Towards a knowledge of chytrids. Part 3. Spirospora paradoxa nov.gen. - nov.spec. [Translation from: Archiv fur Protistenkunde 54 510-528 [pages 522-525 and plate 28 only], 1926]

This partial translation of the original paper provides morphological observations on the fungus Spirospora paradoxa. Illustrations are included here.

To the knowledge of the genus Pseudospora Cienkowski [Translation from: Archiv fur Protistenkunde 59, 350-368, 1927]

Morphological observations on the two types of Pseudospora are given. The two Pseudospora whic are described are Pseudospora eudorini and Pseudospora volvocis. The systematic classification of the genus Pseudospora is discussed.

Aplicación del método de series de Uranio al grafismo rupestre de estilo paleolítico: el caso de la cavidad de Covalanas (Ramales de la Victoria, Cantabria)

[EN] Se ensaya la aplicación del procedimiento analítico de series de Uranio para el conocimiento de la seriación artística de obras parietales de estilo paleolítico. El resultado obtenido muestra la validez del método y abre nuevas vías para la obtención de fechas absolutas "ante quem" y "post quem" que sirvan para enmarcar momentos de ejecución de obras rupestres.

A participatory study of the traditional knowledge of fishing communities in the Gulf of Mannar, India

This is the first in a series of case studies undertaken by the International Collective in Support of Fishworkers (ICSF) to document the traditional knowledge of fishing communities dependent on marine and coastal resources in protected and conserved areas in different parts of the world. The study, done with the support of the Bay of Bengal Large Marine Ecosystem (BOBLME) project, documents the traditional knowledge of fishing communities in the Gulf of Mannar in the state of Tamil Nadu. It focuses on two fishing villages, Chinnapalam and Bharathi Nagar, whose communities have traditionally depended on Krusadai and Appa Islands for their livelihood. Traditional knowledge relating to oceanographic, meteorological, biological, ecological and navigational aspects of fisheries was documented. The study will be useful for researchers, students, scientists, policymakers, fishworker organizations, NGOs and anyone interested in the traditional knowledge of local fishing communities related to marine biodiversity and the customary use of fisheries resources and fishing practices.

Coherent Behavior from Incoherent Knowledge Sources in the Automatic Synthesis of Numerical Computer Programs

A fundamental problem in artificial intelligence is obtaining coherent behavior in rule-based problem solving systems. A good quantitative measure of coherence is time behavior; a system that never, in retrospect, applied a rule needlessly is certainly coherent; a system suffering from combinatorial blowup is certainly behaving incoherently. This report describes a rule-based problem solving system for automatically writing and improving numerical computer programs from specifications. The specifications are in terms of "constraints" among inputs and outputs. The system has solved program synthesis problems involving systems of equations, determining that methods of successive approximation converge, transforming recursion to iteration, and manipulating power series (using differing organizations, control structures, and argument-passing techniques).

An investigation of innovation and knowledge creation in virtual worlds

The Internet and World Wide Web have had, and continue to have, an incredible impact on our civilization. These technologies have radically influenced the way that society is organised and the manner in which people around the world communicate and interact. The structure and function of individual, social, organisational, economic and political life begin to resemble the digital network architectures upon which they are increasingly reliant. It is increasingly difficult to imagine how our ‘offline’ world would look or function without the ‘online’ world; it is becoming less meaningful to distinguish between the ‘actual’ and the ‘virtual’. Thus, the major architectural project of the twenty-first century is to “imagine, build, and enhance an interactive and ever changing cyberspace” (Lévy, 1997, p. 10). Virtual worlds are at the forefront of this evolving digital landscape. Virtual worlds have “critical implications for business, education, social sciences, and our society at large” (Messinger et al., 2009, p. 204). This study focuses on the possibilities of virtual worlds in terms of communication, collaboration, innovation and creativity. The concept of knowledge creation is at the core of this research. The study shows that scholars increasingly recognise that knowledge creation, as a socially enacted process, goes to the very heart of innovation. However, efforts to build upon these insights have struggled to escape the influence of the information processing paradigm of old and have failed to move beyond the persistent but problematic conceptualisation of knowledge creation in terms of tacit and explicit knowledge. Based on these insights, the study leverages extant research to develop the conceptual apparatus necessary to carry out an investigation of innovation and knowledge creation in virtual worlds. The study derives and articulates a set of definitions (of virtual worlds, innovation, knowledge and knowledge creation) to guide research. The study also leverages a number of extant theories in order to develop a preliminary framework to model knowledge creation in virtual worlds. Using a combination of participant observation and six case studies of innovative educational projects in Second Life, the study yields a range of insights into the process of knowledge creation in virtual worlds and into the factors that affect it. The study’s contributions to theory are expressed as a series of propositions and findings and are represented as a revised and empirically grounded theoretical framework of knowledge creation in virtual worlds. These findings highlight the importance of prior related knowledge and intrinsic motivation in terms of shaping and stimulating knowledge creation in virtual worlds. At the same time, they highlight the importance of meta-knowledge (knowledge about knowledge) in terms of guiding the knowledge creation process whilst revealing the diversity of behavioural approaches actually used to create knowledge in virtual worlds and. This theoretical framework is itself one of the chief contributions of the study and the analysis explores how it can be used to guide further research in virtual worlds and on knowledge creation. The study’s contributions to practice are presented as actionable guide to simulate knowledge creation in virtual worlds. This guide utilises a theoretically based classification of four knowledge-creator archetypes (the sage, the lore master, the artisan, and the apprentice) and derives an actionable set of behavioural prescriptions for each archetype. The study concludes with a discussion of the study’s implications in terms of future research.

