Street-Ball: The Myth of the Ghetto Basketball Star

In recent decades, countless scholars have examined the developing trend of African American dominance in United States’ professional sports. Many have hypothesized that this over-representation is caused by the presumed reliance on sports as an avenue out of poverty for the African American youths. This trend, it is believed, has a highly detrimental effect the African American community. In actuality, this argument is flawed because it works under the stereotypical assumption that the overwhelming majority of African Americans come from abject poverty. To dispel this fallacy, the author has analyzed the upbringings of each All-National Basketball League First Team player over the past thirty years. The author discovered that while the majority of the players selected were African American, only a small percentage of these athletes were raised in a poverty-stricken environment. Logically, this would not be the case if poor African Americans relied more heavily on sports than middle-class African Americans. This data proves that stereotypes, not empirical evidence, are the substance of nearly every previous study conducted on the topic.

Evaluation of Mycobacterium avium complex lung disease in women

Mycobacterium avium complex (MAC) is a ubiquitous organism responsible for most pulmonary and disseminated disease caused by non-tuberculosis (NTM) mycobacteria. Though MAC lung disease without predisposing factors is uncommon, in recent years it has been increasingly described in middle-aged and elderly women. Recognition and correct diagnosis, is often delayed due to the indolent nature of the disease. It is unclear if these women have significant clinical disease as or if their airways are simply colonized by the bacterium. This study describes the clinical presentation, identifies risk factors, and describes the clinical significance of MAC lung disease in HIV-negative women aged 50 or greater. ^ A hybrid study design utilizing both cross-sectional and case-control methodologies was used. A comparison population was selected from previously identified tuberculosis suspects found throughout Harris County. The study population had at least one acid fast bacillus pulmonary culture performed between 1/1/1998 and 12/31/2000 from a pulmonary source. Clinical presentation and symptoms were analyzed using a cross-sectional design. Past medical history and other risk factors were evaluated using a traditional case-control study design. Differences in categorical variables were estimated with the Chi Square or Fisher's Exact test as appropriate. Odds ratios and 95% confidence intervals were utilized to evaluate associations. Multivariate logistic regression was used to identify predictive factors for MAC. All statistical tests were two-sided and P-values <0.05 were considered statistically significant. ^ Culture confirmed MAC pulmonary cases were more likely to be white, have bronchiectasis, scoliosis, evidence of cavitation and pleural changes on chest radiography and granulomas on histopathologic examination than women whose pulmonary cultures were AFB negative. After controlling for selected risk factors, white race continued to be significantly associated with MAC lung disease (OR = 4.6, 95% CI = 2.3, 9.2). In addition, asthma history, smoking history and alcohol use were less likely to be evident among MAC cases in a multivariate analysis. Right upper and right middle lobe disease was further noted among clinically significant cases. Based on population data, MAC lung disease appears to represent a significant clinical syndrome in HIV-negative women thus supporting the theory of the Lady Windermere Syndrome. ^

The contributions of the hippocampal formation, perirhinal cortex and areas TH/TF to object, spatial and contextual recognition memory in primates

Adult monkeys (Macaca mulatta) with lesions of the hippocampal formation, perirhinal cortex, areas TH/TF, as well as controls were tested on tasks of object, spatial and contextual recognition memory. ^ Using a visual paired-comparison (VPC) task, all experimental groups showed a lack of object recognition relative to controls, although this impairment emerged at 10 sec with perirhinal lesions, 30 sec with areas TH/TF lesions and 60 sec with hippocampal lesions. In contrast, only perirhinal lesions impaired performance on delayed nonmatching-to-sample (DNMS), another task of object recognition memory. All groups were tested on DNMS with distraction (dDNMS) to examine whether the use of active cognitive strategies during the delay period could enable good performance on DNMS in spite of impaired recognition memory (revealed by the VPC task). Distractors affected performance of animals with perirhinal lesions at the 10-sec delay (the only delay in which their DNMS performance was above chance). They did not affect performance of animals with areas TH/TF lesions. Hippocampectomized animals were impaired at the 600-sec delay (the only delay at which prevention of active strategies would likely affect their behavior). ^ While lesions of areas TH/TF impaired spatial location memory and object-in-place memory, hippocampal lesions impaired only object-in-place memory. The pattern of results for perirhinal cortex lesions on the different task conditions indicated that this cortical area is not critical for spatial memory. ^ Finally, all three lesions impaired contextual recognition memory processes. The pattern of impairment appeared to result from the formation of only a global representation of the object and background, and suggests that all three areas are recruited for associating information across sources. ^ These results support the view that (1) the perirhinal cortex maintains storage of information about object and the context in which it is learned for a brief period of time, (2) areas TH/TF maintain information about spatial location and form associations between objects and their spatial relationship (a process that likely requires additional time) and (3) the hippocampal formation mediates associations between objects, their spatial relationship and the general context in which these associations are formed (an integrative function that requires additional time). ^

