Background Subtraction via Generalized Fused Lasso Foreground Modeling

Background Subtraction (BS) is one of the key steps in video analysis. Many background models have been proposed and achieved promising performance on public data sets. However, due to challenges such as illumination change, dynamic background etc. the resulted foreground segmentation often consists of holes as well as background noise. In this regard, we consider generalized fused lasso regularization to quest for intact structured foregrounds. Together with certain assumptions about the background, such as the low-rank assumption or the sparse-composition assumption (depending on whether pure background frames are provided), we formulate BS as a matrix decomposition problem using regularization terms for both the foreground and background matrices. Moreover, under the proposed formulation, the two generally distinctive background assumptions can be solved in a unified manner. The optimization was carried out via applying the augmented Lagrange multiplier (ALM) method in such a way that a fast parametric-flow algorithm is used for updating the foreground matrix. Experimental results on several popular BS data sets demonstrate the advantage of the proposed model compared to state-of-the-arts

The progenitors of Type Ia supernovae with long delay times

The nature of the progenitors of Type Ia supernovae (SNe Ia) is still unclear. In this paper, by considering the effect of the instability of accretion disk on the evolution of white dwarf (WD) binaries, we performed binary evolution calculations for about 2400 close WD binaries, in which a carbon--oxygen WD accretes material from a main-sequence star or a slightly evolved subgiant star (WD + MS channel), or a red-giant star (WD + RG channel) to increase its mass to the Chandrasekhar (Ch) mass limit. According to these calculations, we mapped out the initial parameters for SNe Ia in the orbital period--secondary mass ($\log P^{\rm i}-M^{\rm i}_2$) plane for various WD masses for these two channels, respectively. We confirm that WDs in the WD + MS channel with a mass as low as $0.61 M_\odot$ can accrete efficiently and reach the Ch limit, while the lowest WD mass for the WD + RG channel is $1.0 \rm M_\odot$. We have implemented these results in a binary population synthesis study to obtain the SN Ia birthrates and the evolution of SN Ia birthrates with time for both a constant star formation rate and a single starburst. We find that the Galactic SN Ia birthrate from the WD + MS channel is $\sim$$1.8\times 10^{-3} {\rm yr}^{-1}$ according to our standard model, which is higher than previous results. However, similar to previous studies, the birthrate from the WD + RG channel is still low ($\sim$$3\times 10^{-5} {\rm yr}^{-1}$). We also find that about one third of SNe Ia from the WD + MS channel and all SNe Ia from the WD + RG channel can contribute to the old populations (\ga1 Gyr) of SN Ia progenitors.Comment: 11 pages, 9 figures, 1 table, accepted for publication in MNRA

Interacting heavy fermions in a disordered optical lattice

We have theoretically studied the effect of disorder on ultracold alkaline-earth atoms governed by the Kondo lattice model in an optical lattice via simplified double-well model and hybridization mean-field theory. Disorder-induced narrowing and even complete closure of hybridization gap have been predicted and the compressibility of the system has also been investigated for metallic and Kondo insulator phases in the presence of the disordered potential. To make connection to the experimental situation, we have numerically solved the disordered Kondo lattice model with an external harmonic trap and shown both the melting of Kondo insulator plateau and an compressibility anomaly at low-density

A Mutual Attraction Model for Both Assortative and Disassortative Weighted Networks

In most networks, the connection between a pair of nodes is the result of their mutual affinity and attachment. In this letter, we will propose a Mutual Attraction Model to characterize weighted evolving networks. By introducing the initial attractiveness $A$ and the general mechanism of mutual attraction (controlled by parameter $m$), the model can naturally reproduce scale-free distributions of degree, weight and strength, as found in many real systems. Simulation results are in consistent with theoretical predictions. Interestingly, we also obtain nontrivial clustering coefficient C and tunable degree assortativity r, depending on $m$ and A. Our weighted model appears as the first one that unifies the characterization of both assortative and disassortative weighted networks.Comment: 4 pages, 3 figure