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Given a set $\mathcal{F}$ of graphs, we call a copy of a graph in $\mathcal{F}$ an $\mathcal{F}$-graph. The $\mathcal{F}$-isolation number of a graph $G$, denoted by $\iota(G,\mathcal{F})$, is the size of a smallest subset $D$ of the vertex set $V(G)$ such that the closed neighbourhood of $D$ intersects the vertex sets of the $\mathcal{F}$-graphs contained by $G$ (equivalently, $G - N[D]$ contains no $\mathcal{F}$-graph). Thus, $\iota(G,\{K_1\})$ is the domination number of $G$. The second author showed that if $\mathcal{F}$ is the set of cycles and $G$ is a connected $n$-vertex graph that is not a triangle, then $\iota(G,\mathcal{F}) \leq \left \lfloor \frac{n}{4} \right \rfloor$. This bound is attainable for every $n$ and solved a problem of Caro and Hansberg. A question that arises immediately is how smaller an upper bound can be if $\mathcal{F} = \{C_k\}$ for some $k \geq 3$, where $C_k$ is a cycle of length $k$. The problem is to determine the smallest real number $c_k$ (if it exists) such that for some finite set $\mathcal{E}_k$ of graphs, $\iota(G, \{C_k\}) \leq c_k |V(G)|$ for every connected graph $G$ that is not an $\mathcal{E}_k$-graph. The above-mentioned result yields $c_3 = \frac{1}{4}$ and $\mathcal{E}_3 = \{C_3\}$. The second author also showed that if $k \geq 5$ and $c_k$ exists, then $c_k \geq \frac{2}{2k + 1}$. We prove that $c_4 = \frac{1}{5}$ and determine $\mathcal{E}_4$, which consists of three $4$-vertex graphs and six $9$-vertex graphs. The $9$-vertex graphs in $\mathcal{E}_4$ were fully determined by means of a computer program. A method that has the potential of yielding similar results is introduced.

We introduce the extremal range, a local statistic for studying the spatial extent of extreme events in random fields on $\mathbb{R}^2$. Conditioned on exceedance of a high threshold at a location $s$, the extremal range at $s$ is the random variable defined as the smallest distance from $s$ to a location where there is a non-exceedance. We leverage tools from excursion-set theory to study distributional properties of the extremal range, propose parametric models and predict the median extremal range at extreme threshold levels. The extremal range captures the rate at which the spatial extent of conditional extreme events scales for increasingly high thresholds, and we relate its distributional properties with the bivariate tail dependence coefficient and the extremal index of time series in classical Extreme-Value Theory. Consistent estimation of the distribution function of the extremal range for stationary random fields is proven. For non-stationary random fields, we implement generalized additive median regression to predict extremal-range maps at very high threshold levels. An application to two large daily temperature datasets, namely reanalyses and climate-model simulations for France, highlights decreasing extremal dependence for increasing threshold levels and reveals strong differences in joint tail decay rates between reanalyses and simulations.

We introduce a novel algorithm that converges to level-set convex viscosity solutions of high-dimensional Hamilton-Jacobi equations. The algorithm is applicable to a broad class of curvature motion PDEs, as well as a recently developed Hamilton-Jacobi equation for the Tukey depth, which is a statistical depth measure of data points. A main contribution of our work is a new monotone scheme for approximating the direction of the gradient, which allows for monotone discretizations of pure partial derivatives in the direction of, and orthogonal to, the gradient. We provide a convergence analysis of the algorithm on both regular Cartesian grids and unstructured point clouds in any dimension and present numerical experiments that demonstrate the effectiveness of the algorithm in approximating solutions of the affine flow in two dimensions and the Tukey depth measure of high-dimensional datasets such as MNIST and FashionMNIST.

The stability of an approximating sequence $(A_n)$ for an operator $A$ usually requires, besides invertibility of $A$, the invertibility of further operators, say $B, C, \dots$, that are well-associated to the sequence $(A_n)$. We study this set, $\{A,B,C,\dots\}$, of so-called stability indicators of $(A_n)$ and connect it to the asymptotics of $\|A_n\|$, $\|A_n^{-1}\|$ and $\kappa(A_n)=\|A_n\|\|A_n^{-1}\|$ as well as to spectral pollution by showing that $\limsup {\rm Spec}_\varepsilon A_n= {\rm Spec}_\varepsilon A\cup{\rm Spec}_\varepsilon B\cup{\rm Spec}_\varepsilon C\cup\dots$. We further specify, for each of $\|A_n\|$, $\|A_n^{-1}\|$, $\kappa(A_n)$ and ${\rm Spec}_\varepsilon A_n$, under which conditions even convergence applies.

