23,855 research outputs found
Microanalytical study of some cosmic dust discovered in sea-floor sediments in China
The study of cosmic dust can provide useful data in the investigation of the origin of the Earth and the evolution of celestial bodies. Three types of cosmic dust (ferriginous, siliceous, and glassy) were discovered in the seafloor sediments near China. Their chemical composition and microstructure were examined by X-ray diffraction, fractography, and electron microscopy. The major mineral in an iron-containing cosmic dust is magnetite. The silicate spheres contain sundry metals and metal oxides. Glassy microtektites are similar in composition to tektites, and are found in all the major meteorite areas worldwide
Dr. Yang Zhong: an explorer on the road forever
On the morning of September 25th 2017, grievous news spread from the remote Ordos region of Inner Mongolia to Fudan University campus in Shanghai. Professor Yang Zhong, a famous botanist and the Dean of Fudan University’s graduate school, passed away in a tragic car accident while on a business trip
A New Solution of the Yang-Baxter Equation Related to the Adjoint Representation of
A new solution of the Yang-Baxter equation, that is related to the adjoint
representation of the quantum enveloping algebra , is obtained by
fusion formulas from a non-standard solution.Comment: 16 pages (Latex), Preprint BIHEP-TH-93-3
Data-Driven Forecasting of High-Dimensional Chaotic Systems with Long Short-Term Memory Networks
We introduce a data-driven forecasting method for high-dimensional chaotic
systems using long short-term memory (LSTM) recurrent neural networks. The
proposed LSTM neural networks perform inference of high-dimensional dynamical
systems in their reduced order space and are shown to be an effective set of
nonlinear approximators of their attractor. We demonstrate the forecasting
performance of the LSTM and compare it with Gaussian processes (GPs) in time
series obtained from the Lorenz 96 system, the Kuramoto-Sivashinsky equation
and a prototype climate model. The LSTM networks outperform the GPs in
short-term forecasting accuracy in all applications considered. A hybrid
architecture, extending the LSTM with a mean stochastic model (MSM-LSTM), is
proposed to ensure convergence to the invariant measure. This novel hybrid
method is fully data-driven and extends the forecasting capabilities of LSTM
networks.Comment: 31 page
Determination of Dark Matter Halo Mass from Dynamics of Satellite Galaxies
We show that the mass of a dark matter halo can be inferred from the
dynamical status of its satellite galaxies. Using 9 dark-matter simulations of
halos like the Milky Way (MW), we find that the present-day substructures in
each halo follow a characteristic distribution in the phase space of orbital
binding energy and angular momentum, and that this distribution is similar from
halo to halo but has an intrinsic dependence on the halo formation history. We
construct this distribution directly from the simulations for a specific halo
and extend the result to halos of similar formation history but different
masses by scaling. The mass of an observed halo can then be estimated by
maximizing the likelihood in comparing the measured kinematic parameters of its
satellite galaxies with these distributions. We test the validity and accuracy
of this method with mock samples taken from the simulations. Using the
positions, radial velocities, and proper motions of 9 tracers and assuming
observational uncertainties comparable to those of MW satellite galaxies, we
find that the halo mass can be recovered to within 40%. The accuracy can
be improved to within 25% if 30 tracers are used. However, the dependence
of the phase-space distribution on the halo formation history sets a minimum
uncertainty of 20% that cannot be reduced by using more tracers. We
believe that this minimum uncertainty also applies to any mass determination
for a halo when the phase space information of other kinematic tracers is used.Comment: Accepted for publication in ApJ, 18 pages, 13 figure
Suppression of dephasing by qubit motion in superconducting circuits
We suggest and demonstrate a protocol which suppresses dephasing due to the
low-frequency noise by qubit motion, i.e., transfer of the logical qubit of
information in a system of physical qubits. The protocol requires
only the nearest-neighbor coupling and is applicable to different qubit
structures. We further analyze its effectiveness against noises with arbitrary
correlations. Our analysis, together with experiments using up to three
superconducting qubits, shows that for the realistic uncorrelated noises, qubit
motion increases the dephasing time of the logical qubit as . In
general, the protocol provides a diagnostic tool to measure the noise
correlations.Comment: 5 pages with 3 embedded figures, plus supplementary informatio
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