Study of optimization for cooperative motion between two humans in mutual tracking

Verbal communication is essential for human society and human civilization. Non-verbal communication, on the other hand, is more widely used not only by human but also other kind of animals, and the content of information is estimated even larger than the verbal communication. Among the non-verbal communication mutual motion is the simplest and easiest to study experimentally and analytically. We measured the power spectrum of the hand velocity in various conditions and clarified the following points on the feed-back and feed- forward mechanism as basic knowledge to understand the condition of good communication.

Asset liability modelling and pension schemes: the application of robust optimization to USS

This paper uses a novel numerical optimization technique - robust optimization - that is well suited to solving the asset-liability management (ALM) problem for pension schemes. It requires the estimation of fewer stochastic parameters, reduces estimation risk and adopts a prudent approach to asset allocation. This study is the first to apply it to a real-world pension scheme, and the first ALM model of a pension scheme to maximise the Sharpe ratio. We disaggregate pension liabilities into three components - active members, deferred members and pensioners, and transform the optimal asset allocation into the scheme’s projected contribution rate. The robust optimization model is extended to include liabilities and used to derive optimal investment policies for the Universities Superannuation Scheme (USS), benchmarked against the Sharpe and Tint, Bayes-Stein, and Black-Litterman models as well as the actual USS investment decisions. Over a 144 month out-of-sample period robust optimization is superior to the four benchmarks across 20 performance criteria, and has a remarkably stable asset allocation – essentially fix-mix. These conclusions are supported by six robustness checks.

Optimization of a sponge cake formulation with inulin as fat replacer: structure, physicochemical, and sensory properties

The effects of several fat replacement levels (0%, 35%, 50%, 70%, and 100%) by inulin in sponge cake microstructure and physicochemical properties were studied. Oil substitution for inulin decreased significantly (P < 0.05) batter viscosity, giving heterogeneous bubbles size distributions as it was observed by light microscopy. Using confocal laser scanning microscopy the fat was observed to be located at the bubbles’ interface, enabling an optimum crumb cake structure development during baking. Cryo-SEM micrographs of cake crumbs showed a continuous matrix with embedded starch granules and coated with oil; when fat replacement levels increased, starch granules appeared as detached structures. Cakes with fat replacement up to 70% had a high crumb air cell values; they were softer and rated as acceptable by an untrained sensory panel (n = 51). So, the reformulation of a standard sponge cake recipe to obtain a new product with additional health benefits and accepted by consumers is achieved.

Combining 3D and 2D for less constrained periocular recognition

Periocular recognition has recently become an active topic in biometrics. Typically it uses 2D image data of the periocular region. This paper is the first description of combining 3D shape structure with 2D texture. A simple and effective technique using iterative closest point (ICP) was applied for 3D periocular region matching. It proved its strength for relatively unconstrained eye region capture, and does not require any training. Local binary patterns (LBP) were applied for 2D image based periocular matching. The two modalities were combined at the score-level. This approach was evaluated using the Bosphorus 3D face database, which contains large variations in facial expressions, head poses and occlusions. The rank-1 accuracy achieved from the 3D data (80%) was better than that for 2D (58%), and the best accuracy (83%) was achieved by fusing the two types of data. This suggests that significant improvements to periocular recognition systems could be achieved using the 3D structure information that is now available from small and inexpensive sensors.

An optimization problem concerning multiplicative functions

In this paper we study the problem of maximizing a quadratic form 〈Ax,x〉 subject to ‖x‖q=1, where A has matrix entries View the MathML source with i,j|k and q≥1. We investigate when the optimum is achieved at a ‘multiplicative’ point; i.e. where x1xmn=xmxn. This turns out to depend on both f and q, with a marked difference appearing as q varies between 1 and 2. We prove some partial results and conjecture that for f multiplicative such that 0

Bio-inspired ant colony optimization based clustering algorithm with mobile sinks for applications in consumer home automation networks

With the fast development of wireless communications, ZigBee and semiconductor devices, home automation networks have recently become very popular. Since typical consumer products deployed in home automation networks are often powered by tiny and limited batteries, one of the most challenging research issues is concerning energy reduction and the balancing of energy consumption across the network in order to prolong the home network lifetime for consumer devices. The introduction of clustering and sink mobility techniques into home automation networks have been shown to be an efficient way to improve the network performance and have received significant research attention. Taking inspiration from nature, this paper proposes an Ant Colony Optimization (ACO) based clustering algorithm specifically with mobile sink support for home automation networks. In this work, the network is divided into several clusters and cluster heads are selected within each cluster. Then, a mobile sink communicates with each cluster head to collect data directly through short range communications. The ACO algorithm has been utilized in this work in order to find the optimal mobility trajectory for the mobile sink. Extensive simulation results from this research show that the proposed algorithm significantly improves home network performance when using mobile sinks in terms of energy consumption and network lifetime as compared to other routing algorithms currently deployed for home automation networks.

