Abstract: Provided is a new solid oxygen ionic conductor based field-effect transistor and its manufacturing method. The field-effect transistor includes: a substrate; a gate dielectric layer located on the substrate, where the gate dielectric layer is a solid oxygen ionic conductor thin film; a channel layer covered on a part of the gate dielectric layer; and a source electrode and a drain electrode respectively located on the gate dielectric layer not covered by the channel layer and on a part of the channel layer.

Abstract: A high-efficiency particle analysis method includes the following steps: taking representative air-dried samples and measuring a moisture content; boiling, sieving, weighing and adding a dispersant; conducting a particle analysis test; reading four readings of 1st to 59th and 60th to 90th samples; and drawing a particle size distribution curve showing the relationship between the particle size and the percentage of below a certain diameter. According to the method, a time difference is used to change the measurement mode, and the four readings of the 59th and 90th samples are read in a cycling manner; and a novel test method is provided on the premise of ensuring quality, thus greatly improving the efficiency of a particle analysis test and meeting production requirements.

Abstract: Disclosed is a full-hydraulic automatic diaphragm wall cutting-grooving machine, which includes a supporting frame, a cutting device, a cutting driving device and a verticality detection device. The verticality detection device is used to detect the verticality and flatness of the diaphragm wall during the cutting process, the cutting device is used to drive the supporting frame and an equipment arranged on the supporting frame to move and cut, the cutting driving device is used to drive and adjust the cutting device in different directions. The verticality detection device is used to realize the automatic measurement of the verticality of the wall and the bottom surface and the flatness of the wall during the cutting process.

Abstract: A method of high dimensional data analysis in real-time comprising executing dimension-reducing an input historical data set under a t-SNE model and determining from the resulting dimension-reduced data set a recent; further dimension-reducing the recent group data set under a PCA model; statistical analyzing the further dimension-reduced data set to determine a threshold group for distinguishing abnormal data from normal ones in a real-time data stream. The method may further include training a classifier using the abnormal or normal data set for predicting anomaly in the real-time data source system. Alternatively, a discrepancy training data set is computed from one of the normal and abnormal data sets and be used to train one of independent normal and abnormal data regression models; with the other one trained by transfer learning based on the trained one. The trained regression models are then used to predict discrepancy values.

Abstract: A method for summarizing text with sentence extraction including steps as follows. Sentences are extracted from a document including text by a natural language processing (NLP) based feature extractor. A word vector set with respect to each of the sentences is generated by a processor. The word vector set with respect to each of the sentences is used to generate a n-grams vector set and a phrase-n vector set with respect to each of the sentences. A word score representing similarity between the word vector sets, a n-grams score representing similarity between the n-grams vector sets, and a phrase-n score representing similarity between the phrase-n vector sets are computed. The word, n-grams, and phrase-n scores are combined to compute an edge score. Text features are selected from the sentences using the edge scores of the sentences, so as to output a summary of the document.

Abstract: A gel composition, in particular a gelled electrolyte, comprising: i) fumed alumina particles, wherein the mean primary particle size of the particle is from 5 to 50 nm and the BET specific surface area is from 40 to 400 m2/g; ii) at least two organic solvents; and iii) a lithium salt; wherein the amount of the alumina particles is 0.2-10% by weight based on the total weight of the gel composition. A method to prepare a gelled electrolyte, a Li-ion battery, a Li-ion battery and a device are also provided.

Abstract: A method for applying image encoding recognition in the execution of natural language processing (NLP) tasks, comprising the processing steps as follows. A sentence from a textual source is extracted by an NLP-based feature extractor. A word vector is generated in response to the sentence by the NLP-based feature extractor. The word vector is converted into a feature vector {right arrow over (b)} by the NLP-based feature extractor, in which the feature vector {right arrow over (b)} satisfies {right arrow over (b)}?m and the parameter m is a positive integer. The feature vector is transformed into an image set having a plurality of two-dimensional images by a transformer. The image set is fed to a neural network to execute image recognition by a processor, so as to analyze the sentence.

