Abstract: The present disclosure relates to an integrated chip including a dielectric structure over a substrate. A first capacitor is disposed between sidewalls of the dielectric structure. The first capacitor includes a first electrode between the sidewalls of the dielectric structure and a second electrode between the sidewalls and over the first electrode. A second capacitor is disposed between the sidewalls. The second capacitor includes the second electrode and a third electrode between the sidewalls and over the second electrode. A third capacitor is disposed between the sidewalls. The third capacitor includes the third electrode and a fourth electrode between the sidewalls and over the third electrode. The first capacitor, the second capacitor, and the third capacitor are coupled in parallel by a first contact on a first side of the first capacitor and a second contact on a second side of the first capacitor.

Abstract: A system includes a catalyst for receiving and treating exhaust gas generated by an engine, a passive NOx adsorber (PNA) positioned upstream of the catalyst, a bypass valve positioned upstream of the catalyst and the PNA, and a controller. The controller is configured to: while controlling the bypass valve to direct exhaust gas to the PNA, detect a torque demand that is greater than a threshold value; responsive to detecting that the torque demand is greater than the threshold value, engage a motor, coupled with a battery system, with a drive shaft of the system to meet at least a portion of the torque demand; in response to the engagement of the motor with the drive shaft not meeting all of the torque demand, engage the engine with the drive shaft to meet a remainder of the torque demand.

Abstract: The present invention provides Wnt pathway agonists and related compositions, which may be used in any of a variety of therapeutic methods for the treatment of diseases.

Abstract: A signal transmission method and an apparatus are disclosed. In an embodiment a signal transmission method includes generating a reference signal and sending the reference signal, wherein the reference signal is sent in a specific time-frequency resource, and the specific time-frequency resource is located on symbols corresponding to a synchronization signal block.

Abstract: Training a multi-object tracking model includes: generating a plurality of training images based at least on scene generation information, each training image comprising a plurality of objects to be tracked; generating, for each training image, original simulated data based at least on the scene generation information, the original simulated data comprising tag data for a first object; locating, within the original simulated data, tag data for the first object, based on at least an anomaly alert (e.g., occlusion alert, proximity alert, motion alert) associated with the first object in the first training image; based at least on locating the tag data for the first object, modifying at least a portion of the tag data for the first object from the original simulated data, thereby generating preprocessed training data from the original simulated data; and training a multi-object tracking model with the preprocessed training data to produce a trained multi-object tracker.

Abstract: A conjugate and preparation method therefor, comprising a medication composition comprising the conjugate and a use of the medication composition in preparing medications for the treatment or prevention of diseases.

Abstract: The present invention provides a conjugate and preparation method thereof, a pharmaceutical composition comprising the conjugate and use of the pharmaceutical composition in the manufacture of a medicament for the treatment or prevention of a disease.

Abstract: The present disclosure relates to an integrated chip including a dielectric structure over a substrate. A first capacitor is disposed between sidewalls of the dielectric structure. The first capacitor includes a first electrode between the sidewalls of the dielectric structure and a second electrode between the sidewalls and over the first electrode. A second capacitor is disposed between the sidewalls. The second capacitor includes the second electrode and a third electrode between the sidewalls and over the second electrode. A third capacitor is disposed between the sidewalls. The third capacitor includes the third electrode and a fourth electrode between the sidewalls and over the third electrode. The first capacitor, the second capacitor, and the third capacitor are coupled in parallel by a first contact on a first side of the first capacitor and a second contact on a second side of the first capacitor.

Abstract: The disclosure herein enables tracking of multiple objects in a real-time video stream. For each individual frame received from the video stream, a frame type of the frame is determined. Based on the individual frame being an object detection frame type, a set of object proposals is detected in the individual frame, associations between the set of object proposals and a set of object tracks are assigned, and statuses of the set of object tracks are updated based on the assigned associations. Based on the individual frame being an object tracking frame type, single-object tracking is performed on the frame based on each object track of the set of object tracks and the set of object tracks is updated based on the performed single-object tracking. For each frame received, a real-time object location data stream is provided based on the set of object tracks.

Abstract: Training a multi-object tracking model includes: generating a plurality of training images based at least on scene generation information, each training image comprising a plurality of objects to be tracked; generating, for each training image, original simulated data based at least on the scene generation information, the original simulated data comprising tag data for a first object; locating, within the original simulated data, tag data for the first object, based on at least an anomaly alert (e.g., occlusion alert, proximity alert, motion alert) associated with the first object in the first training image; based at least on locating the tag data for the first object, modifying at least a portion of the tag data for the first object from the original simulated data, thereby generating preprocessed training data from the original simulated data; and training a multi-object tracking model with the preprocessed training data to produce a trained multi-object tracker.

Abstract: Example solutions for image paragraph captioning use a first vision language model to generate visual information (comprising text) for an image. The visual information may include tags, an initial image caption, and information on objects within the image (e.g., further tags and captions, and object attributes and locations within the image). In some examples, the visual information further includes visual clues. A generative language model generates a plurality of image story caption candidates (e.g., descriptive paragraphs) from the visual information. A second vision language model evaluates the plurality of image story caption candidates and selects a caption as the final output caption.

