Anti-woman's rights demonstrators, mostly male, one holding a sign reading, "This morning I made love to my wife - she loved it!"

Inscription: Verso: protestors demonstrate against women's rights, Fifth Avenue, New York.

Studying Recommender Systems to Enhance Distributed Computing Schedulers

Distributed Computing frameworks belong to a class of programming models that allow developers to

launch workloads on large clusters of machines. Due to the dramatic increase in the volume of

data gathered by ubiquitous computing devices, data analytic workloads have become a common

case among distributed computing applications, making Data Science an entire field of

Computer Science. We argue that Data Scientist's concern lays in three main components: a dataset,

a sequence of operations they wish to apply on this dataset, and some constraint they may have

related to their work (performances, QoS, budget, etc). However, it is actually extremely

difficult, without domain expertise, to perform data science. One need to select the right amount

and type of resources, pick up a framework, and configure it. Also, users are often running their

application in shared environments, ruled by schedulers expecting them to specify precisely their resource

needs. Inherent to the distributed and concurrent nature of the cited frameworks, monitoring and

profiling are hard, high dimensional problems that block users from making the right

configuration choices and determining the right amount of resources they need. Paradoxically, the

system is gathering a large amount of monitoring data at runtime, which remains unused.

In the ideal abstraction we envision for data scientists, the system is adaptive, able to exploit

monitoring data to learn about workloads, and process user requests into a tailored execution

context. In this work, we study different techniques that have been used to make steps toward

such system awareness, and explore a new way to do so by implementing machine learning

techniques to recommend a specific subset of system configurations for Apache Spark applications.

Furthermore, we present an in depth study of Apache Spark executors configuration, which highlight

the complexity in choosing the best one for a given workload.

Using parent report to assess early lexical production in children exposed to more than one language

Limited expressive vocabulary skills in young children are considered to be the first warning signs of a potential Specific Language Impairment (SLI) (Ellis & Thal, 2008). In bilingual language learning environments, the expressive vocabulary size in each of the child’s developing languages is usually smaller compared to the number of words produced by monolingual peers (e.g. De Houwer, 2009). Nonetheless, evidence shows children’s total productive lexicon size across both languages to be comparable to monolingual peers’ vocabularies (e.g. Pearson et al., 1993; Pearson & Fernandez, 1994). Since there is limited knowledge as to which level of bilingual vocabulary size should be considered as a risk factor for SLI, the effects of bilingualism and language-learning difficulties on early lexical production are often confounded. The compilation of profiles for early vocabulary production in children exposed to more than one language, and their comparison across language pairs, should enable more accurate identification of vocabulary delays that signal a risk for SLI in bilingual populations. These considerations prompted the design of a methodology for assessing early expressive vocabulary in children exposed to more than one language, which is described in the present chapter. The implementation of this methodological framework is then outlined by presenting the design of a study that measured the productive lexicons of children aged 24-36 months who were exposed to different language pairs, namely Maltese and English, Irish and English, Polish and English, French and Portuguese, Turkish and German as well as English and Hebrew. These studies were designed and coordinated in COST Action IS0804 Working Group 3 (WG3) and will be described in detail in a series of subsequent publications. Expressive vocabulary size was measured through parental report, by employing the vocabulary checklist of the MacArthur-Bates Communicative Development Inventory: Words and Sentences (CDI: WS) (Fenson et al., 1993, 2007) and its adaptations to the participants’ languages. Here we describe the novelty of the study’s methodological design, which lies in its attempt to harmonize the use of vocabulary checklist adaptations, together with parental questionnaires addressing language exposure and developmental history, across participant groups characterized by different language exposure variables. This chapter outlines the various methodological considerations that paved the way for meaningful cross-linguistic comparison of the participants’ expressive lexicon sizes. In so doing, it hopes to provide a template for and encourage further research directed at establishing a threshold for SLI risk in children exposed to more than one language.

