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Variable (feature) selection plays an important role in data analysis and mathematical modeling. This paper aims to address the significant lack of formal evaluation benchmarks for feature selection algorithms (FSAs). To evaluate FSAs effectively, controlled environments are required, and the use of synthetic datasets offers significant advantages. We introduce a set of ten synthetically generated datasets with known relevance, redundancy, and irrelevance of features, derived from various mathematical, logical, and geometric sources. Additionally, eight FSAs are evaluated on these datasets based on their relevance and novelty. The paper first introduces the datasets and then provides a comprehensive experimental analysis of the performance of the selected FSAs on these datasets including testing the FSAs' resilience on two types of induced data noise. The analysis has guided the grouping of the generated datasets into four groups of data complexity. Lastly, we provide public access to the generated datasets to facilitate bench-marking of new feature selection algorithms in the field via our Github repository. The contributions of this paper aim to foster the development of novel feature selection algorithms and advance their study. [ABSTRACT FROM AUTHOR]

The generation of mathematical models for the analysis of buildings with multiple thermal zones is a large and complex task. Furthermore, the order and complexity of the dynamical model are increased by the number of included thermal zones. To overcome this problem, this paper presents an algorithm to define the mathematical model automatically, using the geometric and physics parameters as inputs. Additionally, the spatial position of each thermal zone must be recorded in an arrangement called a contact matrix. The algorithm for modeling systems with multiple thermal zones is the main contribution of this work. This algorithm is presented in pseudocode format and as an annex, an implementation in MATLAB software. One of the advantages of this methodology is that it allows us to work with parallelepipeds and not necessarily cubic thermal zones. The algorithm allows us to generate mathematical models with symbolic variables, starting from the knowledge of how many thermal zones compose the system and its geometric organization. This information must be organized in a matrix arrangement called a contact matrix. Different arrays of thermal zones were constructed with wooden boxes to verify the functionality of the models generated with the algorithm. Each case provided information that allowed us to adjust the mathematical models and their simulations, obtaining a range of errors between experimental and simulated temperatures from 2.08 to 5.6 , depending on the number of thermal zones studied. [ABSTRACT FROM AUTHOR]

This paper continues formalization in the Mizar system [2, 1] of basic notions of the composition-nominative approach to program semantics [14] which was started in [8, 12, 10]. The composition-nominative approach studies mathematical models of computer programs and data on various levels of abstraction and generality and provides tools for reasoning about their properties. In particular, data in computer systems are modeled as nominative data [15]. Besides formalization of semantics of programs, certain elements of the composition-nominative approach were applied to abstract systems in a mathematical systems theory [4, 6, 7, 5, 3]. In the paper we give a formal definition of the notions of a binominative function over given sets of names and values (i.e. a partial function which maps simple-named complex-valued nominative data to such data) and a nominative predicate (a partial predicate on simple-named complex-valued nominative data). The sets of such binominative functions and nominative predicates form the carrier of the generalized Glushkov algorithmic algebra for simple-named complex-valued nominative data [15]. This algebra can be used to formalize algorithms which operate on various data structures (such as multidimensional arrays, lists, etc.) and reason about their properties. In particular, we formalize the operations of this algebra which require a specification of a data domain and which include the existential quantifier, the assignment composition, the composition of superposition into a predicate, the composition of superposition into a binominative function, the name checking predicate. The details on formalization of nominative data and the operations of the algorithmic algebra over them are described in [11, 13, 9]. [ABSTRACT FROM AUTHOR]

