381 research outputs found

    Quantum Repeaters based on Single Trapped Ions

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    We analyze the performance of a quantum repeater protocol based on single trapped ions. At each node, single trapped ions embedded into high finesse cavities emit single photons whose polarization is entangled with the ion state. A specific detection of two photons at a central station located half-way between two nodes heralds the entanglement of two remote ions. Entanglement can be extended to long distances by applying successive entanglement swapping operations based on two-ion gate operations that have already been demonstrated experimentally with high precision. Our calculation shows that the distribution rate of entanglement achievable with such an ion-based quantum repeater protocol is higher by orders of magnitude than the rates that are achievable with the best known schemes based on atomic ensemble memories and linear optics. The main reason is that for trapped ions the entanglement swapping operations are performed deterministically, in contrast to success probabilities below 50 percent per swapping with linear optics. The scheme requires efficient collection of the emitted photons, which can be achieved with cavities, and efficient conversion of their wavelength, which can be done via stimulated parametric down-conversion. We also suggest how to realize temporal multiplexing, which offers additional significant speed-ups in entanglement distribution, with trapped ions

    Reliability analysis of distribution systems with photovoltaic generation using a power flow simulator and a parallel Monte Carlo approach

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    This paper presents a Monte Carlo approach for reliability assessment of distribution systems with distributed generation using parallel computing. The calculations are carried out with a royalty-free power flow simulator, OpenDSS (Open Distribution System Simulator). The procedure has been implemented in an environment in which OpenDSS is driven from MATLAB. The test system is an overhead distribution system represented by means of a three-phase model that includes protective devices. The paper details the implemented procedure, which can be applied to systems with or without distributed generation, includes an illustrative case study and summarizes the results derived from the analysis of the test system during one year. The goal is to evaluate the test system performance considering different scenarios with different level of system automation and reconfiguration, and assess the impact that distributed photovoltaic generation can have on that performance. Several reliability indices, including those related to the impact of distributed generation, are obtained for every scenario.Postprint (published version

    Temporal relation discovery between events and temporal expressions identified in clinical narrative

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    AbstractThe automatic detection of temporal relations between events in electronic medical records has the potential to greatly augment the value of such records for understanding disease progression and patients’ responses to treatments. We present a three-step methodology for labeling temporal relations using machine learning and deterministic rules over an annotated corpus provided by the 2012 i2b2 Shared Challenge. We first create an expanded training network of relations by computing the transitive closure over the annotated data; we then apply hand-written rules and machine learning with a feature set that casts a wide net across potentially relevant lexical and syntactic information; finally, we employ a voting mechanism to resolve global contradictions between the local predictions made by the learned classifier. Results over the testing data illustrate the contributions of initial prediction and conflict resolution

    End to end approach for i2b2 2012 challenge based on Cross-lingual models

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    BACKGROUND - We propose a Cross-lingual approach to i2b2 2012 challenge for Clinical Records focused on the temporal relations in clinical narratives. Corpus of discharge summaries annotated with temporal information was provided for automatically extracting : (1) clinically significant events, including both clinical concepts such as problems, tests, treatments, and clinical departments, and events relevant to the patient’s clinical timeline, such as admissions, transfers between departments, etc; (2) temporal expressions, referring to the dates, times, duration, or frequencies in the clinical text. The values of the extracted temporal expressions had to be normalized to an ISO specification standard; and (3) temporal relations, among the clinical events and temporal expressions. GOALS - The objectives involved in the current work consists on outperforming previous State of the Art for the i2b2 2012 challenge and adapting Cross-lingual model into clinical specific domain with low Data resources available. METHODS - The task has been conceived as a pipeline of different modules, an event and temporal expression token-classifier and a text-classifier for relation extraction, each of them independently developed from the other. We used XLM-RoBERTa Cross-lingual model. RESULTS - For event detection, the proposed token-classifier obtains a 0.91 Span F1. For temporal expressions, our sentence-classifier achieves a 0.91 Span F1. For temporal relation, we propose sentence classifier based on sequential-taggers that performs at 0.29 F1 measure.DESKRIBAPENA - Narratiba klinikoen domeinuan i2b2 2012 erronkarako hizkuntzarteko ikuspegia jorratzen duen soluzioa proposatzen dugu. Erronka honek txosten medikuetan islatzen diren gertaeren arteko denbora-erlazioak iragartzea du helburu. Horretarako, lan hau alde batetik (1) klinikoki esanguratsuak diren gertaerak, adibidez, kontzeptu klinikoak, probak, tratamenduak, sail klinikoak eta bestetik, (2) denbora-adierazpenak, adibidez, txostenak esleituta duen data, denbora, iraupen edo maiztasuna adierazten duten espresioak antzeman eta bukatzeko gertaera klinikoen eta (3) denbora-adierazpenen arteako erlazioak anotatuta duen corpus batetik abiatzen da. HELBURUAK - Lanaren helburuak i2b2 2012 artearen egoera hobetzea eta Cross-lingual modeloa Data baliabide baxuak dituen domeinu kliniko espezifikora egokitzea dira. METODOAK - Lana modulu desberdinetako hobi gisa ulertu da, gertaera eta denbora-adierazpenetarako sekuentzia-markatzaileak, eta denbora-erlaziorako perpaus-sailkatzailea, independenteki garatu dira. XLM-RoBERTa Cross-lingual modeloa erabili izan da lan honetan. EMAITZAK - Gertaerak atzemateko, 0.91 Span F1 exekutatzen duen sekuentzia-markatzailea proposatzen dugu. Denbora-adierazpenetarako, 0.91 Span F1 egiten duen sekuentzia-markatzailea bat proposatzen dugu. Denbora-erlaziorako, 0.29 F1 neurria egiten duten sekuentzia-markatzaileetan oinarritutako perpaus-sailkatzailea proposatzen dugu

    Semantic transfer in Verbmobil

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    This paper is a detailed discussion of semantic transfer in the context of the Verbmobil Machine Translation project. The use of semantic transfer as a translation mechanism is introduced and justified by comparison with alternative approaches. Some criteria for evaluation of transfer frameworks are discussed and a comparison is made of three different approaches to the representation of translation rules or equivalences. This is followed by a discussion of control of application of transfer rules and interaction with a domain description and inference component

    Automatic case acquisition from texts for process-oriented case-based reasoning

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    This paper introduces a method for the automatic acquisition of a rich case representation from free text for process-oriented case-based reasoning. Case engineering is among the most complicated and costly tasks in implementing a case-based reasoning system. This is especially so for process-oriented case-based reasoning, where more expressive case representations are generally used and, in our opinion, actually required for satisfactory case adaptation. In this context, the ability to acquire cases automatically from procedural texts is a major step forward in order to reason on processes. We therefore detail a methodology that makes case acquisition from processes described as free text possible, with special attention given to assembly instruction texts. This methodology extends the techniques we used to extract actions from cooking recipes. We argue that techniques taken from natural language processing are required for this task, and that they give satisfactory results. An evaluation based on our implemented prototype extracting workflows from recipe texts is provided.Comment: Sous presse, publication pr\'evue en 201
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