463 results about "Expression data" patented technology

The invention provides proteins from Neisseria meningitidis (strains A & B), including amino acid sequences, the corresponding nucleotide sequences, expression data, and serological data. The proteins are useful antigens for vaccines, immunogenic compositions, and/or diagnostics.

A virtual human interface for conducting survey questioning comprises a system and method which may include a script file which may include survey question data, response pattern data, expression data, advertising data, entertainment data, lobbying data and/or processing instructions. The expression data may include mouth and eye expression data along with respective duration data indicative of the length of time of respective visual representations. The virtual human interface may interact With a user by representing a character object that personifies communicative behavior to make interaction more natural and enjoyable.

The invention relates to a virtual learning environment micro-expression recognition and interaction method based on a double-flow convolutional neural network, and the method comprises the followingsteps: S1, carrying out the preprocessing of micro-expression data: carrying out the Euler video amplification of a micro-expression video, extracting an image sequence, carrying out the face positioning of the image sequence, and carrying out the cutting of the image sequence, and obtaining the RGB data of a micro-expression; extracting optical flow information from the data amplified by the Euler video to obtain an optical flow image of the micro-expression; s2, dividing the preprocessed data into a training set and a test set, and constructing a double-flow convolutional neural network by using a transfer learning method so as to learn space and time domain information of micro expressions; s3, carrying out maximum value fusion on the output of the double-flow convolutional neural network to enhance the recognition accuracy and obtain a final micro-expression recognition model; and S4, creating a virtual learning environment interaction system by using the micro-expression recognition model, and obtaining a user face image sequence through Kinect to carry out a micro-expression recognition task.

In an image communication system for compositing and generating an image for enhancing the communication, a composite image generator in a communication terminal analyzes voice data and image data, and an analyzer analyzes voice data to detect an emotion parameter. Basic emotion data are generated by a movement controller, an emotion movement pattern storage and a basic emotion generator, based on the emotion parameter. An expression compositor composites the basic emotion data with expression data, extracted from image data, to generate composite expression data. An image compositor composites the composite expression data with character data to generate a composite character image.

Techniques for measuring the emotional state of minds in a person based upon a set of biological data captured from a person in a natural environment, where the person is not restricted at all in his movement. Various sensing data from sensors are captured with or without the intervention of the person. In addition, expression data is also captured before, during or after the sensing signals are captured. The data sets are processed to filter out those uncorrelated. A dedicated computing device is provided to collect the data along with other necessary data available on the Internet, where the measurement of the emotion is measured, derived or calculated based on the collected data, and/or any historical measurements of the person. The result may be shared with a list of contacts selected by the person who may also views theirs as well.

The invention provides an automatic micro-expression identification method of a macro-to-micro conversion model based on depth learning. The method comprises steps that A, micro-expression sample processing, 1), a micro-expression data set sample and a macro-expression data set sample are pre-processed; 2), sample pairs of a cross-modal tuple loss function are constructed; B, cross-modal macro-to-micro conversion model training, 3), the AU detection network is trained, an AU detection network parameter is initialized, and a flexible maximum value loss function is trained; 4), the AU detection network parameter is fixed, a cross-modal macro-to-micro conversion model parameter is initialized, and the cross-modal macro-to-micro conversion model is trained; and C, micro-expression identification, according to a trained convolutional neural network model, a test parameter is initialized, a test sample is inputted to the trained convolutional neural network model, and an identification rate is outputted after forward network propagation. The method is advantaged in that robustness is realized compared with methods in the prior art.

Provided is a breast pump for the mother's milk, which makes it possible to obtain universal milk expression data without being affected by an individual difference, for example. The breast pump is composed of a collection container, a milk expressing member, and a negative pressure source for producing pulsated negative pressure. The milk expressing member is provided at an end with a teat cup and at an opposite end with a connector plug to the negative pressure source. The milk expressing member is divided by a partition plate. A flexible teat cup liner is replaceably fitted in the teat cup, and configured to be brought into contact with an areola and nipple of a breast.

The invention discloses a face expression recognition method of multi-scale feature extraction and global feature fusion. A human face expression data set is selected as raw data, and the raw data isdivided into training set data and test set data. A TensorFlow artificial intelligence learning system is used to construct a convolution neural network with multi-scale feature extraction and globalfeature fusion. The convolution neural network reads the training set data, pretreats the training set data and performs model training, then reads the test set data, recognizes the expression types of each expression in the test set data in turn, and calculates the average accuracy rate and average F1 of all expressions after completing the recognition of all expressions. Score indicators, the final completion of the process of facial expression recognition. The invention has the advantages of high recognition speed under the condition of ensuring high recognition accuracy, and can be adaptedto multiple illumination environments with strong robustness, so as to effectively meet the practical application requirements.

The invention provides a real time holographic projection method, a device and a system; the method comprises the following steps: capturing and collecting first motion data of a target in real time, wherein the first motion data comprises at least one of the following list: expression data, movement data, special effect data and a control order; receiving and processing the first motion data in real time so as to generate first motion driving data, driving and rendering a virtual target to generate first animation image data according to the first motion driving data, wherein a visual angle number of the first animation image data is between 1 and N, and the N is a natural number; projecting the first animation image data on a holographic device. The real time holographic projection method solves the problems that image data needed by holographic projection cannot be generated in real time in the prior art, thus affecting comprehensive application of the holographic projection technology; the real time holographic projection method, device and system can real time generate the image data needed by holographic projection, thus expanding holographic projection technology application effect.

