Abstract: In a claw-type gearshift, a blocking ring is arranged axially between a hub body having a sliding sleeve and a clutch body such that it is rotatable between a release position and two locking positions. The blocking ring is adapted to be displaced toward the clutch body until conical friction surfaces on the blocking ring and on the clutch body come into contact. The blocking ring constitutes a form-locking blockade for the sliding sleeve against displacement of the sliding sleeve teeth between the clutch body teeth when an axial shifting force is applied in the non-synchronized state. When the claw clutch is shifted, a difference in speed between the clutch body and the hub body is reduced and the sliding sleeve is deflected in the axial direction toward the speed change gear to be shifted, as a result of which a friction surface of the blocking ring and a mating friction surface of the clutch body come into contact.

Abstract: A fully-connected neural network may be configured for execution by a processor as a fully-fused neural network by limiting slow global memory accesses to reading and writing inputs to and outputs from the fully-connected neural network. The computational cost of fully-connected neural networks scale quadratically with its width, whereas its memory traffic scales linearly. Modern graphics processing units typically have much greater computational throughput compared with memory bandwidth, so that for narrow, fully-connected neural networks, the linear memory traffic is the bottleneck. The key to improving performance of the fully-connected neural network is to minimize traffic to slow “global” memory (off-chip memory and high-level caches) and to fully utilize fast on-chip memory (low-level caches, “shared” memory, and registers), which is achieved by the fully-fused approach.

Abstract: Image editing on a wearable device includes a system which obtains sensor data via the wearable device. The sensor data includes a representation of hand movement, head movement or voice command associated with a user. The system executes an application for editing an image based on the obtained sensor data. The system provides for display a list of image adjustment types associated with the application. The system selects an image adjustment type based on one or more of the hand movement, the head movement or the voice command. The system provides for display a prompt having options to adjust a property of the selected image adjustment type. The system selects one of the options included in the prompt. The system modifies an image based on the selected option. The system then provides the modified image for storage in a data structure of a memory unit in the wearable device.

Abstract: An illumination apparatus for a motor vehicle includes a lighting device having one or more light sources, a reflector arrangement having one or more scatter reflectors, and one or more transparent bodies each having a surface made of a plurality of flat facets. The illumination apparatus is designed such that light generated by the light source(s) of the lighting device is scattered at the scatter reflector(s) of the reflector arrangement, as a result of which scattered light is created, which is refracted at least partially on facets of the transparent body or bodies and then exits the illumination apparatus in order to generate a light distribution.

Abstract: A lighting device for a motor vehicle includes a lighting apparatus which has one or more light sources and one or more transparent bodies which each have a surface made of a plurality of flat facets. The lighting device is configured such that light which comes from a light source at least in part passes through a transparent body and is refracted at facets of the transparent body. The light which passes through the transparent body at least in part exits from the lighting device in order to create a light distribution. An associated transparent body is a molded component having one or more recesses which are integrally molded in the molded component, the molded component being clamped in the lighting device by the engagement of one or more projections into the one or more recesses.

Abstract: A lighting device for a motor vehicle includes a lighting apparatus which has one or more light sources and one or more transparent bodies which each have a surface made of a plurality of flat facets. The lighting device is configured such that light which comes from a light source at least in part passes through a transparent body and is refracted at facets of the transparent body. The light which passes through the transparent body at least in part exits from the lighting device in order to create a light distribution. An associated transparent body is a molded component having one or more recesses which are integrally molded in the molded component, the molded component being clamped in the lighting device by the engagement of one or more projections into the one or more recesses.

Abstract: Methods for producing acetaldehyde from ethanol are provided. In embodiments, such a method comprises (a) exposing ethanol and furfural to a biocatalyst comprising yeast alcohol dehydrogenase 1, yeast alcohol dehydrogenase 2, and yeast alcohol dehydrogenase 3, and a biocatalyst cofactor under conditions that oxidize the ethanol to acetaldehyde and reduce the furfural to furfuryl alcohol to provide a product mixture comprising the acetaldehyde and the furfuryl alcohol; and (b) recovering the acetaldehyde from the product mixture as it is being produced in step (a).