This paper continues formalization in Mizar [2, 1] of basic notions of the composition-nominative approach to program semantics [13] which was started in [8, 11]. The composition-nominative approach studies mathematical models of computer programs and data on various levels of abstraction and generality and provides tools for reasoning about their properties. Besides formalization of semantics of programs, certain elements of the composition-nominative approach were applied to abstract systems in a mathematical systems theory [4, 6, 7, 5, 3]. In the paper we introduce a definition of the notion of a binominative function over a set D understood as a partial function which maps elements of D to D. The sets of binominative functions and nominative predicates [11] over given sets form the carrier of the generalized Glushkov algorithmic algebra [14]. This algebra can be used to formalize algorithms which operate on various data structures (such as multidimensional arrays, lists, etc.) and reason about their properties. We formalize the operations of this algebra (also called compositions) which are valid over uninterpretated data and which include among others the sequential composition, the prediction composition, the branching composition, the monotone Floyd-Hoare composition, and the cycle composition. The details on formalization of nominative data and the operations of the algorithmic algebra over them are described in [10, 12, 9]. [ABSTRACT FROM AUTHOR]

The clonal selection algorithm(CSA) is a core method in artificial immune system, which is famous for its intelligent evolution in artificial intelligence application. However, There are some shortcomings in the algorithm, such as local optima and low convergence speed, which make its practical effects not ideal. Culture algorithm(CA) is driven by knowledge, which can significantly improve the evolutionary efficiency. Chaos mechanism can make the algorithm have better problem space coverage ability. Therefore, a culture&chaos-inspired CSA(CC-CSA) is proposed in this paper to deal with the problems mentioned before. CC-CSA adopts the double-layer evolutionary framework of CA to extract knowledge and guide the crossover and chaotic mutation operation to complete the evolution process. The implicit knowledge is used to adaptively control the chaotic mutation scale, guide the individuals to jump out of the local optima, and realize the accurate search in the latter evolution cycle to gradually approach the optimal solution. It can be seen from the mathematical model analysis that CC-CSA can converge to the global optimal solution. Compared with the experimental results of the original CSA and its representative, up-to-date improved methods, CC-CSA has the fastest convergence speed and the best detection performances. It is also proved that CC-CSA can solve the problems of local optima and slow convergence speed by using the knowledge guidance of CA's double-layer framework and good coverage ability of chaos mechanism to the problem space. [ABSTRACT FROM AUTHOR]

In this paper, we prove that for a linearly ordered skewfield the groups of quotients of the semigroup Gn(픻) coincides with the group GLn(픻) for n ≥ 3. [ABSTRACT FROM AUTHOR]

In this paper a problem of specifying HIV-infection parameters and immune response using additional measurements of the concentrations of the T-lymphocytes, the free virus and the immune effectors at fixed times for a mathematical model of HIV dynamics is investigated numerically. The problem of the parameter specifying of the mathematical model (an inverse problem) is reduced to a problem of minimizing an objective function describing the deviation of the simulation results from the experimental data. A genetic algorithm for solving the least squares function minimization problem is implemented and investigated. The results of a numerical solution of the inverse problem are analyzed. [ABSTRACT FROM AUTHOR]

In this paper, we consider the discrete unit square cover (DUSC) problem as follows: given a set 𝒫 of n points and a set 𝒮 of m axis-aligned unit squares in ℝ 2 , the objective is (i) to check whether the union of the squares in 𝒮 covers all the points in 𝒫 , and (ii) if the answer is yes, then select a minimum cardinality subset 𝒮 ∗ ⊆ 𝒮 such that each point in 𝒫 is covered by at least one square in 𝒮 ∗ . For the DUSC problem: (i) we propose a (2 + 4 k − 2) -approximation algorithm, where k (> 2) is an integer parameter that defines a trade-off between the running time and the approximation factor of the algorithm. The running time of our proposed algorithm is O (k m k n + n log n). Our solution of the DUSC problem is based on a simple (1 + 2 k − 2) -approximation algorithm for the subproblem strip square cover (SSC) problem, where all the points in 𝒫 are lying within a horizontal strip of unit height. (ii) we also propose a 2-approximation algorithm, which runs in O (m 4 n) time. The 2-approximation algorithm is based on an algorithm for the subproblem SSC problem. The algorithm for the subproblem is developed using plane sweep and graph search traversal techniques. We also extend this algorithm to get 2-approximation result for the weighted DUSC problem where the squares are assigned weights, and the aim is to choose a subset 𝒮 ∗ ⊆ 𝒮 such that each point in 𝒫 is covered by at least one square in 𝒮 ∗ and the sum of the weights of squares in 𝒮 ∗ is minimized. [ABSTRACT FROM AUTHOR]