A distributed architecture for the monitoring and analysis of time series data

It is estimated that the quantity of digital data being transferred, processed or stored at any one time currently stands at 4.4 zettabytes (4.4 × 2 70 bytes) and this figure is expected to have grown by a factor of 10 to 44 zettabytes by 2020. Exploiting this data is, and will remain, a significant challenge. At present there is the capacity to store 33% of digital data in existence at any one time; by 2020 this capacity is expected to fall to 15%. These statistics suggest that, in the era of Big Data, the identification of important, exploitable data will need to be done in a timely manner. Systems for the monitoring and analysis of data, e.g. stock markets, smart grids and sensor networks, can be made up of massive numbers of individual components. These components can be geographically distributed yet may interact with one another via continuous data streams, which in turn may affect the state of the sender or receiver. This introduces a dynamic causality, which further complicates the overall system by introducing a temporal constraint that is difficult to accommodate. Practical approaches to realising the system described above have led to a multiplicity of analysis techniques, each of which concentrates on specific characteristics of the system being analysed and treats these characteristics as the dominant component affecting the results being sought. The multiplicity of analysis techniques introduces another layer of heterogeneity, that is heterogeneity of approach, partitioning the field to the extent that results from one domain are difficult to exploit in another. The question is asked can a generic solution for the monitoring and analysis of data that: accommodates temporal constraints; bridges the gap between expert knowledge and raw data; and enables data to be effectively interpreted and exploited in a transparent manner, be identified? The approach proposed in this dissertation acquires, analyses and processes data in a manner that is free of the constraints of any particular analysis technique, while at the same time facilitating these techniques where appropriate. Constraints are applied by defining a workflow based on the production, interpretation and consumption of data. This supports the application of different analysis techniques on the same raw data without the danger of incorporating hidden bias that may exist. To illustrate and to realise this approach a software platform has been created that allows for the transparent analysis of data, combining analysis techniques with a maintainable record of provenance so that independent third party analysis can be applied to verify any derived conclusions. In order to demonstrate these concepts, a complex real world example involving the near real-time capturing and analysis of neurophysiological data from a neonatal intensive care unit (NICU) was chosen. A system was engineered to gather raw data, analyse that data using different analysis techniques, uncover information, incorporate that information into the system and curate the evolution of the discovered knowledge. The application domain was chosen for three reasons: firstly because it is complex and no comprehensive solution exists; secondly, it requires tight interaction with domain experts, thus requiring the handling of subjective knowledge and inference; and thirdly, given the dearth of neurophysiologists, there is a real world need to provide a solution for this domain

Comparison of pattern detection methods in microarray time series of the segmentation clock.