Bacteremia and mortality within 30 days of catheter-associated bacteriuria

Background: The distinction between catheter-associated asymptomatic bacteriuria (CAABU) and catheter-associated urinary tract infection (CAUTI) has only recently been widely appreciated. Our aims were to describe the relationship between CAUTI/CAABU and subsequent bacteremia and to investigate whether CAUTI/CAABU and antimicrobial use was associated with either bacteremia or mortality within 30 days. ^ Methods: Our study design was retrospective cohort. Patients with a urinary catheter and a positive urine culture between October 2010 and June 2011 at a large tertiary care facility were included. A multivariable model for analysis was constructed which controlled for age, race, Charlson co-morbidity score, catheter type and duration, category of organism,antimicrobials and classification of the catheter-associated bacteriuria as CAUTI or CAABU. ^ Results: Data from 444 catheter associated urine culture episodes in 308 unique patients were included in the analysis. Overall mortality was 21.1% (61 of 308 patients) within 30 days. Among the 444 urine culture episodes, 402 (90.5%) of these episodes were associated with antibiotic use. 52 (11.7%) of episodes were associated with bacteremia, but only 3 episodes of bacteremia (0.7% of 444 CAB episodes) were caused by an organism from the urinary tract. One of these episodes was CAABU and the other 2 were CAUTI. Bacteremia within 30 days was associated with having CAUTI rather than CAABU and having an indwelling urinary catheter rather than a condom catheter. The variables which were found to be significant for mortality within 30 days were a higher Charlson co-morbidity score and the presence of Candida in the urine culture. Use of antimicrobial agents to treat the bacteriuria was not associated with an increase or decrease in either bacteremia or mortality. ^ Conclusions: Our findings call into question the practice of giving antimicrobial agents to treat bacteriuria in an inpatient population with nearly universal antimicrobial use. A better practice may be targeted treatment of bacteriuria in patients with risk factors predictive of bacteremia and mortality.^

Multiscale object-based classification of satellite images merging multispectral information with panchromatic textural features

Once admitted the advantages of object-based classification compared to pixel-based classification; the need of simple and affordable methods to define and characterize objects to be classified, appears. This paper presents a new methodology for the identification and characterization of objects at different scales, through the integration of spectral information provided by the multispectral image, and textural information from the corresponding panchromatic image. In this way, it has defined a set of objects that yields a simplified representation of the information contained in the two source images. These objects can be characterized by different attributes that allow discriminating between different spectral&textural patterns. This methodology facilitates information processing, from a conceptual and computational point of view. Thus the vectors of attributes defined can be used directly as training pattern input for certain classifiers, as for example artificial neural networks. Growing Cell Structures have been used to classify the merged information.

Cross-lingual ontology matching as a challenge for the Multilingual Semantic Web

Recently, the Semantic Web has experienced significant advancements in standards and techniques, as well as in the amount of semantic information available online. Nevertheless, mechanisms are still needed to automatically reconcile information when it is expressed in different natural languages on the Web of Data, in order to improve the access to semantic information across language barriers. In this context several challenges arise [1], such as: (i) ontology translation/localization, (ii) cross-lingual ontology mappings, (iii) representation of multilingual lexical information, and (iv) cross-lingual access and querying of linked data. In the following we will focus on the second challenge, which is the necessity of establishing, representing and storing cross-lingual links among semantic information on the Web. In fact, in a “truly” multilingual Semantic Web, semantic data with lexical representations in one natural language would be mapped to equivalent or related information in other languages, thus making navigation across multilingual information possible for software agents.