The conditional diagnosis is a very important measure of the reliability and the fault-tolerance of networks. The ‘condition’ means that no faulty set contains all neighbours of any node. Under this assumption, for any systemG, every component ofhas more than 1 node, whereFis the faulty set ofG. Theg-extra conditional diagnosability is defined under the assumption that every component ofhas more thannodes. ‘A system with at mosttfaulty nodes is defined as sequentiallyt-diagnosable if at least one faulty node can be repaired, so that the testing can be continued using the repaired node to eventually diagnose all faulty nodes’ [E.P. Duarte Jr., R.P. Ziwich, and L.C.P. Albini,A survey of comparison-based system-level diagnosis, ACM Comput. Surv. 43(3) (2011), article 22]. To increase the degree of the sequentialt-diagnosability of a system, sequential-diagnosis strategy is proposed in this paper. It is allowed that there are at mostkmisdiagnosed nodes. In this paper, we determine theg-extra conditional diagnosability of hypercubes and propose sequential-diagnosis algorithms for hypercubes with low time complexities under the Preparata, Metze, and Chien (PMC) model and the MM* model which is a special case of the Maeng and Malek (MM) model. [ABSTRACT FROM PUBLISHER]

In the past decade the humanitarian supply chain (HSC) has attracted the attention of researchers due to the increasing frequency of disasters. The uncertainty in time, location, and severity of disaster during predisaster phase and poor conditions of available infrastructure during postdisaster phase make HSC operations difficult to handle. In order to overcome the difficulties during these phases, we need to assure that HSC operations are designed in an efficient manner to minimize human and economic losses. In the recent times, several mathematical optimization techniques and algorithms have been developed to increase the efficiency of HSC operations. These techniques and algorithms developed for the field of HSC motivate the need of a systematic literature review. Owing to the importance of mathematical modelling techniques, this paper presents the review of the mathematical contributions made in the last decade in the field of HSC. A systematic literature review methodology is used for this paper due to its transparent procedure. There are two objectives of this study: the first one is to conduct an up-to-date survey of mathematical models developed in HSC area and the second one is to highlight the potential research areas which require attention of the researchers. [ABSTRACT FROM AUTHOR]

This paper presents a review of the most relevant current techniques that deal with hole-filling in 3D models. Contrary to earlier reports, which approach mesh repairing in a sparse and global manner, the objective of this review is twofold. First, a specific and comprehensive review of hole-filling techniques (as a relevant part in the field of mesh repairing) is carried out. We present a brief summary of each technique with attention paid to its algorithmic essence, main contributions and limitations. Second, a solid comparison between 34 methods is established. To do this, we define 19 possible meaningful features and properties that can be found in a generic hole-filling process. Then, we use these features to assess the virtues and deficiencies of the method and to build comparative tables. The purpose of this review is to make a comparative hole-filling state-of-the-art available to researchers, showing pros and cons in a common framework. [ABSTRACT FROM AUTHOR]

