Abstract: The present invention provides a phase-change storage unit containing a TiSiN material layer and a method for preparing the same. The phase-change storage unit includes a phase-change material layer and a lower electrode located there below, the phase-change material layer and the lower electrode are connected by a TiSiN material layer, the lower electrode includes a bottom and a sheet side connected to the bottom, the sheet side is perpendicular to the bottom to form a blade structure, and the top of the sheet side contacts the TiSiN material layer. The present invention adopts annealing to increase the grain size of the electrode so as to reduce the overall resistance of the device and form a TiSiN material layer on the top of the lower electrode so as to reduce the effective operation region. The phase-change storage unit of the present invention is applied to a phase-change memory to achieve the advantages such as low power consumption, high density and high data retention performance.

Abstract: A video chatting method and system are provided. The method and system describe collection of facial vector data, audio data, and interactive motion information of a user of a first client. The collected data may be transmitted to a second client. The second client, in turn, may generate a virtual avatar model of the user of the first client based on the received data. The second client may further display the virtual model, play sound in the audio data. The second client may also render the interactive motion information and facial data information of a user of the second client, and generate and display a virtual avatar model of the user of the second client. The provided method and system may decreases amount of data that may be transferred over the network. This may allow data transmission rate during video communication to be high enough for a smooth operation.

Abstract: The present invention relates to a metal element doped phase-change material in the field of micro-electronics technologies, specifically to an antimony-rich high-speed phase-change material used in a phase-change memory (PCRAM), a preparing method and an application thereof. The antimony-rich high-speed phase-change material used in a PCRAM has a chemical formula being Ax(Sb2Te)1?x, x is an atom percent, where A is selected from W, Ti, Ta, and Mn, and 0<x<0.5 The phase-change material provided in the present invention is similar to a usual GeSbTe material, so as to be propitious to implement high-density storage. The material may perform reversible phase-change under an effect of an externally electrically driven nano-second (ns) pulse. A phase-change speed of the W—Sb—Te is 3 times of the GeSbTe material, so as to be propitious to implement the high-speed PCRAM.

Abstract: The present invention provides a phase-change storage unit for replacing DRAM and FLASH and a manufacturing method thereof, and the phase-change storage unit includes a phase-change material layer and a cylindrical lower electrode being in contact with and located below the phase-change material layer, where the phase-change material layer is formed by connecting a side wall layer and a round bottom layer, forms a hollow cylinder or hollow inverted conical frustum having an opening at an upper part, and the hollow cylinder or hollow inverted conical frustum is internally filled with a medium layer.

Abstract: An interactive method, apparatus based on an image and a server are provided according to embodiments of the present invention. The method includes: recognizing a face region in an image; generating a face box corresponding to the face region; generating a label box corresponding to the face box; and representing label information corresponding to the face region in the label box by performing one of the following modes: obtaining the label information corresponding to the face region from a server, representing the label information obtained from the server in the label box; and receiving the label information corresponding to the face region inputted by a user, representing the label information inputted by the user in the label box. Thus, based on the label information provided by the server or the user, information associated with a circled region is customized (e.g., reviews information), and is further pushed to an associated friend.

Abstract: A method and an apparatus are provided for retrieving keyword. The apparatus configures at least two types of language models in a model file, where each type of language model includes a recognition model and a corresponding decoding model; the apparatus extracts a speech feature from the to-be-processed speech data; performs language matching on the extracted speech feature by using recognition models in the model file one by one, and determines a recognition model based on a language matching rate; and determines a decoding model corresponding to the recognition model; decoding the extracted speech feature by using the determined decoding model, and obtains a word recognition result after the decoding; and matches a keyword in a keyword dictionary and the word recognition result, and outputs a matched keyword.

