Abstract: The album creation application of the present disclosure displays image data, to which trimming is performed, and a template, which includes a slot in which the image data is arranged, so that a slot and image data to be arranged in the slot are selected by use of an input device. Position information of a point of interest in the image to be arranged in the slot is obtained. Composition patterns applicable to the image with designation of the point of interest are presented to the user, and the composition pattern to be applied, which is selected by the user from among the presented composition patterns, is obtained. Trimming is performed based on the point of interest and the selected composition pattern selected. The trimmed images are listed, so that multiple trimmed images are presented to the user as trimming proposals.

Abstract: There is provided a computerized system and method of generating a unique identification associated with a gemstone, usable for unique identification of the gemstone. The method comprises: obtaining one or more images of the gemstone, the one or more images captured at one or more viewing angles relative to the gemstone and to a light pattern, thus giving rise to a representative group of images; processing the representative group of images to generate a set of rotation-invariant values informative of rotational cross-correlation relationship characterizing the images in the representative group; and using the generated set of rotation-invariant values to generate a unique identification associated with the gemstone. The unique identification associated with the gemstone can be further compared with an independently generated unique identification associated with the gemstone in question, or with a class-indicative unique identification.

Abstract: A method includes accessing a training sample including an image of a scene, depth measurements of the scene, and a predetermined 3D position of an object in the scene. The method includes training a 3D-detection model for detecting 3D positions of objects based the depth measurements and the predetermined 3D position, and training a 2D-detection model for detecting 2D positions of objects within images. Training the 2D-detection model includes generating an estimated 2D position of the object by processing the image using the 2D-detection model, determining a subset of the depth measurements that correspond to the object based on the estimated 2D position and a viewpoint from which the image is captured, generating an estimated 3D position of the object based on the subset of the depth measurements, and updating the 2D-detection model based on a comparison between the estimated 3D position and the predetermined 3D position.

Abstract: Methods and systems fort operating one or more light sources to project adversarial patterns generated to disorient a machine learning based detection system, comprising generating one or more adversarial patterns configured to disorient the machine learning based detection system and operating one or more light sources configured to project one or more of the adversarial pattern(s) in association with the targeted object in order to disorient the machine learning based detection system.

Abstract: A tangible object virtualization station including a base capable of stably resting on a surface and a head component unit connected to the base. The head component unit extends upwardly from the base. At an end of the head component opposite the base, the head component comprises a camera situated to capture a downward view of the surface proximate the base, a lighting array that directs light downward toward the surface proximate the base. The tangible object virtualization station further comprises a display interface included in the base. The display interface is configured to hold a display device in an upright position and connect the display device to the camera and the lighting array.

Abstract: Methods, systems, and articles of manufacture, including computer program products, are provided for synthesizing images for machine learning. The method may include selecting one or more image preprocessing transformations to apply on the foreground object image; applying the selected one or more image preprocessing transformations to the foreground object image; selecting a background image from a set of background images depicting a variety of different backgrounds which may be associated with the foreground object image; merging the selected background image with the foreground object image to form a synthesized image; selecting one or more image transformations to apply on the synthesized image; applying the selected one or more image transformations to the synthesized image; and storing the synthesized image in a collection of synthesized images to train a machine learning model.

Abstract: Techniques are disclosed for generating a vector image from a raster image, where the raster image is, for instance, a photographed or scanned version of a hand-drawn sketch. While drawing a sketch, an artist may perform multiple strokes to draw a line, and the resultant raster image may have adjacent or partially overlapping salient and non-salient lines, where the salient lines are representative of the artist's intent, and the non-salient (or auxiliary) lines are formed due to the redundant strokes or otherwise as artefacts of the creation process. The raster image may also include other auxiliary features, such as blemishes, non-white background (e.g., reflecting the canvas on which the hand-sketch was made), and/or uneven lighting. In an example, the vector image is generated to include the salient lines, but not the non-salient lines or other auxiliary features. Thus, the generated vector image is a cleaner version of the raster image.

Abstract: A technique for performing a ray intersection test, the method comprising: receiving a request for an early termination ray intersection test for a ray; testing the ray against one or more early termination box nodes and one or more normal box nodes of a bounding volume hierarchy; and based on the test of the ray against the one or more early termination box nodes, determining whether to end traversal of the bounding volume hierarchy and determine whether the ray intersects geometry for the purpose of the ray intersection test.

Abstract: One embodiment provides a method, including: receiving, at an information handling device having a display, an indication to capture an image of a scene using a camera sensor positioned underneath the display; capturing, responsive to the receiving, a plurality of partial images of the scene, wherein the capturing comprises adjusting, using an adjustment mechanism, a physical position of the camera sensor after each of the plurality of partial images of the scene are captured; and stitching, subsequent to the capturing, the plurality of partial images together to form the image of the scene. Other aspects are described and claimed.

Abstract: A System for Measuring Body Kinetics includes a wearable device configured to be wrapped around a joint. A microprocessor is attached to the wearable device. One or more Inertial Measurement Units (IMUs) are connected to the microprocessor and arranged on the wearable device. The IMUs are arranged and configured to provide kinetic data concerning the joint to the microprocessor. A wireless transmission component is connected to the microprocessor. The microprocessor is configured to receive kinetic data from the IMUs, and to transmit the kinetic data by way of the wireless transmission component to a central processor or other device. An algorithm resides within the microprocessor or the central processor or other device, and is configured to determine the position of each IMU from the kinetic data. The wearable device may be constructed of fabric, strap, adhesive tape, or a combination thereof.

