Abstract: A method includes providing a computer system, the computer system including at least a processor and a memory, the memory including at least an operating system, executing a process in the memory, the process including providing a recurrent circuit model, and converting the recurrent circuit model into a recurrent neural network that can be fit with gradient descent.

Abstract: The invention relates to a method for preparing a solid comprising the mixture of a set of compounds comprising at least one Cu2(OH)2CO3 powder, one metal oxide powder selected from the group of metals consisting of copper, zinc, iron, manganese and mixtures thereof, and at least one binder as well as the use of the solid prepared by means of this method.

Abstract: A method includes providing a computer system, the computer system including at least a processor and a memory, the memory including at least an operating system, executing a process in the memory, the process including providing a recurrent circuit model, and converting the recurrent circuit model into a recurrent neural network that can be fit with gradient descent.

Abstract: The invention relates to a method for preparing a solid comprising a step of mixing a set of compounds comprising at least two Cu2(OH)2CO3 powders of different particle sizes and at least one binder and the use of the solid prepared by means of this method.

Abstract: A device for determining the density of volumes of material to be imaged is provided, the device comprising a gas detector having first and second chambers separated by a micro-screen, making it possible to detect a stream of ionising particles, to calculate the path of each ionising particle and the stream of ionising particles passing through the first chamber, and comprising computing means for converting the calculations of paths and streams into information on the volume density of the material to be imaged.

Abstract: A device for determining the density of volumes of material to be imaged is provided, the device comprising a gas detector having first and second chambers separated by a micro-screen, making it possible to detect a stream of ionising particles, to calculate the path of each ionising particle and the stream of ionising particles passing through the first chamber, and comprising computing means for converting the calculations of paths and streams into information on the volume density of the material to be imaged.

Abstract: The method and system includes a plurality of test subject containers, each having a test subject therein. A plurality of video cameras is focused on the test subject containers to capture video of the behavior of each of the test subjects. The system and method also includes a storage system for storing the plurality of video from the cameras. The storage system may be local or cloud-based as is known in the art. The system further has one or more computers with a neural network configured to (a) retrieve the video from the storage system of the test subjects, (b) analyze the video to identify a plurality of observable behaviors in the test subjects, (c) annotate the video with the observed behavior classifications, and (d) store the annotated video in the storage system.

Abstract: The invention relates to a method for preparing a solid comprising a step of mixing a set of compounds comprising at least two Cu2(OH)2CO3 powders of different particle sizes and at least one binder and the use of the solid prepared by means of this method.

Abstract: The invention relates to a method for preparing a solid comprising the mixture of a set of compounds comprising at least one Cu2(OH)2CO3 powder, one metal oxide powder selected from the group of metals consisting of copper, zinc, iron, manganese and mixtures thereof, and at least one binder as well as the use of the solid prepared by means of this method.

Abstract: The method and system includes a plurality of test subject containers, each having a test subject therein. A plurality of video cameras is focused on the test subject containers to capture video of the behavior of each of the test subjects. The system and method also includes a storage system for storing the plurality of video from the cameras. The storage system may be local or cloud-based as is known in the art. The system further has one or more computers with a neural network configured to (a) retrieve the video from the storage system of the test subjects, (b) analyze the video to identify a plurality of observable behaviors in the test subjects, (c) annotate the video with the observed behavior classifications, and (d) store the annotated video in the storage system.

Abstract: An artificial visual recognition system and method employ a digital processor and a model executed by the digital processor. The model has a loose hierarchy of layers. Each layer, from a lowest hierarchy level to a top level, provides relatively increasing selectivity and invariance of the input image. The hierarchy allows bypass routes between layers. On output, the model produces feature recognition and classification of an object in the input image. In some embodiments, windowing means provide windows of the input image to the model, and the model responds to shape-based objects in the input image. In another feature, segmenting means segment the input image and enables the model to determine texture-based objects in the input image.

Abstract: An artificial visual recognition system and method employ a digital processor and a model executed by the digital processor. The model has a loose hierarchy of layers. Each layer, from a lowest hierarchy level to a top level, provides relatively increasing selectivity and invariance of the input image. The hierarchy allows bypass routes between layers. On output, the model produces feature recognition and classification of an object in the input image. In some embodiments, windowing means provide windows of the input image to the model, and the model responds to shape-based objects in the input image. In another feature, segmenting means segment the input image and enables the model to determine texture-based objects in the input image.

Abstract: Techniques are disclosed for multi-modal identification that utilize a classifier combination framework. One embodiment of the present invention provides a multi-modal identification system that includes a collection of classifiers that classify feature streams derived from audio and/or video sources. A classifier combination scheme is used to combine the classifier outputs having varying degrees of confidence, but in a robust way by using a confidence-based weighting scheme that operates on a “per-class” basis, rather than (or in addition to) the traditional “per-classifier” basis. The system can be distributed across several machines running independent feature classifiers on the subscription basis.