All Fact is Beautiful Theory: The Romantic Philip Selznick

Properly understood, Philip Selznick is a chastened romantic of the Left and is mischaracterized as a man of the Right. To Marx, Selznick added insights derived form Freud and Dewey. He was committed to the moral primacy of facts and the conditions under which they realized values. Selznick’s organicism is discussed and critiqued

All Fact is Beautiful Theory: The Romantic Philip Selznick

Properly understood, Philip Selznick is a chastened romantic of the Left and is mischaracterized as a man of the Right. To Marx, Selznick added insights derived form Freud and Dewey. He was committed to the moral primacy of facts and the conditions under which they realized values. Selznick’s organicism is discussed and critiqued

The Framing Effects of Professionalism: Is There a Lawyer Cast of Mind? Lessons from Compliance Programs

Professionals working inside companies may bring with them frames of mind set by their professional experience and socialization. Lawyers, in particular, are said to think like a lawyer -to have a lawyer cast of mind. In seeking power within a company and in exercising the power that they obtain, professionals may draw on their professional background to frame, name, diagnose, and prescribe a remedy for the company\u27s problems. In making decisions about their compliance with the law, companies are constrained not only by their environment, but also by their agents\u27 understanding of whose (or what) interests the company should serve. In particular, compliance managers\u27 understandings will frame and influence their companies\u27 calculations of the value, benefits, and costs of compliance activities. The profession of the compliance manager then may influence how the company complies with the law. This Article uses data from a survey of 999 large Australian businesses to examine the professional background of the person in charge of compliance and (1) how they analyze the costs, benefits and risks of non-compliance; and (2) their company\u27s structures and practices of compliance. Contrary to our hypotheses, we find that the professional background of the individual responsible for compliance has little impact on a company\u27s compliance management structures and practices or assessment of stakeholders. The exceptions are that having a lawyer in charge of compliance is associated with the company\u27s perception of heightened legal risk; and where the person in charge of compliance is a lawyer, the company compliance efforts will be marked by manuals and training programs, but not more fulsome compliance structures, which are present when a compliance specialist leads the department. Unfortunately, our data also reveals that these compliance structures are generally merely formal-and likely largely symbolic

The Framing Effects of Professionalism: Is There a Lawyer Cast of Mind? Lessons from Compliance Programs

Professionals working inside companies may bring with them frames of mind set by their professional experience and socialization. Lawyers, in particular, are said to think like a lawyer -to have a lawyer cast of mind. In seeking power within a company and in exercising the power that they obtain, professionals may draw on their professional background to frame, name, diagnose, and prescribe a remedy for the company\u27s problems. In making decisions about their compliance with the law, companies are constrained not only by their environment, but also by their agents\u27 understanding of whose (or what) interests the company should serve. In particular, compliance managers\u27 understandings will frame and influence their companies\u27 calculations of the value, benefits, and costs of compliance activities. The profession of the compliance manager then may influence how the company complies with the law. This Article uses data from a survey of 999 large Australian businesses to examine the professional background of the person in charge of compliance and (1) how they analyze the costs, benefits and risks of non-compliance; and (2) their company\u27s structures and practices of compliance. Contrary to our hypotheses, we find that the professional background of the individual responsible for compliance has little impact on a company\u27s compliance management structures and practices or assessment of stakeholders. The exceptions are that having a lawyer in charge of compliance is associated with the company\u27s perception of heightened legal risk; and where the person in charge of compliance is a lawyer, the company compliance efforts will be marked by manuals and training programs, but not more fulsome compliance structures, which are present when a compliance specialist leads the department. Unfortunately, our data also reveals that these compliance structures are generally merely formal-and likely largely symbolic

NiftyNet: a deep-learning platform for medical imaging

Medical image analysis and computer-assisted intervention problems are increasingly being addressed with deep-learning-based solutions. Established deep-learning platforms are flexible but do not provide specific functionality for medical image analysis and adapting them for this application requires substantial implementation effort. Thus, there has been substantial duplication of effort and incompatible infrastructure developed across many research groups. This work presents the open-source NiftyNet platform for deep learning in medical imaging. The ambition of NiftyNet is to accelerate and simplify the development of these solutions, and to provide a common mechanism for disseminating research outputs for the community to use, adapt and build upon. NiftyNet provides a modular deep-learning pipeline for a range of medical imaging applications including segmentation, regression, image generation and representation learning applications. Components of the NiftyNet pipeline including data loading, data augmentation, network architectures, loss functions and evaluation metrics are tailored to, and take advantage of, the idiosyncracies of medical image analysis and computer-assisted intervention. NiftyNet is built on TensorFlow and supports TensorBoard visualization of 2D and 3D images and computational graphs by default. We present 3 illustrative medical image analysis applications built using NiftyNet: (1) segmentation of multiple abdominal organs from computed tomography; (2) image regression to predict computed tomography attenuation maps from brain magnetic resonance images; and (3) generation of simulated ultrasound images for specified anatomical poses. NiftyNet enables researchers to rapidly develop and distribute deep learning solutions for segmentation, regression, image generation and representation learning applications, or extend the platform to new applications.Comment: Wenqi Li and Eli Gibson contributed equally to this work. M. Jorge Cardoso and Tom Vercauteren contributed equally to this work. 26 pages, 6 figures; Update includes additional applications, updated author list and formatting for journal submissio