5,182 research outputs found
meson production of high-energy nuclear collisions at NLO
The transverse momentum spectrum of meson in relativistic heavy-ion
collisions is studied at the next-to-leading-order (NLO) within the
perturbative QCD, where the jet quenching effect in the QGP is incorporated
with the effectively medium-modified fragmentation functions using the
higher-twist approach. We show that the theoretical simulations could give nice
descriptions of PHENIX data on meson in both and central collisions at the RHIC, and also provide numerical predictions of
spectra in central collisions with ~TeV at the
LHC. The ratios of in and in central
collisions at ~GeV are found to overlap in a wide region, which
matches well the measured ratio by PHENIX. We demonstrate that,
at the asymptotic region when the ratios of
in both and are almost determined only by
quark jets fragmentation and thus approach to the one in
scattering; in addition, the almost identical gluon (quark) contribution
fractions to and to result in a rather moderate variation of
distribution at intermediate and high region in
relative to that in ; while a slightly higher at small
in can be observed due to larger suppression of gluon
contribution fraction to as compared to the one to . The
theoretical prediction for at the LHC has also been presented.Comment: 7 pages, 8 figures, 2 typos corrected, revision for publicatio
Nuclear suppression of meson yields with large at the RHIC and the LHC
We calculate meson transverse momentum spectra in p+p collisions as
well as their nuclear suppressions in central A+A collisions both at the RHIC
and the LHC in LO and NLO with the QCD-improved parton model. We have included
the parton energy loss effect in hot/dense QCD medium with the effectively
medium-modified fragmentation functions in the higher-twist approach of
jet quenching. The nuclear modification factors of meson in central
Au+Au collisions at the RHIC and central Pb+Pb collisions at the LHC are
provided, and a nice agreement of our numerical results at NLO with the ALICE
measurement is observed. Predictions of yield ratios of neutral mesons such as
, and at large in relativistic
heavy-ion collisions are also presented for the first time.Comment: 7 pages, 8 figure
Genetic variants in ELOVL2 and HSD17B12 predict melanoma‐specific survival
Fatty acids play a key role in cellular bioenergetics, membrane biosynthesis and intracellular signaling processes and thus may be involved in cancer development and progression. In the present study, we comprehensively assessed associations of 14,522 common single‐nucleotide polymorphisms (SNPs) in 149 genes of the fatty‐acid synthesis pathway with cutaneous melanoma disease‐specific survival (CMSS). The dataset of 858 cutaneous melanoma (CM) patients from a published genome‐wide association study (GWAS) by The University of Texas M.D. Anderson Cancer Center was used as the discovery dataset, and the identified significant SNPs were validated by a dataset of 409 CM patients from another GWAS from the Nurses’ Health and Health Professionals Follow‐up Studies. We found 40 noteworthy SNPs to be associated with CMSS in both discovery and validation datasets after multiple comparison correction by the false positive report probability method, because more than 85% of the SNPs were imputed. By performing functional prediction, linkage disequilibrium analysis, and stepwise Cox regression selection, we identified two independent SNPs of ELOVL2 rs3734398 T>C and HSD17B12 rs11037684 A>G that predicted CMSS, with an allelic hazards ratio of 0.66 (95% confidence interval = 0.51–0.84 and p = 8.34 × 10−4) and 2.29 (1.55–3.39 and p = 3.61 × 10−5), respectively. Finally, the ELOVL2 rs3734398 variant CC genotype was found to be associated with a significantly increased mRNA expression level. These SNPs may be potential markers for CM prognosis, if validated by additional larger and mechanistic studies
Meta contrastive label correction for financial time series
Financial applications such as stock price forecasting, usually face an issue
that under the predefined labeling rules, it is hard to accurately predict the
directions of stock movement. This is because traditional ways of labeling,
taking Triple Barrier Method, for example, usually gives us inaccurate or even
corrupted labels. To address this issue, we focus on two main goals. One is
that our proposed method can automatically generate correct labels for noisy
time series patterns, while at the same time, the method is capable of boosting
classification performance on this new labeled dataset. Based on the
aforementioned goals, our approach has the following three novelties: First, we
fuse a new contrastive learning algorithm into the meta-learning framework to
estimate correct labels iteratively when updating the classification model
inside. Moreover, we utilize images generated from time series data through
Gramian angular field and representative learning. Most important of all, we
adopt multi-task learning to forecast temporal-variant labels. In the
experiments, we work on 6% clean data and the rest unlabeled data. It is shown
that our method is competitive and outperforms a lot compared with benchmarks
First Principles Studies on 3-Dimentional Strong Topological Insulators: Bi2Te3, Bi2Se3 and Sb2Te3
Bi2Se3, Bi2Te3 and Sb2Te3 compounds are recently predicted to be
3-dimentional (3D) strong topological insulators. In this paper, based on
ab-initio calculations, we study in detail the topological nature and the
surface states of this family compounds. The penetration depth and the
spin-resolved Fermi surfaces of the surface states will be analyzed. We will
also present an procedure, from which highly accurate effective Hamiltonian can
be constructed, based on projected atomic Wannier functions (which keep the
symmetries of the systems). Such Hamiltonian can be used to study the
semi-infinite systems or slab type supercells efficiently. Finally, we discuss
the 3D topological phase transition in Sb2(Te1-xSex)3 alloy system.Comment: 8 pages,17 figure
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