A scalable expressive ensemble learning using Random Prism: a MapReduce approach

The induction of classification rules from previously unseen examples is one of the most important data mining tasks in science as well as commercial applications. In order to reduce the influence of noise in the data, ensemble learners are often applied. However, most ensemble learners are based on decision tree classifiers which are affected by noise. The Random Prism classifier has recently been proposed as an alternative to the popular Random Forests classifier, which is based on decision trees. Random Prism is based on the Prism family of algorithms, which is more robust to noise. However, like most ensemble classification approaches, Random Prism also does not scale well on large training data. This paper presents a thorough discussion of Random Prism and a recently proposed parallel version of it called Parallel Random Prism. Parallel Random Prism is based on the MapReduce programming paradigm. The paper provides, for the first time, novel theoretical analysis of the proposed technique and in-depth experimental study that show that Parallel Random Prism scales well on a large number of training examples, a large number of data features and a large number of processors. Expressiveness of decision rules that our technique produces makes it a natural choice for Big Data applications where informed decision making increases the user’s trust in the system.

Vertical structure and physical processes of the Madden-Julian oscillation: Linking hindcast fidelity to simulated diabatic heating and moistening

Many theories for the Madden-Julian oscillation (MJO) focus on diabatic processes, particularly the evolution of vertical heating and moistening. Poor MJO performance in weather and climate models is often blamed on biases in these processes and their interactions with the large-scale circulation. We introduce one of three components of a model-evaluation project, which aims to connect MJO fidelity in models to their representations of several physical processes, focusing on diabatic heating and moistening. This component consists of 20-day hindcasts, initialised daily during two MJO events in winter 2009-10. The 13 models exhibit a range of skill: several have accurate forecasts to 20 days' lead, while others perform similarly to statistical models (8-11 days). Models that maintain the observed MJO amplitude accurately predict propagation, but not vice versa. We find no link between hindcast fidelity and the precipitation-moisture relationship, in contrast to other recent studies. There is also no relationship between models' performance and the evolution of their diabatic-heating profiles with rain rate. A more robust association emerges between models' fidelity and net moistening: the highest-skill models show a clear transition from low-level moistening for light rainfall to mid-level moistening at moderate rainfall and upper-level moistening for heavy rainfall. The mid-level moistening, arising from both dynamics and physics, may be most important. Accurately representing many processes may be necessary, but not sufficient for capturing the MJO, which suggests that models fail to predict the MJO for a broad range of reasons and limits the possibility of finding a panacea.

Vertical structure and physical processes of the Madden-Julian oscillation: Biases and uncertainties at short range

An analysis of diabatic heating and moistening processes from 12-36 hour lead time forecasts from 12 Global Circulation Models are presented as part of the "Vertical structure and physical processes of the Madden-Julian Oscillation (MJO)" project. A lead time of 12-36 hours is chosen to constrain the large scale dynamics and thermodynamics to be close to observations while avoiding being too close to the initial spin-up for the models as they adjust to being driven from the YOTC analysis. A comparison of the vertical velocity and rainfall with the observations and YOTC analysis suggests that the phases of convection associated with the MJO are constrained in most models at this lead time although the rainfall in the suppressed phase is typically overestimated. Although the large scale dynamics is reasonably constrained, moistening and heating profiles have large inter-model spread. In particular, there are large spreads in convective heating and moistening at mid-levels during the transition to active convection. Radiative heating and cloud parameters have the largest relative spread across models at upper levels during the active phase. A detailed analysis of time step behaviour shows that some models show strong intermittency in rainfall and differences in the precipitation and dynamics relationship between models. The wealth of model outputs archived during this project is a very valuable resource for model developers beyond the study of the MJO. In addition, the findings of this study can inform the design of process model experiments, and inform the priorities for field experiments and future observing systems.

