Abstract: The present disclosure includes methods, apparatuses, and systems for aspirating the airway of a patient. The apparatus includes a main body having a pump and a storage cannister housing coupled to the main body and containing a storage container that is at least partially collapsible. In some configurations, the main body can include a pressure sensor, a controller in communication with the pressure sensor, and having a processor, a memory, and a power source in communication with the controller. The storage cannister housing can include a first end coupled to the main body, and a second end having a weighted portion and configured to be coupled to the first end to permit free rotation, such that the second end gravitationally rotates.

Abstract: Provided herein are luminescently labeled oligonucleotide structures, which may be used in systems and methods for polypeptide sequencing and/or nucleic acid sequencing.

Abstract: Embodiments are disclosed for expanding a seed scene using proposals from a generative model of scene graphs. The method may include clustering subgraphs according to respective one or more maximal connected subgraphs of a scene graph. The scene graph includes a plurality of nodes and edges. The method also includes generating a scene sequence for the scene graph based on the clustered subgraphs. A first machine learning model determines a predicted node in response to receiving the scene sequence. A second machine learning model determines a predicted edge in response to receiving the scene sequence and the predicted node. A scene graph is output according to the predicted node and the predicted edge.

Abstract: In some examples, a system includes processing circuitry configured to receive a first set of information, the first set of information comprising information of actual therapy delivered to a patient over a plurality of instances of therapy delivery. The processing circuitry may determine, based upon the first set of information, a therapy usage pattern. The processing circuitry may determine a modification to a programmed therapy schedule based on the therapy usage pattern. The processing circuitry may generate for output the modification to the programmed therapy schedule.

Abstract: Metrics that characterize one or more computing devices are received. A time value associated with a performance of the one or more computing devices based on the received metrics is determined. A first scheduling parameter based on the time value is determined, wherein the first scheduling parameter is associated with a first discovery process that is associated with at least a portion of the one or more computing devices. Execution of the first discovery process is executed according to the first scheduling parameter.

Abstract: Exemplary substrate support assemblies may include an electrostatic chuck body that defines a substrate support surface. The substrate support surface may define a plurality of protrusions that extend upward from the substrate support surface. A density of the plurality of protrusions within an outer region of the substrate support surface may be greater than in an inner region of the substrate support surface. The substrate support assemblies may include a support stem coupled with the electrostatic chuck body. The substrate support assemblies may include an electrode embedded within the electrostatic chuck body.

Abstract: Exemplary substrate support assemblies may include a chuck body defining a substrate support surface. The substrate support surface may define a plurality of protrusions that extend upward from the substrate support surface. The substrate support surface may define an annular groove and/or ridge. A subset of the plurality of protrusions may be disposed within the annular groove and/or ridge. The substrate support assemblies may include a support stem coupled with the chuck body.

Abstract: Disclosed are compositions and methods for inhibiting aldehyde dehydrogenases. In further aspects, treatment of cancers by inhibiting aldehyde dehydrogenases with the disclosed compositions are also disclosed.

Abstract: Systems and methods for data analytics are described. One or more embodiments of the present disclosure receive target time series data and candidate time series data, where the candidate time series data includes data corresponding to each of a plurality of candidate metrics, train a prediction network to predict the target time series data based on the candidate time series data by setting temporal attention weights corresponding to a plurality of rolling time windows and by setting candidate attention weights corresponding to the plurality of candidate metrics, identify a leading indicator metric for the target time series data from the plurality of candidate metrics based on the temporal attention weights and the candidate attention weights, and signal the leading indicator metric for the target time series data.

Abstract: An electronic device is provided. The electronic device includes a display, a camera module disposed under the display, and a processor electrically connected to the display and the camera module. The processor is configured to acquire a sample frame by using the camera module, identify whether a light source object is included in the sample frame, determine an imaging parameter for acquisition of first multiple frames when the light source object is identified to be included in the sample frame, acquire multiple frames, based on the imaging parameter, composite the multiple frames to generate a composite frame, identify an attribute of the light source object included in the composite frame, and perform frame correction of the composite frame, based on the identified attribute.

Abstract: A first processing node of a cluster of processing nodes issues a first alert when first event data satisfies a trigger condition, and sends, to an alert data store external to the cluster, a first alert record of the first alert and suppression information based at least in part on the first alert. A second processing node of the cluster determines that second event data satisfies the trigger condition, obtains, from the alert data store, the suppression information indicating that an expiration time for suppressing the first alert is unexpired, and sends, to the alert data store, a second alert record of a second alert without issuing the second alert.

