4356 results about "Study methods" patented technology

The present invention relates generally to consumer research methods for measuring emotive response to visual stimuli.

A learning system according to the present invention includes an event list database for storing a plurality of event lists, each of the event lists being a set including a series of state-action pairs which reaches a state-action pair immediately before earning a reward, an event list managing section for classifying state-action pairs into the plurality of event lists for storing, and a learning control section for updating expectation of reward of a state-action pair which is an element of each of the event lists.

Compositions and methods for modulating the activity of RNA and DNA are disclosed. In accordance with preferred embodiments, antisense compositions are prepared comprising targeting and reactive portions. Reactive portions which act, alternatively, through phosphorodiester bond cleavage, through backbone sugar bond cleavage or through base modification are preferrably employed. Groups which improve the pharmacodynamic and pharmacokinetic properties of the oligonucleotides are also useful in accordance with certain embodiments of this invention. Delivery of the reactive or non-reactive functionalities into the minor groove formed by the hybridization of the composition with the target RNA is also preferrably accomplished. Therapeutics, diagnostics and research methods and also disclosed. Synthetic nucleosides and nucleoside fragments are also provided useful for elaboration of oligonucleotides and oligonucleotide analogs for such purposes.

Disclosed is a base station of a wireless communications system including an adaptive antenna array and beam forming means for forming simultaneous multiple forward link beams. Preferably mobile stations are separated into groups of mobile stations corresponding to a maximum number of simultaneous forward link beams for determining which of said groups can be served by compatible simultaneous forward link beams. Preferably, if mobiles remain outside of a compatible group, the number of simultaneous forward link beams is increased and grouping of the mobile stations is repeated until all the mobile stations are included in compatible groups. Preferably simultaneous data beams are formed to mobiles of a said group during a time interval accorded to said group, such that every mobile station receives service data during a full cycle of said time intervals at a rate equal to or in excess of a target service data rate for that mobile.

A facial expression recognition system that uses a face detection apparatus realizing efficient learning and high-speed detection processing based on ensemble learning when detecting an area representing a detection target and that is robust against shifts of face position included in images and capable of highly accurate expression recognition, and a learning method for the system, are provided. When learning data to be used by the face detection apparatus by Adaboost, processing to select high-performance weak hypotheses from all weak hypotheses, then generate new weak hypotheses from these high-performance weak hypotheses on the basis of statistical characteristics, and select one weak hypothesis having the highest discrimination performance from these weak hypotheses, is repeated to sequentially generate a weak hypothesis, and a final hypothesis is thus acquired. In detection, using an abort threshold value that has been learned in advance, whether provided data can be obviously judged as a non-face is determined every time one weak hypothesis outputs the result of discrimination. If it can be judged so, processing is aborted. A predetermined Gabor filter is selected from the detected face image by an Adaboost technique, and a support vector for only a feature quantity extracted by the selected filter is learned, thus performing expression recognition.

The present invention provides a deep and reinforcement learning-based real-time online path planning method. According to the method, the high-level semantic information of an image is obtained through using a deep learning method, the path planning of the end-to-end real-time scenes of an environment can be completed through using a reinforcement learning method. In a training process, image information collected in the environment is brought into a scene analysis network as a current state, so that an analytical result can be obtained; the analytical result is inputted into a designed deep cyclic neural network; and the decision-making action of each step of an intelligent body in a specific scene can be obtained through training, so that an optimal complete path can be obtained. In an actual application process, image information collected by a camera is inputted into a trained deep and reinforcement learning network, so that the direction information of the walking of the intelligent body can be obtained. With the method of the invention, obtained image information can be utilized to the greatest extent under a premise that the robustness of the method is ensured and the method slightly depends on the environment, and real-time scene walking information path planning can be realized.

A learning method and system are disclosed that assess a learner's understanding of the subject matter and the learner's preferred learning style by presenting and reviewing the information in various types of teaching strategies and then selecting the teaching strategies in which the student learns best. As the student responds to questions presented during the course, a learning bias model is developed for the learner based on which teaching styles provide the best level of comprehension for the learner and then presents concepts from the course within those learning strategies most suitable to the student.