The invention discloses a method and a device for acquiring expression meanings, and belongs to the technical field of communications. The method comprises the following steps of: acquiring expression data in a picture to be detected, or acquiring corresponding expression data according to expression characteristics input by a user; comparing the acquired expression data with the expression data in a micro expression library, wherein the micro expression library is pre-established and contains at least two expressions, and each expression has corresponding expression data; and if the micro expression library has matched expression data, acquiring and presenting the expressions corresponding to the matched expression data and the meanings of the expressions. The device comprises an expression data acquisition module and an expression acquisition module. The acquired expression data are compared with the pre-established micro expression library to acquire the matched expression data so as to acquire the expressions and the meanings to be acquired. The method and the device have low requirements for operation personnel, are easily popularized, do not need expensive special instruments, save the cost, and are simple, practical and effective.

The invention is related to Digital Rights Management (DRM) or Intellectual Property Management and Protection (IPMP) for a generic digital content. The concept of IPMP Control Graph and REL-IPMP Control Graph is introduced to carry protection signalling and rights expression data. Both content packaging and terminal processing relating to the above control graph are clearly defined and elaborated.

The invention provides a face expression identification method based on parallel convolutional neural network characteristic graph fusion. The method comprises the steps of designing a convolutional neural network with a parallel structure through simulating vision channels of double eyes of a man, fusing the characteristic graph of the parallel channels after a convolutional pooled layer; on a full connecting layer structure, using sparse full-connection output to one channel, and using dense full-connection output to the other channel, and finally fusing the two outputs and performing classification. After the face expression data are used for performing model training and a relatively high identification rate is realized, a model identification effect is tested by means of a testing sample, and furthermore a relatively high identification accuracy is acquired. The face expression identification method is a new method for emotion analysis and face expression identification.

A system and method predict alternative splicing transcripts using DNA chip expression data as a primary data source. The system and method may perform prediction of alternative splicing of pre-messenger RNA that may be used, for example, for regulating eukaryotic gene expression.

The invention discloses a pharmaceutical activity prediction and selection method based on genetic expressions and drug targets. The method comprises following steps: 1) obtaining target genes corresponding to drugs to be detected according to information in drug target database; obtaining genetic expression data of disease tissues of patients and corresponding compression data and obtaining important genetic lists of morbidity processes of patients by evaluating importance of genes through a systems biology method; 2) searching for whether the target genes obtained in the step 1) target the important gene lists during mobility of the patients or not through statistical analysis of in order to predict pharmaceutical activity of patients and select drugs suited to patients. The method is easily used with high efficiency and broad application scope. The prediction method can be used for selecting drugs suitable for individual patients such that a personalized treatment schme can be provided for patients.

A method for the joint analysis of molecular expression data and biological networks by clustering comprising the steps of defining a matrix of distances between molecules or sets of molecules that incorporate both the relation of corresponding expression profiles and information on their relation within a biological network; and clustering the molecules based on said distances.

The invention discloses a differentially expressed gene identification method based on combined constraint non-negative matrix factorization. The method comprises the following steps of 1, representing a cancer-gene expression data set with a non-negative matrix X, 2, constructing a diagonal matrix Q and an element-full matrix E, 3, introducing manifold learning in the classical non-negative matrix factorization method, conducting orthogonal-constraint sparseness and constraint on a coefficient matrix G, and obtaining a combined constraint non-negative matrix factorization target function, 4, calculating the target function, and obtaining iterative formulas of a basis matrix F and the coefficient matrix G, 5, conducting semi-supervision non-negative matrix factorization on the non-negative data set X, and obtaining the basis matrix F and the coefficient matrix G after iteration convergence, 6, obtaining an evaluation vector (the formula is shown in the description), sorting elements in the evaluation vector (the formula is shown in the description) from large to small according to the basis matrix F, and obtaining differentially expressed genes, 7, testing and analyzing the identified differentially expressed genes through a GO tool. The identification method can effectively extract the differentially expressed genes where cancer data is concentrated, and be applied in discovering differential features in a human disease gene database. The identification method has important clinical significance for early diagnosis and target treatment of diseases.

The present invention discloses an informational computation method for classifying objects Specifically, the invention is a system, method, and computer-readable media for classifying tumors using a nonparametric statistical classifier in conjunction with an artificial neural network. The invention classifies unknown tumor types based on the correlation of unknown tumor's genetic expression compared to the genetic expression of know tumor types by first performing a nonparametric statistical analysis on the know data, training a artificial neural network with the known data, and then inputting the unknown tumor data into the neural network to calculate the probability that the sample tumor is a member of a class of tumors. By using a statistical classifier in conjunction with a neural network, the invention classifies unknown tumors more accurately then conventionally possible. Advantageously, by using a variety of tumor genetic expression data sets, including both published data sets and generated data sets, a tumor classifier, robust and accurate enough for clinical application, is provided.

The invention discloses an action-capture-data-driving-based two-dimensional cartoon expression animation production method, which comprises the following steps of: first capturing the expression data of an actor by using action capturing equipment, and marking characteristic points forming a topology structure which is the same as that formed by the data of the actor on a target cartoon face by a user at the same time; then aligning the first frame of the expression data of the actor with the characteristic points of the target cartoon face by using a shifting-ratio-based reorientation method, and mapping the expression data of the actor onto the target cartoon face according to an alignment ratio; and vectorizing the target cartoon face, and driving the deformation of the target cartoon face in a vector image form by using reoriented action capture data to achieve an animation effect. By a vectorization technology and a shifting-ratio-based reorientation technology, a cartoon expression animation with rich expressions and a high restoration degree can be obtained in the field of texture-detail-irrespective animation of unreal cartoon faces.

The invention provides proteins from gonococcus (Neisseria gonorrhoeae), including amino acid sequences, the corresponding nucleotide sequences, expression data, and serological data. The proteins are useful antigens for vaccines, immunogenic compositions, and/or diagnostics. They are also useful for distinguishing between gonococcus and meningococcus and, in particular, between gonococcus and serogroup B meningococcus.