The question of how the human brain represents conceptual knowledge has been debated in many scientific fields. Brain imaging studies have shown that different spatial patterns of neural activation are associated with thinking about different semantic categories of pictures and words (for example, tools, buildings, and animals). We present a computational model that predicts the functional magnetic resonance imaging (fMRI) neural activation associated with words for which fMRI data are not yet available. This model is trained with a combination of data from a trillion-word text corpus and observed fMRI data associated with viewing several dozen concrete nouns. Once trained, the model predicts fMRI activation for thousands of other concrete nouns in the text corpus, with highly significant accuracies over the 60 nouns for which we currently have fMRI data.

Transfer printing represents a set of techniques for deterministic assembly of micro-and nanomaterials into spatially organized, functional arrangements with two and three-dimensional layouts. Such processes provide versatile routes not only to test structures and vehicles for scientific studies but also to high-performance, heterogeneously integrated functional systems, including those in flexible electronics, three-dimensional and/or curvilinear optoelectronics, and bio-integrated sensing and therapeutic devices. This article summarizes recent advances in a variety of transfer printing techniques, ranging from the mechanics and materials aspects that govern their operation to engineering features of their use in systems with varying levels of complexity. A concluding section presents perspectives on opportunities for basic and applied research, and on emerging use of these methods in high throughput, industrial-scale manufacturing.

Reversible control of adhesion is an important feature of many desired, existing, and potential systems, including climbing robots, medical tapes, and stamps for transfer printing. We present experimental and theoretical studies of pressure modulated adhesion between flat, stiff objects and elastomeric surfaces with sharp features of surface relief in optimized geometries. Here, the strength of nonspecific adhesion can be switched by more than three orders of magnitude, from strong to weak, in a reversible fashion. Implementing these concepts in advanced stamps for transfer printing enables versatile modes for deterministic assembly of solid materials in micro/nanostructured forms. Demonstrations in printed two- and three-dimensional collections of silicon platelets and membranes illustrate some capabilities. An unusual type of transistor that incorporates a printed gate electrode, an air gap dielectric, and an aligned array of single walled carbon nanotubes provides a device example.

Whereas people learn many different types of knowledge from diverse experiences over many years, and become better learners over time, most current machine learning systems are much more narrow, learning just a single function or data model based on statistical analysis of a single data set. We suggest that people learn better than computers precisely because of this difference, and we suggest a key direction for machine learning research is to develop software architectures that enable intelligent agents to also learn many types of knowledge, continuously over many years, and to become better learners over time. In this paper we define more precisely this never-ending learning paradigm for machine learning, and we present one case study: the Never-Ending Language Learner (NELL), which achieves a number of the desired properties of a never-ending learner. NELL has been learning to read the Web 24hrs/ day since January 2010, and so far has acquired a knowledge base with 120mn diverse, confidence-weighted beliefs (e.g., servedWith(tea,biscuits)), while learning thousands of interrelated functions that continually improve its reading competence over time. NELL has also learned to reason over its knowledge base to infer new beliefs it has not yet read from those it has, and NELL is inventing new relational predicates to extend the ontology it uses to represent beliefs. We describe the design of NELL, experimental results illustrating its behavior, and discuss both its successes and shortcomings as a case study in never-ending learning. NELL can be tracked online at http://rtw.ml.cmu.edu, and followed on Twitter at @CMUNELL. 2. RELATED WORK Previous research has considered the problem of designing machine learning agents that persist over long periods research highlights

Negative-index metamaterials (NIMs) are engineered structures with optical properties that cannot be obtained in naturally occurring materials. Recent work has demonstrated that focused ion beam and layer-by-layer electron-beam lithography can be used to pattern the necessary nanoscale features over small areas (hundreds of µm(2)) for metamaterials with three-dimensional layouts and interesting characteristics, including negative-index behaviour in the optical regime. A key challenge is in the fabrication of such three-dimensional NIMs with sizes and at throughputs necessary for many realistic applications (including lenses, resonators and other photonic components). We report a simple printing approach capable of forming large-area, high-quality NIMs with three-dimensional, multilayer formats. Here, a silicon wafer with deep, nanoscale patterns of surface relief serves as a reusable stamp. Blanket deposition of alternating layers of silver and magnesium fluoride onto such a stamp represents a process for 'inking' it with thick, multilayer assemblies. Transfer printing this ink material onto rigid or flexible substrates completes the fabrication in a high-throughput manner. Experimental measurements and simulation results show that macroscale, three-dimensional NIMs (>75 cm(2)) nano-manufactured in this way exhibit a strong, negative index of refraction in the near-infrared spectral range, with excellent figures of merit.

We consider the problem of semi-supervised learning to extract categories (e.g., academic fields, athletes) and relations (e.g., PlaysSport(athlete, sport)) from web pages, starting with a handful of labeled training examples of each category or relation, plus hundreds of millions of unlabeled web documents. Semi-supervised training using only a few labeled examples is typically unreliable because the learning task is underconstrained. This paper pursues the thesis that much greater accuracy can be achieved by further constraining the learning task, by coupling the semi-supervised training of many extractors for different categories and relations. We characterize several ways in which the training of category and relation extractors can be coupled, and present experimental results demonstrating significantly improved accuracy as a result.

Objective: To identify specific cultural and behavioural factors that might be influenced to increase colostrum feeding in a rural village in Northern Ethiopia to improve infant health. Design: Background interviews were conducted with six community health workers and two traditional birth attendants. A semi-structured tape-recorded interview was conducted with twenty mothers, most with children under the age of 5 years. Variables were: parental age and education; mother's ethnicity; number of live births and children's age; breast-feeding from birth through to weaning; availability and use of formula; and descriptions of colostrum v. other stages of breast milk. Participant interviews were conducted in Amharic and translated into English. Setting: Kossoye, a rural Amhara village with high prevalence rates of stunting: inappropriate neonatal feeding is thought to be a factor. Subjects: Women (20-60 years of age) reporting at least one live birth (range: 1-8, mean: ,4). Results: Colostrum (inger) and breast milk (yetut wotet) were seen as different substances. Colostrum was said to cause abdominal problems, but discarding a portion was sufficient to mitigate this effect. Almost all (nineteen of twenty) women breast-fed and twelve (63 %) reported ritual prelacteal feeding. A majority (fifteen of nineteen, 79 %) reported discarding colostrum and breast-feeding within 24 h of birth. Prelacteal feeding emerged as an additional factor to be targeted through educational intervention. Conclusions: To maximize neonatal health and growth, we recommend culturally tailored education delivered by community health advocates and traditional health practitioners that promotes immediate colostrum feeding and discourages prelacteal feeding.

Applications of ferroelectric ceramics, ranging from components for sensors, memory devices, microelectromechanical systems, and energy convertors, all involve planar and rigid layouts. The brittle nature of such materials and their high-temperature processing requirements limit applications to devices that involve only very small mechanical deformations and narrow classes of substrates. Here, we report a strategy for integrating nanoribbons of one of the most widely used ferroelectric ceramics, lead zirconate titanate, in "wavy" geometries, on soft, elastomeric supports to achieve reversible, linear elastic responses to large strain deformations (i.e., stretchable properties), without any loss in ferroelectric or piezoelectric properties. Theoretical and computational analysis of the mechanics account for these characteristics and also show that the amplitudes of the waves can be continuously tuned with an applied electric field, to achieve a vertical (normal) displacement range that is near 1000 times larger than is possible in conventional planar layouts. The results suggest new design and application possibilities in piezoelectric devices.