Abstract: The present disclosure is directed to methods for training and using an artificial intelligence model for use with predicting a task output. The method includes generating a plurality of individual datasets, each of the plurality of individual datasets comprising data of at least one answer to at least one of a plurality of questions of each of a plurality of questionnaires, generating a first batch of individual datasets, the first batch of individual datasets comprising one or more of the plurality of individual datasets, inputting the first batch of individual datasets into the artificial intelligence model, and encoding the data of the first batch of individual datasets with an autoencoder.

Abstract: The present disclosure relates to bicyclic heterocycles of Formula (I), and pharmaceutical compositions of the same, that are modulators, antagonists, or inhibitors of the G protein-coupled receptor MRGPRX2 and are useful in the treatment of MRGPRX2 dependent conditions such as inflammatory diseases.

Abstract: The present disclosure relates to bicyclic heterocycles of Formula (I), and pharmaceutical compositions of the same, that are modulators, antagonists, or inhibitors of the G protein-coupled receptor MRGPRX2 and are useful in the treatment of MRGPRX2 dependent conditions such as inflammatory diseases.

Abstract: Disclosed is an anode assembly comprising an anode layer and a solid electrolyte layer prepared by directly coating a slurry comprising a solid electrolyte and a binder on the anode layer. The interfacial resistance between the anode layer and the solid electrolyte membrane is lower than that of an assembly prepared without direct coating. In one embodiment, the solid electrolyte is a sulfide electrolyte. In one embodiment, the binder is a nonfibrillizable binder. In one embodiment, the electrolyte membrane does not comprise a scaffold layer such as non-woven fabric.

Abstract: Software that performs the following operations: (i) receiving a set of graph predictions corresponding to an input text, where graph predictions of the set of graph predictions are generated by different respective machine learning models; (ii) blending the graph predictions of the set of graph predictions to generate a plurality of candidate blended graphs, where nodes and edges of the candidate blended graphs have respective selection metric values, generated using a selection metric function, that meet a minimum threshold; and (iii) selecting as an output blended graph a candidate blended graph of the plurality of candidate blended graphs having a highest total combination of selection metric values among the plurality of candidate blended graphs.

Abstract: A device for training a binary deep neural network, where the device includes a processor, is configured to: generate a training signal in dependence on an error between an output of a prototype version of the binary deep neural network and an expected output, the prototype version of the binary deep neural network having multiple binary weights each having a respective value; and in dependence on the training signal, output for each binary weight of the prototype version of the binary deep neural network a respective decision to invert or maintain the respective value of the respective binary weight. This may allow the device to train a deep neural network including binary parameters directly in the binary domain without the need for gradient processing methods.

Abstract: The present invention relates to bicyclic heterocycles, and pharmaceutical compositions of the same, that are inhibitors of the FGFR enzyme and are useful in the treatment of FGFR-associated diseases such as cancer.

Abstract: The present invention relates to a pharmaceutical composition for preventing or treating neurodegenerative diseases comprising a diterpene, or a pharmaceutically acceptable salt thereof. Specifically, the diterpene of the present invention can prevent or treat neurodegenerative diseases caused by inhibition of Nurr1 activity by activating Nurr1 and inhibiting the inflammatory response.

Abstract: A surface cleaning apparatus adapted for movement across a surface to be cleaned. The surface cleaning apparatus can dock within a storage tray and charge a power supply. Electrical contacts on the surface cleaning apparatus and the storage tray can be shielded when the surface cleaning apparatus is not docked within the storage tray. Furthermore, the storage tray can include a reservoir for a self-cleaning mode.

Abstract: Embodiments are provided for generating a reasonable language model learning for text data in a knowledge graph in a computing system by a processor. One or more data sources and one or more triples may be analyzed from a knowledge graph. Training data having one or more candidate labels associated with one or more of the triples may be generated. One or more reasonable language models may be trained based on the training data.

Abstract: The present teachings generally relate to an acoustic panel including a backing layer, honeycomb core attached to the backing layer, a mesh layer attached to the honeycomb core, and a face sheet including a flexible acoustic fabric face sheet attached to the mesh layer. The acoustic fabric face sheet can provide improved sound dampening characteristics over, for example, a perforated face sheet, and at a lower manufacturing cost, weight, and a simplified manufacturing process. The acoustic fabric face sheet can be or include a flexible woven and/or knitted fabric including a polymer such as an aromatic polyamide that includes a meta-aramid fiber, or a fiberglass fabric.