Optimizing Functional Network Representation of Multivariate Time Series

By combining complex network theory and data mining techniques, we provide objective criteria for optimization of the functional network representation of generic multivariate time series. In particular, we propose a method for the principled selection of the threshold value for functional network reconstruction from raw data, and for proper identification of the network's indicators that unveil the most discriminative information on the system for classification purposes. We illustrate our method by analysing networks of functional brain activity of healthy subjects, and patients suffering from Mild Cognitive Impairment, an intermediate stage between the expected cognitive decline of normal aging and the more pronounced decline of dementia. We discuss extensions of the scope of the proposed methodology to network engineering purposes, and to other data mining tasks.

Ontology-based access to sensor data streams

Sensor networks are increasingly becoming one of the main sources of Big Data on the Web. However, the observations that they produce are made available with heterogeneous schemas, vocabularies and data formats, making it difficult to share and reuse these data for other purposes than those for which they were originally set up. In this thesis we address these challenges, considering how we can transform streaming raw data to rich ontology-based information that is accessible through continuous queries for streaming data. Our main contribution is an ontology-based approach for providing data access and query capabilities to streaming data sources, allowing users to express their needs at a conceptual level, independent of implementation and language-specific details. We introduce novel query rewriting and data translation techniques that rely on mapping definitions relating streaming data models to ontological concepts. Specific contributions include: • The syntax and semantics of the SPARQLStream query language for ontologybased data access, and a query rewriting approach for transforming SPARQLStream queries into streaming algebra expressions. • The design of an ontology-based streaming data access engine that can internally reuse an existing data stream engine, complex event processor or sensor middleware, using R2RML mappings for defining relationships between streaming data models and ontology concepts. Concerning the sensor metadata of such streaming data sources, we have investigated how we can use raw measurements to characterize streaming data, producing enriched data descriptions in terms of ontological models. Our specific contributions are: • A representation of sensor data time series that captures gradient information that is useful to characterize types of sensor data. • A method for classifying sensor data time series and determining the type of data, using data mining techniques, and a method for extracting semantic sensor metadata features from the time series.

Using modular neural networks to model self-consciousness and self-representation for artificial entities

Self-consciousness implies not only self or group recognition, but also real knowledge of one’s own identity. Self-consciousness is only possible if an individual is intelligent enough to formulate an abstract self-representation. Moreover, it necessarily entails the capability of referencing and using this elf-representation in connection with other cognitive features, such as inference, and the anticipation of the consequences of both one’s own and other individuals’ acts. In this paper, a cognitive architecture for self-consciousness is proposed. This cognitive architecture includes several modules: abstraction, self-representation, other individuals'representation, decision and action modules. It includes a learning process of self-representation by direct (self-experience based) and observational learning (based on the observation of other individuals). For model implementation a new approach is taken using Modular Artificial Neural Networks (MANN). For model testing, a virtual environment has been implemented. This virtual environment can be described as a holonic system or holarchy, meaning that it is composed of autonomous entities that behave both as a whole and as part of a greater whole. The system is composed of a certain number of holons interacting. These holons are equipped with cognitive features, such as sensory perception, and a simplified model of personality and self-representation. We explain holons’ cognitive architecture that enables dynamic self-representation. We analyse the effect of holon interaction, focusing on the evolution of the holon’s abstract self-representation. Finally, the results are explained and analysed and conclusions drawn.

Mars: a personalised mobile activity recognition system

Mobile activity recognition focuses on inferring the current activities of a mobile user by leveraging the sensory data that is available on today’s smart phones. The state of the art in mobile activity recognition uses traditional classification learning techniques. Thus, the learning process typically involves: i) collection of labelled sensory data that is transferred and collated in a centralised repository; ii) model building where the classification model is trained and tested using the collected data; iii) a model deployment stage where the learnt model is deployed on-board a mobile device for identifying activities based on new sensory data. In this paper, we demonstrate the Mobile Activity Recognition System (MARS) where for the first time the model is built and continuously updated on-board the mobile device itself using data stream mining. The advantages of the on-board approach are that it allows model personalisation and increased privacy as the data is not sent to any external site. Furthermore, when the user or its activity profile changes MARS enables promptly adaptation. MARS has been implemented on the Android platform to demonstrate that it can achieve accurate mobile activity recognition. Moreover, we can show in practise that MARS quickly adapts to user profile changes while at the same time being scalable and efficient in terms of consumption of the device resources.

