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Animated example of a breadth-first search. Black: explored, grey: queued to be explored later on BFS on Maze-solving algorithm Top part of Tic-tac-toe game tree. Breadth-first search (BFS) is an algorithm for searching a tree data structure for a node that satisfies a given property.
The breadth-first-search algorithm is a way to explore the vertices of a graph layer by layer. It is a basic algorithm in graph theory which can be used as a part of other graph algorithms. For instance, BFS is used by Dinic's algorithm to find maximum flow in a graph.
Flux (also known as FLUX.1) is a text-to-image model developed by Black Forest Labs, based in Freiburg im Breisgau, Germany. Black Forest Labs were founded by former employees of Stability AI. As with other text-to-image models, Flux generates images from natural language descriptions, called prompts.
Best-first search is a class of search algorithms which explores a graph by expanding the most promising node chosen according to a specified rule.. Judea Pearl described best-first search as estimating the promise of node n by a "heuristic evaluation function () which, in general, may depend on the description of n, the description of the goal, the information gathered by the search up to ...
For example, the rule {,} {} found in the sales data of a supermarket would indicate that if a customer buys onions and potatoes together, they are likely to also buy hamburger meat. Such information can be used as the basis for decisions about marketing activities such as, e.g., promotional pricing or product placements .
The algorithm is called lexicographic breadth-first search because the order it produces is an ordering that could also have been produced by a breadth-first search, and because if the ordering is used to index the rows and columns of an adjacency matrix of a graph then the algorithm sorts the rows and columns into lexicographical order.
CSV and PDF Natural language processing, QnA 2021 The Atticus Project: Vietnamese Image Captioning Dataset (UIT-ViIC) Vietnamese Image Captioning Dataset 19,250 captions for 3,850 images CSV and PDF Natural language processing, Computer vision 2020 [112] Lam et al. Vietnamese Names annotated with Genders (UIT-ViNames)
Foreground detection is one of the major tasks in the field of computer vision and image processing whose aim is to detect changes in image sequences. Background subtraction is any technique which allows an image's foreground to be extracted for further processing (object recognition etc.).