Inverse Problems for Sumset Sizes of Finite Sets of Integers

Introduction 1.Counting values 2. Large cardinals 3. Function assignments 4. Proofs using large cardinals 5. Independence proofs 5.1.Existence of finite solutions of certain recursive definitions with strong indiscernibles 5.2.Existence of finite towers of finite sets with strong atomic indiscernibles, weak staggered comprehension, and a weak least-element principle 5.3.Existence of infinite linearly ordered predicates with strong atomicindiscernibles, comprehension for bounded formulas of limited complexity, and a weak least-element principle 5.4.Existence of linearly ordered predicates with limit points, strong indiscernibles for bounded formulas of limited arity, and comprehension and the least-element principle for bounded formulas 5.5.Existence of pairing functions defined by bounded formulas 5.6.Existence of linearly ordered binary relations with pairing function, limit points, strong indiscernibles, comprehension, and the least element principle 5.7.Construction of the cumulative hierarchy 5.8.Existence of a model of set theory with large cardinals, and application of the second incompleteness theorem References *This research was partially

An $(\boldsymbol {M},\boldsymbol {K},\boldsymbol {N},\delta _{\max })$ -quasi-complementary sequence set (QCSS) is referred to as a set of $\boldsymbol {M}$ 2-D matrices of order $\boldsymbol {K}\times \boldsymbol {N}$ with periodic tolerance $\delta _{\max }$ . In a multicarrier code-division multiple-access (MC-CDMA) communication system, the set size $\boldsymbol {M}$ of a QCSS is equal to the maximum number of users it can support, and the periodic tolerance $\delta _{\max }$ determines the interference performance. For the application of a QCSS, it is desirable that the set size should be as large as possible, and the periodic tolerance should be as small as possible. In this paper, a framework of a periodic QCSS is proposed from additive and multiplicative characters associated with a specific integer set. It is then discovered that the parameters of the obtained QCSS are determined by the employed integer set. From this discovery, new classes of periodic QCSSs with large set sizes and low periodic tolerances are constructed and associated with some known integer sets.

A finite set is “hidden” if its elements are not directly enumerable or if its size cannot be ascertained via a deterministic query. In public health, epidemiology, demography, ecology and intelligence analysis, researchers have developed a wide variety of indirect statistical approaches, under different models for sampling and observation, for estimating the size of a hidden set. Some methods make use of random sampling with known or estimable sampling probabilities, and others make structural assumptions about relationships (e.g. ordering or network information) between the elements that comprise the hidden set. In this review, we describe models and methods for learning about the size of a hidden finite set, with special attention to asymptotic properties of estimators. We study the properties of these methods under two asymptotic regimes, “infill” in which the number of fixed-size samples increases, but the population size remains constant, and “outfill” in which the sample size and population size grow together. Statistical properties under these two regimes can be dramatically different.

We study SET COVER for orthants: Given a set of points in a d-dimensional Euclidean space and a set of orthants of the form (-infty,p_1] x ... x (-infty,p_d], select a minimum number of orthants so that every point is contained in at least one selected orthant. This problem draws its motivation from applications in multi-objective optimization problems. While for d=2 the problem can be solved in polynomial time, for d>2 no algorithm is known that avoids the enumeration of all size-k subsets of the input to test whether there is a set cover of size k. Our contribution is a precise understanding of the complexity of this problem in any dimension d >= 3, when k is considered a parameter: - For d=3, we give an algorithm with runtime n^O(sqrt{k}), thus avoiding exhaustive enumeration. - For d=3, we prove a tight lower bound of n^Omega(sqrt{k}) (assuming ETH). - For d >=slant 4, we prove a tight lower bound of n^Omega(k) (assuming ETH). Here n is the size of the set of points plus the size of the set of orthants. The first statement comes as a corollary of a more general result: an algorithm for SET COVER for half-spaces in dimension 3. In particular, we show that given a set of points U in R^3, a set of half-spaces D in R^3, and an integer k, one can decide whether U can be covered by the union of at most k half-spaces from D in time |D|^O(sqrt{k})* |U|^O(1). We also study approximation for SET COVER for orthants. While in dimension 3 a PTAS can be inferred from existing results, we show that in dimension 4 and larger, there is no 1.05-approximation algorithm with runtime f(k)* n^o(k) for any computable f, where k is the optimum.

