A composition theorem for the Fourier Entropy-Influence conjecture

The Fourier Entropy-Influence (FEI) conjecture of Friedgut and Kalai [FK96] seeks to relate two fundamental measures of Boolean function complexity: it states that $H[f] \leq C Inf[f]$ holds for every Boolean function $f$, where $H[f]$ denotes the spectral entropy of $f$, $Inf[f]$ is its total influence, and $C > 0$ is a universal constant. Despite significant interest in the conjecture it has only been shown to hold for a few classes of Boolean functions. Our main result is a composition theorem for the FEI conjecture. We show that if $g_1,...,g_k$ are functions over disjoint sets of variables satisfying the conjecture, and if the Fourier transform of $F$ taken with respect to the product distribution with biases $E[g_1],...,E[g_k]$ satisfies the conjecture, then their composition $F(g_1(x^1),...,g_k(x^k))$ satisfies the conjecture. As an application we show that the FEI conjecture holds for read-once formulas over arbitrary gates of bounded arity, extending a recent result [OWZ11] which proved it for read-once decision trees. Our techniques also yield an explicit function with the largest known ratio of $C \geq 6.278$ between $H[f]$ and $Inf[f]$, improving on the previous lower bound of 4.615

Estimating Variable Returns to Scale Production Frontiers with Alternative Stochastic Assumptions

A stochastic production frontier model is formulated within the generalized production function framework popularized by Zellner and Revankar (1969) and Zellner and Ryu (1998). This framework is convenient for parsimonious modeling of a production function with variable returns to scale specified as a function of output. Two alternatives for introducing the stochastic inefficiency term and the stochastic error are considered, one where they are appended to the existing equation for the production relationship and one where the existing equation is solved for the log of output before the stochastic terms are added. The latter alternative is novel, but it is needed to preserve the usual definition of firm efficiency. The two alternative stochastic assumptions are considered in conjunction with two returns to scale functions, making a total of four models that are considered. A Bayesian framework for estimating all four models is described. The techniques are applied to USDA state-level data on agricultural output and four inputs. Posterior distributions for all parameters, firm efficiencies and the efficiency rankings of firms are obtained. The sensitivity of the results to the returns to scale specification and to the stochastic specification is examined.

The Cyber Physical Implementation of Cloud Manufactuirng Monitoring Systems

AbstractThe rise of the industrial internet has been envisaged as a key catalyst for creating the intelligent manufacturing plant of the future through enabling open data distribution for cloud manufacturing. The context supporting these systems has been defined by Service Oriented Architectures (SOA) that facilitate data resource and computational functions as services available on a network. SOA has been at the forefront EU research over the past decade and several industrially implemented SOA technologies exist on the manufacturing floor. However it is still unclear whether SOA can meet the multi-layered requirements present within state-of-the-art manufacturing Cyber Physical Systems (CPS). The focus of this research is to identify the capability of SOA to be implemented at different execution layers present in a manufacturing CPS. The state-of-the-art for manufacturing CPS is represented by the ISA-95 standard and is correlated with different temporal analysis scales, and manufacturing computational requirements. Manufacturing computational requirements are identified through a review of open and closed loop machine control orientations, and continuous and discrete control methods. Finally the Acquire Recognise Cluster (ARC) SOA for reconfigurable manufacturing process monitoring systems is reviewed, to provide a topological view of data flow within a field level manufacturing SOA

Fiber-Flux Diffusion Density for White Matter Tracts Analysis: Application to Mild Anomalies Localization in Contact Sports Players

We present the concept of fiber-flux density for locally quantifying white matter (WM) fiber bundles. By combining scalar diffusivity measures (e.g., fractional anisotropy) with fiber-flux measurements, we define new local descriptors called Fiber-Flux Diffusion Density (FFDD) vectors. Applying each descriptor throughout fiber bundles allows along-tract coupling of a specific diffusion measure with geometrical properties, such as fiber orientation and coherence. A key step in the proposed framework is the construction of an FFDD dissimilarity measure for sub-voxel alignment of fiber bundles, based on the fast marching method (FMM). The obtained aligned WM tract-profiles enable meaningful inter-subject comparisons and group-wise statistical analysis. We demonstrate our method using two different datasets of contact sports players. Along-tract pairwise comparison as well as group-wise analysis, with respect to non-player healthy controls, reveal significant and spatially-consistent FFDD anomalies. Comparing our method with along-tract FA analysis shows improved sensitivity to subtle structural anomalies in football players over standard FA measurements

