Second language influence on first language motion event encoding and categorization in Spanish-speaking children learning L2 English

Studies show cross-linguistic differences in motion event encoding, such that English speakers preferentially encode manner of motion more than Spanish speakers, who preferentially encode path of motion. Focusing on native Spanish speaking children (aged 5;00-9;00) learning L2 English, we studied path and manner verb preferences during descriptions of motion stimuli, and tested the linguistic relativity hypothesis by investigating categorization preferences in a non-verbal similarity judgement task of motion clip triads. Results revealed L2 influence on L1 motion event encoding, such that bilinguals used more manner verbs and fewer path verbs in their L1, under the influence of English. We found no effects of linguistic structure on non-verbal similarity judgements, and demonstrate for the first time effects of L2 on L1 lexicalization in child L2 learners in the domain of motion events. This pattern of verbal behaviour supports theories of bilingual semantic representation that postulate a merged lexico-semantic system in early bilinguals.

DUCTAL CARCINOMA IN-SITU OF THE BREAST - HISTOLOGIC CATEGORIZATION AND ITS RELATIONSHIP TO PLOIDY AND IMMUNOHISTOCHEMICAL EXPRESSION OF HORMONE RECEPTORS, P53, AND C-ERBB-2 PROTEIN

Background. Ductal carcinoma in situ (DCIS) of the breast has been diagnosed increasingly since the advent of mammography. However, the natural history of these lesions remains uncertain. Ductal carcinoma in situ of the breast does not represent a single entity but a heterogeneous group with histologic and clinical differences. The histologic subtype of DCIS seems to have an influence on its biologic behavior, but there are few studies correlating subtype with biologic markers.Methods. The authors studied a consecutive series of 40 cases of DCIS and after its histologic categorization verified its relationship with ploidy using image analysis and analyzing estrogen receptor (ER), progesterone receptor (PR), p53 and c-erbB-2 expression using immunohistochemistry.Results. The three groups proposed according to the grade of malignancy were correlated significantly with some of the additional parameters studied, including aneuploidy and c-erB-2 expression. Aneuploidy was detected in 77.5% of cases of DCIS mainly in high and intermediate grade subtypes (100% and 80% vs. 35.7% in low grade) whereas immunoreactivity for c-erbB-2 was detected in 45% of cases of DCIS mainly in the high grade group. Expression of ER and PR were observed frequently in this study (63.9% and 65.7% respectively), but without correlation with the histologic subtype of DCIS, although we found a somewhat significant association between high grade DCIS and lack of ER. p53 protein expression was detected in 36.8% of these cases, but no relationship between this expression and histologic subtype or grading of DCIS was found.Conclusions. These results provide further evidence for the morphologic and biologic heterogeneity of DCIS. Besides histologic classification and nuclear grading, some biologic markers such as aneuploidy and c-erbB-2 expression constitute additional criteria of high grade of malignancy.

Image categorization through optimum path forest and visual words

Different from the first attempts to solve the image categorization problem (often based on global features), recently, several researchers have been tackling this research branch through a new vantage point - using features around locally invariant interest points and visual dictionaries. Although several advances have been done in the visual dictionaries literature in the past few years, a problem we still need to cope with is calculation of the number of representative words in the dictionary. Therefore, in this paper we introduce a new solution for automatically finding the number of visual words in an N-Way image categorization problem by means of supervised pattern classification based on optimum-path forest. © 2011 IEEE.

Semantic Categorization of Unfamiliar Tunes in Older and Younger Adults

The process of learning the categories of new tunes in older and younger adults was examined for this study. Tunes were presented either one or three times along with a category name to see if multiple repetitions aid in category memory. Additionally, toexamine if an association may help some listeners, especially older ones, to better remember category information, some tunes were presented with a short associative fact; this fact was either neutral or emotional. Participants were tested on song recognition,fact recognition, and category memory. For all tasks, there was a benefit of three presentations. There were no age differences in fact recognition. For both song recognition and categorization, the memory burden of a neutral association was lessened when the association was emotional.

External auditory canal cholesteatoma: reassessment of and amendments to its categorization, pathogenesis, and treatment in 34 patients

OBJECTIVE: External auditory canal cholesteatoma (EACC) is a rarity. Although there have been numerous case reports, there are only few systematic analyses of case series, and the pathogenesis of idiopathic EACC remains enigmatic. STUDY DESIGN: In a tertiary referral center for a population of 1.5 million inhabitants, 34 patients with 35 EACC (13 idiopathic [1 bilateral] and 22 secondary) who were treated between 1994 and 2006 were included in the study. RESULTS: EACC cardinal symptoms were longstanding otorrhea (65%) and dull otalgia (12%). Focal bone destruction in the external auditory canal with retained squamous debris and an intact tympanic membrane were characteristic. Only 27% of the patients showed conductive hearing loss exceeding 20 dB. Patients with idiopathic EACC had lesions typically located on the floor of the external auditory canal and were older, and the mean smoking intensity was also greater (p < 0.05) compared with patients with secondary EACC. The secondary lesions were assigned to categories (poststenotic [n = 6], postoperative [n = 6], and posttraumatic EACC [n = 4]) and rare categories (radiogenic [n = 2], postinflammatory [n = 1], and postobstructive EACC [n = 1]). In addition, we describe 2 patients with EACC secondary to the complete remission of a Langerhans cell histiocytosis of the external auditory canal. Thirty of 34 patients were treated surgically and became all free of recurrence, even after extensive disease. DISCUSSION: For the development of idiopathic EACC, repeated microtrauma (e.g., microtrauma resulting from cotton-tipped applicator abuse or from hearing aids) and diminished microcirculation (e.g., from smoking) might be risk factors. A location other than in the inferior portion of the external auditory canal indicates a secondary form of the disease, as in the case of 2 patients with atypically located EACC after years of complete remission of Langerhans cell histiocytosis, which we consider as a new posttumorous category and specific late complication of this rare disease.

