The effects of caffeine consumption on direct and indirect majority and minority influence

A study is reported that examines the effect of caffeine consumption on majority and minority influence. In a double blind procedure, 72 participants consumed an orange drink, which either contained caffeine (3.5mg per kilogram of body weight) or did not (placebo). After a 40-minute delay, participants read a counter-attitudinal message (antivoluntary euthanasia) endorsed by either a numerical majority or minority. Both direct (message issue, i.e., voluntary euthanasia) and indirect (message issue-related, i.e., abortion) change was assessed by attitude scales completed before and after exposure to the message. In the placebo condition, the findings replicated the predictions of Moscovici's (1980) conversion theory; namely, majorities leading to compliance (direct influence) and minorities leading to conversion (indirect influence). When participants had consumed caffeine, majorities not only led to more direct influence than in the placebo condition but also to indirect influence. Minorities, by contrast, had no impact on either level of influence. The results suggest that moderate levels of caffeine increase systematic processing of the message but the consequences of this vary for each source. When the source is a majority there was increased indirect influence while for a minority there was decreased indirect influence. The results show the need to understand how contextual factors can affect social influence processes.

Mechanisms of chromium deposition and dissolution under direct and pulse reverse plating conditions

Baths containing sulphuric acid as catalyst and others with selected secondary catalysts (methane sulphonic acid - MSA, SeO2, a KBrO3/KIO3 mixture, indium, uranium and commercial high speed catalysts (HEEF-25 and HEEF-405)) were studied. The secondary catalysts influenced CCE, brightness and cracking. Chromium deposition mechanisms were studied in Part II using potentiostatic and potentiodynamic electroanalytical techniques under stationary and hydrodynamic conditions. Sulphuric acid as a primary catalyst and MSA, HEEF-25, HEEF-405 and sulphosalycilic acid as co-catalysts were explored for different rotation, speeds and scan rates. Maximum current was resolved into diffusion and kinetically limited components, and a contribution towards understanding the electrochemical mechanism is proposed. Reaction kinetics were further studied for H2SO4, MSA and methane disulphonic acid catalysed systems and their influence on reaction mechanisms elaborated. Charge transfer coefficient and electrochemical reaction rate orders for the first stage of the electrodeposition process were determined. A contribution was made toward understanding of H2SO4 and MSA influence on the evolution rate of hydrogen. Anodic dissolution of chromium in the chromic acid solution was studied with a number of techniques. An electrochemical dissolution mechanism is proposed, based on the results of rotating gold ring disc experiments and scanning electron microscopy. Finally, significant increases in chromium electrodeposition rates under non-stationary conditions (PRC mode) were studied and a deposition mechanisms is elaborated based on experimental data and theoretical considerations.

Market orientation of service networks:direct and indirect effects on sustained competitive advantage

Market orientation is an organization-wide concept that helps explain sustained competitive advantage (SCA). Since networks become ever more important, especially in the service sector, there is need to expand the concept of MO to a network setting. In line with Narver and Slater (1990), the concept of Market Orientation of Networks (MONW) is developed. This study indicates how MONW relates to the resource-based view (RBV) of the firm and the industrial organization (IO) view in explaining SCA. It is argued that MONW has direct and indirect effects on SCA. More precisely, the antecedent effect of MONW to resources and industry structure is considered.

False recognition of objects in visual scenes:findings from a combined direct and indirect memory test

We report an extension of the procedure devised by Weinstein and Shanks (Memory & Cognition 36:1415-1428, 2008) to study false recognition and priming of pictures. Participants viewed scenes with multiple embedded objects (seen items), then studied the names of these objects and the names of other objects (read items). Finally, participants completed a combined direct (recognition) and indirect (identification) memory test that included seen items, read items, and new items. In the direct test, participants recognized pictures of seen and read items more often than new pictures. In the indirect test, participants' speed at identifying those same pictures was improved for pictures that they had actually studied, and also for falsely recognized pictures whose names they had read. These data provide new evidence that a false-memory induction procedure can elicit memory-like representations that are difficult to distinguish from "true" memories of studied pictures. © 2012 Psychonomic Society, Inc.

Estimating direct and indirect effects of foreign direct investment on firm productivity in the presence of interactions between firms

We implement a method to estimate the direct effects of foreign-ownership on foreign firms' productivity and the indirect effects (or spillovers) from the presence of foreign-owned firms on other foreign and domestic firms' productivity in a unifying framework, taking interactions between firms into account. To do so, we relax a fundamental assumption made in empirical studies examining a direct causal effect of foreign ownership on firm productivity, namely that of no interactions between firms. Based on our approach, we are able to combine direct and indirect effects of foreign ownership and calculate the total effect of foreign firms on local productivity. Our results show that all these effects vary with the level of foreign presence within a cluster, an important finding for the academic literature and policy debate on the benefits of attracting foreign owned firms.

