3,468 research outputs found
Link Prediction in Complex Network via Penalizing Noncontribution Relations of Endpoints
Similarity based link prediction algorithms become the focus in complex network research. Although endpoint degree as source of influence diffusion plays an important role in link prediction, some noncontribution links, also called noncontribution relations, involved in the endpoint degree serve nothing to the similarity between the two nonadjacent endpoints. In this paper, we propose a novel link prediction algorithm to penalize those endpoints’ degrees including many null links in influence diffusion, namely, noncontribution relations penalization algorithm, briefly called NRP. Seven mainstream baselines are introduced for comparison on nine benchmark datasets, and numerical analysis shows great improvement of accuracy performance, measured by the Area Under roc Curve (AUC). At last, we simply discuss the complexity of our algorithm
Two Solar Tornadoes Observed with the Interface Region Imaging Spectrograph
The barbs or legs of some prominences show an apparent motion of rotation,
which are often termed solar tornadoes. It is under debate whether the apparent
motion is a real rotating motion, or caused by oscillations or
counter-streaming flows. We present analysis results from spectroscopic
observations of two tornadoes by the Interface Region Imaging Spectrograph.
Each tornado was observed for more than 2.5 hours. Doppler velocities are
derived through a single Gaussian fit to the Mg~{\sc{ii}}~k~2796\AA{}~and
Si~{\sc{iv}}~1393\AA{}~line profiles. We find coherent and stable red and blue
shifts adjacent to each other across the tornado axes, which appears to favor
the interpretation of these tornadoes as rotating cool plasmas with
temperatures of K- K. This interpretation is further supported by
simultaneous observations of the Atmospheric Imaging Assembly on board the
Solar Dynamics Observatory, which reveal periodic motions of dark structures in
the tornadoes. Our results demonstrate that spectroscopic observations can
provide key information to disentangle different physical processes in solar
prominences.Comment: 14 figures, accepted by Ap
Inverse Projection Representation and Category Contribution Rate for Robust Tumor Recognition
Sparse representation based classification (SRC) methods have achieved
remarkable results. SRC, however, still suffer from requiring enough training
samples, insufficient use of test samples and instability of representation. In
this paper, a stable inverse projection representation based classification
(IPRC) is presented to tackle these problems by effectively using test samples.
An IPR is firstly proposed and its feasibility and stability are analyzed. A
classification criterion named category contribution rate is constructed to
match the IPR and complete classification. Moreover, a statistical measure is
introduced to quantify the stability of representation-based classification
methods. Based on the IPRC technique, a robust tumor recognition framework is
presented by interpreting microarray gene expression data, where a two-stage
hybrid gene selection method is introduced to select informative genes.
Finally, the functional analysis of candidate's pathogenicity-related genes is
given. Extensive experiments on six public tumor microarray gene expression
datasets demonstrate the proposed technique is competitive with
state-of-the-art methods.Comment: 14 pages, 19 figures, 10 table
Dynamics of Quincke Particles with Tunable Memory
Memory can remarkably modify the collective behaviors of active particles. In
Quincke systems driven by electric fields, the memory of particles, in the form
of relaxation of polarization, has been taken to account for the run-and-tumble
behaviors under periodical driving. However, we show that the memory of Quincke
particles is generally a product of multiple mechanisms including inertia and
depolarization. The memory of Quincke particles can be tuned and enhanced by
the parameters of electric fields. Moreover, the interplay between inertia and
propulsion results in a frequency-dependent mobility such that a balance
between activity and attraction can be reached, giving rise to the formation of
dense active clusters. The memory in dense clusters becomes even more
significant because of the strong electrostatic interactions. Combining the
tunable memory and the adjustable mobility, a rich variety of collective
motions can be realized. These findings offer new insights into the dynamics of
active matter and have broad interests in periodically driven active systems.Comment: 14 pages,6 figures, 3 movie
TripleSurv: Triplet Time-adaptive Coordinate Loss for Survival Analysis
A core challenge in survival analysis is to model the distribution of
censored time-to-event data, where the event of interest may be a death,
failure, or occurrence of a specific event. Previous studies have showed that
ranking and maximum likelihood estimation (MLE)loss functions are widely-used
for survival analysis. However, ranking loss only focus on the ranking of
survival time and does not consider potential effect of samples for exact
survival time values. Furthermore, the MLE is unbounded and easily subject to
outliers (e.g., censored data), which may cause poor performance of modeling.
To handle the complexities of learning process and exploit valuable survival
time values, we propose a time-adaptive coordinate loss function, TripleSurv,
to achieve adaptive adjustments by introducing the differences in the survival
time between sample pairs into the ranking, which can encourage the model to
quantitatively rank relative risk of pairs, ultimately enhancing the accuracy
of predictions. Most importantly, the TripleSurv is proficient in quantifying
the relative risk between samples by ranking ordering of pairs, and consider
the time interval as a trade-off to calibrate the robustness of model over
sample distribution. Our TripleSurv is evaluated on three real-world survival
datasets and a public synthetic dataset. The results show that our method
outperforms the state-of-the-art methods and exhibits good model performance
and robustness on modeling various sophisticated data distributions with
different censor rates. Our code will be available upon acceptance.Comment: 9 pages,6 figure
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