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    ์ƒํƒœ๊ณ„์—์„œ์˜ ๊ฒฝ์Ÿ ๊ด€์ ์œผ๋กœ ๊ทธ๋ž˜ํ”„์™€ ์œ ํ–ฅ๊ทธ๋ž˜ํ”„์˜ ๊ตฌ์กฐ ์—ฐ๊ตฌ

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    ํ•™์œ„๋…ผ๋ฌธ(๋ฐ•์‚ฌ) -- ์„œ์šธ๋Œ€ํ•™๊ต๋Œ€ํ•™์› : ์‚ฌ๋ฒ”๋Œ€ํ•™ ์ˆ˜ํ•™๊ต์œก๊ณผ, 2023. 2. ๊น€์„œ๋ น.In this thesis, we study m-step competition graphs, (1, 2)-step competition graphs, phylogeny graphs, and competition-common enemy graphs (CCE graphs), which are primary variants of competition graphs. Cohen [11] introduced the notion of competition graph while studying predator-prey concepts in ecological food webs.An ecosystem is a biological community of interacting species and their physical environment. For each species in an ecosystem, there can be m conditions of the good environment by regarding lower and upper bounds on numerous dimensions such as soil, climate, temperature, etc, which may be represented by an m-dimensional rectangle, so-called an ecological niche. An elemental ecological truth is that two species compete if and only if their ecological niches overlap. Biologists often describe competitive relations among species cohabiting in a community by a food web that is a digraph whose vertices are the species and an arc goes from a predator to a prey. In this context, Cohen [11] defined the competition graph of a digraph as follows. The competition graph C(D) of a digraph D is defined to be a simple graph whose vertex set is the same as V (D) and which has an edge joining two distinct vertices u and v if and only if there are arcs (u, w) and (v, w) for some vertex w in D. Since Cohen introduced this definition, its variants such as m-step competition graphs, (i, j)-step