Abstract: A composition of materials including a first compound having a structure according to Formula I

Abstract: Compounds containing indolocarbazole and terphenyl building blocks are disclosed in this application. These compounds are useful for application in organic electroluminescent devices.

Abstract: Provided are a response receiving and sending method, a retransmission method, a communication device, and a storage medium. The method includes: sending a transport block to a first communication device through a pre-configured period resource, and receiving a correct response corresponding to the transport block on a pre-configured correct response resource.

Abstract: Particular embodiments may detect, by a core network, a change in network traffic types from a first network traffic type to a second network traffic type. The core network includes one or more network functionality components. Each of the one or more network functionality components is decomposed into multiple service types. The core network may determine several service instances for deployment in response to the change in the network traffic types. Each of the service instances may belong to one of the multiple decomposed service types. The core network may deploy several service instances to one or more server machines of the core network according to a decomposed service type of a respective service instance.

Abstract: The present disclosure relates to a method for patient-specific optimization of imaging protocols. According to an embodiment, the present disclosure relates to a method for generating a patient-specific imaging protocol, comprising acquiring scout scan data, the scout scan data including scout scan information and scout scan parameters, generating a simulated image based on the acquired scout scan data, deriving a simulated dose map from the generated simulated image, determining image quality of the generated simulated image by applying machine learning to the generated simulated image, the neural network being trained to generate at least one probabilistic quality representation corresponding to at least one region of the generated simulated image, evaluating the determined image quality relative to a image quality threshold and the derived simulated dose map relative to a dosage threshold, optimizing.

Abstract: A method of processing information acquired by imaging performed by a medical image diagnostic apparatus, the method including but not limited to at least one of (A) acquiring a training image volume including at least one three-dimensional object having an embedded three-dimensional feature having a first cross-sectional area in a first three-dimensional plane; selecting a second cross-sectional area in a second three-dimensional plane containing the embedded three-dimensional feature, wherein the second cross-sectional area is larger than the first cross-sectional area; and training an untrained neural network with an image of the second cross-sectional area generated from the training image volume; and (B) acquiring a first set of training data; determining a first distribution of tissue density information from the first set of training data; generating from the first set of training data a second set of training data by performing at least one of a tissue-density shifting process and a tissue-density sca

Abstract: A method, apparatus, and computer-readable storage medium for controlling exposure/irradiation during a main three-dimensional X-ray imaging scan using at least one spatially-distributed characteristic of a pre-scan/scout scan preceding the main scan. The at least one spatially-distributed characteristic includes (1) a spatially-distributed noise characteristic of the pre-scan and/or (2) a spatially-distributed identification of exposure-sensitive tissue types. The at least one spatially-distributed characteristic can be calculated from images reconstructed from sinogram/projection data and/or from sinogram/projection directly using a neural network.

Abstract: Particular embodiments may detect, by a core network, a change in network traffic types from a first network traffic type to a second network traffic type. The core network includes one or more network functionality components. Each of the one or more network functionality components is decomposed into multiple service types. The core network may determine several service instances for deployment in response to the change in the network traffic types. Each of the service instances may belong to one of the multiple decomposed service types. The core network may deploy several service instances to one or more server machines of the core network according to a decomposed service type of a respective service instance.

Abstract: Particular embodiments may receive a request to perform a task to a core network by a user device via an access point. The user device may be authenticated by the core network which comprises one or more network functionality components, and each of the one or more network functionality components may be decomposed into multiple service types. The core network may identify service instances for deployment based on the task. Each of the service instances may belong to one of the multiple decomposed service types. The service instances may be deployed to one or more server machines to accomplish the task by the core network based on resource requirements of the service instances and current resource availability of the one or more server machines.

Abstract: A method and system enable to-be-processed medical image data and its corresponding noise characteristic information to be normalized to resemble noise characteristic information of training data used to train at least one neural network for at least one ultrasound data acquisition mode. After normalizing, this processed medical image data is input into the trained neural network for producing output data used for generating cleaner images. Noise characteristic information can be used directly in training a neural network, generating a trained neural network that can handle medical image data with various noise characteristics.

Abstract: An information processing method of an embodiment is a processing method of information acquired by imaging performed by a medical image diagnostic apparatus, the information processing method includes the steps of: acquiring noise data by imaging a phantom using a medical imaging apparatus; based on first subject projection data acquired by the imaging performed by a medical image diagnostic modality of a same kind as the medical image diagnostic apparatus and the noise data, acquiring synthesized subject data in which noise based on the noise data is added to the first subject projection data; and acquiring a noise reduction processing model by machine learning using the synthesized subject data and second subject projection data acquired by the imaging performed by the medical image diagnostic modality.

