Abstract: An urban data prediction method based on a generative causal interpretation model is provided. The generative causal interpretation model includes exogenous variables, spatio-temporal conditional parent variables, controlled causal transition functions, and spatio-temporal mixing functions. By inferring the model's exogenous variables, causal descriptors, spatio-temporal conditional parent variables, and other causal latent variables from the observation data and fitting the corresponding functions such as the controlled causal transfer function and the spatio-temporal mixing function, the invention can predict the spatio-temporal data in city level based on the model. The observation data of the urban complex system can be decomposed into causal descriptors with physical meanings. Under the influence of stable causal structure, the robustness and applicability of the model can be improved, so that the prediction results are more in line with the operation of urban complex systems.

Abstract: The present invention relates to systems and methods of express tracking for patient flow management in a distributed environment. Particularly, aspects are directed to a computer implemented method that includes initiating a check-in process that includes prompting a user to scan an identifier, processing the identifier using a cascade machine-learning architecture comprising of a multi-task convolutional neural network model and a recurrent neural network model to obtain the classification of the identifier and member identification information, determining whether the user is known user based on the member identification information, when the user is a known user, verifying user data saved in the computing system associated with the identifier, once the user data is verified, determining whether the user has a scheduled appointment; and when the user has a scheduled appointment, checking the user in for the scheduled appointment.

Abstract: Techniques are provided for generating a digital image of simulated handwriting using an encoder-decoder neural network trained on images of natural handwriting samples. The simulated handwriting image can be generated based on a style of a handwriting sample and a variable length coded text input. The style represents visually distinctive characteristics of the handwriting sample, such as the shape, size, slope, and spacing of the letters, characters, or other markings in the handwriting sample. The resulting simulated handwriting image can include the text input rendered in the style of the handwriting sample. The distinctive visual appearance of the letters or words in the simulated handwriting image mimics the visual appearance of the letters or words in the handwriting sample image, whether the letters or words in the simulated handwriting image are the same as in the handwriting sample image or different from those in the handwriting sample image.

Abstract: The invention is in the field of iron and steel metallurgy, specifically a method and system for determining the amount of alloy added during the converter tapping process. Given that the LSTM neural network has a strong ability to capture nonlinear relationships, the invention builds an alloy element yield prediction model based on the LSTM neural network. Because different alloy elements have different factors that affect their yield, that is, different model input variables, different LSTM models are established for training. Furthermore, the invention uses integer linear programming to combine the yield prediction results to determine the alloy addition amount. This method not only finds the optimal alloy proportioning scheme quickly, but it also improves the component hit rate and the stability of steel products in the converter steelmaking process, obtains the lowest total cost, effectively reduces alloying costs, and has a good application prospect.

Abstract: A method enables a communication between a process client and a wireless process server to carry out an area-specific process in an area. The method includes a step of transmitting a message from a wireless process server to a wireless communication device operated by the process client. The message includes information related to the area-specific process for enabling the process-client to better arrange time before the area-specific process is scheduled to carry out in the area. In a specific embodiment, the wireless communication server sends a message to notify the process client about a location and an estimate time for the process client to carry out the area-specific process. In another preferred embodiment, the communication system enables the process client to employ a wireless communication device owned by the process client.

Abstract: According to one embodiment, a file transmission/reception device includes a communication direction managing unit and an application unit. The communication direction managing unit, in near field communication, cuts off a connection with an opposing device in a case where a conflict occurs with the opposing device, and, after being reconnected to the opposing device, switches the file transmission/reception device to any one mode of a master mode and a slave mode. The application unit performs transmission, reception, or transmission/reception of a file between the opposing device and the file transmission/reception device in the master mode or the slave mode in accordance with a mode specified by the communication direction managing unit.

Abstract: A technique making use of a few-shot model to determine graphical features present in an image based on a small set of examples with known graphical features. Where a support set including a number of images that each have a known combination of graphical features, the image recognition can identify unknown combinations of those graphical features in any number of query images. In an embodiment of the present disclosure examples of a filled-out form are used to interpret any number of additional filled out versions of the form.

Abstract: Methods and devices for offloading and/or aggregation of resources to coordinate uplink transmissions when interacting with different schedulers are disclosed herein. A method in a WTRU incudes functionality for coordinating with a different scheduler for each eNB associated with the WTRU's configuration. Disclosed methods include autonomous WTRU grand selection and power scaling, and dynamic prioritization of transmission and power scaling priority.

Abstract: A method for fine-tuning a convolutional neural network (CNN) and a sensor system based on a CNN are disclosed. The sensor system may be deployed at a deployment location. The CNN may be fine-tuned for the deployment location using sensor data, e.g., images, captured by a sensor device of the sensor system at the deployment location. The sensor data may include objects that are not present in an initial data set used for training the CNN. The sensor data and the initial data set may be input to the CNN to train the CNN and obtain fine-tuned parameters of the CNN. The CNN can thus be fine-tuned to the deployment location of the sensor system, with an increased chance of recognizing objects when using the sensor system and the CNN to recognize objects in captured sensor data.

