Terminologia Anatomica; Considered from the Perspective of Next-Generation Knowledge Sources

This report examines the semantic structure of Terminologia Anatomica, taking one randomly selected page as an example. The focus of analysis is the meaning imparted to an anatomical term by virtue of its location within the structured list. Terminologia’s structure expressed through hierarchies of headings, varied typographical styles, indentations and an alphanumeric code implies specific relationships between the terms embedded in the list. Together, terms and relationships can potentially capture essential elements of anatomical knowledge. The analysis focuses on these knowledge elements and evaluates the consistency and logic in their representation. Most critical of these elements are class inclusion and part-whole relationships, which are implied, rather than explicitly modeled by Terminologia. This limits the use of the term list to those who have some knowledge of anatomy and excludes computer programs from navigating through the terminology. Assuring consistency in the explicit representation of anatomical relationships would facilitate adoption of Terminologia as the anatomical standard by the various controlled medical terminology (CMT) projects. These projects are motivated by the need for computerizing the patient record, and their aim is to generate machineunderstandable representations of biomedical concepts, including anatomy. Because of the lack of a consistent and explicit representation of anatomy, each of these CMTs has generated it own anatomy model. None of these models is compatible with each other, yet each is consistent with textbook descriptions of anatomy. The analysis of the semantic structure of Terminologia Anatomica leads to some suggestions for enhancing the term list in ways that would facilitate its adoption as the standard for anatomical knowledge representation in biomedical informatics

Symbolic inductive bias for visually grounded learning of spoken language

A widespread approach to processing spoken language is to first automatically transcribe it into text. An alternative is to use an end-to-end approach: recent works have proposed to learn semantic embeddings of spoken language from images with spoken captions, without an intermediate transcription step. We propose to use multitask learning to exploit existing transcribed speech within the end-to-end setting. We describe a three-task architecture which combines the objectives of matching spoken captions with corresponding images, speech with text, and text with images. We show that the addition of the speech/text task leads to substantial performance improvements on image retrieval when compared to training the speech/image task in isolation. We conjecture that this is due to a strong inductive bias transcribed speech provides to the model, and offer supporting evidence for this.Comment: ACL 201

Bar Model: A Beneficial Tool in Learning Percentage

Percentage is found to be a familiar and challenging topic at the same time. Percentage has many application in daily life, yet many students still find it difficult. It might because many schools teach merely a formal procedure and pay less attention to the understanding of the reasoning behinds percentage ideas. This small study employed a design research and aimed at supporting students in understanding percentage by using bar model. Eleven 6th and 7th grade students of American International School of Rotterdam were participated in this study. A sequence of learning activities was designed to help students construct their understanding of percentage. The activities consisted of some rich contextual problems that lead to the emergence of bar model. The result revealed that the model can help students construct students’ knowledge of the relation among numbers in percentage, and also help them solve percentage problems systematically through some intermediate steps which include benchmarks

Solving Problems with the Percentage Bar

At the end of primary school all children more of less know what a percentage is, but yet they often struggle with percentage problems. This article describes a study in which students of 13 and 14 years old were given a written test with percentage problems and a week later were interviewed about the way they solved some of these problems. In a teaching experiment the students were then taught the use of the percentage bar. Although the teaching experiment was very short - just one lesson - the results confirm that the percentage bar is a powerful model that deserves a central place in the teaching of percentages

A quantitative evaluation of the AVITEWRITE model of handwriting learning

Much sensory-motor behavior develops through imitation, as during the learning of handwriting by children. Such complex sequential acts are broken down into distinct motor control synergies, or muscle groups, whose activities overlap in time to generate continuous, curved movements that obey an intense relation between curvature and speed. The Adaptive Vector Integration to Endpoint (AVITEWRITE) model of Grossberg and Paine (2000) proposed how such complex movements may be learned through attentive imitation. The model suggest how frontal, parietal, and motor cortical mechanisms, such as difference vector encoding, under volitional control from the basal ganglia, interact with adaptively-timed, predictive cerebellar learning during movement imitation and predictive performance. Key psycophysical and neural data about learning to make curved movements were simulated, including a decrease in writing time as learning progresses; generation of unimodal, bell-shaped velocity profiles for each movement synergy; size scaling with isochrony, and speed scaling with preservation of the letter shape and the shapes of the velocity profiles; an inverse relation between curvature and tangential velocity; and a Two-Thirds Power Law relation between angular velocity and curvature. However, the model learned from letter trajectories of only one subject, and only qualitative kinematic comparisons were made with previously published human data. The present work describes a quantitative test of AVITEWRITE through direct comparison of a corpus of human handwriting data with the model's performance when it learns by tracing human trajectories. The results show that model performance was variable across subjects, with an average correlation between the model and human data of 89+/-10%. The present data from simulations using the AVITEWRITE model highlight some of its strengths while focusing attention on areas, such as novel shape learning in children, where all models of handwriting and learning of other complex sensory-motor skills would benefit from further research.Defense Advanced Research Projects Agency and the Office of Naval Research (N00014-95-1-0409); National Institutes of Health (1-R29-DC02952-01); Office of Naval Research (N00014-92-J-1309, N00014-01-1-0624); Air Force Office of Scientific Research (F49620-01-1-0397); National Institute of Neurological Disorders and Stroke (NS 33173

