7,147 research outputs found
Neural Generative Question Answering
This paper presents an end-to-end neural network model, named Neural
Generative Question Answering (GENQA), that can generate answers to simple
factoid questions, based on the facts in a knowledge-base. More specifically,
the model is built on the encoder-decoder framework for sequence-to-sequence
learning, while equipped with the ability to enquire the knowledge-base, and is
trained on a corpus of question-answer pairs, with their associated triples in
the knowledge-base. Empirical study shows the proposed model can effectively
deal with the variations of questions and answers, and generate right and
natural answers by referring to the facts in the knowledge-base. The experiment
on question answering demonstrates that the proposed model can outperform an
embedding-based QA model as well as a neural dialogue model trained on the same
data.Comment: Accepted by IJCAI 201
Equations of motion of test particles for solving the spin-dependent Boltzmann-Vlasov equation
A consistent derivation of the equations of motion (EOMs) of test particles
for solving the spin-dependent Boltzmann-Vlasov equation is presented. The
resulting EOMs in phase space are similar to the canonical equations in
Hamiltonian dynamics, and the EOM of spin is the same as that in the Heisenburg
picture of quantum mechanics. Considering further the quantum nature of spin
and choosing the direction of total angular momentum in heavy-ion reactions as
a reference of measuring nucleon spin, the EOMs of spin-up and spin-down
nucleons are given separately. The key elements affecting the spin dynamics in
heavy-ion collisions are identified. The resulting EOMs provide a solid
foundation for using the test-particle approach in studying spin dynamics in
heavy-ion collisions at intermediate energies. Future comparisons of model
simulations with experimental data will help constrain the poorly known
in-medium nucleon spin-orbit coupling relevant for understanding properties of
rare isotopes and their astrophysical impacts.Comment: 5 page
Enhanced Gas-Flow-Induced Voltage in Graphene
We show by systemically experimental investigation that gas-flow-induced
voltage in monolayer graphene is more than twenty times of that in bulk
graphite. Examination over samples with sheet resistances ranging from 307 to
1600 {\Omega}/sq shows that the induced voltage increase with the resistance
and can be further improved by controlling the quality and doping level of
graphene. The induced voltage is nearly independent of the substrate materials
and can be well explained by the interplay of Bernoulli's principle and the
carrier density dependent Seebeck coefficient. The results demonstrate that
graphene has great potential for flow sensors and energy conversion devices
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