Abstract: This application discloses a method implemented of recognizing a keyword in a speech that includes a sequence of audio frames further including a current frame and a subsequent frame. A candidate keyword is determined for the current frame using a decoding network that includes keywords and filler words of multiple languages, and used to determine a confidence score for the audio frame sequence. A word option is also determined for the subsequent frame based on the decoding network, and when the candidate keyword and the word option are associated with two distinct types of languages, the confidence score of the audio frame sequence is updated at least based on a penalty factor associated with the two distinct types of languages. The audio frame sequence is then determined to include both the candidate keyword and the word option by evaluating the updated confidence score according to a keyword determination criterion.

Abstract: A method, system and computer storage medium for visual searching based on cloud service is disclosed. The method includes: receiving, from a client, an image recognition request of cloud service, the request containing image data; forwarding, according to a set classified forwarding rule, the image data to a corresponding classified visual search service; recognizing, by the respective corresponding classified visual search services, corresponding classified type information in the image data, and determining a corresponding name of the image data in accordance with the respective classified type information, and obtaining a classified visual search result; summarizing and sending, to a client, the classified visual search result of the corresponding classified visual search service.

Abstract: A method and a device for training a DNN model includes: at a device including one or more processors and memory: establishing an initial DNN model; dividing a training data corpus into a plurality of disjoint data subsets; for each of the plurality of disjoint data subsets, providing the data subset to a respective training processing unit of a plurality of training processing units operating in parallel, wherein the respective training processing unit applies a Stochastic Gradient Descent (SGD) process to update the initial DNN model to generate a respective DNN sub-model based on the data subset; and merging the respective DNN sub-models generated by the plurality of training processing units to obtain an intermediate DNN model, wherein the intermediate DNN model is established as either the initial DNN model for a next training iteration or a final DNN model in accordance with a preset convergence condition.

Abstract: The present invention discloses an Al—Sb—Te phase change material used for PCM and fabrication method thereof. Said phase change material, which can be prepared by PVD, CVD, ALD, PLD, EBE, and ED, is a mixture of three elements aluminum (Al), antimony (Sb) and tellurium (Te) with a general formula of Alx(SbyTe1)1-x, where 0<x?0.85, 0.67?y?7. Said material is electrically driven from outside. By adjusting the content of three elements in the mixture, storage materials with different crystallization temperatures, melting temperatures and activation energies of crystallization can be achieved. Any two elements of aluminum, antimony and tellurium can be bonded to each other, so the adjustability is very high, maintaining the phase change properties in a wide range.

Abstract: A method, apparatus and system for payment validation have been disclosed. The method includes: receiving a payment validation request from a terminal, wherein the payment validation request includes identification information and a current voice signal; detecting whether the identification information is identical to a pre-stored identification information; if identical: extracting voice characteristics associated with an identity information and a text password from the current voice signal; matching the current voice characteristics to a pre-stored speaker model; if successfully matched: sending an validation reply message to the terminal to indicate that payment request has been authorized. The validation reply message is utilized by the terminal to proceed with a payment transaction. The identity information identifies an owner's current voice signal, and the text password is indicated by the current voice signal.

Abstract: A method includes: acquiring data samples; performing categorized sentence mining in the acquired data samples to obtain categorized training samples for multiple categories; building a text classifier based on the categorized training samples; classifying the data samples using the text classifier to obtain a class vocabulary and a corpus for each category; mining the corpus for each category according to the class vocabulary for the category to obtain a respective set of high-frequency language templates; training on the templates for each category to obtain a template-based language model for the category; training on the corpus for each category to obtain a class-based language model for the category; training on the class vocabulary for each category to obtain a lexicon-based language model for the category; building a speech decoder according to an acoustic model, the class-based language model and the lexicon-based language model for any given field, and the data samples.

Abstract: Systems and methods are provided for voice identification. For example, audio characteristics are extracted from acquired voice signals; a syllable confusion network is identified based on at least information associated with the audio characteristics; a word lattice is generated based on at least information associated with the syllable confusion network and a predetermined phonetic dictionary; and an optimal character sequence is calculated in the word lattice as an identification result.