Abstract: The generating of actionable recommendations for tuning model metrics of an Artificial Intelligence (AI) system includes partitioning a key performance indicator (KPI) range associated with a target system into a plurality of buckets. Log data including at least one KPI of the target system and one or more AI model metrics is partitioned and distributed across the plurality of buckets. For each bucket, an aggregate value of the one or more AI model metrics across the log data is computed and weighted according to the volume of log data in that bucket. A correlation factor between the aggregate value and a representative KPI value for each bucket is determined. A model tuning recommendation to increase ranking of the AI model metrics according to the determined correlation factor is provided to an output device and/or to the AI system for updating the one or more AI model.

Abstract: An electronic device includes a network monitor configured to acquire network environment information related to a radio frequency (RF) transmission signal; a transceiver configured to generate an envelope signal of the RF transmission signal; a transmission (Tx) module including a power amplifier for receiving the RF transmission signal from the transceiver and amplifying the RF transmission signal; and an envelope tracking (ET) modulator configured to receive the envelope signal from the transceiver and to provide a bias of a power amplifier to correspond to the envelope signal, wherein the ET modulator determines a magnitude of the bias of the power amplifier based on the network environment information acquired by the network monitor.

Abstract: An apparatus comprising memory configured to store data to be machine-recognized (710), and at least one processing core configured to run an adaptive boosting machine learning algorithm with the data, wherein a plurality of learning algorithms are applied, wherein a feature space is partitioned into bins, wherein a distortion function is applied to features of the feature space (720), and wherein a first derivative of the distortion function is not constant (730).

Abstract: An apparatus comprises processing circuitry configured to receive a plurality of training image data sets and a plurality of predetermined displacements. The processing circuitry is further configured to use the training image data sets and predetermined displacements to train a transformation regressor in combination with a discriminator in an adversarial fashion by repeatedly alternating a transformation regressor training process in which the transformation regressor is trained to predict displacements, and a discriminator training process in which the discriminator is trained to distinguish between predetermined displacements and displacements predicted by the transformation regressor.

Abstract: The present disclosure generally relates to managing location-related communications of an electronic device associated with a user account. In some embodiments, techniques are discussed for managing location-related communications by pausing display of location-related communications while location-related alerts are enabled. In some embodiments, an option for pausing the alerts is displayed with a set of one or more alert conditions. In some embodiments, techniques are discussed for managing location-related communications by automatically generating a second alert after a first alert was generated in response to a set of alert criteria being met.

Abstract: A method for determining a size of a garment, comprising: receiving an image comprising a representation of the garment and a representation of a reference element, the reference element having a predefined shape and predefined dimensions; identifying the representation of the garment within the received image; identifying the representation of the reference element within the received image; determining first dimensions of the representation of the reference element within the received image and second dimensions of the representation of the garment within the received image; determining real dimensions of the garment using the first dimensions of the representation of the reference element within the received image, the second dimensions of the representation of the garment within the received image and the predefined dimensions of the reference element; and outputting the real dimensions of the garment.

Abstract: An ear saddle point of a subject's ear is determined to produce specifications to fit a wearable apparatus to a subject's head. A boundary of the subject's ear in a profile image is determined using a first model. A map of the subject's ear is generated indicating probable locations of a two-dimensional ear saddle point. A most probable location of the two-dimensional ear saddle point is determined based on the probability map. The two-dimensional ear saddle point is projected onto a three-dimensional mesh surface representing the subject's head. A maximum depth of the three-dimensional mesh surface is determined in a defined region around the projected two-dimensional ear saddle point. The ear saddle point is computed based on the projected two-dimensional ear saddle point and the determined maximum depth. Specifications of a wearable apparatus are generated to fit the apparatus to the subject's head based on the computed ear saddle point.

Abstract: A computing system can include an embedding model and a clustering model. The computing system input each of the plurality of inputs into the embedding model and receiving respective embeddings for the plurality of inputs as outputs of the embedding model. The computing system can input the respective embeddings for the plurality of inputs into the clustering model and receiving respective cluster assignments for the plurality of inputs as outputs of the clustering model. The computing system can evaluate a clustering loss function that evaluates a first average, across the plurality of inputs, of a respective first entropy of each respective probability distribution; and a second entropy of a second average of the probability distributions for the plurality of inputs. The computing system can modify parameter(s) of one or both of the clustering model and the embedding model based on the clustering loss function.

Abstract: The present disclosure generally relates to managing location-related communications of an electronic device associated with a user account.

Abstract: Various embodiments of the present disclosure relate to an electronic device and a feedback information acquisition method therefor. The feedback information acquisition method of the electronic device includes: acquiring input feedback information of a user and first response information of the user, which are related to a specific function; training a feedback estimation model by using the input feedback information and the first response information; acquiring second response information of the user related to the specific function; and acquiring feedback information related to the specific function by applying the second response information to the trained feedback estimation model.