Vertex Cover is one of the most well studied problems in the realm of parameterized algorithms and admits a kernel with O(l^2) edges and 2*l vertices. Here, l denotes the size of a vertex cover we are seeking for. A natural question is whether Vertex Cover admits a polynomial kernel (or a parameterized algorithm) with respect to a parameter k, that is, provably smaller than the size of the vertex cover. Jansen and Bodlaender [STACS 2011, TOCS 2013] raised this question and gave a kernel for Vertex Cover of size O(f^3), where f is the size of a feedback vertex set of the input graph. We continue this line of work and study Vertex Cover with respect to a parameter that is always smaller than the solution size and incomparable to the size of the feedback vertex set of the input graph. Our parameter is the number of vertices whose removal results in a graph of maximum degree two. While vertex cover with this parameterization can easily be shown to be fixed-parameter tractable (FPT), we show that it has a polynomial sized kernel. The input to our problem consists of an undirected graph G, S \subseteq V(G) such that |S| = k and G[V(G)\S] has maximum degree at most 2 and a positive integer l. Given (G,S,l), in polynomial time we output an instance (G',S',l') such that |V(G')|<= O(k^5), |E(G')|<= O(k^6) and G has a vertex cover of size at most l if and only if G' has a vertex cover of size at most l'. When G[V(G)\S] has maximum degree at most 1, we improve the known kernel bound from O(k^3) vertices to O(k^2) vertices (and O(k^3) edges). In general, if G[V(G)\S] is simply a collection of cliques of size at most d, then we transform the graph in polynomial time to an equivalent hypergraph with O(k^d) vertices and show that, for d >= 3, a kernel with O(k^{d-epsilon}) vertices is unlikely to exist for any epsilon >0 unless NP is a subset of coNO/poly.

Groups with few maximal sum-free sets

Parallelized ultrasound homodyned-K imaging based on a generalized artificial neural network estimator

In the past years, extensions of monadic second-order logic (MSO) that can specify boundedness properties by the use of operators referring to the sizes of sets have been considered. In particular, the logics costMSO introduced by T. Colcombet and MSO+U by M. Bojanczyk were analyzed and connections to automaton models have been established to obtain decision procedures for these logics. In this work, we propose the logic quantitative counting MSO (qcMSO for short), which combines aspects from both costMSO and MSO+U. We show that both logics can be embedded into qcMSO in a natural way. Moreover, we provide a decidability proof for the theory of its weak variant (quantification only over finite sets) for the natural numbers with order and the infinite binary tree. These decidability results are obtained using a regular cost function extension of automatic structures called resource-automatic structures.

Let \(\mathbb {F}\) be a finite field consisting of \(q\) elements and let \(n \ge 1\) be an integer. In this paper, we study the size of local Kakeya sets with respect to subsets of \(\mathbb {F}^{n}\) and obtain upper and lower bounds for the minimum size of a (local) Kakeya set with respect to an arbitrary set \({\mathcal T} \subseteq \mathbb {F}^{n}\).

Reduced bases have been introduced for the approximation of parametrized PDEs in applications where many online queries are required. Their numerical efficiency for such problems has been theoretically confirmed in Binev et al. (SIAM J. Math. Anal. 43 (2011) 1457–1472) and DeVore et al. (Constructive Approximation 37 (2013) 455–466), where it is shown that the reduced basis space Vn of dimension n, constructed by a certain greedy strategy, has approximation error similar to that of the optimal space associated to the Kolmogorov n-width of the solution manifold. The greedy construction of the reduced basis space is performed in an offline stage which requires at each step a maximization of the current error over the parameter space. For the purpose of numerical computation, this maximization is performed over a finite training set obtained through a discretization of the parameter domain. To guarantee a final approximation error ε for the space generated by the greedy algorithm requires in principle that the snapshots associated to this training set constitute an approximation net for the solution manifold with accuracy of order ε. Hence, the size of the training set is the ε covering number for M and this covering number typically behaves like exp(Cε−1/s) for some C &gt; 0 when the solution manifold has n-width decay O(n−s). Thus, the shear size of the training set prohibits implementation of the algorithm when ε is small. The main result of this paper shows that, if one is willing to accept results which hold with high probability, rather than with certainty, then for a large class of relevant problems one may replace the fine discretization by a random training set of size polynomial in ε−1. Our proof of this fact is established by using inverse inequalities for polynomials in high dimensions.

