Linear Phase Second Order Recursive Digital Integrators and Differentiators

In this paper, design of linear phase second order recursive digital integrators and differentiators is discussed. New second order integrators have been designed by using Genetic Algorithm (GA) optimization method. Thereafter, by modifying the transfer function of these integrators appropriately, new digital differentiators have been obtained. The proposed digital integrators and differentiators accurately approximate the ideal ones and have linear phase response over almost entire Nyquist frequency range. The proposed operators also outperform the existing operators in terms of both magnitude and phase response

Composite Fermions and Landau Level Mixing in the Fractional Quantum Hall Effect

The reduction of the energy gap due to Landau level mixing, characterized by the dimensionless parameter $\lambda = (e^2/\epsilon l_0)/\hbar\omega_c$, has been calculated by variational Monte Carlo for the fractional quantum Hall effect at filling fractions $\nu=1/3$ and 1/5 using a modified version of Jain's composite fermion wave functions. These wave functions exploit the Landau level mixing already present in composite fermion wave functions by introducing a partial Landau level projection operator. Results for the energy gaps are consistent with experimental observations in $n$-type GaAs, but we conclude that Landau level mixing alone cannot account for the significantly smaller energy gaps observed in $p$-type systems.Comment: 11 pages, RevTex, 2 figures in compressed tar .ps forma

Quantitative surface field analysis: learning causal models to predict ligand binding affinity and pose.

We introduce the QuanSA method for inducing physically meaningful field-based models of ligand binding pockets based on structure-activity data alone. The method is closely related to the QMOD approach, substituting a learned scoring field for a pocket constructed of molecular fragments. The problem of mutual ligand alignment is addressed in a general way, and optimal model parameters and ligand poses are identified through multiple-instance machine learning. We provide algorithmic details along with performance results on sixteen structure-activity data sets covering many pharmaceutically relevant targets. In particular, we show how models initially induced from small data sets can extrapolatively identify potent new ligands with novel underlying scaffolds with very high specificity. Further, we show that combining predictions from QuanSA models with those from physics-based simulation approaches is synergistic. QuanSA predictions yield binding affinities, explicit estimates of ligand strain, associated ligand pose families, and estimates of structural novelty and confidence. The method is applicable for fine-grained lead optimization as well as potent new lead identification

Validated Stability Indicating RP-HPLC Method for Simultaneous Estimation of Ofloxacin and Cefixime in their Combined Dosage Form

The objective of the current study was to develop and validate a simple, accurate, precise and selective stability-indicating gradient reverse phase high performance liquid chromatographic method for simultaneous estimation of Ofloxacin and Cefixime in pharmaceutical formulation in presence of degradation products. The chromatographic separation of Ofloxacin and Cefixime was achieved on Shimadzu LC-20AT series HPLC having C18-ODS bonded column (250 x 4.6 mm, 40 °C, 10 µL) using UV/Visible detector at 276 nm. The optimized mobile phase was consisted of a methanol: phosphate buffer (50:50) at a flow rate of 1.0 ml/m. The retention times were 4.799 and 1.602 m for Ofloxacin and Cefixime respectively. The proposed method provided linear responses within the concentration ranges 5-25 µg/ml for Ofloxacin and Cefixime both. The limit of detection (LOD) and limit of quantification (LOQ) values were found to be 0.0259, 0.078 µg/ml and 0.0206, 0.062 µg/ml for Ofloxacin and Cefixime F respectively. The developed method was validated as per ICH guidelines with respect to specificity, linearity, accuracy, precision, robustness and ruggedness. The studies data revealed that developed method was convenient, fairly reliable, sensitive, less expensive and reproducible

Simple Pricing Schemes for the Cloud

The problem of pricing the cloud has attracted much recent attention due to the widespread use of cloud computing and cloud services. From a theoretical perspective, several mechanisms that provide strong efficiency or fairness guarantees and desirable incentive properties have been designed. However, these mechanisms often rely on a rigid model, with several parameters needing to be precisely known in order for the guarantees to hold. In this paper, we consider a stochastic model and show that it is possible to obtain good welfare and revenue guarantees with simple mechanisms that do not make use of the information on some of these parameters. In particular, we prove that a mechanism that sets the same price per time step for jobs of any length achieves at least 50% of the welfare and revenue obtained by a mechanism that can set different prices for jobs of different lengths, and the ratio can be improved if we have more specific knowledge of some parameters. Similarly, a mechanism that sets the same price for all servers even though the servers may receive different kinds of jobs can provide a reasonable welfare and revenue approximation compared to a mechanism that is allowed to set different prices for different servers.Comment: To appear in the 13th Conference on Web and Internet Economics (WINE), 2017. A preliminary version was presented at the 12th Workshop on the Economics of Networks, Systems and Computation (NetEcon), 201