Abstract: A deep neural network-based system for detection and recognition of construction elements in construction engineering drawings. A first neural network performs object detection and recognition to detect and recognize ducts and fittings. A second neural network performs text detection to detect text. A third neural network performs text recognition. Pixel tracing of the construction engineering drawing is performed using the bounding box for detected text as a reference point. The recognized text is filtered to output system types and system sizes from the recognized text. Each system type and size instance is correlated to a detected duct, pipe or fitting instance to determine object coordinates, system type, item type, size and length. A list of construction elements is generated for all detected duct, pipe and fitting instances. An interactive visualization of the construction engineering drawing based on the list of construction elements and/or a costing may be provided.

Abstract: A system and method relating to constructing an encoder and decoder neural network for providing semantic image segmentation includes generating an encoder comprising encoding convolution layers, each of the encoding convolution layers specifying an encoding filter operation using a respective first filter kernel, generating a decoder corresponding to the encoder, the decoder comprising decoding convolution layers, each of the decoding convolution layers being associated with a corresponding encoding convolution layer, and each of the decoding convolution layers specifying a decoding filter operation using a respective second filter kernel derived from the first filter kernel of the corresponding encoder convolution layer, and providing an input image to the encoder and the decoder for semantic image segmentation.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for performing machine learning using a query engine. One of the methods includes obtaining, from a user device and by a query engine that is configured to access one or more databases, a command to execute a user-defined function, wherein the user-defined function includes an inference call to a machine learning model, wherein the command comprises one or more model inputs to the machine learning model; obtaining, by the query engine and from the one or more databases, trained parameter values for the machine learning model; executing, by the query engine, the user-defined function, comprising processing the one or more model inputs using the machine learning model according to the obtained parameter values of the machine learning model to generate respective model outputs; and providing, to the user device and by the query engine, the generated model outputs.

Abstract: A system and method relating to object detection may include receiving an image frame comprising an array of pixels captured by an image sensor associated with the processing device, identifying a near-field image segment and a far-field image segment in the image frame, applying a first neural network trained for near-field image segments to the near-field image segment for detecting the objects presented in the near-field image segment, and applying a second neural network trained for far-field image segments to the far-field image segment for detecting the objects presented in the near-field image segment.

Abstract: An intelligent light system installed on a motor vehicle includes a light source to provide illumination for the motor vehicle, wherein a wavelength of a light beam generated by the light source is adjustable, a plurality of sensors for capturing sensor data of an environment surrounding the motor vehicle, and a processing device to receive the sensor data captured by the plurality of sensors, provide the sensor data to a neural network to determine a first state of the environment, and issue a control signal to adjust the wavelength of the light beam based on the determined first state of the environment.

Abstract: A system and method relating to object detection using multiple sensor devices include receiving a range data comprising a plurality of points, each of plurality of points being associated with an intensity value and a depth value, determining, based on the intensity values and depth values of the plurality of points, abounding box surrounding a cluster of points among the plurality of points, receiving a video image comprising an array of pixels, determining a region in the video image corresponding to the bounding box, and applying a first neural network to the region to determine an object captured by the range data and the video image.

Abstract: A washing machine including a rotatable cylinder comprising a washing chamber to hold washables, one or more sensors, and a processing device, communicatively connected to the one or more sensors to control an operation of the washing machine, to receive sensor data captured by the one or more sensors, determine, using a machine learning model based on the sensor data, a plurality of properties associated with the washables, determine a setting for the washing machine based on the plurality of properties, and cause the washing machine to operate according to the setting.

Abstract: Disclosed herein is a method for the identification of a treatment for overproduction of mucin during otitis media (OM) and chronic obstructive pulmonary disease (COPD). The method uses a MUC5AC plasmid to identify novel cytoplasmic proteins of Nontypeable Haemophilus influenzae, a common mediator of OM and COPD, which up-regulate human MUC5AC mucin transcription via a positive p38 MAP kinase pathway and a negative PI 3-Kinase-Akt pathway. These proteins can be used to identify or design inhibitors of the p38 MAP kinase pathway and activators of the PI 3-kinase Akt pathway.