Abstract: Methods, non-transitory computer readable media, and causal explanation computing apparatus that assists with generating and providing causal explanation of artificial intelligence models includes obtaining a dataset as an input for an artificial intelligence model, wherein the obtained dataset is filtered to a disentangled low-dimensional representation. Next, a plurality of first factors from the disentangled low-dimensional representation of the obtained data that affect an output of the artificial intelligence model is identified. Further, a generative mapping from the disentangled low-dimensional representation between the identified plurality of first factors and the output of the artificial intelligence model, using causal reasoning is determined. An explanation data is generated using the determined generative mapping, wherein the generated explanation data provides a description of an operation leading to the output of the artificial intelligence model using the identified plurality of first factors.

Abstract: Systems and methods for preference and similarity learning arc disclosed. The systems and methods improve efficiency for both searching datasets and embedding objects within the datasets. The systems and methods for preference embedding include identifying paired comparisons closest to a user's true preference point. The processes include removing obvious paired comparisons and/or ambiguous paired comparisons from subsequent queries The systems and methods for similarity learning include providing larger rank orderings of tuples to increase the context of the information in a dataset In each embodiment, the systems and methods can embed user responses in a Euclidean space such that distances between objects are indicative of user preference or similarity.

Abstract: A system including a processor, and memory having stored thereon instructions that, when executed by the processor, control the processor to receive image data of a sequence of images, and a current image of the sequence of images being after a previous image in the sequence of images, each of the current and previous images including a cell, filter the current image to remove noise, iteratively deconvolve the filtered current image to identify edges of the cell within the current image based on determined edges of the cell within the previous image, and segment the deconvolved current image to determine edges of the cell within the current image.

Abstract: A system including a processor, and memory having stored thereon instructions that, when executed by the processor, control the processor to receive image data of a sequence of images, and a current image of the sequence of images being after a previous image in the sequence of images, each of the current and previous images including a cell, filter the current image to remove noise, iteratively deconvolve the filtered current image to identify edges of the cell within the current image based on determined edges of the cell within the previous image, and segment the deconvolved current image to determine edges of the cell within the current image.

Abstract: A system including: at least one processor; and at least one memory having stored thereon instructions that, when executed by the at least one processor, control the at least one processor to: receive image data of a sequence of images, and a current image of the sequence of images being after a previous image in the sequence of images, each of the current and previous images including a cell; filter the current image to remove noise; iteratively deconvolve the filtered current image to identify edges of the cell within the current image based on determined edges of the cell within the previous image; and segment the deconvolved current image to determine edges of the cell within the current image.

Abstract: A system including: at least one processor; and at least one memory having stored thereon instructions that, when executed by the at least one processor, control the at least one processor to: receive image data of a sequence of images, and a current image of the sequence of images being after a previous image in the sequence of images, each of the current and previous images including a cell; filter the current image to remove noise; iteratively deconvolve the filtered current image to identify edges of the cell within the current image based on determined edges of the cell within the previous image; and segment the deconvolved current image to determine edges of the cell within the current image.

Abstract: A composition comprising a nanoparticle and at least one adsorbate associated with the nanoparticle, wherein the adsorbate displays at least one chemically responsive optical property. A method comprising associating an adsorbate with a nanoparticle, wherein the nanoparticle comprises a shell surrounding a core material with a lower conductivity than the shell material and the adsorbate displays at least one chemically responsive optical property, and engineering the nanoparticle to enhance the optical property of the adsorbate. A method comprising determining an optical response of an adsorbate associated with a nanoparticle as a function of a chemical parameter, and parameterizing the optical response to produce a one-dimensional representation of at least a portion of a spectral window of the optical response in a high dimensional vector space.

Abstract: A parallel dynamical system for computing sparse representations of data, i.e., where the data can be fully represented in terms of a small number of non-zero code elements, and for reconstructing compressively sensed images. The system is based on the principles of thresholding and local competition that solves a family of sparse approximation problems corresponding to various sparsity metrics. The system utilizes Locally Competitive Algorithms (LCAs), nodes in a population continually compete with neighboring units using (usually one-way) lateral inhibition to calculate coefficients representing an input in an over complete dictionary.

Abstract: A parallel dynamical system for computing sparse representations of data, i.e., where the data can be fully represented in terms of a small number of non-zero code elements, and for reconstructing compressively sensed images. The system is based on the principles of thresholding and local competition that solves a family of sparse approximation problems corresponding to various sparsity metrics. The system utilizes Locally Competitive Algorithms (LCAs), nodes in a population continually compete with neighboring units using (usually one-way) lateral inhibition to calculate coefficients representing an input in an over complete dictionary.

Abstract: A composition comprising a nanoparticle and at least one adsorbate associated with the nanoparticle, wherein the adsorbate displays at least one chemically responsive optical property. A method comprising associating an adsorbate with a nanoparticle, wherein the nanoparticle comprises a shell surrounding a core material with a lower conductivity than the shell material and the adsorbate displays at least one chemically responsive optical property, and engineering the nanoparticle to enhance the optical property of the adsorbate. A method comprising determining an optical response of an adsorbate associated with a nanoparticle as a function of a chemical parameter, and parameterizing the optical response to produce a one-dimensional representation of at least a portion of a spectral window of the optical response in a high dimensional vector space.