Abstract: The present disclosure relates to compositions for increasing acetylated alpha-tubulin levels in peripheral neuronal cells. The present disclosure also relates to the use of tubulin deacetylase inhibitors, or activators of tubulin acetylation for increasing acetylated tubulin levels in the cells. The present disclosure also relates to methods for assessing risk of developing peripheral neuropathy by assessing the levels of acetylated alpha-tubulin. The present disclosure further relates to methods for treating a subject suffering from a peripheral neuropathy using the compositions described herein.

Abstract: A method of forming a lithium-free glass article includes thermally treating the glass article to impart a fictive temperature and ion exchanging the thermally treated glass article. The thermal treatment includes heating the glass article to a hold temperature from 130° C. below to 20° C. above an annealing point of the glass article; holding the glass article at the hold temperature for a hold period; cooling the glass article at a first cooling rate from the hold temperature to an intermediate temperature; and cooling the glass article at a second cooling rate from the intermediate temperature to room temperature. The ion exchanged glass article includes a compressive stress greater than or equal to 900 MPa, as measured for an article having a thickness of 1.1 mm; and a warp less than or equal to 0.3 mm, as measured for an article having a length less than 200 mm.

Abstract: The invention relates to formulation for oral delivery of peptides and/or drug molecules and a method of manufacturing the same. The oral formulation is useful for delivering peptides to the gastrointestinal tract, preferably at the ileo-caecal junction of colon.

Abstract: A method for analyzing a PDF document includes: modifying, by an analyzer, a background color of the document to a second color to generate a modified document; converting, by a noise removal module (NRM), the modified document into an image; making, by the NRM, a first determination that the image comprises a background noise, in which the noise is reduced by modifying an area within the image; generating, by a fraud detection module (FDM), at least one image patch from the image; generating, by the FDM, an embedding for the at least one image patch, in which the embedding is processed using a convolutional neural network (CNN) layer; making, by the FDM and based on an output of the CNN layer, a second determination that the document is invalid; and notifying, by the FDM, a user about the second determination using a GUI of the client.

Abstract: A vehicle interior system having a first frame having a first end and a second end and a second frame having a third end and a fourth end. The third end of the second frame is arranged proximal to the second end of the first frame, and the first frame and the second frame define a bending axis between the second end and the third end. A continuous cover material is bonded to the first and second frame and across the bending axis. The vehicle interior system also includes a first display device positioned in a first aperture of the first frame. The system has a first configuration in which the first frame is planar with the second frame and a second configuration in which the first frame is not planar with the second frame. The system is configured to reversibly transition between the first and second configurations.

Abstract: Data flow analysis is provided. A program level data flow analysis is performed for each respective data flow path in a set of control flow chains corresponding to each respective program called by a particular job step in each respective job of a plurality of jobs in a sequence of job execution corresponding to an application. A particular field of a plurality of fields is identified in a record of each dataset of a plurality of datasets corresponding to the application that is included in a particular data flow path to form a field level data flow analysis for each particular data flow path. Results of the field level data flow analysis for each particular data flow path in the set of control flow chains corresponding to each respective program called by the particular job step in each respective job of the plurality of jobs is aggregated.

Abstract: Aspects of the disclosure relate to repositioning a rooftop sensor of an autonomous vehicle when needed to reduce the overall height of the autonomous vehicle. For instance, while an autonomous vehicle is being controlled in an autonomous driving mode, a low clearance zone may be identified. An activation location may be determined based on the low clearance zone and a current speed of the autonomous vehicle. Once the activation location is reached by the autonomous vehicle, a motor may be caused to reposition the rooftop sensor. In addition, in some instances, after the autonomous vehicle has passed the low clearance zone, the motor may be caused to reposition the rooftop sensor again.

Abstract: In some embodiments, a process comprises fixing a first portion of a flexible glass substrate into a first fixed shape with a first rigid support structure and attaching a first display to the first portion of the flexible glass substrate or to the first rigid support structure. After fixing the first portion and attaching the first display, and while maintaining the first fixed shape of the first portion of the flexible glass substrate and the attached first display, cold-forming a second portion of the flexible glass substrate to a second fixed shape and fixing the second portion of the flexible glass substrate into the second fixed shape with a second rigid support structure.

Abstract: A method for information extraction from an image-based asset includes: generating, by an encoder, at least one image patch from the asset; generating, by the encoder, an input embedding for the at least one image patch; generating, by the encoder, an output embedding based on the input embedding; inferring, by a decoder, a detail of the image-based asset based on the output embedding and a formatted asset, in which the output embedding is sent by the encoder, wherein the formatted asset is sent by a parser; generating, by the decoder, a decoder output based on the detail, in which the detail comprises at least a feature and a second feature; converting, by a converter, the decoder output into an output asset, in which the decoder output is sent by the decoder; and sending, by the converter, the output asset to a user using a graphical user interface (GUI).

Abstract: Methods, systems, and computer program products for automatically classifying heterogeneous documents using machine learning techniques are provided herein. A computer-implemented method includes extracting multiple features from one or more sets of heterogeneous documents; arranging at least a portion of the extracted features into multiple hierarchically-defined classes; selecting one or more of the documents associated with each of the multiple hierarchically-defined classes; training at least one machine learning model using at least a portion of the selected documents; and automatically classifying at least one input document into one or more of the multiple hierarchically-defined classes by processing one or more portions of the at least one input document using the at least one trained machine learning model.

