Multiprobabilistic prediction in early medical diagnoses

This paper describes the methodology of providing multiprobability predictions for proteomic mass spectrometry data. The methodology is based on a newly developed machine learning framework called Venn machines. Is allows to output a valid probability interval. The methodology is designed for mass spectrometry data. For demonstrative purposes, we applied this methodology to MALDI-TOF data sets in order to predict the diagnosis of heart disease and early diagnoses of ovarian cancer and breast cancer. The experiments showed that probability intervals are narrow, that is, the output of the multiprobability predictor is similar to a single probability distribution. In addition, probability intervals produced for heart disease and ovarian cancer data were more accurate than the output of corresponding probability predictor. When Venn machines were forced to make point predictions, the accuracy of such predictions is for the most data better than the accuracy of the underlying algorithm that outputs single probability distribution of a label. Application of this methodology to MALDI-TOF data sets empirically demonstrates the validity. The accuracy of the proposed method on ovarian cancer data rises from 66.7 % 11 months in advance of the moment of diagnosis to up to 90.2 % at the moment of diagnosis. The same approach has been applied to heart disease data without time dependency, although the achieved accuracy was not as high (up to 69.9 %). The methodology allowed us to confirm mass spectrometry peaks previously identified as carrying statistically significant information for discrimination between controls and cases.

Clustering with instance and attribute level side information

Selecting a suitable proximity measure is one of the fundamental tasks in clustering. How to effectively utilize all available side information, including the instance level information in the form of pair-wise constraints, and the attribute level information in the form of attribute order preferences, is an essential problem in metric learning. In this paper, we propose a learning framework in which both the pair-wise constraints and the attribute order preferences can be incorporated simultaneously. The theory behind it and the related parameter adjusting technique have been described in details. Experimental results on benchmark data sets demonstrate the effectiveness of proposed method.

Development of enhanced curriculum through student-based research

The introduction of the new “Early Years Learning Framework” (DEEWR, 2009 has shifted, the focus in Early Childhood Centres (ECC) from incidental learning through play, to planning curriculum with play as the vehicle to achieve learning. Our Teaching and Learning Grant Project was intended to identify instances of educator practice that fit with the new framework, and video practitioners’ sessions to select vignettes to use as part of an Early Childhood Science Education Unit. The method of identifying and collecting such illustrations is examined here for its feasibility as a research design to collect data to explore a range of educator practices that fit with the new framework. The data collection method was found useful for these purposes.

Partnerships in early childhood education and care : empowering parents or empowering practitioners

Research acknowledges that outcomes for young children are enhanced when effective partnerships are developed between educators and families. The Australian Early Years Learning Framework provides direction for the professional practice of early childhood educators by acknowledging the importance of educators working in partnership with families. In the Victorian state-based early years framework, family-centred practice has been included as the practice model. Family-centred practice has as its core a philosophy of professionals supporting the empowerment of parents as active decision makers for their child. The early childhood education and care sector in Australia, however, is made up of a workforce which is largely perceived as being undervalued as a profession. This raises questions as to the capacity of these educators to support the empowerment of parents when they themselves are coming from a position of disempowerment due to their professional status. This article reports on findings from a small-scale study of childhood educators working in a long day-care setting which aimed to identify perceptions of the partnerships that exist between themselves and parents. In the course of the investigation, it became evident that some of educators felt disempowered in the relationships that exist with some families.

Finding common ground: enhancing interaction between domestic and international students in higher education

A feature of Australian Higher Education over the last 10 years has been the increased numbers of international students. This feature has been perceived to have great potential for enhanced learning for all students – both international and domestic. Yet, student surveys and research clearly indicate that there is very little interaction occurring between domestic and international students. This article reports on a study that investigated the extent to which university teaching can promote interaction between students from diverse cultural and linguistic background. Using an innovative video-analysis methodology, the research found that academics engage in a variety of activities to encourage interaction between student groups. In order to assist academics in planning interaction, one of the main findings of the study was the development of ‘The Interaction for Learning Framework (ILF)’, that identifies key dimensions for curriculum design that can be used by academics to inform ways that they can enhance interaction between diverse student groups within teaching and learning contexts.

The intentional teaching of science in early childhood settings

The introduction of the new “Early Years Learning Framework” (DEEWR, 2009) has shifted the focus in Early Childhood Centres (ECC) from incidental learning through play, to planning curriculum with play as the vehicle to achieve learning in science. Using student research, we identified instances of educator practice that fitted with the new framework, and videoed practitioners’ sessions to select vignettes to use as part of an Early Childhood Science Education Unit. This presentation will discuss the varied practitioners’ approaches we saw and students’ and teachers’ interpretations of forms of ‘intentional teaching, with particular reference to science. We formulated an ‘Intentional Teaching Spectrum’ for the purpose of locating along it early childhood educator practice that differed in the opportunities that practice provided for creative and innovative children’s activity. As this work is not yet complete, full analysis of the video material is yet to be completed, so descriptive aspects only will be discussed.

