Optimising a nonlinear utility function in multi-objective integer programming

In this paper we develop an algorithm to optimise a nonlinear utility function of multiple objectives over the integer efficient set. Our approach is based on identifying and updating bounds on the individual objectives as well as the optimal utility value. This is done using already known solutions, linear programming relaxations, utility function inversion, and integer programming. We develop a general optimisation algorithm for use with k objectives, and we illustrate our approach using a tri-objective integer programming problem.Comment: 11 pages, 2 tables; v3: minor revisions, to appear in Journal of Global Optimizatio

Impact of stirring regime on piezocatalytic dye degradation using BaTiO3 nanoparticles

There is increasing demand to use readily accessible waste energy to drive environmentally friendly processes. Piezocatalysis, the process of converting mechanical energy such as vibration into a chemical process, is a breakthrough next generation approach to meet this challenge. However, these systems currently focus on using ultrasound to drive the chemical reaction and are therefore expensive to operate. We show that by using simple mechanical stirring and BaTiO3 particles we can remove Rhodamine B dye molecules from solution. After evaluating a range of stirring parameters, we demonstrate that there is an interplay between stirring speed, volume of liquid, catalyst structure and rate of dye removal. Our maximum degradation rate was 12.05 mg. g-1 catalyst after 1 hour of mechanical stirring at favourable conditions. This development provides a new insight into a low energy physical technique that can be used in environmental remediation processes

Understanding the effect of saturated gases on catalytic performance of graphitic‐carbon nitride (g‐C₃N₄) for H₂O₂ generation and dye degradation in the presence of ultrasound

This paper examines the effect of saturated gases on H2O2 generation and dye degradation using graphitic‐carbon nitride (g‐C3N4) as a piezoelectric catalyst. A detailed catalytic evaluation was carried out using a double‐bath sono‐reactor, where the performance of g‐C3N4 for H2O2 production and degradation of rhodamine B and indigo carmine dyes was evaluated for a range of catalyst dosage levels and saturated gases. Specific gases were selected to understand their role in the sonochemical production of reactive oxygen species (ROS) and reactive nitrogen species (RNS) and to elucidate the potential catalytic mechanism. The use of an Ar‐O2 gas mixture led to the highest yield for H2O2 production and dye degradation due to the positive effect of argon and oxygen in the generation of H2O2 and reactive oxygen species, respectively. The presence of nitrogen in both air and in an Ar‐air mixture increased H2O2 generation since reactive nitrogen species improved the conversion of •OH into H2O2. In contrast, air and Ar‐air negatively influenced the generation of ROS, which resulted in a low rate of dye degradation. This work provides new insights of the mechanisms of sonochemical and piezocatalytic processes in the use of graphitic‐carbon nitride in catalytic applications.This article is protected by copyright. All rights reserved

Multiorder coherent Raman scattering of a quantum probe field

We study the multiorder coherent Raman scattering of a quantum probe field in a far-off-resonance medium with a prepared coherence. Under the conditions of negligible dispersion and limited bandwidth, we derive a Bessel-function solution for the sideband field operators. We analytically and numerically calculate various quantum statistical characteristics of the sideband fields. We show that the multiorder coherent Raman process can replicate the statistical properties of a single-mode quantum probe field into a broad comb of generated Raman sidebands. We also study the mixing and modulation of photon statistical properties in the case of two-mode input. We show that the prepared Raman coherence and the medium length can be used as control parameters to switch a sideband field from one type of photon statistics to another type, or from a non-squeezed state to a squeezed state and vice versa.Comment: 12 pages, 7 figures, to be published in Phys. Rev.

Application of Time Transfer Function to McVittie Spacetime: Gravitational Time Delay and Secular Increase in Astronomical Unit

We attempt to calculate the gravitational time delay in a time-dependent gravitational field, especially in McVittie spacetime, which can be considered as the spacetime around a gravitating body such as the Sun, embedded in the FLRW (Friedmann-Lema\^itre-Robertson-Walker) cosmological background metric. To this end, we adopt the time transfer function method proposed by Le Poncin-Lafitte {\it et al.} (Class. Quant. Grav. 21:4463, 2004) and Teyssandier and Le Poncin-Lafitte (Class. Quant. Grav. 25:145020, 2008), which is originally related to Synge's world function $\Omega(x_A, x_B)$ and enables to circumvent the integration of the null geodesic equation. We re-examine the global cosmological effect on light propagation in the solar system. The round-trip time of a light ray/signal is given by the functions of not only the spacial coordinates but also the emission time or reception time of light ray/signal, which characterize the time-dependency of solutions. We also apply the obtained results to the secular increase in the astronomical unit, reported by Krasinsky and Brumberg (Celest. Mech. Dyn. Astron. 90:267, 2004), and we show that the leading order terms of the time-dependent component due to cosmological expansion is 9 orders of magnitude smaller than the observed value of $d{\rm AU}/dt$, i.e., $15 \pm 4$ ~[m/century]. Therefore, it is not possible to explain the secular increase in the astronomical unit in terms of cosmological expansion.Comment: 13 pages, 2 figures, accepted for publication in General Relativity and Gravitatio

Using Heavy Quark Spin Symmetry in Semileptonic BcB_c Decays

The form factors parameterizing the B_c semileptonic matrix elements can be related to a few invariant functions if the decoupling of the spin of the heavy quarks in B_c and in the mesons produced in the semileptonic decays is exploited. We compute the form factors as overlap integral of the meson wave-functions obtained using a QCD relativistic potential model, and give predictions for semileptonic and non-leptonic B_c decay modes. We also discuss possible experimental tests of the heavy quark spin symmetry in B_c decays.Comment: RevTex, 22 pages, 2 figure

