Quantum Computing: Pro and Con

I assess the potential of quantum computation. Broad and important applications must be found to justify construction of a quantum computer; I review some of the known quantum algorithms and consider the prospects for finding new ones. Quantum computers are notoriously susceptible to making errors; I discuss recently developed fault-tolerant procedures that enable a quantum computer with noisy gates to perform reliably. Quantum computing hardware is still in its infancy; I comment on the specifications that should be met by future hardware. Over the past few years, work on quantum computation has erected a new classification of computational complexity, has generated profound insights into the nature of decoherence, and has stimulated the formulation of new techniques in high-precision experimental physics. A broad interdisciplinary effort will be needed if quantum computers are to fulfill their destiny as the world's fastest computing devices. (This paper is an expanded version of remarks that were prepared for a panel discussion at the ITP Conference on Quantum Coherence and Decoherence, 17 December 1996.)Comment: 17 pages, LaTeX, submitted to Proc. Roy. Soc. Lond. A, minor correction

Magnetic Monopoles as Agents of Chiral Symmetry Breaking in U(1) Lattice Gauge Theory

We present results suggesting that magnetic monopoles can account for chiral symmetry breaking in abelian gauge theory. Full U(1) configurations from a lattice simulation are factorized into magnetic monopole and photon contributions. The expectation is computed using the monopole configurations and compared to results for the full U(1) configurations. It is shown that excellent agreement between the two values of is obtained if the effect of photons, which "dress" the composite operator psibarpsi, is included. This can be estimated independently by measurements of the physical fermion mass in the photon background.Comment: 14 pages REVTeX, including 5 figure

Reply to Comments of Bassi, Ghirardi, and Tumulka on the Free Will Theorem

We show that the authors in the title have erred in claiming that our axiom FIN is false by conflating it with Bell locality. We also argue that the predictions of quantum mechanics, and in particular EPR, are fully Lorentz invariant, whereas the Free Will Theorem shows that theories with a mechanism of reduction, such as GRW, cannot be made fully invariant.Comment: We sharpen our theorem by replacing axiom FIN by a weaker axiom MIN to answer the above authors' objection

What Is Not Where: the Challenge of Integrating Spatial Representations Into Deep Learning Architectures

This paper examines to what degree current deep learning architectures for image caption generation capture spatial lan- guage. On the basis of the evaluation of examples of generated captions from the literature we argue that systems capture what objects are in the image data but not where these objects are located: the cap- tions generated by these systems are the output of a language model conditioned on the output of an object detector that cannot capture fine-grained location information. Although language models provide useful knowledge for image captions, we argue that deep learning image captioning architectures should also model geometric rela- tions between objects

Back to the Future: Logic and Machine Learning

In this paper we argue that since the beginning of the natural language processing or computational linguistics there has been a strong connection between logic and machine learning. First of all, there is something logical about language or linguistic about logic. Secondly, we argue that rather than distinguishing between logic and machine learning, a more useful distinction is between top-down approaches and data-driven approaches. Examining some recent approaches in deep learning we argue that they incorporate both properties and this is the reason for their very successful adoption to solve several problems within language technology

Recruitment and retention of farm owners and workers for a six-month prospective injury study in New Zealand: a feasibility study

<p>Abstract</p> <p>Background</p> <p>Agricultural workers experience high rates of occupational injury. There is a lack of analytic studies which provide detailed occupational exposure information to inform intervention development.</p> <p>Methods</p> <p>A feasibility study simulating a six month prospective cohort study was designed and undertaken. The levels of farm and worker participation and retention were analysed to determine the feasibility of the methods for wider deployment.</p> <p>Results</p> <p>Recruitment levels were comparable with other studies, with 24% of farms and 36% of non-owner workers participating. Once recruited, retention was high at 85% and 86% respectively.</p> <p>Conclusions</p> <p>The main challenges identified were in the recruitment process. Once recruited, farms and workers tended to complete the study, indicating that prospective studies in this the agricultural workforce may be feasible. Issues encountered and potential solutions for future studies are discussed.</p

What is not where: the challenge of integrating spatial representations into deep learning architectures

This paper examines to what degree current deep learning architectures for image caption generation capture spatial language. On the basis of the evaluation of examples of generated captions from the literature we argue that systems capture what objects are in the image data but not where these objects are located: the captions generated by these systems are the output of a language model conditioned on the output of an object detector that cannot capture fine-grained location information. Although language models provide useful knowledge for image captions, we argue that deep learning image captioning architectures should also model geometric relations between objects.Comment: 15 pages, 10 figures, Appears in CLASP Papers in Computational Linguistics Vol 1: Proceedings of the Conference on Logic and Machine Learning in Natural Language (LaML 2017), pp. 41-5

Modular Mechanistic Networks: On Bridging Mechanistic and Phenomenological Models with Deep Neural Networks in Natural Language Processing

Natural language processing (NLP) can be done using either top-down (theory driven) and bottom-up (data driven) approaches, which we call mechanistic and phenomenological respectively. The approaches are frequently considered to stand in opposition to each other. Examining some recent approaches in deep learning we argue that deep neural networks incorporate both perspectives and, furthermore, that leveraging this aspect of deep learning may help in solving complex problems within language technology, such as modelling language and perception in the domain of spatial cognition