Josephson and Persistent Spin Currents in Bose-Einstein Condensates of Magnons

Using the Aharonov-Casher (A-C) phase, we present a microscopic theory of the Josephson and persistent spin currents in quasi-equilibrium Bose-Einstein condensates (BECs) of magnons in ferromagnetic insulators. Starting from a microscopic spin model that we map onto a Gross-Pitaevskii Hamiltonian, we derive a two-state model for the Josephson junction between the weakly coupled magnon-BECs. We then show how to obtain the alternating-current (ac) Josephson effect with magnons as well as macroscopic quantum self-trapping in a magnon-BEC. We next propose how to control the direct-current (dc) Josephson effect electrically using the A-C phase, which is the geometric phase acquired by magnons moving in an electric field. Finally, we introduce a magnon-BEC ring and show that persistent magnon-BEC currents flow due to the A-C phase. Focusing on the feature that the persistent magnon-BEC current is a steady flow of magnetic dipoles that produces an electric field, we propose a method to directly measure it experimentally.Comment: 8 pages, 6 figures, updated into published versio

Occurrence and abundance of fungus-dwelling beetles (Ciidae) in boreal forests and clearcuts: habitat associations at two spatial scales

Insect material (> 30,000 individuals) reared from the fruiting bodies of wood-decaying Trametes fungi was compared between old-growth boreal forests and adjacent clearcuts in Finland. Sulcacis affinis and Cis hispidus occurred more frequently and were, on average, more abundant in the clearcuts. Interestingly, Octotemnus glabriculus and Cis boleti had a slightly higher frequency of occurrence in the forests, despite lower resource availability. The former also showed a higher average abundance. On average, the cluster size of Trametes fruiting bodies occurring on woody debris was higher in the clearcuts than in the forests and had a positive effect on species occurrence and abundance in these clusters. The independent effect of the macrohabitat (forest or clearcut) underscores the importance of the macrohabitat where specific resources occur, and this may override the positive effects of resource availability

THE FINE-GRAINED PLIO-PLEISTOCENE DEPOSITS IN ACHAIA – GREECE AND THEIR DISTINCTION IN CHARACTERISTIC GEOTECHNICAL UNITS

The fine grained Plio-Pleistocene sediments encountered along the Patras Ring Road project (PRR) were distinguished into two lithological units, the Upper Geotechnical and the Lower Geotechnical Unit, based on the detailed engineering geological – geotechnical mapping, at a scale of 1:5000, on fieldwork, as well as on data gained from the boreholes drilled during the design and construction of the project. These units are distinguishable, stratigraphically successive and present basic differences in lithological composition, consistency and permeability and therefore different mechanical behaviour during construction

Neighbor Selection and Weighting in User-Based Collaborative Filtering: A Performance Prediction Approach

This is the author's version of the work. It is posted here for your personal use. Not for redistribution. The definitive Version of Record was published in ACM Transactions on the Web, http://dx.doi.org/10.1145/2579993User-based collaborative filtering systems suggest interesting items to a user relying on similar-minded people called neighbors. The selection and weighting of these neighbors characterize the different recommendation approaches. While standard strategies perform a neighbor selection based on user similarities, trust-aware recommendation algorithms rely on other aspects indicative of user trust and reliability. In this article we restate the trust-aware recommendation problem, generalizing it in terms of performance prediction techniques, whose goal is to predict the performance of an information retrieval system in response to a particular query. We investigate how to adopt the preceding generalization to define a unified framework where we conduct an objective analysis of the effectiveness (predictive power) of neighbor scoring functions. The proposed framework enables discriminating whether recommendation performance improvements are caused by the used neighbor scoring functions or by the ways these functions are used in the recommendation computation. We evaluated our approach with several state-of-the-art and novel neighbor scoring functions on three publicly available datasets. By empirically comparing four neighbor quality metrics and thirteen performance predictors, we found strong predictive power for some of the predictors with respect to certain metrics. This result was then validated by checking the final performance of recommendation strategies where predictors are used for selecting and/or weighting user neighbors. As a result, we have found that, by measuring the predictive power of neighbor performance predictors, we are able to anticipate which predictors are going to perform better in neighbor-scoring-powered versions of a user-based collaborative filtering algorithm.This research was supported by the Spanish Ministry of Science and Research (TIN2011-28538-C02-01). Part of this work was carried out during the tenure of an ERCIM “Alain Bensoussan” Fellowship Programme, funded by European Comission FP7 grant agreement no. 246016

A Top-N Recommender System Evaluation Protocol Inspired by Deployed Systems

The evaluation of recommender systems is crucial for their development. In today's recommendation landscape there are many standardized recommendation algorithms and approaches, however, there exists no standardized method for experimental setup of evaluation -- not even for widely used measures such as precision and root-mean-squared error. This creates a setting where comparison of recommendation results using the same datasets becomes problematic. In this paper, we propose an evaluation protocol specifically developed with the recommendation use-case in mind, i.e. the recommendation of one or several items to an end user. The protocol attempts to closely mimic a scenario of a deployed (production) recommendation system, taking specific user aspects into consideration and allowing a comparison of small and large scale recommendat

Better Contextual Suggestions in ClueWeb12 Using Domain Knowledge Inferred from The Open Web

This paper provides an overview of our participation in the Contextual Suggestion Track. The TREC 2014 Contextual Suggestion Track allowed participants to submit personalized rankings using documents either from the Open Web or from an archived, static Web collection (ClueWeb12) collection. One of the main steps in recommending attractions for a particular user in a given context is the selection of the candidate documents. This task is more challenging when relying on ClueWeb12 collection rather than public tourist APIs for finding suggestions. In this paper, we present our approach for selecting candi- date suggestions from the entire ClueWeb12 collection using the tourist domain knowledge available in the Open Web. We show that the generated recommendations to the provided user profiles and contexts improve significantly using this inferred domain knowledge

Artist popularity: do web and social music services agree?

Recommendations based on the most popular products in a catalogue is a common technique when information about users is scarce or absent. In this paper we explore different ways to measure popularity in the music domain; more specifically, we define four indices based on three social music services and on web clicks. Our study shows, first, that for most of the indices the popularity is a rather stable signal, since it barely changes over time; and second, that the ranking of popular artists is heavily dependent on the actual index used to measure the artist's popularity

Information Retrieval and User-Centric Recommender System Evaluation

Traditional recommender system evaluation focuses on raising the accuracy, or lowering the rating prediction error of the recommendation algorithm. Recently, however, discrepancies between commonly used metrics (e.g. precision, recall, root-mean-square error) and the experienced quality from the users' have been brought to light. This project aims to address these discrepancies by attempting to develop novel means of recommender systems evaluation which encompasses qualities identified through traditional evaluation metrics and user-centric factors, e.g. diversity, serendipity, novelty, etc., as well as bringing further insights in the topic by analyzing and translating the problem of evaluation from an Information Retrieval perspective