10,705 research outputs found
On Thermal Gravitational Contribution to Particle Production and Dark Matter
We investigate the particle production from thermal gravitational
annihilation in the very early universe, which is an important contribution for
particles that might not be in thermal equilibrium or/and only have
gravitational interaction, such as dark matter (DM). For particles with spin 0,
1/2 and 1 we calculate the relevant cross sections through gravitational
annihilation and give the analytic formulas with full mass-dependent terms. We
find that DM with mass between TeV and GeV could have the relic
abundance that fits the observation, with small dependence on its spin. We also
discuss the effects of gravitational annihilation from inflatons.
Interestingly, contributions from inflatons could be dominant and have the same
power dependence on Hubble parameter of inflation as that from vacuum
fluctuation. Also, fermion production from inflatons, in comparison to boson,
is suppressed by its mass due to helicity selection.Comment: 10 pages, 3 figures and 2 tables, published versio
Pure Gravitational Dark Matter, Its Mass and Signatures
In this study, we investigate a scenario that dark matter (DM) has only
gravitational interaction. In the framework of effective field theory of
gravity, we find that DM is still stable at tree level even if there is no
symmetry to protect its longevity, but could decay into standard model
particles due to gravitational loop corrections. The radiative corrections can
lead to both higher- and lower-dimensional effective operators. We also first
explore how DM can be produced in the early universe. Through gravitational
interaction at high temperature, DM is then found to have mass around TeV
GeV to get the right relic abundance. When DM
decays, it mostly decays into gravitons, which could be tested by current and
future CMB experiments. We also estimate the resulting fluxes for cosmic rays,
gamma-ray and neutrino.Comment: 6 pages, 3 figure
End-to-end Learning for Short Text Expansion
Effectively making sense of short texts is a critical task for many real
world applications such as search engines, social media services, and
recommender systems. The task is particularly challenging as a short text
contains very sparse information, often too sparse for a machine learning
algorithm to pick up useful signals. A common practice for analyzing short text
is to first expand it with external information, which is usually harvested
from a large collection of longer texts. In literature, short text expansion
has been done with all kinds of heuristics. We propose an end-to-end solution
that automatically learns how to expand short text to optimize a given learning
task. A novel deep memory network is proposed to automatically find relevant
information from a collection of longer documents and reformulate the short
text through a gating mechanism. Using short text classification as a
demonstrating task, we show that the deep memory network significantly
outperforms classical text expansion methods with comprehensive experiments on
real world data sets.Comment: KDD'201
Radar-on-Lidar: metric radar localization on prior lidar maps
Radar and lidar, provided by two different range sensors, each has pros and
cons of various perception tasks on mobile robots or autonomous driving. In
this paper, a Monte Carlo system is used to localize the robot with a rotating
radar sensor on 2D lidar maps. We first train a conditional generative
adversarial network to transfer raw radar data to lidar data, and achieve
reliable radar points from generator. Then an efficient radar odometry is
included in the Monte Carlo system. Combining the initial guess from odometry,
a measurement model is proposed to match the radar data and prior lidar maps
for final 2D positioning. We demonstrate the effectiveness of the proposed
localization framework on the public multi-session dataset. The experimental
results show that our system can achieve high accuracy for long-term
localization in outdoor scenes
Post-Learning Activities and Memory Consolidation: the Effect of Physical and Cognitive Activities on Memory Consolidation
Memory consolidation is the process during which short-term memory is stabilized and long-term memory is formed. This study aims at investigating how physical and cognitive activities affect memory consolidation during wakefulness. There were four conditions: sit, sit-puzzle, walk and walk-puzzle and a repeated measure, within subject design was adopted. Participants engage in each condition for ten minutes immediately following a learning session, and this process was repeated for four times. Word recall was collected twice, both immediately after engaging in the task, and in the second day. Results revealed that engaging in physical activity alone (walk) led to the best recall performance. Recall score was diminished when physical activity was coupled with cognitive activity (walk-puzzle), and there was no difference between the two physically inactive conditions (sit and sit-puzzle). In addition, it was shown that physical activity provided favorable condition for memory consolidation especially when participants\u27 were fatigue. Based on the results of this study, suggestions can be made to students that engagement in moderate exercise such as walking immediately following learning is beneficial to memorization
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