Microelectromechanical systems vibration powered electromagnetic generator for wireless sensor applications

This paper presents a silicon microgenerator, fabricated using standard silicon micromachining techniques, which converts external ambient vibrations into electrical energy. Power is generated by an electromagnetic transduction mechanism with static magnets positioned on either side of a moving coil, which is located on a silicon structure designed to resonate laterally in the plane of the chip. The volume of this device is approximately 100 mm3. ANSYS finite element analysis (FEA) has been used to determine the optimum geometry for the microgenerator. Electromagnetic FEA simulations using Ansoft’s Maxwell 3D software have been performed to determine the voltage generated from a single beam generator design. The predicted voltage levels of 0.7–4.15 V can be generated for a two-pole arrangement by tuning the damping factor to achieve maximum displacement for a given input excitation. Experimental results from the microgenerator demonstrate a maximum power output of 104 nW for 0.4g (g=9.81 m s1) input acceleration at 1.615 kHz. Other frequencies can be achieved by employing different geometries or material

First principles calculation of uniaxial magnetic anisotropy and magnetostriction in strained CMR films

We performed first - principles relativistic full-potential linearized augmented plane wave calculations for strained tetragonal ferromagnetic La(Ba)MnO$_3$ with an assumed experimental structure of thin strained tetragonal La$_{0.67}$Ca$_{0.33}$MnO$_3$ (LCMO) films grown on SrTiO$_3$[001] and LaAlO$_3$[001] substrates. The calculated uniaxial magnetic anisotropy energy (MAE) values, are in good quantitative agreement with experiment for LCMO films on SrTiO$_3$ substrate. We also analyze the applicability of linear magnetoelastic theory for describing the stain dependence of MAE, and estimate magnetostriction coefficient $\lambda_{001}$.Comment: Talk given at APS99 Meeting, Atlanta, 199

Thermodynamic metrics and optimal paths

A fundamental problem in modern thermodynamics is how a molecular-scale machine performs useful work, while operating away from thermal equilibrium without excessive dissipation. To this end, we derive a friction tensor that induces a Riemannian manifold on the space of thermodynamic states. Within the linear-response regime, this metric structure controls the dissipation of finite-time transformations, and bestows optimal protocols with many useful properties. We discuss the connection to the existing thermodynamic length formalism, and demonstrate the utility of this metric by solving for optimal control parameter protocols in a simple nonequilibrium model.Comment: 5 page

The Physicians’ Practice Assessment Questionnaire on asthma and COPD

SummaryWe describe a new tool, the Physicians’ Practice Assessment Questionnaire (PPAQ), designed for the global self-assessment of implementation of asthma and COPD guidelines, as determined by the percentage of patients in whom physicians estimate that they implement guidelines key recommendations. Some of its properties were assessed by a group of 47 general practitioners (GPs), and test–retest data were obtained in repeating the questionnaire at a 5-week interval without intervention in a sub-group of 28 practitioners. Answers to the various questions were globally reproducible. The lowest scores (recommendations implemented in less than 50% of their patients) were: 1) for both asthma and COPD: referral for patient education, provision of a written action plan and regular assessment of inhaler technique, 2) for asthma: referral to a specialist for difficult to control asthma or uncertain diagnosis, and 3) for COPD: assessment of lung function and disability according to specific criteria and referral to a rehabilitation program. The analysis showed sufficient internal consistency for both questionnaires (Cronbach alphas 0.7617 for asthma and 0.8317 for COPD). Pearson’s correlations indicated good test–retest (r = 0.6421, p = 0.0002 for asthma; r = 0.6801, p < 0.0001 for COPD). In conclusion, the PPAQ is a new tool to assess implementation of asthma and COPD guidelines; it has the potential to identify care gaps that can be specifically targeted for intervention