Abstract: The disclosure describes novel systems, methods, and compositions for the manipulation of nucleic acids in a targeted fashion. The disclosure describes non-naturally occurring, engineered CRISPR systems, components, and methods for targeted modification of nucleic acids. Each system includes one or more protein components and one or more nucleic acid components that together target nucleic acids.

Abstract: An imaging system, optionally an intra-oral camera, includes a blue light source and a barrier filter over a camera sensor. Optionally, the imaging system can also take white light images. Optionally, the system includes positively charged nanoparticles with fluorescein. The fluorescent nanoparticles can be identified on an image of a tooth by machine vision or machine learning algorithms on a pixel level basis. Either white light or fluorescent images can be used, with machine learning or artificial intelligence algorithms, to score the lesions. However, the white light image is not useful for determining whether lesions, particularly ICDAS 0-2 lesions, are active or inactive. A fluorescent image, with the fluorescent nanoparticles, can be used to detect and score active lesions. Optionally using a white light image and a fluorescent image together allows for all lesions, active and inactive, to be located and scored, and for their activity to be determined.

Abstract: A data management system (DMS) may facilitate an investigation procedure for a set of one or more virtual machines (VMs) hosted in a first environment. The DMS may receive an indication of a selection of one or more sets of VMs and corresponding snapshots for the investigation procedure. The corresponding snapshots may be stored in a second environment, and each snapshot may be associated with a version of a respective set of VMs. The DMS may mount the one or more sets of VMs to one or more testing environments different than the first and second environments. The mounting may use the snapshots stored in the second environment to provide the testing environments with query access to respective versions of the sets of VMs hosted in the first environment. The DMS may perform query operations between the first environment and the testing environments as part of the investigation procedure.

Abstract: Examples are described for applying different settings for image capture to different portions of image data. For example, an image sensor can capture image data of a scene and can send the image data to an image signal processor (ISP) and a classification engine for processing. The classification engine can determine that a first object image region depicts a first category of object, and a second object image region depicts a second category of object. Different confidence regions of the image data can identify different degrees of confidence in the classifications. The ISP can generate an image by applying a different settings to the different portions of the image data. The different portions of the image data can be identified based on the object image regions and confidence regions.

Abstract: Technologies for a field effect transistor (FET) with a ferroelectric gate dielectric are disclosed. In an illustrative embodiment, a perovskite stack is grown on a buffer layer as part of manufacturing a transistor. The perovskite stack includes one or more doped semiconductor layers alternating with other lattice-matched layers, such as undoped semiconductor layers. Growing the doped semiconductor layers on lattice-matched layers can improve the quality of the doped semiconductor layers. The lattice-matched layers can be preferentially etched away, leaving the doped semiconductor layers as fins for a ribbon FET. In another embodiment, an interlayer can be deposited on top of a semiconductor layer, and a ferroelectric layer can be deposited on the interlayer. The interlayer can bridge a gap in lattice parameters between the semiconductor layer and the ferroelectric layer.

Abstract: The disclosure describes novel systems, methods, and compositions for the manipulation of nucleic acids in a targeted fashion. The disclosure describes non-naturally occurring, engineered CRISPR systems, components, and methods for targeted modification of nucleic acids. Each system includes one or more protein components and one or more nucleic acid components that together target nucleic acids.

Abstract: In one embodiment, transistor device includes a first source or drain material on a substrate, a semiconductor material on the first source or drain material, a second source or drain material on the semiconductor material, a dielectric layer on the substrate and adjacent the first source or drain material, a ferroelectric (FE) material on the dielectric layer and adjacent the semiconductor material, and a gate material on or adjacent to the FE material. The FE material may be a perovskite material and may have a lattice parameter that is less than a lattice parameter of the semiconductor material.

Abstract: The present invention relates to nucleases or nucleic acids encoding the nucleases, RNA guides or nucleic acids encoding the RNA guides, processes for characterizing the nucleases and/or RNA guides, compositions comprising the nucleases and/or RNA guides, and kits and/or methods for preparing and/or using the nucleases and/or RNA guides.

Abstract: In one embodiment, a transistor device includes a gate material layer on a substrate, a ferroelectric (FE) material layer on the gate material, a semiconductor channel material layer on the FE material layer, a first source/drain material on the FE material layer and adjacent the semiconductor channel material layer, and a second source/drain material on the FE material layer and adjacent the semiconductor channel material layer and on an opposite side of the semiconductor channel material layer from the first source/drain material. A first portion of the FE material layer is directly between the gate material and the first source/drain material, and a second portion of the FE material layer is directly between the gate material and the second source/drain material.

Abstract: A composite foam article is disclosed herein. The composite foam article comprises a polyurethane foam core presenting a first surface and a second surface facing opposite the first surface. A first skin is disposed on the first surface and a second skin is disposed on the second surface. The polyurethane foam core has a density of 15-80 kg/m3. The first and second skins comprise a plurality of fibers and a polymeric binder. The composite foam article has a weight per unit area of 500-1000 g/m2 and a strength of greater than 17 N at a post-compression thickness of greater than 2 mm when tested in according with SAE J949 at 23° C.