competition graphs, phylogeny graphs, CCE graphs, p-competition graphs, and niche graphs have been introduced and studied. As part of these studies, we show that the connected triangle-free m-step competition graph on n vertices is a tree and completely characterize the digraphs of order n whose m-step competition graphs are star graphs for positive integers 2 โ‰ค m < n. We completely identify (1,2)-step competition graphs C_{1,2}(D) of orientations D of a complete k-partite graph for some k โ‰ฅ 3 when each partite set of D forms a clique in C_{1,2}(D). In addition, we show that the diameter of each component of C_{1,2}(D) is at most three and provide a sharp upper bound on the domination number of C_{1,2}(D) and give a sufficient condition for C_{1,2}(D) being an interval graph. On the other hand, we study on phylogeny graphs and CCE graphs of degreebounded acyclic digraphs. An acyclic digraph in which every vertex has indegree at most i and outdegree at most j is called an (i, j) digraph for some positive integers i and j. If each vertex of a (not necessarily acyclic) digraph D has indegree at most i and outdegree at most j, then D is called an hi, ji digraph. We give a sufficient condition on the size of hole of an underlying graph of an (i, 2) digraph D for the phylogeny graph of D being a chordal graph where D is an (i, 2) digraph. Moreover, we go further to completely characterize phylogeny graphs of (i, j) digraphs by listing the forbidden induced subgraphs. We completely identify the graphs with the least components among the CCE graphs of (2, 2) digraphs containing at most one cycle and exactly two isolated vertices, and their digraphs. Finally, we gives a sufficient condition for CCE graphs being interval graphs.