Abstract: A question and answer (Q&A) system is enhanced to support natural language queries into any document format regardless of where the underlying documents are stored. The Q&A system may be implemented “as-a-service,” e.g., a network-accessible information retrieval platform. Preferably, the techniques herein enable a user to quickly and reliably locate a document, page, chart, or data point that he or she is looking for across many different datasets. This provides for a unified view of all of the user's (or, more generally, an enterprise's) information assets (such as Adobe® PDFs, Microsoft® Word documents, Microsoft Excel spreadsheets, Microsoft PowerPoint presentations, Google Docs, scanned materials, etc.), and to be able to deeply search all of these sources for the right document, page, sheet, chart, or even answer to a question.

Abstract: Methods, apparatuses, and systems for transcribing and performing analysis on patient data are disclosed. Data is collected from one or more medical professionals as well as sensors and imaging devices positioned on or oriented towards a patient. An analysis is performed on the patient data and the data is presented to a medical professional via a verbal interface in a conversational manner, allowing the medical professional to provide additional data such as observations or instructions which may be used for further analysis or to perform actions related to the patient's care.

Abstract: A method for lossy image or video encoding, transmission and decoding, the method comprising the steps of: receiving an input image at a first computer system; encoding the input image using a first trained neural network to produce a latent representation; identifying one or more regions of the input image associated with high visual sensitivity; encoding the one or more regions of the input image associated with high visual sensitivity using a second trained neural network to produce one or more region latent representations; performing a quantization process on the latent representation and the one or more region latent representations; transmitting the result of the quantization process to a second computer system; decoding the result of the quantization process to produce an output image, wherein the output image is an approximation of the input image.

Abstract: Systems and methods for automated indexing and extraction of information in digital documents are disclosed. A method may comprise identifying a page containing targeted information; inputting an image of the page into a visual machine learning network (visual ML), wherein the visual ML is trained to recognize text associated with the targeted information in an image; identifying by the visual ML, a section of the image that contains the targeted information; inputting the digital document, and coordinates of the section into an extraction module; and extracting the targeted information by the extraction module from the section.

Abstract: Disclosed herein are systems, methods, and software for providing a platform for complex image data analysis using artificial intelligence and/or machine learning algorithms. One or more subsystems allow for the capturing of user input such as eye gaze and dictation for automated generation of findings. Additional features include quality metric tracking and feedback, and worklist management system and communications queueing.

Abstract: Systems and Methods are disclosed herein for automatically indexing multiple informational fields in digital data records, the method comprising: identifying, based on rules defining target information fields, for each target field of the target information fields, at least one page in a digital data record comprising content related to the target field; extracting, for each target field, from the identified at least one page, at least one portion of text comprising the content; feeding, for each target field, a pre-processed version of the at least one portion of text into a machine learning (ML) model, wherein the ML model is trained on the target field; determining, for each target field, via the ML model trained on the target field, at least one candidate text comprising the content; and extracting, for each target field, the at least one candidate text.

Abstract: Disclosed embodiments determine, at an early stage, suitability of an intraoperative image for further intraoperative surgical analysis. The determination of suitability may be made using a first angle (such as a first obturator angle) based on at least three pelvic feature points in a preoperative image, a corresponding second angle (such as a corresponding second obturator angle) based on at least three corresponding pelvic feature points in an intraoperative image, and by comparing the first angle and the corresponding second angle to determine intraoperative image suitability. The first intra-operative image is indicated as suitable for further intraoperative analysis when an absolute value of a difference between the first angle and the corresponding second angle does not exceed a threshold. When the intraoperative image is determined as unsuitable for further intraoperative analysis, an indication of a movement direction for a fluoroscopy camera used to capture the intraoperative image is provided.

Abstract: A method of estimating chlorophyll content of a leaf including providing a device wherein the device includes a mobile computing device with a digital camera; and a peripheral removably attached to the mobile computing device, the peripheral including a light source aligned with the digital camera, wherein the peripheral provides for a space between the light source and the digital camera; capturing by the digital camera a first image of the light source turned on without the leaf interposed between the digital camera and the light source; retrieving exposure data of the first image; capturing by the digital camera a second image with the leaf interposed between the digital camera and the turned on light source; retrieving exposure data of the second image; and using a processor, estimating the chlorophyll content of the leaf based on the retrieved exposure data of the first and second images.

Abstract: The present disclosure discloses a method and an apparatus for classifying a brain anomaly and apparatus based on a 3D MRI image, wherein the classifying method comprises: receiving a to-be-processed 3D MRI image, performing a convolution operation on an imaging sequence corresponding to the 3D MRI image based on a first neural network algorithm to obtain segment masks; and performing a classification operation on the imaging sequence corresponding to the 3D MRI image based on a second neural network algorithm and the segment masks to obtain a classification result of the brain anomaly. Supported by the technologies of artificial intelligence and big data processing, embodiments of the present disclosure enable classification of a brain anomaly shown in the received MRI image through training an MRI recognition model, thereby effectively improving classification accuracy of the brain anomaly and further enhancing diagnosis accuracy of the brain anomaly based on the MRI image.

