5,767 research outputs found
The 125 GeV boson: A composite scalar?
Assuming that the 125 GeV particle observed at the LHC is a composite scalar
and responsible for the electroweak gauge symmetry breaking, we consider the
possibility that the bound state is generated by a non-Abelian gauge theory
with dynamically generated gauge boson masses and a specific chiral symmetry
breaking dynamics motivated by confinement. The scalar mass is computed with
the use of the Bethe-Salpeter equation and its normalization condition as a
function of the SU(N) group and the respective fermionic representation. If the
fermions that form the composite state are in the fundamental representation of
the SU(N) group, we can generate such light boson only for one specific number
of fermions for each group. In the case of small groups, like SU(2) to SU(5),
and two fermions in the adjoint representation we find that is quite improbable
to generate such light composite scalar.Comment: 24 pages, 5 figures, discussion extended, references added; version
to appear in Phys. Rev.
Functional Distributional Semantics
Vector space models have become popular in distributional semantics, despite the challenges they face in capturing various semantic phenomena. We propose a novel probabilistic framework which draws on both formal semantics and recent advances in machine learning. In particular, we separate predicates from the entities they refer to, allowing us to perform Bayesian inference based on logical forms. We describe an implementation of this framework using a combination of Restricted Boltzmann Machines and feedforward neural networks. Finally, we demonstrate the feasibility of this approach by training it on a parsed corpus and evaluating it on established similarity datasets
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Leveraging a semantically annotated corpus to disambiguate prepositional phrase attachment
Accurate parse ranking requires semantic information, since a sentence may have many candidate parses involving common syntactic constructions. In this paper, we propose a probabilistic frame- work for incorporating distributional semantic information into a maximum entropy parser. Further- more, to better deal with sparse data, we use a modified version of Latent Dirichlet Allocation to smooth the probability estimates. This LDA model generates pairs of lemmas, representing the two arguments of a semantic relation, and can be trained, in an unsupervised manner, on a corpus anno- tated with semantic dependencies. To evaluate our framework in isolation from the rest of a parser, we consider the special case of prepositional phrase attachment ambiguity. The results show that our semantically-motivated feature is effective in this case, and moreover, the LDA smoothing both produces semantically interpretable topics, and also improves performance over raw co-occurrence frequencies, demonstrating that it can successfully generalise patterns in the training data.This is the final version of the article. It first appeared from Association for Computational Linguistics via http://www.aclweb.org/anthology/W15-0101
Efficient Symmetry Reduction and the Use of State Symmetries for Symbolic Model Checking
One technique to reduce the state-space explosion problem in temporal logic
model checking is symmetry reduction. The combination of symmetry reduction and
symbolic model checking by using BDDs suffered a long time from the
prohibitively large BDD for the orbit relation. Dynamic symmetry reduction
calculates representatives of equivalence classes of states dynamically and
thus avoids the construction of the orbit relation. In this paper, we present a
new efficient model checking algorithm based on dynamic symmetry reduction. Our
experiments show that the algorithm is very fast and allows the verification of
larger systems. We additionally implemented the use of state symmetries for
symbolic symmetry reduction. To our knowledge we are the first who investigated
state symmetries in combination with BDD based symbolic model checking
Randomized benchmarking of single and multi-qubit control in liquid-state NMR quantum information processing
Being able to quantify the level of coherent control in a proposed device
implementing a quantum information processor (QIP) is an important task for
both comparing different devices and assessing a device's prospects with
regards to achieving fault-tolerant quantum control. We implement in a
liquid-state nuclear magnetic resonance QIP the randomized benchmarking
protocol presented by Knill et al (PRA 77: 012307 (2008)). We report an error
per randomized pulse of with a
single qubit QIP and show an experimentally relevant error model where the
randomized benchmarking gives a signature fidelity decay which is not possible
to interpret as a single error per gate. We explore and experimentally
investigate multi-qubit extensions of this protocol and report an average error
rate for one and two qubit gates of for a three
qubit QIP. We estimate that these error rates are still not decoherence limited
and thus can be improved with modifications to the control hardware and
software.Comment: 10 pages, 6 figures, submitted versio
Incoherent Noise and Quantum Information Processing
Incoherence in the controlled Hamiltonian is an important limitation on the
precision of coherent control in quantum information processing. Incoherence
can typically be modelled as a distribution of unitary processes arising from
slowly varying experimental parameters. We show how it introduces artifacts in
quantum process tomography and we explain how the resulting estimate of the
superoperator may not be completely positive. We then go on to attack the
inverse problem of extracting an effective distribution of unitaries that
characterizes the incoherence via a perturbation theory analysis of the
superoperator eigenvalue spectra.Comment: 15 pages, 5 figures, replaced with future JCP published versio
The \u3ci\u3eA\u3c/i\u3e Series of Allelomorphs in Relation to Pigmentation in Maize
Introduction ... 503
The allelomorph Ab ... 504
The allelomorph ap ... 505
Dominance ... 508
Summary ... 508
Literature Cited ... 50
The \u3ci\u3eA\u3c/i\u3e Series of Allelomorphs in Relation to Pigmentation in Maize
Introduction ... 503
The allelomorph Ab ... 504
The allelomorph ap ... 505
Dominance ... 508
Summary ... 508
Literature Cited ... 50
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Words are vectors, dependencies are matrices: Learning word embeddings from dependency graphs
Distributional Semantic Models (DSMs) construct vector representations of word meanings based on their contexts. Typically, the contexts of a word are defined as its closest neighbours, but they can also be retrieved from its syntactic dependency relations. In this work, we propose a new dependency-based DSM. The novelty of our model lies in associating an independent meaning representation, a matrix, with each dependency-label. This allows it to capture specifics of the relations between words and contexts, leading to good performance on both intrinsic and extrinsic evaluation tasks. In addition to that, our model has an inherent ability to represent dependency chains as products of matrices which provides a straightforward way of handling further contexts of a word
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