Abstract: A system generates a user interface that enables a user to interact with an interactive chart associated with a statement of a data processing package. Via one or more user interactions with the user interface, the system may receive one or more chart parameters for the chart. Using a statement from the data processing package and the one or more chart parameters, the system may generate an additional statement and append the generated statement to the data processing package to form an enriched data processing package. The system may communicate the enriched data processing package to a search service for execution. The system may display the results in the chart.

Abstract: The present invention relates to hydrogel prodrug compositions comprising cross-linked hyaluronic acid (HA), or a derivative or a salt thereof, wherein the cross-linker system comprises a biodegradable spacer, wherein the cross-linked HA comprises a conjugated drug-linker, and wherein the linker is capable of releasing the drug under physiological conditions. The present invention further relates to methods for preparing the hydrogel prodrug compositions. The present invention further relates to methods for treating an ocular condition using the hydrogel compositions.

Abstract: A system generates a user interface that enables a user to generate a chart from one or more statements of a data processing package. Via one or more user interactions with the user interface, the system may receive one or more chart parameters for a chart. Using a statement from the data processing package and the one or more chart parameters, the system may generate an additional statement and append the generated statement to the data processing package to form an enriched data processing package. The system may communicate the enriched data processing package to a search service for execution. The system may display the results in an interactive chart.

Abstract: The present disclosure provides embodiments for joint twist generation for animation. The system can utilize a neural network, also referred to as a deep neural network, which utilizes machine learning processes in order to create animation data that are more life-like and realistic. The system can obtain a set of axis vectors for a rig of a virtual character model; obtain a twist model for the rig; input the set of axis vectors to the twist model to obtain a set of twist vectors; and determine animation data based on the set of axis vectors and the set of twist vectors.

Abstract: A system generates a user interface that enables a user to modify time ranges associated with search-related statements of a data processing package. Via one or more user interactions with the user interface, the system may receive a modified time range for the statement. The modified time range may be appended to the data processing package to form an enriched data processing package. The system may communicate the enriched data processing package to a search service for execution. The system may display the results in the user interface.

Abstract: A system is described that receives a query model of a query that includes one or more query commands. The query model includes a command model that corresponds to at least query command of the one or more query commands. The system uses the command model to generate an interactive action model summary and causes a user interface to display the query and the interactive action model summary in a query actions panel. A modification to the query in the user interface causes an update to the query actions panel and a modification to the action model summary causes an update to the at least one query command of the query.

Abstract: A system generates a user interface that enables a user to generate a data summarization statement for a data processing package. Via one or more user interactions with the user interface, the system may receive one or more parameters for the summarization statement. Using the parameters, the system may generate a summarization statement for execution by a data service, an action model display object, a statement action model display object, and/or a filter token object for display in the user interface.

Abstract: Systems and methods are provided for enhanced animation generation based on generative control models. An example method includes accessing an autoencoder trained based on character control information generated using motion capture data, the character control information indicating, at least, trajectory information associated with the motion capture data, and the autoencoder being trained to reconstruct, via a latent feature space, the character control information. First character control information associated with a trajectory of an in-game character of an electronic game is obtained. A latent feature representation is generated and the latent feature representation is modified. A control signal is output to a motion prediction network for use in updating a character pose of the in-game character.

Abstract: A computing system may provide functionality for controlling an animated model to perform actions and to perform transitions therebetween. The system may determine, from among a plurality of edges from a first node of a control graph to respective other nodes of the control graph, a selected edge from the first control node to a selected node. The system may then determine controls for an animated model in a simulation based at least in part on the selected edge, control data associated with the selected node, a current simulation state of the simulation, and a machine learned algorithm, determine an updated simulation state of the simulation based at least in part on the controls for the animated model, and adapt one or more parameters of the machine learned algorithm based at least in part on the updated simulation state and a desired simulation state.

Abstract: In some embodiments, the dynamic animation generation system can provide a deep learning framework to produce a large variety of martial arts movements in a controllable manner from unstructured motion capture data. The system can imitate animation layering using neural networks with the aim to overcome challenges when mixing, blending and editing movements from unaligned motion sources. The system can synthesize movements from given reference motions and simple user controls, and generate unseen sequences of locomotion, but also reconstruct signature motions of different fighters. For achieving this task, the dynamic animation generation system can adopt a modular framework that is composed of the motion generator, that maps the trajectories of a number of key joints and root trajectory to the full body motion, and a set of different control modules that map the user inputs to such trajectories.

