Fast Discovery of Reliable k-terminal Subgraphs

Peer reviewe

Mass Renormalization in the Su-Schrieffer-Heeger Model

This study of the one dimensional Su-Schrieffer-Heeger model in a weak coupling perturbative regime points out the effective mass behavior as a function of the adiabatic parameter $\omega_{\pi}/J$, $\omega_{\pi}$ is the zone boundary phonon energy and $J$ is the electron band hopping integral. Computation of low order diagrams shows that two phonons scattering processes become appreciable in the intermediate regime in which zone boundary phonons energetically compete with band electrons. Consistently, in the intermediate (and also moderately antiadiabatic) range the relevant mass renormalization signals the onset of a polaronic crossover whereas the electrons are essentially undressed in the fully adiabatic and antiadiabatic systems. The effective mass is roughly twice as much the bare band value in the intermediate regime while an abrupt increase (mainly related to the peculiar 1D dispersion relations) is obtained at $\omega_{\pi}\sim \sqrt{2}J$.Comment: To be published in Phys.Rev.B - 3 figure

Lattice dynamics effects on small polaron properties

This study details the conditions under which strong-coupling perturbation theory can be applied to the molecular crystal model, a fundamental theoretical tool for analysis of the polaron properties. I show that lattice dimensionality and intermolecular forces play a key role in imposing constraints on the applicability of the perturbative approach. The polaron effective mass has been computed in different regimes ranging from the fully antiadiabatic to the fully adiabatic. The polaron masses become essentially dimension independent for sufficiently strong intermolecular coupling strengths and converge to much lower values than those tradition-ally obtained in small-polaron theory. I find evidence for a self-trapping transition in a moderately adiabatic regime at an electron-phonon coupling value of .3. Our results point to a substantial independence of the self-trapping event on dimensionality.Comment: 8 pages, 5 figure

Incremental Declarative Process Mining

Business organizations achieve their mission by performing a number of processes. These span from simple sequences of actions to complex structured sets of activities with complex interrelation among them. The field of Business Processes Management studies how to describe, analyze, preserve and improve processes. In particular the subfield of Process Mining aims at inferring a model of the processes from logs (i.e. the collected records of performed activities). Moreover, processes can change over time to reflect mutated conditions, therefore it is often necessary to update the model. We call this activity Incremental Process Mining. To solve this problem, we modify the process mining system DPML to obtain IPM (Incremental Process Miner), which employs a subset of the SCIFF language to represent models and adopts techniques developed in Inductive Logic Programming to perform theory revision. The experimental results show that is more convenient to revise a theory rather than learning a new one from scratch

Robust kk-DNF Learning via Inductive Belief Merging

International audienceA central issue in logical concept induction is the prospect of inconsistency. This problem may arise due to noise in the training data, or because the target concept does not fit the underlying concept class. In this paper, we introduce the paradigm of inductive belief merging which handles this issue within a uniform framework. The key idea is to base learning on a belief merging operator that selects the concepts which are as close as possible to the set of training examples. From a computational perspective, we apply this paradigm to robust k-DNF learning. To this end, we develop a greedy algorithm which approximates the optimal concepts to within a logarithmic factor. The time complexity of the algorithm is polynomial in the size of k. Moreover, the method bidirectional and returns one maximally specific concept and one maximally general concept. We present experimental results showing the effectiveness of our algorithm on both nominal and numerical datasets

Approximate Relational Reasoning by Stochastic Propositionalization

For many real-world applications it is important to choose the right representation language. While the setting of First Order Logic (FOL) is the most suitable one to model the multi-relational data of real and complex domains, on the other hand it puts the question of the computational complexity of the knowledge induction process. A way of tackling the complexity of such real domains, in which a lot of relationships are required to model the objects involved, is to use a method that reformulates a multi-relational learning task into an attribute-value one. In this chapter we present an approximate reasoning method able to keep low the complexity of a relational problem by using a stochastic inference procedure. The complexity of the relational language is decreased by means of a propositionalization technique, while the NP-completeness of the deduction is tackled using an approximate query evaluation. The proposed approximate reasoning technique has been used to solve the problem of relational rule induction as well as the task of relational clustering. An anytime algorithm has been used for the induction, implemented by a population based method, able to efficiently extract knowledge from relational data, while the clustering task, both unsupervised and supervised, has been solved using a Partition Around Medoid (PAM) clustering algorithm. The validity of the proposed techniques has been proved making an empirical evaluation on real-world datasets