Band Connectivity for Topological Quantum Chemistry: Band Structures As A Graph Theory Problem

The conventional theory of solids is well suited to describing band structures locally near isolated points in momentum space, but struggles to capture the full, global picture necessary for understanding topological phenomena. In part of a recent paper [B. Bradlyn et al., Nature 547, 298 (2017)], we have introduced the way to overcome this difficulty by formulating the problem of sewing together many disconnected local "k-dot-p" band structures across the Brillouin zone in terms of graph theory. In the current manuscript we give the details of our full theoretical construction. We show that crystal symmetries strongly constrain the allowed connectivities of energy bands, and we employ graph-theoretic techniques such as graph connectivity to enumerate all the solutions to these constraints. The tools of graph theory allow us to identify disconnected groups of bands in these solutions, and so identify topologically distinct insulating phases.Comment: 19 pages. Companion paper to arXiv:1703.02050 and arXiv:1706.08529 v2: Accepted version, minor typos corrected and references added. Now 19+epsilon page

Short-Range B-site Ordering in Inverse Spinel Ferrite NiFe2O4

The Raman spectra of single crystals of NiFe2O4 were studied in various scattering configurations in close comparison with the corresponding spectra of Ni0.7Zn0.3Fe2O4 and Fe3O4. The number of experimentally observed Raman modes exceeds significantly that expected for a normal spinel structure and the polarization properties of most of the Raman lines provide evidence for a microscopic symmetry lower than that given by the Fd-3m space group. We argue that the experimental results can be explained by considering the short range 1:1 ordering of Ni2+ and Fe3+ at the B-sites of inverse spinel structure, most probably of tetragonal P4_122/P4_322 symmetry.Comment: 10 pages, 5 figures, 6 table

Crowd vs Experts: Nichesourcing for Knowledge Intensive Tasks in Cultural Heritage

The results of our exploratory study provide new insights to crowdsourcing knowledge intensive tasks. We designed and performed an annotation task on a print collection of the Rijksmuseum Amsterdam, involving experts and crowd workers in the domain-specific description of depicted flowers. We created a testbed to collect annotations from flower experts and crowd workers and analyzed these in regard to user agreement. The findings show promising results, demonstrating how, for given categories, nichesourcing can provide useful annotations by connecting crowdsourcing to domain expertise

Computational Controversy

Climate change, vaccination, abortion, Trump: Many topics are surrounded by fierce controversies. The nature of such heated debates and their elements have been studied extensively in the social science literature. More recently, various computational approaches to controversy analysis have appeared, using new data sources such as Wikipedia, which help us now better understand these phenomena. However, compared to what social sciences have discovered about such debates, the existing computational approaches mostly focus on just a few of the many important aspects around the concept of controversies. In order to link the two strands, we provide and evaluate here a controversy model that is both, rooted in the findings of the social science literature and at the same time strongly linked to computational methods. We show how this model can lead to computational controversy analytics that have full coverage over all the crucial aspects that make up a controversy.Comment: In Proceedings of the 9th International Conference on Social Informatics (SocInfo) 201

Using Neural Networks for Relation Extraction from Biomedical Literature

Using different sources of information to support automated extracting of relations between biomedical concepts contributes to the development of our understanding of biological systems. The primary comprehensive source of these relations is biomedical literature. Several relation extraction approaches have been proposed to identify relations between concepts in biomedical literature, namely, using neural networks algorithms. The use of multichannel architectures composed of multiple data representations, as in deep neural networks, is leading to state-of-the-art results. The right combination of data representations can eventually lead us to even higher evaluation scores in relation extraction tasks. Thus, biomedical ontologies play a fundamental role by providing semantic and ancestry information about an entity. The incorporation of biomedical ontologies has already been proved to enhance previous state-of-the-art results.Comment: Artificial Neural Networks book (Springer) - Chapter 1

Adaptive Web-Based Educational Hypermedia

This chapter describes recent and ongoing research to automatically personalize a learning experience through adaptive educational hypermedia. The Web had made it possible to give a very large audience access to the same learning material. Rather than offering several versions of learning material about a certain subject, for different types of leaners, adaptive educational hypermedia offers personalized learning material without the need to know a detailed classification of users before starting the learning process. We describe different approaches to making a learning experience personalized, all using adaptive hypermedia technology. We include research on authoring for adaptive learning material (the AIMS and MOT projects) and research on modeling adaptive educational applications (the LAOS project). We also cover some of our ongoing work on the AHA! system, which has been used mostly for educational hypermedia but has the potential to be used in very different application areas as wel

Pillows as adaptive interfaces in ambient environments

We have developed a set of small interactive throw pillows containing intelligent touch-sensing surfaces, in order to explore new ways to model the environment, participants, artefacts, and their interactions, in the context of expressive non-verbal interaction. We present the overall architecture of the environment, describing a model of the user, the interface (the interactive pillows and the devices it can interact with) and the context engine. We describe the representation and process modules of the context engine and demonstrate how they support real-time adaptation. We present an evaluation of the current prototype and conclude with plans for future work

Property-based interest propagation in ontology-based user model

We present an approach for propagation of user interests in ontology-based user models taking into account the properties declared for the concepts in the ontology. Starting from initial user feedback on an object, we calculate user interest in this particular object and its properties and further propagate user interest to other objects in the ontology, similar or related to the initial object. The similarity and relatedness of objects depends on the number of properties they have in common and their corresponding values. The approach we propose can support finer recommendation modalities, considering the user interest in the objects, as well as in singular properties of objects in the recommendation process. We tested our approach for interest propagation with a real adaptive application and obtained an improvement with respect to IS-A-propagation of interest values