Abstract: A method for full body motion tracking includes receiving tracking signals from a plurality of sensors associated with an upper body of a person, and based on the tracking signals, determining motion features and joint features. The method further includes training a diffusion model that includes a multi-layer perceptron (MLP) network, and generating a multiple inputs to the trained diffusion model, the inputs including the motion features and the joint features. The method includes providing the inputs to the trained diffusion model to generate multiple outputs. The outputs include sequences of full body poses, the sequences of full body poses including upper body poses and lower body poses.

Abstract: A computing system may provide functionality for controlling an animated model to perform actions and to perform transitions therebetween. The system may determine, from among a plurality of edges from a first node of a control graph to respective other nodes of the control graph, a selected edge from the first control node to a selected node. The system may then determine controls for an animated model in a simulation based at least in part on the selected edge, control data associated with the selected node, a current simulation state of the simulation, and a machine learned algorithm, determine an updated simulation state of the simulation based at least in part on the controls for the animated model, and adapt one or more parameters of the machine learned algorithm based at least in part on the updated simulation state and a desired simulation state.

Abstract: Systems and methods are provided for enhanced animation generation based on using motion mapping with local bone phases. An example method includes accessing first animation control information generated for a first frame of an electronic game including local bone phases representing phase information associated with contacts of a plurality of rigid bodies of an in-game character with an in-game environment. Executing a local motion matching process for each of the plurality of local bone phases and generating a second pose of the character model based on the plurality of matched local poses for a second frame of the electronic game.

Abstract: System and methods for using a deep learning framework to customize animation of an in-game character of a video game. The system can be preconfigured with animation rule sets corresponding to various animations. Each animation can be comprised of a series of distinct poses that collectively form the particular animation. The system can provide an animation-editing interface that enables a user of the video game to make modifications to at least one pose or frame of the animation. The system can realistically extrapolate these modifications across some or all portions of the animation. In addition or alternatively, the system can realistically extrapolate the modifications across other types of animations.

Abstract: A method for unloading a payload at a loading station includes selecting the payload to be unloaded from different payloads and registering a total target quantity of the selected payload to be unloaded at the loading station as a target quantity unit, a current total quantity of the selected payload that has already been unloaded at the loading station as an actual quantity unit, a number of already performed unloading procedures corresponding to a quantity of the selected payload that has already been unloaded at the loading station, or a difference between the target quantity unit and the actual quantity unit.

Abstract: A method for tipping out a cargo load from a reception unit includes providing the reception unit on a lifting device of a work machine, performing a tipping out process of a mass of cargo load from the reception unit, detecting a predetermined residual mass of the cargo load remaining in the reception unit, detecting a predetermined loading mass tipped out during the tipping step, and completing the tipping out process as a function of the predetermined residual mass or the predetermined loading mass.

Abstract: The present invention relates to hydrogel prodrug compositions comprising cross-linked hyaluronic acid (HA), or a derivative or a salt thereof, wherein the cross-linker system comprises a biodegradable spacer, wherein the cross-linked HA comprises a conjugated drug-linker, and wherein the linker is capable of releasing the drug under physiological conditions. The present invention further relates to methods for preparing the hydrogel prodrug compositions. The present invention further relates to methods for treating an ocular condition using the hydrogel compositions.

Abstract: An apparatus for forming glass tubes is provided. The apparatus includes a Danner-pipe, a heating device, and a heating chamber having the Danner-pipe and the heating device arranged therein. The Danner-pipe is inclined with respect to a horizontal plane by a Danner-angle. The heating device is inclined with respect to the horizontal plane by a heating-angle. The Danner-angle and/or the heating angle are in a range of 1° to 45°.

Abstract: Embodiments of the systems and methods described herein provide a dynamic animation generation system that can apply a real-life video clip with a character in motion to a first neural network to receive rough motion data, such as pose information, for each of the frames of the video clip, and overlay the pose information on top of the video clip to generate a modified video clip. The system can identify a sliding window that includes a current frame, past frames, and future frames of the modified video clip, and apply the modified video clip to a second neural network to predict a next frame. The dynamic animation generation system can then move the sliding window to the next frame while including the predicted next frame, and apply the new sliding window to the second neural network to predict the following frame to the next frame.

Abstract: In some embodiments, the dynamic animation generation system can provide a deep learning framework to produce a large variety of martial arts movements in a controllable manner from unstructured motion capture data. The system can imitate animation layering using neural networks with the aim to overcome challenges when mixing, blending and editing movements from unaligned motion sources. The system can synthesize movements from given reference motions and simple user controls, and generate unseen sequences of locomotion, but also reconstruct signature motions of different fighters. For achieving this task, the dynamic animation generation system can adopt a modular framework that is composed of the motion generator, that maps the trajectories of a number of key joints and root trajectory to the full body motion, and a set of different control modules that map the user inputs to such trajectories.