LWE/RLWE-based cryptosystems require sampling error term from discrete Gaussian distribution. However, some existing samplers are somehow slow under certain circumstances therefore efficiency of such schemes is restricted. In this paper, we introduce a more efficient discretized Gaussian sampler based on ziggurat sampling algorithm. We also analyze statistical quality of our sampler to prove that it can be adopted in LWE/RLWE-based cryptosystems. Compared with ziggurat-based sampler by Buchmann et al. related samplers by Peikert, Ducas et al. and Knuth-Yao, our sampler achieves more than 2x speedup when standard deviation is large. This can benefit constructions rely on noise flooding (e.g., homomorphic encryption). We also present two applications: First, we use our sampler to optimize the RLWE-based authenticated key exchange (AKE) protocol by Zhang et al. We achieve 1.14x speedup on total runtime of this protocol over major parameter choices. Second, we give practical post-quantum Transport Layer Security (TLS) ciphersuite. Our ciphersuite inherits advantages from TLS and the optimized AKE protocol. Performance of our proof-of-concept implementation is close to TLS v1.2 ciphersuites and one post-quantum TLS construction.

We introduce a new concept of strong vanishing property and investigate this property for minimally elliptic singularities. Acomparison theorem about strong vanishing property is also obtained. We use Zariski decomposition to give a formula of plurigenera for Gorenstein singularities and use it to test whether a divisor is f-nef.

We describe the potential functions for N= 4B supersymmetric quantum mechanics with D (2, 1; ) symmetry.

We develop an expectation-maximization algorithm with local adaptivity for image segmentation and classification. The key idea of our approach is to combine global statistics extracted from the Gaussian mixture model or other proper statistical models with local statistics and geometrical information, such as local probability distribution, orientation, and anisotropy. The combined information is used to design an adaptive local classification strategy that improves the robustness of the algorithm and also keeps fine features in the image. The proposed methodology is flexible and can be easily generalized to deal with other inferred information/quantities and statistical methods/models.

Greedy forwarding with geographical locations in a wireless sensor network may fail at a local minimum. In this paper we propose to use conformal mapping to compute a new embedding of the sensor nodes in the plane such that greedy forwarding with the virtual coordinates guarantees delivery. In particular, we extract a planar triangulation of the sensor network with non-triangular faces as holes, by either using the nodes' location or using a landmark-based scheme without node location. The conformal map is computed with Ricci flow such that all the non-triangular faces are mapped to perfect circles. Thus greedy forwarding will never get stuck at an intermediate node. The computation of the conformal map and the virtual coordinates is performed at a preprocessing phase and can be implemented by local gossip-style computation. The method applies to both unit disk graph models and quasi-unit disk graph