The Euler genus of a graph is a fundamental and well-studied parameter in graph theory and topology. Computing it has been shown to be NP-hard by Thomassen [J. Algorithms, 10 (1989), pp. 568{576; J. Combin. Theory, Ser. B, 57 (1993), pp. 196{206], and it is known to be fixed-parameter tractable. However, the approximability of the Euler genus is wide open. While the existence of an O(1)-approximation is not ruled out, only an O( p n)-approximation [J. Chen, S. P. Kanchi, and A. Kanevsky, Inform. Process. Lett., 61 (1997), pp. 317{322] is known even in bounded-degree graphs. In this paper we give a polynomial-time algorithm which, given a boundeddegree graph of Euler genus g, computes a drawing in a surface of Euler genus gO(1) · logO(1) n. Combined with the upper bound from [J. Chen, S. P. Kanchi, and A. Kanevsky, Inform. Process. Lett., 61 (1997), pp. 317{322], our result also implies a O(n1/2-α)-approximation for some constant α > 0. Using our algorithm for approximating the Euler genus as a subroutine, we obtain, in a uniform fashion, algorithms with approximation ratios of the form OPTO(1) · logO(1) n for several related problems on bounded-degree graphs. These include the problems of orientable genus, crossing number, and planar edge and vertex deletion. Our algorithm and proof of correctness for the crossing number problem are simpler compared to the long and difficult proof in the recent breakthrough by Chuzhoy [Proceedings of the ACM Symposium on Theory of Computing, 2011, pp. 303{312], while essentially obtaining a qualitatively similar result. For planar edge and vertex deletion problems our results are the first to obtain a bound of the form poly(OPT; log n). We also highlight some further applications of our results in the design of algorithms for graphs with small genus. Many such algorithms require that a drawing of the graph is given as part of the input. Our results imply that in several interesting cases, we can implement such algorithms even when the drawing is unknown. [ABSTRACT FROM AUTHOR]

The block inverse iteration and block Newton’s methods proposed by the authors of this paper for computing invariant pairs of regular linear matrix pencils are generalized to the case of regular nonlinear matrix pencils. Numerical properties of the proposed methods are demonstrated with a typical quadratic eigenproblem. [ABSTRACT FROM AUTHOR]

Music plays a prominent role in society and companies have even started studying its aspects for commercial purposes. It is only natural to ask what characteristics make certain songs appealing. While much research has been conducted on the mathematical principles of sound, there has been less focus on analyzing the structure of popular songs from a mathematical perspective. One mathematical tool that researchers have used to study musical structure is seriation, ordering. This paper applies several types of seriation algorithms to conduct a mathematical analysis of the structural qualities of several musical pieces. This paper focuses on 10 popular artists and their musical influences. The artists chosen for this research are linked because of the influences they cite, musical genre, and the popularity of their music. Results show that an artist's songs have a higher quantitatively measured connection with the artists they cite as influences rather than the artists who they never mention as musical influences. [ABSTRACT FROM AUTHOR]

The article presents information related to the periodical that focuses on mathematics. Topics discussed include declaration on equal opportunities, diversity and scientific collaboration; new trends in algorithmic randomness and computable analysis; nonlocal conservation laws; mathematical models; and mathematical analysis and quantum systems.

In this paper, we first propose a weak convergence algorithm, called the linesearch algorithm, for solving a split equilibrium problem and nonexpansive mapping (SEPNM) in real Hilbert spaces, in which the first bifunction is pseudomonotone with respect to its solution set, the second bifunction is monotone, and fixed point mappings are nonexpansive. In this algorithm, we combine the extragradient method incorporated with the Armijo linesearch rule for solving equilibrium problems and the Mann method for finding a fixed point of an nonexpansive mapping. We then combine the proposed algorithm with hybrid cutting technique to get a strong convergence algorithm for SEPNM. Special cases of these algorithms are also given. [ABSTRACT FROM AUTHOR]

This paper is developed to the study on the general properties of the relation between the orlicz space of χMπ and χMπ (a). [ABSTRACT FROM AUTHOR]

