A linear programming approach for estimating the structure of a sparse linear genetic network from transcript profiling data

Background: A genetic network can be represented as a directed graph in which a node corresponds to a gene and a directed edge specifies the direction of influence of one gene on another. The reconstruction of such networks from transcript profiling data remains an important yet challenging endeavor. A transcript profile specifies the abundances of many genes in a biological sample of interest. Prevailing strategies for learning the structure of a genetic network from high-dimensional transcript profiling data assume sparsity and linearity. Many methods consider relatively small directed graphs, inferring graphs with up to a few hundred nodes. This work examines large undirected graphs representations of genetic networks, graphs with many thousands of nodes where an undirected edge between two nodes does not indicate the direction of influence, and the problem of estimating the structure of such a sparse linear genetic network (SLGN) from transcript profiling data. Results: The structure learning task is cast as a sparse linear regression problem which is then posed as a LASSO (l1-constrained fitting) problem and solved finally by formulating a Linear Program (LP). A bound on the Generalization Error of this approach is given in terms of the Leave-One-Out Error. The accuracy and utility of LP-SLGNs is assessed quantitatively and qualitatively using simulated and real data. The Dialogue for Reverse Engineering Assessments and Methods (DREAM) initiative provides gold standard data sets and evaluation metrics that enable and facilitate the comparison of algorithms for deducing the structure of networks. The structures of LP-SLGNs estimated from the INSILICO1, INSILICO2 and INSILICO3 simulated DREAM2 data sets are comparable to those proposed by the first and/or second ranked teams in the DREAM2 competition. The structures of LP-SLGNs estimated from two published Saccharomyces cerevisae cell cycle transcript profiling data sets capture known regulatory associations. In each S. cerevisiae LP-SLGN, the number of nodes with a particular degree follows an approximate power law suggesting that its degree distributions is similar to that observed in real-world networks. Inspection of these LP-SLGNs suggests biological hypotheses amenable to experimental verification. Conclusion: A statistically robust and computationally efficient LP-based method for estimating the topology of a large sparse undirected graph from high-dimensional data yields representations of genetic networks that are biologically plausible and useful abstractions of the structures of real genetic networks. Analysis of the statistical and topological properties of learned LP-SLGNs may have practical value; for example, genes with high random walk betweenness, a measure of the centrality of a node in a graph, are good candidates for intervention studies and hence integrated computational – experimental investigations designed to infer more realistic and sophisticated probabilistic directed graphical model representations of genetic networks. The LP-based solutions of the sparse linear regression problem described here may provide a method for learning the structure of transcription factor networks from transcript profiling and transcription factor binding motif data.

The Networks of Learning in Technological Innovation : The Emergence of Collaboration Across Fields of Expertise

This study addresses four issues concerning technological product innovations. First, the nature of the very early phases or "embryonic stages" of technological innovation is addressed. Second, this study analyzes why and by what means people initiate innovation processes outside the technological community and the field of expertise of the established industry. In other words, this study addresses the initiation of innovation that occurs without the expertise of established organizations, such as technology firms, professional societies and research institutes operating in the technological field under consideration. Third, the significance of interorganizational learning processes for technological innovation is dealt with. Fourth, this consideration is supplemented by considering how network collaboration and learning change when formalized product development work and the commercialization of innovation advance. These issues are addressed through the empirical analysis of the following three product innovations: Benecol margarine, the Nordic Mobile Telephone system (NMT) and the ProWellness Diabetes Management System (PDMS). This study utilizes the theoretical insights of cultural-historical activity theory on the development of human activities and learning. Activity-theoretical conceptualizations are used in the critical assessment and advancement of the concept of networks of learning. This concept was originally proposed by the research group of organizational scientist Walter Powell. A network of learning refers to the interorganizational collaboration that pools resources, ideas and know-how without market-based or hierarchical relations. The concept of an activity system is used in defining the nodes of the networks of learning. Network collaboration and learning are analyzed with regard to the shared object of development work. According to this study, enduring dilemmas and tensions in activity explain the participants' motives for carrying out actions that lead to novel product concepts in the early phases of technological innovation. These actions comprise the initiation of development work outside the relevant fields of expertise and collaboration and learning across fields of expertise in the absence of market-based or hierarchical relations. These networks of learning are fragile and impermanent. This study suggests that the significance of networks of learning across fields of expertise becomes more and more crucial for innovation activities.

Towards vision-based deep reinforcement learning for robotic motion control

This paper introduces a machine learning based system for controlling a robotic manipulator with visual perception only. The capability to autonomously learn robot controllers solely from raw-pixel images and without any prior knowledge of configuration is shown for the first time. We build upon the success of recent deep reinforcement learning and develop a system for learning target reaching with a three-joint robot manipulator using external visual observation. A Deep Q Network (DQN) was demonstrated to perform target reaching after training in simulation. Transferring the network to real hardware and real observation in a naive approach failed, but experiments show that the network works when replacing camera images with synthetic images.

