arXiv Math @arxiv_math@qoto.org

The next-generation radio access network (RAN), known as Open RAN, is poised to feature an AI-native interface for wireless cellular networks, including emerging satellite-terrestrial systems, making deep learning integral to its operation. In this paper, we address the nonconvex optimization challenge of joint subcarrier and power allocation in Open RAN, with the objective of minimizing the total power consumption while ensuring users meet their transmission data rate requirements. We propose OpenRANet, an optimization-based deep learning model that integrates machine-learning techniques with iterative optimization algorithms. We start by transforming the original nonconvex problem into convex subproblems through decoupling, variable transformation, and relaxation techniques. These subproblems are then efficiently solved using iterative methods within the standard interference function framework, enabling the derivation of primal-dual solutions. These solutions integrate seamlessly as a convex optimization layer within OpenRANet, enhancing constraint adherence, solution accuracy, and computational efficiency by combining machine learning with convex analysis, as shown in numerical experiments. OpenRANet also serves as a foundation for designing resource-constrained AI-native wireless optimization strategies for broader scenarios like multi-cell systems, satellite-terrestrial networks, and future Open RAN deployments with complex power consumption requirements.

Information theoretic leakage metrics quantify the amount of information about a private random variable $X$ that is leaked through a correlated revealed variable $Y$. They can be used to evaluate the privacy of a system in which an adversary, from whom we want to keep $X$ private, is given access to $Y$. Global information theoretic leakage metrics quantify the overall amount of information leaked upon observing $Y$, whilst their pointwise counterparts define leakage as a function of the particular realisation $y$ that the adversary sees, and thus can be viewed as random variables. We consider an adversary who observes a large number of independent identically distributed realisations of $Y$. We formalise the essential asymptotic behaviour of an information theoretic leakage metric, considering in turn what this means for pointwise and global metrics. With the resulting requirements in mind, we take an axiomatic approach to defining a set of pointwise leakage metrics, as well as a set of global leakage metrics that are constructed from them. The global set encompasses many known measures including mutual information, Sibson mutual information, Arimoto mutual information, maximal leakage, min entropy leakage, $f$-divergence metrics, and g-leakage. We prove that both sets follow the desired asymptotic behaviour. Finally, we derive composition theorems which quantify the rate of privacy degradation as an adversary is given access to a large number of independent observations of $Y$. It is found that, for both pointwise and global metrics, privacy degrades exponentially with increasing observations for the adversary, at a rate governed by the minimum Chernoff information between distinct conditional channel distributions. This extends the work of Wu et al. (2024), who have previously found this to be true for certain known metrics, including some that fall into our more general set.

We verify a conjecture of Beem and the first author stating that a certain family of physically motivated BRST reductions of beta-gamma systems and free fermions is isomorphic to $L_1(\mathfrak{psl}_{n|n})$, and that its associated variety is isomorphic as a Poisson variety to the minimal nilpotent orbit closure $\overline{\mathbb{O}_{\mathrm{min}}(\mathfrak{sl}_n)}$. This shows in particular that $L_1(\mathfrak{psl}_{n|n})$ is quasi-lisse. Combining this with other results in the literature (in particular work of Ballin et al.), this paper provides a concrete and important example of how one can extract two symplectic dual varieties from a rather well-known vertex operator algebra.

This paper proposes a model predictive controller for discrete-time linear systems with additive, possibly unbounded, stochastic disturbances and subject to chance constraints. By computing a polytopic probabilistic positively invariant set for constraint tightening with the help of the computation of the minimal robust positively invariant set, the chance constraints are guaranteed, assuming only the mean and covariance of the disturbance distribution are given. The resulting online optimization problem is a standard strictly quadratic programming, just like in conventional model predictive control with recursive feasibility and stability guarantees and is simple to implement. A numerical example is provided to illustrate the proposed method.

