2,825 research outputs found
Probing Majorana neutrinos in rare K and D, D_s, B, B_c meson decays
We study lepton number violating decays of charged K, D, D_s, B and B_c
mesons of the form M^+\to {M'}^-\ell^+\ell^+, induced by the existence of
Majorana neutrinos. These processes provide information complementary to
neutrinoless double nuclear beta decays, and are sensitive to neutrino masses
and lepton mixing. We explore neutrino mass ranges m_N from below 1 eV to
several hundred GeV. We find that in many cases the branching ratios are
prohibitively small, however in the intermediate range m_\pi < m_N < m_{B_c},
in specific channels and for specific neutrino masses, the branching ratios can
be at the reach of high luminosity experiments like those at the LHC-b and
future Super flavor-factories, and can provide bounds on the lepton mixing
parameters.Comment: 25 page
Consequences of the partial restoration of chiral symmetry in AdS/QCD
Chiral symmetry is an essential concept in understanding QCD at low energy.
We treat the chiral condensate, which measures the spontaneous breaking of
chiral symmetry, as a free parameter to investigate the effect of partially
restored chiral symmetry on the physical quantities in the frame work of an
AdS/QCD model. We observe an interesting scaling behavior among the nucleon
mass, pion decay constant and chiral condensate. We propose a phenomenological
way to introduce the temperature dependence of a physical quantity in the
AdS/QCD model with the thermal AdS metric.Comment: 11 pages, 6 figure
Application of Artificial Neural Network to Search for Gravitational-Wave Signals Associated with Short Gamma-Ray Bursts
We apply a machine learning algorithm, the artificial neural network, to the
search for gravitational-wave signals associated with short gamma-ray bursts.
The multi-dimensional samples consisting of data corresponding to the
statistical and physical quantities from the coherent search pipeline are fed
into the artificial neural network to distinguish simulated gravitational-wave
signals from background noise artifacts. Our result shows that the data
classification efficiency at a fixed false alarm probability is improved by the
artificial neural network in comparison to the conventional detection
statistic. Therefore, this algorithm increases the distance at which a
gravitational-wave signal could be observed in coincidence with a gamma-ray
burst. In order to demonstrate the performance, we also evaluate a few seconds
of gravitational-wave data segment using the trained networks and obtain the
false alarm probability. We suggest that the artificial neural network can be a
complementary method to the conventional detection statistic for identifying
gravitational-wave signals related to the short gamma-ray bursts.Comment: 30 pages, 10 figure
Characteristics of light-absorbing aerosol depositions over Greenland ice sheet derived from the NASA’s MERRAero aerosol reanalysis data
第6回極域科学シンポジウム[OM] 極域気水圏11月16日(月) 統計数理研究所 セミナー室2(D304
Coulomb Driven New Bound States at the Integer Quantum Hall States in GaAs/Al(0.3)Ga(0.7)As Single Heterojunctions
Coulomb driven, magneto-optically induced electron and hole bound states from
a series of heavily doped GaAs/Al(0.3)Ga(0.7)As single heterojunctions (SHJ)
are revealed in high magnetic fields. At low magnetic fields (nu > 2), the
photoluminescence spectra display Shubnikov de-Haas type oscillations
associated with the empty second subband transition. In the regime of the
Landau filling factor nu < 1 and 1 < nu <2, we found strong bound states due to
Mott type localizations. Since a SHJ has an open valence band structure, these
bound states are a unique property of the dynamic movement of the valence holes
in strong magnetic fields
General Chemical Reaction Network Theory for Olfactory Sensing Based on G-Protein-Coupled Receptors : Elucidation of Odorant Mixture Effects and Agonist-Synergist Threshold
This work presents a general chemical reaction network theory for olfactory
sensing processes that employ G-protein-coupled receptors as olfactory
receptors (ORs). The theory is applicable to general mixtures of odorants and
an arbitrary number of ORs. Reactions of ORs with G-proteins, both in the
presence and the absence of odorants, are explicitly considered. A unique
feature of the theory is the definition of an odor activity vector consisting
of strengths of odorant-induced signals from ORs relative to those due to
background G-protein activity in the absence of odorants. It is demonstrated
that each component of the odor activity defined this way reduces to a
Michaelis-Menten form capable of accounting for cooperation or competition
effects between different odorants. The main features of the theory are
illustrated for a two-odorant mixture. Known and potential mixture effects,
such as suppression, shadowing, inhibition, and synergy are quantitatively
described. Effects of relative values of rate constants, basal activity, and
G-protein concentration are also demonstrated
NuTrea: Neural Tree Search for Context-guided Multi-hop KGQA
Multi-hop Knowledge Graph Question Answering (KGQA) is a task that involves
retrieving nodes from a knowledge graph (KG) to answer natural language
questions. Recent GNN-based approaches formulate this task as a KG path
searching problem, where messages are sequentially propagated from the seed
node towards the answer nodes. However, these messages are past-oriented, and
they do not consider the full KG context. To make matters worse, KG nodes often
represent proper noun entities and are sometimes encrypted, being uninformative
in selecting between paths. To address these problems, we propose Neural Tree
Search (NuTrea), a tree search-based GNN model that incorporates the broader KG
context. Our model adopts a message-passing scheme that probes the unreached
subtree regions to boost the past-oriented embeddings. In addition, we
introduce the Relation Frequency-Inverse Entity Frequency (RF-IEF) node
embedding that considers the global KG context to better characterize ambiguous
KG nodes. The general effectiveness of our approach is demonstrated through
experiments on three major multi-hop KGQA benchmark datasets, and our extensive
analyses further validate its expressiveness and robustness. Overall, NuTrea
provides a powerful means to query the KG with complex natural language
questions. Code is available at https://github.com/mlvlab/NuTrea.Comment: Neural Information Processing Systems (NeurIPS) 202
Influence of Social Motivations on Spectator Consumption Behavior of a Formula One Grand Prix Event
The purpose of this study was to examine the relationship between spectator motivation and sport consumption behavior in the context of F-1 events. Respondents were spectators from three Formula One (F-1) races held in Shanghai, China. Through a structural equation modeling analysis, Achievement Seeking and Salubrious Effects were found to be related to repurchase intentions. Three motivating factors (i.e., Achievement Seeking, Entertainment, and Catharsis) were also found to be associated with Word-of-Mouth intentions concerning F-1 events
- …