Optimal estimation of qubit states with continuous time measurements

We propose an adaptive, two steps strategy, for the estimation of mixed qubit states. We show that the strategy is optimal in a local minimax sense for the trace norm distance as well as other locally quadratic figures of merit. Local minimax optimality means that given $n$ identical qubits, there exists no estimator which can perform better than the proposed estimator on a neighborhood of size $n^{-1/2}$ of an arbitrary state. In particular, it is asymptotically Bayesian optimal for a large class of prior distributions. We present a physical implementation of the optimal estimation strategy based on continuous time measurements in a field that couples with the qubits. The crucial ingredient of the result is the concept of local asymptotic normality (or LAN) for qubits. This means that, for large $n$, the statistical model described by $n$ identically prepared qubits is locally equivalent to a model with only a classical Gaussian distribution and a Gaussian state of a quantum harmonic oscillator. The term `local' refers to a shrinking neighborhood around a fixed state $\rho_{0}$. An essential result is that the neighborhood radius can be chosen arbitrarily close to $n^{-1/4}$. This allows us to use a two steps procedure by which we first localize the state within a smaller neighborhood of radius $n^{-1/2+\epsilon}$, and then use LAN to perform optimal estimation.Comment: 32 pages, 3 figures, to appear in Commun. Math. Phy

Towards Machine Wald

The past century has seen a steady increase in the need of estimating and predicting complex systems and making (possibly critical) decisions with limited information. Although computers have made possible the numerical evaluation of sophisticated statistical models, these models are still designed \emph{by humans} because there is currently no known recipe or algorithm for dividing the design of a statistical model into a sequence of arithmetic operations. Indeed enabling computers to \emph{think} as \emph{humans} have the ability to do when faced with uncertainty is challenging in several major ways: (1) Finding optimal statistical models remains to be formulated as a well posed problem when information on the system of interest is incomplete and comes in the form of a complex combination of sample data, partial knowledge of constitutive relations and a limited description of the distribution of input random variables. (2) The space of admissible scenarios along with the space of relevant information, assumptions, and/or beliefs, tend to be infinite dimensional, whereas calculus on a computer is necessarily discrete and finite. With this purpose, this paper explores the foundations of a rigorous framework for the scientific computation of optimal statistical estimators/models and reviews their connections with Decision Theory, Machine Learning, Bayesian Inference, Stochastic Optimization, Robust Optimization, Optimal Uncertainty Quantification and Information Based Complexity.Comment: 37 page

Multi-basin depositional framework for moisture-balance reconstruction during the last 1300 years at Lake Bogoria, central Kenya Rift Valley

Multi-proxy analysis of sediment cores from five key locations in hypersaline, alkaline Lake Bogoria (central Kenya Rift Valley) has allowed reconstruction of its history of depositional and hydrological change during the past 1300years. Analyses including organic matter and carbonate content, granulometry, mineralogical composition, charcoal counting and high-resolution scanning of magnetic susceptibility and elemental geochemistry resulted in a detailed sedimentological and compositional characterization of lacustrine deposits in the three lake basins and on the two sills separating them. Thesepalaeolimnological data were supplemented with information on present-day sedimentation conditions based on seasonal sampling of settling particles and on measurement of physicochemical profiles through the water column. A new age model based on Pb-210, Cs-137 and C-14 dating captures the sediment chronology of this hydrochemically complex and geothermally fed lake. An extensive set of chronological tie points between the equivalent high-resolution proxy time series of the five sediment sequences allowed transfer of radiometric dates between the basins, enabling interbasin comparison of sedimentation dynamics through time. The resulting reconstruction demonstrates considerable moisture-balance variability through time, reflecting regional hydroclimate dynamics over the past 1300years. Between ca 690 and 950AD, the central and southern basins of Lake Bogoria were reduced to shallow and separated brine pools. In the former, occasional near-complete desiccation triggered massive trona precipitation. Between ca 950 and 1100AD, slightly higher water levels allowed the build-up of high pCO(2) leading to precipitation of nahcolite still under strongly evaporative conditions. Lake Bogoria experienced a pronounced highstand between ca 1100 and 1350AD, only to recede again afterwards. For a substantial part of the time between ca 1350 and 1800AD, the northern basin was probably disconnected from the united central and southern basins. Throughout the last two centuries, lake level has been relatively high compared to the rest of the past millennium. Evidence for increased terrestrial sediment supply in recent decades, due to anthropogenic soil erosion in the wider Bogoria catchment, is a reason for concern about possible adverse impacts on the unique ecosystem of Lake Bogoria

Contribution of Color Information in Visual Saliency Model for Videos

International audienceMuch research has been concerned with the contribution of the low level features of a visual scene to the deployment of visual attention. Bottom-up saliency models have been developed to predict the location of gaze according to these features. So far, color besides to brightness, contrast and motion is considered as one of the primary features in computing bottom-up saliency. However, its contribution in guiding eye movements when viewing natural scenes has been debated. We investigated the contribution of color information in a bottom-up visual saliency model. The model efficiency was tested using the experimental data obtained on 45 observers who were eye tracked while freely exploring a large data set of color and grayscale videos. The two datasets of recorded eye positions, for grayscale and color videos, were compared with a luminance-based saliency model. We incorporated chrominance information to the model. Results show that color information improves the performance of the saliency model in predicting eye positions