Maximal Subset Research Articles

Aircraft Control During Cruise Flight in Windshear Conditions: Viability Approach

This paper addresses the analysis of aircraft control capabilities during the cruise phase (flying at the established level with practically constant configuration and speed) in the presence of windshears. The study uses a point-mass aircraft model describing flight in a vertical plane. The problem is formulated as a differential game against wind disturbances. The first player, autopilot, controls the angle of attack and the power setting, whereas the second player, wind, produces dangerous gusts. The state variables of the model are subjected to constraints expressing aircraft safety conditions. Namely, the altitude, path inclination, and velocity are constrained. Viability theory is used to find the so-called viability kernel, the maximal subset of the state constraint where the aircraft trajectories can remain arbitrary long if the first player utilizes an appropriate feedback control, and the second player generates any admissible disturbances. The computations are based on grid methods developed by the authors and implemented on a multiprocessor computer system.

Online interval scheduling with a bounded number of failures

We consider the problem of scheduling intervals on m identical machines where each interval can be seen as a job with fixed start and end time. The goal is to accept a maximum cardinality subset of the given intervals and assign these intervals to the machines subject to the constraint that no two intervals assigned to the same machine overlap. We analyze an online version of this problem where, initially, a set of n potential intervals and an upper bound k on the number of failing intervals is given. If an interval fails, it can be accepted neither by the online algorithm nor by the adversary. An online algorithm learns that an interval fails at the time when it is supposed to be started. If a non-failing interval is accepted, it cannot be aborted and must be processed non-preemptively until completion. For different settings of this problem, we present deterministic and randomized online algorithms and prove lower bounds on the competitive ratio.

Suborbital graphs for the Atkin--Lehner group

We investigate suborbital graphs for an imprimitive action of the Atkin-Lehner group on a maximal subset of extended rational numbers on which a transitive action is also satisfied. Obtaining edge and some circuit conditions, we examine some combinatorial properties of these graphs.

A CONSISTENT DATA FUSION APPROACH FOR UNCERTAINTY QUANTIFICATION IN RIGID MUSCULOSKELETAL SIMULATION

Uncertainty quantification in rigid musculoskeletal modeling is essential to analyze the risks related to the simulation outcomes. Data fusion from multiple sources is a potential solution to reduce data uncertainties. This present study aimed at proposing a new data fusion rule leading to a more consistent and coherent data for uncertainty quantification. Moreover, a new uncertainty representation was developed using imprecise probability approach. A biggest maximal coherent subsets (BMCS) operator was defined to fuse interval-valued data ranges from multiple sources. Fusion-based probability-box structure was developed to represent the data uncertainty. Case studies were performed for uncertainty propagation through inverse dynamics and static optimization algorithms. Hip joint moment and muscle force estimation were computed under effect of the uncertainties of thigh mass and muscle properties. Respective p-boxes of these properties were generated. Regarding the uncertainty propagation analysis, correlation coefficients showed a very good value ([Formula: see text]) for the proposed fusion operator according to classical operators. Muscle force variation of the rectus femoris was computed. Peak-to-peak (i.e., difference between maximal values) rectus femoris forces showed deviations of 55[Formula: see text]N and 40[Formula: see text]N for the first and second peaks, respectively. The development of the new fusion operator and fusion-based probability-box leads to a more consistent uncertainty quantification. This allows the estimation of risks associated with the simulation outcomes under input data uncertainties for rigid musculoskeletal modeling and simulation.

Combining techniques of bounded model checking and constraint programming to aid for error localization

A model checker can produce a trace of counter-example for erroneous program, which is often difficult to exploit to locate errors in source code. In my thesis, we proposed an error localization algorithm from counter-examples, named LocFaults, combining approaches of Bounded Model-Checking (BMC) with constraint satisfaction problem (CSP). This algorithm analyzes the paths of CFG (Control Flow Graph) of the erroneous program to calculate the subsets of suspicious instructions to correct the program. Indeed, we generate a system of constraints for paths of control flow graph for which at most k conditional statements can be wrong. Then we calculate the MCSs (Minimal Correction Sets) of limited size on each of these paths. Removal of one of these sets of constraints gives a maximal satisfiable subset, in other words, a maximal subset of constraints satisfying the postcondition. To calculate the MCSs, we extend the generic algorithm proposed by Liffiton and Sakallah in order to deal with programs with numerical instructions more efficiently. This approach has been experimentally evaluated on a set of academic and realistic programs.