Ontology Localization

In the context of the Semantic Web, resources on the net can be enriched by well-defined, machine-understandable metadata describing their associated conceptual meaning. These metadata consisting of natural language descriptions of concepts are the focus of the activity we describe in this chapter, namely, ontology localization. In the framework of the NeOn Methodology, ontology localization is defined as the activity of adapting an ontology to a particular language and culture. This adaptation mainly involves the translation of the natural language descriptions of the ontology from a source natural language to a target natural language, with the final objective of obtaining a multilingual ontology, that is, an ontology documented in several natural languages. The purpose of this chapter is to provide detailed and prescriptive methodological guidelines to support the performance of this activity.

Merging person-specific bio-markers for predicting oral cancer recurrence through an ontology

One of the major problems related to cancer treatment is its recurrence. Without knowing in advance how likely the cancer will relapse, clinical practice usually recommends adjuvant treatments that have strong side effects. A way to optimize treatments is to predict the recurrence probability by analyzing a set of bio-markers. The NeoMark European project has identified a set of preliminary bio-markers for the case of oral cancer by collecting a large series of data from genomic, imaging, and clinical evidence. This heterogeneous set of data needs a proper representation in order to be stored, computed, and communicated efficiently. Ontologies are often considered the proper mean to integrate biomedical data, for their high level of formality and for the need of interoperable, universally accepted models. This paper presents the NeoMark system and how an ontology has been designed to integrate all its heterogeneous data. The system has been validated in a pilot in which data will populate the ontology and will be made public for further research.

Classical vs. biometric features in the 2013 speaker recognition evaluation in mobile environments

MFCC coefficients extracted from the power spectral density of speech as a whole, seems to have become the de facto standard in the area of speaker recognition, as demonstrated by its use in almost all systems submitted to the 2013 Speaker Recognition Evaluation (SRE) in Mobile Environment [1], thus relegating to background this component of the recognition systems. However, in this article we will show that selecting the adequate speaker characterization system is as important as the selection of the classifier. To accomplish this we will compare the recognition rates achieved by different recognition systems that relies on the same classifier (GMM-UBM) but connected with different feature extraction systems (based on both classical and biometric parameters). As a result we will show that a gender dependent biometric parameterization with a simple recognition system based on GMM- UBM paradigm provides very competitive or even better recognition rates when compared to more complex classification systems based on classical features

Analysis of pattern recognition and dimensionality reduction techniques for odor biometrics

In this paper, we analyze the performance of several well-known pattern recognition and dimensionality reduction techniques when applied to mass-spectrometry data for odor biometric identification. Motivated by the successful results of previous works capturing the odor from other parts of the body, this work attempts to evaluate the feasibility of identifying people by the odor emanated from the hands. By formulating this task according to a machine learning scheme, the problem is identified with a small-sample-size supervised classification problem in which the input data is formed by mass spectrograms from the hand odor of 13 subjects captured in different sessions. The high dimensionality of the data makes it necessary to apply feature selection and extraction techniques together with a simple classifier in order to improve the generalization capabilities of the model. Our experimental results achieve recognition rates over 85% which reveals that there exists discriminatory information in the hand odor and points at body odor as a promising biometric identifier.