UDC 519.1 A Sidon set (also called a Golomb ruler) is a B 2 sequence and a 1 -thin set is a set of integers containing no nontrivial solutions to the equation a + b = c + d . We improve the lower bound for the diameter of a Sidon set with k elements, namely, if k is sufficiently large and 𝒜 is a Sidon set with k elements, then ⅆ i a m ( 𝒜 ) ≥ k 2 - 1.99405 k 3 / 2 . Alternatively, if n is sufficiently large, then the cardinality of the largest subset of { 1,2 , … , n } , which is a Sidon set, does not exceed n 1 / 2 + 0.99703 n 1 / 4 .

Optimizing criteria for choosing a confidence set for a parameter are formulated as mathematical programming problems. The two optimizing criteria, probability of coverage and size of set, give rise to a pair of inverse programming problems. Several examples are worked out. The programming problems are then formulated to allow the incorporation of partial information about the parameter. By varying the family of prior distributions, a continuum of problems from the frequency approach to a Bayesian approach is obtained. Some examples are considered in which the family of priors contains more than one but not all prior distributions.

In this paper, we investigate the factorization of singular partial self-maps on a finite set into products of the least number of nilpotent elements. This research demonstrates that the semigroup of such maps can be expressed within a union of nilpotent-generated sets, specifically up to the third power. Some of our key findings include the determination of the nilpotent rank and the nilpotent depth for these maps, which vary based on whether the set size is even or odd. Additionally, this study surveys the relationship between these results and Stirling numbers, leveraging the Vagner Theorem and digraphic representations. We also examine stable quasi-idempotents, which correspond to specific digraphic paths and chains, providing further insights into the structure of partialtransformation semigroups.

There are various inverse problems -- including reconstruction problems arising in medical imaging where one is often aware of the forward operator that maps variables of interest to the observations. It is therefore natural to ask whether such knowledge of the forward operator can be exploited in deep learning approaches increasingly used to solve inverse problems. In this paper, we provide one such way via an analysis of the generalisation error of deep learning approaches to inverse problems. In particular, by building on the algorithmic robustness framework, we offer a generalisation error bound that encapsulates key ingredients associated with the learning problem such as the complexity of the data space, the size of the training set, the Jacobian of the deep neural network and the Jacobian of the composition of the forward operator with the neural network. We then propose a ‘plug-and-play’ regulariser that leverages the knowledge of the forward map to improve the generalization of the network. We likewise also use a new method allowing us to tightly upper bound the Jacobians of the relevant operators that is much more computationally efficient than existing ones. We demonstrate the efficacy of our model-aware regularised deep learning algorithms against other state-of-the-art approaches on inverse problems involving various sub-sampling operators such as those used in classical compressed sensing tasks, image super-resolution problems and accelerated Magnetic Resonance Imaging (MRI) setups.

Dynamical modeling of gene regulation via network models constitutes a key problem for genomics. The long-run characteristics of a dynamical system are critical and their determination is a primary aspect of system analysis. In the other direction, system synthesis involves constructing a network possessing a given set of properties. This constitutes the inverse problem. Generally, the inverse problem is ill-posed, meaning there will be many networks, or perhaps none, possessing the desired properties. Relative to long-run behavior, we may wish to construct networks possessing a desirable steady-state distribution. This paper addresses the long-run inverse problem pertaining to Boolean networks (BNs). The long-run behavior of a BN is characterized by its attractors. The rest of the state transition diagram is partitioned into level sets, the j-th level set being composed of all states that transition to one of the attractor states in exactly j transitions. We present two algorithms for the attractor inverse problem. The attractors are specified, and the sizes of the predictor sets and the number of levels are constrained. Algorithm complexity and performance are analyzed. The algorithmic solutions have immediate application. Under the assumption that sampling is from the steady state, a basic criterion for checking the validity of a designed network is that there should be concordance between the attractor states of the model and the data states. This criterion can be used to test a design algorithm: randomly select a set of states to be used as data states; generate a BN possessing the selected states as attractors, perhaps with some added requirements such as constraints on the number of predictors and the level structure; apply the design algorithm; and check the concordance between the attractor states of the designed network and the data states. The software and supplementary material is available at http://gsp.tamu.edu/Publications/BNs/bn.htm