Abstract: A method for applying image encoding recognition in the execution of natural language processing (NLP) tasks, comprising the processing steps as follows. A sentence from a textual source is extracted by an NLP-based feature extractor. A word vector is generated in response to the sentence by the NLP-based feature extractor. The word vector is converted into a feature vector {right arrow over (b)} by the NLP-based feature extractor, in which the feature vector {right arrow over (b)} satisfies {right arrow over (b)}?m and the parameter m is a positive integer. The feature vector is transformed into an image set having a plurality of two-dimensional images by a transformer. The image set is fed to a neural network to execute image recognition by a processor, so as to analyze the sentence.

Abstract: A method for summarizing text with sentence extraction including steps as follows. Sentences are extracted from a document including text by a natural language processing (NLP) based feature extractor. A word vector set with respect to each of the sentences is generated by a processor. The word vector set with respect to each of the sentences is used to generate a n-grams vector set and a phrase-n vector set with respect to each of the sentences. A word score representing similarity between the word vector sets, a n-grams score representing similarity between the n-grams vector sets, and a phrase-n score representing similarity between the phrase-n vector sets are computed. The word, n-grams, and phrase-n scores are combined to compute an edge score. Text features are selected from the sentences using the edge scores of the sentences, so as to output a summary of the document.

Abstract: A signal processing method and circuit of a mobile terminal and a mobile terminal are provided, by sending a signal detected by operating an antenna body as a sensing pad of a sensor to a microprocessor, the microprocessor determines a usage state of the mobile terminal according to the signal, generates a control signal according to the usage state of the mobile terminal, and controls a tuning circuit to switch tuning paths through the control signal so as to perform a tuning process on a signal from a mobile terminal signal source or the antenna body.

Abstract: A method for annotating a batch of original samples is provided. A first subset of original samples, selected from the batch and determined by minimizing an entropy-mean difference between the first subset and the batch, is used for human annotation to yield human-annotated samples. The human-annotated samples are used as training data to configure an annotation process for annotating an input sample to yield an annotated output sample, and a check process for verifying annotation accuracy of the annotated output sample. Remaining original samples in the hatch are processed by the annotation process to yield machine-annotated samples, whose accuracy is verified by the check process. In one embodiment, part of the original samples corresponding to erroneous machine-annotated samples are selected for human annotation. Resultant additional human-annotated samples are used to update the two processes. The remaining original samples not yet annotated are then processed by the two processes.

Abstract: A method for recognizing and extracting named entities in a natural language input text processing comprising: performing, by a compressed named entities recognition (NER)-model-based named entity recognizer, a first stage NER on the input text to generate a first stage determination of whether at least one named entity exists in the input text; if the first stage NER determines no named entity exists in the input text, performing, by a rule based named entity recognizer, a second stage NER on the input text to generate a second stage NER result; if the first stage NER determines at least one named entity exists in the input text, generating, by the compressed NER-model-based named entity recognizer, a first stage NER result; and integrating, by a NER result integrator, the first stage NER result and the second stage NER result to generate a final NER result.

Abstract: A method of high dimensional data analysis in real-time comprising executing dimension-reducing an input historical data set under a t-SNE model and determining from the resulting dimension-reduced data set a recent; further dimension-reducing the recent group data set under a PCA model; statistical analyzing the further dimension-reduced data set to determine a threshold group for distinguishing abnormal data from normal ones in a real-time data stream. The method may further include training a classifier using the abnormal or normal data set for predicting anomaly in the real-time data source system. Alternatively, a discrepancy training data set is computed from one of the normal and abnormal data sets and be used to train one of independent normal and abnormal data regression models; with the other one trained by transfer learning based on the trained one. The trained regression models are then used to predict discrepancy values.

