Abstract: Identifying documents that contain potential code words using a machine learning model. In some embodiments, a method may include receiving documents, identifying a first corpus and a second corpus in the documents, extracting a first set of word embeddings from the first corpus and a second set of word embeddings from the second corpus, generating a first vector space for the first set of word embeddings and a second vector space for the second set of word embeddings using a machine learning model, performing a vector rotation to improve alignment of the first set of word embeddings with the second set of word embeddings, identifying a word embedding in the first vector space that is not aligned with a corresponding word embedding in the second vector space as a potential code word, and identifying one or more documents that contain the potential code word in the first corpus.

Abstract: An internal combustion engine is operated at fuel-rich conditions by adjusting one or more operating parameters such as, for example, a throttle, an ignition timing, a load coupled to the engine, a fuel pressure, power to a supercharger, and power to a preheater to maintain a specified engine speed and a temperature of an exhaust gas. Operating the engine under these conditions allows the engine to function as a reformer producing a synthesis gas comprising hydrogen and carbon monoxide.

Abstract: Identifying documents that contain potential code words using a machine learning model. In some embodiments, a method may include receiving documents, identifying a first corpus and a second corpus in the documents, extracting a first set of word embeddings from the first corpus and a second set of word embeddings from the second corpus, generating a first vector space for the first set of word embeddings and a second vector space for the second set of word embeddings using a machine learning model, performing a vector rotation to improve alignment of the first set of word embeddings with the second set of word embeddings, identifying a word embedding in the first vector space that is not aligned with a corresponding word embedding in the second vector space as a potential code word, and identifying one or more documents that contain the potential code word in the first corpus.

Abstract: An internal combustion engine is operated at fuel-rich conditions by adjusting one or more operating parameters such as, for example, a throttle, an ignition timing, a load coupled to the engine, a fuel pressure, power to a supercharger, and power to a preheater to maintain a specified engine speed and a temperature of an exhaust gas. Operating the engine under these conditions allows the engine to function as a reformer producing a synthesis gas comprising hydrogen and carbon monoxide.

Abstract: A method for identifying and ranking potentially privileged documents using a machine learning topic model may include receiving a set of documents. The method may also include, for each of two or more documents in the set of documents, extracting a set of spans from the document, generating, using a machine learning topic model, a set of topics and a subset of legal topics for the set of spans, generating a vector of probabilities for each span with a probability being assigned to each topic in the set of topics for the span, assigning a score to one or more spans in the set of spans by summing the probabilities in the vector that are assigned to a topic in the subset of legal topics, and assigning a score to the document. The method may further include ranking the two or more documents by their assigned scores.

Abstract: Identifying documents that contain potential code words using a machine learning model. In some embodiments, a method may include receiving documents, identifying a first corpus and a second corpus in the documents, extracting a first set of word embeddings from the first corpus and a second set of word embeddings from the second corpus, generating a first vector space for the first set of word embeddings and a second vector space for the second set of word embeddings using a machine learning model, performing a vector rotation to improve alignment of the first set of word embeddings with the second set of word embeddings, identifying a word embedding in the first vector space that is not aligned with a corresponding word embedding in the second vector space as a potential code word, and identifying one or more documents that contain the potential code word in the first corpus.

Abstract: A method to automatically classify emails may include generating multiple entity data objects using entities identified in receiver and sender fields of emails and categorizing the multiple entity data objects into a first set of data objects and a second set of data objects. The method may also include extracting all tokens from each email and searching the extracted tokens for tokens associated with the data objects of the first set of data objects. The method may further include identifying the emails that include the extracted tokens that are associated with the data objects of the first set of data objects, identifying a particular data object of the first set of data objects to which an identified email corresponds, and automatically classifying the identified email in the first category in response to identifying the particular data object of the first set of data objects to which an identified email corresponds.

Abstract: A method to automatically classify emails may include generating multiple entity data objects using entities identified in receiver and sender fields of emails and categorizing the multiple entity data objects into a first set of data objects and a second set of data objects. The method may also include extracting all tokens from each email and searching the extracted tokens for tokens associated with the data objects of the first set of data objects. The method may further include identifying the emails that include the extracted tokens that are associated with the data objects of the first set of data objects, identifying a particular data object of the first set of data objects to which an identified email corresponds, and automatically classifying the identified email in the first category in response to identifying the particular data object of the first set of data objects to which an identified email corresponds.

Abstract: A method to automatically classify emails may include generating multiple entity data objects using entities identified in receiver and sender fields of emails and categorizing the multiple entity data objects into a first set of data objects and a second set of data objects. The method may also include extracting all tokens from each email and searching the extracted tokens for tokens associated with the data objects of the first set of data objects. The method may further include identifying the emails that include the extracted tokens that are associated with the data objects of the first set of data objects, identifying a particular data object of the first set of data objects to which an identified email corresponds, and automatically classifying the identified email in the first category in response to identifying the particular data object of the first set of data objects to which an identified email corresponds.

Abstract: A method to automatically classify emails may include generating multiple entity data objects using entities identified in receiver and sender fields of emails and categorizing the multiple entity data objects into a first set of data objects and a second set of data objects. The method may also include extracting all tokens from each email and searching the extracted tokens for tokens associated with the data objects of the first set of data objects. The method may further include identifying the emails that include the extracted tokens that are associated with the data objects of the first set of data objects, identifying a particular data object of the first set of data objects to which an identified email corresponds, and automatically classifying the identified email in the first category in response to identifying the particular data object of the first set of data objects to which an identified email corresponds.