Disclosed are a system, a method, and a computer program product to provide for the optimization of the output transfer load balance between the peer computers transferring data to one or more storage devices. The peer computers receive, organize and transfer the data to storage devices. The data set is composed of a plurality of data transfers. After an initial division of the data transfers between the two peers, each peer will have assigned responsibility for a number of data transfers. If the one of the peer computers completes offloading transactions earlier than the other peer, then the peer that is still transferring data will employ the other peer to execute a portion of the remaining data transfers. The operation of the system is symmetrical in that either peer may assist the other peer depending upon which peer has idle time. In addition the operation is autonomous and self-adjusting resulting in the peer nodes optimizing the size of the portion of data transfers that are reassigned during the operation of the invention resulting in the minimization of idle time for either peer. The self-adjusting feature allows the system to react to changing conditions that affect data transfer rates to the storage devices.

A dynamic learning method and an adaptive normal behavior profile (NBP) architecture for providing fast protection of enterprise applications are disclosed. The adaptive NBP architecture includes a plurality of profile items. Each profile item includes a plurality of profile properties holding the descriptive values of the respective item. An application-level security system can identify and prevent attacks targeted at enterprise applications by matching application events against at least a single profile item in the adaptive NBP.

Methods for training machines to categorize data, and / or recognize patterns in data, and machines and systems so trained. More specifically, variations of the invention relates to methods for training machines that include providing one or more training data samples encompassing one or more data classes, identifying patterns in the one or more training data samples, providing one or more data samples representing one or more unknown classes of data, identifying patterns in the one or more of the data samples of unknown class(es), and predicting one or more classes to which the data samples of unknown class(es) belong by comparing patterns identified in said one or more data samples of unknown class with patterns identified in said one or more training data samples. Also provided are tools, systems, and devices, such as support vector machines (SVMs) and other methods and features, software implementing the methods and features, and computers or other processing devices incorporating and / or running the software, where the methods and features, software, and processors utilize specialized methods to analyze data.

he invention discloses a medical information extraction system and method based on depth learning and distributed semantic features. The system is composed of a pretreatment module, a linguistic-model-based word vector training module, a massive medical knowledge base reinforced learning module, and a depth-artificial-neural-network-based medical term entity identification module. With a depth learning method, generation of the probability of a linguistic model is used as an optimization objective; and a primary word vector is trained by using medical text big data; on the basis of the massive medical knowledge base, a second depth artificial neural network is trained, and the massive knowledge base is combined to the feature leaning process of depth learning based on depth reinforced learning, so that distributed semantic features for the medical field are obtained; and then Chinese medical term entity identification is carried out by using the depth learning method based on the optimized statement-level maximum likelihood probability. Therefore, the word vector is generated by using lots of unmarked linguistic data, so that the tedious feature selection and optimization adjustment process during medical natural language process can be avoided.

Disclosed are systems, methods and computer-readable media for controlling a computing device to provide contextual responses to user inputs. The method comprises receiving a user input, generating a set of features characterizing an association between the user input and a conversation context based on at least a semantic and syntactic analysis of user inputs and system responses, determining with a data-driven machine learning approach whether the user input begins a new topic or is associated with a previous conversation context and if the received question is associated with the existing topic, then generating a response to the user input using information associated with the user input and any previous user input associated with the existing topic.

An enterprise-wide query relaxative support vector machine ranking algorithm approach provides enhanced functionality for query execution in a heterogeneous enterprise environment. Improved query results are obtained by adjusting ranking functions using machine learning methods to automatically train ranking functions. The improved query results are obtained using a list of document-query pairs that are modeled as a binary classification training problem, combination function which requires ranking and learning functions to be implemented representing document attributes and metadata utilizing query relaxation techniques and adjusted ranking functions. Machine learning methods implement user feedback to automatically train ranking functions.

A learning device includes: a plurality of learning modules, each of which performs update learning to update a plurality of model parameters of a pattern learning model that learns a pattern using input data; model parameter sharing means for causing two or more learning modules from among the plurality of learning modules to share the model parameters; and sharing strength updating means for updating sharing strengths between the learning modules so as to minimize learning errors when the plurality of model parameters are updated by the update learning.

