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 $10^{16}$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 $\lesssim m_X \lesssim 10^{11}$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