Abstract: A method and system for connecting a power tool with a mobile device. The mobile device receives a user request to connect to a power tool and transmits a short-range advertisement. The power tool receives the short-range advertisement and transmits a signal. The mobile device receives the signal from the power tool and determines a signal strength. The mobile device compares the signal strength to a predetermined signal strength value. When the signal strength value exceeds the predetermined signal strength value, the mobile device connects with the power tool.

Abstract: A computer-implemented method for a classification and training a neural network includes receiving input at the neural network, wherein the input includes a plurality of resolution inputs of varying resolutions, outputting a plurality of feature tensors for each corresponding resolution of the plurality of resolution inputs, fusing the plurality of feature tensors utilizing upsampling or down sampling for the vary resolutions, utilizing an equilibrium solver to identify one or more prediction vectors from the plurality of feature tensors, and outputting a loss in response to the one or more prediction vectors.

Abstract: A latent code defined in an input space is processed by the mapping neural network to produce an intermediate latent code defined in an intermediate latent space. The intermediate latent code may be used as appearance vector that is processed by the synthesis neural network to generate an image. The appearance vector is a compressed encoding of data, such as video frames including a person's face, audio, and other data. Captured images may be converted into appearance vectors at a local device and transmitted to a remote device using much less bandwidth compared with transmitting the captured images. A synthesis neural network at the remote device reconstructs the images for display.

Abstract: A latent code defined in an input space is processed by the mapping neural network to produce an intermediate latent code defined in an intermediate latent space. The intermediate latent code may be used as appearance vector that is processed by the synthesis neural network to generate an image. The appearance vector is a compressed encoding of data, such as video frames including a person's face, audio, and other data. Captured images may be converted into appearance vectors at a local device and transmitted to a remote device using much less bandwidth compared with transmitting the captured images. A synthesis neural network at the remote device reconstructs the images for display.

Abstract: A style-based generative network architecture enables scale-specific control of synthesized output data, such as images. During training, the style-based generative neural network (generator neural network) includes a mapping network and a synthesis network. During prediction, the mapping network may be omitted, replicated, or evaluated several times. The synthesis network may be used to generate highly varied, high-quality output data with a wide variety of attributes. For example, when used to generate images of people's faces, the attributes that may vary are age, ethnicity, camera viewpoint, pose, face shape, eyeglasses, colors (eyes, hair, etc.), hair style, lighting, background, etc. Depending on the task, generated output data may include images, audio, video, three-dimensional (3D) objects, text, etc.

Abstract: A style-based generative network architecture enables scale-specific control of synthesized output data, such as images. During training, the style-based generative neural network (generator neural network) includes a mapping network and a synthesis network. During prediction, the mapping network may be omitted, replicated, or evaluated several times. The synthesis network may be used to generate highly varied, high-quality output data with a wide variety of attributes. For example, when used to generate images of people's faces, the attributes that may vary are age, ethnicity, camera viewpoint, pose, face shape, eyeglasses, colors (eyes, hair, etc.), hair style, lighting, background, etc. Depending on the task, generated output data may include images, audio, video, three-dimensional (3D) objects, text, etc.

Abstract: According to certain embodiments, an electronic device comprises a first communication circuit configured to support a first communication protocol; a display; and at least one processor operatively connected to the first communication circuit and the display, wherein the at least one processor is configured to: configure a cluster with at least one external electronic based on the first communication protocol device through the first communication circuit, receive through the first communication circuit, from the at least one external electronic device, information identifying at least one short-range communication device connected to the at least one external electronic device via a second communication protocol, and control the display to display at least one indicator of the at least one short-range communication device based on the information received from the at least one external electronic device.

Abstract: Techniques are provided for determining confident data samples for machine learning (ML) models on unseen data. In one embodiment, a method is provided that comprises extracting, by a system comprising a processor, a feature vector for a data sample based on projection of the data sample onto a standard feature space. The method further comprises processing, by the system, the feature vector using an outlier detection model to determine whether the data sample is within a scope of a training dataset used to train a machine learning model, wherein the outlier detection model was trained using features extracted from the training dataset based on projection of data samples included in the training dataset onto the standard feature space.

Abstract: Disclosed are a test instrument and testing methods for audibly providing signal metrics (such as signal strength and/or signal quality) of fifth-generation network (5G) beams to assist installation of 5G Customer Premises Equipment (CPE) antenna at a premises. A test instrument may obtain signal metrics and provide audio output based on the signal metrics at various locations of the premises. The audio output may be transmitted to a headphone device worn by a user. In this manner, the user may select an appropriate location on the premises at which to install the 5G CPE antenna via audible queues that are based on the measured signal metric at a given location. The test instrument may provide fine-tuning capabilities by also audibly providing directional information that indicates where the 5G CPE antenna should be pointed or moved to align the 5G CPE antenna to a 5G beam.

