Abstract: Embodiments of systems and methods for detecting expected user intervention across multiple blades during a Keyboard, Video, and Mouse (KVM) session are discussed. In an embodiment, a chassis may include an Enclosure Controller (EC) coupled to a plurality of Information Handling Systems (IHSs) in a chassis, the EC comprising: a processor; and a memory coupled to the processor, the memory having program instructions stored thereon that, upon execution, cause the EC to: select a first IHS to initiate a first KVM session; register for a notification from the first IHS while the first IHS performs one or more operations; switch to a second IHS to initiate a second KVM session; and during the second KVM session, receive the notification.

Abstract: Methods, systems, and computer program products for determining model-related bias associated with training data are provided herein. A computer-implemented method includes obtaining, via execution of a first model, class designations attributed to data points used to train the first model; identifying any of the data points associated with an inaccurate class designation and/or a low-confidence class designation; training a second model using the data points from the dataset, but excluding the identified data points; determining bias related to at least a portion of those data points used to train the second model by: modifying one or more of the data points used to train the second model; executing the first model using the modified data points; and identifying a change to one or more class designations attributed to the modified data points as compared to before the modifying; and outputting identifying information pertaining to the determined bias.

Abstract: Systems and methods for correlating item data are disclosed. A system for correlating item data may include a memory storing instructions and at least one processor configured to execute instructions to perform operations including: receiving text and image data associated with a reference item from a remote device; converting, using a computer-modeled embedding layer, at least one image to an image embedding; comparing the image embedding to reference embeddings stored in a database; selecting a subset of the candidate item text as candidate text data based on the comparison; selecting a subset of the candidate item images as candidate image data based on the comparison; selecting a text correlation model; determining a first similarity score; selecting an image correlation model; determining a second similarity score; calculating a confidence score based on the first and second similarity scores; and performing a responsive action based on the calculated confidence score.

Abstract: The present application provides an image feature acquisition method and a corresponding apparatus. According to an example of the method, a classification model may be trained by using preset classes of training images, and similar image pairs may be determined based on the training images; classification results from the classification model are tested by using verification images to determine nonsimilar image pairs; and the classification model is optimized based on the similar image pairs and the nonsimilar image pairs. In this way, the optimized classification model may be used to acquire image features.

Abstract: A crop prediction system performs various machine learning operations to predict crop production and to identify a set of farming operations that, if performed, optimize crop production. The crop prediction system uses crop prediction models trained using various machine learning operations based on geographic and agronomic information. Responsive to receiving a request from a grower, the crop prediction system can access information representation of a portion of land corresponding to the request, such as the location of the land and corresponding weather conditions and soil composition. The crop prediction system applies one or more crop prediction models to the access information to predict a crop production and identify an optimized set of farming operations for the grower to perform.

Abstract: A three-dimensional morphological vessel model (20) can be obtained by assigning diameters (14,15) along the vessel derived from a two-dimensional morphological projection (10) at locations in the three-dimensional model defined by the temporal locations (21,22) of a trackable instrument (5). An apparatus (7), a system (1) and a method (100) for use of the system (1) in characterizing the vessel of a living being (2) by rendering a three-5 dimensional morphological vessel model (20) are presented.

Abstract: For determination of motion artifact in MR imaging, motion of the patient in three dimensions is used with a measurement k-space line order based on one or more actual imaging sequences to generate training data. The MR scan of the ground truth three-dimensional (3D) representation subjected to 3D motion is simulated using the realistic line order. The difference between the resulting reconstructed 3D representation and the ground truth 3D representation is used in machine-based deep learning to train a network to predict motion artifact or level given an input 3D representation from a scan of a patient. The architecture of the network may be defined to deal with anisotropic data from the MR scan.

Abstract: The present disclosure relates to signal decoding and icon (e.g., a logo, shape, icon, etc.) detection. In some implementations, a series of filters are applied to scanlines to determine whether an icon is present. Other aspects, combinations and implementations are described as well.

Abstract: The following provides an exemplary method. An image is generated. Scan data generated by the sensing of a subject brain is received and a sequence of volumetric-images are accessed. For at least some of the voxels in at least some of the volumetric images, a change value is determined for the voxel by comparing the current-intensity value of the voxel to the adjacent-intensity value of the comparison voxel. For at least some of the plurality volumetric images, a first-aggregate is determined using at least a mean value of change values of the volumetric image and a second-aggregate is determined using at least a median value of change values of the volumetric image. At least one, but not all, of the volumetric images is determined as invalid as a result of the aggregates.

Abstract: Provided herein is technology relating to analysis of images and particularly, but not exclusively, to methods and systems for determining the area and/or volume of a region of interest using optical coherence tomography data. Some embodiments provide for determining the area and/or volume of a lesion in retinal tissue using three-dimensional optical coherence tomography data and a two-dimensional optical coherence tomography fundus image.