This paper proposes a new stochastic method based on u-transform (UT) to capture the uncertainties of the reconfiguration in a correlated environment. The proposed stochastic framework makes use of the idea covariance matrix to reach this goal. Also, in order to reach the maximum efficiency of the reconfiguration strategy, reliability indices are considered along with the traditional targets in the multi-objective framework. Therefore, the objective functions to be optimized are System Average Interruption Frequency Index (SAIFI), Average Energy Not Supplied (AENS), total active power losses and the total network costs. Since the problem investigated is a complex, nonlinear optimization problem, a new optimization method based on krill herd algorithm (KH) is introduced. In addition, a new modification method is proposed for the KH algorithm to improve its total search ability. The feasibility and performance of the proposed method are examined on the IEEE standard test system. [ABSTRACT FROM AUTHOR]

The purpose of the paper is to study the proximal split feasibility problems. For solving the problems, we present new self-adaptive algorithms with the regularization technique. By using these algorithms, we give some strong convergence theorems for the proximal split feasibility problems. [ABSTRACT FROM AUTHOR]

Quasi-subtractive varieties are a generalization of subtractive varieties, introduced to account for some correspondence theorems between ideals and congruences in the literature that are not corollaries of general theorems in the theory of subtractive varieties. The main tool for the investigation of quasi-subtractive varieties is the concept of open filter, that extends the notion of ideal due to Gumm and Ursini. In this paper, we explore further the fundamental properties of open filters and congruences in this context; moreover, we introduce and investigate a notion of commutator of open filters. [ABSTRACT FROM AUTHOR]

We develop a method to compute the generating function of the number of vertices inside certain regions of the Uniform Infinite Planar Triangulation (UIPT). The computations are mostly combinatorial in flavour and the main tool is the decomposition of the UIPT into layers, called the skeleton decomposition, introduced by Krikun [20]. In particular, we get explicit formulas for the generating functions of the number of vertices inside hulls (or completed metric balls) centred around the root, and the number of vertices inside geodesic slices of these hulls. We also recover known results about the scaling limit of the volume of hulls previously obtained by Curien and Le Gall by studying the peeling process of the UIPT in [17]. [ABSTRACT FROM AUTHOR]

Markov models of queuing-inventory systems with demands of two types are proposed. In these systems, two restocking policies are used: policies with fixed and variable demand size. High-priority demands have no access constraints while low-priority demands are accepted if the total number of demands in the system is less than a given threshold. Methods are developed to calculate performance measures of the systems under study and problems of their optimization are solved. Results of the numerical experiments are shown. [ABSTRACT FROM AUTHOR]

We generalize the reduction mechanism for linear programming problems and semidefinite programming problems from Braun et al. (Inapproximability of combinatorial problems via small LPs and SDPs, 2015) in two ways (1) relaxing the requirement of affineness, and (2) extending to fractional optimization problems. As applications we provide several new LP-hardness and SDP-hardness results, e.g., for the problem, the problem, and the problem and show how to extend ad-hoc reductions between Sherali-Adams relaxations to reductions between LPs. [ABSTRACT FROM AUTHOR]

For linear processes with independent identically distributed innovations that are regularly varying with tail index α ∈ (0, 2), we study the functional convergence of the joint partial-sum and partial-maxima processes. We derive a functional limit theorem under certain assumptions on the coefficients of the linear processes, which enable the functional convergence in the space of ℝ2-valued càdlàg functions on [0, 1] with the Skorokhod weak M2 topology.We also obtain a joint convergence in the M2 topology on the first coordinate and in theM1 topology on the second coordinate. [ABSTRACT FROM AUTHOR]

Let $$C$$ be a smooth curve embedded in a smooth quasi-projective three-fold $$Y$$, and let $$Q^n_C={\text{Quot}}_n(\mathscr I_C)$$ be the Quot scheme of length $$n$$ quotients of its ideal sheaf. We show the identity $$\tilde\chi(Q^n_C)=(-1)^n\chi(Q^n_C)$$, where $$\tilde\chi$$ is the Behrend weighted Euler characteristic. When $$Y$$ is a projective Calabi–Yau three-fold, this shows that the Donaldson–Thomas (DT) contribution of a smooth rigid curve is the signed Euler characteristic of the moduli space. This can be rephrased as a DT/PT wall-crossing type formula, which can be formulated for arbitrary smooth curves. In general, such wall-crossing formula is shown to be equivalent to a certain Behrend function identity. [ABSTRACT FROM AUTHOR]