For $r\geq 3$ and $g= \frac{r(r+1)}{2}$, we study the Prym-Brill-Noether variety $V^r(C,η)$ associated to Prym curves $[C,η]$. The locus $\mathcal{R}_g^r$ in $\mathcal{R}_g$ parametrizing Prym curves $(C, η)$ with nonempty $V^r(C,η)$ is a divisor. We compute some key coefficients of the class $[\overline{\mathcal{R}}_g^r]$ in $\mathrm{Pic}_\mathbb{Q}(\overline{\mathcal{R}}_g)$. Furthermore, we examine a strongly Brill-Noether divisor in $\overline{\mathcal{M}}_{g-1,2}$: we show its irreducibility and compute some of its coefficients in $\mathrm{Pic}_\mathbb{Q}(\overline{\mathcal{M}}_{g-1,2})$. As a consequence of our results, the moduli space $\mathcal{R}_{14,2}$ is of general type.

In his 1996 paper, Talagrand highlighted that the Law of Large Numbers (LLN) for independent random variables can be viewed as a geometric property of multidimensional product spaces. This phenomenon is known as the concentration of measure. To illustrate this profound connection between geometry and probability theory, we consider a seemingly intractable geometric problem in multidimensional Euclidean space and solve it using standard probabilistic tools such as the LLN and the Central Limit Theorem (CLT).

We extend the work of Dyda and Kijaczko by establishing the corresponding weighted fractional Hardy inequalities with singularities on any flat submanifolds. While they derived weighted fractional Hardy inequalities with singularities at a point and on a half-space, we generalize these results to handle singularities on any flat submanifold of codimension $k$, where $1<k<d$. Furthermore, we also address the critical case $sp=k+α+ β$ and establish weighted fractional Hardy inequality with appropriate logarithmic weight function.

Let $X\rightarrow S$ be a fibration of relative dimension at most two and let $(X,Δ)$ be a klt pair for which $K_X+Δ\equiv_S 0$. We show that there are only finitely many Mori chambers and Mori faces in the movable effective cone $\mathcal{M}^e(X/S)$ up to the action of relative pseudo-automorphisms of $X/S$ preserving $Δ$.

In this paper we extend the results of Ballico and Malaspina on regularity and splitting conditions on multiprojective spaces $\mathbb{P}^{n_1}\times\cdots\times\mathbb{P}^{n_s}$.

For an oriented graph $G$, the least number of colours required to oriented colour $G$ is called the oriented chromatic number of $G$ and denoted $χ_o(G)$.For a non-negative integer $g$ let $χ_o(g)$ be the least integer such that $χ_o(G) \leq χ_o(g)$ for every oriented graph $G$ with Euler genus at most $g$. We will prove that $χ_o(g)$ is nearly linear in the sense that $Ω(\frac{g}{\log(g)}) \leq χ_o(g) \leq O(g \log(g))$. This resolves a question of the author, Bradshaw, and Xu, by improving their bounds of the form $Ω((\frac{g^2}{\log(g)})^{1/3}) \leq χ_o(g)$ and $χ_o(g) \leq O(g^{6400})$.

This paper introduces the Quantum Contextual Topos (QCT), a novel framework that extends traditional quantum logic by embedding contextual elements within a topos-theoretic structure. This framework seeks to provide a classically-obedient tool for exploring the logical foundations of quantum mechanics. The QCT framework aims to address the limitations of classical quantum logic, particularly its challenges in capturing the dynamic and contextual nature of quantum phenomena. By integrating modal operators and classical propositional logic within a topos structure, the QCT offers a unified approach to modeling quantum systems. The main result of this work is demonstrating that the internal logic of QCT corresponds to a form of classical propositional polymodal logic. We do this by generalizing Stone's Representation Theorem for a specific case of polymodal algebras and their underlying Stone Spaces.

