Document Filtering for Long-tail Entities

Filtering relevant documents with respect to entities is an essential task in the context of knowledge base construction and maintenance. It entails processing a time-ordered stream of documents that might be relevant to an entity in order to select only those that contain vital information. State-of-the-art approaches to document filtering for popular entities are entity-dependent: they rely on and are also trained on the specifics of differentiating features for each specific entity. Moreover, these approaches tend to use so-called extrinsic information such as Wikipedia page views and related entities which is typically only available only for popular head entities. Entity-dependent approaches based on such signals are therefore ill-suited as filtering methods for long-tail entities. In this paper we propose a document filtering method for long-tail entities that is entity-independent and thus also generalizes to unseen or rarely seen entities. It is based on intrinsic features, i.e., features that are derived from the documents in which the entities are mentioned. We propose a set of features that capture informativeness, entity-saliency, and timeliness. In particular, we introduce features based on entity aspect similarities, relation patterns, and temporal expressions and combine these with standard features for document filtering. Experiments following the TREC KBA 2014 setup on a publicly available dataset show that our model is able to improve the filtering performance for long-tail entities over several baselines. Results of applying the model to unseen entities are promising, indicating that the model is able to learn the general characteristics of a vital document. The overall performance across all entities---i.e., not just long-tail entities---improves upon the state-of-the-art without depending on any entity-specific training data.Comment: CIKM2016, Proceedings of the 25th ACM International Conference on Information and Knowledge Management. 201

Bio-Inspired Multi-Layer Spiking Neural Network Extracts Discriminative Features from Speech Signals

Spiking neural networks (SNNs) enable power-efficient implementations due to their sparse, spike-based coding scheme. This paper develops a bio-inspired SNN that uses unsupervised learning to extract discriminative features from speech signals, which can subsequently be used in a classifier. The architecture consists of a spiking convolutional/pooling layer followed by a fully connected spiking layer for feature discovery. The convolutional layer of leaky, integrate-and-fire (LIF) neurons represents primary acoustic features. The fully connected layer is equipped with a probabilistic spike-timing-dependent plasticity learning rule. This layer represents the discriminative features through probabilistic, LIF neurons. To assess the discriminative power of the learned features, they are used in a hidden Markov model (HMM) for spoken digit recognition. The experimental results show performance above 96% that compares favorably with popular statistical feature extraction methods. Our results provide a novel demonstration of unsupervised feature acquisition in an SNN

Information processing using a single dynamical node as complex system

Novel methods for information processing are highly desired in our information-driven society. Inspired by the brain's ability to process information, the recently introduced paradigm known as 'reservoir computing' shows that complex networks can efficiently perform computation. Here we introduce a novel architecture that reduces the usually required large number of elements to a single nonlinear node with delayed feedback. Through an electronic implementation, we experimentally and numerically demonstrate excellent performance in a speech recognition benchmark. Complementary numerical studies also show excellent performance for a time series prediction benchmark. These results prove that delay-dynamical systems, even in their simplest manifestation, can perform efficient information processing. This finding paves the way to feasible and resource-efficient technological implementations of reservoir computing

Towards a framework for critical citizenship education

Increasingly countries around the world are promoting forms of "critical" citizenship in the planned curricula of schools. However, the intended meaning behind this term varies markedly and can range from a set of creative and technical skills under the label "critical thinking" to a desire to encourage engagement, action and political emancipation, often labelled "critical pedagogy". This paper distinguishes these manifestations of the "critical" and, based on an analysis of the prevailing models of critical pedagogy and citizenship education, develops a conceptual framework for analysing and comparing the nature of critical citizenship

Non-invasive beam profile monitor for medical accelerators

A beam profile monitor based on a supersonic gas-curtain is currently under development for transverse profile diagnostics of electron and proton beams in the High Luminosity LHC. This monitor uses a thin supersonic gas curtain that crosses the primary beam to be characterized under an angle of 45 degrees. The fluorescence caused by the interaction between the beam and gas-curtain is detected using a specially designed imaging system to determine the 2D transverse profile of the primary beam. Another prototype monitor based on beam induced ionization is installed at The Cockcroft Institute. This paper presents the design features of both the monitors, the gas-jet curtain formation and various experimental tests, including profile measurements of an electron beam, using helium, nitrogen and neon as gases. Such a non-invasive online beam profile monitor would be highly desirable also for medical LINAC’s and storage rings as it can characterize the beam without stopping machine operation. The paper discusses opportunities for simplifying the monitor design for integration into a medical accelerator and expected monitor performance

