Abstract: Herein is described a training method, comprising: receiving data representing an amino acid sequence and a DNA sequence; based on the received data, generating a set of pairs of corresponding zinc finger array sequences of the amino acid sequence and DNA sub-sequences of the DNA sequence, wherein a DNA sub-sequence comprises a binding site for a corresponding zinc finger array sequence; generating training data based at least in part the set of pairs of corresponding zinc finger array sequences and DNA subsequences; training, using the training data, a machine learning model to predict, from an input DNA sequence comprising a set of input DNA sub-sequences, a set of output zinc finger array sequences, an output zinc finger array sequence of the set of output zinc finger array sequences corresponding to an input DNA sub-sequence of the set of input DNA sub-sequences.

Abstract: A pet management system to identify each of a plurality of different pets and to manage pet services assigned to each of the plurality of pets based on an identification of each pet and a determination as to whether a specific pet is within a predetermined range of the assigned pet service.

Abstract: Exemplary systems and methods are directed to generating synthetic data for computer vision. A processing device generates a synthetic three-dimensional (3D) image of an object. A background image is selected, and a composite image is generated by combining the 3D image of the object and the background image. The processing device simulates: reflection or emission of at least one type of radiant energy from the surface of the object and/or the background according to a set of parameters associated with at least one of the object and the background image; and a reflectance or emittance measurement of the at least one type of radiant energy from the surface of the object by a sensor device configured for detecting the at least one type of radiant energy. The processing device generates a plurality of two-dimensional (2D) simulated images of different perspectives of the object based on simulation data.

Abstract: A computer-implemented method for assessing an image sentiment. The method can include receiving an image to identify a region of interest in the image; determining an image sentiment value associated with the image based on the region of interest, or a classification of the image; and displaying an indication of the image sentiment value with a spatial (e.g. geographical) location.

Abstract: A computer-implemented method for assessing an image sentiment. The method can include receiving an image to identify a region of interest in the image; determining an image sentiment value associated with the image based on the region of interest, or a classification of the image; and displaying an indication of the image sentiment value with a spatial (e.g. geographical) location.