Abstract: Systems and methods for multi-modal image generation are provided. One or more aspects of the systems and methods includes obtaining a text prompt and layout information indicating a target location for an element of the text prompt within an image to be generated and computing a text feature map including a plurality of values corresponding to the element of the text prompt at pixel locations corresponding to the target location. Then the image is generated based on the text feature map using a diffusion model. The generated image includes the element of the text prompt at the target location.

Abstract: A system for categorizing, visualizing, and recommending Cannabis products based on objective data of the chemical composition of products is described. This system allows Cannabis products to be visualized and compared based on their chemical composition, even by laypeople with little or no knowledge of the underlying objective data. A recommendation system is built upon this which can recommend products for consumption, including a user interface for receiving user input, which includes at least one of demographic data, desired level of psychoactivity, or prior experience with the product, a merchant interface for receiving merchant input, including at least one of general data descriptive of the regulated product, lab data descriptive of the regulated products chemical composition, and user data descriptive of subjective effects of the regulated product on a user; and a decision engine making a recommendation of product according to correlation of the lab data with the user data.

Abstract: A system, apparatus, and/or method is disclosed for—inter alia—producing a product. One or more financial characteristics relating to one or more sample products may be received. For each of the sample products, a value for each of one or more respective properties of the sample product may be received. The received values of the one or more respective properties of the sample products, the one or more received financial characteristics relating to the sample products, and a desired financial characteristic for a potential product may be input into the machine learning model. A value for each of the one or more respective properties for the potential product may be determined based on the desired financial characteristic for the potential product. A product having the determined value for each of the one or more respective properties for the potential product may be produced.

Abstract: Personal care formulation is provided having multistage polymer, comprising: acrylate rich stage comprising: (a) structural units of monomer selected from C1-22 alkyl (meth)acrylates and structural units of first carbosiloxane monomer of formula (I); and (b) carbosiloxane rich stage, comprising: structural units of second carbosiloxane monomer of formula (I); wherein a is 0 to 3; wherein d is 0 or 1; wherein R1 is selected from hydrogen, C1-10 alkyl group and aryl group; wherein R2 is selected from hydrogen and C1-10 alkyl group; wherein R8 is —O—Si(CH3)3 group; wherein Y is selected from formula (II), (III) and (IV); wherein R4 and R6 are selected from hydrogen and methyl group; wherein R3 and R5 are C1-10 alkylene group; wherein R7 is C1-10 alkyl group; wherein b is 0 to 4 and wherein c is 0 or 1; and wherein the first and second carbosiloxane monomer of formula (I) are same or different.

Abstract: A multistage polymer is provided, having: acrylate rich stage comprising: (a) structural units of monomer selected from C1-22 alkyl (meth)acrylates and structural units of first carbosiloxane monomer of formula (I); and (b) carbosiloxane rich stage, comprising: structural units of second carbosiloxane monomer of formula (I); wherein a is 0 to 3; wherein d is 0 or 1; wherein R1 is selected from hydrogen, C1-10 alkyl group and aryl group; wherein R2 is selected from hydrogen and C1-10 alkyl group; wherein R8 is —O—Si(CH3)3 group; wherein Y is selected from formula (II), (III) and (IV); wherein R4 and R6 are selected from hydrogen and methyl group; wherein R3 and R5 are a C1-10 alkylene group; wherein R7 is C1-10 alkyl group; wherein b is 0 to 4 and wherein c is 0 or 1; and wherein the first and second carbosiloxane monomer of formula (I) are same or different.

Abstract: A system may include a decentralized communication network and multiple processing devices on the network. Each processing device may have an artificial intelligence (AI) chip, the device may be configured to generate an AI model, determine the performance value of the AI model on the AI chip, receive a chain from the network where the chain contains a performance measure. If the performance value of the AI model is better than the performance measure, then the processing device may broadcast the AI model to the network for verification. If the AI model is verified by the network, the device may update the chain with the performance value so that the chain can be shared by the multiple processing devices on the network. Any processing device on the network may also verify an AI model broadcasted by any other device. Methods for generating the AI model are also provided.

Abstract: A system may include a decentralized communication network and multiple processing devices on the network. Each processing device may have an artificial intelligence (AI) chip, the device may be configured to generate an AI model, determine the performance value of the AI model on the AI chip, receive a chain from the network where the chain contains a performance measure. If the performance value of the AI model is better than the performance measure, then the processing device may broadcast the AI model to the network for verification. If the AI model is verified by the network, the device may update the chain with the performance value so that the chain can be shared by the multiple processing devices on the network. Any processing device on the network may also verify an AI model broadcasted by any other device. Methods for generating the AI model are also provided.

Abstract: A system may include a decentralized communication network and multiple processing devices on the network. Each processing device may have an artificial intelligence (AI) chip, the device may be configured to generate an AI model, determine the performance value of the AI model on the AI chip, receive a chain from the network where the chain contains a performance measure. If the performance value of the AI model is better than the performance measure, then the processing device may broadcast the AI model to the network for verification. If the AI model is verified by the network, the device may update the chain with the performance value so that the chain can be shared by the multiple processing devices on the network. Any processing device on the network may also verify an AI model broadcasted by any other device. Methods for generating the AI model are also provided.

Abstract: A system may include a decentralized communication network and multiple processing devices on the network. Each processing device may have an artificial intelligence (AI) chip, the device may be configured to generate an AI model, determine the performance value of the AI model on the AI chip, receive a chain from the network where the chain contains a performance measure. If the performance value of the AI model is better than the performance measure, then the processing device may broadcast the AI model to the network for verification. If the AI model is verified by the network, the device may update the chain with the performance value so that the chain can be shared by the multiple processing devices on the network. Any processing device on the network may also verify an AI model broadcasted by any other device. Methods for generating the AI model are also provided.

Abstract: A method comprising: sending an I/O message to an I/O device on an I/O bus, wherein the I/O device comprises an I/O device state, wherein the first I/O message is a I/O traffic type; receiving a LSR suspend request comprising a suspend instruction to suspend the I/O traffic type, an expected impact, and an I/O device identifier associated with the I/O device; setting the I/O device state to suspended in response to the receiving the LSR suspend request, wherein no I/O messages of the I/O traffic type are sent to the I/O device while the I/O device state is suspended; receiving a LSR resume request comprising a resume instruction to resume the I/O traffic type, the expected impact, and the I/O device identifier; setting the I/O device state to online in response to the receiving the LSR resume request; and sending a second I/O message to the I/O device.