Embodiments of the present invention are directed to methods and resulting structures for integrated circuits having metal-insulator-metal (MIM) capacitors that serve as both decoupling capacitors and crack stops. In a non-limiting embodiment, an interconnect is formed on a first portion of a substrate in an interior region of the integrated circuit. A second portion of the substrate is exposed in an edge region of the integrated circuit. A MIM capacitor is formed over the second portion of the substrate in the edge region. The MIM capacitor includes two or more plates and one or more dielectric layers. Each dielectric layer is positioned between an adjacent pair of the two or more plates and a portion of the two or more plates extends over the interconnect in the interior region. A plate of the two or more plates is electrically coupled to a last metal wiring level of the interconnect.

An image processing method and an electronic device are provided. The image processing method includes: receiving a first input performed by a user; identifying a first text region on a target image in response to the first input; training a text image of the first text region on the target image to obtain a first font style model of the first text region; receiving a second input performed by the user; in response to the second input, obtaining a first input text, and training the first input text according to the first font style model to obtain a second text matching a font style of the first text region; and replacing text of the first text region with the second text on the target image.

An apparatus and methods for generating automated communication simulation using artificial intelligence is disclosed. The apparatus comprises at least a processor, a memory communicatively connected to the processor, wherein the memory includes instructions configuring the at least a processor to receive a prior communication datum from a first user, parse the prior communication datum to extract at least a contextual datum relating to a second user, generate a correspondence simulation from the first user to the second suer as a function of the at least a contextual datum, receive a prior handwriting image datum from the first user, classify at least a portion of the prior handwriting image datum to at least a glyph, identify at least a semantic match to the at least a glyph in the correspondence simulation, generate and transmit an automated communication simulation using the at least a semantic match and the correspondence simulation.

A method and an apparatus for pushing information are provided. The method may include: obtaining a prescription image presenting prescription information; recognizing the prescription image to obtain a recognition result inputting the recognition result into a pre-trained prescription image analyzing model to obtain the prescription information presented in the prescription image; and pushing information based on the prescription information.

The present teaching relates to system, method, medium for in-situ perception in an autonomous driving vehicle. A plurality of types of sensor data are received, which are acquired by a plurality of types of sensors deployed on the vehicle to provide information about surrounding of the vehicle. Based on at least one model, one or more surrounding items are tracked from a first of the plurality of types of sensor data acquired by a first type sensors. At least some of the tracked items are automatically labeled via cross validation and are used to locally adapt, on-the-fly, the at least one model. Model update information is received which from a model update center, which is derived based on the labeled at least some items. The at least one model is updated using the model update information.

The present disclosure provides a character recognition model training method and apparatus, a character recognition method and apparatus, a device and a medium, relating to the technical field of artificial intelligence, and specifically to the technical fields of deep learning, image processing and computer vision, which can be applied to scenarios such as character detection and recognition technology. The specific implementing solution is: partitioning an untagged training sample into at least two sub-sample images; dividing the at least two sub-sample images into a first training set and a second training set; where the first training set includes a first sub-sample image with a visible attribute, and the second training set includes a second sub-sample image with an invisible attribute; performing self-supervised training on a to-be-trained encoder by taking the second training set as a tag of the first training set, to obtain a target encoder.

A system for generating textual instructions for manufacturers from hybrid textual and image data includes a manufacturing instruction generator that may generate a language processing module from a first training set including at least a training annotated file describing at least a first product to manufacture, the at least an annotated file containing one or more textual data, and at least an instruction set containing one or more manufacturing instructions to manufacture the at least a first product. Manufacturing instruction generator may use the language processing to generate textual instructions for manufacturers from at least an annotated file and may initiate manufacture using the generated manufacturing instructions.

Systems, methods and computer program products for detecting objects using a multi-detector are disclosed, according to various embodiments. In one aspect, a computer-implemented method includes defining an analysis profile comprising an initial number of analysis cycles dedicated to each of a plurality of detectors, where each detector is independently configured to detect objects according to a unique set of analysis parameters and/or a unique detector algorithm. The method also includes: receiving digital video data that depicts at least one object; analyzing the digital video data using some or all of the detectors in accordance with the analysis profile, where the analyzing produces an analysis result for each detector used in the analysis. Further, the method includes updating the analysis profile by adjusting the number of analysis cycles dedicated to at least one of the detectors based on the analysis results.

A method for providing a user interface (UI) for generating training data for an artificial intelligence (AI) model may include providing, for display via the UI, image information that depicts an object, a set of operations of the object, and a process associated with the set of operations. The method may include providing, for display via the UI, text information that describes the object, the set of operations of the object, and the process associated with the set of operations. The method may include receiving, via the UI, a user input that associates respective image information of the image information with corresponding text information of the text information. The method may include generating association information that associates the respective image information with the corresponding text information, based on the user input. The method may include generating discourse and semantic information from the text information associated to the image information.

Described embodiments relate to a method comprising: determining a candidate document comprising image data and character data and extracting the image data and the character data from the candidate document. The method comprises providing, to an image-based numerical representation generation model, the image data, and generating, by the image-based numerical representation generation model, an image-based numerical representation of the image data. The method comprises providing, to a character-based numerical representation generation model, the character data; and generating, by the character-based numerical representation generation model, a character-based numerical representation of the character data. The method comprises providing, to a consolidated image-character based numerical representation generation model, the image-based numerical representation and the character-based numerical representation; and generating, by the consolidated image-character based numerical representation generation model, a combined image-character based numerical representation of the candidate document.