Abstract: An activity recognition system is disclosed. A plurality of temporal features is generated from a digital representation of an observed activity using a feature detection algorithm. An observed activity graph comprising one or more clusters of temporal features generated from the digital representation is established, wherein each one of the one or more clusters of temporal features defines a node of the observed activity graph. At least one contextually relevant scoring technique is selected from similarity scoring techniques for known activity graphs, the at least one contextually relevant scoring technique being associated with activity ingestion metadata that satisfies device context criteria defined based on device contextual attributes of the digital representation, and a similarity activity score is calculated for the observed activity graph as a function of the at least one contextually relevant scoring technique, the similarity activity score being relative to at least one known activity graph.

Abstract: Apparatus, methods and systems of providing AR content are disclosed. Embodiments of the inventive subject matter can obtain an initial map of an area, derive views of interest, obtain AR content objects associated with the views of interest, establish experience clusters and generate a tile map tessellated based on the experience clusters. A user device could be configured to obtain and instantiate at least some of the AR content objects based on at least one of a location and a recognition.

Abstract: An activity recognition system is disclosed. A plurality of temporal features is generated from a digital representation of an observed activity using a feature detection algorithm. An observed activity graph comprising one or more clusters of temporal features generated from the digital representation is established, wherein each one of the one or more clusters of temporal features defines a node of the observed activity graph. At least one contextually relevant scoring technique is selected from similarity scoring techniques for known activity graphs, the at least one contextually relevant scoring technique being associated with activity ingestion metadata that satisfies device context criteria defined based on device contextual attributes of the digital representation, and a similarity activity score is calculated for the observed activity graph as a function of the at least one contextually relevant scoring technique, the similarity activity score being relative to at least one known activity graph.

Abstract: An immune gene expression signature is associated with favorable clinical features in Treg-enriched tumor samples and can be used to predict immunogenicity of a tumor, overall survival, and/or chemosensitivity.

Abstract: An object recognition ingestion system is presented. The object ingestion system captures image data of objects, possibly in an uncontrolled setting. The image data is analyzed to determine if one or more a priori know canonical shape objects match the object represented in the image data. The canonical shape object also includes one or more reference PoVs indicating perspectives from which to analyze objects having the corresponding shape. An object ingestion engine combines the canonical shape object along with the image data to create a model of the object. The engine generates a desirable set of model PoVs from the reference PoVs, and then generates recognition descriptors from each of the model PoVs. The descriptors, image data, model PoVs, or other contextually relevant information are combined into key frame bundles having sufficient information to allow other computing devices to recognize the object at a later time.

Abstract: Disclosed systems and methods include executing a convolutional neural network to detect a level of microbiome in a whole slide image associated with a patient, based on an output of the convolutional neural network, categorize the whole slide image as one of microbiome-low and microbiome-high, and, based on the categorization of the whole slide image, determine a characteristic of a cancer associated with the patient.

Abstract: A method of preparing a vaccine includes providing an immune epitope database; providing a neural network; receiving data corresponding to at least one protein into the neural network; receiving data corresponding to one or more candidate peptides corresponding to potential cleavage products of the at least one protein, or determining, using the neural network, data corresponding to one or more candidate peptides corresponding to potential cleavage products of the at least one protein; calculating, using the neural network, a probability of cleavage of the protein to result in each of the one or more candidate peptides; and outputting a signal corresponding to the calculated probability. An architecture having two channel output, i.e., output of a C-terminal cleavage and an N-terminal cleavage, is described. Related devices, apparatuses, systems, techniques, articles and non-transitory computer-readable storage medium are also described.

Abstract: An immune gene expression signature is associated with favorable clinical features in Treg-enriched tumor samples and can be used to predict immunogenicity of a tumor, overall survival, and/or chemosensitivity.

Abstract: An object recognition ingestion system is presented. The object ingestion system captures image data of objects, possibly in an uncontrolled setting. The image data is analyzed to determine if one or more a priori know canonical shape objects match the object represented in the image data. The canonical shape object also includes one or more reference PoVs indicating perspectives from which to analyze objects having the corresponding shape. An object ingestion engine combines the canonical shape object along with the image data to create a model of the object. The engine generates a desirable set of model PoVs from the reference PoVs, and then generates recognition descriptors from each of the model PoVs. The descriptors, image data, model PoVs, or other contextually relevant information are combined into key frame bundles having sufficient information to allow other computing devices to recognize the object at a later time.

Abstract: Methods for rendering augmented reality (AR) content are presented. An a priori defined 3D albedo model of an object is leveraged to adjust AR content so that is appears as a natural part of a scene. Disclosed devices recognize a known object having a corresponding albedo model. The devices compare the observed object to the known albedo model to determine a content transformation referred to as an estimated shading (environmental shading) model. The transformation is then applied to the AR content to generate adjusted content, which is then rendered and presented for consumption by a user.

Abstract: Techniques are provided for predicting MHC-peptide binding affinity. A plurality of training peptide sequences is obtained, and a neural network model is trained to predict MHC-peptide binding affinity using the training peptide sequences. An encoder of the neural network model comprising an RNN is configured to process an input training peptide sequence to generate a fixed-dimension encoding output by applying a final hidden state of the RNN at intermediate state outputs of the RNN to generate attention weighted outputs, and linearly combining the attention weighted outputs. A fully connected layer following the encoder is configured to process the fixed-dimension encoding output to generate an MHC-peptide binding affinity prediction output. A computing device is configured to use the trained neural network to predict MHC-peptide binding affinity for a test peptide sequence.