Enumerating solution-free sets in the integers

Given a linear equation L, a set A⊆[n] is L-free if A does not contain any ‘non-trivial’ solutions to L. In this paper we consider the following three general questions:(i)What is the size of the largest L-free subset of [n]?(ii)How many L-free subsets of [n] are there?(iii)How many maximal L-free subsets of [n] are there? We completely resolve (i) in the case when L is the equation px+qy=z for fixed p,q∈N where p≥2. Further, up to a multiplicative constant, we answer (ii) for a wide class of such equations L, thereby refining a special case of a result of Green [15]. We also give various bounds on the number of maximal L-free subsets of [n] for three-variable homogeneous linear equations L. For this, we make use of container and removal lemmas of Green [15].

Co-constructive development of a green chemistry-based model for the assessment of nanoparticles synthesis

Nanomaterials (materials at the nanoscale, 10−9 meters) are extensively used in several industry sectors due to the improved properties they empower commercial products with. There is a pressing need to produce these materials more sustainably. This paper proposes a Multiple Criteria Decision Aiding (MCDA) approach to assess the implementation of green chemistry principles as applied to the protocols for nanoparticles synthesis. In the presence of multiple green and environmentally oriented criteria, decision aiding is performed with a synergy of ordinal regression methods; preference information in the form of desired assignment for a subset of reference protocols is accepted. The classification models, indirectly derived from such information, are composed of an additive value function and a vector of thresholds separating the pre-defined and ordered classes. The method delivers a single representative model that is used to assess the relative importance of the criteria, identify the possible gains with improvement of the protocol’s evaluations and classify the non-reference protocols. Such precise recommendation is validated against the outcomes of robustness analysis exploiting the sets of all classification models compatible with all maximal subsets of consistent assignment examples. The introduced approach is used with real-world data concerning silver nanoparticles. It is proven to effectively resolve inconsistency in the assignment examples, tolerate ordinal and cardinal measurement scales, differentiate between inter- and intra-criteria attractiveness and deliver easily interpretable scores and class assignments. This work thoroughly discusses the learning insights that MCDA provided during the co-constructive development of the classification model.

Context-dependent combination of sensor information in Dempster\u2013Shafer theory for BDI

There has been much interest in the belief–desire–intention (BDI) agent-based model for developing scalable intelligent systems, e.g. using the AgentSpeak framework. However, reasoning from sensor information in these large-scale systems remains a significant challenge. For example, agents may be faced with information from heterogeneous sources which is uncertain and incomplete, while the sources themselves may be unreliable or conflicting. In order to derive meaningful conclusions, it is important that such information be correctly modelled and combined. In this paper, we choose to model uncertain sensor information in Dempster–Shafer (DS) theory. Unfortunately, as in other uncertainty theories, simple combination strategies in DS theory are often too restrictive (losing valuable information) or too permissive (resulting in ignorance). For this reason, we investigate how a context-dependent strategy originally defined for possibility theory can be adapted to DS theory. In particular, we use the notion of largely partially maximal consistent subsets (LPMCSes) to characterise the context for when to use Dempster’s original rule of combination and for when to resort to an alternative. To guide this process, we identify existing measures of similarity and conflict for finding LPMCSes along with quality of information heuristics to ensure that LPMCSes are formed around high-quality information. We then propose an intelligent sensor model for integrating this information into the AgentSpeak framework which is responsible for applying evidence propagation to construct compatible information, for performing context-dependent combination and for deriving beliefs for revising an agent’s belief base. Finally, we present a power grid scenario inspired by a real-world case study to demonstrate our work.

