The spectral properties of the Falicov-Kimball model in the weak-coupling limit

The $f$ and $d$ electron density of states of the one-dimensional Falicov-Kimball model are studied in the weak-coupling limit by exact diagonalization calculations. The resultant behaviors are used to examine the $d$-electron gap ($\Delta_{d}$), the $f$-electron gap ($\Delta_{f}$), and the $fd$-electron gap ($\Delta_{fd}$) as functions of the $f$-level energy $E_f$ and hybridization $V$. It is shown that the spinless Falicov-Kimball model behaves fully differently for zero and finite hybridization between $f$ and $d$ states. At zero hybridization the energy gaps do not coincide ($\Delta_{d}\neq \Delta_{f} \neq \Delta_{fd}$), and the activation gap $\Delta_{fd}$ vanishes discontinuously at some critical value of the $f$-level energy $E_{fc}$. On the other hand, at finite hybridization all energy gaps coincide and vanish continuously at the insulator-metal transition point $E_f=E_{fc}$. The importance of these results for a description of real materials is discussed.Comment: 10 pages, 7 figures, LaTe

Generic Subsequence Matching Framework: Modularity, Flexibility, Efficiency

Subsequence matching has appeared to be an ideal approach for solving many problems related to the fields of data mining and similarity retrieval. It has been shown that almost any data class (audio, image, biometrics, signals) is or can be represented by some kind of time series or string of symbols, which can be seen as an input for various subsequence matching approaches. The variety of data types, specific tasks and their partial or full solutions is so wide that the choice, implementation and parametrization of a suitable solution for a given task might be complicated and time-consuming; a possibly fruitful combination of fragments from different research areas may not be obvious nor easy to realize. The leading authors of this field also mention the implementation bias that makes difficult a proper comparison of competing approaches. Therefore we present a new generic Subsequence Matching Framework (SMF) that tries to overcome the aforementioned problems by a uniform frame that simplifies and speeds up the design, development and evaluation of subsequence matching related systems. We identify several relatively separate subtasks solved differently over the literature and SMF enables to combine them in straightforward manner achieving new quality and efficiency. This framework can be used in many application domains and its components can be reused effectively. Its strictly modular architecture and openness enables also involvement of efficient solutions from different fields, for instance efficient metric-based indexes. This is an extended version of a paper published on DEXA 2012.Comment: This is an extended version of a paper published on DEXA 201

Accelerating Metric Filtering by Improving Bounds on Estimated Distances

Filtering is a fundamental strategy of metric similarity indexes to minimise the number of computed distances. Given a triple of objects for which distances of two pairs are known, the lower and upper bounds on the third distance can be set as the difference and the sum of these two already known distances, due to the triangle inequality rule of the metric space. For efficiency reasons, the tightness of bounds is crucial, but as angles within triangles of distances can be arbitrary, the worst case with zero and straight angles must also be considered for correctness. However, in data of real-life applications, the distribution of possible angles is skewed and extremes are very unlikely to occur. In this paper, we enhance the existing definition of bounds on the unknown distance with information about possible angles within triangles. We show that two lower bounds and one upper bound on each distance exist in case of limited angles. We analyse their filtering power and confirm high improvements of efficiency by experiments on several real-life datasets

Secure Metric-Based Index for Similarity Cloud

We propose a similarity index that ensures data privacy and thus is suitable for search systems outsourced in a cloud. The proposed solution can exploit existing efficient metric indexes based on a fixed set of reference points. The method has been fully implemented as a security extension of an existing established approach called M-Index. This Encrypted M-Index supports evaluation of standard range and nearest neighbors queries both in precise and approximate manner. In the first part of this work, we analyze various levels of privacy in existing or future similarity search systems; the proposed solution tries to keep a reasonable privacy level while relocating only the necessary amount of work from server to an authorized client. The Encrypted M-Index has been tested on three real data sets with focus on various cost components