This paper proposes low-complexity robust adaptive beamforming (RAB) techniques based on shrinkage methods. We firstly briefly review a Low-Complexity Shrinkage-Based Mismatch Estimation (LOCSME) batch algorithm to estimate the desired signal steering vector mismatch, in which the interference-plus-noise covariance (INC) matrix is also estimated with a recursive matrix shrinkage method. Then we...

The study of rotary steering drilling technology is currently one of the hot topics in the drilling engineering field. It requires accurate well trajectory control instructions when rotary steerable tools are applied to achieve the well trajectory control goal. A drilling trajectory prediction model will benefit this progress. According to the continuous beam theory, a mechanical model of push-...

As the precious resource for quantum information processing, quantum coherence can be created remotely if the involved two sites are quantum correlated. It can be expected that the amount of coherence created should depend on the quantity of the shared quantum correlation, which is also a resource. Here, we establish an operational connection between coherence induced by steering and the quantu...

The ongoing transition from uniprocessor to multi-core computers requires support from the operating system kernel. Although many general-purpose multiprocessor operating systems exist, there is a large number of specialized operating systems which require porting in order to work on multiprocessors. In this paper we describe the multiprocessor port of a cluster operating system kernel from a p...

Kernel PCA is a widely used nonlinear dimension reduction technique in machine learning, but storing the kernel matrix is notoriously challenging when the sample size is large. Inspired by [YPCC16], where the idea of partial matrix sampling followed by nonconvex optimization is proposed for matrix completion and robust PCA, we apply a similar approach to memoryefficient Kernel PCA. In theory, w...

Invariance to nuisance transformations is one of the desirable properties of effective representations. We consider transformations that form a group and propose an approach based on kernel methods to derive local group invariant representations. Locality is achieved by defining a suitable probability distribution over the group which in turn induces distributions in the input feature space. We...

The efficiency and performance of the Twin Support Vector Machines (TWSVM) are better than the traditional support vector machines when it deals with the problems. However, it also has the problem of selecting kernel functions. Generally, TWSVM selects the Gaussian radial basis kernel function. Although it has a strong learning ability, its generalization ability is relatively weak. In a certai...

Kernel-based machine learning algorithms are versatile tools for biological sequence data analysis. Special sequence kernels can endow Support Vector Machines with biological knowledge to perform accurate classification of diverse sequence data. The kernels relative strengths and weaknesses are difficult to evaluate on single data sets. We examine a range of recent kernels tailor-made for biolo...

In this paper we give a characterization of kernel-perfect (and of critical kernel-imperfect) arc-local tournament digraphs. As a consequence, we prove that arc-local tournament digraphs satisfy a strenghtened form of the following interesting conjecture which constitutes a bridge between kernels and perfectness in digraphs, stated by C. Berge and P. Duchet in 1982: a graph G is perfect if and ...