While genome-wide gene expression data are generated at an increasing rate, the repertoire of approaches for pattern discovery in these data is still limited. Identifying subtle patterns of interest in large amounts of data (tens of thousands of profiles) associated with a certain level of noise remains a challenge. A microarray time series was recently generated to study the transcriptional program of the mouse segmentation clock, a biological oscillator associated with the periodic formation of the segments of the body axis. A method related to Fourier analysis, the Lomb-Scargle periodogram, was used to detect periodic profiles in the dataset, leading to the identification of a novel set of cyclic genes associated with the segmentation clock. Here, we applied to the same microarray time series dataset four distinct mathematical methods to identify significant patterns in gene expression profiles. These methods are called: Phase consistency, Address reduction, Cyclohedron test and Stable persistence, and are based on different conceptual frameworks that are either hypothesis- or data-driven. Some of the methods, unlike Fourier transforms, are not dependent on the assumption of periodicity of the pattern of interest. Remarkably, these methods identified blindly the expression profiles of known cyclic genes as the most significant patterns in the dataset. Many candidate genes predicted by more than one approach appeared to be true positive cyclic genes and will be of particular interest for future research. In addition, these methods predicted novel candidate cyclic genes that were consistent with previous biological knowledge and experimental validation in mouse embryos. Our results demonstrate the utility of these novel pattern detection strategies, notably for detection of periodic profiles, and suggest that combining several distinct mathematical approaches to analyze microarray datasets is a valuable strategy for identifying genes that exhibit novel, interesting transcriptional patterns.

Real-Time and Data-Driven Operation Optimization and Knowledge Discovery for an Enterprise Information System

An enterprise information system (EIS) is an integrated data-applications platform characterized by diverse, heterogeneous, and distributed data sources. For many enterprises, a number of business processes still depend heavily on static rule-based methods and extensive human expertise. Enterprises are faced with the need for optimizing operation scheduling, improving resource utilization, discovering useful knowledge, and making data-driven decisions.

This thesis research is focused on real-time optimization and knowledge discovery that addresses workflow optimization, resource allocation, as well as data-driven predictions of process-execution times, order fulfillment, and enterprise service-level performance. In contrast to prior work on data analytics techniques for enterprise performance optimization, the emphasis here is on realizing scalable and real-time enterprise intelligence based on a combination of heterogeneous system simulation, combinatorial optimization, machine-learning algorithms, and statistical methods.

On-demand digital-print service is a representative enterprise requiring a powerful EIS.We use real-life data from Reischling Press, Inc. (RPI), a digit-print-service provider (PSP), to evaluate our optimization algorithms.

In order to handle the increase in volume and diversity of demands, we first present a high-performance, scalable, and real-time production scheduling algorithm for production automation based on an incremental genetic algorithm (IGA). The objective of this algorithm is to optimize the order dispatching sequence and balance resource utilization. Compared to prior work, this solution is scalable for a high volume of orders and it provides fast scheduling solutions for orders that require complex fulfillment procedures. Experimental results highlight its potential benefit in reducing production inefficiencies and enhancing the productivity of an enterprise.

We next discuss analysis and prediction of different attributes involved in hierarchical components of an enterprise. We start from a study of the fundamental processes related to real-time prediction. Our process-execution time and process status prediction models integrate statistical methods with machine-learning algorithms. In addition to improved prediction accuracy compared to stand-alone machine-learning algorithms, it also performs a probabilistic estimation of the predicted status. An order generally consists of multiple series and parallel processes. We next introduce an order-fulfillment prediction model that combines advantages of multiple classification models by incorporating flexible decision-integration mechanisms. Experimental results show that adopting due dates recommended by the model can significantly reduce enterprise late-delivery ratio. Finally, we investigate service-level attributes that reflect the overall performance of an enterprise. We analyze and decompose time-series data into different components according to their hierarchical periodic nature, perform correlation analysis,

and develop univariate prediction models for each component as well as multivariate models for correlated components. Predictions for the original time series are aggregated from the predictions of its components. In addition to a significant increase in mid-term prediction accuracy, this distributed modeling strategy also improves short-term time-series prediction accuracy.

In summary, this thesis research has led to a set of characterization, optimization, and prediction tools for an EIS to derive insightful knowledge from data and use them as guidance for production management. It is expected to provide solutions for enterprises to increase reconfigurability, accomplish more automated procedures, and obtain data-driven recommendations or effective decisions.

BioHackathon series in 2011 and 2012: penetration of ontology and linked data in life science domains.

The application of semantic technologies to the integration of biological data and the interoperability of bioinformatics analysis and visualization tools has been the common theme of a series of annual BioHackathons hosted in Japan for the past five years. Here we provide a review of the activities and outcomes from the BioHackathons held in 2011 in Kyoto and 2012 in Toyama. In order to efficiently implement semantic technologies in the life sciences, participants formed various sub-groups and worked on the following topics: Resource Description Framework (RDF) models for specific domains, text mining of the literature, ontology development, essential metadata for biological databases, platforms to enable efficient Semantic Web technology development and interoperability, and the development of applications for Semantic Web data. In this review, we briefly introduce the themes covered by these sub-groups. The observations made, conclusions drawn, and software development projects that emerged from these activities are discussed.