Abstract: The present invention, pertaining to the field of speech recognition, discloses a reminder setting method and apparatus. The method includes: acquiring speech signals; acquiring time information in speech signals by using keyword recognition, and determining reminder time for reminder setting according to the time information; acquiring text sequence corresponding to the speech signals by using continuous speech recognition, and determining reminder content for reminder setting according to the time information and the text sequence; and setting a reminder according to the reminder time and the reminder content.

Abstract: Described is a new stand-alone diguanylate cyclase polypeptide having a GGDEF motif and a mutated I-site that does not bind c-di-GMP. We demonstrate that the production yield of c-di-GMP and analogues was significantly increased by mutation of a conserved residue in the putative regulatory I-site.

Abstract: A method of recognizing speech is provided that includes generating a decoding network that includes a primary sub-network and a classification sub-network. The primary sub-network includes a classification node corresponding to the classification sub-network. The classification sub-network corresponds to a group of uncommon words. A speech input is received and decoded by instantiating a token in the primary sub-network and passing the token through the primary network. When the token reaches the classification node, the method includes transferring the token to the classification sub-network and passing the token through the classification sub-network. When the token reaches an accept node of the classification sub-network, the method includes returning a result of the token passing through the classification sub-network to the primary sub-network. The result includes one or more words in the group of uncommon words. A string corresponding to the speech input is output that includes the one or more words.

Abstract: An electronic device with one or more processors and memory trains an acoustic model with an international phonetic alphabet (IPA) phoneme mapping collection and audio samples in different languages, where the acoustic model includes: a foreground model; and a background model. The device generates a phone decoder based on the trained acoustic model. The device collects keyword audio samples, decodes the keyword audio samples with the phone decoder to generate phoneme sequence candidates, and selects a keyword phoneme sequence from the phoneme sequence candidates. After obtaining the keyword phoneme sequence, the device detects one or more keywords in an input audio signal with the trained acoustic model, including: matching phonemic keyword portions of the input audio signal with phonemes in the keyword phoneme sequence with the foreground model; and filtering out phonemic non-keyword portions of the input audio signal with the background model.

Abstract: A computer-implemented method is performed at a server having one or more processors and memory storing programs executed by the one or more processors for authenticating a user from video and audio data. The method includes: receiving a login request from a mobile device, the login request including video data and audio data; extracting a group of facial features from the video data; extracting a group of audio features from the audio data and recognizing a sequence of words in the audio data; identifying a first user account whose respective facial features match the group of facial features and a second user account whose respective audio features match the group of audio features. If the first user account is the same as the second user account, retrieve the sequence of words associated with the user account and compare the sequences of words for authentication purpose.

Abstract: A method and a device for training an acoustic language model, include: conducting word segmentation for training samples in a training corpus using an initial language model containing no word class labels, to obtain initial word segmentation data containing no word class labels; performing word class replacement for the initial word segmentation data containing no word class labels, to obtain first word segmentation data containing word class labels; using the first word segmentation data containing word class labels to train a first language model containing word class labels; using the first language model containing word class labels to conduct word segmentation for the training samples in the training corpus, to obtain second word segmentation data containing word class labels; and in accordance with the second word segmentation data meeting one or more predetermined criteria, using the second word segmentation data containing word class labels to train the acoustic language model.

Abstract: A computer-implemented method is performed at a device having one or more processors and memory storing programs executed by the one or more processors. The method comprises: selecting a target word in a target sentence; from the target sentence, acquiring a first sequence of words that precede the target word and a second sequence of words that succeed the target word; from a sentence database, searching and acquiring a group of words, each of which separates the first sequence of words from the second sequence of words in a sentence; creating a candidate sentence for each of the candidate words by replacing the target word in the target sentence with each of the candidate words; determining the fittest sentence among the candidate sentences according to a linguistic model; and suggesting the candidate word within the fittest sentence as a correction.