Abstract: The present invention discloses an intelligent image sensing device for sensing-computing-cloud integration based on a federated learning framework. The device comprises: intelligent image sensors, edge servers and a remote cloud, wherein the intelligent image sensor is used for perceiving and generating images, and uploading the images to the edge server; the edge server is used as a client; the remote cloud is used as a server; the clients train a convolutional fuzzy rough neural network based on the received images and the proposed federated learning framework; and the intelligent image sensors download the weight parameters of the trained convolutional fuzzy rough neural network from the clients, and classify and recognize the images based on the trained weight parameters. The present invention searches a lightweight deep learning architecture through neuroevolution, and deploys the lightweight deep learning architecture in the image sensors to automatically discriminate and analyze the perceived images.

Abstract: Systems for monitoring respiratory function of a person include a garment configured to be worn on a torso of the person. The garment includes one or more reference patterns positioned on the anterior torso of the person when the garment is worn. A camera is used to capture, at a first time, at least one image of the person immediately prior to inhalation, and immediately prior to exhalation, and to capture, at a second time, at least one image of the person immediately prior to inhalation, and immediately prior to exhalation. A computing device is used to transmit the images to a remote computing device for analysis.

Abstract: Examples disclosed herein may involve a computing system that is configured to (i) obtain previously-derived perception data for a collection of sensor data including a sequence of frames observed by a vehicle within one or more scenes, where the previously-derived perception data includes a respective set of object-level information for each of a plurality of objects detected within the sequence of frames, (ii) derive supplemental object-level information for at least one object detected within the sequence of frames that adds to the previously-derived object-level information for the at least one object, (iii) augment the previously-derived perception data to include the supplemental object-level information for the at least one object, and (iv) store the augmented perception data in an arrangement that encodes a hierarchical relationship between the plurality of objects, the sequence of frames, and the one or more scenes.

Abstract: Systems, methods, and storage media for performing actions in response to a determined spoken command of a user are disclosed.

Abstract: The invention discloses an apparatus (100) for processing a medical image associated with a subject. The apparatus comprises a clinical information extractor (102) for determining clinical information associated with the medical image; a plurality of image processors (104), each image processor for performing at least one image processing task in respect of the medical image; and an image processing manager (106) for determining, based at least on the determined clinical information associated with the medical image, at least one image processor of the plurality of image processors to perform one or more tasks in respect of the medical image.

Abstract: Aspects relate to systems and methods for parsing and correlating solicitation video content. An exemplary system includes a computing device configured to receive a solicitation video related to a subject, where the solicitation video includes at least an image component and at least an audio component, where the audio component includes audible verbal content related to at least an attribute of the subject, transcribe at least a keyword as a function of the audio component, and associate the subject with at least a job description as a function of the at least a keyword.

Abstract: A system and method for performing a teleoperational medical procedure is provided. In an example, a medical system includes an imaging instrument, a processor coupled to the imaging instrument, and a non-transitory computer memory coupled to the processor. The non-transitory computer memory stores machine-executable instructions that, when executed, cause the processor to: receive, from the imaging instrument, an image of a patient anatomy, wherein the patient anatomy includes a tissue within a tissue bed; receive an ex vivo model of the tissue after removal of the tissue from the tissue bed; and determine an arrangement of the tissue in the tissue bed from the ex vivo model and the image of the patient anatomy.

Abstract: The present invention provides a frequency modulation dynamic modeling method and device for a wind farm, and an electronic device. The method includes: acquiring first frequency modulation data measured at a grid-connected point of the wind farm under a plurality of preset working conditions; establishing a state space model corresponding to each of the plurality of working conditions according to the first frequency modulation data; measuring the nonlinearity between the state space models corresponding to each two of the plurality of working conditions by using a gap measurement method; combining the first frequency modulation data according to the nonlinearity to obtain second frequency modulation data; and training a preset initial LSTM neural network according to the second frequency modulation data until a preset training requirement is met, and obtaining a trained frequency modulation dynamic model of the wind farm.

Abstract: Remote distribution of multiple neural network models to various client devices over a network can be implemented by identifying a native neural network and remotely converting the native neural network to a target neural network based on a given client device operating environment. The native neural network can be configured for execution using efficient parameters, and the target neural network can use less efficient but more precise parameters.

Abstract: This invention predicts results for a media clip posted to a social media influencer channel by maintaining a database of results data for media clips where an influencer channel includes media clips that include unstructured data, and structured data, and then provide to a first machine learning model a first set of channel data, extracting a first set of features, predicting a value for the first target variable, providing to a second machine learning model a second set of channel data including a second selection of structured data, and the predicted value of the first target variable, extracting a second set of features, and predicting a value for the second target variable.

Abstract: A method for obstacle identification for a vehicle. In order to provide means for reliable obstacle identification for a vehicle, the method includes: performing a first obstacle search with a radar sensor using a radio frequency signal; performing a second obstacle search with a night vision camera; and identifying an obstacle if a first detection of the first obstacle search corresponds to a second detection of the second obstacle search, wherein a reliability of the first detection is assessed and if the reliability is above a first threshold, the obstacle is identified independently of the second obstacle search.