Abstract: A physical card has a body with dynamic region(s) configured to appear opaque for human viewing in a first phase and translucent for human viewing in a second phase. The card also has a computer readable chip, a power supply configured to power the one or more dynamic regions, a communication device, one or more processors, and memory storing instructions that, when executed, are configured to cause the card to perform a method. The card may receive an authorization signal from a recognized user device associated with a cardholder, direct dynamic region(s) to transition from being opaque in the first phase to being translucent in the second phase, and direct the dynamic region(s) to transition from being translucent in the second phase to being opaque in the first phase upon hitting a predetermined time threshold in the second phase.

Abstract: A system may comprise one or more processors, a memory in communication with the one or more processors, and storing instructions, that when executed by the one or more processors, are configured to cause the system to perform a method for decrypting an encrypted message. The method may include providing an encrypted code to a first user, receiving one or more first images comprising the encrypted code from a first user device associated with the first user. The method may include determining whether the first user device is a trusted device for the encrypted code. The method may include mapping the encrypted code to a first sequence of alphanumeric characters associated with the encrypted code. The method may include providing the first decrypted code comprising the first sequence of alphanumeric characters to the first user device for display in augmented reality proximate the encrypted code.

Abstract: A physical card has a body with dynamic region(s) configured to appear opaque for human viewing in a first phase and translucent for human viewing in a second phase. The card also has a computer readable chip, a power supply configured to power the one or more dynamic regions, a communication device, one or more processors, and memory storing instructions that, when executed, are configured to cause the card to perform a method. The card may receive an authorization signal from a recognized user device associated with a cardholder, direct dynamic region(s) to transition from being opaque in the first phase to being translucent in the second phase, and direct the dynamic region(s) to transition from being translucent in the second phase to being opaque in the first phase upon hitting a predetermined time threshold in the second phase.

Abstract: In some implementations, a server device may receive, from a client device, a login credential associated with a user and a request to access a resource. The server device may identify a confidant associated with the user and a confidant device associated with the confidant, wherein the confidant device is different from a user device associated with the user. The server device may transmit, to the confidant device, a request to verify an identity of the user. The server device may determine whether a verification of the identity of the user is received from the confidant device. The server device may grant or deny access to the resource based on determining whether the verification of the identity of the user is received from the confidant device.

Abstract: A method, system and computer program product for generating answers to questions. In one embodiment, the method comprises receiving a query, conducting a search through one or more data sources to identify candidate answers to the query, and providing each of the candidate answers with a preliminary score. The method further comprises filtering out any of the candidate answers with a preliminary score that does not satisfy a defined condition. The candidate answers having preliminary scores that satisfy this condition form a subset of the candidate answers. Each of the candidate answers in this subset is processed to produce further scores. A ranking function is applied to these further scores to determine a ranking for each of the candidate answers in the subset; and after this ranking function is applied, one or more of the candidate answers are selected as one or more final answers to the query.

Abstract: In some implementations, a terminal device may obtain exchange information associated with an exchange initiated at the terminal device. The exchange information may identify a medium used in the exchange. The terminal device may be configured to present a terminal-side user interface for performing a signing operation of the exchange. The terminal device may selectively obtain user information associated with the medium used in the exchange. The user information may identify a user device associated with the medium. The terminal device may transmit, to the user device and based on obtaining the user information, presentation information that identifies content for a user-side user interface for performing the signing operation at the user device. The terminal device may receive, from the user device, signing information associated with a performance of the signing operation at the user device via the user-side user interface.

Abstract: Herein are described the preparation of a series of synthetic rocaglates, amidino- and amino-rocaglates, which display inhibition of protein translation and tumor cell proliferation (in vitro and in vivo). The methods described herein allow the preparation of libraries of modified rocaglates. This chemical modification of the rocaglate scaffold changes the C8b-hydroxyl of the natural product series to a more optimal hydrogen bond donor/acceptor.

Abstract: A dynamic reasoning system may include a symbolic reasoning engine that iteratively calls a dynamic rule generator to answer an input query. The symbolic reasoning engine may determine a primary goal and/or secondary goals to generate proofs for the answer. The symbolic reasoning engine may call a rules component to provide rules to prove a current input goal. The rules component may use a static rule knowledge base and/or the dynamic rule generator to retrieve and rank rules relevant to the current input goal. The dynamic rule generator may generate new rules that lead to the current input goal. The dynamic rule generator may include a statistical model that generates unstructured or structured probabilistic rules based on context related to the input query. The symbolic reasoning engine may return a list of rules with confidence for explaining the answer to the input goal.

Abstract: A method, system and computer program product for generating answers to questions. In one embodiment, the method comprises receiving an input query, identifying a plurality of candidate answers to the query; and for at least one of these candidate answers, identifying at least one proof of the answer. This proof includes a series of premises, and a multitude of documents are identified that include references to the premises. A set of these documents is selected that include references to all of the premises. This set of documents is used to generate one or more scores for the one of the candidate answers. A defined procedure is applied to the candidate answers to determine a ranking for the answers, and this includes using the one or more scores for the at least one of the candidate answers in the defined procedure to determine the ranking for this one candidate answer.

