Abstract: An optical device includes a sensor and a mask structure. The mask structure is configured to provide a first mask covering at least a part of the sensor and a second mask covering at least a part of the sensor. The second mask is the anti-mask of the first mask, and the sensor is configured to provide first output signals for light transmitted through the first mask and second output signals for light transmitted through the second mask.

Abstract: Examples of the present disclosure describe systems and methods for probabilistic neural network architecture generation. In an example, an underlying distribution over neural network architectures based on various parameters is sampled using probabilistic modeling. Training data is evaluated in order to iteratively update the underlying distribution, thereby generating a probability distribution over the neural network architectures. The distribution is iteratively trained until the parameters associated with the neural network architecture converge. Once it is determined that the parameters have converged, the resulting probability distribution may be used to generate a resulting neural network architecture. As a result, intermediate architectures need not be fully trained, which dramatically reduces memory usage and/or processing time.

Abstract: According to a first aspect of the present invention there is provided a method of measuring the optical reflectance R of a target using a detection system comprising a light emitter and a light detector spaced apart from one another. The method comprises illuminating the target with the light emitter, detecting light reflected from the target using the light detector, wherein the light detector provides an electrical output signal SS indicative of the intensity of the detected light, and determining the optical reflectance R of the target according to (Formula 1), where RR is the spectral reflectance of a reference standard, SR is the detector electrical output signal with the reference standard in place, SH is the detector electrical output signal with no target in front of the light emitter and light detector, and M is a calibration factor.

Abstract: Examples of the present disclosure describe systems and methods for probabilistic neural network architecture generation. In an example, an underlying distribution over neural network architectures based on various parameters is sampled using probabilistic modeling. Training data is evaluated in order to iteratively update the underlying distribution, thereby generating a probability distribution over the neural network architectures. The distribution is iteratively trained until the parameters associated with the neural network architecture converge. Once it is determined that the parameters have converged, the resulting probability distribution may be used to generate a resulting neural network architecture. As a result, intermediate architectures need not be fully trained, which dramatically reduces memory usage and/or processing time.

Abstract: The present disclosure relates to a discovery platform including machine-learning techniques for using medical imaging data to study a phenotype of interest, such as complex diseases with weak or unknown genetic drivers. An exemplary method identifying a covariant of interest with respect to drug response phenotype (DRP) of a treatment is disclosed.

Abstract: Systems and methods, for selecting a neural network for a machine learning (ML) problem, are disclosed. A method includes accessing an input matrix, and accessing an ML problem space associated with an ML problem and multiple untrained candidate neural networks for solving the ML problem. The method includes computing, for each untrained candidate neural network, at least one expressivity measure capturing an expressivity of the candidate neural network with respect to the ML problem. The method includes computing, for each untrained candidate neural network, at least one trainability measure capturing a trainability of the candidate neural network with respect to the ML problem. The method includes selecting, based on the at least one expressivity measure and the at least one trainability measure, at least one candidate neural network for solving the ML problem. The method includes providing an output representing the selected at least one candidate neural network.

Abstract: The present disclosure relates to a discovery platform including machine-learning techniques for using medical imaging data to study a phenotype of interest, such as complex diseases with weak or unknown genetic drivers. An exemplary method of identifying a patient subgroup of interest, comprising: inputting a plurality of medical images obtained from a group of clinical subjects into a trained unsupervised machine-learning model to obtain a plurality of embeddings in a latent space; clustering the plurality of embeddings to generate one or more clusters of embeddings; identifying one or more patient subgroups corresponding to the one or more clusters of embeddings; and associating each patient subgroup of the one or more patient subgroups with a covariant to identify the patient subgroup of interest.

Abstract: The present disclosure relates to a discovery platform including machine-learning techniques for using medical imaging data to study a phenotype of interest, such as complex diseases with weak or unknown genetic drivers. An exemplary method of identifying a covariant of interest with respect to a phenotype comprises: receiving covariant information of a covariate class and corresponding phenotypic image data related to the phenotype obtained from a group of clinical subjects; inputting the phenotypic image data into a trained unsupervised machine-learning model to obtain a plurality of embeddings in a latent space, each embedding corresponding to a phenotypic state reflected in the phenotypic image data; and determining, based on the covariant information for the group of clinical subjects, the plurality of embeddings, and one or more linear regression models, an association between each candidate covariant of a plurality of candidate covariants and the phenotype state to identify the covariant of interest.

Abstract: Systems and methods for selecting a neural network for a machine learning problem are disclosed. A method includes accessing an input matrix. The method includes accessing a machine learning problem space associated with a machine learning problem and multiple untrained candidate neural networks for solving the machine learning problem. The method includes computing, for each untrained candidate neural network, at least one expressivity measure capturing an expressivity of the candidate neural network with respect to the machine learning problem. The method includes computing, for each untrained candidate neural network, at least one trainability measure capturing a trainability of the candidate neural network with respect to the machine learning problem. The method includes selecting, based on the at least one expressivity measure and the at least one trainability measure, at least one candidate neural network for solving the machine learning problem.

