Abstract: The present invention includes compounds of formula (I) and methods for its preparation and treatment of disorders with activated peroxisome preoliferator-activated receptor gamma (PPARG), particularly cancer.

Abstract: The present disclosure provides compositions and methods for the treatment of PPARG activated cancer. For example, the present disclosure provides PPARG signaling modulators for the treatment of bladder cancer. In particular, therapeutic and/or prophylactic compositions and uses of PPARG inverse-agonists are described.

Abstract: A computer accesses a plurality of image frames. The computer identifies, within the plurality of image frames, a plurality of vehicle front vehicle back detections. The computer pairs at least a subset of the plurality of vehicle back detections with vehicle front detections. A given vehicle back detection is paired with a given vehicle front detection based on camera angle relative to a predefined axis. The computer assigns, using each of a plurality of pools, a score to each vehicle front detection—vehicle back detection pair, each non-paired vehicle front detection, and each non-paired vehicle back detection. Each pool comprises a data structure representing a scoring mechanism and a set of detections. The computer assigns each detection to a pool that assigned a highest score to that detection. Upon determining that a given pool comprises at least n detections: the computer labels the given pool as representing a specific vehicle.

Abstract: A drug-delivery device comprising a drug-reservoir assembly, displacement-generating actuator, and drug administration unit is described, wherein the drug-reservoir assembly comprises at least one flexible wall and a constraining ring, such that the displacement generated by the actuator collapses a flexible wall of the assembly expelling the drug contents of the drug-reservoir towards said drug administration means.

Abstract: A computer obtains image frames. The computer identifies a chip within the image frames, the chip having a position and dimensions determined based on a lane width. Based on a speed and a length of a vehicle passing through a field of view of the camera, the computer selects a subset of the image frames. The computer takes, from each of the image frames in the subset, the identified chip for use as input to an artificial neural network (ANN). The computer individually provides each taken chip as input to the ANN to generate an ANN output. Based on a combination of the ANN outputs, the computer identifies a shape, a number of axles, and a number of segments of the vehicle. The computer provides a tuple representing the vehicle shape, the number of axles, and the number of segments.

Abstract: The present application provides a self-powered drug-delivery device. The device includes a chamber having a wall. The chamber contains a fluid and is in connection with an administration means. The device also includes a displacement-generating battery cell. The device further includes an electrically-controlled battery unit, which includes the displacement-generating battery cell coupled to the chamber by a coupling means. The displacement-generating battery cell includes an element that changes shape as a result of charge or discharge of the battery cell so as to cause a displacement within the battery unit. The arrangement of the battery unit, the coupling means, the wall, the chamber, and the administration means is such that the displacement derived from the battery unit is conveyed by the coupling means to cause displacement of the wall of the chamber such that the fluid is expelled from the chamber to force a drug towards the administration means.

Abstract: A machine receives a large image having large image dimensions that exceed memory threshold dimensions. The large image includes metadata. The machine adjusts an orientation and a scaling of the large image based on the metadata. The machine divides the large image into a plurality of image tiles, each image tile having tile dimensions smaller than or equal to the memory threshold dimensions. The machine provides the plurality of image tiles to an artificial neural network. The machine identifies, using the artificial neural network, at least a portion of the target in at least one image tile. The machine identifies the target in the large image based on at least the portion of the target being identified in at least one image tile.

Abstract: A computer obtains image frames. The computer identifies a chip within the image frames, the chip having a position and dimensions determined based on a lane width. Based on a speed and a length of a vehicle passing through a field of view of the camera, the computer selects a subset of the image frames. The computer takes, from each of the image frames in the subset, the identified chip for use as input to an artificial neural network (ANN). The computer individually provides each taken chip as input to the ANN to generate an ANN output. Based on a combination of the ANN outputs, the computer identifies a shape, a number of axles, and a number of segments of the vehicle. The computer provides a tuple representing the vehicle shape, the number of axles, and the number of segments.

Abstract: Discussed herein are systems, devices, and methods for automatic target recognition based on a non-visible input image. A method can include providing, as input to a first machine learning (ML) model for object classification, pixel data of a non-visible image, the first ML model including an encoder from a second ML model, the second ML model trained to generate a visible image representation of an input non-visible image, and receiving, from the first ML model, data indicating one or more objects present in the non-visible image.

Abstract: Systems, devices, methods, and computer-readable media for. A method can include receiving, from a laser scan device of a tolling station, a time series of distance measurements, determining, based on the time series of distance measurements, height measurements indicating a height of a vehicle from a surface of a road. generating, based on the height measurements, an image of the height measurements, and classifying, using the image as input to a convolutional neural network (CNN), the vehicle.