Let $\mathcal{P} \subset \mathbb{R}^d$ be a lattice polytope of dimension $d$. Let $b(\mathcal{P})$ denote the number of lattice points belonging to the boundary of $\mathcal{P}$ and $c(\mathcal{P})$ that to the interior of $\mathcal{P}$. It follows from the lower bound theorem of Ehrhart polynomials that, when $c > 0$, \[ {\rm vol}(\mathcal{P}) \geq (d \cdot c(\mathcal{P}) + (d-1) \cdot b(\mathcal{P}) - d^2 + 2)/d!, \] where ${\rm vol}(\mathcal{P})$ is the (Lebesgue) volume of $\mathcal{P}$. Pick's formula guarantees that, when $d = 2$, the above inequality is an equality. In the present paper several classes of lattice polytopes for which the equality here holds will be presented.

Stochastic gradient descent (SGD) is a popular algorithm for minimizing objective functions that arise in machine learning. For constant step-sized SGD, the iterates form a Markov chain on a general state space. Focusing on a class of separable (non-convex) objective functions, we establish a "Doeblin-type decomposition," in that the state space decomposes into a uniformly transient set and a disjoint union of absorbing sets. Each of the absorbing sets contains a unique invariant measure, with the set of all invariant measures being the convex hull. Moreover the set of invariant measures are shown to be global attractors to the Markov chain with a geometric convergence rate. The theory is highlighted with examples that show: (1) the failure of the diffusion approximation to characterize the long-time dynamics of SGD; (2) the global minimum of an objective function may lie outside the support of the invariant measures (i.e., even if initialized at the global minimum, SGD iterates will leave); and (3) bifurcations may enable the SGD iterates to transition between two local minima. Key ingredients in the theory involve viewing the SGD dynamics as a monotone iterated function system and establishing a "splitting condition" of Dubins and Freedman 1966 and Bhattacharya and Lee 1988.

In this paper, we consider a kind of fully coupled slow fast motion, in which the slow variable satisfies the non Lipschitz condition. We prove that the stochastic flow of the slow variable exists and moreover, satisfies the large deviation principle. The argument is mainly based on Khasminskii's averaging principle, the variational representation of the exponential functional of the Brownian motion, and the weak convergence framework proposed by Budhiraja and Dupuis.

Let $G$ be a countable group that is the fundamental group of a graph of groups with finite edge groups and vertex groups that satisfy the strong Atiyah conjecture over $K \subseteq \mathbb{C}$ a field closed under complex conjugation. Assume that the orders of finite subgroups of $G$ are bounded above. We show that $G$ satisfies the strong Atiyah conjecture over $K$. In particular, this implies that the strong Atiyah conjecture is closed under free products. Moreover, we prove that the $\ast$-regular closure of $K[G]$ in $\mathcal{U}(G)$, $\mathcal{R}_{\small K[G]}$, is a universal localization of the graph of rings associated to the graph of groups, where the rings are the corresponding $\ast$-regular closures. As a result, we obtain that the algebraic and center-valued Atiyah conjecture over $K$ are also closed under the graph of groups construction provided that the edge groups are finite. We also infer some consequences on the structure of the $K_0$ and $K_1$-groups of $\mathcal{R}_{\small K[G]}$. The techniques developed allow us to prove that $K[G]$ fulfills the strong, algebraic and center-valued Atiyah conjectures and that $\mathcal{R}_{\small K[G]}$ is the universal localization of $K[G]$ over the set of all matrices that become invertible in $\mathcal{U}(G)$ if $G$ lies in a certain class of groups $\mathcal{T}_{\small \mathcal{VLI}}$, which contains in particular virtually-{locally indicable} groups that are the fundamental group of a graph of virtually free groups.

$K_σ$ sets involving sticky maps $σ$ have been used in the theory of differentiation of integrals to probabilistically construct Kakeya-type sets that imply certain types of directional maximal operators are unbounded on $L^p(\mathbb{R}^2)$ for all $1 \leq p < \infty$. We indicate limits to this approach by showing that, given $ε> 0$ and a natural number $N$, there exists a tree $\mathcal{T}_{N, ε}$ of finite height that is lacunary of order $N$ but such that, for \emph{every} sticky map $σ: \mathcal{B}^{h(\mathcal{T}_{N, ε})} \rightarrow \mathcal{T}_{N, ε}$, one has $|K_σ \cap ((1,2) \times \mathbb{R})| \geq 1 - ε$.

