830 research outputs found
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 teachersâ 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 teachersâ 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 âtsunamiâ. 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
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