In various embodiments, the systems and methods described herein relate to generative models. The generative models may be trained using machine learning approaches, with training sets comprising chemical compounds and biological or chemical information that relate to the chemical compounds. Deep learning architectures may be used. In various embodiments, the generative models are used to generate chemical compounds that have desired characteristics, e.g. activity against a selected target. The generative models may be used to generate chemical compounds that satisfy multiple requirements.

Aspects of the invention relate to machine-learning-based hotspot detection techniques. These hotspot detection techniques employ machine learning models constructed using two feature encoding schemes. When two-level machine learning methods are also employed, a total four machine learning models are constructed: scheme-one level-one, scheme-one level-two, scheme-two level-one and scheme-two level-two. The four models are applied to test patterns to derive scheme-one hotspot information and scheme-two hotspot information, which are then used to determine final hotspot information.

A computer-implemented adaptive learning method is disclosed. The method is intended for performance within the context of a task being carried out by a user. At least one of a sequence of elements presented to the user as part of the task is designated as a learning item. A learning object is selected in dependence upon the designated learning item, information relating to previous performance of the learning method in relation to the user, and a predetermined scheme devised to manage an overall learning process for the user. Presentation of the selected learning object to the user is intended to advance the user's knowledge of the designated learning item in some way. Once the learning object has been presented to the user, the information is updated in dependence upon the presented learning object and / or how the user interacts with or responds to the presented learning object.

The present invention relates to a system and methodology to facilitate multiattribute adjustments and control associated with messages and other communications and informational items that are directed to a user via automated systems. An interface, specification language, and controls are provided for defining a plurality of variously configured groups that may attempt to communicate respective items. Controls include the specification of priorities and preferences as well as the modification of priorities and preferences that have been learned from training sets via machine learning methods. The system provides both a means for assessing parameters used in the control of messaging and communications and for the inspection and modification of parameters that have been learned autonomously.

The present invention relates to the field of deep learning techniques, in particular to a deep convolution neural network training method and a device. The deep convolution neural network training method and the device comprise the steps of a, pretraining the DCNN on a large scale data set, and pruning the DCNN; b, performing the migration learning on the pruned DCNN; c, performing the model compression and the pruning on the migrated DCNN with the small-scale target data set, In the process of migrating learning of large-scale source data set to small-scale target data set, the model compression and the pruning are performed on the DCNN by the migration learning method and the advantages of model compression technology, so as to improve the migration learning ability to reduce the risk of overfitting and the deployment difficulty on the small-scale target data set and improve the prediction ability of the model on the target data set.

A collaborative Web research method (and system), includes organizing a plurality of documents in a N-dimensional space according to a collection of subject words, and based on the organizing, retrieving, by a user, said documents organized in the N-dimensional space according to the collection of subject words.

Disclosed is a method of estimating repeatable runout (RRO) for a disk drive. The method includes: determining a plurality of position error signal (PES) values for a plurality of servo wedges of a track of a disk; and estimating repeatable runout (RRO) in the PES values for the servo wedges of the track by averaging determined PES values over a pre-determined number of revolutions of the disk of the disk drive and scaling the averaged PES values by a pre-determined ratio of the variance of non-repeatable runout (NRRO) to the variance of an optimal estimation of the RRO for the disk drive.

The present invention relates to ultrafiltration. In particular, the present invention provides nanoporous membranes having pores for generating in vitro and in vivo ultrafiltrate, devices and bioartificial organs utilizing such nanoporous membranes, and related methods (e.g., diagnostic methods, research methods, drug screening). The present invention further provides nanoporous membranes configured to avoid protein fouling with, for example, a polyethylene glycol surface coating.