Abstract: The disclosure describes novel systems, methods, and compositions for the manipulation of nucleic acids in a targeted fashion. The disclosure describes non-naturally occurring, engineered CRISPR systems, components, and methods for targeted modification of nucleic acids such as DNA. Each system includes one or more protein components and one or more nucleic acid components that together target nucleic acids.

Abstract: Examples are described for applying different settings for image capture to different portions of image data. For example, an image sensor can capture image data of a scene and can send the image data to an image signal processor (ISP) and a classification engine for processing. The classification engine can determine that a first object image region depicts a first category of object, and a second object image region depicts a second category of object. Different confidence regions of the image data can identify different degrees of confidence in the classifications. The ISP can generate an image by applying a different settings to the different portions of the image data. The different portions of the image data can be identified based on the object image regions and confidence regions.

Abstract: A method of generating a set of examples for explaining decisions made by a machine learning program, involving receiving a set of training data for training the program, and for given subsets of the training data, determining each of (a) a probability of a user correctly inferring a future decision of the program after observing the respective decisions of the program for the given subset of the training data, (b) a suitability of a size of the given subset, and (c) an average probability of the user correctly inferring a future decision of the program after observing the respective decisions of the program for an unspecified subset of the training data. The determinations (a), (b) and (c) are used to score each of the given subsets of training data, and a subset of training data is selected as the generated set of examples based on the scores.

Abstract: Aspects relate to an image signal processor that processes frames at changing frame rates. An example method includes receiving, by an image signal processor, a first sequence of image frames from an image sensor at a first frame rate, processing each image frame of the first sequence of image frames at the first frame rate, and receiving from the image sensor an indication of a frame rate change from the first frame rate to a second frame rate. The method also includes configuring one or more filters of the image signal processor to process image frames from the image sensor in response to receiving the indication of the frame rate change from the image sensor, receiving a second sequence of image frames from the image sensor at the second frame rate, and processing each image frame of the second sequence of image frames at the second frame rate.

Abstract: Aspects relate to an image signal processor that processes frames from different image sensors. An example device includes a memory and an image signal processor coupled to the memory. The image signal processor is configured to provide a first trigger to a first image sensor (the first image sensor being coupled to the image signal processor), receive a first frame from the first image sensor at a first time in response to the first trigger being received by the first image sensor, process the first frame, provide a second trigger to the second image sensor (the second image sensor being coupled to the image signal processor), receive a second frame from the second image sensor at a second time in response to the second trigger being received by the second image sensor (with the second time subsequent to the first time), and process the second frame.

Abstract: Techniques and systems are provided for processing image data. For example, an image signal processor can obtain (e.g., from a host processor) a first setting change indicator value indicating a change in parameter settings of the image signal processor. The image signal processor can obtain an image frame from an image sensor, and can determine a second setting change indicator value from the image frame. The second setting change indicator value can be provided to the image sensor from the host processor. The second setting change indicator value indicates a change in parameter settings of the image sensor. The image signal processor can compare the first setting change indicator value to the second setting change indicator value, and can determine whether to process the image frame or to drop the image frame based on comparing the first setting change indicator value to the second setting change indicator value.

Abstract: Examples are described for applying different settings for image capture to different portions of image data. For example, an image sensor can capture image data of a scene and can send the image data to an image signal processor (ISP) and a classification engine for processing. The classification engine can determine that a first object image region depicts a first category of object, and a second object image region depicts a second category of object. Different confidence regions of the image data can identify different degrees of confidence in the classifications. The ISP can generate an image by applying a different settings to the different portions of the image data. The different portions of the image data can be identified based on the object image regions and confidence regions.

Abstract: Aspects relate to an image signal processor that processes frames at changing frame rates. An example method includes receiving, by an image signal processor, a first sequence of image frames from an image sensor at a first frame rate, processing each image frame of the first sequence of image frames at the first frame rate, and receiving from the image sensor an indication of a frame rate change from the first frame rate to a second frame rate. The method also includes configuring one or more filters of the image signal processor to process image frames from the image sensor in response to receiving the indication of the frame rate change from the image sensor, receiving a second sequence of image frames from the image sensor at the second frame rate, and processing each image frame of the second sequence of image frames at the second frame rate.

Abstract: Techniques and systems are provided for processing image data. For example, an image signal processor can obtain (e.g., from a host processor) a first setting change indicator value indicating a change in parameter settings of the image signal processor. The image signal processor can obtain an image frame from an image sensor, and can determine a second setting change indicator value from the image frame. The second setting change indicator value can be provided to the image sensor from the host processor. The second setting change indicator value indicates a change in parameter settings of the image sensor. The image signal processor can compare the first setting change indicator value to the second setting change indicator value, and can determine whether to process the image frame or to drop the image frame based on comparing the first setting change indicator value to the second setting change indicator value.