Abstract: A method may include obtaining a set of documents. Text objects from a text object database generated based on the set of documents may be compared to a search term object to identify relevant text objects that match the search term object. A context object for each of the relevant text objects that indicate usage of the relevant text objects within the documents corresponding to the relevant text objects may be determined, and context objects may be grouped according to similarities between the context objects. A first or second classification may be applied to each context object based on one or more criteria, and the first and second classifications may also be applied to each group based on the classifications of the context objects within the groups. Documents within the set of documents may be given the first or second classifications based on relations and similarities to the classified groups.

Abstract: An internal combustion engine is operated at fuel-rich conditions by adjusting one or more operating parameters such as, for example, a throttle, an ignition timing, a load coupled to the engine, a fuel pressure, power to a supercharger, and power to a preheater to maintain a specified engine speed and a temperature of an exhaust gas. Operating the engine under these conditions allows the engine to function as a reformer producing a synthesis gas comprising hydrogen and carbon monoxide.

Abstract: According to an aspect, a heterogeneous graph in a data store is accessed. The heterogeneous graph includes a plurality of nodes having a plurality of node types. The nodes are connected by edges having a plurality of relation types. One or more intermediary graphs are created based on the heterogeneous graph. The intermediary graphs include intermediary nodes that are the relation types of the edges of the heterogeneous graph and include intermediary links between the intermediary nodes based on shared instances of the nodes between relation types in the heterogeneous graph. The intermediary graphs are traversed to find sets of relations based on intermediary links according to a template. An inference rule is extracted from the heterogeneous graph based on finding sets of relations in the intermediary graphs. The inference rule defines an inferred relation type between at least two of the nodes of the heterogeneous graph.

Abstract: According to an aspect, a heterogeneous graph in a data store is accessed. The heterogeneous graph includes a plurality of nodes having a plurality of node types. The nodes are connected by edges having a plurality of relation types. One or more intermediary graphs are created based on the heterogeneous graph. The intermediary graphs include intermediary nodes that are the relation types of the edges of the heterogeneous graph and include intermediary links between the intermediary nodes based on shared instances of the nodes between relation types in the heterogeneous graph. The intermediary graphs are traversed to find sets of relations based on intermediary links according to a template. An inference rule is extracted from the heterogeneous graph based on finding sets of relations in the intermediary graphs. The inference rule defines an inferred relation type between at least two of the nodes of the heterogeneous graph.

Abstract: According to an aspect, a heterogeneous graph in a data store is accessed. The heterogeneous graph includes a plurality of nodes having a plurality of node types. The nodes are connected by edges having a plurality of relation types. One or more intermediary graphs are created based on the heterogeneous graph. The intermediary graphs include intermediary nodes that are the relation types of the edges of the heterogeneous graph and include intermediary links between the intermediary nodes based on shared instances of the nodes between relation types in the heterogeneous graph. The intermediary graphs are traversed to find sets of relations based on intermediary links according to a template. An inference rule is extracted from the heterogeneous graph based on finding sets of relations in the intermediary graphs. The inference rule defines an inferred relation type between at least two of the nodes of the heterogeneous graph.

Abstract: Methods/systems receive a question and automatically search sources of data containing passages to produce candidate answers to the question. The searching identifies passages that support each of the candidate answers based on scoring features that indicate whether the candidate answers are correct answers to the question. These methods/systems automatically create a scoring feature-specific matrix for each scoring feature. Each scoring feature-specific matrix has a score field for each different combination of text passage and question term (vector), and each score field holds a score value (vector value) indicating how each different combination of text passage and question term supports the candidate answers as being a correct answer to the question. Next, such methods/systems automatically combine multiple such vectors to produce a combined vector score for each of the candidate answers, and then rank the candidate answers based on the combined scores.

Abstract: Methods/systems receive a question and automatically search sources of data containing passages to produce candidate answers to the question. The searching identifies passages that support each of the candidate answers based on scoring features that indicate whether the candidate answers are correct answers to the question. These methods/systems automatically create a scoring feature-specific matrix for each scoring feature. Each scoring feature-specific matrix has a score field for each different combination of text passage and question term (vector), and each score field holds a score value (vector value) indicating how each different combination of text passage and question term supports the candidate answers as being a correct answer to the question. Next, such methods/systems automatically combine multiple such vectors to produce a combined vector score for each of the candidate answers, and then rank the candidate answers based on the combined scores.

Abstract: Methods and systems are provided for facilitating refueling operations in vehicles operating with multiple fuels. A vehicle operator may be assisted in refueling the multiple fuel tanks of the vehicle by being provided one or more refueling profiles that take into account the vehicle's future trip plans, the predicted environmental conditions along a planned route, and the operator's preferences.

Abstract: Methods/systems receive a question and automatically search sources of data containing passages to produce candidate answers to the question. The searching identifies passages that support each of the candidate answers based on scoring features that indicate whether the candidate answers are correct answers to the question. These methods/systems automatically create a scoring feature-specific matrix for each scoring feature. Each scoring feature-specific matrix has a score field for each different combination of text passage and question term (vector), and each score field holds a score value (vector value) indicating how each different combination of text passage and question term supports the candidate answers as being a correct answer to the question. Next, such methods/systems automatically combine multiple such vectors to produce a combined vector score for each of the candidate answers, and then rank the candidate answers based on the combined scores.

Abstract: Methods/systems receive a question and automatically search sources of data containing passages to produce candidate answers to the question. The searching identifies passages that support each of the candidate answers based on scoring features that indicate whether the candidate answers are correct answers to the question. These methods/systems automatically create a scoring feature-specific matrix for each scoring feature. Each scoring feature-specific matrix has a score field for each different combination of text passage and question term (vector), and each score field holds a score value (vector value) indicating how each different combination of text passage and question term supports the candidate answers as being a correct answer to the question. Next, such methods/systems automatically combine multiple such vectors to produce a combined vector score for each of the candidate answers, and then rank the candidate answers based on the combined scores.