Entangled Mechanical Oscillators

Hallmarks of quantum mechanics include superposition and entanglement. In the context of large complex systems, these features should lead to situations like Schrodinger's cat, which exists in a superposition of alive and dead states entangled with a radioactive nucleus. Such situations are not observed in nature. This may simply be due to our inability to sufficiently isolate the system of interest from the surrounding environment -- a technical limitation. Another possibility is some as-of-yet undiscovered mechanism that prevents the formation of macroscopic entangled states. Such a limitation might depend on the number of elementary constituents in the system or on the types of degrees of freedom that are entangled. One system ubiquitous to nature where entanglement has not been previously demonstrated is distinct mechanical oscillators. Here we demonstrate deterministic entanglement of separated mechanical oscillators, consisting of the vibrational states of two pairs of atomic ions held in different locations. We also demonstrate entanglement of the internal states of an atomic ion with a distant mechanical oscillator.Comment: 7 pages, 2 figure

Neural Network Parameterizations of Electromagnetic Nucleon Form Factors

The electromagnetic nucleon form-factors data are studied with artificial feed forward neural networks. As a result the unbiased model-independent form-factor parametrizations are evaluated together with uncertainties. The Bayesian approach for the neural networks is adapted for chi2 error-like function and applied to the data analysis. The sequence of the feed forward neural networks with one hidden layer of units is considered. The given neural network represents a particular form-factor parametrization. The so-called evidence (the measure of how much the data favor given statistical model) is computed with the Bayesian framework and it is used to determine the best form factor parametrization.Comment: The revised version is divided into 4 sections. The discussion of the prior assumptions is added. The manuscript contains 4 new figures and 2 new tables (32 pages, 15 figures, 2 tables

NLO Higgs boson production plus one and two jets using the POWHEG BOX, MadGraph4 and MCFM

We present a next-to-leading order calculation of Higgs boson production plus one and two jets via gluon fusion interfaced to shower Monte Carlo programs, implemented according to the POWHEG method. For this implementation we have used a new interface of the POWHEG BOX with MadGraph4, that generates the codes for generic Born and real processes automatically. The virtual corrections have been taken from the MCFM code. We carry out a simple phenomenological study of our generators, comparing them among each other and with fixed next-to-leading order results.Comment: 27 pages, 21 figure

Factorization and NNLL Resummation for Higgs Production with a Jet Veto

Using methods of effective field theory, we derive the first all-order factorization theorem for the Higgs-boson production cross section with a jet veto, imposed by means of a standard sequential recombination jet algorithm. Like in the case of small-q_T resummation in Drell-Yan and Higgs production, the factorization is affected by a collinear anomaly. Our analysis provides the basis for a systematic resummation of large logarithms log(m_H/p_T^veto) beyond leading-logarithmic order. Specifically, we present predictions for the resummed jet-veto cross section and efficiency at next-to-next-to-leading logarithmic order. Our results have important implications for Higgs-boson searches at the LHC, where a jet veto is required to suppress background events.Comment: 28 pages, 5 figures; v2: published version; note added in proo

Teacher resilience in adverse contexts: issues of professionalism and professional identity

Teacher resilience is a construct that is relative, developmental and dynamic; it is socially constructed and depends on personal and professional dispositions. Issues of commitment, professionalism, and professional identity, for instance, need to be taken into account if teacher resilience is to be fully understood. In this chapter I draw upon a larger piece of research aimed at investigating teachers’ work and lives in challenging circumstances. Data were collected through a national survey (n=2702 teachers), focus group (n=99 teachers) and interviews to 11 school principals. Findings suggest the connection between teacher commitment and resilience which are associated with issues of school culture and leadership, a sense of vocation, and teachers’ beliefs and professional values. Factors and sources of teacher motivation and resilience are also identified within a context marked by teacher intensification, lack of trust, worsening of teaching conditions, lower social and economic status and legislative “tsunami”. The chapter ends with the discussion of the importance of relationships in the teaching profession and issues of motivation and professionalism which entails given ways of being and feeling as a teacher.Financial Support by CIEC (Research Centre on Child Studies, IE, UMinho; FCT R&D unit 317, Portugal) by the Strategic Project UID/CED/00317/2013, with financial support of National Funds through the FCT (Foundation for Science and Technology) and co-financed by European Regional Development Funds (FEDER) through the COMPETE 2020 - Competitiveness and Internationalization Operational Program (POCI) with the reference POCI-01-0145-FEDER-00756

