Confidence-Based Feature Acquisition

Confidence-based Feature Acquisition (CFA) is a novel, supervised learning method for acquiring missing feature values when there is missing data at both training (learning) and test (deployment) time. To train a machine learning classifier, data is encoded with a series of input features describing each item. In some applications, the training data may have missing values for some of the features, which can be acquired at a given cost. A relevant JPL example is that of the Mars rover exploration in which the features are obtained from a variety of different instruments, with different power consumption and integration time costs. The challenge is to decide which features will lead to increased classification performance and are therefore worth acquiring (paying the cost). To solve this problem, CFA, which is made up of two algorithms (CFA-train and CFA-predict), has been designed to greedily minimize total acquisition cost (during training and testing) while aiming for a specific accuracy level (specified as a confidence threshold). With this method, it is assumed that there is a nonempty subset of features that are free; that is, every instance in the data set includes these features initially for zero cost. It is also assumed that the feature acquisition (FA) cost associated with each feature is known in advance, and that the FA cost for a given feature is the same for all instances. Finally, CFA requires that the base-level classifiers produce not only a classification, but also a confidence (or posterior probability)

Progressive Neural Networks

Learning to solve complex sequences of tasks--while both leveraging transfer and avoiding catastrophic forgetting--remains a key obstacle to achieving human-level intelligence. The progressive networks approach represents a step forward in this direction: they are immune to forgetting and can leverage prior knowledge via lateral connections to previously learned features. We evaluate this architecture extensively on a wide variety of reinforcement learning tasks (Atari and 3D maze games), and show that it outperforms common baselines based on pretraining and finetuning. Using a novel sensitivity measure, we demonstrate that transfer occurs at both low-level sensory and high-level control layers of the learned policy

Building a CTU Orientation Handbook iPad® application for first-year residents

Background: The General Internal Medicine Clinical Teaching Unit (CTU) is a challenging rotation for new residents and the optimal format of orientation has not been determined. We hypothesized that an iPad®application (app) would be a useful reference tool after residents completed their traditional large group orientation. Methods: Postgraduate year 1 (PGY1) residents were sent a link to download the free app one week before the start of their rotation. A pre-usage survey at initial login collected basic demographics. Usage data was collected to determine the sections, duration, and the timeframe from which the app was utilized.Results: Pre-usage survey data revealed that 63% of participants were female, 69% felt the app would improve orientation, and 94% were comfortable using mobile technology for medical education. Usage data showed “Teaching Sessions and Schedules,” “The Consult Note,” and “Admission Orders” were the three sections most commonly used. The most usage was during the evening call shift (10pm to 6am), followed by the morning shift (6am to 5pm).   Conclusion: The CTU Orientation App was a useful supplement to the traditional orientation. Researchers may not be able to predict what content would be most valuable in an iPad® app, thus pre-development needs-assessments and usage feedback are crucial.

Doing Things with Research through Design: With What, with Whom, and Towards What Ends?

This workshop provides a venue within CHI for research through design (RtD) practitioners to present their work and discuss how, with whom, and why it is used. Building on the success of prior RtD and design research workshops at CHI, this workshop will focus on how RtD artifacts are used, with the goal of connecting diverse works with broader methodologies in HCI and Design

The motion of a fluid-rigid disc system at the zero limit of the rigid disc radius

We consider the two-dimensional motion of the coupled system of a viscous incompressible fluid and a rigid disc moving with the fluid, in the whole plane. The fluid motion is described by the Navier-Stokes equations and the motion of the rigid body by conservation laws of linear and angular momentum. We show that, assuming that the rigid disc is not allowed to rotate, as the radius of the disc goes to zero, the solution of this system converges, in an appropriate sense, to the solution of the Navier-Stokes equations describing the motion of only fluid in the whole plane. We also prove that the trajectory of the centre of the disc, at the zero limit of its radius, coincides with a fluid particle trajectory.Comment: 29 pages, 0 figure

Patient-Specific 3D Printed Models for Education, Research and Surgical Simulation

3D printing techniques are increasingly used in engineering science, allowing the use of computer aided design (CAD) to rapidly and inexpensively create prototypes and components. There is also growing interest in the application of these techniques in a clinical context for the creation of anatomically accurate 3D printed models from medical images for therapy planning, research, training and teaching applications. However, the techniques and tools available to create 3D models of anatomical structures typically require specialist knowledge in image processing and mesh manipulation to achieve. In this book chapter we describe the advantages of 3D printing for patient education, healthcare professional education, interventional planning and implant development. We also describe how to use medical image data to segment volumes of interest, refine and prepare for 3D printing. We will use a lung as an example. The information in this section will allow anyone to create own 3D printed models from medical image data. This knowledge will be of use to anyone with little or no previous experience in medical image processing who have identified a potential application for 3D printing in a medical context, or those with a more general interest in the techniques