Approximation for Maximum Surjective Constraint Satisfaction Problems

Maximum surjective constraint satisfaction problems (Max-Sur-CSPs) are computational problems where we are given a set of variables denoting values from a finite domain B and a set of constraints on the variables. A solution to such a problem is a surjective mapping from the set of variables to B such that the number of satisfied constraints is maximized. We study the approximation performance that can be acccchieved by algorithms for these problems, mainly by investigating their relation with Max-CSPs (which are the corresponding problems without the surjectivity requirement). Our work gives a complexity dichotomy for Max-Sur-CSP(B) between PTAS and APX-complete, under the assumption that there is a complexity dichotomy for Max-CSP(B) between PO and APX-complete, which has already been proved on the Boolean domain and 3-element domains

From high-scale leptogenesis to low-scale one-loop neutrino mass generation

We show that a high-scale leptogenesis can be consistent with a low-scale one-loop neutrino mass generation. Our models are based on the SU(3)_c\times SU(2)_L\times U(1)_Y\times U(1)_{B-L} gauge groups. Except a complex singlet scalar for the U(1)_{B-L} symmetry breaking, the other new scalars and fermions (one scalar doublet, two or more real scalar singlets/triplets and three right-handed neutrinos) are odd under an unbroken Z_2 discrete symmetry. The real scalar decays can produce an asymmetry stored in the new scalar doublet which subsequently decays into the standard model lepton doublets and the right-handed neutrinos. The lepton asymmetry in the standard model leptons then can be partially converted to a baryon asymmetry by the sphaleron processes. By integrating out the heavy scalar singlets/triplets, we can realize an effective theory to radiatively generate the small neutrino masses at the TeV scale. Furthermore, the lightest right-handed neutrino can serve as a dark matter candidate.Comment: 8 pages, 4 figure

El camino del 'desarrollo armonioso' de China

Las dos cuestiones esenciales que se plantean en este programa, tanto el crecimiento de la economía como la armonía interna en la sociedad necesaria para conseguirlo, se han convertido en el foco del futuro desarrollo social chino. La aparición de la 'armonía' como condición previa por primera vez en el diseño de camino de este país socialista atraído la atención de los estudiosos

Redevelopment of Urban Village in Shenzhen

Urban Villages are a specific phenomenon raised in modern China due to the high-speed economic development and urbanization in recent three decades. And there are social, economic, cultural and architectural transformations happened in these villages during these years. They appear on both the outskirts and the downtown segments of major cities, and surrounded by skyscrapers, transportation infrastructures, and other modern urban constructions. They are commonly inhabited by the poor and transient. Most of Urban Villages are heavily populated, overdeveloped, and lack of basic infrastructure. Some villages\u27 building density is higher than 70%. They are composed of overcrowded multi-story buildings from three to five (or more) floors, also with narrow alleys, which are difficult for vehicles to pass through. Inside these villages, it is dark and damp year round and the lights have to be kept on during daytime. However, they are also among the liveliest areas in some cities and are notable for affording economic opportunity for newcomers to the city. However, Urban Villages are rejected by the governor and face demolition–redevelopment programs in order to replace them with formal urban neighborhoods. But the demolition-redevelopment approach would be devastating not only for the rural migrants, but also for the city’s economy which is largely based on labor-intensive sectors. In my study, I take Gangsha Village, a typical urban village in Shenzhen City, as a study case, to explore an appropriate reformation approach that combines urban design and architectural strategy to solve social, economic and cultural problems in Urban Village. To provide them a better living condition, and make the village better serves the city

Bidirectional-Convolutional LSTM Based Spectral-Spatial Feature Learning for Hyperspectral Image Classification

This paper proposes a novel deep learning framework named bidirectional-convolutional long short term memory (Bi-CLSTM) network to automatically learn the spectral-spatial feature from hyperspectral images (HSIs). In the network, the issue of spectral feature extraction is considered as a sequence learning problem, and a recurrent connection operator across the spectral domain is used to address it. Meanwhile, inspired from the widely used convolutional neural network (CNN), a convolution operator across the spatial domain is incorporated into the network to extract the spatial feature. Besides, to sufficiently capture the spectral information, a bidirectional recurrent connection is proposed. In the classification phase, the learned features are concatenated into a vector and fed to a softmax classifier via a fully-connected operator. To validate the effectiveness of the proposed Bi-CLSTM framework, we compare it with several state-of-the-art methods, including the CNN framework, on three widely used HSIs. The obtained results show that Bi-CLSTM can improve the classification performance as compared to other methods