Extraction of unexpected rules from Twitter hashtags and its application to sport events

Twitter has become a dependable microblogging tool for real time information dissemination and newsworthy events broadcast. Its users sometimes break news on the network faster than traditional newsagents due to their presence at ongoing real life events at most times. Different topic detection methods are currently used to match Twitter posts to real life news of mainstream media. In this paper, we analyse tweets relating to the English FA Cup finals 2012 by applying our novel method named TRCM to extract association rules present in hash tag keywords of tweets in different time-slots. Our system identify evolving hash tag keywords with strong association rules in each time-slot. We then map the identified hash tag keywords to event highlights of the game as reported in the ground truth of the main stream media. The performance effectiveness measure of our experiments show that our method perform well as a Topic Detection and Tracking approach.

A survey of data mining techniques for social media analysis

Social network has gained remarkable attention in the last decade. Accessing social network sites such as Twitter, Facebook LinkedIn and Google+ through the internet and the web 2.0 technologies has become more affordable. People are becoming more interested in and relying on social network for information, news and opinion of other users on diverse subject matters. The heavy reliance on social network sites causes them to generate massive data characterised by three computational issues namely; size, noise and dynamism. These issues often make social network data very complex to analyse manually, resulting in the pertinent use of computational means of analysing them. Data mining provides a wide range of techniques for detecting useful knowledge from massive datasets like trends, patterns and rules [44]. Data mining techniques are used for information retrieval, statistical modelling and machine learning. These techniques employ data pre-processing, data analysis, and data interpretation processes in the course of data analysis. This survey discusses different data mining techniques used in mining diverse aspects of the social network over decades going from the historical techniques to the up-to-date models, including our novel technique named TRCM. All the techniques covered in this survey are listed in the Table.1 including the tools employed as well as names of their authors.

Fast adaptive real-time classification for data streams with concept drift

An important application of Big Data Analytics is the real-time analysis of streaming data. Streaming data imposes unique challenges to data mining algorithms, such as concept drifts, the need to analyse the data on the fly due to unbounded data streams and scalable algorithms due to potentially high throughput of data. Real-time classification algorithms that are adaptive to concept drifts and fast exist, however, most approaches are not naturally parallel and are thus limited in their scalability. This paper presents work on the Micro-Cluster Nearest Neighbour (MC-NN) classifier. MC-NN is based on an adaptive statistical data summary based on Micro-Clusters. MC-NN is very fast and adaptive to concept drift whilst maintaining the parallel properties of the base KNN classifier. Also MC-NN is competitive compared with existing data stream classifiers in terms of accuracy and speed.

Enhanced long-range forecast skill in boreal winter following stratospheric strong vortex conditions

There has been a great deal of recent interest in producing weather forecasts on the 2–6 week sub-seasonal timescale, which bridges the gap between medium-range (0–10 day) and seasonal (3–6 month) forecasts. While much of this interest is focused on the potential applications of skilful forecasts on the sub-seasonal range, understanding the potential sources of sub-seasonal forecast skill is a challenging and interesting problem, particularly because of the likely state-dependence of this skill (Hudson et al 2011). One such potential source of state-dependent skill for the Northern Hemisphere in winter is the occurrence of stratospheric sudden warming (SSW) events (Sigmond et al 2013). Here we show, by analysing a set of sub-seasonal hindcasts, that there is enhanced predictability of surface circulation not only when the stratospheric vortex is anomalously weak following SSWs but also when the vortex is extremely strong. Sub-seasonal forecasts initialized during strong vortex events are able to successfully capture the associated surface temperature and circulation anomalies. This results in an enhancement of Northern annular mode forecast skill compared to forecasts initialized during the cases when the stratospheric state is close to climatology. We demonstrate that the enhancement of skill for forecasts initialized during periods of strong vortex conditions is comparable to that achieved for forecasts initialized during weak events. This result indicates that additional confidence can be placed in sub-seasonal forecasts when the stratospheric polar vortex is significantly disturbed from its normal state.

