Abstract: A display control apparatus according to the present invention includes: an axis detection unit configured to detect, based on an image, a rotation axis about which a celestial body included in the image as an object rotates in response to the rotation of the earth; and a control unit configured to perform control such that a partial area of the image is displayed as a display area, and rotation display is performed by changing the display area while changing an angle corresponding to the display area around a position corresponding to the rotation axis detected by the axis detection unit.

Abstract: Disclosed is an ego motion estimation method and apparatus, wherein the apparatus calculates a scene flow field from a plurality of spaces of an input image, clusters the plurality of spaces based on a scene flow, updates a probability vector map for clustered spaces, identifies a stationary background based on the updated probability vector map, and estimates ego motion information based on the identified stationary background.

Abstract: An imaging system includes a phase grating overlying a two-dimensional array of pixels, which may be thermally sensitive pixels for use in infrared imaging. The phase grating comprises a two-dimensional array of identical subgratings that define a system of Cartesian coordinates. The subgrating and pixel arrays are sized and oriented such that the pixels are evenly distributed with respect to the row and column intersections of the subgratings. The location of each pixel thus maps to a unique location beneath a virtual archetypical subgrating. Portions of the phase grating extend beyond the edges of the pixels array to interference pattern in support of Fourier-domain imaging.

Abstract: Disclosed are a speech processing method and a speech processing device, for performing speech processing by executing artificial intelligence (AI) algorithms and/or machine learning algorithms installed thereon, thus enabling the communication between a user terminal and a server in a 5G communication environment. The speech processing method according to an embodiment of the present disclosure includes receiving a user spoken utterance, outputting a voice actor spoken utterance in a voice actor's voice having the highest degree of similarity with a user's voice by using a user-voice actor mapping learning model, the voice actor spoken utterance corresponding to the user spoken utterance, and performing speech recognition for the voice actor spoken utterance.

Abstract: Methods and systems for determining a status of a hydrocarbon production site are provided. The systems and methods include defining an area about a hydrocarbon well location and obtaining a time series of mobile geolocation data for at least one mobile device located within the area about the hydrocarbon well location. The times series of mobile geolocation data is processed and probabilities that the mobile geolocation data is associated with a drilling activity, a fracking activity, a tie-in activity, and/or an absence of drilling activity, fracking activity, and/or tie-in activity are output. The mobile geolocation data is categorized, based on the probabilities, as being associated with one of drilling activity, fracking activity, or absence of drilling activity and fracking activity.

Abstract: An electronic device and a voice recognition method thereof are provided. A voice recognition method of an electronic device includes receiving trigger voice, storing a characteristic of the trigger voice for voice verification by analyzing the trigger voice. When a user issues a voice command, determining whether the user voice command comprises the characteristic belonging to the trigger voice, and performing function of the electronic device corresponding to the user voice command based on the determination. Accordingly, users may easily use the voice recognition function of the electronic device.

Abstract: Projection images of reduced resolution are generated by reducing resolution of filtered projection images and/or reducing the number of filtered projection images. Volume data of reduced resolution is generated by performing CT reconstruction using the projection images of reduced resolution. Each voxel of the volume data of reduced resolution is provisionally divided. The provisionally divided voxels are compared in voxel value before and after provisional division. If a difference in voxel value before and after the provisional division is greater than a threshold, the provisional division is determined to be valid, and division is further continued. If the difference in voxel value before and after the provisional division is less than or equal to the threshold, the provisional division is determined to be invalid and the voxel ends being divided.

Abstract: Computing systems for efficient incident management in large scale computer systems are described herein. In one embodiment, an incident management system can be configured to, in response to receiving a user input requesting an unidentified incident management service, convert an alphanumerical string of the user input into a pixelated matrix having multiple pixels individually corresponding to a character or number in the alphanumerical string. The incident management system can then feed the converted pixelated matrix into a neural network to identify one or more incident management services corresponding to the received user input with a corresponding probability value. The incident management system can then perform an application programming interface (API) call to execute a computer application to provide one of the identified incident management services to the user.

Abstract: Disclosed is a multi-channel image processing method including the steps of: storing a multi-channel image in one or several 2D texture units each formed of a pack of four tiles in a server; taking a selection of one or several channels of the multi-channel image into account; taking transformation and projection parameters (real-time user input) for the selected channels into account; applying the transformation on the selected channels, according to received parameters, all pixels of the selected channels being processed at the same time; projecting the selected channels into an RGB color space, according to the received parameters, all pixels of the selected channels being processed at the same time.

Abstract: Techniques are described for collecting descriptions of an entity from different data sources and using a numeric comparison and textual centrality measure to automatically select a best description. In one implementation, a method includes: retrieving a real property description dataset, the real property description dataset including descriptions from multiple data sources that describe the real property; extracting, from each of the descriptions, numbers that identify the property; performing a numerical comparison of the numbers extracted from each of the descriptions to determine if any descriptions needs to be discarded from further consideration; applying a text cleaning process to normalize the descriptions; and performing a textual centrality measure of remaining descriptions to determine a level agreement of each of the remaining descriptions with each of the other remaining descriptions; and using at least the textual centrality measure to select a description.