Parareal algorithm is a very powerful parallel computation method and has received much interest from many researchers over the past few years. The aim of this paper is to investigate the performance of parareal algorithm implemented with IMEX Runge-Kutta (RK) methods. A stability criterion of the parareal algorithm coupled with IMEX RK methods is established and the advantage (in the sense of stability) of implementing with this kind of RK methods is numerically investigated. Finally, numerical examples are given to illustrate the efficiency and performance of different parareal methods. [ABSTRACT FROM AUTHOR]

Constraint programming effectively solves constraint satisfaction and optimization problems by basically building, pruning, and exploring a search tree of potential solutions. In this context, a main component is the enumeration strategy, which is responsible for selecting the order in which variables and values are selected to build a possible solution. This process is known to be quite important; indeed a correct selection can reach a solution without failed explorations. However, it is well known that selecting the right strategy is quite challenging as their performance is notably hard to predict. During the last years, adaptive enumeration appeared as a proper solution to this problem. Adaptive enumeration allows the solving algorithm being able to autonomously modifying its strategies in solving time depending on performance information. In this way, the most suitable order for variables and values is employed along the search. In this paper, we present a new and more lightweight approach for performing adaptive enumeration. We incorporate a powerful classification technique named Top-k in order to adaptively select strategies along the resolution. We report results on a set of well-known benchmarks where the proposed approach noticeably competes with classical and modern adaptive enumeration methods for constraint satisfaction. [ABSTRACT FROM AUTHOR]

Summary: We propose a model for unbalanced longitudinal data, where the univariate margins can be selected arbitrarily and the dependence structure is described with the help of a D‐vine copula. We show that our approach is an extremely flexible extension of the widely used linear mixed model if the correlation is homogeneous over the considered individuals. As an alternative to joint maximum–likelihood a sequential estimation approach for the D‐vine copula is provided and validated in a simulation study. The model can handle missing values without being forced to discard data. Since conditional distributions are known analytically, we easily make predictions for future events. For model selection, we adjust the Bayesian information criterion to our situation. In an application to heart surgery data our model performs clearly better than competing linear mixed models. [ABSTRACT FROM AUTHOR]

The connection between the rules and derivations of Gentzen’s calculi NJ and LJ will be explained by several steps (i.e., systems), and an analysis of the well-known problems of the connection between reduction steps of normalization and cut elimination, from Zucker (1974) and Urban (2014), will be given. [ABSTRACT FROM AUTHOR]

One of the main objectives of empirical analysis of experiments and quasi‐experiments is to inform policy decisions that determine the allocation of treatments to individuals with different observable covariates. We study the properties and implementation of the Empirical Welfare Maximization (EWM) method, which estimates a treatment assignment policy by maximizing the sample analog of average social welfare over a class of candidate treatment policies. The EWM approach is attractive in terms of both statistical performance and practical implementation in realistic settings of policy design. Common features of these settings include: (i) feasible treatment assignment rules are constrained exogenously for ethical, legislative, or political reasons, (ii) a policy maker wants a simple treatment assignment rule based on one or more eligibility scores in order to reduce the dimensionality of individual observable characteristics, and/or (iii) the proportion of individuals who can receive the treatment is a priori limited due to a budget or a capacity constraint. We show that when the propensity score is known, the average social welfare attained by EWM rules converges at least at n−1/2 rate to the maximum obtainable welfare uniformly over a minimally constrained class of data distributions, and this uniform convergence rate is minimax optimal. We examine how the uniform convergence rate depends on the richness of the class of candidate decision rules, the distribution of conditional treatment effects, and the lack of knowledge of the propensity score. We offer easily implementable algorithms for computing the EWM rule and an application using experimental data from the National JTPA Study. [ABSTRACT FROM AUTHOR]

