Bidding Dynamics of Rational Advertisers in Sponsored Search Auctions on the Web

In this paper, we address a key problem faced by advertisers in sponsored search auctions on the web: how much to bid, given the bids of the other advertisers, so as to maximize individual payoffs? Assuming the generalized second price auction as the auction mechanism, we formulate this problem in the framework of an infinite horizon alternative-move game of advertiser bidding behavior. For a sponsored search auction involving two advertisers, we characterize all the pure strategy and mixed strategy Nash equilibria. We also prove that the bid prices will lead to a Nash equilibrium, if the advertisers follow a myopic best response bidding strategy. Following this, we investigate the bidding behavior of the advertisers if they use Q-learning. We discover empirically an interesting trend that the Q-values converge even if both the advertisers learn simultaneously.

A new load distribution strategy for linear network with communication delays

In this paper, we propose a new load distribution strategy called `send-and-receive' for scheduling divisible loads, in a linear network of processors with communication delay. This strategy is designed to optimally utilize the network resources and thereby minimizes the processing time of entire processing load. A closed-form expression for optimal size of load fractions and processing time are derived when the processing load originates at processor located in boundary and interior of the network. A condition on processor and link speed is also derived to ensure that the processors are continuously engaged in load distributions. This paper also presents a parallel implementation of `digital watermarking problem' on a personal computer-based Pentium Linear Network (PLN) topology. Experiments are carried out to study the performance of the proposed strategy and results are compared with other strategies found in literature.

An Efficient Load Distribution Strategy for a Distributed Linear Network of Processors with Communication Delays

In this paper, we present an improved load distribution strategy, for arbitrarily divisible processing loads, to minimize the processing time in a distributed linear network of communicating processors by an efficient utilization of their front-ends. Closed-form solutions are derived, with the processing load originating at the boundary and at the interior of the network, under some important conditions on the arrangement of processors and links in the network. Asymptotic analysis is carried out to explore the ultimate performance limits of such networks. Two important theorems are stated regarding the optimal load sequence and the optimal load origination point. Comparative study of this new strategy with an earlier strategy is also presented.

A 1h And 13c Magnetic Resonance And Infrared Spectroscopic Study Of Coordinated 2-Aminocyclopentene-1-Dithiocarboxylic Acid And Its S-Methyl Ester

The ligating properties of 2-aminocyclopentene-1-dithiocarboxylic acid and its S-methyl esters were investigated. Complexes with Zn(II), Cd(II) and Hg(II) halides were synthesized and characterized by infrared and proton and carbon-13 NMR studies. The results are concordant with a bidentate coordination of the -CS2 group to the metal ions

Efficient implementations of a pseudodynamical stochastic filtering strategy for static elastography

A computationally efficient pseudodynamical filtering setup is established for elasticity imaging (i.e., reconstruction of shear modulus distribution) in soft-tissue organs given statically recorded and partially measured displacement data. Unlike a regularized quasi-Newton method (QNM) that needs inversion of ill-conditioned matrices, the authors explore pseudodynamic extended and ensemble Kalman filters (PD-EKF and PD-EnKF) that use a parsimonious representation of states and bypass explicit regularization by recursion over pseudotime. Numerical experiments with QNM and the two filters suggest that the PD-EnKF is the most robust performer as it exhibits no sensitivity to process noise covariance and yields good reconstruction even with small ensemble sizes.

A Strategy For Resolving Indias Oil Crisis

India is the midst of oil crisis.Many long term solution have been suggested.The question that is being asked is: can something be done immediately? Prof. A.K.N Reddy, who leads the group on the application of science & Techonology to rural area at the Indian Institute of Science has come with simple solutions which appears to well within our present technological capability.

Evidence for Non-coordinated synthesis of 5 S RNA in the thermophilic fungus, Thermomyces lanuginosus

Analysis of ribosomes and the post ribosomal supernatant fraction of actively growing cells ofThermomyces lanuginosus showed the presence of free 5 S RNA in the supernatant fraction. This 5 S RNA was identical to the ribosomal 5 S RNA in its electrophoretic mobility on 10% Polyacrylamide gel and in its base composition. 5 S RNA from both the sources gave evidence for the presence of diphosphate at the 5’ end. Most of the 5 S RNA that appeared in the cytoplasm was that transported from the nucleus during the isolation. This could be prevented by the use of a hexylene glycol-HEPES buffer.