Logical ontology for mediating between nursing intervention terminology systems

Objectives: Several researchers have proposed the use of logical ontologies as reference terminologies'. However, there are a number of unresolved issues. This article describes the development of a logical ontology for nursing interventions and presents the results of evaluation. Methods: Initially this study involved the development in GRAIL of two separate experimental ontologies: an ontology based on the textual content of informal definitions for nursing interventions drown from the Nursing interventions Classification; and an ontology based On labels for the some nursing interventions. Following initial bench-testing, the ontology based on labels was selected for extension (to accommodate also nursing intervention components of the Home Health Care Classification System and the Omaha System), for further testing and for external evaluation. Results: A hierarchy of nursing interventions generated automatically from the experimental antolagy based on informal definitions cantained only 3 hierarchical relationships, compared to 214 for the initial ontology based on labels. For the final extended ontology based on labels, the generated hierarchy contained the three source terminology systems in entirety - there were a total of 2861 hierarchical relationships. While the results of comporative bench testing of the final ontology were fovourable, the results of external evaluation were mixed and showed little agreement between reviewers. Conclusion: This study suggests that while a logical ontology based on labels might be a useful tool for mediating between nursing intervention terminology systems, a formative consensus type development methodology might improve the approach by helping to harmonise ideological differences that may exist across the nursing profession

Solving Problems with The Percentage Bar

At the end of primary school all children more of less know what a percentage is, but yet they often struggle with percentage problems. This article describes a study in which students of 13 and 14 years old were given a written test with percentage problems and a week later were interviewed about the way they solved some of these problems. In a teaching experiment the students were then taught the use of the percentage bar. Although the teaching experiment was very short - just one lesson  -  the results confirm that the percentage bar is a powerful model that deserves a central place in the teaching of percentages

Becoming Rasuwa Relief: Practices of Multiple Engagement in Post-Earthquake Nepal

In this article, we reflect on the multiple nature of our engagements in the wake of the 7.8m earthquake that struck Nepal on April 25th 2015. Specifically, we trace the events, experiences, decisions, positions, and processes that constituted our work with a post-earthquake volunteer initiative we helped to form, called Rasuwa Relief. Using the concept of multiplicity (cf. Mol 2002), we consider the uncertain process by which Rasuwa Relief began to cohere, as a collective of diverse efforts, interventions, projects, and commitments, and how Rasuwa Relief was continually and multiply enacted through practices of engagement. As a collaborative effort that coordinated and consolidated many of our post-earthquake interventions over a period of two years, Rasuwa Relief was always in a state of becoming. This process of becoming, we suggest, indexed and informed the multiple ways that we participated and intervened in the aftermath of the earthquake—as accidental humanitarians or ‘relief workers’, as early-career scholars, and as people attempting to balance diverse personal, academic, and ethical commitments within and beyond Nepal. Based on a reflexive analysis of these multiple engagements, we also present an embedded critique of ‘humanitarian reason’ (Fassin 2012), inclusive of our own decisions and actions, alongside a selfcritical analysis of the affective factors that shaped our own ‘need to help’ (Malkki 2015)

Ontology Based Integration of Distributed and Heterogeneous Data Sources in ACGT.

In this work, we describe the set of tools comprising the Data Access Infrastructure within Advancing Clinic-genomic Trials on Cancer (ACGT), a R&D Project funded in part by the European. This infrastructure aims at improving Post-genomic clinical trials by providing seamless access to integrated clinical, genetic, and image databases. A data access layer, based on OGSA-DAI, has been developed in order to cope with syntactic heterogeneities in databases. The semantic problems present in data sources with different nature are tackled by two core tools, namely the Semantic Mediator and the Master Ontology on Cancer. The ontology is used as a common framework for semantics, modeling the domain and acting as giving support to homogenization. SPARQL has been selected as query language for the Data Access Services and the Mediator. Two experiments have been carried out in order to test the suitability of the selected approach, integrating clinical and DICOM image databases