Formal evolution and content in Asturian newspapers. From World War One to the Primo de Rivera Dictatorship

Between 1914 and 1923, and in general during the 1910s and 1920s, important changes occurred in the Asturian press that can be seen, for example, in the type of dailies which achieved certain readership levels. The form and number of pages evolved, images became more common, so-called citizen journalism was more freely practised, and there was a trend -when there was an opportunity- towards publishing articles which involved the author travelling to distant lands. Simultaneously, the consideration of sport as a show and the treatment given to other content both helped outline a new media scenario.

Extracellular vesicle biogenesis in helminths: more than one route to the surface?

The quite recent discovery that parasites release extracellular vesicles (EVs) that can transfer a range of effector molecules to host cells has made us re-think our understanding of the host-parasite interface. In this opinion article we will consider how recent proteomics and transcriptomics studies, together with ultrastructural observations, suggest that more than one mechanism of EV biogenesis can occur in helminths. We propose that distinct EV sub-types have roles in immune-modulation and repair of drug-induced damage, and put forward the case for targeting EV biogenesis pathways to achieve parasite control. In doing so we raise a number of outstanding research questions that must be addressed before this can happen.

’Letting the right one in’: provider contexts for recruitment to initial teacher education in the United Kingdom

We exploit policy differences within the UK to investigate provider context and recruitment to initial teacher education (ITE). We identify three dimensions of variation: conceptions of professionalism, universal or context specific preparation and costs and benefits to providers. University-led ITE programmes used similar criteria and processes in each jurisdiction, but there were differences between university-led and school-led recruitment. Our study suggests that the current shortfall in recruitment to ITE in England may be a product of the contextual constraints which schools experience. It also suggests that school-led recruitment may tend to emphasise short-term and school-specific needs.

Evaluation of One-Dimensional Seismic Site Response Analyses at Small to Large Strain Levels

Thesis (Master's)--University of Washington, 2016-08

An extreme planetary system around HD 219828: one long-period super Jupiter to a hot-Neptune host star

Context. With about 2000 extrasolar planets confirmed, the results show that planetary systems have a whole range of unexpected properties. This wide diversity provides fundamental clues to the processes of planet formation and evolution. Aims: We present a full investigation of the HD 219828 system, a bright metal-rich star for which a hot Neptune has previously been detected. Methods: We used a set of HARPS, SOPHIE, and ELODIE radial velocities to search for the existence of orbiting companions to HD 219828. The spectra were used to characterise the star and its chemical abundances, as well as to check for spurious, activity induced signals. A dynamical analysis is also performed to study the stability of the system and to constrain the orbital parameters and planet masses. Results: We announce the discovery of a long period (P = 13.1 yr) massive (m sini = 15.1 MJup) companion (HD 219828 c) in a very eccentric orbit (e = 0.81). The same data confirms the existence of a hot Neptune, HD 219828 b, with a minimum mass of 21 M⊕ and a period of 3.83 days. The dynamical analysis shows that the system is stable, and that the equilibrium eccentricity of planet b is close to zero. Conclusions: The HD 219828 system is extreme and unique in several aspects. First, ammong all known exoplanet systems it presents an unusually high mass ratio. We also show that systems like HD 219828, with a hot Neptune and a long-period massive companion are more frequent than similar systems with a hot Jupiter instead. This suggests that the formation of hot Neptunes follows a different path than the formation of their hot jovian counterparts. The high mass, long period, and eccentricity of HD 219828 c also make it a good target for Gaia astrometry as well as a potential target for atmospheric characterisation, using direct imaging or high-resolution spectroscopy. Astrometric observations will allow us to derive its real mass and orbital configuration. If a transit of HD 219828 b is detected, we will be able to fully characterise the system, including the relative orbital inclinations. With a clearly known mass, HD 219828 c may become a benchmark object for the range in between giant planets and brown dwarfs.

The chess player / by George Walker; to which are added the three games played at one and the same time by Philidor; sixty openings, mates and situations, by W. S. Kenny, with remarks, anecdotes, &c. &c., and an explanation of the round chess board.

Mode of access: Internet.

