Abstract: The invention relates to a process for the preparation of ribociclib of formula V or its salts. The invention provides novel crystalline forms of ribociclib succinate and ribociclib trifluoroacetate. The present invention also relates to pharmaceutical compositions comprising a crystalline form of ribociclib succinate and at least a pharmaceutically acceptable carrier. It further relates to the use of such compositions in the treatment of cancer.

Abstract: Systems and methods for detection and localization of image and document forgery. The method can include the step of receiving a dataset having a plurality of authentic images and a plurality of manipulated images. The method can also include the step of benchmarking a plurality of image forgery algorithms using the dataset. The method can further include the step of generating a plurality of receiver operating characteristic (ROC) curves for each of the plurality of image forgery algorithms. The method also includes the step of calculating a plurality of area under curve metrics for each of the plurality of ROC curves. The method further includes the step of training a neural network for image forgery based on the plurality of area under curve metrics.

Abstract: Computer vision systems and methods for localizing image forgery are provided. The system generates a constrained convolution via a plurality of learned rich filters. The system trains a convolutional neural network with the constrained convolution and a plurality of images of a dataset to learn a low level representation of each image among the plurality of images. The low level representation is indicative of a statistical signature of at least one source camera model of each image. The system can determine a splicing manipulation localization by the trained convolutional neural network.

Abstract: Computer vision systems and methods for high-fidelity representation of complex 3D surfaces using deep unsigned distance embeddings are provided. The system receives data associated with the 3D surface. The system processes the data based at least in part on one or more computer vision models to predict an unsigned distance field and a normal vector field. The unsigned distance field is indicative of proximity to the 3D surface and includes a predicted closest unsigned distance to a surface point of the 3D surface from a given point in a 3D space. The normal vector field is indicative of a surface orientation of the 3D surface and includes a predicted normal vector to the surface point closest to the given point. The system further determines the 3D surface representation based at least in part on the unsigned distance field and the normal vector field.

Abstract: Machine learning (ML) systems and methods for fact extraction and claim verification are provided. The system receives a claim and retrieves a document from a dataset. The document has a first relatedness score higher than a first threshold, which indicates that ML models of the system determine that the document is most likely to be relevant to the claim. The dataset includes supporting documents and claims including a first group of claims supported by facts from more than two supporting documents and a second group of claims not supported by the supporting documents. The system selects a set of sentences from the document. The set of sentences have second relatedness scores higher than a second threshold, which indicate that the ML models determine that the set of sentences are most likely to be relevant to the claim. The system determines whether the claim includes facts from the set of sentences.

Abstract: Machine learning systems and methods for multiscale Alzheimer's dementia recognition through spontaneous speech are provided. The system retrieves one or more audio samples and processes the one or more audio samples to extract acoustic features from audio samples. The system further processes the one or more audio samples to extract linguistic features from the audio samples. Machine learning is performed on the extracted acoustic and linguistic features, and the system indicates a likelihood of Alzheimer's disease based on output of machine learning performed on the extracted acoustic and linguistic features.

Abstract: Computer vision systems and methods for vehicle damage detection are provided. An embodiment of the system generates a dataset and trains a neural network with a plurality of images of the dataset to learn to detect an attribute of a vehicle present in an image of the dataset and to classify at least one feature of the detected attribute. The system can detect the attribute of the vehicle and classify the at least one feature of the detected attribute by the trained neural network. In addition, an embodiment of the system utilizes a neural network to reconstruct a vehicle from one or more digital images.

Abstract: The present invention relates to a process for the purification of compound of formula II, wherein X may be independently selected from trifluoroacetic acid, hydrochloric acid, hydrobromic acid, p-toluene sulfonic acid and phosphoric acid; its isolation as solid and use for the preparation of carfilzomib.

Abstract: Computer vision systems and methods for image to image translation are provided. The system samples a first image and a second image of a dataset. The system utilizes a variational auto-encoder to execute a cycle consistent forward cycle and a cycle consistent reverse cycle on each of the first image and the second image to generate a disentanglement representation of the first image and a disentanglement representation of the second image, and generate a first reconstructed image and a second reconstructed image based on the first image disentanglement representation and the second image disentanglement representation.