Seasonal forecasts of tropical cyclone activity in a high-atmospheric-resolution coupled prediction system

Seasonal forecast skill of the basinwide and regional tropical cyclone (TC) activity in an experimental coupled prediction system based on the ECMWF System 4 is assessed. As part of a collaboration between the Center for Ocean–Land–Atmosphere Studies (COLA) and the ECMWF called Project Minerva, the system is integrated at the atmospheric horizontal spectral resolutions of T319, T639, and T1279. Seven-month hindcasts starting from 1 May for the years 1980–2011 are produced at all three resolutions with at least 15 ensemble members. The Minerva system demonstrates statistically significant skill for retrospective forecasts of TC frequency and accumulated cyclone energy (ACE) in the North Atlantic (NA), eastern North Pacific (EP), and western North Pacific. While the highest scores overall are achieved in the North Pacific, the skill in the NA appears to be limited by an overly strong influence of the tropical Pacific variability. Higher model resolution improves skill scores for the ACE and, to a lesser extent, the TC frequency, even though the influence of large-scale climate variations on these TC activity measures is largely independent of resolution changes. The biggest gain occurs in transition from T319 to T639. Significant skill in regional TC forecasts is achieved over broad areas of the Northern Hemisphere. The highest-resolution hindcasts exhibit additional locations with skill in the NA and EP, including land-adjacent areas. The feasibility of regional intensity forecasts is assessed. In the presence of the coupled model biases, the benefits of high resolution for seasonal TC forecasting may be underestimated.

Towards expressive rule induction on IP network event streams

In order to gain insights into events and issues that may cause errors and outages in parts of IP networks, intelligent methods that capture and express causal relationships online (in real-time) are needed. Whereas generalised rule induction has been explored for non-streaming data applications, its application and adaptation on streaming data is mostly undeveloped or based on periodic and ad-hoc training with batch algorithms. Some association rule mining approaches for streaming data do exist, however, they can only express binary causal relationships. This paper presents the ongoing work on Online Generalised Rule Induction (OGRI) in order to create expressive and adaptive rule sets real-time that can be applied to a broad range of applications, including network telemetry data streams.

ADCK3, an ancestral kinase, is mutated in a form of recessive ataxia associated with coenzyme Q(10) deficiency

Muscle coenzyme Q(10) (CoQ(10) or ubiquinone) deficiency has been identified in more than 20 patients with presumed autosomal-recessive ataxia. However, mutations in genes required for CoQ(10) biosynthetic pathway have been identified only in patients with infantile-onset multisystemic diseases or isolated nephropathy. Our SNP-based genome-wide scan in a large consanguineous family revealed a locus for autosomal-recessive ataxia at chromosome 1q41. The causative mutation is a homozygous splice-site mutation in the aarF-domain-containing kinase 3 gene (ADCK3). Five additional mutations in ADCK3 were found in three patients with sporadic ataxia, including one known to have CoQ(10) deficiency in muscle. All of the patients have childhood-onset cerebellar ataxia with slow progression, and three of six have mildly elevated lactate levels. ADCK3 is a mitochondrial protein homologous to the yeast COQ8 and the bacterial UbiB proteins, which are required for CoQ biosynthesis. Three out of four patients tested showed a low endogenous pool of CoQ(10) in their fibroblasts or lymphoblasts, and two out of three patients showed impaired ubiquinone synthesis, strongly suggesting that ADCK3 is also involved in CoQ(10) biosynthesis. The deleterious nature of the three identified missense changes was confirmed by the introduction of them at the corresponding positions of the yeast COQ8 gene. Finally, a phylogenetic analysis shows that ADCK3 belongs to the family of atypical kinases, which includes phosphomositide and choline kinases, suggesting that ADCK3 plays an indirect regulatory role in ubiquinone biosynthesis possibly as part of a feedback loop that regulates ATP production.

Effect of cholesterol on the interaction of the amphibian antimicrobial peptide DD K with liposomes

DD K is an antimicrobial peptide previously isolated from the skin of the amphibian Phyllomedusa distincta. The effect of cholesterol on synthetic DD K binding to egg lecithin liposomes was investigated by intrinsic fluorescence of tryptophan residue, measurements of kinetics of 5(6)-carboxyfluorescein (CF) leakage, dynamic light scattering and isothermal titration microcalorimetry. An 8 nm blue shift of tryptophan maximum emission fluorescence was observed when DD K was in the presence of lecithin liposomes compared to the value observed for liposomes containing 43 mol% cholesterol. The rate and the extent of CF release were also significantly reduced by the presence of cholesterol. Dynamic light scattering showed that lecithin liposome size increase from 115 to 140 nm when titrated with DD K but addition of cholesterol reduces the liposome size increments. Isothermal titration microcalorimetry studies showed that DD K binding both to liposomes containing cholesterol as to liposomes devoid of it is more entropically than enthalpically favored. Nevertheless, the peptide concentration necessary to furnish an adjustable titration curve is much higher for liposomes containing cholesterol at 43 mol% (2 mmol L-1) than in its absence (93 mu mol L-1). Apparent binding constant values were 2160 and 10,000 L mol(-1), respectively. The whole data indicate that DD K binding to phosphatidylcholine liposomes is significantly affected by cholesterol, which contributes to explain the low hemolytic activity of the peptide. (C) 2007 Elsevier Inc. All rights reserved.