Abstract: Methods and systems are described for providing end-to-end beamforming. For example, end-to-end beamforming systems include end-to-end relays and ground networks to provide communications to user terminals located in user beam coverage areas. The ground segment can include geographically distributed access nodes and a central processing system. Return uplink signals, transmitted from the user terminals, have multipath induced by a plurality of receive/transmit signal paths in the end to end relay and are relayed to the ground network. The ground network, using beamformers, recovers user data streams transmitted by the user terminals from return downlink signals. The ground network, using beamformers generates forward uplink signals from appropriately weighted combinations of user data streams that, after relay by the end-end-end relay, produce forward downlink signals that combine to form user beams.

Abstract: Methods, systems, and computer program products are provided for stroke attribute matrices. User input strokes may be converted into attributes encoded in one or more stroke attribute matrices (SAMs), such as bitmaps, for image or other multidimensional analysis. One or more convolutional neural networks (CNNs) may recognize letters, symbols, shapes and gestures in SAMs. A selector may select output classifications from among multiple CNNs. A sequence analyzer may select a sequence of selected CNN outputs. Stroke information may comprise, for example, velocity (e.g. direction and speed), tilt, pressure, line width, pen up/down events, hover height, etc. Stroke information may be stored, for example, in bitmap color channels (e.g. to facilitate human review). For example, an x, y velocity vector and x, y tilt may be encoded, respectively, as RGBA components of pixel data. Stroke crossings may be encoded, for example, by combining attribute values at pixels where strokes intersect.

Abstract: A device for and a computer implemented method of digital signal processing. The method includes providing a first set of data, mapping the first set of data with to a second set of data, and determining an output of the digital signal processing depending on the second set of data. The second set of data is determined depending on a sum of a finite series of terms. At least one term of the series is determined depending on a result of a convolution of the first set of data with a kernel and at least one term of the series is determined depending on the first set of data and independent of the kernel.

Abstract: Upon identifying image data associated with an object, radar data associated with the object is identified. A semantic point cloud image is generated based on the image data and the radar data. Transformed semantic point cloud image from a perspective of the object is determined with a variational auto-encoder neural network trained to accept the semantic point cloud image of the object and to generate the transformed semantic point cloud image from the perspective of the object. Physical characteristics of the object are determined based on the transformed semantic point cloud image.

Abstract: A method and an image processing system for detecting an object in an image are described. A set of line segments are detected in the image. A subset of the line segments is identified based on a projection space orientation that defines a projection space. Each one of the line segments of the subset of line segments is projected into the projection space to obtain a set of projected line segments, where each projected line segment of the set of projected line segments is represented by a respective set of projection parameters. A determination is performed, in the projection space, based on the sets of projection parameters and a shape criterion that characterizes the object, of whether the image includes an instance of the object. In response to determining that the image includes the instance of the object, the instance of the object is output.

Abstract: A computer-implemented system and method for generating a medical image is provided. In some embodiments, the medical image is generated by determining a location and an alignment for a first tracking detector with respect to a particle beam system. The direction of a beam generated from the particle beam system is determined. A first position of a first particle from a detected particle hit on the first tracking detector is also determined. A determination is made as to a first residual range of the first particle from a detected particle hit on a residual range detector. The system reconstructs a path for the first particle based on the location, the alignment, the first position, and the first residual range of the first particle. The resulting medical image that is generated by the system is based on the reconstructed path for the first particle.

Abstract: A computer platform implements a precision agriculture system that predicts output conditions, such as diseases, salt damage, soil problems, water leaks and generic anomalies, for orchards under analysis. The computer platform stores site and crop datasets and processed satellite image for the orchards. An orchard data learned model predicts a propensity for existence of output conditions associated with the permanent crops based on the data values for the variables of the site and crop datasets. Also, a satellite model predicts a propensity for existence of the output conditions at the orchard based on processed satellite images. A precision agriculture management model is disclosed that integrates the orchard data learned model with the satellite model to accurately predict the output conditions.

Abstract: An image is accepted by one or more processing circuits from a user depicting the user's facial skin. Machine learning models stored in one or more memory circuits are applied to the image to classify facial skin characteristics. A regimen recommendation is provided to the user based on the classified facial skin characteristics.

Abstract: The present disclosure provide a method for identifying power equipment targets based on human-level concept learning, including: creating a dataset of power equipment images, and annotating power equipment in power equipment images; training neural network and Bayesian network with the annotated dataset and respectively acquire identification results and conditional probabilities; calculating probabilities of unions with the conditional probabilities; and filtering the identification result corresponding to the highest probability of the union as identification result of the dataset of the power equipment images and complete the identification of the power equipment. The present disclosure combines Mask R-CNN and probabilistic graphical model. The bottom layer uses Mask R-CNN, and the top layer uses Bayesian network to train in identifying power equipment images, so that a small amount of data samples can achieve good recognition, which improved the performance of Mask R-CNN model.

Abstract: A method of clustering spatial data includes receiving a point cloud comprised of a plurality of points defined within three-dimensional (3D) space. The method further includes selecting one or more adaptable clustering parameters and traversing each of the plurality of points in the point cloud and selectively adding each of the points to one or more clusters based on the selected clustering parameters associated with each point.

Abstract: Systems, apparatuses, and methods for cooperative security in wireless sensor networks are described herein. A wireless node may organize itself into a cluster with other wireless nodes. The wireless node may cooperate with other wireless nodes in the cluster to select a leader node. The wireless node may describe its expected behaviors. The wireless node may detect a compromised wireless node within the cluster. The wireless node may prevent the compromised wireless node from compromising another wireless node.