We conjecture that the level k U(N) Chern-Simons theory coupled to free anticommuting scalar matter in the fundamental is dual to non-minmal higher-spin Vasiliev gravity in dS4 with parity-violating phase \theta0 = \pi N/2k and Neumann boundary conditions for the scalar. Related conjectures are made for fundamental commuting spinor matter and critical theories. This generalizes a recent conjecture relating the minimal Type A Vasiliev theory in dS4 to the Sp(N) model with fundamental real anti-commuting scalars.

We combine the method of searching for an invariant subspace of the unbalanced Oil and Vinegar signature scheme and the Minrank method to defeat the new TTS signature scheme, which was suggested for low-cost smart card applications at CHES 2004. We show that the attack complexity is less than 2^{50}.

Automatic reconstruction of 3D objects from 2D orthographic views has been a major research issue in CAD/CAM. In this paper, two accelerating techniques to improve the eÆciency of reconstruction are presented. First, some pseudo elements are removed by depth and topology information as soon as the wire-frame is constructed, which reduces the searching space. Second, the proposed algorithm does not establish all possible surfaces in the process of generating 3D faces. The surfaces and edge loops are generated by using the relationship between the boundaries of 3D faces and their projections. This avoids the growth in combinational complexity of previous methods that have to check all possible pairs of 3D candidate edges.

Remeshing aims to produce a more regular mesh from a given input mesh, while representing the original geometry as accurately as possible. Many existing remeshing methods focus on where to place new mesh vertices; these samples are placed exactly on the input mesh. However, considering the output mesh as a piecewise linear approximation of some geometry, this simple scheme leads to significant systematic error in non-planar regions. Here, we use parameterised meshes and the recent mathematical development of orthogonal approximation using Sobolev-type inner products to develop a novel sampling scheme which allows vertices to lie in space near the input surface, rather than exactly on it. The algorithm requires little extra computational effort and can be readily incorporated into many remeshing approaches. Experimental results show that on average, approximation error can be reduced by 40% with the same number of vertices.

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