Investigation and optimization of parameters affecting the multiply charged ion yield in AP-MALDI MS

Liquid matrix-assisted laser desorption/ionization (MALDI) allows the generation of predominantly multiply charged ions in atmospheric pressure (AP) MALDI ion sources for mass spectrometry (MS) analysis. The charge state distribution of the generated ions and the efficiency of the ion source in generating such ions crucially depend on the desolvation regime of the MALDI plume after desorption in the AP-tovacuum inlet. Both high temperature and a flow regime with increased residence time of the desorbed plume in the desolvation region promote the generation of multiply charged ions. Without such measures the application of an electric ion extraction field significantly increases the ion signal intensity of singly charged species while the detection of multiply charged species is less dependent on the extraction field. In general, optimization of high temperature application facilitates the predominant formation and detection of multiply charged compared to singly charged ion species. In this study an experimental setup and optimization strategy is described for liquid AP-MALDI MS which improves the ionization effi- ciency of selected ion species up to 14 times. In combination with ion mobility separation, the method allows the detection of multiply charged peptide and protein ions for analyte solution concentrations as low as 2 fmol/lL (0.5 lL, i.e. 1 fmol, deposited on the target) with very low sample consumption in the low nL-range.

Heterogeneous tensor decomposition for clustering via manifold optimization

Tensor clustering is an important tool that exploits intrinsically rich structures in real-world multiarray or Tensor datasets. Often in dealing with those datasets, standard practice is to use subspace clustering that is based on vectorizing multiarray data. However, vectorization of tensorial data does not exploit complete structure information. In this paper, we propose a subspace clustering algorithm without adopting any vectorization process. Our approach is based on a novel heterogeneous Tucker decomposition model taking into account cluster membership information. We propose a new clustering algorithm that alternates between different modes of the proposed heterogeneous tensor model. All but the last mode have closed-form updates. Updating the last mode reduces to optimizing over the multinomial manifold for which we investigate second order Riemannian geometry and propose a trust-region algorithm. Numerical experiments show that our proposed algorithm compete effectively with state-of-the-art clustering algorithms that are based on tensor factorization.

A constrained recursive least squares algorithm for adaptive combination of multiple models

In this paper, we develop a novel constrained recursive least squares algorithm for adaptively combining a set of given multiple models. With data available in an online fashion, the linear combination coefficients of submodels are adapted via the proposed algorithm.We propose to minimize the mean square error with a forgetting factor, and apply the sum to one constraint to the combination parameters. Moreover an l1-norm constraint to the combination parameters is also applied with the aim to achieve sparsity of multiple models so that only a subset of models may be selected into the final model. Then a weighted l2-norm is applied as an approximation to the l1-norm term. As such at each time step, a closed solution of the model combination parameters is available. The contribution of this paper is to derive the proposed constrained recursive least squares algorithm that is computational efficient by exploiting matrix theory. The effectiveness of the approach has been demonstrated using both simulated and real time series examples.

Coupling an aerosol box model with one-dimensional flow: a tool for understanding observations of new particle formation events

Field observations of new particle formation and the subsequent particle growth are typically only possible at a fixed measurement location, and hence do not follow the temporal evolution of an air parcel in a Lagrangian sense. Standard analysis for determining formation and growth rates requires that the time-dependent formation rate and growth rate of the particles are spatially invariant; air parcel advection means that the observed temporal evolution of the particle size distribution at a fixed measurement location may not represent the true evolution if there are spatial variations in the formation and growth rates. Here we present a zero-dimensional aerosol box model coupled with one-dimensional atmospheric flow to describe the impact of advection on the evolution of simulated new particle formation events. Wind speed, particle formation rates and growth rates are input parameters that can vary as a function of time and location, using wind speed to connect location to time. The output simulates measurements at a fixed location; formation and growth rates of the particle mode can then be calculated from the simulated observations at a stationary point for different scenarios and be compared with the ‘true’ input parameters. Hence, we can investigate how spatial variations in the formation and growth rates of new particles would appear in observations of particle number size distributions at a fixed measurement site. We show that the particle size distribution and growth rate at a fixed location is dependent on the formation and growth parameters upwind, even if local conditions do not vary. We also show that different input parameters used may result in very similar simulated measurements. Erroneous interpretation of observations in terms of particle formation and growth rates, and the time span and areal extent of new particle formation, is possible if the spatial effects are not accounted for.