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