A Soft Computing Approach to Acute Coronary Syndrome

Acute Coronary Syndrome (ACS) is transversal to a broad and heterogeneous set of human beings, and assumed as a serious diagnosis and risk stratification problem. Although one may be faced with or had at his disposition different tools as biomarkers for the diagnosis and prognosis of ACS, they have to be previously evaluated and validated in different scenarios and patient cohorts. Besides ensuring that a diagnosis is correct, attention should also be directed to ensure that therapies are either correctly or safely applied. Indeed, this work will focus on the development of a diagnosis decision support system in terms of its knowledge representation and reasoning mechanisms, given here in terms of a formal framework based on Logic Programming, complemented with a problem solving methodology to computing anchored on Artificial Neural Networks. On the one hand it caters for the evaluation of ACS predisposing risk and the respective Degree-of-Confidence that one has on such a happening. On the other hand it may be seen as a major development on the Multi-Value Logics to understand things and ones behavior. Undeniably, the proposed model allows for an improvement of the diagnosis process, classifying properly the patients that presented the pathology (sensitivity ranging from 89.7% to 90.9%) as well as classifying the absence of ACS (specificity ranging from 88.4% to 90.2%).

“Biomarkers in a One Health perspective: current knowledge of their ability to merge human and animal health

Biomarkers are nowadays essential tools to be one step ahead for fighting disease, enabling an enhanced focus on disease prevention and on the probability of its occurrence. Research in a multidisciplinary approach has been an important step towards the repeated discovery of new biomarkers. Biomarkers are defined as biochemical measurable indicators of the presence of disease or as indicators for monitoring disease progression. Currently, biomarkers have been used in several domains such as oncology, neurology, cardiovascular, inflammatory and respiratory disease, and several endocrinopathies. Bridging biomarkers in a One Health perspective has been proven useful in almost all of these domains. In oncology, humans and animals are found to be subject to the same environmental and genetic predisposing factors: examples include the existence of mutations in BR-CA1 gene predisposing to breast cancer, both in human and dogs, with increased prevalence in certain dog breeds and human ethnic groups. Also, breast feeding frequency and duration has been related to a decreased risk of breast cancer in women and bitches. When it comes to infectious diseases, this parallelism is prone to be even more important, for as much as 75% of all emerging diseases are believed to be zoonotic. Examples of successful use of biomarkers have been found in several zoonotic diseases such as Ebola, dengue, leptospirosis or West Nile virus infections. Acute Phase Proteins (APPs) have been used for quite some time as biomarkers of inflammatory conditions. These have been used in human health but also in the veterinary field such as in mastitis evaluation and PRRS (porcine respiratory and reproductive syndrome) diagnosis. Advantages rely on the fact that these biomarkers can be much easier to assess than other conventional disease diagnostic approaches (example: measured in easy to collect saliva samples). Another domain in which biomarkers have been essential is food safety: the possibility to measure exposure to chemical contaminants or other biohazards present in the food chain, which are sometimes analytical challenges due to their low bioavailability in body fluids, is nowadays a major breakthrough. Finally, biomarkers are considered the key to provide more personalized therapies, with more efficient outcomes and fewer side effects. This approach is expected to be the correct path to follow also in veterinary medicine, in the near future.

An Integrated Soft Computing Approach to Hughes Syndrome Risk Assessment

The AntiPhospholipid Syndrome (APS) is an acquired autoimmune disorder induced by high levels of antiphospholipid antibodies that cause arterial and veins thrombosis, as well as pregnancy-related complications and morbidity, as clinical manifestations. This autoimmune hypercoagulable state, usually known as Hughes syndrome, has severe consequences for the patients, being one of the main causes of thrombotic disorders and death. Therefore, it is required to be preventive; being aware of how probable is to have that kind of syndrome. Despite the updated of antiphospholipid syndrome classification, the diagnosis remains difficult to establish. Additional research on clinically relevant antibodies and standardization of their quantification are required in order to improve the antiphospholipid syndrome risk assessment. Thus, this work will focus on the development of a diagnosis decision support system in terms of a formal agenda built on a Logic Programming approach to knowledge representation and reasoning, complemented with a computational framework based on Artificial Neural Networks. The proposed model allows for improving the diagnosis, classifying properly the patients that really presented this pathology (sensitivity higher than 85%), as well as classifying the absence of APS (specificity close to 95%).