Abstract: The first parameter generation unit 811 generates a first parameter, which is a parameter of a first recognizer, using first learning data including a combination of data to be recognized, a correct label of the data, and domain information indicating a collection environment of the data. The second parameter generation unit 812 generates a second parameter, which is a parameter of a second recognizer, using second learning data including a combination of data to be recognized that is collected in a predetermined collection environment, a correct label of the data, and target domain information indicating the predetermined collection environment, based on the first parameter. The third parameter generation unit 813 integrates the first parameter and the second parameter to generate a third parameter to be used for pattern recognition of input data by learning using the first learning data.

Abstract: One exemplary implementation facilitates object detection using multiple scans of an object in different lighting conditions. For example, a first scan of the object can be created by capturing images of the object by moving an image sensor on a first path in a first lighting condition, e.g., bright lighting. A second scan of the object can then be created by capturing additional images of the object by moving the image sensor on a second path in a second lighting condition, e.g., dim lighting. Implementations determine a transform that associates the scan data from these multiple scans to one another and use the transforms to generate a 3D model of the object in a single coordinate system. Augmented content can be positioned relative to that object in the single coordinate system and thus will be displayed in the appropriate location regardless of the lighting condition in which the physical object is later detected.

Abstract: A latent code defined in an input space is processed by the mapping neural network to produce an intermediate latent code defined in an intermediate latent space. The intermediate latent code may be used as appearance vector that is processed by the synthesis neural network to generate an image. The appearance vector is a compressed encoding of data, such as video frames including a person's face, audio, and other data. Captured images may be converted into appearance vectors at a local device and transmitted to a remote device using much less bandwidth compared with transmitting the captured images. A synthesis neural network at the remote device reconstructs the images for display.

Abstract: A method is for matching a set of first classes assigned to a first data set with a set of second classes assigned to a second data set. The method includes constructing, via a set of pre-processing functions, a plurality of alignment profiles such that at least one alignment profile is assigned to each of the first classes and each of the second classes. The method includes generating a comparison matrix for each group of the alignment profiles, such that each group includes at least one of the first classes and at least one of the second classes. The method includes training a first machine learning model, through supervised training, based on the generated comparison matrices and based on probabilistic labels generated by a second machine learning model.

Abstract: A generative adversarial network device and a training method thereof. The generative adversarial network device includes a generator and a discriminator. The generator is configured to generate a first sample according to an input data; the discriminator is coupled to the generator, and is configured to receive the first sample and be trained based on the first sample; the generator includes a first memristor array serving as a first weight array. The generative adversarial network device can omit a process of adding noise to fake samples generated by the generator, thereby saving training time, reducing resource consumption and improving training speed of the generative adversarial network.

Abstract: There is provided an electronic device having Bluetooth communication function. The electronic device confirms whether a current packet received in a receive slot is a retransmitted packet according to a SEQN bit in the packet header so as to determine whether to continuously turn on an RF receiver in the receive slot or early turn off the RF receiver to save power.

Abstract: Approaches for private and interpretable machine learning systems include a system for processing a query. The system includes one or more teacher modules for receiving a query and generating a respective output, one or more privacy sanitization modules for privacy sanitizing the respective output of each of the one or more teacher modules, and a student module for receiving a query and the privacy sanitized respective output of each of the one or more teacher modules and generating a result. Each of the one or more teacher modules is trained using a respective private data set. The student module is trained using a public data set. In some embodiments, human understandable interpretations of an output from the student module is provided to a model user.

Abstract: A method for the artifact correction of three-dimensional volume image data of an object is disclosed. In an embodiment, the method includes receiving first volume image data via a first interface, the first volume image data being based on projection measurement data acquired via a computed tomography device, the computed tomography device including a system axis, and the first volume image data including an artifact including high-frequency first portions in a direction of a system axis and including second portions, being low-frequency relative to the high-frequency first portions, in a plane perpendicular to the system axis; ascertaining, via a computing unit, artifact-corrected second volume image data by applying a trained function to the first volume image data received; and outputting the artifact-corrected second volume image data via a second interface.

Abstract: A novel concept for Near Field Communication (NFC) is described herein. In accordance with the embodiments described herein, orthogonal carrier signals are transmitted between NFC transmitter and NFC receiver circuit. In one arrangement, a carrier signal is unmodulated to ensure uninterrupted energy transfer to the receiver circuit. The orthogonal carriers are used to implement full-duplex Near Field Communication.

Abstract: An example system includes a processor to receive an input and a model trained to classify inputs. The processor is to iteratively generate a perturbed input that optimizes a saliency metric including a classification term, a sparsity term, and a smoothness term, while keeping parameters of the model constant. The processor is to also detect that a predefined number of iterations is exceeded or a convergence of values of the perturbed input. The processor is to further generate a saliency mask based on a perturbation of the perturbed input in response to detecting the predefined number of iterations is exceeded or the convergence.

Abstract: Field effect transistors including a source region and a drain region on a substrate, a fin base protruding from a top surface of the substrate, a plurality of fin portions extending upward from the fin base and connecting the source region with the drain region, a gate electrode on the fin portions, and a gate dielectric between the fin portions and the gate electrode may be provided. A top surface of the substrate may include a plurality of grooves (e.g., a plurality of convex portions and a plurality of concave portions). Further, a device isolation layer may be provided to expose upper portions of the plurality of fin portions and to cover top surfaces of the plurality of grooves.