Abstract: Computer vision systems and methods for image to image translation are provided. The system receives a first input image and a second input image and applies a content adversarial loss function to the first input image and the second input image to determine a disentanglement representation of the first input image and a disentanglement representation of the second input image. The system trains a network to generate at least one output image by applying a cross cycle consistency loss function to the first disentanglement representation and the second disentanglement representation to perform multimodal mapping between the first input image and the second input image.

Abstract: Computer vision systems and methods for compositional pixel prediction are provided. The system receives an input image frame having a plurality of entities where each entity has a location at a first time step. The system processes the input image frame to extract a representation of each entity. The system utilizes an entity predictor to determine a predicted representation of each extracted entity representation at a next time step based on each extracted entity representation and a latent variable and utilizes a frame decoder to generate a predicted frame based on the input image frame and the predicted entity representations. The system trains an encoder to predict a distribution over the latent variable based on the input image frame and a final frame of a ground truth video associated with the input image frame.

Abstract: Computer vision systems and methods for optimizing correlation clustering for image segmentation are provided. The system receives input data and generates a correlation clustering formulation for Benders Decomposition for optimized correlation clustering of the input data. The system optimizes the Benders Decomposition for the generated correlation clustering formulation and performs image segmentation using the optimized Benders Decomposition.

Abstract: Computer vision systems and methods for text classification are provided. The system detects a plurality of text regions in an image and generates a bounding box for each detected text region. The system utilizes a neural network to recognize text present within each bounding box and classifies the recognized text, based on at least one extracted feature of each bounding box and the recognized text present within each bounding box, according to a plurality of predefined tags. The system can associate a key with a value and return a key-value pair for each predefined tag.

Abstract: Computer vision systems and methods for automatic parcel alignment are provided. The system receives a geotagged aerial image, parcel information, and semantic information where each of the parcel and semantic information are overlaid on the geotagged aerial image. The system cleans the parcel information and the semantic information. The system optimizes the parcel information by grouping geo-registered parcels present in the geotagged aerial image into a plurality of islands and generating a plurality of parcel alignment solutions for each island of the plurality of islands. The system refines the plurality of parcel alignment solutions for each island and regularizes each island. The system generates a composite parcel alignment solution based on the refined plurality of parcel alignment solutions for each regularized island to align the geo-registered parcels of each regularized island with the geotagged aerial image.

Abstract: Computer vision systems and methods for object detection with reinforcement learning are provided. The system includes a reinforcement learning agent configured to detect an object pertaining to a target object class and a plurality of objects pertaining to different target object classes, such that the reinforcement learning agent determines a bounding box for each of the detected of objects. The system first sets parameters of the reinforcement learning agent. The system then detects an object and/or objects in an image based on the set parameters. Finally, the system determines a bounding box and/or bounding boxes for each of the detected objects.

Abstract: A packaged oral care implement that includes a package and at least one oral care implement. The package may include a blister package having a receiving cavity and a flat panel coupled to the blister package to close an open end of the receiving cavity. The flat panel may include a first window aperture. A transparent film may be placed onto a front surface of the flat panel to cover or otherwise close the first window aperture.

Abstract: Computer vision systems and methods for end-to end training of neural networks are provided. The system generates a fixed point algorithm for dual-decomposition of a maximum-a-posteriori inference problem and trains the convolutional neural network and a conditional random field with the fixed point algorithm and a plurality of images of a dataset to learn to perform semantic image segmentation. The system can segment an attribute of an image of the dataset by the trained neural network and the conditional random field.

Abstract: Computer vision systems and methods for noisy contour alignment are provided. The system generates a loss function and trains a convolutional neural network with the loss function and a plurality of images of a dataset to learn to align contours with progressively increasing complex forward and backward transforms over increasing scales. The system can align an attribute of an image of the dataset by the trained neural network.

Abstract: Machine learning systems and methods for performing entity resolution. The system receives a dataset of observations and utilizes a machine learning algorithm to apply a blocking technique to the dataset to identify and generate a subset of pairs of observations of the dataset that could represent a same real world entity. The system generates a probability score for each pair of observations of the subset where the probability score is defined over a given pair of observations and denotes a probability that each pair is associated with a common entity in ground truth. The system utilizes a flexible minimum weight set packing framework to determine problem specific cost terms of a single hypothesis associated with the subset of pairs of observations and to perform entity resolution by partitioning the subset of pairs of observations into hypotheses based on the cost terms.