A ética em Smith nos neo-clássicos e suas implicações na ética em negócios

Esta pesquisa procurará discutir as relações entre ética em economia e administração. O enfoque adotado demonstrará que a ética da economia clássica representada pelo pensamento de Adam Smith é completamente diferente daquela encontrada nos pensadores neoclássicos representados por Hayek, Von Mises e Friedman. Decorre daí que apesar do mundo dos negócios adotar algumas perspectivas econômicas de Smith, os critérios de avaliação de desempenho empresarial decorrem da economia neoclássica e de forma subjacente incorpora seus valores éticos. Ao se ignorar este relacionamento entre economia e negócios, a discussão sobre ética nos negócios é conduzida por um caminho que impede qualquer consenso ou aplicação prática.

O behaviorismo radical de B. F. Skinner e sua aplicabilidade socialmente relevante

o presente trabalho tem como objetivo a avaliação da capacidade do behaviorismo radical skinneriano'para fundamentar aplicações práticas dotadas de efetiva relevância humana e social, assim definidas em' função do caráter politicamente progressista e democratizante que se lhes possa imprimir. Essa avaliação é desenvolvida através de dois distintos, mas interrelacionados, procedimentos acadêmicos: uma argumentação discursiva baseada em pesquisa bibliográfica pertinente, que se complementapor um estudo empírico de campo a nível exploratório. A apreciação discursiva do behaviorismo radical é inicialmente conduzida sob uma perspectiva histórico-acadêmica, cujo foco contínuo de referência é o próprio agente individual basicamente responsável pelo gradativo processo de constituição desse sistema científico - B. F. Skinner. Em uma da etapa, o behaviorismo radical vem a ser discutido deforma mais impessoalizada, contra os sucessivos panos de fundo da filosofia da ciência, das relações sociais humanas e da ética implicada nas intervenções práticas sobre tais relações. O estudo exploratório, que então-se segue, consiste no julgamento da utilidade potencial de um instrumento de educação política popular, elaborado previamente a partir dos princípios behavioristas radicais básicos e seus desdobramentos psicossociológicos, e destinado a uma clientela sócio-economicamente desprivi1egiada, constituída de indivíduos adultos, de ambos os sexos, com escolaridade 'em torno do 19 grau completo. A avaliação dessa Cartilha de Contracontole Social através de entrevistas semi-estruturadas e um breve questionário objetivo, por quatorze lnformantes - qualificados em termos de sua 1iderança e/ou militância em associações de moradores de bairros e de favelas, sindicatos de trabalhadores, núcleos comunitários re1igiosos, entidades assistenciais autônomas e associações de servidores em organizações de trabalho - vem a apresentar, em linhas gerais, resultados muito positivos. A iniciativa é considerada extremamente séria e responsável; e o conteúdo da Carti1ha, bastante compatível com os objetivos desses movimentos populares, assim como muito útil para dotar as suas atividades de uma maior eficácia social. Evidencia-se, não obstante, uma restrição generalizada quanto à possibilidade de compreensão do texto por parte da população-alvo; o que deve, portanto, ensejar sua reformu1ação para o emprego em futuros projetos de prosseguimento dessa linha de pesquisa. Conclui-se, enfim, que a tese em seu todo parece apta a contribuir, se suficientemente divulgada, para a promoção de um maior interesse pelo behaviorismo de Skinner no âmbito acadêmico do Rio de Janeiro, bem como para a produção de um exercício mais eficaz de contracontro1e sobre as atuais práticas aversivas e/ou exploratórias de controle social.