The invasive hydromedusae Blackfordia virginica Mayer, 1910 (Cnidaria: Blackfordiidae) in southern Brazil, with comments on taxonomy and distribution of the genus Blackfordia

The invasive brackish-water hydrozoan Blackfordia virginica is reported from estuaries and harbours in southeastern and southern Brazil. Medusae of the species were collected for the first time in Cananeia, Guaratuba Bay, and Babitonga Bay. They were also found in Paranagua Bay where they were previously known to occur. Based on material examined here, a comparative redescription is given of B. virginica, and its distribution worldwide is reviewed. The three nominal species of Blackfordia are assessed.

Flavonoids and the taxonomy of Camarea (Malpighiaceae)

Camarea is a South-American endemic genus comprising eight species. In the present work leaf flavonoids of seven species of Camarea were identified, aiming to evaluate the usefulness of their distribution as a taxonomic aid. A total of 12 flavonoids were isolated and identified. Free aglycones, such as apigenin, chrysoeriol, kaempferol and quercetin, as well as 7-O-glycosides of apigenin and luteolin, 3-O-glycosides of kaempferol and quercetin were identified. Flavonoid distribution in Camarea species, taking into account aglycones and aglycone moieties of glycosides, was used to obtain a phenogram of chemical affinities. Apigenin, chrysoeriol and kaempferol were the main discriminating characters for links establishment. The resultant tree suggests the links: 1) Camarea hirsuta, Camarea affinis and C. affinis x C. hirsuta; 2) Camarea elongata and Camarea axillaris; 3) Camarea sericea and Camarea humifusa. The results are in agreement with morphological similarities and disagree with several points of n-alkane evidence. The results support the recognition of Camarea triphylla as synonymy of C axillaris. (C) 2009 Elsevier Ltd. All rights reserved.

Stem anatomy of Rhipsalis (Cactaceae) and its relevance for taxonomy

Several species of the genus Rhipsalis (Cactaceae) are extremely important as ornamentals and are endangered in their natural habitat. However, only a few studies have addressed its taxonomy, morphology (including anatomy), phylogeny and evolutionary history. Consequently, the limited knowledge of the genus coupled with the problematic delimitation of species had led to problems in the identification of taxa. In the current work six species of Rhipsalis, R. cereoides, R. elliptica, R. grandiflora, R. paradoxa, R. pentaptera and R. teres were studied to evaluate the relevance of anatomical characters for the taxonomy of the genus. An anatomical characterization of the primary structure of the stem of Rhipsalis is provided highlighting the differences between species. Features of the stem epidermis are found to discriminate best between species and therefore provide clear and useful characters for the separation of species.

Taxonomy of the Threadsnakes of the tribe Epictini (Squamata: Serpentes: Leptotyphlopidae) in Colombia

Threadsnakes of the tribe Epictini are endemic to the New World, occurring from the United States to Argentina, mostly in the Neotropical region. Currently, the taxonomic status of most species is unclear and there has been no previous attempt of a comprehensive taxonomic revision of Neotropical taxa. Taxonomy of the group is a difficult task due to the paucity of geographic samples, general homogeneous morphology and brevity of species descriptions. Therefore, the only way to address the taxonomic status of existing names is through detailed characterization of the types and the search for additional material of the poorly known species. In this study, we evaluated the taxonomic status of the Colombian threadsnakes and report on geographical variation of meristic, morphometric, colour pattern, and hemipenis characters. On the basis of available samples we recognize the following species in Colombia: Epictia goudotii, E. magnamaculata, E. signata, Rena nicefori, Tricheilostoma brevissimum, T. dugandi, T. joshuai and T. macrolepis. We discuss the systematic position of Rena nicefori and propose its allocation in the genus Tricheilostoma based on a unique combination of morphological characters. Furthermore, we provide a key to the representatives of the tribe Epictini in Colombia.

Contributions to the taxonomy of the Normanellidae (Copepoda, Harpacticoida): description of a new genus from the Brazilian continental shelf and re-assignment of Pseudocletodes vararensis Scott & Scott, 1893 (ex Nannopodidae)

A new genus and species of Normanellidae (Copepoda, Harpacticoida), Paranaiara inajae gen. et sp. nov., is described from the continental shelf off the northern coast of Sao Paulo State, Brazil. The new genus differs from the type genus Normanella Brady, 1880 and Sagamiella Lee & Huys, 1999 in its presence of lamelliform caudal rami, a maxillulary endopod represented by 2 setae, an unarmed maxillipedal syncoxa, and reduced setation on P2 enp-2 (without outer spine) and P3 enp-2 (with only 2 inner setae). All these apomorphic character states are shared with the genus Pseudocletodes Scott & Scott, 1893, formerly placed in the family Nannopodidae (ex Huntemanniidae) and here assigned to the Normanellidae. Pseudocletodes can be differentiated from Paranaiara by the loss of the P1 endopod and of the inner seta on P2-P4 enp-1, the presence of only 2 inner setae on P2 enp-2 (instead of 3) and only 1 inner seta on P4 exp-3 (instead of 2), the presence of a second inner seta on P4 enp-2 (instead of 1), the morphology of the fifth pair of legs which are not medially fused and have only 3 endopodal elements (instead of 4) in the male, and the well developed caudal ramus seta V (instead of rudimentary). It is postulated that prehensility of the P1 endopod was secondarily lost in the common ancestor of Paranaiara and Pseudocletodes. An updated family diagnosis of the Normanellidae and a dichotomous identification key to the 22 currently valid species are presented.

