the congestion formation and dissipation scenarios from various datasets.

The dual challenges of prohibitive communication overhead and the impracticality of gradient computation due to data privacy or black-box constraints in distributed systems motivate this work on communication-constrained gradient-free optimization. We propose a stochastic distributed zeroth-order algorithm (Com-DSZO) requiring only two function evaluations per iteration, integrated with general compression operators. Rigorous analysis establishes its sublinear convergence rate for both smooth and nonsmooth objectives, while explicitly elucidating the compression-convergence trade-off. Furthermore, we develop a variance-reduced variant (VR-Com-DSZO) under stochastic mini-batch feedback. The empirical algorithm performance are illustrated with numerical examples.

This work presents an optimization framework for tailoring the nonlinear dynamic response of lightly damped mechanical systems using Spectral Submanifold (SSM) reduction. We derive the SSM-based backbone curve and its sensitivity with respect to parameters up to arbitrary polynomial orders, enabling efficient and accurate optimization of the nonlinear frequency-amplitude relation. We use the adjoint method to derive sensitivity expressions, which drastically reduces the computational cost compared to direct differentiation as the number of parameters increases. An important feature of this framework is the automatic adjustment of the expansion order of SSM-based ROMs using user-defined error tolerances during the optimization process. We demonstrate the effectiveness of the approach in optimizing the nonlinear response over several numerical examples of mechanical systems. Hence, the proposed framework extends the applicability of SSM-based optimization methods to practical engineering problems, offering a robust tool for the design and optimization of nonlinear mechanical structures.

The aim of the present paper is to extend the concept of a congruence from lattices to posets. We use an approach different from that used by the first author and V. Snášel. By using our definition we show that congruence classes are convex. If the poset in question satisfies the Ascending Chain Condition as well as the Descending Chain Condition, then these classes turn out to be intervals. If the poset has a top element 1 then the 1-class of every congruence is a so-called strong filter. We study congruences on relatively pseudocomplemented posets which form a formalization of intuitionistic logic. For such posets we define so-called deductive systems and we show how they are connected with congruence kernels. We prove that every strong filter F of a relatively pseudocomplemented poset induces a congruence having F as its kernel. Finally, we consider Boolean posets which form a natural generalization of Boolean algebras. We show that congruences on Boolean posets in general do not share properties known from Boolean algebras, but congruence kernels of Boolean posets still have some interesting properties.

We make an asymptotic analysis via singularity analysis of generating functions of a number sequence that involves the Fibonacci numbers and generalizes the binomial coefficients.

Reduced-order models (ROMs) remain generally unreliable for convection-dominated problems, such as those encountered in hypersonic flows, due to the slowly decaying Kolmogorov $n$-width of linear subspace approximations, known as the Kolmogorov barrier. This limitation hinders the accuracy of traditional ROMs and necessitates impractical amounts of training data during the offline phase. To address this challenge, we introduce a novel landmark-based registration procedure tailored for ROMs of convection-dominated problems. Our approach leverages limited training data and incorporates a nonlinear transformation of the data using a landmark-based registration technique combined with radial basis function (RBF) interpolation. During the offline phase, we align dominant convective features in a reference domain, resulting in a rapid decay of error relative to the reduced space dimension. Landmarks are generated through a three-step process: (1) detecting shocks via edge detection techniques, (2) sampling using Monte Carlo methods, and (3) domain partitioning with $k$-means clustering, where cluster centroids serve as landmarks. Accurate landmark correspondence is achieved by minimizing pairing distances for similar features. The online phase integrates standard minimum-residual ROM methodologies, extending the optimization space to include admissible domain mappings. We validate our approach on two test cases: a space-time Burgers' equation parameterized by the initial condition, and a hypersonic viscous flow over a cylinder parameterized by the Mach number. Results demonstrate the efficacy of the proposed method in overcoming the Kolmogorov barrier and enhancing the reliability of ROMs for convection-dominated problems.

It is a well-known and easily established fact that every Euclidean domain is also a principal ideal domain. However, the converse statement is not true, and this is usually shown by exhibiting as a counterexample the ring of algebraic integers in a certain, very specific quadratic field, and the proof that this works is quite unnatural and technical. In this article, we will present a family of counterexamples built from real closed fields.

This paper provides a complete characterization of global hypoellipticity and solvability with loss of derivatives for Fourier multiplier operators on the $n$-dimensional torus. We establish necessary and sufficient conditions for these properties and examine their connections with classical notions of global hypoellipticity and solvability, particularly in relation to the closedness of the operator's range. As an application, we explore the interplay between these properties and number theory in the context of differential operators on the two-torus. Specifically, we prove that the loss of derivatives in the solvability of the vector field $\partial_{x_1} - α\partial_{x_2}$ is precisely determined by the well-known irrationality measure $μ(α)$ of its coefficient $α$. Furthermore, we analyze the wave operator $\partial_{x_1}^2 - η^2 Δ_{\mathbb{T}^n}$ and show how the loss of derivatives depends explicitly on the parameter $η> 0$.

This chapter is dedicated to recent developments in the field of wavelet analysis for scattered data. We introduce the concept of samplets, which are signed measures of wavelet type and may be defined on sets of arbitrarily distributed data sites in possibly high dimension. By employing samplets, we transfer well-known concepts known from wavelet analysis, namely the fast basis transform, data compression, operator compression and operator arithmetics to scattered data problems. Especially, samplet matrix compression facilitates the rapid solution of scattered data interpolation problems, even for kernel functions with nonlocal support. Finally, we demonstrate that sparsity constraints for scattered data approximation problems become meaningful and can efficiently be solved in samplet coordinates.

