Normal Factor Graphs and Holographic Transformations

This paper stands at the intersection of two distinct lines of research. One line is "holographic algorithms," a powerful approach introduced by Valiant for solving various counting problems in computer science; the other is "normal factor graphs," an elegant framework proposed by Forney for representing codes defined on graphs. We introduce the notion of holographic transformations for normal factor graphs, and establish a very general theorem, called the generalized Holant theorem, which relates a normal factor graph to its holographic transformation. We show that the generalized Holant theorem on the one hand underlies the principle of holographic algorithms, and on the other hand reduces to a general duality theorem for normal factor graphs, a special case of which was first proved by Forney. In the course of our development, we formalize a new semantics for normal factor graphs, which highlights various linear algebraic properties that potentially enable the use of normal factor graphs as a linear algebraic tool.Comment: To appear IEEE Trans. Inform. Theor

Development and Application of Novel Sodium Silicate Microcapsule-Based Self-Healing Oil Well Cement

A majority of well integrity problems originate from cracks of oil well cement. To address the crack issues, bespoke sodium silicate microcapsules were used in this study for introducing autonomous crack healing ability to oil well cement under high-temperature service conditions at 80 °C. Two types of sodium silicate microcapsule, which differed in their polyurea shell properties, were first evaluated on their suitability for use under the high temperature of 80 °C in the wellbore. Both types of microcapsules showed good thermal stability and survivability during mixing. The microcapsules with a more rigid shell were chosen over microcapsule with a more rubbery shell for further tests on the self-healing efficiency since the former had much less negative effect on the oil well cement strength. It was found that oil well cement itself showed very little healing capability when cured at 80 °C, but the addition of the microcapsules significantly promoted its self-healing performance. After healing for 7 days at 80 °C, the microcapsule-containing cement pastes achieved crack depth reduction up to ~58%, sorptivity coefficient reduction up to ~76%, and flexural strength regain up to ~27%. The microstructure analysis further confirmed the stability of microcapsules and their self-healing reactions upon cracking in the high temperature oil well cement system. These results provide a promising perspective for the development of self-healing microcapsule-based oil well cements

Forecasting the Number of Monthly Active Facebook and Twitter Worldwide Users Using ARMA Model

In this study, an Auto-Regressive Moving Average (ARMA) Model with optimal order has been developed to estimate and forecast the short term future numbers of the monthly active Facebook and Twitter worldwide users. In order to pickup the optimal estimation order, we analyzed the model order vs. the corresponding model error in terms of final prediction error. The simulation results showed that the optimal model order to estimate the given Facebook and Twitter time series are ARMA[5, 5] and ARMA[3, 3], respectively, since they correspond to the minimum acceptable prediction error values. Besides, the optimal models recorded a high-level of estimation accuracy with fit percents of 98.8% and 96.5% for Facebook and Twitter time series, respectively. Eventually, the developed framework can be used accurately to estimate the spectrum for any linear time series

Development and Application of Novel Sodium Silicate Microcapsule-Based Self-Healing Oil Well Cement.

A majority of well integrity problems originate from cracks of oil well cement. To address the crack issues, bespoke sodium silicate microcapsules were used in this study for introducing autonomous crack healing ability to oil well cement under high-temperature service conditions at 80 °C. Two types of sodium silicate microcapsule, which differed in their polyurea shell properties, were first evaluated on their suitability for use under the high temperature of 80 °C in the wellbore. Both types of microcapsules showed good thermal stability and survivability during mixing. The microcapsules with a more rigid shell were chosen over microcapsule with a more rubbery shell for further tests on the self-healing efficiency since the former had much less negative effect on the oil well cement strength. It was found that oil well cement itself showed very little healing capability when cured at 80 °C, but the addition of the microcapsules significantly promoted its self-healing performance. After healing for 7 days at 80 °C, the microcapsule-containing cement pastes achieved crack depth reduction up to ~58%, sorptivity coefficient reduction up to ~76%, and flexural strength regain up to ~27%. The microstructure analysis further confirmed the stability of microcapsules and their self-healing reactions upon cracking in the high temperature oil well cement system. These results provide a promising perspective for the development of self-healing microcapsule-based oil well cements.Shell, Schlumberger Foundation Faculty for the Future, Cambridge Trust and EPSRC Resilient Materials for Life (RM4L) Programme Grant (EP/P02081X/1

Statistical analysis of the owl:sameAs network for aligning concepts in the linking open data cloud

The massively distributed publication of linked data has brought to the attention of scientific community the limitations of classic methods for achieving data integration and the opportunities of pushing the boundaries of the field by experimenting this collective enterprise that is the linking open data cloud. While reusing existing ontologies is the choice of preference, the exploitation of ontology alignments still is a required step for easing the burden of integrating heterogeneous data sets. Alignments, even between the most used vocabularies, is still poorly supported in systems nowadays whereas links between instances are the most widely used means for bridging the gap between different data sets. We provide in this paper an account of our statistical and qualitative analysis of the network of instance level equivalences in the Linking Open Data Cloud (i.e. the sameAs network) in order to automatically compute alignments at the conceptual level. Moreover, we explore the effect of ontological information when adopting classical Jaccard methods to the ontology alignment task. Automating such task will allow in fact to achieve a clearer conceptual description of the data at the cloud level, while improving the level of integration between datasets. <br/

Aggregated Learning: A Vector-Quantization Approach to Learning Neural Network Classifiers

We consider the problem of learning a neural network classifier. Under the information bottleneck (IB) principle, we associate with this classification problem a representation learning problem, which we call "IB learning". We show that IB learning is, in fact, equivalent to a special class of the quantization problem. The classical results in rate-distortion theory then suggest that IB learning can benefit from a "vector quantization" approach, namely, simultaneously learning the representations of multiple input objects. Such an approach assisted with some variational techniques, result in a novel learning framework, "Aggregated Learning", for classification with neural network models. In this framework, several objects are jointly classified by a single neural network. The effectiveness of this framework is verified through extensive experiments on standard image recognition and text classification tasks.Comment: Proof of theoretical results are provide

Compact coplanar waveguide bandpass filter based on coupled S-shaped split ring resonators

This letter is focused on the application of coupled single-layer S-shaped split ring resonators (S-SRRs) to the design of highly compact bandpass filters in coplanar waveguide (CPW) technology. S-SRRs have been previously demonstrated as miniaturized resonators, particularly suited for applications in conjunction with coplanar line geometries. However, size reduction of CPW filters based on impedance inverters and S-SRRs is limited by the inverters. Therefore, this letter proposes an alternative geometry of CPW bandpass filters employing S-SRRs in a configuration based on the theory of coupled resonators. A highly compact third-order bandpass filter is designed using this principle, and the proposed approach is validated through experiment, demonstrating competitive filter performance achieved in an extremely small area