Outlier detection using default reasoning

Default logics are usually used to describe the regular behavior and normal properties of domain elements. In this paper we suggest, conversely, that the framework of default logics can be exploited for detecting outliers. Outliers are observations expressed by sets of literals that feature unexpected semantical characteristics. These sets of literals are selected among those explicitly embodied in the given knowledge base. Hence, essentially we perceive outlier detection as a knowledge discovery technique. This paper defines the notion of outlier in two related formalisms for specifying defaults: Reiter's default logic and extended disjunctive logic programs. For each of the two formalisms, we show that finding outliers is quite complex. Indeed, we prove that several versions of the outlier detection problem lie over the second level of the polynomial hierarchy. We believe that a thorough complexity analysis, as done here, is a useful preliminary step towards developing effective heuristics and exploring tractable subsets of outlier detection problems.     

A Neuro-Fuzzy Network for the Design of Circular and Triangular Equilateral Microstrip Antennas

A fast method for the design of circular and equilateral triangular microstrip antennas, based on a Neuro-Fuzzy Network, is presented. Numerical results obtained by using this technique agree quite well with the Moment Method results. The proposed technique can be fruitfully used in microwave and millimeter wave CAD applications.     

Outlier detection for simple default theories

It was noted recently that the framework of default logics can be exploited for detecting outliers. Outliers are observations expressed by sets of literals that feature unexpected properties. These observations are not explicitly provided in input (as it happens with abduction) but, rather, they are hidden in the given knowledge base. Unfortunately, in the two related formalisms for specifying defaults — Reiter's default logic and extended disjunctive logic programs — the most general outlier detection problems turn out to lie at the third level of the polynomial hierarchy. In this note, we analyze the complexity of outlier detection for two very simple classes of default theories, namely NU and DNU, for which the entailment problem is solvable in polynomial time. We show that, for these classes, checking for the existence of an outlier is anyway intractable. This result contributes to further showing the inherent intractability of outlier detection in default reasoning.     

Condensed nearest neighbor data domain description

A simple yet effective unsupervised classification rule to discriminate between normal and abnormal data is based on accepting test objects whose nearest neighbors distances in a reference data set, assumed to model normal behavior, lie within a certain threshold. This work investigates the effect of using a subset of the original data set as the reference set of the classifier. With this aim, the concept of a reference consistent subset is introduced and it is shown that finding the minimum cardinality reference consistent subset is intractable. Then, the CNNDD algorithm is described, which computes a reference consistent subset with only two reference set passes. Experimental results revealed the advantages of condensing the data set and confirmed the effectiveness of the proposed approach. A thorough comparison with related methods was accomplished, pointing out the strengths and weaknesses of one-class nearest-neighbor-based training set consistent condensation.     

Use of radar and optical remotely sensed data for soil moisture retrieval over vegetated areas

This work assesses the possibility of obtaining soil moisture maps of vegetated fields using information derived from radar and optical images. The sensor and field data were acquired during the SMEX'02 experiment. The retrieval was obtained by using a Bayesian approach, where the key point is the evaluation of probability density functions (pdfs) based on the knowledge of soil parameter measurements and of the corresponding remotely sensing data. The purpose is to determine a useful parameterization of vegetation backscattering effects through suitable pdfs to be later used in the inversion algorithm. The correlation coefficients between measured and extracted soil moisture values are R=0.68 for C-band and R=0.60 for L-band. The pdf parameters have been found to be correlated to the vegetation water content estimated from a Landsat image with correlation coefficients of R=0.65 and 0.91 for C- and L-bands, respectively. In consideration of these correlations, a second run of the Bayesian procedure has been performed where the pdf parameters are variable with vegetation water content. This second procedure allows the improvement of inversion results for the L-band. The results derived from the Bayesian approach have also been compared with a classical inversion method that is based on a linear relationship between soil moisture and the backscattering coefficients for horizontal and vertical polarizations.     