An End-Use Oriented Global Energy Strategy

Abstract is not available.

Ant colony optimization for reporting cell planning in mobile computing using selective paging strategy

Location management problem that arise in mobile computing networks is addressed. One method used in location management is to designate sonic of the cells in the network as "reporting cells". The other cells in the network are "non-reporting cells". Finding an optimal set of reporting cells (or reporting cell configuration) for a given network. is a difficult combinatorial optimization problem. In fact this is shown to be an NP-complete problem. in an earlier study. In this paper, we use the selective paging strategy and use an ant colony optimization method to obtain the best/optimal set of reporting cells for a given a network.

Preparation of Palladium(II) Complexes of Isonitrosoacetylacetoneimines Having O- and N-Coordinated Isonitroso Groups

Methods for the preparation of palladium(II) complexes of the type Pd(R-IAI)(IAI'), where IAI' is the anion of isonitrosoacetylacetoneimine and R-IAI, its N-alkyl or N-aryl derivative, are given.

A Control Strategy for Reference Wave Adaptive Current Generation

Speed control of ac motors requires variable frequency, variable current, or variable voltage supply. Variable frequency supply can be obtained directly from a fixed frequency supply by using a frequency converter or from a dc source using inverters. In this paper a control technique for reference wave adaptive-current generation by modulating the inverter voltage is explained. Extension of this technique for three-phase induction-motor speed control is briefly explained. The oscillograms of the current waveforms obtained from the experimental setup are also shown.

A strategy for scheduling tightly coupled parallel applications on clusters

Although various strategies have been developed for scheduling parallel applications with independent tasks, very little work exists for scheduling tightly coupled parallel applications on cluster environments. In this paper, we compare four different strategies based on performance models of tightly coupled parallel applications for scheduling the applications on clusters. In addition to algorithms based on existing popular optimization techniques, we also propose a new algorithm called Box Elimination that searches the space of performance model parameters to determine the best schedule of machines. By means of real and simulation experiments, we evaluated the algorithms on single cluster and multi-cluster setups. We show that our Box Elimination algorithm generates up to 80% more efficient schedule than other algorithms. We also show that the execution times of the schedules produced by our algorithm are more robust against the performance modeling errors.

A Kalman filter based strategy for linear structural system identification based on multiple static and dynamic test data

The problem of identification of stiffness, mass and damping properties of linear structural systems, based on multiple sets of measurement data originating from static and dynamic tests is considered. A strategy, within the framework of Kalman filter based dynamic state estimation, is proposed to tackle this problem. The static tests consists of measurement of response of the structure to slowly moving loads, and to static loads whose magnitude are varied incrementally; the dynamic tests involve measurement of a few elements of the frequency response function (FRF) matrix. These measurements are taken to be contaminated by additive Gaussian noise. An artificial independent variable τ, that simultaneously parameterizes the point of application of the moving load, the magnitude of the incrementally varied static load and the driving frequency in the FRFs, is introduced. The state vector is taken to consist of system parameters to be identified. The fact that these parameters are independent of the variable τ is taken to constitute the set of ‘process’ equations. The measurement equations are derived based on the mechanics of the problem and, quantities, such as displacements and/or strains, are taken to be measured. A recursive algorithm that employs a linearization strategy based on Neumann’s expansion of structural static and dynamic stiffness matrices, and, which provides posterior estimates of the mean and covariance of the unknown system parameters, is developed. The satisfactory performance of the proposed approach is illustrated by considering the problem of the identification of the dynamic properties of an inhomogeneous beam and the axial rigidities of members of a truss structure.

Deploy and search strategy for multi-agent systems using Voronoi partitions

In this paper we analyze a deploy and search strategy for multi-agent systems. Mobile agents equipped with sensors carry out search operation in the search space. The lack of information about the search space is modeled as an uncertainty density distribution over the space, and is assumed to be known to the agents a priori. In each step, the agents deploy themselves in an optimal way so as to maximize per step reduction in the uncertainty density. We analyze the proposed strategy for convergence and spatial distributedness. The control law moving the agents has been analyzed for stability and convergence using LaSalle's invariance principle, and for spatial distributedness under a few realistic constraints on the control input such as constant speed, limit on maximum speed, and also sensor range limits. The simulation experiments show that the strategy successfully reduces the average uncertainty density below the required level.