A note on the random greedy independent set algorithm

Let r be a fixed constant and let be an r‐uniform, D‐regular hypergraph on N vertices. Assume further that for some . Consider the random greedy algorithm for forming an independent set in . An independent set is chosen at random by iteratively choosing vertices at random to be in the independent set. At each step we chose a vertex uniformly at random from the collection of vertices that could be added to the independent set (i.e. the collection of vertices v with the property that v is not in the current independent set I and contains no edge in ). Note that this process terminates at a maximal subset of vertices with the property that this set contains no edge of ; that is, the process terminates at a maximal independent set.We prove that if satisfies certain degree and codegree conditions then there are vertices in the independent set produced by the random greedy algorithm with high probability. This result generalizes a lower bound on the number of steps in the H‐free process due to Bohman and Keevash and produces objects of interest in additive combinatorics. © 2016 Wiley Periodicals, Inc. Random Struct. Alg., 49, 479–502, 2016

Realizing secret sharing with general access structure

Secret sharing (SS) is one of the most important cryptographic primitives used for data outsourcing. The (t, n) SS was introduced by Shamir and Blakley separately in 1979. The secret sharing policy of the (t, n) threshold SS is far too simple for many applications because it assumes that every shareholder has equal privilege to the secret or every shareholder is equally trusted. Ito et al. introduced the concept of a general secret sharing scheme (GSS). In a GSS, a secret is divided among a set of shareholders in such a way that any “qualified” subset of shareholders can access the secret, but any “unqualified” subset of shareholders cannot access the secret. The secret access structure of GSS is far more flexible than threshold SS. In this paper, we propose an optimized implementation of GSS. Our proposed scheme first uses Boolean logic to derive two important subsets, one is called Min which is the minimal positive access subset and the other is called Max which is the maximal negative access subset, of a given general secret sharing structure. Then, conditions of parameters of a GSS are established based on these two important subsets. Furthermore, integer linear/non-linear programming is used to optimize the size of shares of a GSS. The complexity of linear/non-linear programming is O(n), where n is the number of shares generated by the dealer. This proposed design can be applied to implement GSS based on any classical SS. However, our proposed method is limited to be applicable to some general secret sharing policies. We use two GSSs, one is based on Shamir's weighted SS (WSS) using linear polynomial and the other is based on Asmuth-Bloom's SS using Chinese Remainder Theorem (CRT), to demonstrate our design. In comparing with existing GSSs, our proposed scheme is more efficient and can be applied to all classical SSs.

The link between orthology relations and gene trees: a correction perspective.

BackgroundWhile tree-oriented methods for inferring orthology and paralogy relations between genes are based on reconciling a gene tree with a species tree, many tree-free methods are also available (usually based on sequence similarity). Recently, the link between orthology relations and gene trees has been formally considered from the perspective of reconstructing phylogenies from orthology relations. In this paper, we consider this link from a correction point of view. Indeed, a gene tree induces a set of relations, but the converse is not always true: a set of relations is not necessarily in agreement with any gene tree. A natural question is thus how to minimally correct an infeasible set of relations. Another natural question, given a gene tree and a set of relations, is how to minimally correct a gene tree so that the resulting gene tree fits the set of relations.ResultsWe consider four variants of relation and gene tree correction problems, and provide hardness results for all of them. More specifically, we show that it is NP-Hard to edit a minimum of set of relations to make them consistent with a given species tree. We also show that the problem of finding a maximum subset of genes that share consistent relations is hard to approximate. We then demonstrate that editing a gene tree to satisfy a given set of relations in a minimum way is NP-Hard, where “minimum” refers either to the number of modified relations depicted by the gene tree or the number of clades that are lost. We also discuss some of the algorithmic perspectives given these hardness results.

An evaluation of exact methods for the multiple subset maximum cardinality selection problem.

The maximum cardinality subset selection problem requires finding the largest possible subset from a set of objects, such that one or more conditions are satisfied. An important extension of this problem is to extract multiple subsets, where the addition of one more object to a larger subset would always be preferred to increases in the size of one or more smaller subsets. We refer to this as the multiple subset maximum cardinality selection problem (MSMCSP). A recently published branch-and-bound algorithm solves the MSMCSP as a partitioning problem. Unfortunately, the computational requirement associated with the algorithm is often enormous, thus rendering the method infeasible from a practical standpoint. In this paper, we present an alternative approach that successively solves a series of binary integer linear programs to obtain a globally optimal solution to the MSMCSP. Computational comparisons of the methods using published similarity data for 45 food items reveal that the proposed sequential method is computationally far more efficient than the branch-and-bound approach.

