Some Model Assisted Estimators Using Functional Form Calibration Approach

The Model assisted estimators are approximately design unbiased, consistent and provides robustness in the case of large sample sizes. The model assisted estimators result in reduction of the design variance if underlying model reasonably defines the regression relationship. If the model is misspecified, then model assisted estimators might result in an increase of the design variance but remain approximately design unbiased and show robustness against model-misspecification. The well-known model assisted estimators, generalized regression estimators are members of a larger class of calibration estimators. Calibration method generates calibration weights that meet the calibration constraints and have minimum distance from the sampling design weights. By using different distance measures, classical calibration approach generates different calibration estimators but with asymptotically identical properties. The constraint of distance minimization was reduced for studying the properties of calibration estimators by proposing a simple functional form approach. The approach generates calibration weights that prove helpful to control the changes in calibration weights by using different choices of auxiliary variable’s functions. This paper is an extended work on model assisted approach by using functional form of calibration weights. Some new model assisted estimators are considered to get efficient and stabilized regression weights by introducing a control matrix. The asymptotic un-biasedness of the proposed estimators is verified and the expressions for MSE are derived in three different cases. A simulation study is done to compare and evaluate the efficiency of the proposed estimators with some existing model assisted estimators.

Joint feature enhancement mapping and reservoir computing for improving fault diagnosis performance

Complicated industrial robot structure and harsh working conditions may cause signal features collected in the condition monitoring process to be seriously disturbed. In this paper, a joint feature enhancement mapping and reservoir computing (FEM-RC) method is presented to handle the industrial robot fault diagnosis problem. Firstly, a feature enhancement mapping (FEM) method is proposed to achieve intraclass distance minimization and interclass distance equalization to obtain an enhanced feature matrix. Then, the first reservoir computing (RC) network is adopted to map the original feature matrix to the feature enhancement matrix, and the second RC network is for fault type classification. The results of the experiment carried out on a six-axial industrial robot demonstrate that compared with other peer models, the present FEM-RC has better fault diagnosis performance and robustness.

On the Impact of Affine Loop Transformations in Qubit Allocation

Most quantum compiler transformations and qubit allocation techniques to date are either peep-hole focused or rely on sliding windows that depend on a number of external parameters including the topology of the quantum processor. Thus, global optimization criteria are still lacking. In this article, we explore the synergies and impact of affine loop transformations in the context of qubit allocation and mapping. With this goal in mind, we designed and implemented AXL , a domain specific language and source-to-source compiler for quantum circuits that can be directly described with affine relations. We conduct an extensive evaluation spanning circuits from the recently introduced QUEKO benchmark suite, eight quantum circuits taken from the literature, three distinct coupling graphs, four affine transformations (including the Pluto dependence distance minimization and Feautrier’s minimum latency algorithms), four qubit allocators, and two back-end quantum compilers. Our results demonstrate that affine transformations using global optimization criteria can cooperate effectively in several scenarios with quantum qubit mapping algorithms to reduce the circuit depth, size and allocation time.

Dimensionality reduction for tensor data based on projection distance minimization and hilbert-schmidt independence criterion maximization1

Tensor data are becoming more and more common in machine learning. Compared with vector data, the curse of dimensionality of tensor data is more serious. The motivation of this paper is to combine Hilbert-Schmidt Independence Criterion (HSIC) and tensor algebra to create a new dimensionality reduction algorithm for tensor data. There are three contributions in this paper. (1) An HSIC-based algorithm is proposed in which the dimension-reduced tensor is determined by maximizing HSIC between the dimension-reduced and high-dimensional tensors. (2) A tensor algebra-based algorithm is proposed, in which the high-dimensional tensor are projected onto a subspace and the projection coordinate is set to be the dimension-reduced tensor. The subspace is determined by minimizing the distance between the high-dimensional tensor data and their projection in the subspace. (3) By combining the above two algorithms, a new dimensionality reduction algorithm, called PDMHSIC, is proposed, in which the dimensionality reduction must satisfy two criteria at the same time: HSIC maximization and subspace projection distance minimization. The proposed algorithm is a new attempt to combine HSIC with other algorithms to create new algorithms and has achieved better experimental results on 8 commonly-used datasets than the other 7 well-known algorithms.

Mind your weight: ‘Motionlessly’ sitting between the object and the verb in Japanese

The object in Japanese is often displaced from its canonical position next to the sentence-final verb, due to motivations such as information structure or animacy. Such flexibility allows for an adverb to be placed between the object and the verb. In the literature, there are suggestions for an almost equal preference to place Japanese manner adverbs before or after the object, inferred from both online and offline results. We will present a corpus study with a representative Japanese manner adverb zitto ‘motionlessly’ to show that either order may be preferred in different accounts of word order variation, but none can satisfy both requirements of distance minimization and accessibility, which are manifested in competing directions in Japanese, a verb-final language. In both accounts, weight has immense effect and should not be neglected. By using two heuristic methods to measure the weight effect, we propose that this case study with an object and an adverb sheds new light on the explanatory power of the distance minimization account, in particular by the Mimimize Domains principle (Hawkins 1994), which operates at both levels of (1) the constituency construction of the full VP, which favors the object-first order, and (2) the Phrasal Combination Domain between the head of object and the verb, which favors the adverb-first order. It is also proposed to implement a complement-and-adjunct distinction in the MiD principle, as a step toward a more effective study method of weight effect which I shall call efficiency profiling.