Evaluating large-scale knowledge resources across languages

This paper presents an empirical evaluation in a multilingual scenario of the semantic knowledge present on publicly available large-scale knowledge resources. The study covers a wide range of manually and automatically derived large-scale knowledge resources for English and Spanish. In order to establish a fair and neutral comparison, the knowledge resources are evaluated using the same method on two Word Sense Disambiguation tasks (Senseval-3 English and Spanish Lexical Sample Tasks). First, this study empirically demonstrates that the combination of the knowledge contained in these resources surpass the most frequent sense classi er for English. Second, we also show that this large-scale topical knowledge acquired from one language can be successfully ported to other languages.Peer ReviewedPostprint (author’s final draft

Large-scale Knowledge Distillation with Elastic Heterogeneous Computing Resources

Although more layers and more parameters generally improve the accuracy of the models, such big models generally have high computational complexity and require big memory, which exceed the capacity of small devices for inference and incurs long training time. In addition, it is difficult to afford long training time and inference time of big models even in high performance servers, as well. As an efficient approach to compress a large deep model (a teacher model) to a compact model (a student model), knowledge distillation emerges as a promising approach to deal with the big models. Existing knowledge distillation methods cannot exploit the elastic available computing resources and correspond to low efficiency. In this paper, we propose an Elastic Deep Learning framework for knowledge Distillation, i.e., EDL-Dist. The advantages of EDL-Dist are three-fold. First, the inference and the training process is separated. Second, elastic available computing resources can be utilized to improve the efficiency. Third, fault-tolerance of the training and inference processes is supported. We take extensive experimentation to show that the throughput of EDL-Dist is up to 3.125 times faster than the baseline method (online knowledge distillation) while the accuracy is similar or higher.Comment: To appear in Concurrency and Computation: Practice and Experience, 16 pages, 7 figures, 5 table

Quantum system characterization with limited resources

The construction and operation of large scale quantum information devices presents a grand challenge. A major issue is the effective control of coherent evolution, which requires accurate knowledge of the system dynamics that may vary from device to device. We review strategies for obtaining such knowledge from minimal initial resources and in an efficient manner, and apply these to the problem of characterization of a qubit embedded into a larger state manifold, made tractable by exploiting prior structural knowledge. We also investigate adaptive sampling for estimation of multiple parameters

Systematic identification and correction of spelling errors in the Foundational Model of Anatomy

We describe a method for automating the detection and correction of spelling errors in the Foundational Model of Anatomy (FMA). The FMA was tokenized into 4893 distinct words; misspellings were identified and corrected using the National Library of Medicine’s SPECIALIST GSpell Spelling Suggestion API. We identified 43 errors occurring in 97 terms, and 6 words of questionable or inconsistent spelling occurring in 26 terms. These errors are replicated in other reference terminologies that use the FMA. Our approach may be useful as part of a quality assurance process for other large-scale biomedical knowledge resources

From knowledge dependence to knowledge creation: Industrial growth and the technological advance of the Japanese electronics industry

The thrust of the argument put forward in this paper is that the postwar technological advance of the Japanese electronics industry was in essence a product not a primary cause of industrial growth. We demonstrate that the industry's surge forward resulted from the interaction of a unique combination of political, economic and cultural forces. Business leaders took full advantage by investing on a massive scale in physical, organizational, human and technological resources. It was success in the marketplace and strong cash flows that allowed Japanese firms to import technology on a large scale, invest in scientists and engineers, and progressively develop world class technological capabilities. In establishing themselves as global players, Japanese electronics firms moved over the years from a position of knowledge dependence to one of knowledge creation. We explore how this transformation was achieved and how they learned to control and exploit knowledge creating systems and processes. In particular, we establish the multi-faceted context and complex set of relationships that have conditioned strategic decision making and the creation of technological capabilities

Interdisciplinary perspectives on the development, integration and application of cognitive ontologies

We discuss recent progress in the development of cognitive ontologies and summarize three challenges in the coordinated development and application of these resources. Challenge 1 is to adopt a standardized definition for cognitive processes. We describe three possibilities and recommend one that is consistent with the standard view in cognitive and biomedical sciences. Challenge 2 is harmonization. Gaps and conflicts in representation must be resolved so that these resources can be combined for mark-up and interpretation of multi-modal data. Finally, Challenge 3 is to test the utility of these resources for large-scale annotation of data, search and query, and knowledge discovery and integration. As term definitions are tested and revised, harmonization should enable coordinated updates across ontologies. However, the true test of these definitions will be in their community-wide adoption which will test whether they support valid inferences about psychological and neuroscientific data

RegenBase: a knowledge base of spinal cord injury biology for translational research.

Spinal cord injury (SCI) research is a data-rich field that aims to identify the biological mechanisms resulting in loss of function and mobility after SCI, as well as develop therapies that promote recovery after injury. SCI experimental methods, data and domain knowledge are locked in the largely unstructured text of scientific publications, making large scale integration with existing bioinformatics resources and subsequent analysis infeasible. The lack of standard reporting for experiment variables and results also makes experiment replicability a significant challenge. To address these challenges, we have developed RegenBase, a knowledge base of SCI biology. RegenBase integrates curated literature-sourced facts and experimental details, raw assay data profiling the effect of compounds on enzyme activity and cell growth, and structured SCI domain knowledge in the form of the first ontology for SCI, using Semantic Web representation languages and frameworks. RegenBase uses consistent identifier schemes and data representations that enable automated linking among RegenBase statements and also to other biological databases and electronic resources. By querying RegenBase, we have identified novel biological hypotheses linking the effects of perturbagens to observed behavioral outcomes after SCI. RegenBase is publicly available for browsing, querying and download.Database URL:http://regenbase.org

Designing Open Educational Resources through Knowledge Maps to enhance Meaningful learning

This paper demonstrates some pedagogical strategies for developing Open Educational Resources (OERs) using the knowledge mapping tool Compendium. It also describes applications of Knowledge Maps to facilitate meaningful learning by focusing on specific OER examples. The study centres on the OpenLearn project, a large scale online environment that makes a selection of higher education learning resources freely available via the internet. OpenLearn, which is supportedby William and Flora Hewlett Foundation, was launched in October 2006 and in the two year period of its existence hasreleased over 8,100 learning hours of the OU's distance learning resources for free access and modification by learnersand educators under the Creative Commons license. OpenLearn also offers three knowledge media tools: Compendium(knowledge mapping software), MSG (instant messaging application with geolocation maps) and FM (web-based videoconferencing application). Compendium is a software tool for visual thinking, used to connect ideas, concepts, arguments, websites and documents. There are numerous examples of OERs that have been developed and delivered by institutions across the world, for example, MIT, Rice, Utah State, Core, Paris Tech, JOCW. They present a wide variety of learning materials in terms of styles as well as differing subject content. Many such offerings are based upon original lecture notes, hand-outs and other related papers used in face-to-face teaching. Openlearn OERs, however, are reconstructed from original self study distance learning materials developed at the Open University and from a vast academic catalogue of materials. Samples of these “units” comprise a variety of formats: text, images, audio and video. In this study, our findings illustratethe benefits of sharing some OER content through knowledge maps, the possibility of condensing high volumes of information,accessing resources in a more attractive way, visualising connections between diverse learning materials, connecting new ideas to familiar references, organising thinking and gaining new insights into subject specific content