Minimizing the effect of sinusoidal trends in detrended fluctuation analysis

The detrended fluctuation analysis (DFA) [Peng et al., 1994] and its extensions (MF-DFA) [Kantelhardt et al., 2002] have been used extensively to determine possible long-range correlations in self-affine signals. While the DFA has been claimed to be a superior technique, recent reports have indicated its susceptibility to trends in the data. In this report, a smoothing filter is proposed to minimize the effect of sinusoidal trends and distortion in the log-log plots obtained by DFA and MF-DFA techniques

Ground Instrumentation for Mariner IV OCCULTATION Experiment

Deep Space Instrumentation Facility /DSIF/ GROUND receiver stations for Mariner IV space probe occulation experimen

Data processing method for a weak, moving telemetry signal

Method of processing data from a spacecraft, where the carrier has a low signal-to-noise ratio and wide unpredictable frequency shifts, consists of analogue recording of the noisy signal along with a high-frequency tone that is used as a clock to trigger a digitizer

Simulation of turbulent transonic separated flow over an airfoil

A code developed for simulating high Reynolds number transonic flow fields of arbitrary configuration is described. This code, in conjunction with laboratory experiments, is used to devise and test turbulence transport models which may be suitable in the prediction of such flow fields, with particular emphasis on regions of flow separation. The solutions describe the flow field, including both the shock-induced and trailing-edge separation regions, in sufficient detail to provide the profile and friction drag

Fast Matrix Factorization for Online Recommendation with Implicit Feedback

This paper contributes improvements on both the effectiveness and efficiency of Matrix Factorization (MF) methods for implicit feedback. We highlight two critical issues of existing works. First, due to the large space of unobserved feedback, most existing works resort to assign a uniform weight to the missing data to reduce computational complexity. However, such a uniform assumption is invalid in real-world settings. Second, most methods are also designed in an offline setting and fail to keep up with the dynamic nature of online data. We address the above two issues in learning MF models from implicit feedback. We first propose to weight the missing data based on item popularity, which is more effective and flexible than the uniform-weight assumption. However, such a non-uniform weighting poses efficiency challenge in learning the model. To address this, we specifically design a new learning algorithm based on the element-wise Alternating Least Squares (eALS) technique, for efficiently optimizing a MF model with variably-weighted missing data. We exploit this efficiency to then seamlessly devise an incremental update strategy that instantly refreshes a MF model given new feedback. Through comprehensive experiments on two public datasets in both offline and online protocols, we show that our eALS method consistently outperforms state-of-the-art implicit MF methods. Our implementation is available at https://github.com/hexiangnan/sigir16-eals.Comment: 10 pages, 8 figure

The phase-dependent linear conductance of a superconducting quantum point contact

The exact expression for the phase-dependent linear conductance of a weakly damped superconducting quantum point contact is obtained. The calculation is performed by summing up the complete perturbative series in the coupling between the electrodes. The failure of any finite order perturbative expansion in the limit of small voltage and small quasi-particle damping is analyzed in detail. In the low transmission regime this nonperturbative calculation yields a result which is at variance with standard tunnel theory. Our result predicts the correct sign of the quasi-particle pair interference term and exhibits an unusual phase-dependence at low temperatures in qualitative agreement with the available experimental data.Comment: 12 pages (revtex) + 1 postscript figure. Submitted to Phys. Rev. Let

Petawatt laser absorption bounded

The interaction of petawatt ($10^{15}\ \mathrm{W}$) lasers with solid matter forms the basis for advanced scientific applications such as table-top particle accelerators, ultrafast imaging systems and laser fusion. Key metrics for these applications relate to absorption, yet conditions in this regime are so nonlinear that it is often impossible to know the fraction of absorbed light $f$, and even the range of $f$ is unknown. Here using a relativistic Rankine-Hugoniot-like analysis, we show for the first time that $f$ exhibits a theoretical maximum and minimum. These bounds constrain nonlinear absorption mechanisms across the petawatt regime, forbidding high absorption values at low laser power and low absorption values at high laser power. For applications needing to circumvent the absorption bounds, these results will accelerate a shift from solid targets, towards structured and multilayer targets, and lead the development of new materials