Aiming at the identification of Chinese fine-grained implicit discourse relation and taking the directionality characteristic in account, the authors propose a feature learning algorithm based on the distant supervision to label explicit discourse data automatically. The relative position information between conjunction and words are applied to train the intensive word representation. Then the rhetorical function of words and the directionality of relations are encoded into the representation of intensive words, which is applied to the relation classification of fine-grained implicit discourses. From the experimental studies of the proposed approach, the classification accuracy reaches 49.79%, which are better than those approaches neglecting the directionality of discourse relations.

Nested named entities relationship extraction research lacks corresponding benchmark corpora. To solve this problem, manual annotation with machine learning are combined to extract their semantic relationships from an existing Chinese named entity recognition corpus. The authors manually annotate a Chinese nested named entity relation corpus from existing Chinese named entity recognition and conduct experiments with relation extraction between nested named entities via support vector machines (SVM) and convolutional neural network (CNN) models respectively. The experimental results show that the nested entity relation extraction performs excellently on the corpus with manually labeled entities, obtaining an F1 score of over 95%, while it falls short of expectations with automatically recognized entities.

A mixed-monotonic capacitor switching scheme which can provide stable common-mode voltage (Vcm) without any additional voltage regulator and compensation capacitor array is proposed for the successive approximation register (SAR) analog-to-digital (ADC). The proposed scheme contains two equal amplitude but opposite monotonicity switched capacitor arrays, the stabilization of the common mode voltage is achieved with self-complementation of the differential voltage. Based on this technique, a 10-bit 50 MS/s prototype is designed in CMOS 0.18 μm technology. A window opening SAR logic is used to reduce the transmission time from the comparator out to DAC control signal. An adaptive delay chain is used in the comparator loop to reduce the conversion time of lower bit in the SAR ADC. Measurement result shows that the SAR ADC can achieve a SNDR equal to 57.31 dB, and INL and DNL are equal to 1.81 LSB and 0.98 LSB respectively.

The two kinds of generalized gradient systems are proposed and the characteristics of the two systems are studied. The conditions under which the Nielsen equations can be considered as one of the two generalized gradient systems are obtained. The characteristics of the generalized gradient systems can be used to study the stability of solution of the Nielsen equations. Some examples are given to illustrate the application of the results.

Using the matrix expression form of computer vision projection equation, the collinear equation is constructed into matrix equation. With the projection matrix element as a composite function, this paper realizes the unification derivation of each variable of the collinear equation based on the matrix analysis method. Compared with the traditional analytical method of linearization, the form of matrix analysis process is quite succinct and easy to understand, which can be used to the numerical solution of linear library application. For the various construction form of the rotation matrix, this method has better adaptability. The constructed matrix of collinear equation has important enlightenment significance for using computer vision method.