Abstract: Methods for rendering augmented reality (AR) content are presented. An a priori defined 3D albedo model of an object is leveraged to adjust AR content so that is appears as a natural part of a scene. Disclosed devices recognize a known object having a corresponding albedo model. The devices compare the observed object to the known albedo model to determine a content transformation referred to as an estimated shading (environmental shading) model. The transformation is then applied to the AR content to generate adjusted content, which is then rendered and presented for consumption by a user.

Abstract: Techniques are provided for predicting MHC-peptide binding affinity. A plurality of training peptide sequences is obtained, and a neural network model is trained to predict MHC-peptide binding affinity using the training peptide sequences. An encoder of the neural network model comprising an RNN is configured to process an input training peptide sequence to generate a fixed-dimension encoding output by applying a final hidden state of the RNN at intermediate state outputs of the RNN to generate attention weighted outputs, and linearly combining the attention weighted outputs. A fully connected layer following the encoder is configured to process the fixed-dimension encoding output to generate an MHC-peptide binding affinity prediction output. A computing device is configured to use the trained neural network to predict MHC-peptide binding affinity for a test peptide sequence.

Abstract: Systems and methods are presented that allow for determination and prediction of payload toxicity in therapeutic viruses. Disclosed herein are methods of determining payload toxicity of an expressed polypeptide in a cell, comprising: generating or procuring a plurality of expression vectors, each containing a different recombinant nucleic acid sequence that encodes a corresponding recombinant polypeptide; expressing the recombinant nucleic acid sequence in a plurality of host cells while culturing the host cells; sequencing the plurality of expression vectors after culturing the host cells; and correlating at least portions of the recombinant nucleic acid sequence with a toxicity measure.

Abstract: An object recognition ingestion system is presented. The object ingestion system captures image data of objects, possibly in an uncontrolled setting. The image data is analyzed to determine if one or more a priori know canonical shape objects match the object represented in the image data. The canonical shape object also includes one or more reference PoVs indicating perspectives from which to analyze objects having the corresponding shape. An object ingestion engine combines the canonical shape object along with the image data to create a model of the object. The engine generates a desirable set of model PoVs from the reference PoVs, and then generates recognition descriptors from each of the model PoVs. The descriptors, image data, model PoVs, or other contextually relevant information are combined into key frame bundles having sufficient information to allow other computing devices to recognize the object at a later time.

Abstract: Techniques are provided for determining pan-HLA binding of viral proteins. A trained classifier model is operable to determine, independently per HLA, at least one of (a) an average binding prediction of overlapping peptides at each position of a viral protein, (b) a maximum value of a binding prediction of overlapping peptides at each position of the viral protein, (c) standard deviation of a binding prediction of overlapping peptides at each position of the viral protein, and (d) a combination of one or more of (a)-(c). A classification engine uses the classifier model to determine average binding predictions of overlapping peptides at each position of the viral protein independently for test HLA-I and HLA-II functional groupings, where a peptide is classified as a binder when an average binding prediction corresponding to the peptide satisfies a binding value threshold.

Abstract: Apparatus, methods and systems of providing AR content are disclosed. Embodiments of the inventive subject matter can obtain an initial map of an area, derive views of interest, obtain AR content objects associated with the views of interest, establish experience clusters and generate a tile map tessellated based on the experience clusters. A user device could be configured to obtain and instantiate at least some of the AR content objects based on at least one of a location and a recognition.

Abstract: An object recognition ingestion system is presented. The object ingestion system captures image data of objects, possibly in an uncontrolled setting. The image data is analyzed to determine if one or more a priori know canonical shape objects match the object represented in the image data. The canonical shape object also includes one or more reference PoVs indicating perspectives from which to analyze objects having the corresponding shape. An object ingestion engine combines the canonical shape object along with the image data to create a model of the object. The engine generates a desirable set of model PoVs from the reference PoVs, and then generates recognition descriptors from each of the model PoVs. The descriptors, image data, model PoVs, or other contextually relevant information are combined into key frame bundles having sufficient information to allow other computing devices to recognize the object at a later time.

Abstract: Apparatus, methods and systems of providing AR content are disclosed. Embodiments of the inventive subject matter can obtain an initial map of an area, derive views of interest, obtain AR content objects associated with the views of interest, establish experience clusters and generate a tile map tessellated based on the experience clusters. A user device could be configured to obtain and instantiate at least some of the AR content objects based on at least one of a location and a recognition.

Abstract: An activity recognition system is disclosed. A plurality of temporal features is generated from a digital representation of an observed activity using a feature detection algorithm. An observed activity graph comprising one or more clusters of temporal features generated from the digital representation is established, wherein each one of the one or more clusters of temporal features defines a node of the observed activity graph. At least one contextually relevant scoring technique is selected from similarity scoring techniques for known activity graphs, the at least one contextually relevant scoring technique being associated with activity ingestion metadata that satisfies device context criteria defined based on device contextual attributes of the digital representation, and a similarity activity score is calculated for the observed activity graph as a function of the at least one contextually relevant scoring technique, the similarity activity score being relative to at least one known activity graph.