The gray matter volume of the amygdala is correlated with the perception of melodic intervals: a voxel-based morphometry study

Music is not simply a series of organized pitches, rhythms, and timbres, it is capable of evoking emotions. In the present study, voxel-based morphometry (VBM) was employed to explore the neural basis that may link music to emotion. To do this, we identified the neuroanatomical correlates of the ability to extract pitch interval size in a music segment (i.e., interval perception) in a large population of healthy young adults (N = 264). Behaviorally, we found that interval perception was correlated with daily emotional experiences, indicating the intrinsic link between music and emotion. Neurally, and as expected, we found that interval perception was positively correlated with the gray matter volume (GMV) of the bilateral temporal cortex. More important, a larger GMV of the bilateral amygdala was associated with better interval perception, suggesting that the amygdala, which is the neural substrate of emotional processing, is also involved in music processing. In sum, our study provides one of first neuroanatomical evidence on the association between the amygdala and music, which contributes to our understanding of exactly how music evokes emotional responses

Linear, Deterministic, and Order-Invariant Initialization Methods for the K-Means Clustering Algorithm

Over the past five decades, k-means has become the clustering algorithm of choice in many application domains primarily due to its simplicity, time/space efficiency, and invariance to the ordering of the data points. Unfortunately, the algorithm's sensitivity to the initial selection of the cluster centers remains to be its most serious drawback. Numerous initialization methods have been proposed to address this drawback. Many of these methods, however, have time complexity superlinear in the number of data points, which makes them impractical for large data sets. On the other hand, linear methods are often random and/or sensitive to the ordering of the data points. These methods are generally unreliable in that the quality of their results is unpredictable. Therefore, it is common practice to perform multiple runs of such methods and take the output of the run that produces the best results. Such a practice, however, greatly increases the computational requirements of the otherwise highly efficient k-means algorithm. In this chapter, we investigate the empirical performance of six linear, deterministic (non-random), and order-invariant k-means initialization methods on a large and diverse collection of data sets from the UCI Machine Learning Repository. The results demonstrate that two relatively unknown hierarchical initialization methods due to Su and Dy outperform the remaining four methods with respect to two objective effectiveness criteria. In addition, a recent method due to Erisoglu et al. performs surprisingly poorly.Comment: 21 pages, 2 figures, 5 tables, Partitional Clustering Algorithms (Springer, 2014). arXiv admin note: substantial text overlap with arXiv:1304.7465, arXiv:1209.196

Astrobiological Complexity with Probabilistic Cellular Automata

Search for extraterrestrial life and intelligence constitutes one of the major endeavors in science, but has yet been quantitatively modeled only rarely and in a cursory and superficial fashion. We argue that probabilistic cellular automata (PCA) represent the best quantitative framework for modeling astrobiological history of the Milky Way and its Galactic Habitable Zone. The relevant astrobiological parameters are to be modeled as the elements of the input probability matrix for the PCA kernel. With the underlying simplicity of the cellular automata constructs, this approach enables a quick analysis of large and ambiguous input parameters' space. We perform a simple clustering analysis of typical astrobiological histories and discuss the relevant boundary conditions of practical importance for planning and guiding actual empirical astrobiological and SETI projects. In addition to showing how the present framework is adaptable to more complex situations and updated observational databases from current and near-future space missions, we demonstrate how numerical results could offer a cautious rationale for continuation of practical SETI searches.Comment: 37 pages, 11 figures, 2 tables; added journal reference belo