Abstract: Disclosed are systems and methods for predicting patient response to a treatment option. In one embodiment, the image slides from patient tissue samples are divided into patches and morphological patterns correlated with a disease outcome are labeled and given a patch-level score, based on whether the morphological patterns occur only in patients with good outcomes or patients with poor outcomes. A patient-level score can be generated based, at least partly, on the patch-level scores. Patch-level scores can identify regions of interest for targeted biomarker identification.

Abstract: Methods and systems disclosed herein collect user data in real-time and organize them (e.g., using collaborative filtering) into groups (e.g., clusters). The system then determines statistical distributions of observed real-time intents (e.g., based on actual selections made by users) for each group. The system then merges this distribution with existing model predictions (e.g., a model trained on historical training data) to balance between historical and dynamically updated information.

Abstract: Methods and systems for dynamically updating machine learning models that provide conversational responses through the use of a configuration file that defines modifications and changes to the machine learning model are disclosed. For example, the configuration file may be used to define an expected behavior and required attributes for instituting modifications and changes (e.g., via a mutation algorithm) to the machine learning model.

Abstract: Methods, systems, and computer program products related to a cognitive assistant for co-generating creative content are provided herein. A computer-implemented method includes obtaining semantic-level inputs from at least one user, wherein the semantic-level inputs pertain to multiple aspects of a desired content narrative; generating textual content based at least in part on the semantic-level inputs, wherein said generating the textual content comprises applying one or more deep learning algorithms to the semantic-level inputs; generating image content based at least in part on the generated textual content; creating the desired content narrative by integrating (i) the generated textual content and (ii) the generated image content; and outputting the desired content narrative to the at least one user.

Abstract: A process for producing acetylene, ethylene, or both is disclosed. The process includes combusting a fuel stream to produce a combustion gas effluent stream and pyrolyzing a feed stream in a pyrolysis zone in the presence of the combustion gas effluent stream to produce a pyrolysis zone effluent stream which is further quenched and compressed. The compressed quenched stream is separated in a solvent separation column to produce a net gas stream comprising hydrogen, methane, and at least one carbon oxide and a product stream. A portion of the carbon oxide of the net gas stream is converted into methane in a methanation reactor and a reactor effluent stream is sent to an amine scrubber where carbon dioxide is removed and a methane containing stream is generated as an effluent. The methane containing stream is then recycled to the pyrolysis zone of the supersonic reactor.

Abstract: An integrated process for producing acetylene is provided. The process comprises separating a gas stream comprising methane from a fuel gas stream in a fuel gas recovery unit of a process. A fuel and an oxidizer are combusted in a combustion zone of a pyrolytic reactor to create a combustion gas stream, wherein the pyrolytic reactor is integrated with the fuel gas recovery unit via the gas stream comprising methane. A light hydrocarbon stream comprising all or a first portion of the gas stream comprising methane is injected into a supersonic combustion gas stream to create a mixed stream. The velocity of the mixed stream is transitioned from supersonic to subsonic in a reaction zone of the pyrolytic reactor to produce a reaction mixture comprising acetylene, methane, carbon oxides, and hydrogen. The reaction mixture is separated to provide an acetylene stream.

Abstract: Text suggestions are generated. A document is received, and a portion score for at least one portion of the document is generated. A global assessment score for at least two portions of the document is also generated. A variation between the portion score and the global assessment score is calculated. It is determined that the variation is above a threshold variation, and at least one text change suggestion is generated.

Abstract: One embodiment provides a method, including: receiving, from a user, input for generating an article, wherein the input comprises (i) an indication of topic-specific requirements for the article that are dependent upon a topic of the article and (ii) an indication of non-topic-specific requirements for the article that are independent of the topic of the article; creating at least one query from the input; retrieving, from secondary sources by performing a search utilizing the query, structured information contextually relevant to the topic; and from the structured information, generating the article, wherein the article comprises a coherent and grammatically fluid structured article generated in view of the topic-specific requirements and the non-topic-specific requirements.

Abstract: A process for producing acetylene, ethylene, or both is disclosed. The process includes combusting a fuel stream to produce a combustion gas effluent stream and pyrolyzing a feed stream in a pyrolysis zone in the presence of the combustion gas effluent stream to produce a pyrolysis zone effluent stream which is further quenched and compressed. The compressed quenched stream is separated in a solvent separation column to produce a net gas stream comprising hydrogen, methane, and at least one carbon oxide and a product stream. A portion of the carbon oxide of the net gas stream is converted into methane in a methanation reactor and a reactor effluent stream is sent to an amine scrubber where carbon dioxide is removed and a methane containing stream is generated as an effluent. The methane containing stream is then recycled to the pyrolysis zone of the supersonic reactor.

Abstract: Methods and systems for identifying and quantifying molecular biomarkers and for predicting patient response to cancer therapy are provided. The disclosed methods and systems make use of artificial intelligence to capture morphometric changes from histopathology tissue that correlate with molecular changes. The system may analyze molecular markers which are predictive of tumor response and have no defined correlation with morphological features. The system may use artificial intelligence to correlate morphometric changes with critical gene modifications and molecular changes.