Theories into practice: understanding and rethinking our work with young children and the EYLF

This book introduces and explores each of the five major groups of theories described in the Early Years Learning Framework that inform practice in the early childhood field. In doing so, it addresses the need of the early childhood educators to better understand how their practice is underpinned by theories of learning and development.Educators draw from a range of perspectives, but it can sometimes be difficult to understand the link between theorists, theories and perspectives and their own practice. This book makes these links clear and easy to understand so educators can talk with more authority to colleagues, families and communities about their work, and reflect constructively on their practice.Each chapter deals with a different group of theories, briefly outlining the major thrust of each group and what it might look like in practice. Some of the major theorists are then focused on in turn. Discussion starter topics follow each theorist to promote discussion on the main topics and threads of the theorist’s thinking. This is followed by questions for reflection that relate more to thinking about the main concepts in relation to practice.

Streaming variational inference for dirichlet process mixtures

Bayesian nonparametric models are theoretically suitable to learn streaming data due to their complexity relaxation to the volume of observed data. However, most of the existing variational inference algorithms are not applicable to streaming applications since they re-quire truncation on variational distributions. In this paper, we present two truncation-free variational algorithms, one for mix-membership inference called TFVB (truncation-free variational Bayes), and the other for hard clustering inference called TFME (truncation-free maximization expectation). With these algorithms, we further developed a streaming learning framework for the popular Dirichlet process mixture (DPM) models. Our ex-periments demonstrate the usefulness of our framework in both synthetic and real-world data.

Autonomous Acquisition of Natural Situated Communication

An important part of human intelligence, both historically and operationally, is our ability to communicate. We learn how to communicate, and maintain our communicative skills, in a society of communicators – a highly effective way to reach and maintain proficiency in this complex skill. Principles that might allow artificial agents to learn language this way are in completely known at present – the multi-dimensional nature of socio-communicative skills are beyond every machine learning framework so far proposed. Our work begins to address the challenge of proposing a way for observation-based machine learning of natural language and communication. Our framework can learn complex communicative skills with minimal up-front knowledge. The system learns by incrementally producing predictive models of causal relationships in observed data, guided by goal-inference and reasoning using forward-inverse models. We present results from two experiments where our S1 agent learns human communication by observing two humans interacting in a realtime TV-style interview, using multimodal communicative gesture and situated language to talk about recycling of various materials and objects. S1 can learn multimodal complex language and multimodal communicative acts, a vocabulary of 100 words forming natural sentences with relatively complex sentence structure, including manual deictic reference and anaphora. S1 is seeded only with high-level information about goals of the interviewer and interviewee, and a small ontology; no grammar or other information is provided to S1 a priori. The agent learns the pragmatics, semantics, and syntax of complex utterances spoken and gestures from scratch, by observing the humans compare and contrast the cost and pollution related to recycling aluminum cans, glass bottles, newspaper, plastic, and wood. After 20 hours of observation S1 can perform an unscripted TV interview with a human, in the same style, without making mistakes.

Synchronous collaboration of virtual and remote laboratories

Virtual and remote laboratories(VRLs) are e-learning resources which enhance the accessibility of experimental setups providing a distance teaching framework which meets the student's hands-on learning needs. In addition, online collaborative communication represents a practical and a constructivist method to transmit the knowledge and experience from the teacher to students, overcoming physical distance and isolation. Thus, the integration of learning environments in the form of VRLs inside collaborative learning spaces is strongly desired. Considering these facts, the authors of this document present an original approach which enables user to share practical experiences while they work collaboratively through the Internet. This practical experimentation is based on VRLs, which have been integrated inside a synchronous collaborative e-learning framework. This article describes the main features of this system and its successful application for science and engineering subjects.

Organisational change and safety culture: the impact of communication

This research examines and explains the links between safety culture and communication. Safety culture is a concept that in recent years has gained prominence but there has been little applied research conducted to investigate the meaning of the concept in 'real life' settings. This research focused on a Train Operating Company undergoing change in a move towards privatisation. These changes were evident in the management of safety, the organisation of the industry and internally in their management. The Train Operating Company's management took steps to improve their safety culture and communications through the development of a cascade communication structure. The research framework employed a qualitative methodology in order to investigate the effect of the new system on safety culture. Findings of the research were that communications in the organisation failed to be effective for a number of reasons, including both cultural and logistical problems. The cultural problems related to a lack of trust in the organisation by the management and the workforce, the perception of communications as management propaganda, and asyntonic communications between those involved, whilst logistical problems related to the inherent difficulties of communicating over a geographically distributed network. An organisational learning framework was used to explain the results. It is postulated that one of the principal reasons why change, either to the safety culture or to communications, did not occur was because of the organisation's inability to learn. The research has also shown the crucial importance of trust between the members of the organisation, as this was one of the fundamental reasons why the safety culture did not change, and why safety management systems were not fully implemented. This is consistent with the notion of mutual trust in the HSC (1993) definition of safety culture. This research has highlighted its relevance to safety culture and its importance for organisational change.