Abstract: Methods, apparatus, and processor-readable storage media for character-based representation learning for information extraction using artificial intelligence techniques are provided herein.

Abstract: Various embodiments of the present invention provide methods, apparatus, systems, computing devices, computing entities, and/or the like for performing predictive data analysis operations for parasomnia episode management. For example, certain embodiments of the present invention utilize systems, methods, and computer program products that perform predictive data analysis operations for parasomnia episode management using at least one of pre-sleep parasomnia episode likelihood prediction machine learning models, in-sleep parasomnia episode likelihood prediction machine learning models, augmented parasomnia episode likelihood prediction machine learning models that are configured to generate conditional likelihood scores for candidate parasomnia reduction interventions, deep reinforcement learning machine learning models that are configured to generate recommended parasomnia reduction interventions, and dynamically-deployable parasomnia episode likelihood prediction machine learning models.

Abstract: Embodiments of a vehicle interior system are disclosed. In one or more embodiments, the system includes a base with a curved surface, and a display or touch panel disposed on the curved surface. The display includes a cold-bent glass substrate with a thickness of 1.5 mm or less and a first radius of curvature of 20 mm or greater, and a display module and/or touch panel attached to the glass substrate having a second radius of curvature that is within 10% of the first radius of curvature. Methods for forming such systems are also disclosed.

Abstract: Various embodiments of the present invention provide methods, apparatus, systems, computing devices, computing entities, and/or the like for performing predictive data analysis operations for parasomnia episode management. For example, certain embodiments of the present invention utilize systems, methods, and computer program products that perform predictive data analysis operations for parasomnia episode management using at least one of pre-sleep parasomnia episode likelihood prediction machine learning models, in-sleep parasomnia episode likelihood prediction machine learning models, augmented parasomnia episode likelihood prediction machine learning models that are configured to generate conditional likelihood scores for candidate parasomnia reduction interventions, deep reinforcement learning machine learning models that are configured to generate recommended parasomnia reduction interventions, and dynamically-deployable parasomnia episode likelihood prediction machine learning models.

Abstract: A voltage detector for measuring a voltage between voltage rails including first and second voltage to current (V2I) converters, a current converter, and a controller. The controller enables the first V2I converter when a selected voltage is between the first voltage rail and an intermediate voltage and enables the second V2I converter when the selected voltage is between the intermediate voltage and the second voltage rail. Each V2I converter converts the selected voltage into an output current when enabled. The current converter converts the output current into a measured value and the controller converts the measured value into an output voltage. The controller may enable either V2I converter and select a reference voltage for determining a reference value, then select an input voltage for determining an input value using the enabled V2I converter, and then compare the reference and input values for determining which V2I converter provides a correct result.

Abstract: Embodiments of a vehicle interior system and methods of forming the same are disclosed. The vehicle interior system includes a base having a curved surface, a display disposed on the curved surface, and a cover glass disposed on the display. The cover glass has a first region including a first major surface, a second major surface opposite the first major surface, and a first thickness defined as a distance between the first major surface and the second major surface. The cover glass also has a second region including the first major surface, a third major surface opposite the first major surface, and a second thickness defined as a distance between the first major surface and the third major surface. The display is attached to the third major surface, and the second region corresponds to a touch-sensitive region of the display. The second thickness is greater than the first thickness.

Abstract: Examples of systems described herein may manage durable handles for seamless failover. Durable handles may be stored in a persistent volume group. The persistent volume group may be moved, together with all or a portion of file share data, to another computing node responsive to an indication of a failure event. To accommodate asynchronous write shares, asynchronous write operations may also be provided to the target computing node. The asynchronous write operations may be processed before reconnecting clients to the file share.

Abstract: Disclosed herein are system, apparatus, article of manufacture, method, and computer program product embodiments for adapting an automated speech recognition system to provide more accurate suggestions to voice queries involving media content including recently created or recently available content. An example computer-implemented method includes transcribing the voice query, identifying respective components of the query such as the media content being requested and the action to be performed, and generating fuzzy candidates that potentially match the media content based on phonetic representations of the identified components. Phonetic representations of domain specific candidates are stored in a domain entities index and is continuously updated with new entries so as to maintain the accuracy of the speech recognition of voice queries for recently created or recently available content.

Abstract: Document retrieval with domain adaptation and out of distribution identification is disclosed. Custom layers are added to a model without impacting base weights. The custom layers are trained using a domain specific dataset and topic identifiers. When executing a query, the custom layers use weights associated with a topic identifier. If a potential result is sufficiently similar, the result is returned in response to the query. Otherwise, the result is out of distribution and no result is returned.

Abstract: Various embodiments of the present invention provide methods, apparatus, systems, computing devices, computing entities, and/or the like for performing predictive data analysis operations for parasomnia episode management. For example, certain embodiments of the present invention utilize systems, methods, and computer program products that perform predictive data analysis operations for parasomnia episode management using at least one of pre-sleep parasomnia episode likelihood prediction machine learning models, in-sleep parasomnia episode likelihood prediction machine learning models, augmented parasomnia episode likelihood prediction machine learning models that are configured to generate conditional likelihood scores for candidate parasomnia reduction interventions, deep reinforcement learning machine learning models that are configured to generate recommended parasomnia reduction interventions, and dynamically-deployable parasomnia episode likelihood prediction machine learning models.