A distributionally robust perspective on uncertainty quantification and chance constrained programming

The objective of uncertainty quantification is to certify that a given physical, engineering or economic system satisfies multiple safety conditions with high probability. A more ambitious goal is to actively influence the system so as to guarantee and maintain its safety, a scenario which can be modeled through a chance constrained program. In this paper we assume that the parameters of the system are governed by an ambiguous distribution that is only known to belong to an ambiguity set characterized through generalized moment bounds and structural properties such as symmetry, unimodality or independence patterns. We delineate the watershed between tractability and intractability in ambiguity-averse uncertainty quantification and chance constrained programming. Using tools from distributionally robust optimization, we derive explicit conic reformulations for tractable problem classes and suggest efficiently computable conservative approximations for intractable ones

Some Variations on Maxwell's Equations

In the first sections of this article, we discuss two variations on Maxwell's equations that have been introduced in earlier work--a class of nonlinear Maxwell theories with well-defined Galilean limits (and correspondingly generalized Yang-Mills equations), and a linear modification motivated by the coupling of the electromagnetic potential with a certain nonlinear Schroedinger equation. In the final section, revisiting an old idea of Lorentz, we write Maxwell's equations for a theory in which the electrostatic force of repulsion between like charges differs fundamentally in magnitude from the electrostatic force of attraction between unlike charges. We elaborate on Lorentz' description by means of electric and magnetic field strengths, whose governing equations separate into two fully relativistic Maxwell systems--one describing ordinary electromagnetism, and the other describing a universally attractive or repulsive long-range force. If such a force cannot be ruled out {\it a priori} by known physical principles, its magnitude should be determined or bounded experimentally. Were it to exist, interesting possibilities go beyond Lorentz' early conjecture of a relation to (Newtonian) gravity.Comment: 26 pages, submitted to a volume in preparation to honor Gerard Emch v. 2: discussion revised, factors of 4\pi corrected in some equation

An investigation of the beneficial effects of adding carbon nanotubes to standard injection grout

Mortar grouting is often used in masonry constructions to mitigate structural decay and repair damage by filling cracks and voids, resulting in an improvement in mechanical properties. This paper presents an original experimental investigation on grout with added carbon nanotubes (CNTs). The samples were prepared with different percentages of CNTs, up to 1.2 wt% with respect to the binder, and underwent three‐point bending tests in crack mouth opening displacement mode and compressive tests. The results showed that very small additions (up to 0.12 wt% of CNTs) increased not only flexural and compressive strengths (+73% and 35%, respectively, in comparison with plain mortar) but also fracture energy (+80%). These results can be explained on the basis of a reduction in porosity, as evidenced by mercury intrusion porosimetry, as well as by a crack bridging mechanism and by the probable formation of nucleation sites for hydration products, as observed through scanning electron microscopy

Frequency dependence of electrical conductivity and dielectric constant of UO2

The dielectric constant and electrical conductivity of single crystal and polycrystalline UO2 are found to be frequency dependent. The dielectric constant measured at low frequencies is anomalously large at room temperature but decreases to a limiting value (~25) below about 130 K. A knee observed in the temperature dependence of the conductivity of polycrystalline UO2 corresponds to a process having an activation energy of 0.15 eV

Type-Constrained Representation Learning in Knowledge Graphs

Large knowledge graphs increasingly add value to various applications that require machines to recognize and understand queries and their semantics, as in search or question answering systems. Latent variable models have increasingly gained attention for the statistical modeling of knowledge graphs, showing promising results in tasks related to knowledge graph completion and cleaning. Besides storing facts about the world, schema-based knowledge graphs are backed by rich semantic descriptions of entities and relation-types that allow machines to understand the notion of things and their semantic relationships. In this work, we study how type-constraints can generally support the statistical modeling with latent variable models. More precisely, we integrated prior knowledge in form of type-constraints in various state of the art latent variable approaches. Our experimental results show that prior knowledge on relation-types significantly improves these models up to 77% in link-prediction tasks. The achieved improvements are especially prominent when a low model complexity is enforced, a crucial requirement when these models are applied to very large datasets. Unfortunately, type-constraints are neither always available nor always complete e.g., they can become fuzzy when entities lack proper typing. We show that in these cases, it can be beneficial to apply a local closed-world assumption that approximates the semantics of relation-types based on observations made in the data

