Abstract: Provided herein are systems, methods, and computer-readable media for generating one or more database queries based on a natural language input data. An example method comprises configuring, using a conversational artificial intelligence (AI) editing module, information for a task. Moreover, the method may further comprise parsing the configured information based on a database schema to obtain a parsed database schema. Further, the method may further comprise generating, using a natural language question-answering system, one or more database queries based on the parsed database schema and natural language input data.

Abstract: Embodiments of this application disclose an image generation method and apparatus, an electronic device, a storage medium, and a program product. The method includes receiving a first source image of a predetermined style and a second source image comprising a predetermined portrait; performing feature extraction on the first source image using at least one image encoder to obtain at least one image feature; performing facial recognition on the second source image using a facial recognition model to obtain a facial feature of the predetermined portrait; concatenating the at least one image feature with the facial feature to obtain a concatenated feature; and inputting the concatenated feature into a trained diffusion model to generate a target image that is in the predetermined style and that comprises the predetermined portrait.

Abstract: Disclosed is a method for training an expression transfer model performed by a computer device, the method including: obtaining a source domain facial image of a first object, a target domain facial image of a second object and a facial feature image associated with the target domain facial image; applying the facial feature image and the source domain facial image to an expression transfer model to obtain a synthesized facial image of the first object; applying the synthesized facial image and the target domain facial image to a discriminative network model obtain two discrimination results; applying the synthesized facial image and the target domain facial image to an image classification model to obtain a category feature vector for identifying a difference between the synthesized facial image and the target domain facial image; and updating the expression transfer model according to the category feature vector and the discrimination results.

Abstract: Provided herein are systems, methods, and computer-readable media for generating one or more database queries based on a natural language input data. An example method comprises configuring, using a conversational artificial intelligence (AI) editing module, information for a task. Moreover, the method may further comprise parsing the configured information based on a database schema to obtain a parsed database schema. Further, the method may further comprise generating, using a natural language question-answering system, one or more database queries based on the parsed database schema and natural language input data.

Abstract: An image processing method and apparatus, a device, and a storage medium. The image processing method includes: performing preliminary segmentation recognition on a raw image by using a first segmentation model to obtain a candidate foreground image region and a candidate background image region of the raw image; recombining the candidate foreground image region, the candidate background image region, and the raw image to obtain a recombined image, pixels in the recombined image being in a one-to-one correspondence with pixels in the raw image; and performing region segmentation recognition on the recombined image by using a second segmentation model to obtain a target foreground image region and a target background image region of the raw image.

Abstract: An eye-gaze tracking apparatus and a method of eye-gaze tracking. The eye-gaze tracking apparatus comprises: a plurality of foreground cameras each arranged to capture at least a portion of a target view area of a user; and an eye-gaze estimation module having at least one eye-gaze camera each arranged to capture an image an eye of the user, so as to estimate a position of a pupil of the eye based on the image of the eye; wherein each of the plurality of foreground cameras includes a field-of-view (FOV) being different from each other.

Abstract: A computing device obtains a to-be-optimized image. The device aligns the to-be-optimized image to obtain a to-be-optimized aligned image. The to-be-optimized aligned image includes a target region having points of objects that are distributed in a standard position. The computing device uses the to-be-optimized aligned image as an inputs to a generation network. The device performs feature extraction on the to-be-optimized aligned image using the generation network, to obtain an optimized image. The generation network is obtained by training a to-be-trained generative adversarial deep neural network model according to a low-quality image pair and a joint loss function. The low-quality image pair includes a target image and a low-quality image corresponding to the target image.

Abstract: The disclosure relates to the use of biomarkers for predicting the response to cancer (e.g., melanoma) treatments, for selecting a treatment for a cancer patient (e.g., using targeted therapy, e.g., using an agent targeting BRAF and/or an agent targeting MEK in combination with an immuno-oncology therapy (e.g., an anti-PD-1 therapy)), for stratifying cancer patients into different treatment groups, for treating cancer patients, and for predicting clinical outcome in cancer.

Abstract: An eye-gaze tracking apparatus and a method of eye-gaze tracking. The eye-gaze tracking apparatus comprises: a plurality of foreground cameras each arranged to capture at least a portion of a target view area of a user; and an eye-gaze estimation module having at least one eye-gaze camera each arranged to capture an image an eye of the user, so as to estimate a position of a pupil of the eye based on the image of the eye; wherein each of the plurality of foreground cameras includes a field-of-view (FOV) being different from each other.

Abstract: The present disclosure provides a high-strength and high-plasticity casting high-entropy alloy (HEA), having a general formula of AlaCobCrcTidFeeNifCug, where 6.0<a?8.0, 18.0<b?23.0, 7.5?c<12.5, 2.0<d?8.5, 15.5<e?20.0, 28.0<f?37.0, 0.2<g?10.0, and a+b+c+d+e+f+g=100. The casting HEA can be prepared in one step and has excellent mechanical properties. The various metal raw materials are environmental-friendly and suitable for large-scale industrial production.

