Probability density function characterization of multipartite entanglement

We propose a method to characterize and quantify multipartite entanglement for pure states. The method hinges upon the study of the probability density function of bipartite entanglement and is tested on an ensemble of qubits in a variety of situations. This characterization is also compared to several measures of multipartite entanglement.Comment: 7 pages, 2 figures; published version; title changed; further explanations and comparison with several measures of multipartite entanglement adde

On Resilient Behaviors in Computational Systems and Environments

The present article introduces a reference framework for discussing resilience of computational systems. Rather than a property that may or may not be exhibited by a system, resilience is interpreted here as the emerging result of a dynamic process. Said process represents the dynamic interplay between the behaviors exercised by a system and those of the environment it is set to operate in. As a result of this interpretation, coherent definitions of several aspects of resilience can be derived and proposed, including elasticity, change tolerance, and antifragility. Definitions are also provided for measures of the risk of unresilience as well as for the optimal match of a given resilient design with respect to the current environmental conditions. Finally, a resilience strategy based on our model is exemplified through a simple scenario.Comment: The final publication is available at Springer via http://dx.doi.org/10.1007/s40860-015-0002-6 The paper considerably extends the results of two conference papers that are available at http://ow.ly/KWfkj and http://ow.ly/KWfgO. Text and formalism in those papers has been used or adapted in the herewith submitted pape

Multipartite entanglement characterization of a quantum phase transition

A probability density characterization of multipartite entanglement is tested on the one-dimensional quantum Ising model in a transverse field. The average and second moment of the probability distribution are numerically shown to be good indicators of the quantum phase transition. We comment on multipartite entanglement generation at a quantum phase transition.Comment: 10 pages, 6 figures, final versio

Characterizing and measuring multipartite Entanglement

A method is proposed to characterize and quantify multipartite entanglement in terms of the probability density function of bipartite entanglement over all possible balanced bipartitions of an ensemble of qubits. The method is tested on a class of random pure states.Comment: 7 pages, 5 figures. Submitted to "International Journal of Quantum Information

Maximally multipartite entangled states

We introduce the notion of maximally multipartite entangled states of n qubits as a generalization of the bipartite case. These pure states have a bipartite entanglement that does not depend on the bipartition and is maximal for all possible bipartitions. They are solutions of a minimization problem. Examples for small n are investigated, both analytically and numerically.Comment: 5 pages, 1 figure, final verso

Statistical mechanics of multipartite entanglement

We characterize the multipartite entanglement of a system of n qubits in terms of the distribution function of the bipartite purity over all balanced bipartitions. We search for those (maximally multipartite entangled) states whose purity is minimum for all bipartitions and recast this optimization problem into a problem of statistical mechanics.Comment: final versio

ANALISIS TINGKAT KEPUASAN DAN TINGKAT KEPENTINGAN PENERAPAN SISTEM INFORMASI UNIVERSITAS MUHAMMADIYAH MALANG

Untuk mengetahui apakah sistem informasi Universitas Muhammadiyah Malang berjalan sebagaimana mestinya, maka diperlukan proses evaluasi terhadap kinerja dari sistem informasi tersebut. Evaluasi dapat dilakukan dengan dengan berbagai cara sesuai dengan tujuan dari evaluasi tersebut. Evaluasi yang dilakukan terhadap sistem informasi Universitas Muhammadiyah Malang dengan menggunakan PIECES Framework. Analisis dilakukan dengan menggunakan 6 fokus analisis yaitu performance, information and data, economy, control and security, efficiency, dan service. Tujuan penelitian ini adalah untuk mengetahui tingkat kepuasan dan tingkat kepentingan sistem informasi Universitas Muhammadiyah Malang, mengetahui kelemahan dan kekuatan dan menganalisis komponen apa saja yang perlu ditingkatkan kualitas pelayanannya. Data didapatkan dari penyebaran kuesioner terhadap 40 pengguna sistem informasi Universitas Muhammadiyah Malang. Hasil penelitian ini diketahui bahwa pengukuran rata-rata tingkat kepuasan berada pada 3, 6 yang berarti sistem informasi Universitas Muhammadiyah Malang telah memberikan kepuasan kepada pengguna, sedangkan untuk nilai pengukuran rata-rata tingkat kepentingan 3, 4 yang berarti sistem informasi Universitas Muhammadiyah Malang dianggap penting oleh pengguna

Time of your hate: The challenge of time in hate speech detection on social media

The availability of large annotated corpora from social media and the development of powerful classification approaches have contributed in an unprecedented way to tackle the challenge of monitoring users' opinions and sentiments in online social platforms across time. Such linguistic data are strongly affected by events and topic discourse, and this aspect is crucial when detecting phenomena such as hate speech, especially from a diachronic perspective. We address this challenge by focusing on a real case study: the "Contro l'odio" platform for monitoring hate speech against immigrants in the Italian Twittersphere. We explored the temporal robustness of a BERT model for Italian (AlBERTo), the current benchmark on non-diachronic detection settings. We tested different training strategies to evaluate how the classification performance is affected by adding more data temporally distant from the test set and hence potentially different in terms of topic and language use. Our analysis points out the limits that a supervised classification model encounters on data that are heavily influenced by events. Our results show how AlBERTo is highly sensitive to the temporal distance of the fine-tuning set. However, with an adequate time window, the performance increases, while requiring less annotated data than a traditional classifier