66 research outputs found
Symmetry breaking effect on determination of polarized and unpolarized parton distributions
We perform a new extraction for unpolarized and polarized parton distribution
functions considering a flavor decompositions for sea quarks and applying very
recent deep inelastic scattering (DIS) and semi inclusive deep inelastic
scattering (SIDIS) data in the fixed flavor number scheme (FFNS) framework. In
the new symmetry breaking scenario the light quark and antiquark densities are
extracted separately and new parametrization forms are determined for them. The
heavy flavors contribution, including charm and bottom quarks, are also taken
to be account for unpolarized distributions.Comment: Talk presented at 16th International QCD Conference (QCD12),
Montpellier, France, July 2- 7, 2012. Submitted to Nuc. Phys. (Proc. Suppl.),
4 pages, 4 figure
New parton distributions in fixed flavour factorization scheme from recent deep-inelastic-scattering data
We present our QCD analysis of the proton structure function
to determine the parton distributions at the next-to-leading order (NLO). The
heavy quark contributions to , with = , have been
included in the framework of the `fixed flavour number scheme' (FFNS). The
results obtained in the FFNS are compared with available results such as the
general-mass variable-flavour-number scheme (GM-VFNS) and other prescriptions
used in global fits of PDFs. In the present QCD analysis, we use a wide range
of the inclusive neutral-current deep-inelastic-scattering (NC DIS) data,
including the most recent data for charm , bottom , longitudinal
structure functions and also the reduced DIS cross sections
from HERA experiments. The most recent HERMES data for
proton and deuteron structure functions are also added. We take into account
ZEUS neutral current DIS inclusive jet cross section data from HERA
together with the recent Tevatron Run-II inclusive jet cross section data from
CDF and D{\O}. The impact of these recent DIS data on the PDFs extracted from
the global fits are studied. We present two families of PDFs, {\tt KKT12} and
{\tt KKT12C}, without and with HERA `combined' data sets on DIS. We
find these are in good agreement with the available theoretical models.Comment: 23 pages, 26 figures and 4 tables. V3: Only few comments and
references added in the replaced version, results unchanged. Code can be
found at http://particles.ipm.ir/links/QCD.ht
Polarized Deeply Inelastic Scattering (DIS) Structure Functions for Nucleons and Nuclei
We extract parton distribution functions (PDFs) and structure functions from
recent experimental data of polarized lepton-DIS on nucleons at next-to-leading
order (NLO) Quantum Chromodynamics. We apply the Jacobi polynomial method to
the DGLAP evolution as this is numerically efficient. Having determined the
polarized proton and neutron spin structure, we extend this analysis to
describe 3He and 3H polarized structure functions, as well as various sum
rules. We compare our results with other analyses from the literature.Comment: LaTeX, 12 pages, 11 figures, 6 tables. Update to match published
versio
User Intent Prediction in Information-seeking Conversations
Conversational assistants are being progressively adopted by the general
population. However, they are not capable of handling complicated
information-seeking tasks that involve multiple turns of information exchange.