Detecting and repairing anomalous evolutions in noisy environments

In systems where agents are required to interact with a partially known and dynamic world, sensors can be used to obtain knowledge about the environment. However, sensors may be unreliable, that is, they may deliver wrong information (due, e.g., to hardware or software malfunctioning) and, consequently, they may cause agents to take wrong decisions, which is a scenario that should be avoided. The paper considers the problem of reasoning in noisy environments in a setting where no (either certain or probabilistic) data is available in advance about the reliability of sensors. Therefore, assuming that each agent is equipped with a background theory encoding its general knowledge about the world, a concept of detecting an anomaly perceived in sensor data and the related concept of agent recovering to a coherent status of information are defined. In this context, the complexities of various anomaly detection and anomaly recovery problems are studied. Finally, rewriting algorithms are proposed that transform recovery problems into equivalent inference problems under answer set semantics, thereby making them effectively realizable on top of available answer set solvers.     

Distance-based outlier queries in data streams: the novel task and algorithms

This work proposes a method for detecting distance-based outliers in data streams under the sliding window model. The novel notion of one-time outlier query is introduced in order to detect anomalies in the current window at arbitrary points-in-time. Three algorithms are presented. The first algorithm exactly answers to outlier queries, but has larger space requirements than the other two. The second algorithm is derived from the exact one, reduces memory requirements and returns an approximate answer based on estimations with a statistical guarantee. The third algorithm is a specialization of the approximate algorithm working with strictly fixed memory requirements. Accuracy properties and memory consumption of the algorithms have been theoretically assessed. Moreover experimental results have confirmed the effectiveness of the proposed approach and the good quality of the solutions.     

Synuclein Analysis in Adult Xenopus laevis

The α-, β- and γ-synucleins are small soluble proteins expressed in the nervous system of mammals and evolutionary conserved in vertebrates. After being discovered in the cartilaginous fish Torpedo californica, synucleins have been sequenced in all vertebrates, showing differences in the number of genes and splicing isoforms in different taxa. Although α-, β- and γ-synucleins share high homology in the N-terminal sequence, suggesting their evolution from a common ancestor, the three isoforms also differ in molecular characteristics, expression levels and tissue distribution. Moreover, their functions have yet to be fully understood. Great scientific interest on synucleins mainly derives from the involvement of α-synuclein in human neurodegenerative diseases, collectively named synucleinopathies, which involve the accumulation of amyloidogenic α-synuclein inclusions in neurons and glia cells. Studies on synucleinopathies can take advantage of the development of new vertebrate models other than mammals. Moreover, synuclein expression in non-mammalian vertebrates contribute to clarify the physiological role of these proteins in the evolutionary perspective. In this paper, gene expression levels of α-, β- and γ-synucleins have been analysed in the main organs of adult Xenopus laevis by qRT-PCR. Moreover, recombinant α-, β- and γ-synucleins were produced to test the specificity of commercial antibodies against α-synuclein used in Western blot and immunohistochemistry. Finally, the secondary structure of Xenopus synucleins was evaluated by circular dichroism analysis. Results indicate Xenopus as a good model for studying synucleinopathies, and provide a useful background for future studies on synuclein functions and their evolution in vertebrates.     

Analysis and design of passive and active microstrip reflectarrays

The design of reflectarrays is a complex and time‐consuming process that very often relies on a trial‐and‐error approach. In this article, a simplified analysis based on a commercial simulator is proposed. This method, valid for both passive and active antennas, uses a simulator to characterize the single radiating cell and evaluates the contribution of the finite ground plane using physical optics approximation. The proposed design technique has been validated by comparing the simulated response of the single element with the experimentally retrieved phase of the active and passive cell. As a proof of the design concept's feasibility, two small reflectarrays, active and passive, have been prototyped and tested. The measured radiation patterns are presented and discussed. © 2003 Wiley Periodicals, Inc. Int J RF and Microwave CAE 13: 370–377, 2003.     

Synthesis of microstrip reflectarrays as planar scatterers for SAR interferometry

The use of printed reflectarrays as planar scatterers for SAR interferometry applications is proposed. A phase-only synthesis is applied to obtain a scattered field in a prescribed direction. A reflectarray working at ERS1/2 frequency (5.3 GHz) is simulated to validate the method. Measurements are performed on a 10 GHz size-reduced prototype.