Cerebellar Pathology Does Not Impair Performance on Identification or Categorization Tasks

In comparison to the basal ganglia, prefrontal cortex, and medial temporal lobes, the cerebellum has been absent from recent research on the neural substrates of categorization and identification, two prominent tasks in the learning and memory literature. To investigate the contribution of the cerebellum to these tasks, we tested patients with cerebellar pathology (seven with bilateral degeneration, six with unilateral lesions, and two with midline damage) on rule-based and information-integration categorization tasks and an identification task. In rule-based tasks, it is assumed that participants learn the categories through an explicit reasoning process. In information-integration tasks, optimal performance requires the integration of information from multiple stimulus dimensions, and participants are typically unaware of the decision strategy. The identification task, in contrast, required participants to learn arbitrary, color-word associations. The cerebellar patients performed similar to matched controls on all three tasks and performance did not vary with the extent of cerebellar pathology. Although the interpretation of these null results requires caution, these data contribute to the current debate on cerebellar contributions to cognition by providing boundary conditions on understanding the neural substrates of categorization and identification, and help define the functional domain of the cerebellum in learning and memory.

Automatic Categorization for Improving Spanish into Spanish Sign Language Machine Translation

This paper describes a preprocessing module for improving the performance of a Spanish into Spanish Sign Language (Lengua de Signos Espanola: LSE) translation system when dealing with sparse training data. This preprocessing module replaces Spanish words with associated tags. The list with Spanish words (vocabulary) and associated tags used by this module is computed automatically considering those signs that show the highest probability of being the translation of every Spanish word. This automatic tag extraction has been compared to a manual strategy achieving almost the same improvement. In this analysis, several alternatives for dealing with non-relevant words have been studied. Non-relevant words are Spanish words not assigned to any sign. The preprocessing module has been incorporated into two well-known statistical translation architectures: a phrase-based system and a Statistical Finite State Transducer (SFST). This system has been developed for a specific application domain: the renewal of Identity Documents and Driver's License. In order to evaluate the system a parallel corpus made up of 4080 Spanish sentences and their LSE translation has been used. The evaluation results revealed a significant performance improvement when including this preprocessing module. In the phrase-based system, the proposed module has given rise to an increase in BLEU (Bilingual Evaluation Understudy) from 73.8% to 81.0% and an increase in the human evaluation score from 0.64 to 0.83. In the case of SFST, BLEU increased from 70.6% to 78.4% and the human evaluation score from 0.65 to 0.82.

Hybrid Approach Combining Machine Learning and a Rule-Based Expert System for Text Categorization

This paper discusses a novel hybrid approach for text categorization that combines a machine learning algorithm, which provides a base model trained with a labeled corpus, with a rule-based expert system, which is used to improve the results provided by the previous classifier, by filtering false positives and dealing with false negatives. The main advantage is that the system can be easily fine-tuned by adding specific rules for those noisy or conflicting categories that have not been successfully trained. We also describe an implementation based on k-Nearest Neighbor and a simple rule language to express lists of positive, negative and relevant (multiword) terms appearing in the input text. The system is evaluated in several scenarios, including the popular Reuters-21578 news corpus for comparison to other approaches, and categorization using IPTC metadata, EUROVOC thesaurus and others. Results show that this approach achieves a precision that is comparable to top ranked methods, with the added value that it does not require a demanding human expert workload to train

Source Language Categorization for improving a Speech into Sign Language Translation System

This paper describes a categorization module for improving the performance of a Spanish into Spanish Sign Language (LSE) translation system. This categorization module replaces Spanish words with associated tags. When implementing this module, several alternatives for dealing with non-relevant words have been studied. Non-relevant words are Spanish words not relevant in the translation process. The categorization module has been incorporated into a phrase-based system and a Statistical Finite State Transducer (SFST). The evaluation results reveal that the BLEU has increased from 69.11% to 78.79% for the phrase-based system and from 69.84% to 75.59% for the SFST.

Text categorization methods for automatic estimation of verbal intelligence

In this paper we investigate whether conventional text categorization methods may suffice to infer different verbal intelligence levels. This research goal relies on the hypothesis that the vocabulary that speakers make use of reflects their verbal intelligence levels. Automatic verbal intelligence estimation of users in a spoken language dialog system may be useful when defining an optimal dialog strategy by improving its adaptation capabilities. The work is based on a corpus containing descriptions (i.e. monologs) of a short film by test persons yielding different educational backgrounds and the verbal intelligence scores of the speakers. First, a one-way analysis of variance was performed to compare the monologs with the film transcription and to demonstrate that there are differences in the vocabulary used by the test persons yielding different verbal intelligence levels. Then, for the classification task, the monologs were represented as feature vectors using the classical TF–IDF weighting scheme. The Naive Bayes, k-nearest neighbors and Rocchio classifiers were tested. In this paper we describe and compare these classification approaches, define the optimal classification parameters and discuss the classification results obtained.