We study percolation on nonamenable groups at the uniqueness threshold $p_u$, the critical value that separates the phase in which there are infinitely many infinite clusters from the phase in which there is a unique infinite cluster. The number of infinite clusters at $p_u$ itself is a subtle question, depending on the choice of group, with only a relatively small number of examples understood. In this paper, we do the following: 1. Prove non-uniqueness at $p_u$ in a new class of examples, namely those groups that contain an amenable, $wq$-normal subgroup of exponential growth. Concrete new examples to which this result applies include lamplighters over nonamenable base groups. 2. Prove a co-heredity property of a certain strong form of non-uniqueness at $p_u$, stating that this property is inherited from a $wq$-normal subgroup to the entire group. Remarkably, this co-heredity property is the same as that proven for the vanishing of the first $\ell^2$ Betti number by Peterson and Thom (Invent. Math. 2011), supporting the conjecture that the two properties are equivalent. Our proof is based on the method of subgroup relativization, and relies in particular on relativized versions of uniqueness monotonicity, the equivalence of non-uniqueness and connectivity decay, the sharpness of the phase transition, and the Burton-Keane theorem. As a further application of the relative Burton-Keane theorem, we resolve a question of Lyons and Schramm (Ann. Probab. 1999) concerning intersections of random walks with percolation clusters.

In this article, we examine a stochastic partial differential equation (SPDE) driven by a symmetric $α$-stable (S$α$S) Lévy noise, that is multiplied by a linear function $σ(u)=u$ of the solution. The solution is interpreted in the mild sense. For this models, in the case of the Gaussian noise, the solution has an explicit Wiener chaos expansion, and is studied using tools from Malliavin calculus. These tools cannot be used for an infinite-variance Lévy noise. In this article, we provide sufficient conditions for the existence of a solution, and we give an explicit series expansion of this solution. To achieve this, we use the multiple stable integrals, which were developed in Samorodnitsky and Taqqu (1990, 1991), and originate from the LePage series representation of the noise. To give a meaning to the stochastic integral which appears in the definition of solution, we embed the space-time Lévy noise into a Lévy basis, and use the stochastic integration theory (Bichteler and Jacod 1983, Bichteler 2002) with respect to this object, as in other studies of SPDEs with heavy-tailed noise: Chong (2017a), Chong (2017b), Chong, Dalang and Humeau (2019). As applications, we consider the heat and wave equations with linear multiplicative noise, also called the parabolic/hyperbolic Anderson models.

Extremum Seeking Control (ESC) is a well-known set of continuous time algorithms for model-free optimization of a cost function. One issue for ESCs is the convergence rates of parameters to extrema of unknown cost functions. The local convergence rate depends on the second, or sometimes higher, order derivatives of the unknown cost function. To mitigate this dependency, we propose the use of the RMSprop optimizer for ESCs as RMSprop is an adaptive gradient-based optimizer which attempts to have a normalized convergence rate in all parameters. Practical stability results are given for this RMSprop ESC (RMSpESC). In particular notability, the proof of practical stability uses Lyapunov function based on observed contracting, attractive sets. Versions of this Lyapunov function could be applied to other areas of applications, in particular for interconnected systems.

By the operator of relative complementation is meant a mapping assigning to every element x of an interval [a,b] of a lattice L the set x^{ab} of all relative complements of x in [a,b]. Of course, if L is relatively complemented then x^{ab} is non-empty for each interval [a,b] and every element x belonging to it. We study the question under what condition a complement of x in L induces a relative complement of x in [a,b] It is well-known that this is the case provided L is modular and complemented. However, we present a more general result. Further, we investigate properties of the operator of relative complementation, in particular in the case when the interval [a,b] is a modular sublattice of L or if it is finite. Moreover, we characterize when the operator of relative complementation is involutive and we show a class of lattices where this identity holds. Finally, we establish sufficient conditions under which two different complements of a given element x of [a,b] induce the same relative complement of x in this interval.