์ด ๋…ผ๋ฌธ์—์„œ ๊ฒฝ์Ÿ๊ทธ๋ž˜ํ”„์˜ ์ฃผ์š” ๋ณ€์ด๋“ค ์ค‘ m-step ๊ฒฝ์Ÿ๊ทธ๋ž˜ํ”„, (1, 2)-step ๊ฒฝ์Ÿ ๊ทธ๋ž˜ํ”„, ๊ณ„ํ†ต ๊ทธ๋ž˜ํ”„, ๊ฒฝ์Ÿ๊ณต์ ๊ทธ๋ž˜ํ”„์— ๋Œ€ํ•œ ์—ฐ๊ตฌ ๊ฒฐ๊ณผ๋ฅผ ์ข…ํ•ฉํ–ˆ๋‹ค. Cohen [11]์€ ๋จน์ด์‚ฌ์Šฌ์—์„œ ํฌ์‹์ž-ํ”ผ์‹์ž ๊ฐœ๋…์„ ์—ฐ๊ตฌํ•˜๋ฉด์„œ ๊ฒฝ์Ÿ๊ทธ๋ž˜ํ”„ ๊ฐœ๋…์„ ๊ณ ์•ˆํ–ˆ๋‹ค. ์ƒํƒœ๊ณ„๋Š” ์ƒํ˜ธ์ž‘์šฉํ•˜๋Š” ์ข…๋“ค๊ณผ ๊ทธ๋“ค์˜ ๋ฌผ๋ฆฌ์  ํ™˜๊ฒฝ์˜ ์ƒ๋ฌผํ•™์  ์ฒด๊ณ„์ด๋‹ค. ์ƒํƒœ๊ณ„์˜ ๊ฐ ์ข…์— ๋Œ€ํ•ด์„œ, ํ† ์–‘, ๊ธฐํ›„, ์˜จ๋„ ๋“ฑ๊ณผ ๊ฐ™์€ ๋‹ค์–‘ํ•œ ์ฐจ์›์˜ ํ•˜๊ณ„ ๋ฐ ์ƒ๊ณ„๋ฅผ ๊ณ ๋ คํ•˜์—ฌ ์ข‹์€ ํ™˜๊ฒฝ์„ m๊ฐœ์˜ ์กฐ๊ฑด๋“ค๋กœ ๋‚˜ํƒ€๋‚ผ ์ˆ˜ ์žˆ๋Š”๋ฐ ์ด๋ฅผ ์ƒํƒœ์  ์ง€์œ„(ecological niche)๋ผ๊ณ  ํ•œ๋‹ค. ์ƒํƒœํ•™์  ๊ธฐ๋ณธ๊ฐ€์ •์€ ๋‘ ์ข…์ด ์ƒํƒœ์  ์ง€์œ„๊ฐ€ ๊ฒน์น˜๋ฉด ๊ฒฝ์Ÿํ•˜๊ณ (compete), ๊ฒฝ์Ÿํ•˜๋Š” ๋‘ ์ข…์€ ์ƒํƒœ์  ์ง€์œ„๊ฐ€ ๊ฒน์นœ๋‹ค๋Š” ๊ฒƒ์ด๋‹ค. ํ”ํžˆ ์ƒ๋ฌผํ•™์ž๋“ค์€ ํ•œ ์ฒด์ œ์—์„œ ์„œ์‹ํ•˜๋Š” ์ข…๋“ค์˜ ๊ฒฝ์Ÿ์  ๊ด€๊ณ„๋ฅผ ๊ฐ ์ข…์€ ๊ผญ์ง“์ ์œผ๋กœ, ํฌ์‹์ž์—์„œ ํ”ผ์‹์ž์—๊ฒŒ๋Š” ์œ ํ–ฅ๋ณ€(arc)์„ ๊ทธ์–ด์„œ ๋จน์ด์‚ฌ์Šฌ๋กœ ํ‘œํ˜„ํ•œ๋‹ค. ์ด๋Ÿฌํ•œ ๋งฅ๋ฝ์—์„œ Cohen [11]์€ ๋‹ค์Œ๊ณผ ๊ฐ™์ด ์œ ํ–ฅ๊ทธ๋ž˜ํ”„์˜ ๊ฒฝ์Ÿ ๊ทธ๋ž˜ํ”„๋ฅผ ์ •์˜ํ–ˆ๋‹ค. ์œ ํ–ฅ๊ทธ๋ž˜ํ”„(digraph) D์˜ ๊ฒฝ์Ÿ๊ทธ๋ž˜ํ”„(competition graph) C(D) ๋ž€ V (D)๋ฅผ ๊ผญ์ง“์  ์ง‘ํ•ฉ์œผ๋กœ ํ•˜๊ณ  ๋‘ ๊ผญ์ง“์  u, v๋ฅผ ์–‘ ๋์ ์œผ๋กœ ๊ฐ–๋Š” ๋ณ€์ด ์กด์žฌํ•œ๋‹ค๋Š” ๊ฒƒ๊ณผ ๊ผญ์ง“์  w๊ฐ€ ์กด์žฌํ•˜์—ฌ (u, w),(v, w)๊ฐ€ ๋ชจ๋‘ D์—์„œ ์œ ํ–ฅ๋ณ€์ด ๋˜๋Š” ๊ฒƒ์ด ๋™์น˜์ธ ๊ทธ๋ž˜ํ”„๋ฅผ ์˜๋ฏธํ•œ๋‹ค. Cohen์ด ๊ฒฝ์Ÿ๊ทธ๋ž˜ํ”„์˜ ์ •์˜๋ฅผ ๋„์ž…ํ•œ ์ดํ›„๋กœ ๊ทธ ๋ณ€์ด๋“ค๋กœ m-step ๊ฒฝ์Ÿ๊ทธ๋ž˜ํ”„(m-step competition graph), (i, j)-step ๊ฒฝ์Ÿ๊ทธ๋ž˜ํ”„((i, j)-step competition graph), ๊ณ„ํ†ต๊ทธ๋ž˜ํ”„(phylogeny graph), ๊ฒฝ์Ÿ๊ณต์ ๊ทธ๋ž˜ํ”„(competition-common enemy graph), p-๊ฒฝ์Ÿ๊ทธ๋ž˜ํ”„(p-competition graph), ๊ทธ๋ฆฌ๊ณ  ์ง€์œ„๊ทธ๋ž˜ํ”„(niche graph)๊ฐ€ ๋„์ž…๋˜์—ˆ๊ณ  ์—ฐ๊ตฌ๋˜๊ณ  ์žˆ๋‹ค. ์ด ๋…ผ๋ฌธ์˜ ์—ฐ๊ตฌ ๊ฒฐ๊ณผ๋“ค์˜ ์ผ๋ถ€๋Š” ๋‹ค์Œ๊ณผ ๊ฐ™๋‹ค. ์‚ผ๊ฐํ˜•์ด ์—†์ด ์—ฐ๊ฒฐ๋œ m-step ๊ฒฝ์Ÿ ๊ทธ๋ž˜ํ”„๋Š” ํŠธ๋ฆฌ(tree)์ž„์„ ๋ณด์˜€์œผ๋ฉฐ 2 โ‰ค m < n์„ ๋งŒ์กฑํ•˜๋Š” ์ •์ˆ˜ m, n์— ๋Œ€ํ•˜์—ฌ ๊ผญ์ง“์ ์˜ ๊ฐœ์ˆ˜๊ฐ€ n๊ฐœ์ด๊ณ  m-step ๊ฒฝ์Ÿ๊ทธ๋ž˜ํ”„๊ฐ€ ๋ณ„๊ทธ๋ž˜ํ”„(star graph)๊ฐ€ ๋˜๋Š” ์œ ํ–ฅ๊ทธ๋ž˜ํ”„๋ฅผ ์™„๋ฒฝํ•˜๊ฒŒ ํŠน์ง•ํ™” ํ•˜์˜€๋‹ค. k โ‰ฅ 3์ด๊ณ  ๋ฐฉํ–ฅ์ง€์–ด์ง„ ์™„์ „ k-๋ถ„ํ•  ๊ทธ๋ž˜ํ”„(oriented complete k-partite graph)์˜ (1, 2)-step ๊ฒฝ์Ÿ๊ทธ๋ž˜ํ”„ C_{1,2}(D)์—์„œ ๊ฐ ๋ถ„ํ• ์ด ์™„์ „ ๋ถ€๋ถ„ ๊ทธ๋ž˜ํ”„๋ฅผ ์ด๋ฃฐ ๋•Œ, C_{1,2}(D)์„ ๋ชจ๋‘ ํŠน์ง•ํ™” ํ•˜์˜€๋‹ค. ๋˜ํ•œ, C_{1,2}(D)์˜ ๊ฐ ์„ฑ๋ถ„(component)์˜ ์ง€๋ฆ„(diameter)์˜ ๊ธธ์ด๊ฐ€ ์ตœ๋Œ€ 3์ด๋ฉฐ C_{1,2}(D)์˜ ์ง€๋ฐฐ์ˆ˜(domination number)์— ๋Œ€ํ•œ ์ƒ๊ณ„์™€ ์ตœ๋Œ“๊ฐ’์„ ๊ตฌํ•˜๊ณ  ๊ตฌ๊ฐ„๊ทธ๋ž˜ํ”„(interval graph)๊ฐ€ ๋˜๊ธฐ ์œ„ํ•œ ์ถฉ๋ถ„ ์กฐ๊ฑด์„ ๊ตฌํ•˜์˜€๋‹ค. ์ฐจ์ˆ˜๊ฐ€ ์ œํ•œ๋œ ์œ ํ–ฅํšŒ๋กœ๋ฅผ ๊ฐ–์ง€ ์•Š๋Š” ์œ ํ–ฅ๊ทธ๋ž˜ํ”„(degree-bounded acyclic digraph)์˜ ๊ณ„ํ†ต๊ทธ๋ž˜ํ”„์™€ ๊ฒฝ์Ÿ๊ณต์ ๊ทธ๋ž˜ํ”„์— ๋Œ€ํ•ด์„œ๋„ ์—ฐ๊ตฌํ•˜์˜€๋‹ค. ์–‘์˜ ์ •์ˆ˜๋“ค i, j์— ๋Œ€ํ•˜์—ฌ (i, j) ์œ ํ–ฅ๊ทธ๋ž˜ํ”„๋ž€ ๊ฐ ๊ผญ์ง“์ ์˜ ๋‚ด์ฐจ์ˆ˜๋Š” ์ตœ๋Œ€ i, ์™ธ์ฐจ์ˆ˜๋Š” ์ตœ๋Œ€ j์ธ ์œ ํ–ฅํšŒ๋กœ ๊ฐ–์ง€ ์•Š๋Š” ์œ ํ–ฅ๊ทธ๋ž˜ํ”„์ด๋‹ค. ๋งŒ์•ฝ ์œ ํ–ฅ๊ทธ๋ž˜ํ”„ D์— ๊ฐ ๊ผญ์ง“์ ์ด ๋‚ด์ฐจ์ˆ˜๊ฐ€ ์ตœ๋Œ€ i, ์™ธ์ฐจ์ˆ˜๊ฐ€ ์ตœ๋Œ€ j ์ธ ๊ฒฝ์šฐ์— D๋ฅผ hi, ji ์œ ํ–ฅ๊ทธ๋ž˜ํ”„๋ผ ํ•œ๋‹ค. D๊ฐ€ (i, 2) ์œ ํ–ฅ๊ทธ๋ž˜ํ”„์ผ ๋•Œ, D์˜ ๊ณ„ํ†ต๊ทธ๋ž˜ํ”„๊ฐ€ ํ˜„๊ทธ๋ž˜ํ”„(chordal graph)๊ฐ€ ๋˜๊ธฐ ์œ„ํ•œ D์˜ ๋ฐฉํ–ฅ์„ ๊ณ ๋ คํ•˜์ง€ ์•Š๊ณ  ์–ป์–ด์ง€๋Š” ๊ทธ๋ž˜ํ”„(underlying graph)์—์„œ ๊ธธ์ด๊ฐ€ 4์ด์ƒ์ธ ํšŒ๋กœ(hole)์˜ ๊ธธ์ด์— ๋Œ€ํ•œ ์ถฉ๋ถ„์กฐ๊ฑด์„ ๊ตฌํ•˜์˜€๋‹ค. ๊ฒŒ๋‹ค๊ฐ€ (i, j) ์œ ํ–ฅ๊ทธ๋ž˜ํ”„์˜ ๊ณ„ํ†ต๊ทธ๋ž˜ํ”„์—์„œ ๋‚˜์˜ฌ ์ˆ˜ ์—†๋Š” ์ƒ์„ฑ ๋ถ€๋ถ„ ๊ทธ๋ž˜ํ”„(forbidden