Adaptive diversity in heterogeneous environments for populations regulated by a mixture of soft and hard selection

The stable co-existence of two haploid genotypes or two species is studied in a spatially heterogeneous environment submitted to a mixture of soft selection (within-patch regulation) and hard selection (outside-patch regulation) and where two kinds of resource are available. This is analysed both at an ecological time-scale (short term) and at an evolutionary time-scale (long term). At an ecological scale, we show that co-existence is very unlikely if the two competitors are symmetrical specialists exploiting different resources. In this case, the most favourable conditions are met when the two resources are equally available, a situation that should favour generalists at an evolutionary scale. Alternatively, low within-patch density dependence (soft selection) enhances the co-existence between two slightly different specialists of the most available resource. This results from the opposing forces that are acting in hard and soft regulation modes. In the case of unbalanced accessibility to the two resources, hard selection favours the most specialized genotype, whereas soft selection strongly favours the less specialized one. Our results suggest that competition for different resources may be difficult to demonstrate in the wild even when it is a key factor in the maintenance of adaptive diversity. At an evolutionary scale, a monomorphic invasive evolutionarily stable strategy (ESS) always exists. When a linear trade-off exists between survival in one habitat versus that in another, this ESS lies between an absolute adjustment of survival to niche size (for mainly soft-regulated populations) and absolute survival (specialization) in a single niche (for mainly hard-regulated populations). This suggests that environments in agreement with the assumptions of such models should lead to an absence of adaptive variation in the long term.

Long-range ferromagnetic dipolar ordering of high-spin molecular clusters

We report the first example of a transition to long-range magnetic order in a purely dipolarly interacting molecular magnet. For the magnetic cluster compound Mn6O4Br4(Et2dbm)6, the anisotropy experienced by the total spin S=12 of each cluster is so small that spin-lattice relaxation remains fast down to the lowest temperatures, thus enabling dipolar order to occur within experimental times at Tc=0.16 K. In high magnetic fields, the relaxation rate becomes drastically reduced and the interplay between nuclear- and electron-spin lattice relaxation is revealed.

Metal complexation by electroanalytical techniques: hard- and soft-modelling approaches

En la investigació de la complexació de metalls mitjançant eines electroanalítiques són emprades dues aproximacions generals. La primera, anomenada de modelatge dur (hardmodelling), es basa en la formulació d'un model fisicoquímic conjunt per als processos electròdic i de complexació i en la resolució analítica o numèrica del model. Posteriorment, l'ajust dels paràmetres del model a les dades experimentals donarà la informació desitjada sobre el procés de complexació. La segona aproximació, anomenada de modelatge tou (soft-modelling), es basa en la identificació d'un model de complexació a partir de l'anàlisi numèrica i estadística de les dades, sense cap assumpció prèvia d'un model. Aquesta aproximació, que ha estat extensivament emprada amb dades espectroscòpiques, ho ha estat poquíssim amb dades electroquímiques. En aquest article tractem de la formulació d'un model (hard-modelling) per a la complexació de metalls en sistemes amb mescles de lligands, incloent-hi lligands macromoleculars, i de l'aplicació d

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Monitoring and information reporting through regulation : an inter-jurisdictional comparison of forestry-related hard laws

[Abstract]

Local optima networks of hard combinatorial landscapes

Combinatorial optimization involves finding an optimal solution in a finite set of options; many everyday life problems are of this kind. However, the number of options grows exponentially with the size of the problem, such that an exhaustive search for the best solution is practically infeasible beyond a certain problem size. When efficient algorithms are not available, a practical approach to obtain an approximate solution to the problem at hand, is to start with an educated guess and gradually refine it until we have a good-enough solution. Roughly speaking, this is how local search heuristics work. These stochastic algorithms navigate the problem search space by iteratively turning the current solution into new candidate solutions, guiding the search towards better solutions. The search performance, therefore, depends on structural aspects of the search space, which in turn depend on the move operator being used to modify solutions. A common way to characterize the search space of a problem is through the study of its fitness landscape, a mathematical object comprising the space of all possible solutions, their value with respect to the optimization objective, and a relationship of neighborhood defined by the move operator. The landscape metaphor is used to explain the search dynamics as a sort of potential function. The concept is indeed similar to that of potential energy surfaces in physical chemistry. Borrowing ideas from that field, we propose to extend to combinatorial landscapes the notion of the inherent network formed by energy minima in energy landscapes. In our case, energy minima are the local optima of the combinatorial problem, and we explore several definitions for the network edges. At first, we perform an exhaustive sampling of local optima basins of attraction, and define weighted transitions between basins by accounting for all the possible ways of crossing the basins frontier via one random move. Then, we reduce the computational burden by only counting the chances of escaping a given basin via random kick moves that start at the local optimum. Finally, we approximate network edges from the search trajectory of simple search heuristics, mining the frequency and inter-arrival time with which the heuristic visits local optima. Through these methodologies, we build a weighted directed graph that provides a synthetic view of the whole landscape, and that we can characterize using the tools of complex networks science. We argue that the network characterization can advance our understanding of the structural and dynamical properties of hard combinatorial landscapes. We apply our approach to prototypical problems such as the Quadratic Assignment Problem, the NK model of rugged landscapes, and the Permutation Flow-shop Scheduling Problem. We show that some network metrics can differentiate problem classes, correlate with problem non-linearity, and predict problem hardness as measured from the performances of trajectory-based local search heuristics.