Abstract: A physical system is simulated using a model including a plurality of elements in a mesh or grid. The elements are divided into partitions processed by different processing units. For some time steps, flux data is transmitted between partitions for updating the state of edge elements of the partitions. Periodically, transmission of flux data is suppressed and flux data is obtained by linear interpolation based on past flux data. Alternatively, flux data is obtained by processing state variables of an edge element and past flux data using a machine learning model, such as a DNN.

Abstract: The present disclosure relates to an improved implantable drug delivery device, method, and system that is powered by compression and expansion of an inert gas contained within a compressible bellows and surrounded by a biocompatible housing that forms a reservoir which is connected to restricted channel for flow rate control and further connected to a flexible reinforced catheter that delivers medicants from the reservoir to the intrathecal space or other desired bodily tissue.

Abstract: An approach is provided for using machine learning to provide compensation for roundoff error in algorithmic computations. The approach includes training a machine learning model based low precision data and corresponding high precision data. The low precision data includes pairs of low precision values of a specific datatype that correspond to pairs of high precision values from the high precision data. The high precision data includes pairs of high precision values of a specific datatype that correspond to the pairs of low precision values from the low precision data. When the machine learning model has been trained, the machine learning model is used as a basis for determining a compensation value is used to compensate for roundoff error in a particular algorithmic computation. Techniques discussed herein provide compensation for roundoff error during otherwise unstable computations, enabling high-performance computing and other scientific applications to use lower precision data types more readily.

Abstract: The present invention operates to significantly reduce over or under drainage of cerebrospinal fluid (CSF) from the brain or spinal cord using an extraventricular drain (EVD) in the brain or lumbar drainage device (LDD) and automated intracranial pressure (ICP) monitoring. The present invention attaches the drainage catheter of the EVD to a flow controller or valve which controls the flow of CSF from the drainage catheter and is electronically controlled by a stepper motor communicating with a pressure transducer receiving pressure signals indicating an ICP without the need for constant manual re-leveling.

Abstract: An electronic device includes a UDC and a UDC controller configured to determine an optimal number of frames required to be captured for a scene to compensate at least one parameter to optimize an output media using the UDC, obtain a multi-frame fusion media by performing at least one multi-frame fusion on the determined optimal number of frames, perform a light source spread correction on the multi-frame fusion media, and optimize the output media based on the light source spread correction on the multi-frame fusion media.

Abstract: Exemplary semiconductor processing chambers may include a chamber body. The chambers may include a substrate support disposed within the chamber body. The substrate support may define a substrate support surface. The chambers may include a showerhead positioned supported atop the chamber body. The substrate support and a bottom surface of the showerhead may at least partially define a processing region within the semiconductor processing chamber. The showerhead may define a plurality of apertures through the showerhead. The bottom surface of the showerhead may define an annular groove or ridge that is positioned directly above at least a portion of the substrate support.

Abstract: Exemplary substrate support assemblies may include a chuck body defining a substrate support surface. The substrate support surface may define a plurality of protrusions that extend upward from the substrate support surface. The substrate support surface may define an annular groove and/or ridge. A subset of the plurality of protrusions may be disposed within the annular groove and/or ridge. The substrate support assemblies may include a support stem coupled with the chuck body.

Abstract: The present disclosure relates to an apparatus for decoding an encoded Unified Audio and Speech stream. The apparatus comprises a core decoder for decoding the encoded Unified Audio and Speech stream. The core decoder includes a fast Fourier transform, FFT, module implementation based on a Cooley-Tuckey algorithm. The FFT module is configured to determine a discrete Fourier transform, DFT. Determining the DFT involves recursively breaking down the DFT into small FFTs based on the Cooley-Tucker algorithm and using radix-4 if a number of points of the FFT is a power of 4 and using mixed radix if the number is not a power of 4. Performing the small FFTs involves applying twiddle factors. Applying the twiddle factors involves referring to pre-computed values for the twiddle factors.

Abstract: Systems and methods for data analytics are described. One or more embodiments of the present disclosure receive target time series data and candidate time series data, where the candidate time series data includes data corresponding to each of a plurality of candidate metrics, train a prediction network to predict the target time series data based on the candidate time series data by setting temporal attention weights corresponding to a plurality of rolling time windows and by setting candidate attention weights corresponding to the plurality of candidate metrics, identify a leading indicator metric for the target time series data from the plurality of candidate metrics based on the temporal attention weights and the candidate attention weights, and signal the leading indicator metric for the target time series data.