Abstract: Disclosed herein are vaccine compositions, in particular, polyvalent icosahedral compositions for antigen presentation. The disclosed compositions may contain an S particle made up of recombinant fusion proteins. The recombinant fusion proteins may include a norovirus (NoV) S domain protein, a linker protein domain operatively connected to the norovirus S domain protein, and an antigen protein domain operatively connected to said linker.

Abstract: Random token segmentation may be implemented for next token prediction. Text data may be received for training a machine learning model to predict a next token given input text tokens. Multiple tokens may be determined from the text data. Different ones of the multiple token may be randomly segmented in to sub-tokens. The machine learning model may then be trained using the multiple tokens including the respective sub-tokens as a training data set.

Abstract: A light fixture is provided herein including: a plurality of side-by-side arrays each containing a set of solid state light generating elements, wherein each array is independently powered; and, a plurality of lenses provided for each of the arrays, the lenses each covering one or more solid state light generating elements and being fixed to a substrate, wherein first lenses of a first array having a first height, second lenses of a second array having a second height greater than the first height, whereby different beam angles and light patterns are achievable by activating different arrays, individually or in various combinations. Advantageously, with the subject invention, a light fixture may be provided where different beam angles and light patterns are achievable without physically changing components of the light fixture.

Abstract: Disclosed herein is a system for mining map data to automatically identify and/or predict turn restrictions. The systems uses street-level imagery to recognize and locate posted signs (e.g., physical signs, electronic signs) that signal, or are in some way related to, a turn restriction at an intersection. Then, the techniques use a cascade of machine learning models to accurately predict whether the recognized and located signs impose the turn restriction at the intersection. Consequently, the human effort required to identify turn restrictions is greatly decreased, if not completely eliminated. Furthermore, electronic maps can more efficiently be expanded and/or updated which improves the experience for vehicle drivers that reply upon the electronic maps for directions from original locations to destination locations.

Abstract: Methods and apparatus for complementary evidence identification in natural language inference. A given question is obtained and a set of N passages is obtained from a database. A probability is determined, for each passage of the set of N passages, of a corresponding passage being a supportive passage for the given question and the set of N passages is ranked based on the determined probabilities. M passages that are ranked 1 to M of the set of N passages are selected. A set of L passages is selected based on a plurality of scores, each score assigned to a set of candidate passages of the set of N passages, each score being based on the determined probabilities, the selected M passages, and a weighted regulation parameter. The set of L passages is provided to a computerized machine learning system to answer the question based on the set of L passages.

Abstract: Mechanisms are provided for performing an automated machine learning (AutoML) operation to configure parameters of a machine learning model. AutoML logic is configured based on an initial parameter sampling configuration for sampling values of parameter(s) of the machine learning (ML) model. An initial AutoML process is executed on the ML model based on a dataset utilizing the initially configured AutoML logic, to generate at least one learned value for the parameter(s) of the ML model. The dataset is analyzed to extract a set of dataset characteristics that define properties of a format and/or a content of the dataset which are stored in association with the at least one learned value as part of a training dataset. A ML prediction model is trained based on the training dataset to predict, for new datasets, corresponding new sampling configuration information based on characteristics of the new datasets.

Abstract: Disclosed herein are vaccine compositions, in particular, polyvalent icosahedral compositions for antigen presentation. The disclosed compositions may contain an S particle made up of recombinant fusion proteins. The recombinant fusion proteins may include a norovirus (NoV) S domain protein, a linker protein domain operatively connected to the norovirus S domain protein, and an antigen protein domain operatively connected to said linker.

Abstract: Disclosed herein is a system for mining map data to automatically identify and/or predict turn restrictions. The systems uses street-level imagery to recognize and locate posted signs (e.g., physical signs, electronic signs) that signal, or are in some way related to, a turn restriction at an intersection. Then, the techniques use a cascade of machine learning models to accurately predict whether the recognized and located signs impose the turn restriction at the intersection. Consequently, the human effort required to identify turn restrictions is greatly decreased, if not completely eliminated. Furthermore, electronic maps can more efficiently be expanded and/or updated which improves the experience for vehicle drivers that reply upon the electronic maps for directions from original locations to destination locations.

Abstract: The instant disclosure relates to pseudovirus nanoparticles (PVNPs) and compositions comprising PVNPs. The disclosed PVNPs may be comprised of fusion proteins that form an icosahedral structure and a nanoparticle shell. The disclosed fusion proteins may comprise a modified norovirus (NoV) S domain protein; a hemagglutinin I (HA1) antigen of the influenza hemagglutinin I (HA1) of influenza vims; and a peptide linker connecting the C-terminus of the NoV S domain to the HA1 antigen. The modified NoV S domain proteins form the interior nanoparticle shell of said PVNP composition and display the 60 HA1 antigens on the surface of the nanoparticle shell. Methods of making and using the PVNPs and compositions containing PVNPs are also disclosed.

