Teaching for creativity : from sage to guide to meddler

This paper serves three purposes. First, it makes a case for seeing creativity as a key learning outcome in our times, and thus the core business of education. It then goes on to examine the nexus of creativity and pedagogy, showing the conceptual work done to demonstrate creativity as a learnable set of dispositions and capabilities. Finally and most importantly, the paper argues the value of a pedagogical approach the author calls “Meddling-in-the-Middle”, in augmenting and enhancing the repertoires of “Sage-on-the-Stage” and “Guide-on-the-Side” in order to build students' creative capacity. Examples are given of what these meta-approaches might look like in relation to the teaching of Shakespeare. The author concludes by arguing the important connection between Meddling pedagogy and creative capacity building.

Practical introduction to development of sage applications

Throughout this workshop session we have looked at various configurations of Sage as well as using the Sage UI to run Sage applications (e.g. the image viewer). More advanced usage of Sage has been demonstrated using a Sage compatible version of Paraview highlighting the potential of parallel rendering. The aim of this tutorial session is to give a practical introduction to developing visual content for a tiled display using the Sage libraries. After completing this tutorial you should have the basic tools required to develop your own custom Sage applications. This tutorial is designed for software developers and intermediate programming knowledge is assumed, along with some introductory OpenGL . You will be required to write small portions of C/C++ code to complete this worksheet. However if you do not feel comfortable writing code (or have never written in C or C++), we will be on hand throughout this session so feel free to ask for some help. We have a number of machines in this lab running a VNC client to a virtual machine running Fedora 12. You should all be able to log in with the username “escience”, and password “escience10”. Some of the commands in this worksheet require you to run them as the root user, so note the password as you may need to use it a few times. If you need to access the Internet, then use the username “qpsf01”, password “escience10”

The SAGE Handbook of Criminological Research Methods

Conducting research into crime and criminal justice carries unique challenges. This Handbook focuses on the application of 'methods' to address the core substantive questions that currently motivate contemporary criminological research. It maps a canon of methods that are more elaborated than in most other fields of social science, and the intellectual terrain of research problems with which criminologists are routinely confronted. Drawing on exemplary studies, chapters in each section illustrate the techniques (qualitative and quantitative) that are commonly applied in empirical studies, as well as the logic of criminological enquiry. Organized into five sections, each prefaced by an editorial introduction, the Handbook covers: • Crime and Criminals • Contextualizing Crimes in Space and Time: Networks, Communities and Culture • Perceptual Dimensions of Crime • Criminal Justice Systems: Organizations and Institutions • Preventing Crime and Improving Justice Edited by leaders in the field of criminological research, and with contributions from internationally renowned experts, The SAGE Handbook of Criminological Research Methods is set to become the definitive resource for postgraduates, researchers and academics in criminology, criminal justice, policing, law, and sociology.

The SAGE Handbook of Early Childhood Research

Recent decades have seen an upsurge of research with and about young children, their families and communities. The Handbook of Early Childhood Research will provide a landmark overview of the field of early childhood research and will set an agenda for early childhood research into the future. It includes 31 chapters provided by internationally recognized experts in early childhood research. The team of international contributors apply their expertise to conceptual and methodological issues in research and to relevant fields of practice and policy. The Handbook recognizes the main contexts of early childhood research: home and family contexts; out-of-home contexts such as services for young children and their families; and broader societal contexts of that evoke risk for young children.

The Gift of a Sage. Life and Wisdom of Rabbi Dr. Friedrich Hillel 1892- 1974

Manuscript by Erwin Tramer: "The Gift of a Sage. Life and Wisdom of Rabbi Dr. Friedrich Hillel". Biography of an orthodox rabbi (1865-1928), born in Wisnitz, Austrian Galicia, and who served in Leipnik, Czechoslovakia.

An efficient and light-weight multi-tasking model to achieve Intra-Agent concurrency for SAGE

Multi-agent systems implicate a high degree of concurrency at both the Inter- and Intra-Agent levels. Scalable, fault tolerant, Agent Grooming Environment (SAGE), the second generation, FIPA compliant MAS requires a built in mechanism to achieve both the Inter- and Intra-Agent concurrency. This paper dilates upon an attempt to provide a reliable, efficient and light-weight solution to provide intra-agent concurrency with-in the internal agent architecture of SAGE. It addresses the issues related to using the JAVA threading model to provide this level of concurrency to the agent and provides an alternative approach that is based on an eventdriven, concurrent and user-scalable multi-tasking model for the agent's internal model. The findings of this paper show that our proposed approach is suitable for providing an efficient and lightweight concurrent task model for SA GE and considerably outweighs the performance of multithreaded tasking model based on JAVA in terms of throughput and efficiency. This has been illustrated using the practical implementation and evaluation of both models. © 2004 IEEE.

A hybrid agent architecture for modeling autonomous agents in SAGE

This paper highlights the Hybrid agent construction model being developed that allows the description and development of autonomous agents in SAGE (Scalable, fault Tolerant Agent Grooming Environment) - a second generation FIPA-Compliant Multi-Agent system. We aim to provide the programmer with a generic and well defined agent architecture enabling the development of sophisticated agents on SAGE, possessing the desired properties of autonomous agents - reactivity, pro-activity, social ability and knowledge based reasoning. © Springer-Verlag Berlin Heidelberg 2005.