Abstract: A lighting device for a motor vehicle includes a lighting component having one or more light sources and one or more transparent facet bodies, each of which bodies has a surface consisting of a plurality of planar first facets. Light originating from the one or more light sources is radiated into the one or more transparent facet bodies as first luminous radiation. The first luminous radiation is at least partially refracted on first facets of the one or more transparent facet bodies and subsequently exits the lighting device as second luminous radiation in order to produce light distribution. One or more translucent optical components are located in the lighting device between the lighting component and the one or more transparent facet bodies.

Abstract: Integrated processes are disclosed for the catalytic conversion of carbohydrate to ethylene glycol and/or propylene glycol using a homogeneous, tungsten-containing retro-aldol catalyst. In these processes, the carbohydrate is subjected to retro-aldol conversion and hydrogenation to provide a reaction product containing ethylene glycol and/or propylene glycol, other reaction process including organic acids, itols and tungsten species. Ethylene glycol and propylene glycol are separated from the reaction product for purification, and at least a portion of the remaining fraction is subjected to ion exclusion chromatography to provide an eluant containing tungsten species and a subsequent eluant containing organic acids and a substantially reduced concentration of tungsten species. At least a portion of the eluant containing tungsten species can be recycled for reuse directly or with intervening unit operations to enhance the catalytic activity of the tungsten species.

Abstract: A lesion detection and classification artificial intelligence (AI) pipeline comprising a plurality of trained machine learning (ML) computer models is provided. First ML model(s) process an input volume of medical images (VOI) to determine whether VOI depicts a predetermined amount of an anatomical structure. The AI pipeline determines whether criteria, such as a predetermined amount of an anatomical structure of interest being depicted in the input volume, are satisfied by output of the first ML model(s). If so, lesion processing operations are performed including: second ML model(s) processing the VOI to detect lesions which correspond to the anatomical structure of interest; third ML model(s) performing lesion segmentation and combining of lesion contours associated with a same lesion; and fourth ML models processing the listing of lesions to classify the lesions. The AI pipeline outputs the listing of lesions and the classifications for downstream computing system processing.

Abstract: A false positive removal engine is provided. The false positive removal engine receives detected objects in one or more images. A machine learning classifier computer model, configured with first operational parameters to implement a first operating point, processes the received input to classify each detected object as being a true positive or a false positive to generate a first set of object classifications. If the first set is empty, the false positive removal engine outputs the first set as a filtered list of objects; otherwise the ML classifier computer model is configured with second operational parameters to implement a second operating point, different from the first operating point, which then processes the received input to classify each detected object and generate a second set of objects classified as true positive, which is output by the false positive removal engine as the filtered list of objects.

Abstract: In an approach for automatically detecting whether an output of a detection and segmentation algorithm is of an acceptable quality, a processor receives an image. A processor applies a detection stage of a detection and segmentation algorithm to the image. A processor computes a set of features from a detection score map output by the detection stage of the detection and segmentation algorithm by analyzing the detection score map at more than one different operating points. A processor inputs the set of features into a classifier that predicts whether a final output of the detection and segmentation algorithm will be of an acceptable quality, wherein the acceptable quality is defined based on whether a detection precision threshold has been reached. A processor receives an output of the classifier.

Abstract: An illumination apparatus for a motor vehicle includes a lighting device having one or more light sources, a reflector arrangement having one or more scatter reflectors, and one or more transparent bodies each having a surface made of a plurality of flat facets. The illumination apparatus is designed such that light generated by the light source(s) of the lighting device is scattered at the scatter reflector(s) of the reflector arrangement, as a result of which scattered light is created, which is refracted at least partially on facets of the transparent body or bodies and then exits the illumination apparatus in order to generate a light distribution.