Abstract: Techniques regarding transferring learning outcomes across machine learning tasks in automated machine learning systems are provided. For example, one or more embodiments described herein can comprise a system, which can comprise a memory that can store computer executable components. The system can also comprise a processor, operably coupled to the memory, and that can execute the computer executable components stored in the memory. The computer executable components can comprise a transfer learning component that can executes a machine learning task using an existing artificial intelligence model on a sample dataset based on a similarity between the sample dataset and a historical dataset. The existing artificial intelligence model can be generated by automated machine learning and trained on the historical dataset.

Abstract: An artificial intelligence (AI) interaction method, system, and computer program product include selecting an artificial intelligence model to respond to a query to generating a response to the query using the selected artificial intelligence model, and receiving the response to the query from the selected artificial intelligence model.

Abstract: The instant disclosure relates to pseudovirus nanoparticles (PVNPs) and compositions comprising PVNPs. The disclosed PVNPs may be comprised of fusion proteins that form an icosahedral structure and a nanoparticle shell. The disclosed fusion proteins may comprise a modified norovirus (NoV) S domain protein; a hemagglutinin I (HA1) antigen of the influenza hemagglutinin I (HA1) of influenza vims; and a peptide linker connecting the C-terminus of the NoV S domain to the HA1 antigen. The modified NoV S domain proteins form the interior nanoparticle shell of said PVNP composition and display the 60 HA1 antigens on the surface of the nanoparticle shell. Methods of making and using the PVNPs and compositions containing PVNPs are also disclosed.

Abstract: Random token segmentation may be implemented for next token prediction. Text data may be received for training a machine learning model to predict a next token given input text tokens. Multiple tokens may be determined from the text data. Different ones of the multiple token may be randomly segmented in to sub-tokens. The machine learning model may then be trained using the multiple tokens including the respective sub-tokens as a training data set.

Abstract: Disclosed herein are vaccine compositions, in particular, polyvalent icosahedral compositions for antigen presentation. The disclosed compositions may contain an S particle made up of recombinant fusion proteins. The recombinant fusion proteins may include a norovirus (NoV) S domain protein, a linker protein domain operatively connected to the norovirus S domain protein, and an antigen protein domain operatively connected to said linker.

Abstract: Systems and techniques that facilitate few-shot temporal action localization based on graph convolutional networks are provided. In one or more embodiments, a graph component can generate a graph that models a support set of temporal action classifications. Nodes of the graph can correspond to respective temporal action classifications in the support set. Edges of the graph can correspond to similarities between the respective temporal action classifications. In various embodiments, a convolution component can perform a convolution on the graph, such that the nodes of the graph output respective matching scores indicating levels of match between the respective temporal action classifications and an action to be classified. In various embodiments, an instantiation component can input into the nodes respective input vectors based on a proposed feature vector representing the action to be classified.

Abstract: A deep learning module classifies messages received from a plurality of entities into one or more conversation threads. In response to receiving a subsequent message, the deep learning module determines which of the one or more conversation threads and a new conversation thread is contextually a best fit for the subsequent message. The subsequent message is added to the determined conversation thread.

Abstract: A computer-implemented method includes using an embedding network to generate prototypical vectors. Each prototypical vector is based on a corresponding label associated with a first domain. The computer-implemented method also includes using the embedding network to generate an in-domain test vector based on at least one data sample from a particular label associated with the first domain and using the embedding network to generate an out-of-domain test vector based on at least one other data sample associated with a different domain. The computer-implemented method also includes comparing the prototypical vectors to the in-domain test vector to generate in-domain comparison values and comparing the prototypical vectors to the out-of-domain test vector to generate out-of-domain comparison values. The computer-implemented method also includes modifying, based on the in-domain comparison values and the out-of-domain comparison values, one or more parameters of the embedding network.

Abstract: Domain specific model compression by providing a weighting parameter for a candidate operation of a neural network, applying the weighting parameter to an output vector of the candidate operation, performing a regularization of the weighting parameter output vector combination, compressing the neural network model according to the results of the regularization, and providing the neural network model after compression.

Abstract: In response to running at least one testing phrase on a previously trained text classifier and identifying a separate predicted classification label based on a score calculated for each respective at least one testing phrase, a text classifier decomposes extracted features summed in the score into word-level scores for each word in the at least one testing phrase. The text classifier assigns a separate heatmap value to each of the word-level scores, each respective separate heatmap value reflecting a weight of each word-level score. The text classifier outputs the separate predicted classification label and each separate heatmap value reflecting the weight of each word-level score for defining a heatmap identifying the contribution of each word in the at least one testing phrase to the separate predicted classification label for facilitating client evaluation of text classification anomalies.