We study the problem of adaptive variable selection in a Gaussian white noise model of intensity $\varepsilon$ under certain sparsity and regularity conditions on an unknown regression function $f$. The $d$-variate regression function $f$ is assumed to be a sum of functions each depending on a smaller number $k$ of variables ($1 \leq k \leq d$). These functions are unknown to us and only few of them are non-zero. We assume that $d=d_\varepsilon \to \infty$ as $\varepsilon \to 0$ and consider the cases when $k$ is fixed and when $k=k_\varepsilon \to \infty$ and $k=o(d)$ as $\varepsilon \to 0$. In this work, we introduce an adaptive selection procedure that, under some model assumptions, identifies exactly all non-zero $k$-variate components of $f$. In addition, we establish conditions under which exact identification of the non-zero components is impossible. These conditions ensure that the proposed selection procedure is the best possible in the asymptotically minimax sense with respect to the Hamming risk.

The k-sample testing problem involves determining whether $k$ groups of data points are each drawn from the same distribution. The standard method for k-sample testing in biomedicine is Multivariate analysis of variance (MANOVA), despite that it depends on strong, and often unsuitable, parametric assumptions. Moreover, independence testing and k-sample testing are closely related, and several universally consistent high-dimensional independence tests such as distance correlation (Dcorr) and Hilbert-Schmidt-Independence-Criterion (Hsic) enjoy solid theoretical and empirical properties. In this paper, we prove that independence tests achieve universally consistent k-sample testing and that k-sample statistics such as Energy and Maximum Mean Discrepancy (MMD) are precisely equivalent to Dcorr. An empirical evaluation of nonparametric independence tests showed that they generally perform better than the popular MANOVA test, even in Gaussian distributed scenarios. The evaluation included several popular independence statistics and covered a comprehensive set of simulations. Additionally, the testing approach was extended to perform multiway and multilevel tests, which were demonstrated in a simulated study as well as a real-world fMRI brain scans with a set of attributes.

Sequences of numbers (either natural integers, or integers or rational) of level $k \in \mathbb{N}$ have been defined in \cite{Fra05,Fra-Sen06} as the sequences which can be computed by deterministic pushdown automata of level $k$. This definition has been extended to sequences of {\em words} indexed by {\em words} in \cite{Sen07,Fer-Mar-Sen14}. We characterise here the sequences of level 3 as the compositions of two HDT0L-systems. Two applications are derived: - the sequences of rational numbers of level 3 are characterised by polynomial recurrences - the equality problem for sequences of rational numbers of level 3 is decidable.

A vertex set $L\subseteq V$ is liar's vertex-edge dominating set of a graph $G=(V,E)$ if for every $e_i\in E$, $|N_G[e_i]\cap L|\geq 2$ and for every pair of distinct edges $e_i$ and $e_j$, $|(N_G[e_i]\cup N_G[e_j])\cap L|\geq 3$. In this paper, we introduce the notion of liar's vertex-edge domination which arise naturally from some application in communication network. Given a graph $G$, the \textsc{Minimum Liar's Vertex-Edge Domination Problem} (\textsc{MinLVEDP}) asks to find a minimum liar's vertex-edge dominating set of $G$ of minimum cardinality. We have studied this problem from algorithmic point of view. We show that \textsc{MinLVEDP} can be solved in linear time for trees, whereas the decision version of this problem is NP-complete for general graphs. We further study approximation algorithms for this problem. We propose an $O(\ln \Delta(G))$-approximation algorithm for \textsc{MinLVEDP} in general graphs, where $\Delta(G)$ is the maximum degree of the input graph. On the negative side, we show that the \textsc{MinLVEDP} cannot be approximated within $\frac{1}{2}(\frac{1}{8}-\epsilon)\ln|V|$ for any $\epsilon >0$, unless $NP\subseteq DTIME(|V|^{O(\log(\log|V|)})$.

Given a function $f: [a,b] \to \mathbb{R}$, if $f(a)<0$ and $f(b)>0$ and $f$ is continuous, the Intermediate Value Theorem implies that $f$ has a root in $[a,b]$. Moreover, given a value-oracle for $f$, an approximate root of $f$ can be computed using the bisection method, and the number of required evaluations is polynomial in the number of accuracy digits. The goal of this paper is to identify conditions under which this polynomiality result extends to a multi-dimensional function that satisfies the conditions of Miranda's theorem -- the natural multi-dimensional extension of the Intermediate Value Theorem. In general, finding an approximate root of $f$ might require an exponential number of evaluations even for a two-dimensional function. We show that, if $f$ is two-dimensional, and at least one component of $f$ is monotone, an approximate root of $f$ can be found using a polynomial number of evalutaions. This result has applications for computing an approximately envy-free cake-cutting among three groups.

We improve the previously best known upper bounds on the sizes of $\theta$-spherical codes for every $\theta<\theta^*\approx 62.997^{\circ}$ at least by a factor of $0.4325$, in sufficiently high dimensions. Furthermore, for sphere packing densities in dimensions $n\geq 2000$ we have an improvement at least by a factor of $0.4325+\frac{51}{n}$. Our method also breaks many non-numerical sphere packing density bounds in smaller dimensions. This is the first such improvement for each dimension since the work of Kabatyanskii and Levenshtein~\cite{KL} and its later improvement by Levenshtein~\cite{Leven79}. Novelties of this paper include the analysis of triple correlations, usage of the concentration of mass in high dimensions, and the study of the spacings between the roots of Jacobi polynomials.