Abstract: A method for recognizing and extracting named entities in a natural language input text processing comprising: performing, by a compressed named entities recognition (NER)-model-based named entity recognizer, a first stage NER on the input text to generate a first stage determination of whether at least one named entity exists in the input text; if the first stage NER determines no named entity exists in the input text, performing, by a rule based named entity recognizer, a second stage NER on the input text to generate a second stage NER result; if the first stage NER determines at least one named entity exists in the input text, generating, by the compressed NER-model-based named entity recognizer, a first stage NER result; and integrating, by a NER result integrator, the first stage NER result and the second stage NER result to generate a final NER result.

Abstract: Methods and systems which provide super-resolution synthesis based on weighted results from a random forest classifier are described. Embodiments apply a trained random forest classifier to low-resolution patches generated from the low-resolution input image to classify the low-resolution input patches. As each low-resolution patch is fed into the random forest classifier, each decision tree in the random forest classifier “votes” for a particular class for each of the low-resolution patches. Each class is associated with a projection matrix. The projection matrices output by the decision trees are combined by a weighted average to calculate an overall projection matrix corresponding to the random forest classifier output, which is used to calculate a high-resolution patch for each low-resolution patch. The high-resolution patches are combined to generate a synthesized high-resolution image corresponding to the low-resolution input image.

Abstract: Methods and systems which provide super-resolution synthesis based on weighted results from a random forest classifier are described, Embodiments apply a trained random forest classifier to low resolution patches generated from the low-resolution input image to classify the low resolution input patches. As each low-resolution patch is fed into the random forest classifier, each decision tree in the random forest classifier “votes” for a particular class for each of the low-resolution patches. Each class is associated with a projection matrix. The projection matrices output by the decision trees are combined by a weighted average to calculate an overall projection matrix corresponding to the random forest classifier output, which is used to calculate a high-resolution patch for each low-resolution patch. The high-resolution patches are combined to generate a synthesized high-resolution image corresponding to the low-resolution input image.

Abstract: A method for organizing and processing feature based data structures that can be used in linguistic spell checking and auto-correction, comprising: splitting an original dictionary into sub-dictionaries based on different values of a common feature such as high frequency words; receiving an input text that contains errors; determining a sub-dictionary selection feature from the input human-readable text; selecting the sub-dictionary based on the determined sub-dictionary selection feature; executing a first matching in the selected sub-dictionary, wherein a match is found if a similarity between the characters, words, or phrases in proximity of the errors in the input text and a character, word, or phrase in the sub-dictionary is above a threshold; if a unique match is found, the result is returned as an output to correct the errors; otherwise, executing a second matching with a raised threshold, and repeating the second matching until a unique match is found.

Abstract: A method for annotating a batch of original samples is provided. A first subset of original samples, selected from the batch and determined by minimizing an entropy-mean difference between the first subset and the batch, is used for human annotation to yield human-annotated samples. The human-annotated samples are used as training data to configure an annotation process for annotating an input sample to yield an annotated output sample, and a check process for verifying annotation accuracy of the annotated output sample. Remaining original samples in the hatch are processed by the annotation process to yield machine-annotated samples, whose accuracy is verified by the check process. In one embodiment, part of the original samples corresponding to erroneous machine-annotated samples are selected for human annotation. Resultant additional human-annotated samples are used to update the two processes. The remaining original samples not yet annotated are then processed by the two processes.

Abstract: A method for organizing and processing feature based data structures that can be used in linguistic spell checking and auto-correction, comprising: splitting an original dictionary into sub-dictionaries based on different values of a common feature such as high frequency words; receiving an input text that contains errors; determining a sub-dictionary selection common feature from the input human-readable text; selecting the sub-dictionary based on the determined sub-dictionary selection feature; executing a first matching in the selected sub-dictionary, wherein a match is found if a similarity between the characters, words, or phrases in proximity of the errors in the input text and a character, word, or phrase in the sub-dictionary is above a threshold; if a unique match is found, the result is returned as an output to correct the errors; otherwise, executing a second matching with a raised threshold, and repeating the second matching until a unique match is found.