Abstract: Methods, systems, apparatus, and articles of manufacture to control a vehicle based on signal blending are disclosed. An example apparatus disclosed herein includes programmable circuitry at least determine a first yaw rate signal based on first signal data output by a yaw rate sensor of a vehicle, determine a second yaw rate signal based on second signal data output by a steering wheel angle sensor of the vehicle, determine a blended yaw rate signal based on the first yaw rate signal and the second yaw rate signal, and adjust a torque to be applied by a motor of the vehicle based on the blended yaw rate signal.

Abstract: In implementations of systems for image inversion using multiple latent spaces, a computing device implements an inversion system to generate a segment map that segments an input digital image into a first image region and a second image region and assigns the first image region to a first latent space and the second image region to a second latent space that corresponds to a layer of a convolutional neural network. An inverted latent representation of the input digital image is computed using a binary mask for the second image region. The inversion system modifies the inverted latent representation of the input digital image using an edit direction vector that corresponds to a visual feature. An output digital image is generated that depicts a reconstruction of the input digital image having the visual feature based on the modified inverted latent representation of the input digital image.

Abstract: A grating regulating device is applied to a 3D displaying device. The 3D displaying device includes a display panel, the display panel includes a first polarizing unit, the grating regulating device is provided on one side of the display panel away from the first polarizing unit, and the grating regulating device includes: a first substrate and a second substrate, wherein the first substrate and the second substrate face each other; a second polarizing unit provided on one side of the second substrate away from the first substrate; and a third polarizing unit provided on one side of the first substrate away from the second substrate; and an absorption axis of the first polarizing unit and an absorption axis of the second polarizing unit are perpendicular, and the absorption axis of the second polarizing unit and an absorption axis of the third polarizing unit are perpendicular.

Abstract: The application relates to the technical field of artificial intelligence, and provides a method, device, electronic equipment and storage medium for positioning macular center in fundus images. The method comprises: acquiring a detection result of the fundus image detection model, wherein the detection result includes an optic disc area, and a first detection block and a first confidence score corresponding to the optic disc area, and a macular area, and a second detection block and a second confidence score corresponding to the macular area; calculating a center point coordinate of the optic disc area according to the first detection block, and calculating a center point coordinate of the macular area according to the second detection block; identifying whether the to-be-detected fundus image is a left eye fundus image or a right eye fundus image, and correcting a center point of the macular area using different correction models.

Abstract: A short contact reaction system for preparing ethylene and propylene from methanol includes an MTO short contact reactor, a riser reactor, a dense bed, and a stripper. The MTO short contact reactor has the following components coaxially distributed from inside to outside: a methanol feeding pipeline, a filter pipe wall, a product gas channel, and a catalyst distributor arranged at the top of the reactor, and a seal pipe arranged at the bottom of the reactor. The seal pipe is located in the stripper. The diameter at the top of the product gas channel is smaller than the diameter at the bottom of the product gas channel. Methanol is in crossflow contact with the descending coked catalyst II in the MTO short contact reactor.

Abstract: A high temperature resistant mannanase mutant, comprising at least one mutation site among D49C/I/N, G58D/E/S, H90L, T99Y, K120Y, N124P, T130D/N/P/Q/R/S, Y132F, S136M, S140L/K, T148K, F206Y, A212N/S/P, L257F, S263R, D268A/E/G/P, G273P, S302M/E, S306M/Q, T315P, T353L/F, D360E/N/K, and A362R. The high temperature resistant mannanase mutant has heat resistance significantly higher than the wild type mannanase, and can be widely used in the field of feeds.

Abstract: A method for etching features in a carbon containing layer below a mask is provided. A simultaneous etch and passivation step is provided comprising flowing an etch gas comprising a boron containing passivant gas and an oxygen containing gas. A plasma is created from the etch gas, wherein the plasma etches features in the carbon containing layer.

Abstract: Embodiments of this application provide a method and an apparatus for resolving an intent conflict that includes: receiving conflict resolution policy information indicating a threshold and a first operation, the first operation includes: when a first preset condition is met, using an alternative solution for achieving a first intent, where the first preset condition includes that a first impact value of a value that is of a first KPI and that corresponds to the solution for a value that is of the first KPI and that corresponds to a second intent target is less than or equal to the threshold; and performing conflict resolution on the solution based on the conflict resolution policy information.

Abstract: This application provides an intent management method. The method includes: A first network element receives first information from a second network element, where the first information indicates that a first intent is not achieved; the first network element receives second information from the second network element, where the second information indicates that an intent operation corresponding to the first intent is not performed; and the first network element determines, based on the first information and the second information, not to adjust the first intent.

Abstract: Exemplary semiconductor processing methods may include providing a silicon-containing precursor to a processing region of a semiconductor processing chamber. A substrate may be disposed within the processing region of the semiconductor processing chamber. The methods may include forming a plasma of the silicon-containing precursor in the processing region. The plasma may be at least partially formed by an RF power operating at between about 50 W and 1,000 W, at a pulsing frequency below about 100,000 Hz, and at a duty cycle between about 5% and 95%. The methods may include forming a layer of material on the substrate. The layer of material may include a silicon-containing material.

Abstract: Disclosed are a method and control device for adjusting an opening size of a sampling window of a biopsy surgical device. The biopsy surgical device includes a motor, an outer cutter tube and an inner cutter tube. The outer cutter tube includes a sampling window on a side of the front end. The motor drives the inner cutter tube to move axially through a transmission mechanism, changing the axial relative position of the inner cutter tube and the sampling window. The method includes: obtaining an input instruction that includes a set value of an sampling window opening length; determining an axial movement distance of the inner cutter tube according to the current position of the inner cutter tube and the set value; calculating the number of rotations of the motor corresponding to the axial movement distance; and controlling the motor to rotate until the sampling window reaches the opening length.

