The bizarre promotional video promises "Face analysis based on best of breed Artificial Intelligence algorithms for Business Intelligence and Digital Signage applications." What follows is footage of a woman pushing her hair behind her ears, a man grimacing and baring his teeth, and an actor in a pinstripe suit being slapped in the face against a green screen. Digitally overlayed on each person's face are colored outlines of rectangles with supposed measurements displayed: "F 25 happiness," "caucasian_latin," "M 38 sadness." The commercial reel advertises just one of the many video analytics tools available for download on an app store monitored by the Internet of Things startup Azena, itself a project from the German kitchen appliance maker Bosch. Bosch, known more for its line of refrigerators, ovens, and dishwashers, also develops and sells an entire suite of surveillance cameras.

Who joins Alphabet (GOOGL), Meta Platforms (FB) (formerly known as Facebook), and Alibaba (BABA) on this list of Warren Buffett stocks based on the investing strategy of the Berkshire Hathaway CEO? In addition to GOOGL, FB and BABA, Sprouts Farmers Market (SFM), Evercore (EVR), Teradyne (TER), and Williams-Sonoma (WSM) are among 21 names featured on this stock screen.

Reliably and physically accurately transferring information between images through deformable image registration with large anatomical differences is an open challenge in medical image analysis. Most existing methods have two key shortcomings: first, they require extensive up-front parameter tuning to each specific registration problem, and second, they have difficulty capturing large deformations and content mismatches between images. There have however been developments that have laid the foundation for potential solutions to both shortcomings. Towards the first shortcoming, a multi-objective optimization approach using the Real-Valued Gene-pool Optimal Mixing Evolutionary Algorithm (RV-GOMEA) has been shown to be capable of producing a diverse set of registrations for 2D images in one run of the algorithm, representing different trade-offs between conflicting objectives in the registration problem. This allows the user to select a registration afterwards and removes the need for up-front tuning. Towards the second shortcoming, a dual-dynamic grid transformation model has proven effective at capturing large differences in 2D images. These two developments have recently been accelerated through GPU parallelization, delivering large speed-ups. Based on this accelerated version, it is now possible to extend the approach to 3D images. Concordantly, this work introduces the first method for multi-objective 3D deformable image registration, using a 3D dual-dynamic grid transformation model based on simplex meshes while still supporting the incorporation of annotated guidance information and multi-resolution schemes. Our proof-of-concept prototype shows promising results on synthetic and clinical 3D registration problems, forming the foundation for a new, insightful method that can include bio-mechanical properties in the registration.

As it is subjected to machines for identification, artificial intelligence (AI) is becoming sophisticated. The greater the number of databases kept for Machine Learning models, the more thorough and nimbler your AI will be in identifying, understanding, and predicting in a variety of circumstances. It is difficult to identify or distinguish items without picture recognition. Because image recognition is critical for computer vision, we must learn more about it. Image recognition, a subset of computer vision, is the art of recognizing and interpreting photographs to identify objects, places, people, or things observable in one's natural surroundings.

Colored figures are available in the online version at https://link.springer.com/journal/11633 He is currently a faculty member with School of Computer and Communication Engineering, University of Science and Technology Beijing, China. His research interests include pattern recognition, classifier ensemble, and document analysis and recognition. Chang Liu received the B. Sc. degree in computer science from University of Science and Technology Beijing, China in 2016, where he is a Ph. His research interests include text detection, few-shot learning, and text recognition.

Graphs are a ubiquitous data structure to model processes and relations in a wide range of domains. Examples include control-flow graphs in programs and semantic scene graphs in images. Identifying subgraph patterns in graphs is an important approach to understanding their structural properties. We propose a visual analytics system GraphQ to support human-in-the-loop, example-based, subgraph pattern search in a database containing many individual graphs. To support fast, interactive queries, we use graph neural networks (GNNs) to encode a graph as fixed-length latent vector representation, and perform subgraph matching in the latent space. Due to the complexity of the problem, it is still difficult to obtain accurate one-to-one node correspondences in the matching results that are crucial for visualization and interpretation. We, therefore, propose a novel GNN for node-alignment called NeuroAlign, to facilitate easy validation and interpretation of the query results. GraphQ provides a visual query interface with a query editor and a multi-scale visualization of the results, as well as a user feedback mechanism for refining the results with additional constraints. We demonstrate GraphQ through two example usage scenarios: analyzing reusable subroutines in program workflows and semantic scene graph search in images. Quantitative experiments show that NeuroAlign achieves 19-29% improvement in node-alignment accuracy compared to baseline GNN and provides up to 100x speedup compared to combinatorial algorithms. Our qualitative study with domain experts confirms the effectiveness for both usage scenarios.

Distance measures provide the foundation for many popular algorithms in Machine Learning and Pattern Recognition. Different notions of distance can be used depending on the types of the data the algorithm is working on. For graph-shaped data, an important notion is the Graph Edit Distance (GED) that measures the degree of (dis)similarity between two graphs in terms of the operations needed to make them identical. As the complexity of computing GED is the same as NP-hard problems, it is reasonable to consider approximate solutions. In this paper we present a QUBO formulation of the GED problem. This allows us to implement two different approaches, namely quantum annealing and variational quantum algorithms that run on the two types of quantum hardware currently available: quantum annealer and gate-based quantum computer, respectively. Considering the current state of noisy intermediate-scale quantum computers, we base our study on proof-of-principle tests of their performance.

Manuscript symbols can be stored, recognized and retrieved from an entropic digital memory that is associative and distributed but yet declarative; memory retrieval is a constructive operation, memory cues to objects not contained in the memory are rejected directly without search, and memory operations can be performed through parallel computations. Manuscript symbols, both letters and numerals, are represented in Associative Memory Registers that have an associated entropy. The memory recognition operation obeys an entropy trade-off between precision and recall, and the entropy level impacts on the quality of the objects recovered through the memory retrieval operation. The present proposal is contrasted in several dimensions with neural networks models of associative memory. We discuss the operational characteristics of the entropic associative memory for retrieving objects with both complete and incomplete information, such as severe occlusions. The experiments reported in this paper add evidence on the potential of this framework for developing practical applications and computational models of natural memory.

AI, like humans, learns from examples. Given enough data and time, an AI model can make sense of the statistical relationships well enough to generate predictions. That's how OpenAI's GPT-3 writes text from poetry to computer code, and how apps like Google Lens recognize objects such as lampshades in photos of bedrooms. Historically, the data to train as well as test AI has come mostly from public sources on the web. But these sources are flawed.

Character recognition is a process that allows computers to recognize written or printed characters such as numbers or letters and to change them into a form that computers can use. As a part of this case study, we are going to recognize "Hindi characters". It is a Character Recognition problem related to computer vision, where our task is to predict the Hindi character present in the image. The Model should predict or recognize the character present in the image in real-time. So the latency of the model should be low.