Can teacher collaboration overcome barriers to interdisciplinary learning in a disciplinary university? A case study using climate change

A teacher network was formed at an Australian university in order to better promote interdisciplinary student learning on the complex social-environmental problem of climate change. Rather than leaving it to students to piece together disciplinary responses, eight teaching academics collaborated on the task of exposing students to different types of knowledge in a way that was more than the summing of disciplinary parts. With a part-time network facilitator providing cohesion, network members were able to teach into each other’s classes, and share material and student activities across a range of units that included business, zoology, marine science, geography and education. Participants reported that the most positive aspects of the project were the collegiality and support for teaching innovation provided by peers. However, participants also reported being time-poor and overworked. Maintaining the collaboration beyond the initial one year project proved difficult because without funding for the network facilitator, participants were unable to dedicate the time required to meet and collaborate on shared activities. In order to strengthen teacher collaboration in a university whose administrative structures are predominantly discipline-based, there is need for recognition of the benefits of interdisciplinary learning to be matched by recognition of the need for financial and other resources to support collaborative teaching initiatives.

In pursuit of becoming a senior coach: The learning culture for Australian Football League coaches

- Background and Purpose Given the turbulent and highly contested environment in which professional coaches work, a prime concern to coach developers is how coaches learn their craft. Understanding the learning and development of senior coaches (SCs) and assistant coaches (ACs) in the Australian Football League (AFL – the peak organisation for Australian Rules Football) is important to better develop the next generation of performance coaches. Hence the focus of this research was to examine the learning of SC and AC in the AFL. Fundamental to this research was an understanding that the AFL and each club within the league be regarded as learning organisations and workplaces with their own learning cultures where learning takes place. The purpose of this paper was to examine the learning culture for AFL coaches. - Method Five SCs, 6 ACs, and 5 administrators (4 of whom were former coaches) at 11 of the 16 AFL clubs were recruited for the research project. First, demographic data were collected for each participant (e.g. age, playing and coaching experience, development and coach development activities). Second, all participants were involved in one semi-structured interview of between 45 and 90 minutes duration. An interpretative (hierarchical content) analysis of the interview data was conducted to identify key emergent themes. - Results Learning was central to AFL coaches becoming a SC. Nevertheless, coaches reported a sense of isolation and a lack of support in developing their craft within their particular learning culture. These coaches developed a unique dynamic social network (DSN) that involved episodic contact with a number of respected confidantes often from diverse fields (used here in the Bourdieuian sense) in developing their coaching craft. Although there were some opportunities in their workplace, much of their learning was unmediated by others, underscoring the importance of their agentic engagement in limited workplace affordances. - Conclusion The variety of people accessed for the purposes of learning (often beyond the immediate workplace) and the long time taken to establish networks of supporters meant that a new way of describing the social networks of AFL coaches was needed; DSN. However, despite the acknowledged utility of learning from others, all coaches reported some sense of isolation in their learning. The sense of isolation brought about by professional volatility in high-performance Australian Football offers an alternative view on Hodkinson, Biesta and James' attempt in overcoming dualisms in learning.

Journalism, media & communication network: Final report 2015

The JoMeC Network project had three key objectives. These were to: 1. Benchmark the pedagogical elements of journalism, media and communication (JoMeC) programs at Australian universities in order to develop a set of minimum academic standards, to be known as Threshold Learning Outcomes (TLOs), which would applicable to the disciplines of Journalism, Communication and/or Media Studies, and Public Relations; 2. Build a learning and teaching network of scholars across the JoMeC disciplines to support collaboration, develop leadership potential among educators, and progress shared priorities; 3. Create an online resources hub to support learning and teaching excellence and foster leadership in learning and teaching in the JoMeC disciplines. In order to benchmark the pedagogical elements of the JoMeC disciplines, the project started with a comprehensive review of the disciplinary settings of journalism, media and communication-related programs within Higher Education in Australia plus an analysis of capstone units (or subjects) offered in JoMeC-related degrees. This audit revealed a diversity of degree titles, disciplinary foci, projected career outcomes and pedagogical styles in the 36 universities that offered JoMeC-related degrees in 2012, highlighting the difficulties of classifying the JoMeC disciplines collectively or singularly. Instead of attempting to map all disciplines related to journalism, media and communication, the project team opted to create generalised TLOs for these fields, coupled with detailed TLOs for bachelor-level qualifications in three selected JoMeC disciplines: Journalism, Communication and/or Media Studies, and Public Relations. The initial review’s outcomes shaped the methodology that was used to develop the TLOs. Given the complexity of the JoMeC disciplines and the diversity of degrees across the network, the project team deployed an issue-framing process to create TLO statements. This involved several phases, including discussions with an issue-framing team (an advisory group of representatives from different disciplinary areas); research into accreditation requirements and industry-produced materials about employment expectations; evaluation of learning outcomes from universities across Australia; reviews of scholarly literature; as well as input from disciplinary leaders in a variety of forms. Draft TLOs were refined after further consultation with industry stakeholders and the academic community via email, telephone interviews, and meetings and public forums at conferences. This process was used to create a set of common TLOs for JoMeC disciplines in general and extended TLO statements for the specific disciplines of Journalism and Public Relations. A TLO statement for Communication and/or Media Studies remains in draft form. The Australian and New Zealand Communication Association (ANZCA) and Journalism Education and Research Association of Australian (JERAA) have agreed to host meetings to review, revise and further develop the TLOs. The aim is to support the JoMeC Network’s sustainability and the TLOs’ future development and use.