Abstract: Disclosed are systems and methods for predicting patient response to a treatment option. In one embodiment, the image slides from patient tissue samples are divided into patches and morphological patterns correlated with a disease outcome are labeled and given a patch-level score, based on whether the morphological patterns occur only in patients with good outcomes or patients with poor outcomes. A patient-level score can be generated based, at least partly, on the patch-level scores. Patch-level scores can identify regions of interest for targeted biomarker identification.

Abstract: Methods, systems and computer program products for multi-style text transformation are provided herein. A computer-implemented method includes selecting at least one set of style specifications for transforming at least a portion of input text. The at least one set of style specifications include one or more target writing style domains selected from a plurality of writing style domains, weights for each of the target writing style domains representing relative impact of the target writing style domains for transformation of at least a portion of the input text, and weights for each of a set of linguistic aspects for transformation of at least a portion of the input text. The computer-implemented method also includes generating one or more style-transformed output texts based at least in part on the at least one set of style specifications utilizing at least one unsupervised neural network.

Abstract: One embodiment provides a method, including: receiving, from a user, input for generating an article, wherein the input comprises (i) an indication of topic-specific requirements for the article that are dependent upon a topic of the article and (ii) an indication of non-topic-specific requirements for the article that are independent of the topic of the article; creating at least one query from the input; retrieving, from secondary sources by performing a search utilizing the query, structured information contextually relevant to the topic; and from the structured information, generating the article, wherein the article comprises a coherent and grammatically fluid structured article generated in view of the topic-specific requirements and the non-topic-specific requirements.

Abstract: Methods, systems, and computer program products for unsupervised tunable stylized text transformations are provided herein. A computer-implemented method includes identifying amendable portions of input text by processing at least a portion of the input text using at least one neural network; determining stylistic text modifications to the amendable portions of the input text, the text modifications encompassing a set of stylistic parameters, wherein said determining comprises processing at least a portion of the set of stylistic parameters using at least one neural network; generating a stylized output set of text by transforming at least a portion of the input text, wherein said transforming comprises modifying at least one of the amendable portions of the input text via at least one of the stylistic text modifications encompassed by the set of stylistic parameters; and outputting the stylized output set of text to at least one user.

Abstract: Methods, systems and computer program products for multi-style text transformation are provided herein. A computer-implemented method includes obtaining input text and selecting a set of style specifications for transforming the input text. The set of style specifications include one or more target writing style domains selected from a plurality of writing style domains, weights for each of the target writing style domains representing relative impact of the target writing style domains for transformation of the input text, and weights for each of a set of linguistic aspects for transformation of the input text. The computer-implemented method also includes generating one or more style-transformed output texts based at least in part on the set of style specifications utilizing an unsupervised neural network.

Abstract: Embodiments of the present invention provide a method, a computer program product, and a system for generating a haptic signal representing a fabric composition. Embodiments of the present invention can be used to generate a haptic signal that is based on a user selection. For example, embodiments of the present invention can combine characteristic signals corresponding to a plurality of textiles to generate the haptic signal for output to a haptic device. Embodiments of the present invention can be used to recommend similar fabric compositions based upon similarity between a characteristic signal of a fabric composition and the haptic signal.

Abstract: Methods, systems, and computer program products for unsupervised tunable stylized text transformations are provided herein. A computer-implemented method includes identifying stylistically amendable portions of input text by applying at least one neural network to the input text; determining stylistic text modifications to the amendable portions of the input text, the text modifications encompassing a set of stylistic parameters, wherein the determining comprises applying at least one neural network to the set of stylistic parameters; generating a stylized output set of text by transforming the input text, wherein the transforming comprises modifying at least one of the stylistically amendable portions of the input text via at least one of the stylistic text modifications encompassed by the set of stylistic parameters; and outputting the stylized output set of text to a user.

Abstract: Techniques for script modification are provided including receiving a script and parsing the script to identify at least one attribute of the script. The identified at least one attribute is presented to a user in a graphical user interface via a display device and an adjustment of at least one element in the graphical user interface that corresponds to the at least one attribute is received from the user via an input device. Modification data corresponding to the at least one attribute are received from a data repository and at least one attribute of the script is modified based on the received adjustment and the obtained modification data corresponding to the at least one attribute. A modified script is generated based on the modified at least one attribute.

Abstract: Methods, systems, and computer program products for bootstrapping of state-based dialog systems are provided herein. A computer-implemented method includes determining parameters for state automata by partitioning an ontology graph into sub-graphs and a knowledge graph into sub-graphs, wherein the ontology graph and the knowledge graph are based on a user question and domain knowledge pertaining to the user question; generating a structured query for each of the sub-graphs; determining intentions of a dialog pertaining to the at least one user question by translating each of the generated structured queries to a respective natural language query; creating one or more dialog states for each of the determined dialog intentions; creating one or more connecting dialog states between pairs of the created dialog states; and generating an automata dialog framework associated with the user question based on the created dialog states and the created connecting dialog states.

Abstract: Methods, systems and computer program products for multi-style text transformation are provided herein. A computer-implemented method includes obtaining input text and selecting a set of style specifications for transforming the input text. The set of style specifications include one or more target writing style domains selected from a plurality of writing style domains, weights for each of the target writing style domains representing relative impact of the target writing style domains for transformation of the input text, and weights for each of a set of linguistic aspects for transformation of the input text. The computer-implemented method also includes generating one or more style-transformed output texts based at least in part on the set of style specifications utilizing an unsupervised neural network.