Abstract: An optical system includes a spatial encoding arrangement for generating spatially encoded light with an initial spatial distribution; a coded aperture defining a mask pattern based on the initial spatial distribution of the spatially encoded light; and—an image sensor. The spatial encoding arrangement—directs the spatially encoded light onto the object, and the object reflects at least a portion of the spatially encoded light to form reflected light. The reflected light is directed through the coded aperture to form spatially decoded light. The spatially decoded light is directed onto the image sensor to form an image thereon, and the image sensor detects the image. The spatial encoding arrangement includes optical emitters spatially arranged, defining the initial spatial pattern of the spatially encoded light. The mask pattern is the inverse of the initial spatial pattern of the spatially encoded light defined by the spatial arrangement of the optical emitters.

Abstract: An optical module includes a display including light emitting elements that emit visible light. The optical module also includes an image sensor layer including infra-red light sensitive elements. The optical module further includes a mask layer configured to block infra-red light having a wavelength in one or more portions of the infra-red optical spectrum and pass visible light. The optical module is lensless.

Abstract: A method for the production of a diagnostic device comprising the steps of: obtaining a blank from a plastic material sheet; the blank comprising one or more support bands provided with electrical tracks; providing one or more sensitive elements, in particular detection pads, for each band, being configured to generate signals, in particular pressure signals and being connected to the electrical tracks for the transmission of signals; applying a first electrically conductive coating on the blank in the area of the sensitive elements; creating a main structure from the blank, in particular by rolling the blank, keeping the first electrically conductive coating radially on the inside of the main structure; and creating an auxiliary matrix which wraps the main structure and fills the gaps between the support bands, so as to obtain a tubular body.

Abstract: An apparatus for a first current sensor for a switching converter has an inductor and a first switch. The first switch is arranged to selectively couple the inductor to a first voltage. The first current sensor generates a first output current that is dependent on an inductor current flowing through the inductor The first current sensor compensates for an error arising due to the first switch in the generation of the first output current. The apparatus provides an improved current sensor for a switching converter that overcomes or mitigates the problem of errors in the measurement of a current.

Abstract: The present disclosure relates to a method for image classes definition and to a method for image multiprocessing and related vision system, which implement said method for image classes definition.

Abstract: The present disclosure relates to a discovery platform including machine-learning techniques for using medical imaging data to study a phenotype of interest, such as complex diseases with weak or unknown genetic drivers. An exemplary method of identifying a covariant of interest with respect to a phenotype comprises: receiving covariant information of a covariate class and corresponding phenotypic image data related to the phenotype obtained from a group of clinical subjects; inputting the phenotypic image data into a trained unsupervised machine-learning model to obtain a plurality of embeddings in a latent space, each embedding corresponding to a phenotypic state reflected in the phenotypic image data; and determining, based on the covariant information for the group of clinical subjects, the plurality of embeddings, and one or more linear regression models, an association between each candidate covariant of a plurality of candidate covariants and the phenotype state to identify the covariant of interest.

Abstract: A substantially planar light replication or re-transmission component having an incident light receiving surface and an opposed light emitting surface. The component comprises a substantially transparent planar substrate, one or more bipolar junction transistors provided on said substrate, the or each transistor comprising a collector region adjacent to said light receiving surface, an emitter region adjacent to said light emitting surface, and a base region between said collector region and said emitter region, and circuitry for biasing the bipolar transistors in use. The or each transistor is configured and biased in use so that said collector and base regions of the transistor operate as a photodiode whilst said base and emitter regions operate as a light emitting diode.

Abstract: A sensing system may include a first coded aperture configured to receive incident light and transmit a coded image of an object. The sensing system comprises a light replication component configured to detect the coded image and emit a replicated coded image. The sensing system may include a second coded aperture configured to receive the replicated coded image and transmit a decoded image. The sensing system may include a sensor configured to detect the decoded image.

Abstract: A storing and issuing module (10) including a storage roller (50), a motor (60) associated with the storage roller (50), at least one transport tape (52) configured to wrap onto the storage roller (50) together with flexible documents, such as banknotes, conveyed in a conveying section provided with holding means (53, 54) actuatable for engaging flexible documents to be stored or issued with the transport tape (52), wherein the storing and issuing module (50) is capable of handling large storage capacity thanks to a mechanical layout that minimizes variations in the conveying section despite the great variations of the outer diameter of the storage roller due to the large capacity thereof.

Abstract: A storing and issuing module (10) including a storage roller (50), a motor (60) associated with the storage roller (50), at least one transport tape (52) configured to wrap onto the storage roller (50) together with flexible documents, such as banknotes, conveyed in a conveying section provided with holding means (53, 54) actuatable for engaging flexible documents to be stored or issued with the transport tape (52), wherein the storing and issuing module (50) is capable of handling large storage capacity thanks to a mechanical layout that minimizes variations in the conveying section despite the great variations of the outer diameter of the storage roller due to the large capacity thereof.