Acceptable Regions for Approximations in Quality Control

The manufacturer\u27s inspection of his own product or the product received from outside vender, serves two purposes: 1. To provide a basis for action with regard to the materials and goods at hand. For instance; to-decide whether the particular article or group of articles should be utilized, or whether some alternative disposition should be made, such as: inspected further, sorted, repaired, reworked or scrapped. 2. To provide a basis for action with regard to the future production process. For instance; to decide whether the process should. be left alone, or whether action taken to find and eliminate disturbing causes

Nonlinearity and Dynamic Phase Transition of Charge-density-wave Lattice

100學年度杜昭宏升等參考著作[[abstract]]We report the investigation of the dynamic behavior of charge-density waves (CDWs) in a quasi-one-dimensional material K0.3 Mo O3 using x-ray scattering and multiple x-ray diffraction. Under the application of voltages, we demonstrate that the occurrence of nonlinear conductivity caused by CDW is through the internal deformation of the CDW lattice, i.e., a phase jump of 2π, as the applied voltage exceeds the threshold. By measuring the evolution of peak width of satellite reflections as a function of the field strength, we also report that the CDW lattice can be driven to move and undergo a dynamic phase transition, i.e., from the disordered pinning state to ordered moving solid state, and finally, to disordered moving liquid.[[notice]]補正完畢[[incitationindex]]SCI[[booktype]]紙本[[booktype]]電子

Brane world unification of quark and lepton masses and its implication for the masses of the neutrinos

A TeV-scale scenario is constructed in an attempt to understand the relationship between quark and lepton masses. This scenario combines a model of early (TeV) unification of quarks and leptons with the physics of large extra dimensions. It demonstrates a relationship between quark and lepton mass scales at rather ``low'' (TeV) energies which will be dubbed as {\em early quark-lepton mass unification}. It also predicts that the masses of the neutrinos are naturally light and Dirac. There is an interesting correlation between neutrino masses and those of the unconventionally charged fermions which are present in the early unification model. If these unconventional fermions were to lie between 200 GeV and 300 GeV, the Dirac neutrino mass scale is predicted to be between 0.07 eV and 1 eV.Comment: ReVTeX, 16 double-column pages. Typos corrected. One added referenc

Computational Aspects of Optional P\'{o}lya Tree

Optional P\'{o}lya Tree (OPT) is a flexible non-parametric Bayesian model for density estimation. Despite its merits, the computation for OPT inference is challenging. In this paper we present time complexity analysis for OPT inference and propose two algorithmic improvements. The first improvement, named Limited-Lookahead Optional P\'{o}lya Tree (LL-OPT), aims at greatly accelerate the computation for OPT inference. The second improvement modifies the output of OPT or LL-OPT and produces a continuous piecewise linear density estimate. We demonstrate the performance of these two improvements using simulations

An Integrated Framework Based on Latent Variational Autoencoder for Providing Early Warning of At-Risk Students

The rapid development of learning technologies has enabled online learning paradigm to gain great popularity in both high education and K-12, which makes the prediction of student performance become one of the most popular research topics in education. However, the traditional prediction algorithms are originally designed for balanced dataset, while the educational dataset typically belongs to highly imbalanced dataset, which makes it more difficult to accurately identify the at-risk students. In order to solve this dilemma, this study proposes an integrated framework (LVAEPre) based on latent variational autoencoder (LVAE) with deep neural network (DNN) to alleviate the imbalanced distribution of educational dataset and further to provide early warning of at-risk students. Specifically, with the characteristics of educational data in mind, LVAE mainly aims to learn latent distribution of at-risk students and to generate at-risk samples for the purpose of obtaining a balanced dataset. DNN is to perform final performance prediction. Extensive experiments based on the collected K-12 dataset show that LVAEPre can effectively handle the imbalanced education dataset and provide much better and more stable prediction results than baseline methods in terms of accuracy and F1.5 score. The comparison of t-SNE visualization results further confirms the advantage of LVAE in dealing with imbalanced issue in educational dataset. Finally, through the identification of the significant predictors of LVAEPre in the experimental dataset, some suggestions for designing pedagogical interventions are put forward

X-ray multiple-wave coherent interaction in a quasi-two-dimensional material NbSe2-2H

[[abstract]]The first observation of the X-ray multiple-wave interaction in an incommensurate charge-density-wave (CDW) modulated structure at low temperatures is reported for an example of a quasi-two-dimensional material, NbSe2-2H. Via the coherent interaction between the X-ray waves propagating in the CDW-modulated structure and the host structure, the phase-dependent intensity variations of a CDW reflection were detected. In accord with a centrosymmetric structure, the phases of the structure-factor triplets of two CDW reflections and a Bragg reflection of the host structure were determined to be either 0 or 180°, and not to vary with temperature. Relative phase differences of the two CDW reflections are also deduced.[[notice]]補正完畢[[booktype]]紙本[[booktype]]電子

S-KMN: Integrating Semantic Features Learning and Knowledge Mapping Network for Automatic Quiz Question Annotation

Quiz question annotation aims to assign the most relevant knowledge point to a question, which is a key technology to support intelligent education applications. However, the existing methods only extract the explicit semantic information that reveals the literal meaning of a question, and ignore the implicit knowledge information that highlights the knowledge intention. To this end, an innovative dual-channel model, the Semantic-Knowledge Mapping Network (S-KMN) is proposed to enrich the question representation from two perspectives, semantic and knowledge, simultaneously. It integrates semantic features learning and knowledge mapping network (KMN) to extract explicit semantic features and implicit knowledge features of questions,respectively. Designing KMN to extract implicit knowledge features is the focus of this study. First, the context-aware and sequence information of knowledge attribute words in the question text is integrated into the knowledge attribute graph to form the knowledge representation of each question. Second, learning a projection matrix, which maps the knowledge representation to the latent knowledge space based on the scene base vectors, and the weighted summations of these base vectors serve as knowledge features. To enrich the question representation, an attention mechanism is introduced to fuse explicit semantic features and implicit knowledge features, which realizes further cognitive processing on the basis of understanding semantics. The experimental results on 19,410 real-world physics quiz questions in 30 knowledge points demonstrate that the S-KMN outperforms the state-of-the-art text classification-based question annotation method. Comprehensive analysis and ablation studies validate the superiority of our model in selecting knowledge-specific features