On the Extraction of One Maximal Information Subset That Does Not Conflict with Multiple Contexts

The efficient extraction of one maximal information subset that does not conflict with multiple contxts or additional information sources is a key basic issue in many A.I. domains, especially when these contexts or sources can be mutually conflicting. In this paper, this question is addressed from a computational point of view in clausal Boolean logic. A new approach is introduced that experimentally outperforms the currently most efficient technique.

The basic principles of uncertain information fusion. An organised review of merging rules in different representation frameworks

We propose and advocate basic principles for the fusion of incomplete or uncertain information items, that should apply regardless of the formalism adopted for representing pieces of information coming from several sources. This formalism can be based on sets, logic, partial orders, possibility theory, belief functions or imprecise probabilities. We propose a general notion of information item representing incomplete or uncertain information about the values of an entity of interest. It is supposed to rank such values in terms of relative plausibility, and explicitly point out impossible values. Basic issues affecting the results of the fusion process, such as relative information content and consistency of information items, as well as their mutual consistency, are discussed. For each representation setting, we present fusion rules that obey our principles, and compare them to postulates specific to the representation proposed in the past. In the crudest (Boolean) representation setting (using a set of possible values), we show that the understanding of the set in terms of most plausible values, or in terms of non-impossible ones matters for choosing a relevant fusion rule. Especially, in the latter case our principles justify the method of maximal consistent subsets, while the former is related to the fusion of logical bases. Then we consider several formal settings for incomplete or uncertain information items, where our postulates are instantiated: plausibility orderings, qualitative and quantitative possibility distributions, belief functions and convex sets of probabilities. The aim of this paper is to provide a unified picture of fusion rules across various uncertainty representation settings.

Algorithms and Complexity Results for Genome Mapping Problems.

Genome mapping algorithms aim at computing an ordering of a set of genomic markers based on local ordering information such as adjacencies and intervals of markers. In most genome mapping models, markers are assumed to occur uniquely in the resulting map. We introduce algorithmic questions that consider repeats, i.e., markers that can have several occurrences in the resulting map. We show that, provided with an upper bound on the copy number of repeated markers and with intervals that span full repeat copies, called repeat spanning intervals, the problem of deciding if a set of adjacencies and repeat spanning intervals admits a genome representation is tractable if the target genome can contain linear and/or circular chromosomal fragments. We also show that extracting a maximum cardinality or weight subset of repeat spanning intervals given a set of adjacencies that admits a genome realization is NP-hard but fixed-parameter tractable in the maximum copy number and the number of adjacent repeats, and tractable if intervals contain a single repeated marker.

The solution of Tingley's problem for the operator norm unit sphere of complex n × n matrices

In this paper, we study geometric structures and isometries of the unit sphere of n×n complex matrices. We introduce the notion of proper exposed points of the unit ball of a Banach space which provides a refinement of that of exposed point provided that the space is separable. As a new geometric interpretation of unitary matrices, it is shown that the set of all proper exposed points of the unit ball of Mn(C) coincides with its unitary group U(n), where Mn(C) denotes the algebra of all n×n complex matrices. Moreover, we show that every maximal convex subset of the unit sphere of Mn(C) can be identified with the unit ball of Mn−1(C) by an affine bijection preserving the unitary matrices. This map is isometric with respect to any unitarily invariant norm. As an application, we completely determine the forms of surjective isometries on the unit sphere of Mn(C) with respect to the operator norm, which provides an affirmative answer to Tingley's problem on Mn(C).

Solving the maximal matching problem with DNA molecules in Adleman–Lipton model

The maximal matching problem (MMP) is to find maximal edge subsets in a given undirected graph, that no pair of edges are adjacent in the subsets. It is a vitally important NP-complete problem in graph theory and applied mathematics, having numerous real life applications in optimal combination and linear programming fields. It can be difficultly solved by the electronic computer in exponential level time. Meanwhile in previous studies deoxyribonucleic acid (DNA) molecular operations usually were used to solve NP-complete continuous path search problems, e.g. HPP, traveling salesman problem, rarely for NP-hard problems with discrete vertices or edges solutions, such as the minimum vertex cover problem, graph coloring problem and so on. In this paper, we present a DNA algorithm for solving the MMP with DNA molecular operations. For an undirected graph with [Formula: see text] vertices and [Formula: see text] edges, we reasonably design fixed length DNA strands representing vertices and edges of the graph, take appropriate steps and get the solutions of the MMP in proper length range using [Formula: see text] time. We extend the application of DNA molecular operations and simultaneously simplify the complexity of the computation.

