Decentralized learning with budgeted network load using Gaussian copulas and classifier ensembles

We examine a network of learners which address the same classification task but must learn from different data sets. The learners cannot share data but instead share their models. Models are shared only one time so as to preserve the network load. We introduce DELCO (standing for Decentralized Ensemble Learning with COpulas), a new approach allowing to aggregate the predictions of the classifiers trained by each learner. The proposed method aggregates the base classifiers using a probabilistic model relying on Gaussian copulas. Experiments on logistic regressor ensembles demonstrate competing accuracy and increased robustness in case of dependent classifiers. A companion python implementation can be downloaded at https://github.com/john-klein/DELC

Techniques for Complex Analysis of Contemporary Data

Contemporary data objects are typically complex, semi-structured, or unstructured at all. Besides, objects are also related to form a network. In such a situation, data analysis requires not only the traditional attribute-based access but also access based on similarity as well as data mining operations. Though tools for such operations do exist, they usually specialise in operation and are available for specialized data structures supported by specific computer system environments. In contrary, advance analyses are obtained by application of several elementary access operations which in turn requires expert knowledge in multiple areas. In this paper, we propose a unification platform for various data analytical operators specified as a general-purpose analytical system ADAMiSS. An extensible data-mining and similarity-based set of operators over a common versatile data structure allow the recursive application of heterogeneous operations, thus allowing the definition of complex analytical processes, necessary to solve the contemporary analytical tasks. As a proof-of-concept, we present results that were obtained by our prototype implementation on two real-world data collections: the Twitter Higg's boson and the Kosarak datasets

Experiences of refugees and asylum seekers in general practice: a qualitative study

Background: There has been much debate regarding the refugee health situation in the UK. However most of the existing literature fails to take account of the opinions of refugees themselves. This study was established to determine the views of asylum seekers and refugees on their overall experiences in primary care and to suggest improvements to their care. Methods: Qualitative study of adult asylum seekers and refugees who had entered the UK in the last 10 years. The study was set in Barnet Refugee Walk in Service, London. 11 Semi structured interviews were conducted and analysed using framework analysis. Results: Access to GPs may be more difficult for failed asylum seekers and those without support from refugee agencies or family. There may be concerns amongst some in the refugee community regarding the access to and confidentiality of professional interpreters. Most participants stated their preference for GPs who offered advice rather than prescriptions. The stigma associated with refugee status in the UK may have led to some refugees altering their help seeking behaviour. Conclusion: The problem of poor access for those with inadequate support may be improved by better education and support for GPs in how to provide for refugees. Primary Care Trusts could also supply information to newly arrived refugees on how to access services. GPs should be aware that, in some situations, professional interpreters may not always be desired and that instead, it may be advisable to reach a consensus as to who should be used as an interpreter. A better doctor-patient experience resulting from improvements in access and communication may help to reduce the stigma associated with refugee status and lead to more appropriate help seeking behaviour. Given the small nature of our investigation, larger studies need to be conducted to confirm and to quantify these results

Normal levels of p27Xic1 are necessary for somite segmentation and determining pronephric organ size

The Xenopus laevis cyclin dependent kinase inhibitor p27Xic1 has been shown to be involved in exit from the cell cycle and differentiation of cells into a quiescent state in the nervous system, muscle tissue, heart and retina. We show that p27Xic1 is expressed in the developing kidney in the nephrostomal regions. Using over-expression and morpholino oligonucleotide (MO) knock-down approaches we show normal levels of p27Xic1 regulate pronephros organ size by regulating cell cycle exit. Knock-down of p27Xic1 expression using a MO prevented myogenesis, as previously reported; an effect that subsequently inhibits pronephrogenesis. Furthermore, we show that normal levels of p27Xic1 are required for somite segmentation also through its cell cycle control function. Finally, we provide evidence to suggest correct paraxial mesoderm segmentation is not necessary for pronephric induction in the intermediate mesoderm. These results indicate novel developmental roles for p27Xic1, and reveal its differentiation function is not universally utilised in all developing tissues

Re-ranking Permutation-Based Candidate Sets with the n-Simplex Projection

In the realm of metric search, the permutation-based approaches have shown very good performance in indexing and supporting approximate search on large databases. These methods embed the metric objects into a permutation space where candidate results to a given query can be efficiently identified. Typically, to achieve high effectiveness, the permutation-based result set is refined by directly comparing each candidate object to the query one. Therefore, one drawback of these approaches is that the original dataset needs to be stored and then accessed during the refining step. We propose a refining approach based on a metric embedding, called n-Simplex projection, that can be used on metric spaces meeting the n-point property. The n-Simplex projection provides upper- and lower-bounds of the actual distance, derived using the distances between the data objects and a finite set of pivots. We propose to reuse the distances computed for building the data permutations to derive these bounds and we show how to use them to improve the permutation-based results. Our approach is particularly advantageous for all the cases in which the traditional refining step is too costly, e.g. very large dataset or very expensive metric function

Fast approximate hierarchical clustering using similarity heuristics

© 2008 Kull and Vilo; licensee BioMed Central Ltd

Search for short baseline nu(e) disappearance with the T2K near detector

8 pages, 6 figures, submitted to PRD rapid communication8 pages, 6 figures, submitted to PRD rapid communicationWe thank the J-PARC staff for superb accelerator performance and the CERN NA61 collaboration for providing valuable particle production data. We acknowledge the support of MEXT, Japan; NSERC, NRC and CFI, Canada; Commissariat `a l’Energie Atomique and Centre National de la Recherche Scientifique–Institut National de Physique Nucle´aire et de Physique des Particules, France; DFG, Germany; INFN, Italy; National Science Centre (NCN), Poland; Russian Science Foundation, RFBR and Ministry of Education and Science, Russia; MINECO and European Regional Development Fund, Spain; Swiss National Science Foundation and State Secretariat for Education, Research and Innovation, Switzerland; STFC, UK; and DOE, USA. We also thank CERN for the UA1/NOMAD magnet, DESY for the HERA-B magnet mover system, NII for SINET4, the WestGrid and SciNet consortia in Compute Canada, GridPP, UK. In addition participation of individual researchers and institutions has been further supported by funds from ERC (FP7), EU; JSPS, Japan; Royal Society, UK; DOE Early Career program, USA

Measurements of π±\pi^\pm, K±K^\pm, KS0K^0_S, Λ\Lambda and proton production in proton-carbon interactions at 31 GeV/cc with the NA61/SHINE spectrometer at the CERN SPS

Measurements of hadron production in p+C interactions at 31 GeV/c are performed using the NA61/ SHINE spectrometer at the CERN SPS. The analysis is based on the full set of data collected in 2009 using a graphite target with a thickness of 4% of a nuclear interaction length. Inelastic and production cross sections as well as spectra of $\pi^\pm$, $K^\pm$, p, $K^0_S$ and $\Lambda$ are measured with high precision. These measurements are essential for improved calculations of the initial neutrino fluxes in the T2K long-baseline neutrino oscillation experiment in Japan. A comparison of the NA61/SHINE measurements with predictions of several hadroproduction models is presented.Comment: v1 corresponds to the preprint CERN-PH-EP-2015-278; v2 matches the final published versio