Abstract: The present disclosure provides a high-strength and high-plasticity casting high-entropy alloy (HEA), having a general formula of AlaCobCrcTidFeeNifCug, where 6.0<a?8.0, 18.0<b?23.0, 7.5?c<12.5, 2.0<d?8.5, 15.5<e?20.0, 28.0<f?37.0, 0.2<g?10.0, and a+b+c+d+e+f+g=100. The casting HEA can be prepared in one step and has excellent mechanical properties. The various metal raw materials are environmental-friendly and suitable for large-scale industrial production.

Abstract: A magnetic resonance imaging apparatus according to an embodiment executes a first imaging prior to a second imaging and includes processing circuitry. The processing circuitry receives, on a first image obtained from the first imaging, a setting of a region in which an RF (Radio Frequency) pulse is to be applied to a subject, generates a three-dimensional image based on the first image, determines, based on an imaging purpose of the second imaging, a translucent region to which translucent processing is to be performed in the three-dimensional image, and displays the translucent region, making the translucent region translucent in the three-dimensional image.

Abstract: An image processing method and apparatus, a device, and a storage medium. The image processing method includes: performing preliminary segmentation recognition on a raw image by using a first segmentation model to obtain a candidate foreground image region and a candidate background image region of the raw image; recombining the candidate foreground image region, the candidate background image region, and the raw image to obtain a recombined image, pixels in the recombined image being in a one-to-one correspondence with pixels in the raw image; and performing region segmentation recognition on the recombined image by using a second segmentation model to obtain a target foreground image region and a target background image region of the raw image.

Abstract: Embodiments of this application disclose a video data processing method and apparatus, and a storage medium. After historical and current video frames are acquired from a video stream, two point position sets of a human body are obtained from the historical video frame and the current video frame, respectively. A jitter amplitude of a human body key point in the current video frame is analyzed according to the point position set of the human body in the current video frame and the point position set of the human body in the historical video frame; an adjustment reference position of the human body in the current video frame is determined according to the jitter amplitude; and subsequently a human body part below the adjustment reference position in the current video frame is adjusted and the adjusted current video frame is outputted for display as part of the video stream.

Abstract: In a method for generating a cartoon face image that is performed by a computing device, a to-be-processed face image is obtained. Face feature information is recognized from the to-be-processed face image, and cartoon materials corresponding to feature parts of a face in the to-be-processed face image are obtained according to the face feature information. The obtained cartoon materials are cartoon face materials. The cartoon face materials include cartoon facial feature materials and another cartoon material other than the cartoon facial feature materials. Each of the cartoon facial feature materials are adjusted by using an adjustment parameter, and the other cartoon material are combined with the cartoon facial feature materials after adjustment to generate the cartoon face image.

Abstract: The present application relates to rolled good feeding device and method. Material feeding devices and methods are provided for conveying material from material rolls. The material roll may be adjusted in the longitudinal direction during the feeding process by a rectifying frame to aid in maintaining alignment during the unrolling. Tension of the unrolled material may be monitored and unrolling speeds of the feeding device can be adjusted to maintain tension of the material within a range for that material. Aspects also contemplate elements to feed multiple rolled materials simultaneously for subsequent processing in combination as layered materials.

Abstract: A computing device obtains a to-be-optimized image. The device aligns the to-be-optimized image to obtain a to-be-optimized aligned image. The to-be-optimized aligned image includes a target region having points of objects that are distributed in a standard position. The computing device uses the to-be-optimized aligned image as an inputs to a generation network. The device performs feature extraction on the to-be-optimized aligned image using the generation network, to obtain an optimized image. The generation network is obtained by training a to-be-trained generative adversarial deep neural network model according to a low-quality image pair and a joint loss function. The low-quality image pair includes a target image and a low-quality image corresponding to the target image.

Abstract: Disclosed is a method for training an expression transfer model performed by a computer device, the method including: obtaining a source domain facial image of a first object, a target domain facial image of a second object and a facial feature image associated with the target domain facial image; applying the facial feature image and the source domain facial image to an expression transfer model to obtain a synthesized facial image of the first object; applying the synthesized facial image and the target domain facial image to a discriminative network model obtain two discrimination results; applying the synthesized facial image and the target domain facial image to an image classification model to obtain a category feature vector for identifying a difference between the synthesized facial image and the target domain facial image; and updating the expression transfer model according to the category feature vector and the discrimination results.

Abstract: An image processing method includes: obtaining audio data corresponding to a reality scene image acquired in real time; dynamically determining attribute information of a virtual object according to the audio data, the attribute information indicating a visual state of the virtual object; identifying a target object from the reality scene image; determining, according to the target object, a fusion location of the virtual object determined according to the attribute information in the reality scene image according to the target object; fusing the virtual object determined according to the attribute information into the reality scene image according to the fusion location, the virtual object presenting different visual states that correspond to different attribute information dynamically determined according to the audio data.

Abstract: The present disclosure provides an image processing method, including: recognizing a source object in a source image, and determining, according to feature points of the source object, an orientation and a size of the source object; adjusting, according to matching relationships between the orientation and the size of the source object and the orientation and the size of the target object, the orientation and the size of the source object; adjusting a shape of the source object and a shape of the target object according to an average shape of the source object and an average shape of the target object; and fusing, in real time, the source image and the target image in a manner of aligning the shape of the source object with the shape of the target object.