induced subgraph)๋ฅผ ํŠน์ง•ํ™” ํ•˜์˜€๋‹ค. (2, 2) ์œ ํ–ฅ๊ทธ๋ž˜ํ”„ D์˜ ๊ฒฝ์Ÿ๊ณต์ ๊ทธ๋ž˜ํ”„ CCE(D)๊ฐ€ 2๊ฐœ์˜ ๊ณ ๋ฆฝ์ (isolated vertex)๊ณผ ์ตœ๋Œ€ 1๊ฐœ์˜ ํšŒ๋กœ๋ฅผ ๊ฐ–์œผ๋ฉด์„œ ๊ฐ€์žฅ ์ ์€ ์„ฑ๋ถ„์„ ๊ฐ–๋Š” ๊ฒฝ์šฐ์ผ ๋•Œ์˜ ๊ตฌ์กฐ๋ฅผ ๊ทœ๋ช…ํ–ˆ๋‹ค. ๋งˆ์ง€๋ง‰์œผ๋กœ, CCE(D)๊ฐ€ ๊ตฌ๊ฐ„๊ทธ๋ž˜ํ”„๊ฐ€ ๋˜๊ธฐ ์œ„ํ•œ ์„ฑ๋ถ„์˜ ๊ฐœ์ˆ˜์— ๋Œ€ํ•œ ์ถฉ๋ถ„์กฐ๊ฑด์„ ๊ตฌํ•˜์˜€๋‹ค.1 Introduction 1 1.1 Graph theory terminology and basic concepts 1 1.2 Competition graphs and its variants 6 1.2.1 A brief background of competition graphs 6 1.2.2 Variants of competition graphs 8 1.2.3 m-step competition graphs 10 1.2.4 (1, 2)-step competition graphs 13 1.2.5 Phylogeny graphs 14 1.2.6 CCE graphs 16 1.3 A preview of the thesis 17 2 Digraphs whose m-step competition graphs are trees 19 2.1 The triangle-free m-step competition graphs 23 2.2 Digraphs whose m-step competition graphs are trees 29 2.3 The digraphs whose m-step competition graphs are star graphs 38 3 On (1, 2)-step competition graphs of multipartite tournaments 47 3.1 Preliminaries 48 3.2 C1,2(D) with a non-clique partite set of D 51 3.3 C1,2(D) without a non-clique partite set of D 66 3.4 C1,2(D) as a complete graph 74 3.5 Diameters and domination numbers of C1,2(D) 79 3.6 Disconnected (1, 2)-step competition graphs 82 3.7 Interval (1, 2)-step competition graphs 84 4 The forbidden induced subgraphs of (i, j) phylogeny graphs 90 4.1 A necessary condition for an (i, 2) phylogeny graph being chordal 91 4.2 Forbidden subgraphs for phylogeny graphs of degree bounded digraphs 99 5 On CCE graphs of (2, 2) digraphs 122 5.1 CCE graphs of h2, 2i digraphs 128 5.2 CCE graphs of (2, 2) digraphs 134 Abstract (in Korean) 168 Acknowledgement (in Korean) 170๋ฐ•