Taxonomy and cladistic analysis of the subgenus Xenomorellia Malloch (Diptera: Muscidae: Morellia Robineau-Desvoidy) with description of two new species

Xenomorellia Malloch, a subgenus of Morellia Robineau-Desvoidy, is revised to include two new species, Morellia (Xenomorellia) inca Nihei and Carvalho sp. nov. from South America, and M. (X.) maia Carvalho and Nihei sp. nov. from Costa Rica and Mexico. Diagnoses for M. (X.) holti (Malloch) and M. (X.) montanhesa (Albuquerque) are provided, as well as an identification key to the four species of the subgenus. A cladistic analysis was performed to test the monophyly of Xenomorellia and to recover the phylogenetic relationships among its species. Tree searches resulted in one single most-parsimonious cladogram, wherein the monophyly of Xenomorellia is supported, as well as a sister-group relationship with the Neotropical subgenus Trichomorellia Stein. Xenomorellia was divided into two clades: one with Caribbean-Andean species (maia + inca), and another with species from southeastern South America (holti + montanhesa).

Towards improving cluster-based feature selection with a simplified silhouette filter

This paper proposes a filter-based algorithm for feature selection. The filter is based on the partitioning of the set of features into clusters. The number of clusters, and consequently the cardinality of the subset of selected features, is automatically estimated from data. The computational complexity of the proposed algorithm is also investigated. A variant of this filter that considers feature-class correlations is also proposed for classification problems. Empirical results involving ten datasets illustrate the performance of the developed algorithm, which in general has obtained competitive results in terms of classification accuracy when compared to state of the art algorithms that find clusters of features. We show that, if computational efficiency is an important issue, then the proposed filter May be preferred over their counterparts, thus becoming eligible to join a pool of feature selection algorithms to be used in practice. As an additional contribution of this work, a theoretical framework is used to formally analyze some properties of feature selection methods that rely on finding clusters of features. (C) 2011 Elsevier Inc. All rights reserved.

An investigation into feature construction to assist word sense disambiguation

Identifying the correct sense of a word in context is crucial for many tasks in natural language processing (machine translation is an example). State-of-the art methods for Word Sense Disambiguation (WSD) build models using hand-crafted features that usually capturing shallow linguistic information. Complex background knowledge, such as semantic relationships, are typically either not used, or used in specialised manner, due to the limitations of the feature-based modelling techniques used. On the other hand, empirical results from the use of Inductive Logic Programming (ILP) systems have repeatedly shown that they can use diverse sources of background knowledge when constructing models. In this paper, we investigate whether this ability of ILP systems could be used to improve the predictive accuracy of models for WSD. Specifically, we examine the use of a general-purpose ILP system as a method to construct a set of features using semantic, syntactic and lexical information. This feature-set is then used by a common modelling technique in the field (a support vector machine) to construct a classifier for predicting the sense of a word. In our investigation we examine one-shot and incremental approaches to feature-set construction applied to monolingual and bilingual WSD tasks. The monolingual tasks use 32 verbs and 85 verbs and nouns (in English) from the SENSEVAL-3 and SemEval-2007 benchmarks; while the bilingual WSD task consists of 7 highly ambiguous verbs in translating from English to Portuguese. The results are encouraging: the ILP-assisted models show substantial improvements over those that simply use shallow features. In addition, incremental feature-set construction appears to identify smaller and better sets of features. Taken together, the results suggest that the use of ILP with diverse sources of background knowledge provide a way for making substantial progress in the field of WSD.

Interactive Vector Field Feature Identification

We introduce a flexible technique for interactive exploration of vector field data through classification derived from user-specified feature templates. Our method is founded on the observation that, while similar features within the vector field may be spatially disparate, they share similar neighborhood characteristics. Users generate feature-based visualizations by interactively highlighting well-accepted and domain specific representative feature points. Feature exploration begins with the computation of attributes that describe the neighborhood of each sample within the input vector field. Compilation of these attributes forms a representation of the vector field samples in the attribute space. We project the attribute points onto the canonical 2D plane to enable interactive exploration of the vector field using a painting interface. The projection encodes the similarities between vector field points within the distances computed between their associated attribute points. The proposed method is performed at interactive rates for enhanced user experience and is completely flexible as showcased by the simultaneous identification of diverse feature types.