Due to the limited communication bandwidth in conversational search, it is
important for conversational assistants to accurately detect and predict user
intent in information-seeking conversations. In this paper, we investigate two
aspects of user intent prediction in an information-seeking setting. First, we
extract features based on the content, structural, and sentiment
characteristics of a given utterance, and use classic machine learning methods
to perform user intent prediction. We then conduct an in-depth feature
importance analysis to identify key features in this prediction task. We find
that structural features contribute most to the prediction performance. Given
this finding, we construct neural classifiers to incorporate context
information and achieve better performance without feature engineering. Our
findings can provide insights into the important factors and effective methods
of user intent prediction in information-seeking conversations.Comment: Accepted to CHIIR 201
Dynamic hydraulic jump and retrograde sedimentation in an open channel induced by sediment supply: experimental study and SPH simulation
Mountainous torrents often carry large amounts of loose materials into the rivers, thus causing strong sediment transport. Experimentally it was found for the first time that when the intensive sediment motion occurs downstream over a gentle slope, the siltation of the riverbed is induced and the sediment particles can move upstream rapidly in the form of a retrograde sand wave, resulting in a higher water level along the river. To further study the complex mechanisms of this problem, a sediment mass model in the framework of the Smoothed Particle Hydrodynamics (SPH) method was presented to simulate the riverbed evolution, sediment particle motion, and the generation and development of dynamic hydraulic jump under the condition of sufficient sediment supply over a steep slope with varying angles. Because the sediment is not a continuous medium, the marker particle tracking approach was proposed to represent a piece of sediment with a marked sediment particle. The two-phase SPH model realizes the interaction between the sediment and fluid by moving the bed boundary particles up and down, so it can reasonably treat the fluid-sediment interfaces with high CPU efficiency. The critical triggering condition of sediment motion, the propagation of the hydraulic jump and the initial siltation position were all systematically studied. The experimental and numerical results revealed the extra disastrous sediment effect in a mountainous flood. The findings will be useful references to the disaster prevention and mitigation in mountainous rivers
Psycho-social factors associated with mental resilience in the Corona lockdown
The SARS-CoV-2 pandemic is not only a threat to physical health but is also having severe impacts on mental health. Although increases in stress-related symptomatology and other adverse psycho-social outcomes, as well as their most important risk factors have been described, hardly anything is known about potential protective factors. Resilience refers to the maintenance of mental health despite adversity. To gain mechanistic insights about the relationship between described psycho-social resilience factors and resilience specifically in the current crisis, we assessed resilience factors, exposure to Corona crisis-specific and general stressors, as well as internalizing symptoms in a cross-sectional online survey conducted in 24 languages during the most intense phase of the lockdown in Europe (22 March to 19 April) in a convenience sample of N = 15,970 adults. Resilience, as an outcome, was conceptualized as good mental health despite stressor exposure and measured as the inverse residual between actual and predicted symptom total score. Preregistered hypotheses (osf.io/r6btn) were tested with multiple regression models and mediation analyses. Results confirmed our primary hypothesis that positive appraisal style (PAS) is positively associated with resilience (p < 0.0001). The resilience factor PAS also partly mediated the positive association between perceived social support and resilience, and its association with resilience was in turn partly mediated by the ability to easily recover from stress (both p < 0.0001). In comparison with other resilience factors, good stress response recovery and positive appraisal specifically of the consequences of the Corona crisis were the strongest factors. Preregistered exploratory subgroup analyses (osf.io/thka9) showed that all tested resilience factors generalize across major socio-demographic categories. This research identifies modifiable protective factors that can be targeted by public mental health efforts in this and in future pandemics
The Large Hadron-Electron Collider at the HL-LHC
The Large Hadron-Electron Collider (LHeC) is designed to move the field of deep inelastic scattering (DIS) to the energy and intensity frontier of particle physics. Exploiting energy-recovery technology, it collides a novel, intense electron beam with a proton or ion beam from the High-Luminosity Large Hadron Collider (HL-LHC). The accelerator and interaction region are designed for concurrent electron-proton and proton-proton operations. This report represents an update to the LHeC's conceptual design report (CDR), published in 2012. It comprises new results on the parton structure of the proton and heavier nuclei, QCD dynamics, and electroweak and top-quark physics. It is shown how the LHeC will open a new chapter of nuclear particle physics by extending the accessible kinematic range of lepton-nucleus scattering by several orders of magnitude. Due to its enhanced luminosity and large energy and the cleanliness of the final hadronic states, the LHeC has a strong Higgs physics programme and its own discovery potential for new physics. Building on the 2012 CDR, this report contains a detailed updated design for the energy-recovery electron linac (ERL), including a new lattice, magnet and superconducting radio-frequency technology, and further components. Challenges of energy recovery are described, and the lower-energy, high-current, three-turn ERL facility, PERLE at Orsay, is presented, which uses the LHeC characteristics serving as a development facility for the design and operation of the LHeC. An updated detector design is presented corresponding to the acceptance, resolution, and calibration goals that arise from the Higgs and parton-density-function physics programmes. This paper also presents novel results for the Future Circular Collider in electron-hadron (FCC-eh) mode, which utilises the same ERL technology to further extend the reach of DIS to even higher centre-of-mass energies.Peer reviewe
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