A seated state detection device according to an embodiment includes a detection unit, a state determination unit, and an output unit. The detection unit detects, from taken image data output from an imaging unit that images a seat to which a mark is added, whether the mark is shielded. The state determination unit determines a seated state of the seat based on a shielded state of the mark. The output unit outputs seating information indicating the seated state.

Various systems are provided for optimizing the searching of a graph for a portion that matches a pattern is provided. A Graph Search Optimization System (GSOS) provides various techniques for reducing the computational expense when searching for patterns within a graph. The GSOS provides techniques that include an edge-count directed (ECD) system, a derived constraint (DC) system, and a sorted property (SP) system. The ECD system matches a pattern in a direction based on the number of edges for that direction. The DC system derives a single-element constraint from a multi-element constraints to avoid having to check multiple elements. The SP system processes edges of a graph in a sorted order based on the value of a property of the edges.

System and methods for relational time-series learning are provided. Unlike traditional time series forecasting techniques, which assume either complete time series independence or complete dependence, the disclosed system and method allow time series forecasting that can be performed on multivariate time series represented as vertices in graphs with arbitrary structures and predicting a future classification for data items represented by one of nodes in the graph. The system and methods also utilize non-relational, relational, temporal data for classification, and allow using fast and parallel classification techniques with linear speedups. The system and methods are well-suited for processing data in a streaming or online setting and naturally handle training data with skewed or unbalanced class labels.

A relative atlas may be used to lay out elements in a digital map used in the control of an autonomous vehicle. A vehicle pose for the autonomous vehicle within a geographical area may be determined, and the relative atlas may be accessed to identify elements in the geographical area and to determine relative poses between those elements. The elements may then be laid out within the digital map using the determined relative poses, e.g., for use in planning vehicle trajectories, for estimating the states of traffic controls, or for tracking and/or identifying dynamic objects, among other purposes.

A relative atlas may be used to lay out elements in a digital map used in the control of an autonomous vehicle. A vehicle pose for the autonomous vehicle within a geographical area may be determined, and the relative atlas may be accessed to identify elements in the geographical area and to determine relative poses between those elements. The elements may then be laid out within the digital map using the determined relative poses, e.g., for use in planning vehicle trajectories, for estimating the states of traffic controls, or for tracking and/or identifying dynamic objects, among other purposes.

System and methods for relational time-series learning are provided. Unlike traditional time series forecasting techniques, which assume either complete time series independence or complete dependence, the disclosed system and method allow time series forecasting that can be performed on multivariate time series represented as vertices in graphs with arbitrary structures and predicting a future classification for data items represented by one of nodes in the graph. The system and methods also utilize non-relational, relational, temporal data for classification, and allow using fast and parallel classification techniques with linear speedups. The system and methods are well-suited for processing data in a streaming or online setting and naturally handle training data with skewed or unbalanced class labels.

System and methods for relational time-series learning are provided. Unlike traditional time series forecasting techniques, which assume either complete time series independence or complete dependence, the disclosed system and method allow time series forecasting that can be performed on multivariate time series represented as vertices in graphs with arbitrary structures and predicting a future classification for data items represented by one of nodes in the graph. The system and methods also utilize non-relational, relational, temporal data for classification, and allow using fast and parallel classification techniques with linear speedups. The system and methods are well-suited for processing data in a streaming or online setting and naturally handle training data with skewed or unbalanced class labels.

A computer implemented method for automatic print analysis, the method comprising: receiving a first image wherein the first image shows one or more of: a latent print, a patent print, an impressed print, and an actual finger, palm, toe and/or foot; and wherein the first image includes characteristic features of at least one of a finger, a palm, a toe and a foot; creating an orientation field by estimating the orientation of one or more features in the first image, wherein the estimating comprises: applying an orientation operator to the first image, the orientation operator being based on a plurality of isotropic filters lying in quadrature.

A skin-attachment sheet for makeup includes a thin-film layer made of polylactic acid, polyglycolic acid, polycaprolactone, or a copolymer thereof, and a biocompatible polymer. The thickness of a first layer thereof is from 10 nm to 500 nm. An ink corresponding to a makeup image is printed on a surface of the thin-film layer on an opposite side of a surface of the thin-film layer attachable to the skin. The sheet also includes a mount that is provided on a surface on the thin-film layer, and that is capable of being peeled off from the surface on which the mount is provided.

A biological image presentation device includes: an acquisition unit; a determination unit determining an image as a standard and an image for comparison; an extraction unit extracting, from the image as a standard, a position where a change of luminance value equal to or larger than a defined value is present; a detection unit detecting a position corresponding to the position extracted from the image for comparison; a division unit dividing the image as a standard on the basis of the position extracted; a mapping unit mapping the image for comparison to an area corresponding to each divided area of the image as a standard while modifying so as to conform to the shape of the divided area; and a display control unit switching and displaying an image for display in a display area by using the image as a standard and an image mapped by the mapping unit.