Comparing direct and imagined intergroup contact among children:effects on outgroup stereotypes and helping intentions

We conducted an experimental intervention aimed at comparing the effectiveness of direct and imagined intergroup contact. Italian elementary school children took part in a three-week intervention with dependent variables assessed one week after the last intervention session. Results revealed that direct and imagined intergroup contact, compared to control conditions of direct and imagined intragroup contact, had an additive impact when it came to reducing negative stereotypes of immigrants and fostering future helping intentions toward this group. The theoretical and practical implications of the findings are discussed.

Periodic nonlinear Fourier transform for fiber-optic communications, Part I:theory and numerical methods

In this work, we introduce the periodic nonlinear Fourier transform (PNFT) method as an alternative and efficacious tool for compensation of the nonlinear transmission effects in optical fiber links. In the Part I, we introduce the algorithmic platform of the technique, describing in details the direct and inverse PNFT operations, also known as the inverse scattering transform for periodic (in time variable) nonlinear Schrödinger equation (NLSE). We pay a special attention to explaining the potential advantages of the PNFT-based processing over the previously studied nonlinear Fourier transform (NFT) based methods. Further, we elucidate the issue of the numerical PNFT computation: we compare the performance of four known numerical methods applicable for the calculation of nonlinear spectral data (the direct PNFT), in particular, taking the main spectrum (utilized further in Part II for the modulation and transmission) associated with some simple example waveforms as the quality indicator for each method. We show that the Ablowitz-Ladik discretization approach for the direct PNFT provides the best performance in terms of the accuracy and computational time consumption.

On the numerical solution of a Cauchy problem in an elastostatic half-plane with a bounded inclusion

We propose an iterative procedure for the inverse problem of determining the displacement vector on the boundary of a bounded planar inclusion given the displacement and stress fields on an infinite (planar) line-segment. At each iteration step mixed boundary value problems in an elastostatic half-plane containing the bounded inclusion are solved. For efficient numerical implementation of the procedure these mixed problems are reduced to integral equations over the bounded inclusion. Well-posedness and numerical solution of these boundary integral equations are presented, and a proof of convergence of the procedure for the inverse problem to the original solution is given. Numerical investigations are presented both for the direct and inverse problems, and these results show in particular that the displacement vector on the boundary of the inclusion can be found in an accurate and stable way with small computational cost.

Canonical solution groups of a homomorphism : manipulator kinematics, nonparametric regression and distributed object systems

The kinematic mapping of a rigid open-link manipulator is a homomorphism between Lie groups. The homomorphisrn has solution groups that act on an inverse kinematic solution element. A canonical representation of solution group operators that act on a solution element of three and seven degree-of-freedom (do!) dextrous manipulators is determined by geometric analysis. Seven canonical solution groups are determined for the seven do! Robotics Research K-1207 and Hollerbach arms. The solution element of a dextrous manipulator is a collection of trivial fibre bundles with solution fibres homotopic to the Torus. If fibre solutions are parameterised by a scalar, a direct inverse funct.ion that maps the scalar and Cartesian base space coordinates to solution element fibre coordinates may be defined. A direct inverse pararneterisation of a solution element may be approximated by a local linear map generated by an inverse augmented Jacobian correction of a linear interpolation. The action of canonical solution group operators on a local linear approximation of the solution element of inverse kinematics of dextrous manipulators generates cyclical solutions. The solution representation is proposed as a model of inverse kinematic transformations in primate nervous systems. Simultaneous calibration of a composition of stereo-camera and manipulator kinematic models is under-determined by equi-output parameter groups in the composition of stereo-camera and Denavit Hartenberg (DH) rnodels. An error measure for simultaneous calibration of a composition of models is derived and parameter subsets with no equi-output groups are determined by numerical experiments to simultaneously calibrate the composition of homogeneous or pan-tilt stereo-camera with DH models. For acceleration of exact Newton second-order re-calibration of DH parameters after a sequential calibration of stereo-camera and DH parameters, an optimal numerical evaluation of DH matrix first order and second order error derivatives with respect to a re-calibration error function is derived, implemented and tested. A distributed object environment for point and click image-based tele-command of manipulators and stereo-cameras is specified and implemented that supports rapid prototyping of numerical experiments in distributed system control. The environment is validated by a hierarchical k-fold cross validated calibration to Cartesian space of a radial basis function regression correction of an affine stereo model. Basic design and performance requirements are defined for scalable virtual micro-kernels that broker inter-Java-virtual-machine remote method invocations between components of secure manageable fault-tolerant open distributed agile Total Quality Managed ISO 9000+ conformant Just in Time manufacturing systems.

