Adjacent Channel Interference in UMTS Networks

One of the purposes of receive filtering in a Universal Mobile Telecommunication System (UMTS) handset receiver is to attenuate out-of-channel interference to provide channel selectivity. A UMTS handset receiver using a receive filter adaptive on out-of-channel interference level can be more computationally efficient than a handset with a fixed receive filter provided that the hand-set operates in low out-of-channel interference conditions often enough. The UMTS Adjacent Channel Selectivity (ACS) test case requires the adaptive receive filter to provide a worst case ACS of 33 dB. An adaptive receive filter is more computationally efficient than a fixed receive filter when the required ACS is less than 23 dB, because the added complexity of measuring the out-of-channel interference is compensated for by the reduction in the required number of filter taps to achieve the ACS. Measurements of the out-of-channel interference show that currently the interference levels for which the maximum ACS of 33 dB is required are hardly ever reached in practice. For the currently measured interference levels an adaptive receive filter will be computationally more efficient than a fixed\ud receive filter 97% of the time. However, the current out-of-channel interference measurements might be on the optimistic side, because the loads of the UMTS networks are low. When these loads increase in the future, the out-of-channel interference levels may increase and the advantage in computational efficiency of the adaptive receive filter will be reduced

Pricing options and computing implied volatilities using neural networks

This paper proposes a data-driven approach, by means of an Artificial Neural Network (ANN), to value financial options and to calculate implied volatilities with the aim of accelerating the corresponding numerical methods. With ANNs being universal function approximators, this method trains an optimized ANN on a data set generated by a sophisticated financial model, and runs the trained ANN as an agent of the original solver in a fast and efficient way. We test this approach on three different types of solvers, including the analytic solution for the Black-Scholes equation, the COS method for the Heston stochastic volatility model and Brent's iterative root-finding method for the calculation of implied volatilities. The numerical results show that the ANN solver can reduce the computing time significantly

Severing the Link between Farm Program Payments and Farm Production: Motivation, International Efforts, and Lessons

Agricultural and Food Policy, International Relations/Trade,

Circulating cell death products predict clinical outcome of colorectal cancer patients.

BackgroundTumor cell death generates products that can be measured in the circulation of cancer patients. CK18-Asp396 (M30 antigen) is a caspase-degraded product of cytokeratin 18 (CK18), produced by apoptotic epithelial cells, and is elevated in breast and lung cancer patients.MethodsWe determined the CK18-Asp396 and total CK18 levels in plasma of 49 colorectal cancer patients, before and after surgical resection of the tumor, by ELISA. Correlations with patient and tumor characteristics were determined by Kruskal-Wallis H and Mann-Whitney U tests. Disease-free survival was determined using Kaplan-Meier methodology with Log Rank tests, and univariate and multivariate Cox proportional hazard analysis.ResultsPlasma CK18-Asp396 and total CK18 levels in colorectal cancer patients were related to disease stage and tumor diameter, and were predictive of disease-free survival, independent of disease-stage, with hazard ratios (HR) of patients with high levels (> median) compared to those with low levels (< or = median) of 3.58 (95% CI: 1.17-11.02) and 3.58 (95% CI: 0.97-7.71), respectively. The CK18-Asp396/CK18 ratio, which decreased with tumor progression, was also predictive of disease-free survival, with a low ratio (< or = median) associated with worse disease-free survival: HR 2.78 (95% CI: 1.06-7.19). Remarkably, the plasma CK18-Asp396 and total CK18 levels after surgical removal of the tumor were also predictive of disease-free survival, with patients with high levels having a HR of 3.78 (95% CI: 0.77-18.50) and 4.12 (95% CI: 0.84-20.34), respectively, indicating that these parameters can be used also to monitor patients after surgery.ConclusionCK18-Asp396 and total CK18 levels in the circulation of colorectal cancer patients are predictive of tumor progression and prognosis and might be helpful for treatment selection and monitoring of these patients

A neural network-based framework for financial model calibration

A data-driven approach called CaNN (Calibration Neural Network) is proposed to calibrate financial asset price models using an Artificial Neural Network (ANN). Determining optimal values of the model parameters is formulated as training hidden neurons within a machine learning framework, based on available financial option prices. The framework consists of two parts: a forward pass in which we train the weights of the ANN off-line, valuing options under many different asset model parameter settings; and a backward pass, in which we evaluate the trained ANN-solver on-line, aiming to find the weights of the neurons in the input layer. The rapid on-line learning of implied volatility by ANNs, in combination with the use of an adapted parallel global optimization method, tackles the computation bottleneck and provides a fast and reliable technique for calibrating model parameters while avoiding, as much as possible, getting stuck in local minima. Numerical experiments confirm that this machine-learning framework can be employed to calibrate parameters of high-dimensional stochastic volatility models efficiently and accurately.Comment: 34 pages, 9 figures, 11 table

On local Fourier analysis of multigrid methods for PDEs with jumping and random coefficients

In this paper, we propose a novel non-standard Local Fourier Analysis (LFA) variant for accurately predicting the multigrid convergence of problems with random and jumping coefficients. This LFA method is based on a specific basis of the Fourier space rather than the commonly used Fourier modes. To show the utility of this analysis, we consider, as an example, a simple cell-centered multigrid method for solving a steady-state single phase flow problem in a random porous medium. We successfully demonstrate the prediction capability of the proposed LFA using a number of challenging benchmark problems. The information provided by this analysis helps us to estimate a-priori the time needed for solving certain uncertainty quantification problems by means of a multigrid multilevel Monte Carlo method