Abstract: The method involves configuring a pretrained text to music AI model that includes a neural network implementing a diffusion model. The process includes receiving audio sample data corresponding to a specific audio concept, generating a concept identifier token based on the audio sample data, adapting a loss function of the diffusion model based on the concept identifier token, selecting pivotal parameters in weight matrices in a self-attention layer of the neural network of the AI model based on the audio sample data, and further training the pivotal parameters of the AI model, to optimize the AI model for the specific audio concept.

Abstract: The present disclosure relates to systems, non-transitory computer-readable media, and methods for utilizing machine learning models to generate modified digital images. In particular, in some embodiments, the disclosed systems generate image editing directions between textual identifiers of two visual features utilizing a language prediction machine learning model and a text encoder. In some embodiments, the disclosed systems generated an inversion of a digital image utilizing a regularized inversion model to guide forward diffusion of the digital image. In some embodiments, the disclosed systems utilize cross-attention guidance to preserve structural details of a source digital image when generating a modified digital image with a diffusion neural network.

Abstract: The present disclosure relates to systems, non-transitory computer-readable media, and methods for utilizing machine learning models to generate modified digital images. In particular, in some embodiments, the disclosed systems generate image editing directions between textual identifiers of two visual features utilizing a language prediction machine learning model and a text encoder. In some embodiments, the disclosed systems generated an inversion of a digital image utilizing a regularized inversion model to guide forward diffusion of the digital image. In some embodiments, the disclosed systems utilize cross-attention guidance to preserve structural details of a source digital image when generating a modified digital image with a diffusion neural network.

Abstract: A method and system for positioning a target in a brain region are provided. The method includes: obtaining datasets of N persons at a first time point and a second time point after stroke; constructing a first lesion mapping functional network based on each resting-state functional magnetic resonance imaging image in a first stroke dataset; constructing an acute phase cognitive-lesion mapping functional network; constructing a chronic phase cognitive-lesion mapping functional network; comparing the acute phase cognitive-lesion mapping functional network with the chronic phase cognitive-lesion mapping functional network to obtain a key improvement network; calculating a whole-brain functional connectivity network with each voxel as a seed point, and performing spatial correlation calculation on the whole-brain functional connectivity network and the key improvement network to obtain a spatial correlation network; and determining a therapeutic target of the functional image to be positioned.

Abstract: A rotating extrusion rheometer includes a control monitoring mechanism, a melt extrusion mechanism, a rotating extrusion rheology machine head, a sensor, a drive chain wheel, a coupler and an electric motor. The control monitoring mechanism, the melt extrusion mechanism, the rotating extrusion rheology machine head are sequentially connected. The rotating extrusion rheology machine head is formed by a connecting pipe (1), a flow dividing support (3), a lower machine neck (12), a machine head piece (15), an opening mold (17), an opening-mold driving chain wheel (20), a core bar (21) and a core-bar driving mechanism. The rheology measurement method comprises the steps where some parameter values of the rheometer are collected first, and then the rheological behaviors of the polymer melt in the rotating extrusion process are obtained by performing calculation by means of using the derived formula.

Abstract: The present disclosure relates to systems, non-transitory computer-readable media, and methods for utilizing machine learning models to generate modified digital images. In particular, in some embodiments, the disclosed systems generate image editing directions between textual identifiers of two visual features utilizing a language prediction machine learning model and a text encoder. In some embodiments, the disclosed systems generated an inversion of a digital image utilizing a regularized inversion model to guide forward diffusion of the digital image. In some embodiments, the disclosed systems utilize cross-attention guidance to preserve structural details of a source digital image when generating a modified digital image with a diffusion neural network.

Abstract: Avatar generation from an image is performed using semi-supervised machine learning. An image space model undergoes unsupervised training from images to generate latent image vectors responsive to image inputs. An avatar parameter space model undergoes unsupervised training from avatar parameter values for avatar parameters to generate latent avatar parameter vectors responsive to avatar parameter value inputs. A cross-modal mapping model undergoes supervised training on image-avatar parameter pair inputs corresponding to the latent image vectors and the latent avatar parameter vectors. The trained image space model generates a latent image vector from an image input. The trained cross-modal mapping model translates the latent image vector to a latent avatar parameter vector. The trained avatar parameter space model generates avatar parameter values from the latent avatar parameter vector. The latent avatar parameter vector can be used to render an avatar having features corresponding to the input image.

Abstract: The present disclosure relates to an information obtaining method and apparatus, a device, and a medium. The method includes setting a label table corresponding to each sample sentence in a sample set, wherein row characters and column characters in the label table are set identically in accordance with an order of characters of the corresponding sample sentence; and marking cells composed of the row characters and the column characters in the label table with corresponding information category labels; taking each sample sentence in the sample set as input information to a model to be trained and the label table corresponding to each sample sentence as output information of the model to be trained, and performing model training according to a preset target function; and generating an information extraction model based on parameters of the trained model to extract target sentence information by the information extraction model.

Abstract: Embodiments of this application provide an intent pre-evaluation method and apparatus. The method includes: A first network element receives an intent target and a pre-evaluation type from a second network element; receives first information from a third network element, where the first information includes one or more of the following: operation impact information or first KPI impact information, the operation impact information indicates mutual exclusion information of the first operation, and the first KPI impact information indicates impact of the first operation on a KPI associated with the intent target or impact of the first operation on the network KPI; and determines, based on the first information, result information corresponding to the pre-evaluation type, and sends the result information to the second network element, where the result information includes pre-evaluation information of an intent achievement result or pre-evaluation information of impact on a KPI.