Too massive neutron stars: The role of dark matter?

The maximum mass of a neutron star is generally determined by the equation of state of the star material. In this study, we take into account dark matter particles, assumed to behave like fermions with a free parameter to account for the interaction strength among the particles, as a possible constituent of neutron stars. We find dark matter inside the star would soften the equation of state more strongly than that of hyperons, and reduce largely the maximum mass of the star. However, the neutron star maximum mass is sensitive to the particle mass of dark matter, and a very high neutron star mass larger than 2 times solar mass could be achieved when the particle mass is small enough. Such kind of dark-matter- admixed neutron stars could explain the recent measurement of the Shapiro delay in the radio pulsar PSR J1614-2230, which yielded a neutron star mass of 2 times solar mass that may be hardly reached when hyperons are considered only, as in the case of the microscopic Brueckner theory. Furthermore, in this particular case, we point out that the dark matter around a neutron star should also contribute to the mass measurement due to its pure gravitational effect. However, our numerically calculation illustrates that such contribution could be safely ignored because of the usual diluted dark matter environment assumed. We conclude that a very high mass measurement of about 2 times solar mass requires a really stiff equation of state in neutron stars, and find a strong upper limit (<= 0.64 GeV) for the particle mass of non-self- annihilating dark matter based on the present model.Comment: Astroparticle Physics (2012) in Pres

Inhomogeneous graph trend filtering via a l2,0 cardinality penalty

We study estimation of piecewise smooth signals over a graph. We propose a $\ell_{2,0}$-norm penalized Graph Trend Filtering (GTF) model to estimate piecewise smooth graph signals that exhibits inhomogeneous levels of smoothness across the nodes. We prove that the proposed GTF model is simultaneously a k-means clustering on the signal over the nodes and a minimum graph cut on the edges of the graph, where the clustering and the cut share the same assignment matrix. We propose two methods to solve the proposed GTF model: a spectral decomposition method and a method based on simulated annealing. In the experiment on synthetic and real-world datasets, we show that the proposed GTF model has a better performances compared with existing approaches on the tasks of denoising, support recovery and semi-supervised classification. We also show that the proposed GTF model can be solved more efficiently than existing models for the dataset with a large edge set.Comment: 21 pages, 3 figures, 4 table

A quantitative study of the relationship between the oxide charge trapping over the drain extension and the off-state drain leakage current

In this letter, we report an approach to quantitative study of the relationship between the oxide charge trapping over the drain extension due to electrical stress and the off-state drain leakage current. It is found that positive charge trapping over the drain extension leads to a significant increase in the off-state drain current if the edge direct tunneling (EDT) is dominant in the drain current but in contrast, it leads to a reduction in the drain current if the band-to-band tunneling in the Si surface is dominant. A quantitative relationship between the charge trapping and the off-state drain leakage current in the EDT regime is established. From the measurement of the off-state current in the EDT regime, the charge trapping can be determined by using the approach developed in this study. © 2004 American Institute of Physics.published_or_final_versio

Influence of interfacial nitrogen on edge charge trapping at the interface of gate oxide/drain extension in metal-oxide-semiconductor transistors

The influence of interfacial nitrogen on edge charge trapping at the interface of gate oxide/drain extension in metal-oxide-semiconductor transistors was investigated. Positive edge charge trapping was observed for both pure and nitrided oxides with an oxide thickness of 6.5 nm. Results showed that nitrogen at the interface enhance the edge charge trapping.published_or_final_versio

Progressive Learning without Forgetting

Learning from changing tasks and sequential experience without forgetting the obtained knowledge is a challenging problem for artificial neural networks. In this work, we focus on two challenging problems in the paradigm of Continual Learning (CL) without involving any old data: (i) the accumulation of catastrophic forgetting caused by the gradually fading knowledge space from which the model learns the previous knowledge; (ii) the uncontrolled tug-of-war dynamics to balance the stability and plasticity during the learning of new tasks. In order to tackle these problems, we present Progressive Learning without Forgetting (PLwF) and a credit assignment regime in the optimizer. PLwF densely introduces model functions from previous tasks to construct a knowledge space such that it contains the most reliable knowledge on each task and the distribution information of different tasks, while credit assignment controls the tug-of-war dynamics by removing gradient conflict through projection. Extensive ablative experiments demonstrate the effectiveness of PLwF and credit assignment. In comparison with other CL methods, we report notably better results even without relying on any raw data