A genetic approach for strategic resource allocation planning

Enterprises often implement a measurement system to monitor their march towards their strategic goals. Although this way it is possible to assess the progress of each goal, there is no structured way to reconsider resource allocation to those goals and to plan an optimal (or near optimal) allocation scheme. In this study we propose a genetic approach to match each goal with an autonomous entity (agent) with a specific resource sharing behavior. The overall performance is evaluated through a set of functions and genetic algorithms are used to eventuate in approximate optimal behavior’s schemes. To outline the strategic goals of the enterprise we used the balanced scorecard method. Letting agents deploy their sharing behavior over simulation time, we measure the scorecard’s performance and detect distinguished behaviors, namely recommendations for resource allocation.     

Deformation Techniques for Sparse Systems

We exhibit a probabilistic symbolic algorithm for solving zero-dimensional sparse systems. Our algorithm combines a symbolic homotopy procedure, based on a flat deformation of a certain morphism of affine varieties, with the polyhedral deformation of Huber and Sturmfels. The complexity of our algorithm is cubic in the size of the combinatorial structure of the input system. This size is mainly represented by the cardinality and mixed volume of Newton polytopes of the input polynomials and an arithmetic analogue of the mixed volume associated to the deformations under consideration. Research was partially supported by the following grants: UBACyT X112 (2004–2007), UBACyT X847 (2006–2009), PIP CONICET 2461, PIP CONICET 5852/05, ANPCyT PICT 2005 17-33018, UNGS 30/3005, MTM2004-01167 (2004–2007), MTM2007-62799 and CIC 2007–2008.     

The Analysis and the Representation of Balanced Complex Polytopes in 2D

In this paper, we deepen the theoretical study of the geometric structure of a balanced complex polytope (b.c.p.), which is the generalization of a real centrally symmetric polytope to the complex space. We also propose a constructive algorithm for the representation of its facets in terms of their associated linear functionals. The b.c.p.s are used, for example, as a tool for the computation of the joint spectral radius of families of matrices. For the representation of real polytopes, there exist well-known algorithms such as, for example, the Beneath–Beyond method. Our purpose is to modify and adapt this method to the complex case by exploiting the geometric features of the b.c.p. However, due to the significant increase in the difficulty of the problem when passing from the real to the complex case, in this paper, we confine ourselves to examine the two-dimensional case. We also propose an algorithm for the computation of the norm the unit ball of which is a b.c.p. This work was supported by INdAM-GNCS.     

A {(\frac 4 3) -} approximation algorithm for a special case of the two machine flow shop problem with several availability constraints

In this paper, we deal with a special case of the two-machine flow shop scheduling problem with several availability constraints on the second machine, under the resumable scenario. We develop an improved algorithm with a relative worst-case error bound of 4/3.     

A minimization version of a directed subgraph homeomorphism problem

We consider a special case of the directed subgraph homeomorphism or topological minor problem, where the host graph has a specific regular structure. Given an acyclic directed pattern graph, we are looking for a host graph of minimal height which still allows for an embedding. This problem has applications in compiler design for certain coarse-grain reconfigurable architectures. In this application domain, the task is to simultaneously schedule, bind and route a so-called data-flow graph, where vertices represent operations and arcs stand for data dependencies between the operations, given an orthogonal grid structure of reconfigurable processing elements (PEs) that have restricted communication abilities. We show that the problem of simultaneously scheduling, binding and routing is NP-complete by describing a logic engine reduction from NAE-3-SAT. This result holds even when the input graph is a directed tree with maximum indegree two. We also give a |V|^3/2-approximation algorithm. J. A. Brenner’s research supported by the DFG Research Center Matheon “Mathematics for key technologies”. J. C. van der Veen’s research supported by DFG Focus Program 1148, “Reconfigurable Architectures”, Grants FE 407/8-1 and FE 407/8-2.     

Adaptive image enhancement based on clustering of wavelet coefficients for infrared sea surveillance systems

Most of the techniques developed for infrared (IR) image enhancement (IE) depend heavily on the scene, environmental conditions, and the properties of the imaging system. So, with a set of predefined scenario properties, a content-based IR-IE technique can be developed for better situational awareness. This study proposes an adaptive IR-IE technique based on clustering of wavelet coefficients of an image for sea surveillance systems. Discrete wavelet transform (DWT) of an image is computed and feature vectors are constructed from subband images. Clustering operation is applied to group similar feature vectors that belong to different scene components such as target or background. Depending on the feature vectors, a weight is assigned to each cluster and these weights are used to compute gain matrices which are used to multiply wavelet coefficients for the enhancement of the original image. Enhancement results are presented and a comparison of the performance of the proposed algorithm is given through subjective tests with other well known frequency and histogram based enhancement techniques. The proposed algorithm outperforms previous ones in the truthfulness, detail visibility of the target, artificiality, and total quality criteria, while providing an acceptable computational load.     

Pairwise clustering using a Monte Carlo Markov Chain

In this work an application of MCMC is proposed for unsupervised data classification, in conjunction with a novel pairwise objective function, which is shown to work well in situations where clusters to be identified have a strong overlap, and the centroid oriented methods (such as K-means) fail by construction. In particular, an exceptionally simple but difficult situation is addressed when cluster centroids coincide, and one can differentiate between the clusters only on the basis of their variance. Performance of the proposed approach is tested on synthetic and real datasets.     

A Response to “On method overfitting”

If special-purpose methods can be overfitted to benchmarks, so can general methods.     

Computer modeling of the dynamic properties of the cAMP-dependent protein kinase catalytic subunit

The structural dynamics of the cAMP-dependent protein kinase catalytic subunit were modeled using molecular dynamics computational methods. It was shown that the structure of this protein as well as its complexes with ATP and peptide ligand PKI(5-24) consisted of a large number of rapidly inter-converting conformations which could be grouped into subsets proceeding from their similarity. This cluster analysis revealed that conformations which correspond to the “opened” and “closed” structures of the protein were already present in the free enzyme, and most surprisingly co-existed in enzyme–ATP and enzyme–PKI(5-24) complexes as well as in the ternary complex, which included both of these ligands. The results also demonstrated that the most mobile structure segments of the protein were located in the regions of substrate binding sites and that their dynamics were most significantly affected by the binding of the ATP and peptide ligand.