Abstract: Obtain, using at least one hardware processor, data characterizing a physical system governed by a physical conservation law. Apply, using the at least one hardware processor, contrastive learning to the data to automatically capture system invariants of the physical system. Employ, using the at least one hardware processor, a neural projection layer to guarantee that a corresponding dynamic machine learning model preserves the captured system invariants. Optionally, predict performance of the physical system using the corresponding dynamic machine learning model.

Abstract: A system and a method include a maintenance control unit configured to: receive a fault message regarding one or more components of an aircraft, determine a priority from the fault message, determine one or more parts for maintenance from the fault message, and determine a time and a location for the maintenance based on the priority and location of the one or more parts.

Abstract: This disclosure relates to a solid electrolyte membrane free of scaffold layer. In one embodiment, the membrane has a lithium-ion conductivity of at least 40% higher than the one prepared with a scaffold layer. Electrochemical devices such as all solid-state battery comprising the solid electrolyte membrane exhibit an improved cycling performance. Methods for preparing the solid electrolyte are also disclosed.

Abstract: Bias supplies and plasma processing systems are disclosed. One bias supply comprises an output node, a return node, and a switch network and at least one power supply coupled to the output node and the return node. The switch network and the at least one power supply configured, in combination, to apply an asymmetric periodic voltage waveform and provide a corresponding current waveform at the output node relative to the return node. A timing parameter estimator receives a digital representation of a full cycle of the voltage and current waveforms, and generates a pulse width control signal based on a crossing time that the current waveform crosses a threshold current value after falling from a positive peak current value to control the switch network.

Abstract: Generative modeling from phylogenetic data is provided. The method comprises creating a multi-sequence alignment (MSA) based on a nucleic acid or protein sequence and generating a phylogenetic tree based on the MSA. The phylogenetic tree is fed into a number of machine learning models, which generate vector representations of the nucleic acid or protein sequences based on the phylogenetic tree. The machine learning models generate from the vector representation predicted nucleic acid or protein sequences for at least one of an evolution sequence, regression sequence, or sibling sequences of nucleic acids or proteins according to the phylogenetic tree.

Abstract: Bias supplies and bias control methods are disclosed. One method comprises applying an asymmetric periodic voltage waveform and providing a corresponding current waveform at an output node relative to a return node; receiving a signal to change from a current state of the asymmetric periodic voltage waveform to a next state of the asymmetric periodic voltage waveform; and adjusting, during a transition from the current state to the next state, at least one portion of the asymmetric periodic voltage waveform and simultaneously adjusting a fundamental frequency of the asymmetric periodic voltage waveform to settle at the next state.

Abstract: A mobile device capable of device-adaptive super-resolution based adaptive streaming may include a super-resolution-based adaptive bitrate (SR-based ABR) application configured to receive an input and determine a quality of a representation of a video to request based on the input, the SR-based ABR application being able to compute a cost of requesting the quality of representation based on a cost function. The mobile device may apply an SR network to the representation of the video to upscale the representation of the video to a desired resolution. The mobile device also may include a display configured to play back a segment of the video. A method for SR-based adaptive streaming may include receiving an input, determining whether to request a lower resolution representation of a video segment based on the input, and applying an SR network to the lower resolution representation to upscale it to a desired resolution.

Abstract: A general embodiment of the invention is a citation bridge platform 31 or a computer-implemented method for receiving and localizing bridge requests. Each bridge request includes or links to a first local document 40A with a first set of pinpoint elements formatted according to a first local convention LC1. Each bridge request also identifies a second local convention LC2 to which the first set of pinpoint elements should be localized to create a second set of pinpoint element for a second local document 40B. The platform 31 or method first globalizes the set of pinpoint elements of the first local document 40A by mapping them to a bridge set 40C of bridge elements formatted according to a bridge convention BC. Second, the platform 31 or method creates the second set of pinpoint elements for the second local document 40B by mapping the bridge set 40C of bridge elements into the second set of pinpoint elements created for the second local document 40B according to a second local convention LC2.

Abstract: In example aspects of this disclosure, a method includes generating, by one or more computing devices, a parametric model that expresses condition states for each of a plurality of assets, and the probability of the assets transitioning between the condition states; generating, by the one or more computing devices, stochastic degradation predictions of a group of the assets, based on the condition states and the probability of transitioning between the condition states for at least some of the assets; and generating, by the one or more computing devices, a maintenance schedule based on: the stochastic degradation predictions of the group of the assets, costs of corrective maintenance for assets in a failed state, and costs of scheduled maintenance for the assets.