We present a digital-twin simulator for a pastillation process. The simulation framework produces realistic thermal image data of the process that is used to train computer vision-based soft sensors based on convolutional neural networks (CNNs); the soft sensors produce output signals for temperature and product flow rate that enable real-time monitoring and feedback control. Pastillation technologies are high-throughput devices that are used in a broad range of industries; these processes face operational challenges such as real-time identification of clog locations (faults) in the rotating shell and the automatic, real-time adjustment of conveyor belt speed and operating conditions to stabilize output. The proposed simulator is able to capture this behavior and generates realistic data that can be used to benchmark different algorithms for image processing and different control architectures. We present a case study to illustrate the capabilities; the study explores behavior over a range of equipment sizes, clog locations, and clog duration. A feedback controller (tuned using Bayesian optimization) is used to adjust the conveyor belt speed based on the CNN output signal to achieve the desired process outputs.

This paper develops a systematic framework for integrating local categories that model logical connectives using higher category theory. By extending these local categories into a unified two-category enriched with natural isomorphisms, the universal properties of logical operations such as negation, conjunction, disjunction, and implication are rigorously captured. Advanced techniques including pseudo-limits, pseudo-colimits, and strictification are employed to transform the resulting weak structure into a strict two-category, thereby simplifying composition rules and coherence verification without loss of semantic content. The framework is validated through detailed diagrammatic proofs and concrete examples, demonstrating its robustness and potential impact in areas such as type theory, programming language semantics, and formal verification.

We consider estimating the proportion of random variables for two types of composite null hypotheses: (i) the means or medians of the random variables belonging to a non-empty, bounded interval; (ii) the means or medians of the random variables belonging to an unbounded interval that is not the whole real line. For each type of composite null hypotheses, uniformly consistent estimators of the proportion of false null hypotheses are constructed for random variables whose distributions are members of a Type I location-shift family. Further, uniformly consistent estimators of certain functions of a bounded null on the means or medians are provided for the random variables mentioned earlier; these functions are continuous and of bounded variation. The estimators are constructed via solutions to Lebesgue-Stieltjes integral equations and harmonic analysis, do not rely on a concept of p-value, and have various applications.

Pre-trained Transformers, through in-context learning (ICL), have demonstrated exceptional capabilities to adapt to new tasks using example prompts without model update. Transformer-based wireless receivers, where prompts consist of the pilot data in the form of transmitted and received signal pairs, have shown high detection accuracy when pilot data are abundant. However, pilot information is often costly and limited in practice. In this work, we propose the DEcision Feedback INcontExt Detection (DEFINED) solution as a new wireless receiver design, which bypasses channel estimation and directly performs symbol detection using the (sometimes extremely) limited pilot data. The key innovation in DEFINED is the proposed decision feedback mechanism in ICL, where we sequentially incorporate the detected symbols into the prompts as pseudo-labels to improve the detection for subsequent symbols. Furthermore, we proposed another detection method where we combine ICL with Semi-Supervised Learning (SSL) to extract information from both labeled and unlabeled data during inference, thus avoiding the errors propagated during the decision feedback process of the original DEFINED. Extensive experiments across a broad range of wireless communication settings demonstrate that a small Transformer trained with DEFINED or IC-SSL achieves significant performance improvements over conventional methods, in some cases only needing a single pilot pair to achieve similar performance of the latter with more than 4 pilot pairs.

Recently, renewed interest in cislunar space spurred by private and public organizations has driven research for future infrastructure in the region. As Earth-Moon traffic increases amidst a growing space economy, monitoring architectures supporting this traffic must also develop. These are likely to be realized as constellations of patrol satellites surveying traffic between the Earth and the Moon. This work investigates the concurrent optimization of patrol satellite phasing and tasking to provide information-maximal coverage of traffic in periodic orbits.

We revisit the results of Kim, and of Katsoulis and Ramsey concerning hyperrigidity for non-degenerate C*-correspondences. We show that the tensor algebra is hyperrigid, if and only if Katsura's ideal acts non-degenerately, if and only if Katsura's ideal acts non-degenerately under any representation. This gives a positive answer to the question of Katsoulis and Ramsey, showing that their necessary condition and their sufficient condition for hyperrigidity of the tensor algebra are equivalent. Non-degeneracy of the left action of Katsura's ideal was also shown by Kim to be equivalent to hyperrigidity for the selfadjoint operator space associated with the C*-correspondence, and our approach provides a simplified proof of this result as well. In the process we revisit Arveson's criterion connecting maximality with the unique extension property and hyperrigidity, in conjunction with the work of Salomon on generating sets.

In this note, we prove a filtered version of Beilinson-type formula for the V-filtration of Kashiwara and Malgrange for any D-module underlying a complex mixed Hodge module along a hypersurface, using Hodge filtrations on the localization. We give some applications to the theory of higher multiplier and Hodge ideals. The main result is that the higher multiplier ideals can be deduced directly from the Hodge ideals by taking a suitable limit. As a corollary, we conclude that the Hodge ideals are left semi-continuous if and only if they coincide with the higher multiplier ideals. In an appendix, we make some general observations about Hodge and higher multiplier ideals. We observe that results of Saito and Chen-Mustaţă give a birational formula for higher multiplier ideals, answering a question of Schnell and the second author, and that the Kodaira vanishing theorem for twisted Hodge modules gives a short proof of the vanishing theorem for Hodge ideals, strengthening a result of B. Chen.