Mining clinical relationships from patient narratives

Background The Clinical E-Science Framework (CLEF) project has built a system to extract clinically significant information from the textual component of medical records in order to support clinical research, evidence-based healthcare and genotype-meets-phenotype informatics. One part of this system is the identification of relationships between clinically important entities in the text. Typical approaches to relationship extraction in this domain have used full parses, domain-specific grammars, and large knowledge bases encoding domain knowledge. In other areas of biomedical NLP, statistical machine learning (ML) approaches are now routinely applied to relationship extraction. We report on the novel application of these statistical techniques to the extraction of clinical relationships. Results We have designed and implemented an ML-based system for relation extraction, using support vector machines, and trained and tested it on a corpus of oncology narratives hand-annotated with clinically important relationships. Over a class of seven relation types, the system achieves an average F1 score of 72%, only slightly behind an indicative measure of human inter annotator agreement on the same task. We investigate the effectiveness of different features for this task, how extraction performance varies between inter- and intra-sentential relationships, and examine the amount of training data needed to learn various relationships. Conclusion We have shown that it is possible to extract important clinical relationships from text, using supervised statistical ML techniques, at levels of accuracy approaching those of human annotators. Given the importance of relation extraction as an enabling technology for text mining and given also the ready adaptability of systems based on our supervised learning approach to other clinical relationship extraction tasks, this result has significance for clinical text mining more generally, though further work to confirm our encouraging results should be carried out on a larger sample of narratives and relationship types

Dynamics and genetics of a disease driven species decline to near extinction: lessons for conservation

Amphibian chytridiomycosis has caused precipitous declines in hundreds of species worldwide. By tracking mountain chicken (Leptodactylus fallax) populations before, during and after the emergence of chytridiomycosis, we quantified the real-time species level impacts of this disease. We report a range-wide species decline amongst the fastest ever recorded, with a loss of over 85% of the population in fewer than 18 months on Dominica and near extinction on Montserrat. Genetic diversity declined in the wild, but emergency measures to establish a captive assurance population captured a representative sample of genetic diversity from Montserrat. If the Convention on Biological Diversity’s targets are to be met, it is important to evaluate the reasons why they appear consistently unattainable. The emergence of chytridiomycosis in the mountain chicken was predictable, but the decline could not be prevented. There is an urgent need to build mitigation capacity where amphibians are at risk from chytridiomycosis

Finishing the euchromatic sequence of the human genome

The sequence of the human genome encodes the genetic instructions for human physiology, as well as rich information about human evolution. In 2001, the International Human Genome Sequencing Consortium reported a draft sequence of the euchromatic portion of the human genome. Since then, the international collaboration has worked to convert this draft into a genome sequence with high accuracy and nearly complete coverage. Here, we report the result of this finishing process. The current genome sequence (Build 35) contains 2.85 billion nucleotides interrupted by only 341 gaps. It covers ∼99% of the euchromatic genome and is accurate to an error rate of ∼1 event per 100,000 bases. Many of the remaining euchromatic gaps are associated with segmental duplications and will require focused work with new methods. The near-complete sequence, the first for a vertebrate, greatly improves the precision of biological analyses of the human genome including studies of gene number, birth and death. Notably, the human enome seems to encode only 20,000-25,000 protein-coding genes. The genome sequence reported here should serve as a firm foundation for biomedical research in the decades ahead

Reflections on the 'History and Historians' of the black woman's role in the community of slaves: enslaved women and intimate partner sexual violence

Taking as points of inspiration Peter Parish’s 1989 book, Slavery: History and Historians, and Angela Davis’s seminal 1971 article, “Reflections on the black woman’s role in the community of slaves,” this probes both historiographically and methodologically some of the challenges faced by historians writing about the lives of enslaved women through a case study of intimate partner violence among enslaved people in the antebellum South. Because rape and sexual assault have been defined in the past as non-consensual sexual acts supported by surviving legal evidence (generally testimony from court trials), it is hard for historians to research rape and sexual violence under slavery (especially marital rape) as there was no legal standing for the rape of enslaved women or the rape of any woman within marriage. This article suggests enslaved women recognized that black men could both be perpetrators of sexual violence and simultaneously be victims of the system of slavery. It also argues women stoically tolerated being forced into intimate relationships, sometimes even staying with “husbands” imposed upon them after emancipation