492 research outputs found
Design of generalized fractional order gradient descent method
This paper focuses on the convergence problem of the emerging fractional
order gradient descent method, and proposes three solutions to overcome the
problem. In fact, the general fractional gradient method cannot converge to the
real extreme point of the target function, which critically hampers the
application of this method. Because of the long memory characteristics of
fractional derivative, fixed memory principle is a prior choice. Apart from the
truncation of memory length, two new methods are developed to reach the
convergence. The one is the truncation of the infinite series, and the other is
the modification of the constant fractional order. Finally, six illustrative
examples are performed to illustrate the effectiveness and practicability of
proposed methods.Comment: 8 pages, 16 figure
Description and Realization for a Class of Irrational Transfer Functions
This paper proposes an exact description scheme which is an extension to the
well-established frequency distributed model method for a class of irrational
transfer functions. The method relaxes the constraints on the zero initial
instant by introducing the generalized Laplace transform, which provides a wide
range of applicability. With the discretization of continuous frequency band,
the infinite dimensional equivalent model is approximated by a finite
dimensional one. Finally, a fair comparison to the well-known Charef method is
presented, demonstrating its added value with respect to the state of art.Comment: 9 pages, 9 figure
Estimating Freeway Travel Times using the General Motors Model
Travel time is a key transportation performance measure because of its diverse applications. Various modeling approaches to estimating freeway travel time have been well developed due to widespread installation of intelligent transportation system sensors. However, estimating accurate travel time using existing freeway travel time models is still challenging under congested conditions. Therefore, this study aimed to develop an innovative freeway travel time estimation model based on the General Motors (GM) car-following model. Since the GM model is usually used in a microsimulation environment, the concepts of virtual leading and virtual following vehicles are proposed to allow the GM model to be used in macroscale environments using aggregated traffic sensor data. Travel time data collected from three study corridors on I-270 in Saint Louis, Missouri, were used to verify the estimated travel times produced by the proposed General Motors travel time estimation (GMTTE) model and two existing models, the instantaneous model and the time-slice model. The results showed that the GMTTE model out-performed the two existing models due to lower mean average percentage errors of 1.62% in free-flow conditions and 6.66% in two congested conditions. Overall, the GMTTE model demonstrated its robustness and accuracy for estimating freeway travel times
Predicting DNA Methylation State of CpG Dinucleotide Using Genome Topological Features and Deep Networks
The hypo- or hyper-methylation of the human genome is one of the epigenetic features of leukemia. However, experimental approaches have only determined the methylation state of a small portion of the human genome. We developed deep learning based (stacked denoising autoencoders, or SdAs) software named DeepMethyl to predict the methylation state of DNA CpG dinucleotides using features inferred from three-dimensional genome topology (based on Hi-C) and DNA sequence patterns. We used the experimental data from immortalised myelogenous leukemia (K562) and healthy lymphoblastoid (GM12878) cell lines to train the learning models and assess prediction performance. We have tested various SdA architectures with different configurations of hidden layer(s) and amount of pre-training data and compared the performance of deep networks relative to support vector machines (SVMs). Using the methylation states of sequentially neighboring regions as one of the learning features, an SdA achieved a blind test accuracy of 89.7% for GM12878 and 88.6% for K562. When the methylation states of sequentially neighboring regions are unknown, the accuracies are 84.82% for GM12878 and 72.01% for K562. We also analyzed the contribution of genome topological features inferred from Hi-C. DeepMethyl can be accessed at http://dna.cs.usm.edu/deepmethyl/
Long Non-Coding RNAs As Prognostic Markers In Human Breast Cancer
Long non-coding RNAs (lncRNAs) have been recently shown to play an important role in gene regulation and normal cellular functions, and disease processes. However, despite the overwhelming number of lncRNAs identified to date, little is known about their role in cancer for vast majority of them. The present study aims to determine whether lncRNAs can serve as prognostic markers in human breast cancer. We interrogated the breast invasive carcinoma dataset of the Cancer Genome Atlas (TCGA) at the cBioPortal consisting of ~ 1,000 cases. Among 2,730 lncRNAs analyzed, 577 lncRNAs had alterations ranging from 1% to 32% frequency, which include mutations, alterations of copy number and RNA expression. We found that deregulation of 11 lncRNAs, primarily due to copy number alteration, is associated with poor overall survival. At RNA expression level, upregulation of 4 lncRNAs (LINC00657, LINC00346, LINC00654 and HCG11) was associated with poor overall survival. A third signature consists of 9 lncRNAs (LINC00705, LINC00310, LINC00704, LINC00574, FAM74A3, UMODL1-AS1, ARRDC1-AS1, HAR1A, and LINC00323) and their upregulation can predict recurrence. Finally, we selected LINC00657 to determine their role in breast cancer, and found that LINC00657 knockout significantly suppresses tumor cell growth and proliferation, suggesting that it plays an oncogenic role. Together, these results highlight the clinical significance of lncRNAs, and thus, these lncRNAs may serve as prognostic markers for breast cancer
Study of the decay
The decay is studied
in proton-proton collisions at a center-of-mass energy of TeV
using data corresponding to an integrated luminosity of 5
collected by the LHCb experiment. In the system, the
state observed at the BaBar and Belle experiments is
resolved into two narrower states, and ,
whose masses and widths are measured to be where the first uncertainties are statistical and the second
systematic. The results are consistent with a previous LHCb measurement using a
prompt sample. Evidence of a new
state is found with a local significance of , whose mass and width
are measured to be and , respectively. In addition, evidence of a new decay mode
is found with a significance of
. The relative branching fraction of with respect to the
decay is measured to be , where the first
uncertainty is statistical, the second systematic and the third originates from
the branching fractions of charm hadron decays.Comment: All figures and tables, along with any supplementary material and
additional information, are available at
https://cern.ch/lhcbproject/Publications/p/LHCb-PAPER-2022-028.html (LHCb
public pages
Measurement of the ratios of branching fractions and
The ratios of branching fractions
and are measured, assuming isospin symmetry, using a
sample of proton-proton collision data corresponding to 3.0 fb of
integrated luminosity recorded by the LHCb experiment during 2011 and 2012. The
tau lepton is identified in the decay mode
. The measured values are
and
, where the first uncertainty is
statistical and the second is systematic. The correlation between these
measurements is . Results are consistent with the current average
of these quantities and are at a combined 1.9 standard deviations from the
predictions based on lepton flavor universality in the Standard Model.Comment: All figures and tables, along with any supplementary material and
additional information, are available at
https://cern.ch/lhcbproject/Publications/p/LHCb-PAPER-2022-039.html (LHCb
public pages
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