The invention discloses a train operation fault automatic detection system and method based on binocular stereoscopic vision, and the method comprises the steps: collecting left and right camera images of different parts of a train based on a binocular stereoscopic vision sensor; achieving the synchronous precise positioning of various types of target regions where faults are liable to happen based on the deep learning theory of a multi-layer convolution neural network or a conventional machine learning method through combining with the left and right image consistency fault (no-fault) constraint of the same part; carrying out the preliminary fault classification and recognition of a positioning region; achieving the synchronous precise positioning of multiple parts in a non-fault region through combining with the priori information of the number of parts in the target regions; carrying out the feature point matching of the left and right images of the same part through employing the technology of binocular stereoscopic vision, achieving the three-dimensional reconstruction, calculating a key size, and carrying out the quantitative description of fine faults and gradually changing hidden faults, such as loosening or playing. The method achieves the synchronous precise detection of the deformation, displacement and falling faults of all big parts of the train, or carries out the three-dimensional quantitative description of the fine and gradually changing hidden troubles, and is more complete, timely and accurate.

Systems and methods are disclosed for automatically segmenting a heart chamber from medical images of a patient. The system may include one or more hardware processors configured to: obtain image data including at least a representation of the patient's heart; obtain a region of interest from the image data; organize the region of interest into an input vector; apply the input vector through a trained graph; obtain an output vector representing a refined region of interest corresponding to the heart based on the application of the input vector through the trained graph; apply a deformable model on the obtained output vector representing the refined region of interest; and identify a segment of a heart chamber from the application of the deformable model on the obtained output vector.

The present invention relates to biologically and behaviorally based methods of measuring audience response to a short stimulus.

The invention relates to a stratification characteristic analysis-based method and an apparatus thereof for on-line identification for TCP, UDP flows. The method comprises the following steps that: an off-line phase determines a common port number of a first layer to-be-identified service type and a characteristic field of a second layer to-be-identified service data flow through a protocol analysis; a port number and characteristic field database is constructed; meanwhile, a third layer Bayesian decision tree model is obtained by training by employing a machine study method; and service type identification on a flow is completed by utilizing the characteristic database and a study model at an on-line classification phase. In addition, the apparatus provided in the invention comprises a data flow separating module, a characteristic extraction module, a characteristic storage module, a characteristic matching module, an attribute extraction module, a model construction and classification module and a classification result display module. According to the embodiment of the invention, various application layer services based on TCP and UDP are accurately identified; moreover, the identification process is simple and highly efficient; therefore, the method and the apparatus are suitable for realization of a hardware apparatus and can be applied for equipment and systems that require on-line flow identification in a high speed backbone network and an access network.

Disclosed are methods for conditionally immortalizing stem cells, including adult and embryonic stem cells, the cells produced by such methods, therapeutic and laboratory or research methods of using such cells, and methods to identify compounds related to cell differentiation and development or to treat diseases, using such cells. A mouse model of acute myeloid leukemia (AML) and cells and methods related to such mouse model are also described.

Tomographic / tomosynthetic image reconstruction systems and methods in the framework of machine learning, such as deep learning, are provided. A machine learning algorithm can be used to obtain an improved tomographic image from raw data, processed data, or a preliminarily reconstructed intermediate image for biomedical imaging or any other imaging purpose. In certain cases, a single, conventional, non-deep-learning algorithm can be used on raw imaging data to obtain an initial image, and then a deep learning algorithm can be used on the initial image to obtain a final reconstructed image. All machine learning methods and systems for tomographic image reconstruction are covered, except for use of a single shallow network (three layers or less) for image reconstruction.

The invention belongs to the field of depth learning, and provides a rail surface defect detection method based on the depth learning network, aiming at solving various problems existing in the priorrail detection methods. The depth learning method first automatically resets the input rail image to 416*416, and then extracts and processes the image. Image extraction mainly by Darknet-53 model complete. The processing output is mainly accomplished by the FPN-like network model. Firstly, the rail image is divided into cells. According to the position of the defects in the cells, the width, height and coordinates of the center point of the defects are calculated by dimension clustering method, and the coordinates are normalized. At the same time, we use logistic regression to predict the fraction of boundary box object, use binary cross-entropy loss to predict the category contained in the boundary box, calculate the confidence level, and then process the convolution in the output, up-sampling, network feature fusion to get the prediction results. The invention can accurately identify defects and effectively improve the detection and identification rate of rail surface defects.