A Parallel Hardware Architecture for Scale and Rotation Invariant Feature Detection

This paper proposes a parallel hardware architecture for image feature detection based on the Scale Invariant Feature Transform algorithm and applied to the Simultaneous Localization And Mapping problem. The work also proposes specific hardware optimizations considered fundamental to embed such a robotic control system on-a-chip. The proposed architecture is completely stand-alone; it reads the input data directly from a CMOS image sensor and provides the results via a field-programmable gate array coupled to an embedded processor. The results may either be used directly in an on-chip application or accessed through an Ethernet connection. The system is able to detect features up to 30 frames per second (320 x 240 pixels) and has accuracy similar to a PC-based implementation. The achieved system performance is at least one order of magnitude better than a PC-based solution, a result achieved by investigating the impact of several hardware-orientated optimizations oil performance, area and accuracy.

U-curve: A branch-and-bound optimization algorithm for U-shaped cost functions on Boolean lattices applied to the feature selection problem

This paper presents the formulation of a combinatorial optimization problem with the following characteristics: (i) the search space is the power set of a finite set structured as a Boolean lattice; (ii) the cost function forms a U-shaped curve when applied to any lattice chain. This formulation applies for feature selection in the context of pattern recognition. The known approaches for this problem are branch-and-bound algorithms and heuristics that explore partially the search space. Branch-and-bound algorithms are equivalent to the full search, while heuristics are not. This paper presents a branch-and-bound algorithm that differs from the others known by exploring the lattice structure and the U-shaped chain curves of the search space. The main contribution of this paper is the architecture of this algorithm that is based on the representation and exploration of the search space by new lattice properties proven here. Several experiments, with well known public data, indicate the superiority of the proposed method to the sequential floating forward selection (SFFS), which is a popular heuristic that gives good results in very short computational time. In all experiments, the proposed method got better or equal results in similar or even smaller computational time. (C) 2009 Elsevier Ltd. All rights reserved.

Nonstationary feature extraction techniques for automatic classification of impact acoustic signals

Condition monitoring of wooden railway sleepers applications are generallycarried out by visual inspection and if necessary some impact acoustic examination iscarried out intuitively by skilled personnel. In this work, a pattern recognition solutionhas been proposed to automate the process for the achievement of robust results. Thestudy presents a comparison of several pattern recognition techniques together withvarious nonstationary feature extraction techniques for classification of impactacoustic emissions. Pattern classifiers such as multilayer perceptron, learning cectorquantization and gaussian mixture models, are combined with nonstationary featureextraction techniques such as Short Time Fourier Transform, Continuous WaveletTransform, Discrete Wavelet Transform and Wigner-Ville Distribution. Due to thepresence of several different feature extraction and classification technqies, datafusion has been investigated. Data fusion in the current case has mainly beeninvestigated on two levels, feature level and classifier level respectively. Fusion at thefeature level demonstrated best results with an overall accuracy of 82% whencompared to the human operator.

Face Detection and Facial Feature Localization for multi-pose faces and complex backgroundimages

The objective of this thesis work, is to propose an algorithm to detect the faces in a digital image with complex background. A lot of work has already been done in the area of face detection, but drawback of some face detection algorithms is the lack of ability to detect faces with closed eyes and open mouth. Thus facial features form an important basis for detection. The current thesis work focuses on detection of faces based on facial objects. The procedure is composed of three different phases: segmentation phase, filtering phase and localization phase. In segmentation phase, the algorithm utilizes color segmentation to isolate human skin color based on its chrominance properties. In filtering phase, Minkowski addition based object removal (Morphological operations) has been used to remove the non-skin regions. In the last phase, Image Processing and Computer Vision methods have been used to find the existence of facial components in the skin regions.This method is effective on detecting a face region with closed eyes, open mouth and a half profile face. The experiment’s results demonstrated that the detection accuracy is around 85.4% and the detection speed is faster when compared to neural network method and other techniques.

Identification of cause of impairment in spiral drawings, using non-stationary feature extraction approach

Parkinson’s disease is a clinical syndrome manifesting with slowness and instability. As it is a progressive disease with varying symptoms, repeated assessments are necessary to determine the outcome of treatment changes in the patient. In the recent past, a computer-based method was developed to rate impairment in spiral drawings. The downside of this method is that it cannot separate the bradykinetic and dyskinetic spiral drawings. This work intends to construct the computer method which can overcome this weakness by using the Hilbert-Huang Transform (HHT) of tangential velocity. The work is done under supervised learning, so a target class is used which is acquired from a neurologist using a web interface. After reducing the dimension of HHT features by using PCA, classification is performed. C4.5 classifier is used to perform the classification. Results of the classification are close to random guessing which shows that the computer method is unsuccessful in assessing the cause of drawing impairment in spirals when evaluated against human ratings. One promising reason is that there is no difference between the two classes of spiral drawings. Displaying patients self ratings along with the spirals in the web application is another possible reason for this, as the neurologist may have relied too much on this in his own ratings.