An ontological characterization of time-series and state-sequences for data mining

Time-series and sequences are important patterns in data mining. Based on an ontology of time-elements, this paper presents a formal characterization of time-series and state-sequences, where a state denotes a collection of data whose validation is dependent on time. While a time-series is formalized as a vector of time-elements temporally ordered one after another, a state-sequence is denoted as a list of states correspondingly ordered by a time-series. In general, a time-series and a state-sequence can be incomplete in various ways. This leads to the distinction between complete and incomplete time-series, and between complete and incomplete state-sequences, which allows the expression of both absolute and relative temporal knowledge in data mining.

A framework for state-based time-series analysis and prediction

Time-series analysis and prediction play an important role in state-based systems that involve dealing with varying situations in terms of states of the world evolving with time. Generally speaking, the world in the discourse persists in a given state until something occurs to it into another state. This paper introduces a framework for prediction and analysis based on time-series of states. It takes a time theory that addresses both points and intervals as primitive time elements as the temporal basis. A state of the world under consideration is defined as a set of time-varying propositions with Boolean truth-values that are dependent on time, including properties, facts, actions, events and processes, etc. A time-series of states is then formalized as a list of states that are temporally ordered one after another. The framework supports explicit expression of both absolute and relative temporal knowledge. A formal schema for expressing general time-series of states to be incomplete in various ways, while the concept of complete time-series of states is also formally defined. As applications of the formalism in time-series analysis and prediction, we present two illustrating examples.

Performing Pedagogies and Exploring Embodied Knowledge

Performing Pedagogies was a week-long performance and exhibition series I organized that took place in Kingston, Ontario between March 15th - March 20th 2016. The motivation for this project came from a desire to explore performative modes of experiencing critical, embodied knowledge. The series featured five performances, a long distance collaboration between thirty-one Queen’s undergraduate students and a Vancouver artist-run free school (The School for Eventual Vacancy), a subsequent exhibition, a panel discussion, and a radical performance pedagogy workshop led by co-artistic director of the international performance art troupe, La Pocha Nostra. Artists featured included Golboo Amani, Basil AlZeri, Caitlin Chaisson, Justin Langlois, Saul Garcia-Lopez, Francisco-Fernando Granados, and Andrew Rabyniuk. By curating examples of performance art that variously incorporated embodied pedagogical interventions, I examined the processes of performance as pedagogy. Performing Pedagogies explored interventions into contemporary contours of neoliberal education paradigms through embodied encounters—fostering conversations about the meanings and limitations of knowledge dissemination and education today and posing questions about possibilities for radical pedagogies, embodied knowledge, and counter curricula.

Oxidation of N,N,N ',N '-tetraalkyl-para-phenylenediamines in a series of room temperature ionic liquids incorporating the bis(trifluoromethylsulfonyl)imide anion

The five room temperature ionic liquids: 1-alkyl-3-methylimidazolium bis(trifluoromethylsulfonyl)imide ([CnMIM][N(Tf)(2)], n = 2, 4, 8, 10) and n-hexyltriethylammonium bis(trifluoromethylsulfonyl)imide ([N-6222][N(Tf)(2)]) were investigated as solvents in which to study the electrochemical oxidation of N,N,N',N'-tetramethyl-para-phenylenediamine (TMPD) and N,N,N',N'-tetrabutyl-paraphenylenediamine (TBPD), using 20 mul micro-samples under vacuum conditions. The effect of dissolved atmospheric gases on the accessible electrochemical window was probed and determined to be less significant than seen previously for ionic liquids containing alternative anions. Chronoamperometric transients recorded at a microdisk electrode were analysed via a process of non-linear curve fitting to yield values for the diffusion coefficients of the electroactive species without requiring a knowledge of their initial concentration. Comparison of experimental and simulated cyclic voltammetry was then employed to corroborate these results and allow diffusion coefficients for the electrogenerated species to be estimated. The diffusion coefficients obtained for the neutral compounds in the five ionic liquids via this analysis were, in units of 10(-11) m(2) s(-1), 2.62, 1.87, 1.12, 1.13 and 0.70 for TMPD. and 1.23, 0.80, 0.40, 0.52 and 0.24 for TBPD (listed using the same order for the ionic liquids as stated above). The most significant consequence of changing the cationic component of the ionic liquid was found to be its effect on the solvent viscosity; the diffusion coefficient of each species was found to be approximately inversely proportional to viscosity across the series of ionic liquids, in accordance with Walden's rule. (C) 2003 Elsevier B.V. All rights reserved.