Types of verbal interaction with instructable robots

An instructable robot is one that accepts instruction in some natural language such as English and uses that instruction to extend its basic repertoire of actions. Such robots are quite different in conception from autonomously intelligent robots, which provide the impetus for much of the research on inference and planning in artificial intelligence. Examined here are the significant problem areas in the design of robots that learn from vebal instruction. Examples are drawn primarily from our earlier work on instructable robots and recent work on the Robotic Aid for the physically disabled. Natural-language understanding by machines is discussed as well as in the possibilities and limits of verbal instruction. The core problem of verbal instruction, namely, how to achieve specific concrete action in the robot in response to commands that express general intentions, is considered, as are two major challenges to instructability: achieving appropriate real-time behavior in the robot, and extending the robot's language capabilities

A natural-language interface to a mobile robot

The present work on robot instructability is based on an ongoing effort to apply modern manipulation technology to serve the needs of the handicapped. The Stanford/VA Robotic Aid is a mobile manipulation system that is being developed to assist severely disabled persons (quadriplegics) in performing simple activities of everyday living in a homelike, unstructured environment. It consists of two major components: a nine degree-of-freedom manipulator and a stationary control console. In the work presented here, only the motions of the Robotic Aid's omnidirectional motion base have been considered, i.e., the six degrees of freedom of the arm and gripper have been ignored. The goal has been to develop some basic software tools for commanding the robot's motions in an enclosed room containing a few objects such as tables, chairs, and rugs. In the present work, the environmental model takes the form of a two-dimensional map with objects represented by polygons. Admittedly, such a highly simplified scheme bears little resemblance to the elaborate cognitive models of reality that are used in normal human discourse. In particular, the polygonal model is given a priori and does not contain any perceptual elements: there is no polygon sensor on board the mobile robot

Spinflop transition in dopped antiferromagnets

In this paper we compute the mean field phase diagram of a doped antiferromagnet, in a magnetic field and with anisotropic exchange. We show that at zero temperature there is a metamagnetic transition from the antiferromagnetic configuration along the z direction to a spin-flop state. In the spin flop phase the system prefers a commensurate magnetic order, at low doping, whereas at larger doping the incommensurate phase is favorable. Contrary to the pure Heisenberg case, the spin flop region does not span an infinite area in the ('Delta',h) plane, where 'Delta' is the exchange anisotropy and h is the external magnetic field. We characterize the magnetic and charge-transport properties of the spin-flop phase, computing the magnetic susceptibility and the Drude weight. This latter quantity presents a sudden variation as the spin-flop to paramagnet phase transition line is crossed. This effect could be used as a possible source of large magneto-resistance. Our findings may have some relevance for doped La_{2-\delta}Sr_{\delta}CuO_4 in a magnetic field.Comment: 18 pages. accepted for Journal of Physics: Condensed Matte

Exploring patient information needs in type 2 diabetes: A cross sectional study of questions

This study set out to analyze questions about type 2 diabetes mellitus (T2DM) from patients and the public. The aim was to better understand people's information needs by starting with what they do not know, discovered through their own questions, rather than starting with what we know about T2DM and subsequently finding ways to communicate that information to people affected by or at risk of the disease. One hundred and sixty-four questions were collected from 120 patients attending outpatient diabetes clinics and 300 questions from 100 members of the public through the Amazon Mechanical Turk crowdsourcing platform. Twenty-three general and diabetes-specific topics and five phases of disease progression were identified; these were used to manually categorize the questions. Analyses were performed to determine which topics, if any, were significant predictors of a question's being asked by a patient or the public, and similarly for questions from a woman or a man. Further analysis identified the individual topics that were assigned significantly more often to the crowdsourced or clinic questions. These were Causes (CI: [-0.07, -0.03], p < .001), Risk Factors ([-0.08, -0.03], p < .001), Prevention ([-0.06, -0.02], p < .001), Diagnosis ([-0.05, -0.02], p < .001), and Distribution of a Disease in a Population ([-0.05,-0.01], p = .0016) for the crowdsourced questions and Treatment ([0.03, 0.01], p = .0019), Disease Complications ([0.02, 0.07], p < .001), and Psychosocial ([0.05, 0.1], p < .001) for the clinic questions. No highly significant gender-specific topics emerged in our study, but questions about Weight were more likely to come from women and Psychosocial questions from men. There were significantly more crowdsourced questions about the time Prior to any Diagnosis ([(-0.11, -0.04], p = .0013) and significantly more clinic questions about Health Maintenance and Prevention after diagnosis ([0.07. 0.17], p < .001). A descriptive analysis pointed to the value provided by the specificity of questions, their potential to disclose emotions behind questions, and the as-yet unrecognized information needs they can reveal. Large-scale collection of questions from patients across the spectrum of T2DM progression and from the public-a significant percentage of whom are likely to be as yet undiagnosed-is expected to yield further valuable insights

CvManGO, a method for leveraging computational predictions to improve literature-based Gene Ontology annotations

The set of annotations at the Saccharomyces Genome Database (SGD) that classifies the cellular function of S. cerevisiae gene products using Gene Ontology (GO) terms has become an important resource for facilitating experimental analysis. In addition to capturing and summarizing experimental results, the structured nature of GO annotations allows for functional comparison across organisms as well as propagation of functional predictions between related gene products. Due to their relevance to many areas of research, ensuring the accuracy and quality of these annotations is a priority at SGD. GO annotations are assigned either manually, by biocurators extracting experimental evidence from the scientific literature, or through automated methods that leverage computational algorithms to predict functional information. Here, we discuss the relationship between literature-based and computationally predicted GO annotations in SGD and extend a strategy whereby comparison of these two types of annotation identifies genes whose annotations need review. Our method, CvManGO (Computational versus Manual GO annotations), pairs literature-based GO annotations with computational GO predictions and evaluates the relationship of the two terms within GO, looking for instances of discrepancy. We found that this method will identify genes that require annotation updates, taking an important step towards finding ways to prioritize literature review. Additionally, we explored factors that may influence the effectiveness of CvManGO in identifying relevant gene targets to find in particular those genes that are missing literature-supported annotations, but our survey found that there are no immediately identifiable criteria by which one could enrich for these under-annotated genes. Finally, we discuss possible ways to improve this strategy, and the applicability of this method to other projects that use the GO for curation

Spin dynamics from time-dependent density functional perturbation theory

We present a new method to model spin-wave excitations in magnetic solids, based on the Liouville-Lanczos approach to time-dependent density functional perturbation theory. This method avoids computationally expensive sums over empty states and naturally deals with the coupling between spin and charge fluctuations, without ever explicitly computing charge-density susceptibilities. Spin-wave excitations are obtained with one Lanczos chain per magnon wave-number and polarization, avoiding the solution of the linear-response problem for every individual value of frequency, as other state-of-the-art approaches do. Our method is validated by computing magnon dispersions in bulk Fe and Ni, resulting in agreement with previous theoretical studies in both cases, and with experiment in the case of Fe. The disagreement in the case of Ni is also comparable with that of previous computations