Abstract: Provided is a method for setting an edge blur according to a brightness value including a first step for providing a plurality of target sheets on each of which a reference pattern for detecting a reference line and a grid pattern for detecting an edge position are provided, and in which changes in brightness values included in the grid patterns are different from each other, a second step for capturing images of the target sheets to obtain target sheet images, a third step for analyzing a reference pattern for each corresponding target sheet to estimate the reference line, and analyzing the grid pattern to extract an edge profile provided in the grid pattern, a fourth step for calculating a gradient of a brightness difference between adjacent pixels on the basis of the edge profile, and acquiring a background edge blur parameter and a foreground edge blur parameter on the basis of the gradient according to brightness contrast present in the image, and a fifth step for generating background edge blur predicti

Abstract: A method and apparatus is provided that uses a deep learning (DL) network to correct projection images acquired using an X-ray source with a large focal spot size. The DL network is trained using a training dataset that includes input data and target data. The input data includes large-focal-spot-size X-ray projection data, and the output data includes small-focal-spot-size X-ray projection data (i.e., smaller than the focal spot of the input data). Thus, the DL network is trained to improve the resolution of projection data acquired using a large focal spot size, and obtain a resolution similar to what is achieved using a small focal spot size. Further, the DL network is can be trained to additional correct other aspects of the projection data (e.g., denoising the projection data).

Abstract: Provided is an information processing apparatus including a determination unit that determines at least whether a photographed image is a non-skin region image obtained by photographing a non-skin region, and an output unit that performs, in the case where the photographed image is determined to be the non-skin region image, a predetermined first output.

Abstract: Methods for compressing shape data for a set of electronic designs include inputting a set of shape data, where the shape data represents a set of shapes for a device fabrication process. A convolutional autoencoder is used on the set of shape data, the convolutional autoencoder having a pre-determined set of convolution layers including a kernel size and filter size for each convolution layer. The set of shape data is encoded to compress the set of shape data, using the pre-determined set of convolution layers of the convolutional autoencoder, to create a set of encoded shape data. The set of shape data comprises an SEM image, and the encoded set of shape data identifies a mask defect.

Abstract: The present disclosure discloses a method and a device for acquiring figure parameters of a user. The method includes the following steps: acquiring a photo of the user; processing the photo to generate a profile of the user; taking a parameterized three-dimensional human body model with a projection profile consistent with the profile of the user as a target parameterized three-dimensional human body model; and taking figure parameters of the target parameterized three-dimensional human body model as figure parameters of the user.

Abstract: Systems and methods for tracking persons by utilizing imagery data captured by a plurality of on-road vehicles. A large number of different persons appear in a corpus of imagery data collectively captured by a plurality of on-road vehicles. An initial and limited-accuracy model of one of the persons is used to search and detect visual occurrences of that person in the corpus of imagery data, thereby starting to track that person, in which such search is limited at first to a confined geo-temporal range, in order to limit the number of different persons over which the initial and limited-accuracy model has to search and detect that person. When the visual occurrences of that person are found, a better model can be constructed, which can now be used to expand the geo-temporal range over which yet additional visual occurrences of that person are found and used to better track that person.

Abstract: In some embodiments, apparatuses and methods are provided herein useful to assess quality of produce at a facility. In some embodiments, there is provided a system for assessing quality of produce at a facility including a produce assessment station configured to provide a staging area to determine a quality classification of a target produce. By one approach, the produce assessment station includes a fixed surface; a rotatable base, a first arm comprising a microphone; a second arm comprising a tapping device; and a local control circuit. In one configuration, the local control circuit configured to rotate the rotatable base at a particular angle and at a particular time interval and receive audio data from the microphone. By one approach, the system includes a plurality of sensors and a portable device configured to provide a signal to the local control circuit to initiate the quality classification of the target produce.

Abstract: The disclosed technology is generally directed to optical character recognition for forms. In one example of the technology, optical character recognition is performed on a plurality of forms. The forms of the plurality of forms include at least one type of form. Anchors are determined for the forms, including corresponding anchors for each type of form of the plurality of forms. Feature rules are determined, including corresponding feature rules for each type of form of the plurality of forms. Features and labels are determined for each form of the plurality of forms. A training model is generated based on a ground truth that includes a plurality of key-value pairs corresponding to the plurality of forms, and further based on the determined features and labels for the plurality of forms.

Abstract: Approaches to zero-shot learning include partitioning training data into first and second sets according to classes assigned to the training data, training a prediction module based on the first set to predict a cluster center based on a class label, training a correction module based on the second set and each of the class labels in the first set to generate a correction to a cluster center predicted by the prediction module, presenting a new class label for a new class to the prediction module to predict a new cluster center, presenting the new class label, the predicted new cluster center, and each of the class labels in the first set to the correction module to generate a correction for the predicted new cluster center, augmenting a classifier based on the corrected cluster center for the new class, and classifying input data into the new class using the classifier.

Abstract: There is provided systems and methods for performing actions based on light signatures. An exemplary system includes a light source, a light detector, a non-transitory memory storing a plurality of light signatures and a hardware processor. The hardware processor executes an executable code to illuminate, using the light source, a target object with a first light, collect, using the light detector, a second light being a reflection of the first light by the target object, match the second light with one of the plurality of light signatures, and perform an action in response to matching the second light with the one of the plurality of light signatures.