Generating highly balanced sudoku problems as hard problems

Sudoku problems are some of the most known and enjoyed pastimes, with a never diminishing popularity, but, for the last few years those problems have gone from an entertainment to an interesting research area, a twofold interesting area, in fact. On the one side Sudoku problems, being a variant of Gerechte Designs and Latin Squares, are being actively used for experimental design, as in [8, 44, 39, 9]. On the other hand, Sudoku problems, as simple as they seem, are really hard structured combinatorial search problems, and thanks to their characteristics and behavior, they can be used as benchmark problems for refining and testing solving algorithms and approaches. Also, thanks to their high inner structure, their study can contribute more than studies of random problems to our goal of solving real-world problems and applications and understanding problem characteristics that make them hard to solve. In this work we use two techniques for solving and modeling Sudoku problems, namely, Constraint Satisfaction Problem (CSP) and Satisfiability Problem (SAT) approaches. To this effect we define the Generalized Sudoku Problem (GSP), where regions can be of rectangular shape, problems can be of any order, and solution existence is not guaranteed. With respect to the worst-case complexity, we prove that GSP with block regions of m rows and n columns with m = n is NP-complete. For studying the empirical hardness of GSP, we define a series of instance generators, that differ in the balancing level they guarantee between the constraints of the problem, by finely controlling how the holes are distributed in the cells of the GSP. Experimentally, we show that the more balanced are the constraints, the higher the complexity of solving the GSP instances, and that GSP is harder than the Quasigroup Completion Problem (QCP), a problem generalized by GSP. Finally, we provide a study of the correlation between backbone variables – variables with the same value in all the solutions of an instance– and hardness of GSP.

Generating hard SAT/CSP instances using expander graphs

In this paper we provide a new method to generate hard k-SAT instances. We incrementally construct a high girth bipartite incidence graph of the k-SAT instance. Having high girth assures high expansion for the graph, and high expansion implies high resolution width. We have extended this approach to generate hard n-ary CSP instances and we have also adapted this idea to increase the expansion of the system of linear equations used to generate XORSAT instances, being able to produce harder satisfiable instances than former generators.

How hard is a commercial puzzle: the eternity II challenge

Recently, edge matching puzzles, an NP-complete problem, have received, thanks to money-prized contests, considerable attention from wide audiences. We consider these competitions not only a challenge for SAT/CSP solving techniques but also as an opportunity to showcase the advances in the SAT/CSP community to a general audience. This paper studies the NP-complete problem of edge matching puzzles focusing on providing generation models of problem instances of variable hardness and on its resolution through the application of SAT and CSP techniques. From the generation side, we also identify the phase transition phenomena for each model. As solving methods, we employ both; SAT solvers through the translation to a SAT formula, and two ad-hoc CSP solvers we have developed, with different levels of consistency, employing several generic and specialized heuristics. Finally, we conducted an extensive experimental investigation to identify the hardest generation models and the best performing solving techniques.

Edge matching puzzles as hard SAT/CSP benchmarks

Recently, edge matching puzzles, an NP-complete problem, have rececived, thanks to money-prized contests, considerable attention from wide audiences. We consider these competitions not only a challenge for SAT/CSP solving techniques but also as an opportunity to showcase the advances in the SAT/CSP community to a general audience. This paper studies the NP-complete problem of edge matching puzzles focusing on providing generation models of problem instances of variable hardness and on its resolution through the application of SAT and CSP techniques. From the generation side, we also identify the phase transition phenomena for each model. As solving methods, we employ both; SAT solvers through the translation to a SAT formula, and two ad-hoc CSP solvers we have developed, with different levels of consistency, employing several generic and specialized heuristics. Finally, we conducted an extensive experimental investigation to identify the hardest generation models and the best performing solving techniques.

Interplay between surface anisotropy and dipolar interactions in an assembly of nanomagnets

We study the interplay between the effects of surface anisotropy and dipolar interactions in monodisperse assemblies of nanomagnets with oriented anisotropy. We derive asymptotic formulas for the assembly magnetization, taking into account temperature, applied field, core and surface anisotropy, and dipolar interparticle interactions. We find that the interplay between surface anisotropy and dipolar interactions is well described by the analytical expression of the assembly magnetization derived here: the overall sign of the product of the two parameters governing the surface and the dipolar contributions determines whether intrinsic and collective terms compete or have synergistic effects on the magnetization. This is illustrated by the magnetization curves of γ-Fe2O3 nanoparticle assemblies in the low concentration limit.

Improving Pitch Tracking Performance in Hard Noise Conditions by a Preprocessing Based on Mathematical Morphology

In this paper we show how a nonlinear preprocessing of speech signal -with high noise- based on morphological filters improves the performance of robust algorithms for pitch tracking (RAPT). This result happens for a very simple morphological filter. More sophisticated ones could even improve such results. Mathematical morphology is widely used in image processing and has a great amount of applications. Almost all its formulations derived in the two-dimensional framework are easily reformulated to be adapted to one-dimensional context