Minimum description length, regularisation and multi-modal data

Relationships between clustering, description length, and regularisation are pointed out, motivating the introduction of a cost function with a description length interpretation and the unusual and useful property of having its minimum approximated by the densest mode of a distribution. A simple inverse kinematics example is used to demonstrate that this property can be used to select and learn one branch of a multi-valued mapping. This property is also used to develop a method for setting regularisation parameters according to the scale on which structure is exhibited in the training data. The regularisation technique is demonstrated on two real data sets, a classification problem and a regression problem.

Minimum description length, regularisation and multi-modal data

Conventional feed forward Neural Networks have used the sum-of-squares cost function for training. A new cost function is presented here with a description length interpretation based on Rissanen's Minimum Description Length principle. It is a heuristic that has a rough interpretation as the number of data points fit by the model. Not concerned with finding optimal descriptions, the cost function prefers to form minimum descriptions in a naive way for computational convenience. The cost function is called the Naive Description Length cost function. Finding minimum description models will be shown to be closely related to the identification of clusters in the data. As a consequence the minimum of this cost function approximates the most probable mode of the data rather than the sum-of-squares cost function that approximates the mean. The new cost function is shown to provide information about the structure of the data. This is done by inspecting the dependence of the error to the amount of regularisation. This structure provides a method of selecting regularisation parameters as an alternative or supplement to Bayesian methods. The new cost function is tested on a number of multi-valued problems such as a simple inverse kinematics problem. It is also tested on a number of classification and regression problems. The mode-seeking property of this cost function is shown to improve prediction in time series problems. Description length principles are used in a similar fashion to derive a regulariser to control network complexity.

Evolutionary neural networks : models and applications

The scaling problems which afflict attempts to optimise neural networks (NNs) with genetic algorithms (GAs) are disclosed. A novel GA-NN hybrid is introduced, based on the bumptree, a little-used connectionist model. As well as being computationally efficient, the bumptree is shown to be more amenable to genetic coding lthan other NN models. A hierarchical genetic coding scheme is developed for the bumptree and shown to have low redundancy, as well as being complete and closed with respect to the search space. When applied to optimising bumptree architectures for classification problems the GA discovers bumptrees which significantly out-perform those constructed using a standard algorithm. The fields of artificial life, control and robotics are identified as likely application areas for the evolutionary optimisation of NNs. An artificial life case-study is presented and discussed. Experiments are reported which show that the GA-bumptree is able to learn simulated pole balancing and car parking tasks using only limited environmental feedback. A simple modification of the fitness function allows the GA-bumptree to learn mappings which are multi-modal, such as robot arm inverse kinematics. The dynamics of the 'geographic speciation' selection model used by the GA-bumptree are investigated empirically and the convergence profile is introduced as an analytical tool. The relationships between the rate of genetic convergence and the phenomena of speciation, genetic drift and punctuated equilibrium arc discussed. The importance of genetic linkage to GA design is discussed and two new recombination operators arc introduced. The first, linkage mapped crossover (LMX) is shown to be a generalisation of existing crossover operators. LMX provides a new framework for incorporating prior knowledge into GAs.Its adaptive form, ALMX, is shown to be able to infer linkage relationships automatically during genetic search.

Performance measurement with multiple interrelated variables and threshold target levels:evidence from retail firms in the US

In this study, we developed a DEA-based performance measurement methodology that is consistent with performance assessment frameworks such as the Balanced Scorecard. The methodology developed in this paper takes into account the direct or inverse relationships that may exist among the dimensions of performance to construct appropriate production frontiers. The production frontiers we obtained are deemed appropriate as they consist solely of firms with desirable levels for all dimensions of performance. These levels should be at least equal to the critical values set by decision makers. The properties and advantages of our methodology against competing methodologies are presented through an application to a real-world case study from retail firms operating in the US. A comparative analysis between the new methodology and existing methodologies explains the failure of the existing approaches to define appropriate production frontiers when directly or inversely related dimensions of performance are present and to express the interrelationships between the dimensions of performance.

Bayesian retrieval of scatterometer wind fields

The retrieval of wind fields from scatterometer observations has traditionally been separated into two phases; local wind vector retrieval and ambiguity removal. Operationally, a forward model relating wind vector to backscatter is inverted, typically using look up tables, to retrieve up to four local wind vector solutions. A heuristic procedure, using numerical weather prediction forecast wind vectors and, often, some neighbourhood comparison is then used to select the correct solution. In this paper we develop a Bayesian method for wind field retrieval, and show how a direct local inverse model, relating backscatter to wind vector, improves the wind vector retrieval accuracy. We compare these results with the operational U.K. Meteorological Office retrievals, our own CMOD4 retrievals and a neural network based local forward model retrieval. We suggest that the neural network based inverse model, which is extremely fast to use, improves upon current forward models when used in a variational data assimilation scheme.