Deeper and wider fully convolutional network coupled with conditional random fields for scene labeling

Deep convolutional neural networks (DCNNs) have been employed in many computer vision tasks with great success due to their robustness in feature learning. One of the advantages of DCNNs is their representation robustness to object locations, which is useful for object recognition tasks. However, this also discards spatial information, which is useful when dealing with topological information of the image (e.g. scene labeling, face recognition). In this paper, we propose a deeper and wider network architecture to tackle the scene labeling task. The depth is achieved by incorporating predictions from multiple early layers of the DCNN. The width is achieved by combining multiple outputs of the network. We then further refine the parsing task by adopting graphical models (GMs) as a post-processing step to incorporate spatial and contextual information into the network. The new strategy for a deeper, wider convolutional network coupled with graphical models has shown promising results on the PASCAL-Context dataset.

Designing a blended learning model for primary school language learning

In Finland, there is a desperate need for flexible, reliable and functional multi-e-learning settings for pupils aged 11-13. Southern Finland has several ongoing e-learning projects, but none that develop a multiple setting, with learning and teaching occurring between more than two schools. In 2006, internet connections were not broadband and data transfer was mainly audio data. Connections and technical problems occurred, which were an obstacle to multi-e-learning. Internet connections today enable web-based learning in major parts of
Lapland and by 2015, broadband will reach even the remotest villages up north. Therefore, it is important to research the possibilities of multi-e-learning and to build collaborative, learner-centred, versatile network models for primary school-aged pupils. The resulting model will facilitate distance learning to extend education to rural, sparsely populated areas, and it will give a model of using mobile devices in language portfolios. This will promote regional equality and prevent exclusion. Working with portfolios provides the opportunity to develop mobility from a pedagogical point of view. It is important to study the pros and cons of mobile devices in producing artefacts on portfolios in e-learning and language learning settings.
The current study represents a design-based research approach. The design research approach includes two important aspects concerning the current research: ‘a teacher as researcher’ aspect, which means there is the possibility to be strongly involved in developing processes and an obstacle-aspect, which means that problems while developing, are seen as a
promoter in evolving the designed model, as apposed to negative results.

Designing a blended learning model for primary school language learning

In Finland, there is a desperate need for flexible, reliable and functional multi-e-learning settings for pupils aged 11-13. Southern Finland has several ongoing e-learning projects, but none that develop a multiple setting, with learning and teaching occurring between more than two schools. In 2006, internet connections were not broadband and data transfer was mainly audio data. Connections and technical problems occurred, which were an obstacle to multi-e-learning. Internet connections today enable web-based learning in major parts of Lapland and by 2015, broadband will reach even the remotest villages up north. Therefore, it is important to research the possibilities of multi-e-learning and to build collaborative, learner-centred, versatile network models for primary school-aged pupils. The resulting model will facilitate distance learning to extend education to rural, sparsely populated areas, and it will give a model of using mobile devices in language portfolios. This will promote regional equality and prevent exclusion. Working with portfolios provides the opportunity to develop mobility from a pedagogical point of view. It is important to study the pros and cons of mobile devices in producing artefacts on portfolios in e-learning and language learning settings. The current study represents a design-based research approach. The design research approach includes two important aspects concerning the current research: ‘a teacher as researcher’ aspect, which means there is the possibility to be strongly involved in developing processes and an obstacle-aspect, which means that problems while developing, are seen as a promoter in evolving the designed model, as apposed to negative results.