Abstract: The present disclosure relates to a method for patient-specific optimization of imaging protocols. According to an embodiment, the present disclosure relates to a method for generating a patient-specific imaging protocol, comprising acquiring scout scan data, the scout scan data including scout scan information and scout scan parameters, generating a simulated image based on the acquired scout scan data, deriving a simulated dose map from the generated simulated image, determining image quality of the generated simulated image by applying machine learning to the generated simulated image, the neural network being trained to generate at least one probabilistic quality representation corresponding to at least one region of the generated simulated image, evaluating the determined image quality relative to a image quality threshold and the derived simulated dose map relative to a dosage threshold, optimizing.

Abstract: A method and apparatus are provided that use deep learning (DL) networks to reduce noise and artifacts in reconstructed computed tomography (CT), positron emission tomography (PET), and magnetic resonance imaging (MRI) images. DL networks are used in both the sinogram and image domains. In each domain, a detection network is used to (i) determine if particular types of artifacts are exhibited (e.g., beam-hardening artifact, ring, motion, metal, photon-starvation, windmill, zebra, partial-volume, cupping, truncation, streak artifact, and/or shadowing artifacts), (ii) determine whether the detected artifact can be corrected through a changed scan protocol or image-processing techniques, and (iii) determine whether the detected artifacts are fatal, in which case the scan is stopped short of completion. When the artifacts can be corrected, corrective measures are taken through a changed scan protocol or through image processing to reduce the artifacts (e.g.

Abstract: Particular embodiments may receive a request to perform a task to a core network by a user device via an access point. The user device may be authenticated by the core network which comprises one or more network functionality components, and each of the one or more network functionality components may be decomposed into multiple service types. The core network may identify service instances for deployment based on the task. Each of the service instances may belong to one of the multiple decomposed service types. The service instances may be deployed to one or more server machines to accomplish the task by the core network based on resource requirements of the service instances and current resource availability of the one or more server machines.

Abstract: Particular embodiments may receive a request to perform a task to a core network by a user device via an access point. The user device may be authenticated by the core network which comprises one or more network functionality components, and each of the one or more network functionality components may be decomposed into multiple service types. The core network may identify a sequence of a service instances based on the task. Each of the service instances may belong to one of the multiple decomposed service types. The sequence of service instances may be scheduled for deployment to accomplish the task by the core network. The core network may deploy the sequence of the service instances to one or more server machines of the core network.

Abstract: A method and apparatus are provided that use deep learning (DL) networks to reduce noise and artifacts in reconstructed computed tomography (CT), positron emission tomography (PET), and magnetic resonance imaging (MRI) images. DL networks are used in both the sinogram and image domains. In each domain, a detection network is used to (i) determine if particular types of artifacts are exhibited (e.g., beam-hardening artifact, ring, motion, metal, photon-starvation, windmill, zebra, partial-volume, cupping, truncation, streak artifact, and/or shadowing artifacts), (ii) determine whether the detected artifact can be corrected through a changed scan protocol or image-processing techniques, and (iii) determine whether the detected artifacts are fatal, in which case the scan is stopped short of completion. When the artifacts can be corrected, corrective measures are taken through a changed scan protocol or through image processing to reduce the artifacts (e.g.

Abstract: Particular embodiments may receive a request to perform a task to a core network by a user device via an access point. The user device may be authenticated by the core network which comprises one or more network functionality components, and each of the one or more network functionality components may be decomposed into multiple service types. The core network may identify a sequence of a service instances based on the task. Each of the service instances may belong to one of the multiple decomposed service types. The sequence of service instances may be scheduled for deployment to accomplish the task by the core network. The core network may deploy the sequence of the service instances to one or more server machines of the core network.

Abstract: Particular embodiments may communicate to a core network by a user device via an access point. The user device may be authenticated by the core network which comprises one or more network functionality components, and each of the one or more network functionality components may be decomposed into multiple service types. The core network may receive a user task associating with service instances. Each of the service instances may belong to one of the multiple decomposed service types and be configured by a service chaining orchestration entity. The service instances may be deployed to one or more of server machines of the core network with respect to the configurations of the service instances, by a service chaining orchestration entity. The capacity of the core network may be scaled up or down by network dimensioning.

Abstract: Particular embodiments may communicate to a core network by a user device via an access point. The user device may be authenticated by the core network which comprises one or more network functionality components, and each of the one or more network functionality components may be decomposed into multiple service types. The core network may receive a user task associating with service instances. Each of the service instances may belong to one of the multiple decomposed service types and be configured by a service chaining orchestration entity. The service instances may be deployed to one or more of server machines of the core network with respect to the configurations of the service instances, by a service chaining orchestration entity. The capacity of the core network may be scaled up or down by network dimensioning.

Abstract: A method and apparatus is provided to obtain projection data representing an intensity of X-ray radiation detected at a plurality of detector elements after traversing an object, the projection data being corrected for a baseline offset, correct the projection data by performing a positivity mapping to generate corrected projection data, perform a logarithm operation on the corrected projection data to generate post-log projection data, correct for a bias of the post-log projection data, using the projection data, to generate bias-corrected projection data, and reconstruct an image of the object from the bias-corrected projection data.