Strong, Light, Multifunctional Fibers of Carbon Nanotubes with Ultrahigh Conductivity

Broader applications of carbon nanotubes to real-world problems have largely gone unfulfilled because of difficult material synthesis and laborious processing. We report high-performance multifunctional carbon nanotube (CNT) fibers that combine the specific strength, stiffness, and thermal conductivity of carbon fibers with the specific electrical conductivity of metals. These fibers consist of bulk-grown CNTs and are produced by high-throughput wet spinning, the same process used to produce high-performance industrial fibers. These scalable CNT fibers are positioned for high-value applications, such as aerospace electronics and field emission, and can evolve into engineered materials with broad long-term impact, from consumer electronics to long-range power transmission

Hyperbolic Diffusion Embedding and Distance for Hierarchical Representation Learning

Finding meaningful representations and distances of hierarchical data is important in many fields. This paper presents a new method for hierarchical data embedding and distance. Our method relies on combining diffusion geometry, a central approach to manifold learning, and hyperbolic geometry. Specifically, using diffusion geometry, we build multi-scale densities on the data, aimed to reveal their hierarchical structure, and then embed them into a product of hyperbolic spaces. We show theoretically that our embedding and distance recover the underlying hierarchical structure. In addition, we demonstrate the efficacy of the proposed method and its advantages compared to existing methods on graph embedding benchmarks and hierarchical datasets

Assessment of a numerical model to reproduce event-scale erosion and deposition distributions in a braided river

Becky Goodsell and Eric Scott are thanked for field assistance. Antony Smith assisted with figure production. The field campaign wa funded by NERC Grant NE/G005427/1 and NERC Geophysical Equipment Facility Loan 892. Richard Williams was funded by NERC Grant NE/G005427/1during fieldwork and by a British Hydrological Society Travel Grant whilst visiting NIW

The efficacy of an automated feedback system for general practitioners

OBJECTIVE: An automated feedback system that produces comments about the non-adherence of general practitioners (GPs) to accepted practice guidelines for ordering diagnostic tests was developed. Before implementing the automated feedback system in daily practice, we assessed the potential effect of the system on the test ordering behaviour of GPs. DESIGN: We used a randomised controlled trial with balanced block design. SETTING: Five times six participant groups of GPs in a computer laboratory setting. INTERVENTION: The GPs reviewed a random sample of 30 request forms they filled in earlier that year. If deemed necessary, they could make changes in the tests requested. Next, the system displayed critical comments about their non-adherence to the guidelines as apparent from the (updated) request forms. SUBJECTS: Twenty-four randomly selected GPs participated. MAIN OUTCOME MEASURES: The number of requested diagnostic tests (17% with 95% confidence interval [CI]: 12-22%) and the fraction of tests ordered that were not in accordance with the practice guidelines (39% with 95% CI: 28-51%) decreased due to the comments of the automated feedback system. The GPs accepted 362 (50%) of the 729 reminders. IMPLICATIONS: Although our experiment cannot predict the size of the actual effect of the automated feedback system in daily practice, the observed effect may be seen as the maximum achievable

On Learning what to Learn: heterogeneous observations of dynamics and establishing (possibly causal) relations among them

Before we attempt to learn a function between two (sets of) observables of a physical process, we must first decide what the inputs and what the outputs of the desired function are going to be. Here we demonstrate two distinct, data-driven ways of initially deciding ``the right quantities'' to relate through such a function, and then proceed to learn it. This is accomplished by processing multiple simultaneous heterogeneous data streams (ensembles of time series) from observations of a physical system: multiple observation processes of the system. We thus determine (a) what subsets of observables are common between the observation processes (and therefore observable from each other, relatable through a function); and (b) what information is unrelated to these common observables, and therefore particular to each observation process, and not contributing to the desired function. Any data-driven function approximation technique can subsequently be used to learn the input-output relation, from k-nearest neighbors and Geometric Harmonics to Gaussian Processes and Neural Networks. Two particular ``twists'' of the approach are discussed. The first has to do with the identifiability of particular quantities of interest from the measurements. We now construct mappings from a single set of observations of one process to entire level sets of measurements of the process, consistent with this single set. The second attempts to relate our framework to a form of causality: if one of the observation processes measures ``now'', while the second observation process measures ``in the future'', the function to be learned among what is common across observation processes constitutes a dynamical model for the system evolution