Cyclone separators are widely used in a variety of industrial applications. A low-mass loading gas cyclone is characterized by two performance parameters, namely the Euler and Stokes numbers. These parameters are highly sensitive to the geometrical design parameters defining the cyclone. Optimizing the cyclone geometry therefore is a complex problem. Testing a large number of cyclone geometries is impractical due to time constraints. Experimental data and even computational fluid dynamics simulations are time-consuming to perform, with a single simulation or experiment taking several weeks. Simpler analytical models are therefore often used to expedite the design process. However, this comes at the cost of model accuracy. Existing techniques used for cyclone shape optimization in literature do not take multiple fidelities into account. This work combines cheap-to-evaluate well-known mathematical models of cyclones, available data from computational fluid dynamics simulations and experimental data to build a triple-fidelity recursive co-Kriging model. This model can be used as a surrogate with a multi-objective optimization algorithm to identify a Pareto set of a finite number of solutions. The proposed scheme is applied to optimize the cyclone geometry, parametrized by seven design variables. [ABSTRACT FROM AUTHOR]

Influential units occur frequently in surveys, especially in business surveys that collect economic variables whose distributions are highly skewed. A unit is said to be influential when its inclusion or exclusion from the sample has an important impact on the sampling error of estimates. We extend the concept of conditional bias attached to a unit and propose a robust version of the double expansion estimator, which depends on a tuning constant. We determine the tuning constant that minimizes the maximum estimated conditional bias. Our results can be naturally extended to the case of unit nonresponse, the set of respondents often being viewed as a second-phase sample. A robust version of calibration estimators, based on auxiliary information available at both phases, is also constructed. [ABSTRACT FROM AUTHOR]

The Two-Handed Tile Assembly Model (2HAM) is a model of algorithmic self-assembly in which large structures, or assemblies of tiles, are grown by the binding of smaller assemblies. In order to bind, two assemblies must have matching glues that can simultaneously touch each other, and stick together with strength that is at least the temperature $$\tau $$ , where $$\tau $$ is some fixed positive integer. We ask whether the 2HAM is intrinsically universal. In other words, we ask: is there a single 2HAM tile set $$U$$ which can be used to simulate any instance of the model? Our main result is a negative answer to this question. We show that for all $$\tau ' < \tau $$ , each temperature- $$\tau '$$ 2HAM tile system does not simulate at least one temperature- $$\tau $$ 2HAM tile system. This impossibility result proves that the 2HAM is not intrinsically universal and stands in contrast to the fact that the (single-tile addition) abstract Tile Assembly Model is intrinsically universal. On the positive side, we prove that, for every fixed temperature $$\tau \ge 2$$ , temperature- $$\tau $$ 2HAM tile systems are indeed intrinsically universal. In other words, for each $$\tau $$ there is a single intrinsically universal 2HAM tile set $$U_{\tau }$$ that, when appropriately initialized, is capable of simulating the behavior of any temperature- $$\tau $$ 2HAM tile system. As a corollary, we find an infinite set of infinite hierarchies of 2HAM systems with strictly increasing simulation power within each hierarchy. Finally, we show that for each $$\tau $$ , there is a temperature- $$\tau $$ 2HAM system that simultaneously simulates all temperature- $$\tau $$ 2HAM systems. [ABSTRACT FROM AUTHOR]

In functional analysis it is well known that every linear functional defined on the dual of a locally convex vector space which is continuous for the weak* topology is the evaluation at a uniquely determined point of the given vector space. M. Schröder and A. Simpson have obtained a similar result for lower semicontinuous linear functionals on the cone of all Scott-continuous valuations on a topological space endowed with the weak* upper topology, an asymmetric version of the weak* topology. This result has given rise to several proofs, originally by Schröder and Simpson themselves and, more recently, by the author of these Notes and by J.Goubault-Larrecq. The proofs developed from very technical arguments to more and more conceptual ones. The present Note continues on this line, presenting a conceptual approach inspired by classical functional analysis which may prove useful in other situations. [ABSTRACT FROM AUTHOR]

