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

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 $\frac{\pi}{2}$ pulse of $1.3 \pm 0.1 \times 10^{-4}$ 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 $4.7 \pm 0.3 \times 10^{-3}$ 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

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