Abstract: A method of cytotoxically treating cancer with aluminum silicate nanorods (SNRs) including aluminum, iron, magnesium, oxygen, potassium, and oxygen comprises contacting the SNRs at a concentration of 0.5 to 2 ?g/mL with a cancerous sample. The aluminum silicate nanorods have a longest dimension of 100 nanometers (nm) to 5500 nm and a diameter of 20 nm to 250 nm. The SNRs are porous with a pore size of 1 nm to 12 nm and include cisplatin in an amount of 20 to 300 mg/g. The cancerous sample has a reduced cell viability after the contacting.

Abstract: The present invention sets forth a technique for performing virtual object placement in a video sequence. The technique includes identifying a planar surface depicted in an input video sequence and selecting a virtual object included in an object library. The technique also includes generating, for a combination of the planar surface and the virtual object, a suitability metric associated with the combination, wherein the suitability metric is based at least on a semantic compatibility between the virtual object and the planar surface. The technique further includes generating, via one or more machine learning models, a modified video sequence based on the suitability metric, where the modified video sequence depicts the virtual object placed on the planar surface.

Abstract: An example computing platform comprising is configured to (i) receive, via one or more cameras positioned on a construction site, a plurality of images, (ii) detect, within the plurality of images, a plurality of objects being worn by respective workers on the construction site, (iii) select, from the plurality of images, a set of images depicting a particular worker, and (iv) based on the selected set of images depicting the particular worker, determine a plurality of trade probabilities for the particular worker, each trade probability in the plurality of trade probabilities indicating a likelihood that the particular worker belongs to a particular trade from among a plurality of trades.

Abstract: A method includes receiving from a client system a user input having input tokens and generating a span-based frame representation based on the input tokens. The span-based frame representation may include intents, slots, and a span. The span may include a first index endpoint associated with a first token and a second index endpoint associated with a second token. The method further includes encoding the user input, based on an encoder of a natural language understanding module, to generate a feature vector for the user input, and determining, by a length module of the natural language understanding module, a length of the span-based frame representation based on the feature vector for the user input. Generating the span-based frame representation may be further based on the length of the span-based frame representation. The method further includes, responsive to the user input, executing tasks based on the span-based frame representation.

Abstract: A method of cytotoxically treating cancer with aluminum silicate nanorods (SNRs) including aluminum, iron, magnesium, oxygen, potassium, and oxygen comprises contacting the SNRs at a concentration of 0.5 to 2 ?g/mL with a cancerous sample. The aluminum silicate nanorods have a longest dimension of 100 nanometers (nm) to 5500 nm and a diameter of 20 nm to 250 nm. The SNRs are porous with a pore size of 1 nm to 12 nm and include cisplatin in an amount of 20 to 300 mg/g. The cancerous sample has a reduced cell viability after the contacting.

Abstract: One embodiment of the present invention sets forth a technique for performing estimation of scene parameters associated with a two-dimensional (2D) input scene. technique includes identifying, based on the input scene, one or more line segments included in the input scene and generating one or more vanishing points associated with the input scene based on the one or more line segments. The technique also includes estimating, based on the one or more vanishing points, one or more scene parameters associated with the scene and inserting a world object into the input scene based on the one or more scene parameters.

Abstract: One embodiment of the present invention sets forth a technique for estimating a real-world size of an object included in an input scene. The technique includes identifying one or more depictions of human faces included in a two-dimensional input scene and generating one or more bounding boxes associated with the input scene, where each bounding box represents a head size associated with a different one of the one or more depictions of human faces. The technique also includes calculating a relative depth value for each of one or more pixels included in the input scene. The technique further includes calculating an average relative head size based on the one or more bounding boxes and relative depth values associated with the one or more pixels and generating a depth scale based on the average relative head size and a known real-world dimension of an average human head.

Abstract: Systems, devices, and methods are provided for depth-guided structure from motion. A system may obtain a plurality of image frames from a digital content item that corresponds to a scene and determine, based at least in part on a correspondence search, a set of 2-D keypoints for the plurality of image frames. A depth estimator may be used to determine a plurality of dense depth map for the plurality of image frames. The set of 2-D keypoints and the plurality of dense depth maps may be used to determine a corresponding set of depth priors. Initialization and/or depth-regularized optimization may be performed using the keypoints and depth priors.

Abstract: An example computing platform comprising is configured to (i) receive, via one or more cameras positioned on a construction site, a plurality of images, (ii) detect, within the plurality of images, a plurality of objects being worn by respective workers on the construction site, (iii) select, from the plurality of images, a set of images depicting a particular worker, and (iv) based on the selected set of images depicting the particular worker, determine a plurality of trade probabilities for the particular worker, each trade probability in the plurality of trade probabilities indicating a likelihood that the particular worker belongs to a particular trade from among a plurality of trades.

Abstract: Techniques are disclosed for detecting an uncovered portion of a body of a person in a frame of video content. In an example, a first machine learning model of a computing system may output a first score for the frame based on a map that identifies a region of the frame associated with an uncovered body part type. Depending on a value of the first score, a second machine learning model that includes a neural network architecture may further analyze the frame to output a second score. The first score and second score may be merged to produce a third score for the frame. A plurality of scores may be determined, respectively, for frames of the video content, and a maximum score may be selected. The video content may be selected for presentation on a display for further evaluation based on the maximum score.