Expected utility theory and inner and outer measures of loss aversion

We introduce a weak rank dependent utility (RDU) model, with one extra parameter compared to the canonical expected utility (EUT) model, which makes many of the same predictions as cumulative prospect theory (CPT). The model extends a set of nonconvex preferences to its maximal inner convex subset, satisfies stochastic dominance principles, resolves the Allais paradox, predicts CPT fourfold pattern of risk attitudes, and characterizes reference dependent preferences. Unlike extant RDU models that transform probability weighting functions (pwfs), our model transforms ranked choice sets while leaving objective probabilities intact. So pwfs are hidden in our model. CPT’s loss aversion index is a special case of the interior solution for the extra parameter for unconstrained utility maximization, and it is driven by tail probabilities in our model. We provide several examples to show how popular formulae for the loss aversion index can be classified into inner and outer measures of loss aversion via an approximate Radon–Nikodym formulation of the model. This resolves sources of disparity in estimating the loss aversion index with experimental data. We show that under extant approaches, the loss aversion index is best estimated by a mixture of inner and outer measures of itself. Furthermore, we identify a CPT paradox: The utility loss aversion index is unmeasurable under CPT nonexpected utility framework for mixed lotteries; but measurable for same under the expected utility paradigm adapted to our model.

Inference of gene interaction networks using conserved subsequential patterns from multiple time course gene expression datasets.

MotivationDeciphering gene interaction networks (GINs) from time-course gene expression (TCGx) data is highly valuable to understand gene behaviors (e.g., activation, inhibition, time-lagged causality) at the system level. Existing methods usually use a global or local proximity measure to infer GINs from a single dataset. As the noise contained in a single data set is hardly self-resolved, the results are sometimes not reliable. Also, these proximity measurements cannot handle the co-existence of the various in vivo positive, negative and time-lagged gene interactions.Methods and resultsWe propose to infer reliable GINs from multiple TCGx datasets using a novel conserved subsequential pattern of gene expression. A subsequential pattern is a maximal subset of genes sharing positive, negative or time-lagged correlations of one expression template on their own subsets of time points. Based on these patterns, a GIN can be built from each of the datasets. It is assumed that reliable gene interactions would be detected repeatedly. We thus use conserved gene pairs from the individual GINs of the multiple TCGx datasets to construct a reliable GIN for a species. We apply our method on six TCGx datasets related to yeast cell cycle, and validate the reliable GINs using protein interaction networks, biopathways and transcription factor-gene regulations. We also compare the reliable GINs with those GINs reconstructed by a global proximity measure Pearson correlation coefficient method from single datasets. It has been demonstrated that our reliable GINs achieve much better prediction performance especially with much higher precision. The functional enrichment analysis also suggests that gene sets in a reliable GIN are more functionally significant. Our method is especially useful to decipher GINs from multiple TCGx datasets related to less studied organisms where little knowledge is available except gene expression data.

ON MAXIMAL SUBSETS OF PAIRWISE NONCOMMUTING ELEMENTS IN FINITE -GROUPS

A subset $X$ of a finite group $G$ is a set of pairwise noncommuting elements if $xy\neq yx$ for all $x\neq y\in X$. If $|X|\geq |Y|$ for any other subset $Y$ of pairwise noncommuting elements, then $X$ is called a maximal subset of pairwise noncommuting elements and the size of such a set is denoted by ${\it\omega}(G)$. In a recent article by Azad et al. [‘Maximal subsets of pairwise noncommuting elements of some finite $p$-groups’, Bull. Iran. Math. Soc.39(1) (2013), 187–192], the value of ${\it\omega}(G)$ is computed for certain $p$-groups $G$. In the present paper, our aim is to generalise these results and find ${\it\omega}(G)$ for some more $p$-groups of interest.