Detecting Distracted Driving with Deep Learning

© Springer International Publishing AG 2017Driver distraction is the leading factor in most car crashes and near-crashes. This paper discusses the types, causes and impacts of distracted driving. A deep learning approach is then presented for the detection of such driving behaviors using images of the driver, where an enhancement has been made to a standard convolutional neural network (CNN). Experimental results on Kaggle challenge dataset have confirmed the capability of a convolutional neural network (CNN) in this complicated computer vision task and illustrated the contribution of the CNN enhancement to a better pattern recognition accuracy.Peer reviewe

myTrustedCloud: Trusted cloud infrastructure for security-critical computation and data managment

Copyright @ 2012 IEEECloud Computing provides an optimal infrastructure to utilise and share both computational and data resources whilst allowing a pay-per-use model, useful to cost-effectively manage hardware investment or to maximise its utilisation. Cloud Computing also offers transitory access to scalable amounts of computational resources, something that is particularly important due to the time and financial constraints of many user communities. The growing number of communities that are adopting large public cloud resources such as Amazon Web Services [1] or Microsoft Azure [2] proves the success and hence usefulness of the Cloud Computing paradigm. Nonetheless, the typical use cases for public clouds involve non-business critical applications, particularly where issues around security of utilization of applications or deposited data within shared public services are binding requisites. In this paper, a use case is presented illustrating how the integration of Trusted Computing technologies into an available cloud infrastructure - Eucalyptus - allows the security-critical energy industry to exploit the flexibility and potential economical benefits of the Cloud Computing paradigm for their business-critical applications

Levers of job satisfaction: participative decision making and individual characteristics

This paper demonstrates that the determinants of job satisfaction do not change if the worker has decision making freedom and that the impact of some individual characteristics on job satisfaction follow interesting patterns as we move through occupational statuses

Developing a health state classification system from NEWQOL for epilepsy using classical psychometric techniques and Rasch analysis: a technical report

Aims: Resource allocation amongst competing health care interventions is informed by evidence of both clinical- and cost-effectiveness. Cost-utility analysis is increasingly used to assess cost effectiveness through the use of Quality Adjusted Life Years (QALYs). This requires health state values. Generic measures of health related quality of life (HRQL) are usually used to produce these values, but there are concerns about their relevance and sensitivity in epilepsy. This study develops a health state classification system for epilepsy from the NEWQOL battery, a validated questionnaire measuring QoL in epilepsy. The classification system will be amenable to valuation for calculating QALYs. Methods: Factor and other psychometric analyses were undertaken to investigate the factor structure of the battery, and assess the validity and responsiveness of the items. These analyses were used alongside Rasch analysis to select the dimensions included in the classification system, and the items used to represent each domain. Analysis was carried out on a trial dataset of patients with epilepsy (n=1611). Rasch and factor analysis were performed on one half of the sample and validated on the remaining half. Dimensions and items were selected that performed well across all analyses. Results: The battery was found to demonstrate reliability and validity but responsiveness across time periods for many of the items was low. A six dimension classification system was developed: worry about seizures, depression, memory, cognition, stigmatism and control, each with four response levels. Conclusions: It is feasible to develop a health state classification system from a battery of instruments using a combination of classical psychometric, factor and Rasch analysis. This is the first condition-specific health state classification developed for epilepsy and the next stage will produce preference weights to enable the measure to be used in cost-utility analysis.quality adjusted life years; health related quality of life; Rasch analysis; preference-based measures of health; health states; epilepsy