Abstract: A lesion detection and classification artificial intelligence (AI) pipeline comprising a plurality of trained machine learning (ML) computer models is provided. First ML model(s) process an input volume of medical images (VOI) to determine whether VOI depicts a predetermined amount of an anatomical structure. The AI pipeline determines whether criteria, such as a predetermined amount of an anatomical structure of interest being depicted in the input volume, are satisfied by output of the first ML model(s). If so, lesion processing operations are performed including: second ML modal(s) processing the VOI to detect lesions which correspond to the anatomical structure of interest; third ML model(s) performing lesion segmentation and combining of lesion contours associated with a same lesion; and fourth ML models processing the listing of lesions to classify the lesions. The AI pipeline outputs the listing of lesions and the classifications for downstream computing system processing.

Abstract: A lesion detection and classification artificial intelligence (AI) pipeline comprising a plurality of trained machine learning (ML) computer models is provided. First ML model(s) process an input volume of medical images (VOI) to determine whether VOI depicts a predetermined amount of an anatomical structure. The AI pipeline determines whether criteria, such as a predetermined amount of an anatomical structure of interest being depicted in the input volume, are satisfied by output of the first ML model(s). If so, lesion processing operations are performed including: second ML model(s) processing the VOI to detect lesions which correspond to the anatomical structure of interest; third ML model(s) performing lesion segmentation and combining of lesion contours associated with a same lesion; and fourth ML models processing the listing of lesions to classify the lesions. The AI pipeline outputs the listing of lesions and the classifications for downstream computing system processing.

Abstract: Some embodiments of the present invention select image data that are valuable for developing and/or training a deep learning based algorithm. A semi-automated system identifies cases that are the most valuable (most impactful, useful, and/or most effective) for developing and/or training the deep learning algorithm. The semi-automated system monitors a degree of uncertainty in the results produced by an image processing algorithm. Cases where the degree of uncertainty is high, and consequently a confidence score is low, are made ready for analysis, classification, and/or annotation by human review. Once analyzed, classified and/or annotated by human review, the data is made available for use in developing and/or training the deep learning algorithm.

Abstract: A false positive removal engine is provided. The false positive removal engine receives detected objects in one or more images. A machine learning classifier computer model, configured with first operational parameters to implement a first operating point, processes the received input to classify each detected object as being a true positive or a false positive to generate a first set of object classifications. If the first set is empty, the false positive removal engine outputs the first set as a filtered list of objects; otherwise the ML classifier computer model is configured with second operational parameters to implement a second operating point, different from the first operating point, which then processes the received input to classify each detected object and generate a second set of objects classified as true positive, which is output by the false positive removal engine as the filtered list of objects.

Abstract: A lesion detection and classification artificial intelligence (AI) pipeline comprising a plurality of trained machine learning (ML) computer models is provided. First ML model(s) process an input volume of medical images (VOI) to determine whether VOI depicts a predetermined amount of an anatomical structure. The AI pipeline determines whether criteria, such as a predetermined amount of an anatomical structure of interest being depicted in the input volume, are satisfied by output of the first ML model(s). If so, lesion processing operations are performed including: second ML modal(s) processing the VOI to detect lesions which correspond to the anatomical structure of interest; third ML model(s) performing lesion segmentation and combining of lesion contours associated with a same lesion; and fourth ML models processing the listing of lesions to classify the lesions. The AI pipeline outputs the listing of lesions and the classifications for downstream computing system processing.

Abstract: Methods of forming lignin prepolymers are provided. In an embodiment, such a method comprises adding an acid to an ozonized reaction mixture, the ozonized reaction mixture comprising ozonized lignin having a backbone, and aromatic monomers cleaved from a lignin, under conditions to react the cleaved aromatic monomers with the backbone of the ozonized lignin to form a lignin prepolymer. The methods may further comprise using the lignin prepolymer to form a lignin resin.

Abstract: Some embodiments of the present invention select image data that are valuable for developing and/or training a deep learning based algorithm. A semi-automated system identifies cases that are the most valuable (most impactful, useful, and/or most effective) for developing and/or training the deep learning algorithm. The semi-automated system monitors a degree of uncertainty in the results produced by an image processing algorithm. Cases where the degree of uncertainty is high, and consequently a confidence score is low, are made ready for analysis, classification, and/or annotation by human review. Once analyzed, classified and/or annotated by human review, the data is made available for use in developing and/or training the deep learning algorithm.