Using transactional distance theory to redesign an online mathematics education course for pre-service primary teachers

This paper examines the impact of a series of design changes to an online mathematics education course in terms of transactional distance between learner and teachers, pre-service education students' attitudes towards mathematics, and their development of mathematical pedagogical knowledge. Transactional distance theory (TDT) was utilised to investigate and describe the interactions among course structure, course dialogue and student autonomy in an online course over a two-year period. Findings indicate that Web 2.0 technologies, when used thoughtfully by teachers, can afford high levels of structure and dialogue. Feedback from pre-service teachers indicated an improved attitude towards mathematics and an increase in their mathematical pedagogical content knowledge. These findings have implications for universities moving towards the delivery of teacher education courses entirely online

Probing context-dependent errors in quantum processors

Gates in error-prone quantum information processors are often modeled using sets of one- and two-qubit process matrices, the standard model of quantum errors. However, the results of quantum circuits on real processors often depend on additional external "context" variables. Such contexts may include the state of a spectator qubit, the time of data collection, or the temperature of control electronics. In this article we demonstrate a suite of simple, widely applicable, and statistically rigorous methods for detecting context dependence in quantum circuit experiments. They can be used on any data that comprise two or more "pools" of measurement results obtained by repeating the same set of quantum circuits in different contexts. These tools may be integrated seamlessly into standard quantum device characterization techniques, like randomized benchmarking or tomography. We experimentally demonstrate these methods by detecting and quantifying crosstalk and drift on the publicly accessible 16-qubit ibmqx3.Comment: 11 pages, 3 figures, code and data available in source file

Moving Through the Rocky Legal Terrain to Find a Safe Royalty Clause or a New Market at the Well

This Article will explore whether re-emergence of a market at the wellhead is legally viable considering judicial rulings related to the issue and specifically, whether such a development would be held to run afoul of the implied covenant to market. This Article will review the law of each state with potential for developing Marcellus and Utica Shale gas, while keeping in mind that some states have no law on the issue, again creating uncertainty because of the divergent rulings by other states\u27 courts. Finally, this Article will discuss the merits of completely abandoning the at the well approach to valuation for purposes of royalty calculation in new leases as the preferred way of avoiding litigation over this issue

Learning a quantum computer's capability using convolutional neural networks

The computational power of contemporary quantum processors is limited by hardware errors that cause computations to fail. In principle, each quantum processor's computational capabilities can be described with a capability function that quantifies how well a processor can run each possible quantum circuit (i.e., program), as a map from circuits to the processor's success rates on those circuits. However, capability functions are typically unknown and challenging to model, as the particular errors afflicting a specific quantum processor are a priori unknown and difficult to completely characterize. In this work, we investigate using artificial neural networks to learn an approximation to a processor's capability function. We explore how to define the capability function, and we explain how data for training neural networks can be efficiently obtained for a capability function defined using process fidelity. We then investigate using convolutional neural networks to model a quantum computer's capability. Using simulations, we show that convolutional neural networks can accurately model a processor's capability when that processor experiences gate-dependent, time-dependent, and context-dependent stochastic errors. We then discuss some challenges to creating useful neural network capability models for experimental processors, such as generalizing beyond training distributions and modelling the effects of coherent errors. Lastly, we apply our neural networks to model the capabilities of cloud-access quantum computing systems, obtaining moderate prediction accuracy (average absolute error around 2-5%)

Social Work and Police Partnership: A Summons To The Village Strategies and Effective Practices

This report addresses the social work/law enforcement relationship and the role of police and other human service agencies in dealing with community problems. Traditionally, law enforcement and human service agencies share the most difficult portion of the others’ client caseloads but there has been little interagency communication or cooperation. Effective intervention and prevention requires more than police action and goes beyond the capability of any single agency. Social service has always been a key part of policing while serving victims of crime and offenders has been a major emphasis of social work. Law enforcement and social work have served the same target groups but with varying success. The community now demands that both institutions combine resources and skills to reach those in crisis and victims of crime. Problem oriented community policing is still a work in progress but there is consensus on four elements: prevention, problem solving, partnerships and organizational change. Using these elements as a foundation, this document describes police/social work partnerships that serve as a community response to crisis situations signaled by calls for police service. Heretofore, community policing has focused on developing relationships with individual citizens through foot/bike patrols, dispersed “community policing” sub-stations and neighborhood improvement. Building partnerships with human service agencies has received far less attention. Social work/police partnerships are the next logical step in the development of community policing. They meet the mandate to work together for the benefit of the whole community and to deal with chronic repeat calls for service. These calls signal a serious problem usually involving multiple forms of abuse and indicate the need for summoning the entire village to provide effective intervention and preventive services. The study was conducted to learn about the development, operation and impact of social work/police partnerships on recurring domestic violence and associated deep-rooted police service delivery problems. This document describes effective practices of five successful social work/police partnership models. Chapters I and II give the background of the problem. Chapter III describes five successful partnership models and Chapter IV provides a composite of critical effective practices gleaned from the study sites. Chapter V outlines steps for assessing the problem. Chapter VI and VII are designed to serve as a project development checklist for program planning, implementation and assessment of effectiveness

Computational translation of genomic responses from experimental model systems to humans

The high failure rate of therapeutics showing promise in mouse models to translate to patients is a pressing challenge in biomedical science. Though retrospective studies have examined the fidelity of mouse models to their respective human conditions, approaches for prospective translation of insights from mouse models to patients remain relatively unexplored. Here, we develop a semi-supervised learning approach for inference of disease-associated human differentially expressed genes and pathways from mouse model experiments. We examined 36 transcriptomic case studies where comparable phenotypes were available for mouse and human inflammatory diseases and assessed multiple computational approaches for inferring human biology from mouse datasets. We found that semi-supervised training of a neural network identified significantly more true human biological associations than interpreting mouse experiments directly. Evaluating the experimental design of mouse experiments where our model was most successful revealed principles of experimental design that may improve translational performance. Our study shows that when prospectively evaluating biological associations in mouse studies, semi-supervised learning approaches, combining mouse and human data for biological inference, provide the most accurate assessment of human in vivo disease processes. Finally, we proffer a delineation of four categories of model system-to-human "Translation Problems" defined by the resolution and coverage of the datasets available for molecular insight translation and suggest that the task of translating insights from model systems to human disease contexts may be better accomplished by a combination of translation-minded experimental design and computational approaches.Boehringer Ingelheim PharmaceuticalsInstitute for Collaborative Biotechnologies (Grant W911NF-09-0001