24,939 research outputs found
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 () 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
, and even the range of is unknown. Here using a relativistic
Rankine-Hugoniot-like analysis, we show for the first time that 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
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