Abstract: Systems, devices, and methods are provided for depth guided structure from motion. A system may obtain a plurality of image frames from a digital content item that corresponds to a scene and determine, based at least in part on a correspondence search, a set of 2-D keypoints for the plurality of image frames. A depth estimator may be used to determine a plurality of dense depth map for the plurality of image frames. The set of 2-D keypoints and the plurality of dense depth maps may be used to determine a corresponding set of depth priors. Initialization and/or depth-regularized optimization may be performed using the keypoints and depth priors.

Abstract: A system can be utilized to retrieve media content and rating schemas, to determine maturity ratings for media content. The media content can be utilized to determine segments of data as building blocks associated with mature content. The building blocks can be mapped to content descriptors and rating levels associated with the rating schemas. The building blocks can be compared the media content to identify portions of the media content that have characteristics represented by the building blocks. The building blocks representing the characteristics in the portions of the media content can be utilized to select content descriptors and rating levels associated with the media content. The selected content descriptor and selected rating levels can be utilized to control how, and/or whether, the media content is made available for output to the consumers.

Abstract: In one embodiment, a method includes receiving a user input comprising input tokens from a client system, parsing the user input to determine ontology tokens and utterance tokens corresponding to the input tokens, decoding the ontology tokens and the utterance tokens to generate a span-based frame representation comprising intents, slots, and a span, wherein the ontology tokens are decoded into the intents and slots, and wherein the utterance tokens are decoded to determine the span comprising one or more tokens of the input tokens, wherein the span comprises a first index endpoint associated with a first token of the one or more tokens and a second index endpoint associated with a second token of the one or more tokens, and executing, responsive to the user input, one or more tasks based on the span-based frame representation.

Abstract: Techniques for a computer-implemented service for virtual product placement in video frames are described. According to some embodiments, a computer-implemented method includes receiving, at a virtual product placement service, a request to place a two-dimensional image of a virtual product into a video, identifying, by a machine learning model of the virtual product placement service, a surface depicted in the video for insertion of the two-dimensional image of the virtual product, inserting, by the virtual product placement service, of the two-dimensional image of the virtual product into one or more frames of the video onto the surface to generate a video including the virtual product, and transmitting the video including the virtual product to a viewer device or a storage location.

Abstract: In one embodiment, a method includes receiving, by a first client system, from one or more remote servers, a current version of a neural network model including multiple model parameters, training the neural network model on multiple examples retrieved from a local data store to generate multiple updated model parameters, wherein each of the examples includes one or more features and one or more labels, calculating a user valuation associated with the first client system, wherein the user valuation represents a measure of utility of training the neural network model on the multiple examples, and sending, to one or more of the remote servers, the trained neural network model and the user valuation, wherein the user valuation is associated with a likelihood of the first client system being selected for a subsequent training of the neural network model.

Abstract: Disclosed are various embodiments for seamless insertion of modified media content. In one embodiment, a modified portion of video content is received. The modified portion has a start cue point and an end cue point that are set relative to a modification to the video content to indicate respectively when the modification approximately begins and ends compared to the video content. A video coding associated with the video content is identified. The start cue point and/or the end cue point are dynamically adjusted to align the modified portion with the video content based at least in part on the video coding.

Abstract: Techniques are disclosed for detecting an uncovered portion of a body of a person in a frame of video content. In an example, a first machine learning model of a computing system may output a first score for the frame based on a map that identifies a region of the frame associated with an uncovered body part type. Depending on a value of the first score, a second machine learning model that includes a neural network architecture may further analyze the frame to output a second score. The first score and second score may be merged to produce a third score for the frame. A plurality of scores may be determined, respectively, for frames of the video content, and a maximum score may be selected. The video content may be selected for presentation on a display for further evaluation based on the maximum score.

Abstract: An example computing platform comprising is configured to (i) receive, via one or more cameras positioned on a construction site, a plurality of images, (ii) detect, within the plurality of images, a plurality of objects being worn by respective workers on the construction site, (iii) select, from the plurality of images, a set of images depicting a particular worker, and (iv) based on the selected set of images depicting the particular worker, determine a plurality of trade probabilities for the particular worker, each trade probability in the plurality of trade probabilities indicating a likelihood that the particular worker belongs to a particular trade from among a plurality of trades.

Abstract: In one embodiment, a method includes receiving a user request to automatically debug a natural-language understanding (NLU) model, accessing a plurality of predicted semantic representations generated by the NLU model, wherein the plurality of predicted semantic representations are associated with a plurality of dialog sessions, respectively, wherein each dialog session is between a user and an assistant xbot associated with the NLU model, generating a plurality of expected semantic representations associated with the plurality of dialog sessions based on an auto-correction model, wherein the auto-correction model is learned from dialog training samples generated based on active learning, identifying incorrect semantic representations of the predicted semantic representations based on a comparison between the predicted semantic representations and the expected semantic representations, and automatically correcting the incorrect semantic representations by replacing them with respective expected semantic repre

Abstract: In one embodiment, a system includes an automatic speech recognition (ASR) module, a natural-language understanding (NLU) module, a dialog manager, one or more agents, an arbitrator, a delivery system, one or more processors, and a non-transitory memory coupled to the processors comprising instructions executable by the processors, the processors operable when executing the instructions to receive a user input, process the user input using the ASR module, the NLU module, the dialog manager, one or more of the agents, the arbitrator, and the delivery system, and provide a response to the user input.