New methodology for the integration of biometric features in speaker recognition systems applied to security environments

La cuestión principal abordada en esta tesis doctoral es la mejora de los sistemas biométricos de reconocimiento de personas a partir de la voz, proponiendo el uso de una nueva parametrización, que hemos denominado parametrización biométrica extendida dependiente de género (GDEBP en sus siglas en inglés). No se propone una ruptura completa respecto a los parámetros clásicos sino una nueva forma de utilizarlos y complementarlos. En concreto, proponemos el uso de parámetros diferentes dependiendo del género del locutor, ya que como es bien sabido, la voz masculina y femenina presentan características diferentes que deberán modelarse, por tanto, de diferente manera. Además complementamos los parámetros clásicos utilizados (MFFC extraídos de la señal de voz), con un nuevo conjunto de parámetros extraídos a partir de la deconstrucción de la señal de voz en sus componentes de fuente glótica (más relacionada con el proceso y órganos de fonación y por tanto con características físicas del locutor) y de tracto vocal (más relacionada con la articulación acústica y por tanto con el mensaje emitido). Para verificar la validez de esta propuesta se plantean diversos escenarios, utilizando diferentes bases de datos, para validar que la GDEBP permite generar una descripción más precisa de los locutores que los parámetros MFCC clásicos independientes del género. En concreto se plantean diferentes escenarios de identificación sobre texto restringido y texto independiente utilizando las bases de datos de HESPERIA y ALBAYZIN. El trabajo también se completa con la participación en dos competiciones internacionales de reconocimiento de locutor, NIST SRE (2010 y 2012) y MOBIO 2013. En el primer caso debido a la naturaleza de las bases de datos utilizadas se obtuvieron resultados cercanos al estado del arte, mientras que en el segundo de los casos el sistema presentado obtuvo la mejor tasa de reconocimiento para locutores femeninos. A pesar de que el objetivo principal de esta tesis no es el estudio de sistemas de clasificación, sí ha sido necesario analizar el rendimiento de diferentes sistemas de clasificación, para ver el rendimiento de la parametrización propuesta. En concreto, se ha abordado el uso de sistemas de reconocimiento basados en el paradigma GMM-UBM, supervectores e i-vectors. Los resultados que se presentan confirman que la utilización de características que permitan describir los locutores de manera más precisa es en cierto modo más importante que la elección del sistema de clasificación utilizado por el sistema. En este sentido la parametrización propuesta supone un paso adelante en la mejora de los sistemas de reconocimiento biométrico de personas por la voz, ya que incluso con sistemas de clasificación relativamente simples se consiguen tasas de reconocimiento realmente competitivas. ABSTRACT The main question addressed in this thesis is the improvement of automatic speaker recognition systems, by the introduction of a new front-end module that we have called Gender Dependent Extended Biometric Parameterisation (GDEBP). This front-end do not constitute a complete break with respect to classical parameterisation techniques used in speaker recognition but a new way to obtain these parameters while introducing some complementary ones. Specifically, we propose a gender-dependent parameterisation, since as it is well known male and female voices have different characteristic, and therefore the use of different parameters to model these distinguishing characteristics should provide a better characterisation of speakers. Additionally, we propose the introduction of a new set of biometric parameters extracted from the components which result from the deconstruction of the voice into its glottal source estimate (close related to the phonation process and the involved organs, and therefore the physical characteristics of the speaker) and vocal tract estimate (close related to acoustic articulation and therefore to the spoken message). These biometric parameters constitute a complement to the classical MFCC extracted from the power spectral density of speech as a whole. In order to check the validity of this proposal we establish different practical scenarios, using different databases, so we can conclude that a GDEBP generates a more accurate description of speakers than classical approaches based on gender-independent MFCC. Specifically, we propose scenarios based on text-constrain and text-independent test using HESPERIA and ALBAYZIN databases. This work is also completed with the participation in two international speaker recognition evaluations: NIST SRE (2010 and 2012) and MOBIO 2013, with diverse results. In the first case, due to the nature of the NIST databases, we obtain results closed to state-of-the-art although confirming our hypothesis, whereas in the MOBIO SRE we obtain the best simple system performance for female speakers. Although the study of classification systems is beyond the scope of this thesis, we found it necessary to analise the performance of different classification systems, in order to verify the effect of them on the propose parameterisation. In particular, we have addressed the use of speaker recognition systems based on the GMM-UBM paradigm, supervectors and i-vectors. The presented results confirm that the selection of a set of parameters that allows for a more accurate description of the speakers is as important as the selection of the classification method used by the biometric system. In this sense, the proposed parameterisation constitutes a step forward in improving speaker recognition systems, since even when using relatively simple classification systems, really competitive recognition rates are achieved.