Abstract: The disclosure herein describes generating an inpainted image from a masked image using a patch-based encoder and an unquantized transformer. An image including a masked region and an unmasked region is received, and the received image is divided into a plurality of patches including masked patches. The plurality of patches is encoded into a plurality of feature vectors, wherein each patch is encoded to a feature vector. Using a transformer, a predicted token is generated for each masked patch using a feature vector encoded from the masked patch, and a quantized vector of the masked patch is determined using generated predicted token and a masked patch-specific codebook. The determined quantized vector of the masked patch is included into a set of quantized vectors associated with the plurality of patches, and an output image is generated from the set of quantized vectors using a decoder.

Abstract: Systems and methods for generating a beam model for radiotherapy treatment planning are discussed. An exemplary system includes a memory to store a trained deep learning model, and a processor circuit to generate a beam model. The deep learning model can be trained to establish a relationship between machine scanning data and values of beam model parameters, and validated for accuracy. The processor circuit can receive machine scanning data indicative of a configuration or an operation status of the radiation therapy device, apply the machine scanning data to the trained deep learning model to determine values for the beam model parameters, and generate a beam model based on the determined values of the plurality of beam model parameters. The beam model may be provided to a user, or a treatment planning system.

Abstract: The present application discloses a woody rootstock for efficient grafting of solanaceous vegetables and an efficient grafting and seedling culture method thereof. According to the present application, a woody rootstock clone with high consistency is provided through tissue culture, efficient grafting is completed through sleeve grafting technology, and the grafting survival rate is improved by regulating the healing environment. A new idea for efficient industrial grafting of solanaceous vegetables is provided, scions are imparted with new features through distant grafting, and the problems of low grafting efficiency and low survival rate are solved. The method has the advantages of strong operability, simplicity, high efficiency and low cost, and provides a technical support for the industrial production of grafted seedlings of solanaceous vegetables.

Abstract: Various aspects of the present disclosure generally relate to wireless communication. In some aspects, a network entity may receive a sounding reference signal (SRS) at a multi-panel system of the network entity, where the multi-panel system includes one or more sounded panels and one or more non-sounded panels. The network entity may estimate a channel to obtain channel state information (CSI) for the one or more sounded panels based at least in part on the SRS. The network entity may estimate CSI for the one or more non-sounded panels based at least in part on the CSI for the one or more sounded panels. The network entity may transmit or receive a communication based at least in part on the CSI for the one or more sounded panels and the CSI for the one or more non-sounded panels. Numerous other aspects are described.

Abstract: Some disclosed systems are configured to obtain a knowledge module configured to receive one or more knowledge inputs corresponding to one or more different modalities and generate a set of knowledge embeddings to be integrated with a set of multi-modal embeddings generated by a multi-modal main model. The systems receive a knowledge input at the knowledge module, identify a knowledge type associated with the knowledge input, and extract a knowledge unit from the knowledge input. The systems select a representation model that corresponds to the knowledge type and select a grounding type configured to ground the at least one knowledge unit into the representation model. The systems then ground the knowledge unit into the representation model according to the grounding type.

Abstract: An intelligent data and knowledge-driven method for modulation recognition includes the following steps: collecting spectrum data; constructing corresponding attribute vector labels for different modulation schemes; constructing and pre-training an attribute learning model based on the attribute vector labels for different modulation schemes; constructing and pre-training a visual model for modulation recognition; constructing a feature space transformation model, and constructing an intelligent data and knowledge-driven model for modulation recognition based on the attribute learning model and the visual model; transferring parameters of the pre-trained visual model and the pre-trained attribute learning model and retraining the transformation model; and determining whether training on a network is completed and outputting a classification result.

Abstract: Image stylization is based on a learning network. A learning network is trained with a plurality of images and a reference image with a particular texture style. A plurality of different sub-networks of the learning network is trained, respectively. Specifically, one of the sub-networks is trained to extract one or more feature maps from the source image and transform the feature maps with the texture style applied thereon to a target image. Each of the feature maps indicates part of feature information of the source image. Another sub-network is trained to apply a specified texture style to the extracted feature maps, such that the target image generated based on the processed feature maps can embody the specified texture style.

Abstract: A system for tracking a target object across a plurality of image frames. The system comprises a logic machine and a storage machine. The storage machine holds instructions executable by the logic machine to calculate a trajectory for the target object over one or more previous frames occurring before a target frame. Responsive to assessing no detection of the target object in the target frame, the instructions are executable to predict an estimated region for the target object based on the trajectory, predict an occlusion center based on a set of candidate occluding locations for a set of other objects within a threshold distance of the estimated region, each location of the set of candidate occluding locations overlapping with the estimated region, and automatically estimate a bounding box for the target object in the target frame based on the occlusion center.

Abstract: According to implementations of the subject matter described herein, there is provided an image colorization solution. The solution includes determining a similarity between contents presented in a grayscale source image and a color reference image and determining a col or target image corresponding to the source image based on the similarity. Specifically, a first and a second sets of blocks similar and dissimilar to the reference image are determined based on the similarity; a first color for the first set of blocks is determined based on a color of corresponding blocks in the reference image; a second color for the second set of blocks is determined independently of the reference image. Through this solution, it is possible to provide user controllability and customized effects in colorization, and there is no strict requirement on correspondence between the color image and grayscale image, achieving more robustness to selection of color reference images.