E-learning in a rural context: Alternative media and contemporary applications

This report has been written as part of the E-ruralnet –project that addresses e-learning as a means for enhancing lifelong learning opportunities in rural areas, with emphasis on SMEs, micro-enterprises, self-employed and persons seeking employment. E-ruralnet is a European network project part-funded by the European Commission in the context of the Lifelong Learning Programme, Transversal projects-ICT. This report aims to address two issues identified as requiring attention in the previous Observatory study: firstly, access to e-learning for rural areas that have not adequate ICT infrastructure; and secondly new learning approaches introduced through new interactive ICT tools such as web 2.0., wikis, podcasts etc. The possibility of using alternative technology in addition to computers is examined (mobile telephones, DVDs) as well as new approaches to learning (simulation, serious games). The first part of the report examines existing literature on e-learning and what e-learning is all about. Institutional users, learners and instructors/teachers are all looked at separately. We then turn to the implementation of e-learning from the organizational point of view and focus on quality issues related to e-learning. The report includes a separate chapter or e-learning from the rural perspective since most of Europe is geographically speaking rural and the population in those areas is that which could most benefit from the possibilities introduced by the e-learning development. The section titled “Alternative media”, in accordance with the project terminology, looks at standalone technology that is of particular use to rural areas without proper internet connection. It also evaluates the use of new tools and media in e-learning and takes a look at m-learning. Finally, the use of games, serious games and simulations in learning is considered. Practical examples and cases are displayed in a box to facilitate pleasant reading.

Network Knowledge versus Cluster Knowledge- The Gordian Knot of Knowledge Transfer Concepts

Both management scholars and economic geographers have studied knowledge and argued that the ability to transfer knowledge is critical to competitive success. Networks and other forms for cooperation are often the context when analyzing knowledge transfer within management research, while economic geographers focus on the role of the cluster for knowledge transfer and creation. With the common interest in knowledge transfer, few attempts to interdisciplinary research have been made. The aim of this paper is to outline the knowledge transfer concepts in the two strands of literature of management and economic geography (EG). The paper takes an analytical approach to review the existing contributions and seek to identify the benefits of further interaction between the disciplines. Furthermore, it offers an interpretation of the concepts of cluster and network, and suggests a clearer distinction between their respective definitions. The paper posits that studies of internal networks transcending national borders and clusters are not necessarily mutually exclusive when it comes to transfer of knowledge and the learning process of the firm. Our conclusion is that researchers in general seem to increasingly acknowledge the importance of studying both the effect of and the need for geographical proximity and external networks for the knowledge transfer process, but that there exists equivocalness in defining clusters and networks.

Discriminative learning of Bayesian networks via factorized conditional log-likelihood

We propose an efficient and parameter-free scoring criterion, the factorized conditional log-likelihood (ˆfCLL), for learning Bayesian network classifiers. The proposed score is an approximation of the conditional log-likelihood criterion. The approximation is devised in order to guarantee decomposability over the network structure, as well as efficient estimation of the optimal parameters, achieving the same time and space complexity as the traditional log-likelihood scoring criterion. The resulting criterion has an information-theoretic interpretation based on interaction information, which exhibits its discriminative nature. To evaluate the performance of the proposed criterion, we present an empirical comparison with state-of-the-art classifiers. Results on a large suite of benchmark data sets from the UCI repository show that ˆfCLL-trained classifiers achieve at least as good accuracy as the best compared classifiers, using significantly less computational resources.

Machine learning modelling for predicting soil liquefaction susceptibility

This study describes two machine learning techniques applied to predict liquefaction susceptibility of soil based on the standard penetration test (SPT) data from the 1999 Chi-Chi, Taiwan earthquake. The first machine learning technique which uses Artificial Neural Network (ANN) based on multi-layer perceptions (MLP) that are trained with Levenberg-Marquardt backpropagation algorithm. The second machine learning technique uses the Support Vector machine (SVM) that is firmly based on the theory of statistical learning theory, uses classification technique. ANN and SVM have been developed to predict liquefaction susceptibility using corrected SPT (N-1)(60)] and cyclic stress ratio (CSR). Further, an attempt has been made to simplify the models, requiring only the two parameters (N-1)(60) and peck ground acceleration (a(max)/g)], for the prediction of liquefaction susceptibility. The developed ANN and SVM models have also been applied to different case histories available globally. The paper also highlights the capability of the SVM over the ANN models.

Learning the global maximum with parameterized learning automata

A feedforward network composed of units of teams of parameterized learning automata is considered as a model of a reinforcement teaming system. The internal state vector of each learning automaton is updated using an algorithm consisting of a gradient following term and a random perturbation term. It is shown that the algorithm weakly converges to a solution of the Langevin equation implying that the algorithm globally maximizes an appropriate function. The algorithm is decentralized, and the units do not have any information exchange during updating. Simulation results on common payoff games and pattern recognition problems show that reasonable rates of convergence can be obtained.