Making Memorial Student-Ready: Reflections on the First Year Success Experience

In eleven short chapters faculty, academic advising staff and student union representatives discuss aspects of Memorial’s First Year Success Program (piloted as a Teaching Learning Framework initiative 2012-2017). Teaching approaches, curriculum content and policy rationales are covered in a broad view of how and why students identified as least likely to succeed at university can be academically supported. Contributors identify the singular importance of the community that First Year Success provided them and its student participants.

Predicting Transcript Production Rates in Yeast With Sparse Linear Models

To provide biological insights into transcriptional regulation, a couple of groups have recently presented models relating the promoter DNA-bound transcription factors (TFs) to downstream gene’s mean transcript level or transcript production rates over time. However, transcript production is dynamic in response to changes of TF concentrations over time. Also, TFs are not the only factors binding to promoters; other DNA binding factors (DBFs) bind as well, especially nucleosomes, resulting in competition between DBFs for binding at same genomic location. Additionally, not only TFs, but also some other elements regulate transcription. Within core promoter, various regulatory elements influence RNAPII recruitment, PIC formation, RNAPII searching for TSS, and RNAPII initiating transcription. Moreover, it is proposed that downstream from TSS, nucleosomes resist RNAPII elongation.

Here, we provide a machine learning framework to predict transcript production rates from DNA sequences. We applied this framework in the S. cerevisiae yeast for two scenarios: a) to predict the dynamic transcript production rate during the cell cycle for native promoters; b) to predict the mean transcript production rate over time for synthetic promoters. As far as we know, our framework is the first successful attempt to have a model that can predict dynamic transcript production rates from DNA sequences only: with cell cycle data set, we got Pearson correlation coefficient Cp = 0.751 and coefficient of determination r2 = 0.564 on test set for predicting dynamic transcript production rate over time. Also, for DREAM6 Gene Promoter Expression Prediction challenge, our fitted model outperformed all participant teams, best of all teams, and a model combining best team’s k-mer based sequence features and another paper’s biologically mechanistic features, in terms of all scoring metrics.

Moreover, our framework shows its capability of identifying generalizable fea- tures by interpreting the highly predictive models, and thereby provide support for associated hypothesized mechanisms about transcriptional regulation. With the learned sparse linear models, we got results supporting the following biological insights: a) TFs govern the probability of RNAPII recruitment and initiation possibly through interactions with PIC components and transcription cofactors; b) the core promoter amplifies the transcript production probably by influencing PIC formation, RNAPII recruitment, DNA melting, RNAPII searching for and selecting TSS, releasing RNAPII from general transcription factors, and thereby initiation; c) there is strong transcriptional synergy between TFs and core promoter elements; d) the regulatory elements within core promoter region are more than TATA box and nucleosome free region, suggesting the existence of still unidentified TAF-dependent and cofactor-dependent core promoter elements in yeast S. cerevisiae; e) nucleosome occupancy is helpful for representing +1 and -1 nucleosomes’ regulatory roles on transcription.

Accessible Immersion Metrics (AIM) for Second Language Acquisition : A Constructivist Innovation for the ESL Classroom

Abstract: This study was designed to validate a constructivist learning framework, herein referred to as Accessible Immersion Metrics (AIM), for second language acquisition (SLA) as well as to compare two delivery methods of the same framework. The AIM framework was originally developed in 2009 and is proposed as a “How to” guide for the application of constructivist learning principles to the second language classroom. Piloted in 2010 at Champlain College St-Lambert, the AIM model allows for language learning to occur, free of a fixed schedule, to be socially constructive through the use of task-based assessments and relevant to the learner’s life experience by focusing on the students’ needs rather than on course content.||Résumé : Cette étude a été principalement conçu pour valider un cadre d'apprentissage constructiviste, ci-après dénommé Accessible Immersion Metrics - AIM, pour l'acquisition d'une langue seconde - SLA. Le cadre de l'AIM est proposé comme un mode d'emploi pour l'application des principes constructivistes à l'apprentissage d’une langue seconde. Créé en 2009 par l'auteur, et piloté en 2010 au Collège Champlain St-Lambert, le modèle de l'AIM permet l'apprentissage des langues à se produire, sans horaire fixe et socialement constructive grâce à l'utilisation des évaluations alignées basées sur des tâches pertinentes à l'expérience de vie de l'étudiant en se concentrant sur les besoins des élèves plutôt que sur le contenu des cours.