Abstract: A computer accesses a plurality of image frames. The computer identifies, within the plurality of image frames, a plurality of vehicle front vehicle back detections. The computer pairs at least a subset of the plurality of vehicle back detections with vehicle front detections. A given vehicle back detection is paired with a given vehicle front detection based on camera angle relative to a predefined axis. The computer assigns, using each of a plurality of pools, a score to each vehicle front detection—vehicle back detection pair, each non-paired vehicle front detection, and each non-paired vehicle back detection. Each pool comprises a data structure representing a scoring mechanism and a set of detections. The computer assigns each detection to a pool that assigned a highest score to that detection. Upon determining that a given pool comprises at least n detections: the computer labels the given pool as representing a specific vehicle.

Abstract: A neural network apparatus includes processing circuitry and memory. The memory stores a plurality of images of a target. The processing circuitry is configured to: access, from the memory, a first image and an identification of a centroid pixel of the target within the first image; generate, based on a geometry of the target and the centroid pixel, a confidence map indicating, for each pixel in the first image, a confidence value that the pixel includes the target; train, using the plurality of images of the target, including the first image and the confidence map, an artificial neural network to identify the target in visual data; and provide an output representing the trained artificial neural network.

Abstract: A method for dynamically scaling scanned images is disclosed. For example, the method is executed by a processor and includes receiving a scanned image, determining that the scanned image is larger than a previously scanned image, adjusting the previously scanned image to be smaller than the scanned image, generating an adjusted previously scanned image, and causing a display to show the scanned image and the adjusted previously scanned image.

Abstract: A machine receives a large image having large image dimensions that exceed memory threshold dimensions. The large image includes metadata. The machine adjusts an orientation and a scaling of the large image based on the metadata. The machine divides the large image into a plurality of image tiles, each image tile having tile dimensions smaller than or equal to the memory threshold dimensions. The machine provides the plurality of image tiles to an artificial neural network. The machine identifies, using the artificial neural network, at least a portion of the target in at least one image tile. The machine identifies the target in the large image based on at least the portion of the target being identified in at least one image tile.

Abstract: A neural network apparatus includes processing circuitry and memory. The memory stores a plurality of images of a target. The processing circuitry is configured to: access, from the memory, a first image and an identification of a centroid pixel of the target within the first image; generate, based on a geometry of the target and the centroid pixel, a confidence map indicating, for each pixel in the first image, a confidence value that the pixel includes the target; train, using the plurality of images of the target, including the first image and the confidence map, an artificial neural network to identify the target in visual data; and provide an output representing the trained artificial neural network.

Abstract: An egg sealing unit for an automated biological sample collection system and method of use. The egg sealing unit seals the section of egg from which the shell has been removed. The egg sealing unit includes a sampler, an applicator and an imaging adaptor providing an optical conduit between the surface of an egg and the CAM. The applicator is configured to deliver exogenous material to the CAM, the sampler is configured to collect samples from the CAM and the imaging adaptor allows the CAM to be monitored.

Abstract: Accommodating a particular kind of out-of-order data; namely, when data arrives out-of-order because the data is generated in systems that may have different application timelines. As data arrives, it arrives with a particular partition key. For instance, the partition key might identify the source of the data. The data from each partition key is kept in-order with respect to all other data from that same partition key. That said, data from one key is permitted to have a different timeline as compared to data from any other key. Thus, the data may not be in-order when viewed globally across keys. Rather, data is tolerated to be out-of-order globally so long as the lag in the arrived data is within some tolerated lag. If the data arrives having a time that exceeds the maximum tolerated lag, then the system applies some policy to determine what to do with the delayed data.

Abstract: Discussed herein are systems, devices, and methods for automatic target recognition based on a non-visible input image. A method can include providing, as input to a first machine learning (ML) model for object classification, pixel data of a non-visible image, the first ML model including an encoder from a second ML model, the second ML model trained to generate a visible image representation of an input non-visible image, and receiving, from the first ML model, data indicating one or more objects present in the non-visible image.

Abstract: A displacement-generating battery cell for driving a drug-delivery device is described. The cell may include at least one volume-changing element. The cell may include a housing formed according to a concertina-shaped design with folds in the walls thereof and may contain an internal chemical reaction system. The chemical reaction system may include an electrode. The electrode may be the volume-changing element. The arrangement of said chemical reaction system may be such that an expansion of the volume-changing element in a direction lengthens the cell and thus reduces the extent of the folds. Drug-delivery devices including the displacement-generating battery cell are also described.

Abstract: The present application provides a self-powered drug-delivery device. The device includes a chamber having a wall. The chamber contains a fluid and is in connection with an administration means. The device also includes a displacement-generating battery cell. The device further includes an electrically-controlled battery unit, which includes the displacement-generating battery cell coupled to the chamber by a coupling means. The displacement-generating battery cell includes an element that changes shape as a result of charge or discharge of the battery cell so as to cause a displacement within the battery unit. The arrangement of the battery unit, the coupling means, the wall, the chamber, and the administration means is such that the displacement derived from the battery unit is conveyed by the coupling means to cause displacement of the wall of the chamber such that the fluid is expelled from the chamber to force a drug towards the administration means.