    Efficient computation of partition of unity interpolants through a block-based searching technique

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    In this paper we propose a new efficient interpolation tool, extremely suitable for large scattered data sets. The partition of unity method is used and performed by blending Radial Basis Functions (RBFs) as local approximants and using locally supported weight functions. In particular we present a new space-partitioning data structure based on a partition of the underlying generic domain in blocks. This approach allows us to examine only a reduced number of blocks in the search process of the nearest neighbour points, leading to an optimized searching routine. Complexity analysis and numerical experiments in two- and three-dimensional interpolation support our findings. Some applications to geometric modelling are also considered. Moreover, the associated software package written in \textsc{Matlab} is here discussed and made available to the scientific community

    Spatial patterns in mesic savannas: the local facilitation limit and the role of demographic stochasticity

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    We propose a model equation for the dynamics of tree density in mesic savannas. It considers long-range competition among trees and the effect of fire acting as a local facilitation mechanism. Despite short-range facilitation is taken to the local-range limit, the standard full spectrum of spatial structures obtained in general vegetation models is recovered. Long-range competition is thus the key ingredient for the development of patterns. The long time coexistence between trees and grass, and how fires affect the survival of trees as well as the maintenance of the patterns is studied. The influence of demographic noise is analyzed. The stochastic system, under the parameter constraints typical of mesic savannas, shows irregular patterns characteristics of realistic situations. The coexistence of trees and grass still remains at reasonable noise intensities.Comment: 12 pages, 7 figure

    Structure and Problem Hardness: Goal Asymmetry and DPLL Proofs in<br> SAT-Based Planning

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    In Verification and in (optimal) AI Planning, a successful method is to formulate the application as boolean satisfiability (SAT), and solve it with state-of-the-art DPLL-based procedures. There is a lack of understanding of why this works so well. Focussing on the Planning context, we identify a form of problem structure concerned with the symmetrical or asymmetrical nature of the cost of achieving the individual planning goals. We quantify this sort of structure with a simple numeric parameter called AsymRatio, ranging between 0 and 1. We run experiments in 10 benchmark domains from the International Planning Competitions since 2000; we show that AsymRatio is a good indicator of SAT solver performance in 8 of these domains. We then examine carefully crafted synthetic planning domains that allow control of the amount of structure, and that are clean enough for a rigorous analysis of the combinatorial search space. The domains are parameterized by size, and by the amount of structure. The CNFs we examine are unsatisfiable, encoding one planning step less than the length of the optimal plan. We prove upper and lower bounds on the size of the best possible DPLL refutations, under different settings of the amount of structure, as a function of size. We also identify the best possible sets of branching variables (backdoors). With minimum AsymRatio, we prove exponential lower bounds, and identify minimal backdoors of size linear in the number of variables. With maximum AsymRatio, we identify logarithmic DPLL refutations (and backdoors), showing a doubly exponential gap between the two structural extreme cases. The reasons for this behavior -- the proof arguments -- illuminate the prototypical patterns of structure causing the empirical behavior observed in the competition benchmarks

    Cross-Device Tracking: Matching Devices and Cookies

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    The number of computers, tablets and smartphones is increasing rapidly, which entails the ownership and use of multiple devices to perform online tasks. As people move across devices to complete these tasks, their identities becomes fragmented. Understanding the usage and transition between those devices is essential to develop efficient applications in a multi-device world. In this paper we present a solution to deal with the cross-device identification of users based on semi-supervised machine learning methods to identify which cookies belong to an individual using a device. The method proposed in this paper scored third in the ICDM 2015 Drawbridge Cross-Device Connections challenge proving its good performance

    Smart matching

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    One of the most annoying aspects in the formalization of mathematics is the need of transforming notions to match a given, existing result. This kind of transformations, often based on a conspicuous background knowledge in the given scientific domain (mostly expressed in the form of equalities or isomorphisms), are usually implicit in the mathematical discourse, and it would be highly desirable to obtain a similar behavior in interactive provers. The paper describes the superposition-based implementation of this feature inside the Matita interactive theorem prover, focusing in particular on the so called smart application tactic, supporting smart matching between a goal and a given result.Comment: To appear in The 9th International Conference on Mathematical Knowledge Management: MKM 201
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