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Sampling design plays an important role in soil survey and soil mapping. Conditioned Latin hypercube sampling (cLHS) has been proven as an efficient sampling strategy and used widely in digital soil m...
This talk is concerned with a direct sampling method for imaging the support of a frequency-dependent source term embedded in a homogeneous and isotropic medium. The source term is given by the Fourie...
Tokyo Electric Power Company (Tepco) has demonstrated its capabilities for accurate and precise measurements of the radionuclides present in the treated water stored at the Fukushima Daiichi site, acc...
High-dimensional sampling problems are ubiquitous in statistics, operations research, machine learning, etc. In this talk, I will introduce quantum algorithms for two important problems in high-dimens...
Graphs, a potentially extensive web of nodes connected by edges, can be used to express and interrogate relationships between data, like social connections, financial transactions, traffic, energy gri...
Physics-informed neural networks (PINNs) have emerged as an effective technique for solving PDEs in a wide range of domains. Recent research has demonstrated, however, that the performance of PINNs ca...
Adaptive computation is of great importance in numerical simulations. The ideas for adaptive computations can be dated back to adaptive finite element methods in 1970s. In this talk, we shall first re...
The arbitrary-centered discrete Gaussian sampler is a fundamental subroutine in implementing lattice trapdoor sampling algorithms. However, existing approaches typically rely on either a fast implemen...
With the rising popularity of lattice-based cryptography, the Learning with Errors (LWE) problem has emerged as a fundamental core of numerous encryption and key exchange schemes. Many LWE-based schem...
We design an efficient method for sampling a large batch of dd independent coins with a given bias p∈[0,1]p∈[0,1]. The folklore secure computation method for doing so requires O(λ+logd)O(λ+log⁡d...
Random sampling is a fundamental primitive in modern algorithms, statistics, and machine learning, used as a generic method to obtain a small yet ``representative'' subset of the data. In this work, w...
Sampling from the lattice Gaussian distribution has emerged as an important problem in coding, decoding and cryptography. In this paper, the classic Metropolis-Hastings (MH) algorithm in Markov chain ...
Cryptographic constructions based on hard lattice problems have emerged as a front runner for the standardization of post quantum public key cryptography. As the standardization process takes place, o...
Sampling from the lattice Gaussian distribution plays an important role in various research fields. In this paper, the Markov chain Monte Carlo (MCMC)-based sampling technique is advanced in several f...
In this paper, we propose a constant-time implementation of the BLISS lattice-based signature scheme. BLISS is possibly the most efficient lattice-based signature scheme proposed so far, with a level ...

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