VeriFast is a leading research prototype tool for the sound modular verification of safety and correctness properties of single-threaded and multithreaded C and Java programs. It has been used as a vehicle for exploration and validation of novel program verification techniques and for industrial case studies; it has served well at a number of program verification competitions; and it has been used for teaching by multiple teachers independent of the authors. However, until now, while VeriFast's operation has been described informally in a number of publications, and specific verification techniques have been formalized, a clear and precise exposition of how VeriFast works has not yet appeared. In this article we present for the first time a formal definition and soundness proof of a core subset of the VeriFast program verification approach. The exposition aims to be both accessible and rigorous: the text is based on lecture notes for a graduate course on program verification, and it is backed by an executable machine-readable definition and machine-checked soundness proof in Coq. [ABSTRACT FROM AUTHOR]

The current work introduces the notion of pdominant sets and studies their recursion-theoretic properties. Here a set A is called pdominant iff there is a partial A-recursive function ψ such that for every partial recursive function ϕ and almost every x in the domain of ϕ there is a y in the domain of ψ with y ≤ x and ψ(y)>ϕ(x). While there is a full Π10- class of nonrecursive sets where no set is pdominant, there is no Π10 class containing only pdominant sets. No weakly 2-generic set is pdominant while there are pdominant 1-generic sets below K. The halves of Chaitin's Ω are pdominant. No set which is low for Martin- Löf random is pdominant. There is a low r.e. set which is pdominant and a high r.e. set which is not pdominant. [ABSTRACT FROM AUTHOR]

This work is concerned with regular languages defined over large alphabets, either infinite or just too large to be expressed enumeratively. We define ageneric model where transitions are labeled by elements of a finite partition of the alphabet. We then extend Angluin's L* algorithm for learning regular languages from examples for such automata. We have implemented this algorithm and we demonstrate its behavior where the alphabet is a subset of the natural or real numbers. We sketch the extension of the algorithm to a class of languages over partially ordered alphabets. [ABSTRACT FROM AUTHOR]

We obtain estimates for the algorithmic complexity of index sets for the class of decidable autostable models and the class of computable models that possess strong constructivizations and are autostable under strong constructivizations. [ABSTRACT FROM AUTHOR]

SUMMARY In many large-scale computations, systems of equations arise in the form Au = b, where A is a linear operation to be performed on the unknown data u, producing the known right-hand side, b, which represents some constraint of known or assumed behavior of the system being modeled. Because such systems can be very large, solving them directly can be too slow. In contrast, a multigrid method removes different components of the error at different resolutions using smoothers that reduce high-frequency components of the error more readily than low. Here, we present an open-source multigrid solver written only in Python. OpenMG is a pure Python experimentation environment for testing multigrid concepts, not a production solver. The particular restriction method implemented is for 'standard' multigrid. By making the code simple and modular, we make the algorithmic details clear. The resulting solver is tested on an implicit pressure reservoir simulation problem with satisfactory results.Copyright © 2013 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

We introduce the notion of a centroidal locating set of a graph G, that is, a set L of vertices such that all vertices in G are uniquely determined by their relative distances to the vertices of L. A centroidal locating set of G of minimum size is called a centroidal basis, and its size is the centroidal dimension [ABSTRACT FROM AUTHOR]

The aim of the present paper is to develop a new generalized form of the fractional kinetic equation involving a generalized k-Mittag-Leffler function Ek,ζ,ηγ,ρ(⋅). The solutions of fractional kinetic equations are discussed in terms of the Mittag-Leffler function. Further, numerical values of the results and their graphical interpretation is interpreted to study the behavior of these solutions. The results established here are quite general in nature and capable of yielding both known and new results. [ABSTRACT FROM AUTHOR]