Abstract: Methods and systems may be used for performing computer-simulated evaluation of treatments. For example, the method may include: for each of a plurality of patients, receiving one or more patient-specific anatomical and/or physiological models; selecting, from the plurality of patients, a set of patients that have one or more common characteristics; for each patient in an experimental group, modifying at least one model of the respective one or more patient-specific models to obtain at least one modified patient-specific model that models an effect of an evaluation treatment on the respective patient, and calculating a value of an evaluation endpoint based on the respective at least one modified patient-specific model; and comparing the calculated values of the evaluation endpoint with one or more control values of the evaluation endpoint for patients that satisfy the one or more selection criterion.

Abstract: Disclosed are systems and methods for receiving a plurality of comments at a particular phase of a transaction with a member of a networked system, classifying one or more of the plurality of comments into one of a set of predetermined sentiment classifications, applying a trained machine learning system to select a category from a set of predefined categories for each of the one or more comments, applying a natural language processing module to generate a sub-category for each of the one or more comments, associating the generated sub-categories with their respective categories for the one or more comments, and generating a display of the determined categories for the particular transaction with the generated sub-categories, each generated sub-category being graphically connected to their respective categories.

Abstract: Systems and methods are disclosed to improve a topic modeling system that tunes a topic model for a set of topics from a corpus of documents, by allowing users to pre-inform the tuning process with bias parameters for desired associations in the topic model. In embodiments, the topic model may be a Latent Dirichlet Allocation (LDA) model. In embodiments, the bias parameter may indicate a fixed association where a particular word in a particular document is associated with a particular topic. In embodiments, the bias parameter may specify a weight value that biases the inference process with regard to a particular association. Advantageously, the disclosed features allow users to specify a small number of parameters to steer the tuning process towards a set of desired topics. As a result, the topic model may be generated more quickly and with more useful topics.

Abstract: A learning device includes a memory and a processing circuit. The processing circuit: (a) obtains, from the memory, a first computational imaging image which includes an object, the first computational imaging image including a plurality of first pixels; (b) obtains, from the memory, a captured image which includes the object, the captured image including a plurality of second pixels; (c) obtains an identification result of identifying the object included in the captured image; (d) generates, with reference to correspondences between the plurality of first pixels and the plurality of second pixels, an identification model for identifying the first computational imaging image based on the identification result of identifying the object included in the captured image; and (e) outputs the identification model to an image identification device which identifies a second computational imaging image.

Abstract: A method clusters each character on a document into one of a plurality of clusters based on widths of at least a portion of the characters on the document and measures distances between characters on the document. A threshold for each of the plurality of clusters is calculated based on at least a portion of the distances between characters in each cluster. The method then segments characters into units using the thresholds for the plurality of clusters. A distance between two characters in the document is compared to a threshold for a cluster to classify the two characters as being part of a unit when the distance is less than the threshold and not being part of the unit when the distance is greater than the threshold. Then, the method performs a recognition process on the document using the units.

Abstract: A method for generating a point cloud data recovery model includes: acquiring at least one 2D image associated with a first point cloud data frame; partitioning the first point cloud data frame into at least one point cloud data set based on attributes of objects in the 2D image; and for each point cloud data set: determining a matching image of the first point cloud data frame from the at least one 2D image; determining 3D position data of a pixel point in the matching image based on the first point cloud data frame and at least one second point cloud data frame; and using 2D position data and the 3D position data of corresponding pixel points in the matching image as training input data and output data of a training model to generate a point cloud data recovery model for the object corresponding to the point cloud data set.

Abstract: Inducing a particular behavior/process among a set of pre-determined behaviors/processes by implicitly signaling the selection of the particular behavior/process though a particular combination of specific values of said set of information data driven the process. Typically, enables re-using parameters already carried with a signal to signal how those parameters are used by a specific process.

Abstract: Identifying insect species integrates image processing, feature selection, unsupervised clustering, and a support vector machine (SVM) learning algorithm for classification. Results with a total of 101 mosquito specimens spread across nine different vector carrying species demonstrate high accuracy in species identification. When implemented as a smart-phone application, the latency and energy consumption were minimal. The currently manual process of species identification and recording can be sped up, while also minimizing the ensuing cognitive workload of personnel. Citizens at large can use the system in their own homes for self-awareness and share insect identification data with public health agencies.

Abstract: Techniques for estimating speed for an object in a video (either stream or file) are described. In particular, once an object has been recognized, additional data (such as size) for that recognized object is used in a determination of a 3D position of that object in a plurality of frames. An estimated speed may be found using a difference between 3D positions of the object in different frames over the time between the frames.

Abstract: Methods and systems are disclosed for an artificial intelligence (AI)-based, conversational insight and action platform for user context and intent-based natural language processing and data insight generation. Using artificial intelligence and semantic analysis techniques, a knowledge graph is generated from structured data, and a word embedding is generated from unstructured data. A semantic meaning is extracted from a user request, and at least one user attribute and context are determined. One or more entities and relationships on the knowledge graph that match the semantic meaning are determined, based on the user attribute, context, and the word embedding. A sequence of analytical instructions is generated from the matching results, and applied to the structured data to generate a data insight response to the user request. If no matches are found, similar entities and relationships are presented to the user, and user selections are used to further train the system.