Scalable fault tolerant agent grooming environment-SAGE

Multi-agent systems (MAS) advocate an agent-based approach to software engineering based on decomposing problems in terms of decentralized, autonomous agents that can engage in flexible, high-level interactions. This chapter introduces scalable fault tolerant agent grooming environment (SAGE), a second-generation Foundation for Intelligent Physical Agents (FIPA)-compliant multi-agent system developed at NIIT-Comtec, which provides an environment for creating distributed, intelligent, and autonomous entities that are encapsulated as agents. The chapter focuses on the highlight of SAGE, which is its decentralized fault-tolerant architecture that can be used to develop applications in a number of areas such as e-health, e-government, and e-science. In addition, SAGE architecture provides tools for runtime agent management, directory facilitation, monitoring, and editing messages exchange between agents. SAGE also provides a built-in mechanism to program agent behavior and their capabilities with the help of its autonomous agent architecture, which is the other major highlight of this chapter. The authors believe that the market for agent-based applications is growing rapidly, and SAGE can play a crucial role for future intelligent applications development. © 2007, IGI Global.

MADS-box基因在水稻和太行花中的相关研究& SAGE 在水稻上应用的生物信息学分析

本论文主要涉及两部分内容：第一部分是水稻和太行花中MADS-box基因的相关研究，第二部分是水稻中SAGE技术的生物信息学分析。 MADS-box基因家族在花发育过程中起着重要的作用。水稻是单子叶植物的模式植物，其基因组序列的公布为在基因组水平上鉴定MADS-box基因提供了条件。根据已发表的水稻MADS-box基因序列，利用关键词搜索、HMMER分析、同源比较、系统进化树分析等手段，对GeneBank和TIGR水稻基因组注释中所有已知和未知的水稻MADS-box基因进行了分析。结果表明，水稻中有64个MADS-box基因编码不同氨基酸，其中已知cDNA序列的为43个。在64个基因中，有23个属于ABCDE类基因，并且其cDNA序列已知，其中的10个已有功能方面的研究。通过3’-RACE和筛选水稻花器官的cDNA文库，得到了一个A类基因片断（A5B2），两个E类基因片断（MW1，MW2）。这三个基因分别与OsMADS14，OsMADS7和OsMADS5相似或相同。禾本科植物花器官的起源，尤其是内外稃的起源仍存在疑问，A功能基因是否以及如何参与其形成成为问题的关键。初步原位杂交分析表明，OsMADS14在花发育的早期开始表达。在小穗原基发育过程中，OsMADS14在内外稃原基中有较强的表达。随着小穗的发育，OsMADS14在整个花序中都有表达。在小穗发育的后期，OsMADS14在胚珠中有较强的表达。OsMADS14的表达模式与FUL类基因一致，并且证明了其在花发育的后期仍然表达强烈，暗示其可能与胚珠发育有重要关系。 SEPALLATA基因被认为是花的特异因子，参与了花的四轮花器官的决定过程。在本文中，通过3’-，5’-RACE，从太行花花芽中克隆了一个MADS-box基因。该基因推导的的氨基酸序列含有典型的M，I，K和C四个结构域，与FBP2和SEP3的相似性较高，系统进化树分析表明该基因属于SEP3亚家族，于是将其命名为TrSEP3。TrSEP3首先在花分生组织中表达，然后在雌雄蕊原基及花瓣中表达;在成熟花中，TrSEP3仅在花瓣和雌蕊中表达。这种表达模式与其它的SEP-like基因有稍不同。TrSEP3拟南芥中过量表达后并未导致表型的改变，暗示其功能与拟南芥的SEP基因可能存在差异。选择压力分析显示TrSEP3受到了不显著的负选择压力。这些结果暗示TrSEP3的功能可能与其它的SEP-like基因有差别，值得进一步研究。 SAGE(Serial Analysis of Gene Expression)是对比样品间转录谱的差异、发现新基因的有效的方法。tag mapping是将SAGE-tag与其转录本匹配的过程，其效率直接影响对转录谱的解释，该过程受多种因素影响。目前，对水稻tag mapping过程缺少详细研究，导致其效率不高。为了确定参考数据库和其它合适的条件，我们利用EST序列和基因组序列构建了不同的参考图谱，从全长cDNA数据库中提取虚拟的SAGE-tag，研究了参考图谱、锚定酶、tag长度以及匹配方法对tag mapping效率的影响，并比较了tag mapping的准确率。结果表明，用EST序列构建的参考图谱能够匹配大多数的SAGE-tag，并具有较高的准确率;利用迭代的方法，可以充分利用基因组序列。各种锚定酶之间的差别不明显，其中NlaIII， HpyCH4V 和 AluI 表现较好;17bp的tag比较适合水稻;而用双锚定酶和17bp的tag则可以显著提高tag mapping的效率和准确率。

Event models for tumor classification with SAGE gene expression data

Serial Analysis of Gene Expression (SAGE) is a relatively new method for monitoring gene expression levels and is expected to contribute significantly to the progress in cancer treatment by enabling a precise and early diagnosis. A promising application of SAGE gene expression data is classification of tumors. In this paper, we build three event models (the multivariate Bernoulli model, the multinomial model and the normalized multinomial model) for SAGE data classification. Both binary classification and multicategory classification are investigated. Experiments on two SAGE datasets show that the multivariate Bernoulli model performs well with small feature sizes, but the multinomial performs better at large feature sizes, while the normalized multinomial performs well with medium feature sizes. The multinomial achieves the highest overall accuracy.