A system for monitoring gluten consumption, especially in celiac people, which allows the feeding of food consumption data and updating, in real time, of the estimated amount of gluten consumed daily. Still, the present invention refers to a system for prediction that associates the possibility of an indisposition being associated or not with an undue consumption of gluten.

A system for monitoring gluten consumption, especially in celiac people, which allows the feeding of food consumption data and updating, in real time, of the estimated amount of gluten consumed daily. Still, the present invention refers to a system for prediction that associates the possibility of an indisposition being associated or not with an undue consumption of gluten.

A system and a method for detecting animals in a region of interest are disclosed. An image that captures a scene in the region of interest is received. The image is fed to an animal detection model to produce a group of probability maps for a group of key points and a group of affinity field maps for a group of key point sets. One or more connection graphs are determined based on the group of probability maps and the group of affinity field maps. Each connection graph outlines a presence of an animal in the image. One or more animals present in the region of interest are detected based on the one or more connection graphs.

Disclosed is a method for multi-center effect compensation based on a PET/CT intelligent diagnosis system. The method includes the following steps: estimating multi-center effect parameters of a test center B relative to a training center A by implementing a nonparametric mathematical method for data of the training center A and the test center B based on a location-scale model about additive and multiplicative multi-center effect parameters, and using the parameters to compensate the data of the test center B to eliminate a multi-center effect between the test center B and the training center A. According to the present disclosure, the multi-center effect between the training center A and the test center B can be compensated, so that the compensated data of the test center B can be used in the model trained by the training center A, and the generalization ability of the model is indirectly improved.

Disclosed is a method for multi-center effect compensation based on a PET/CT intelligent diagnosis system. The method includes the following steps: estimating multi-center effect parameters of a test center B relative to a training center A by implementing a nonparametric mathematical method for data of the training center A and the test center B based on a location-scale model about additive and multiplicative multi-center effect parameters, and using the parameters to compensate the data of the test center B to eliminate a multi-center effect between the test center B and the training center A. According to the present disclosure, the multi-center effect between the training center A and the test center B can be compensated, so that the compensated data of the test center B can be used in the model trained by the training center A, and the generalization ability of the model is indirectly improved.

Techniques and apparatuses are described that enable a device to be unlocked and continuous user authentication without a touch input dedicated to fingerprint requisition. A touch input is received that comprises one or more touches to a touchscreen, and raw image data corresponding to the touches is retrieved from a fingerprint imaging sensor. A pixel-clustering technique is performed on the raw image data to determine a portion of the raw image data that corresponds to each of the touches. Touch embeddings are formed for each of the portions of the raw image data and compared to one or more stored fingerprint embeddings that correspond to respective fingerprints of one or more authorized users. An authentication result is then determined for the touch input based on the comparison results.

Techniques and apparatuses are described that enable a device to be unlocked and continuous user authentication without a touch input dedicated to fingerprint requisition. A touch input is received that comprises one or more touches to a touchscreen, and raw image data corresponding to the touches is retrieved from a fingerprint imaging sensor. A pixel-clustering technique is performed on the raw image data to determine a portion of the raw image data that corresponds to each of the touches. Touch embeddings are formed for each of the portions of the raw image data and compared to one or more stored fingerprint embeddings that correspond to respective fingerprints of one or more authorized users. An authentication result is then determined for the touch input based on the comparison results.

Different from prior works that model the internal distribution of patches within an image implicitly with a top-down latent variable model (e.g., generator), embodiments explicitly represent the statistical distribution within a single image by using an energy-based generative framework, where a pyramid of energy functions, each parameterized by a bottom-up deep neural network, are used to capture the distributions of patches at different resolutions. Also, embodiments of a coarse-to-fine sequential training and sampling strategy are presented to train the model efficiently. Besides learning to generate random samples from white noise, embodiments can learn in parallel with a self-supervised task (e.g., recover an input image from its corrupted version), which can further improve the descriptive power of the learned model. Embodiments does not require an auxiliary model (e.g., discriminator) to assist the training, and embodiments also unify internal statistics learning and image generation in a single framework.

An information processing device includes a processor. The processor obtains an input image, inputs the input image to a machine learning model that executes classification likelihood calculation processing to obtain, for each of candidate objects in the input image, likelihoods of belonging to the plurality of classes, executes first determination on whether or not each of the candidate objects is classified as a first class of the plurality of classes using a likelihood of belonging to the first class that is a likelihood having a negative correlation with likelihoods of belonging to other classes, executes second determination on whether or not each of the candidate objects that have been determined in the first determination as a non-first class is classified as the other classes, and outputting a result of classifying the candidate objects included in the input image using a result of the second determination.

Diagnostic and prognostic assays and a portable point of care system is provided that performs such assays using an automated, artificial intelligence (AI) based molecular analyses of a subject sample. The system provides for rapid, cost-efficient multiplexable assessment of biomarker panels in a cell sample and may be easily used for global and remote applications.