Abstract: A mechanism is provided in a data processing system for exploring knowledge. The mechanism receives a set of known facts. The mechanism traverses paths in an ontology for a domain of knowledge from known facts in the set of known facts to one or more hypotheses. The ontology includes a plurality of entity types and a plurality of relationships between the entity types. The mechanism presents one or more hypotheses to a user.

Abstract: A method, system and computer program product for generating answers to questions. In one embodiment, the method comprises receiving an input query, decomposing the input query into a plurality of different subqueries, and conducting a search in one or more data sources to identify at least one candidate answer to each of the subqueries. A ranking function is applied to each of the candidate answers to determine a ranking for each of these candidate answers; and for each of the subqueries, one of the candidate answers to the subquery is selected based on this ranking. A logical synthesis component is applied to synthesize a candidate answer for the input query from the selected the candidate answers to the subqueries. In one embodiment, the procedure applied by the logical synthesis component to synthesize the candidate answer for the input query is determined from the input query.

Abstract: Herein are described the preparation of a series of synthetic rocaglates, amidino- and amino-rocaglates, which display inhibition of protein translation and tumor cell proliferation (in vitro and in vivo). The methods described herein allow the preparation of libraries of modified rocaglates. This chemical modification of the rocaglate scaffold changes the C8b-hydroxyl of the natural product series to a more optimal hydrogen bond donor/acceptor.

Abstract: An architecture and processes enable computer learning and developing an understanding of arbitrary natural language text through collaboration with humans in the context of joint problem solving. The architecture ingests the text and then syntactically and semantically processes the text to infer an initial understanding of the text. The initial understanding is captured in a story model of semantic and frame structures. The story model is then tested through computer generated questions that are posed to humans through interactive dialog sessions. The knowledge gleaned from the humans is used to update the story model as well as the computing system's current world model of understanding. The process is repeated for multiple stories over time, enabling the computing system to grow in knowledge and thereby understand stories of increasingly higher reading comprehension levels.

Abstract: An architecture and processes enable computer learning and developing an understanding of arbitrary natural language text through collaboration with humans in the context of joint problem solving. The architecture ingests the text and then syntactically and semantically processes the text to infer an initial understanding of the text. The initial understanding is captured in a story model of semantic and frame structures. The story model is then tested through computer generated questions that are posed to humans through interactive dialog sessions. The knowledge gleaned from the humans is used to update the story model as well as the computing system's current world model of understanding. The process is repeated for multiple stories over time, enabling the computing system to grow in knowledge and thereby understand stories of increasingly higher reading comprehension levels.

Abstract: An architecture and processes enable computer learning and developing an understanding of arbitrary natural language text through collaboration with humans in the context of joint problem solving. The architecture ingests the text and then syntactically and semantically processes the text to infer an initial understanding of the text. The initial understanding is captured in a story model of semantic and frame structures. The story model is then tested through computer generated questions that are posed to humans through interactive dialog sessions. The knowledge gleaned from the humans is used to update the story model as well as the computing system's current world model of understanding. The process is repeated for multiple stories over time, enabling the computing system to grow in knowledge and thereby understand stories of increasingly higher reading comprehension levels.

Abstract: An architecture and processes enable computer learning and developing an understanding of arbitrary natural language text through collaboration with humans in the context of joint problem solving. The architecture ingests the text and then syntactically and semantically processes the text to infer an initial understanding of the text. The initial understanding is captured in a story model of semantic and frame structures. The story model is then tested through computer generated questions that are posed to humans through interactive dialog sessions. The knowledge gleaned from the humans is used to update the story model as well as the computing system's current world model of understanding. The process is repeated for multiple stories over time, enabling the computing system to grow in knowledge and thereby understand stories of increasingly higher reading comprehension levels.

Abstract: A method of generating secondary questions in a question-answer system. Missing information is identified from a corpus of data using a computerized device. The missing information comprises any information that improves confidence scores for candidate answers to a question. The computerized device automatically generates a plurality of hypotheses concerning the missing information. The computerized device automatically generates at least one secondary question based on each of the plurality of hypotheses. The hypotheses are ranked based on relative utility to determine an order in which the computerized device outputs the at least one secondary question to external sources to obtain responses.

Abstract: A method of generating secondary questions in a question-answer system. Missing information is identified from a corpus of data using a computerized device. The missing information comprises any information that improves confidence scores for candidate answers to a question. The computerized device automatically generates a plurality of hypotheses concerning the missing information. The computerized device automatically generates at least one secondary question based on each of the plurality of hypotheses. The hypotheses are ranked based on relative utility to determine an order in which the computerized device outputs the at least one secondary question to external sources to obtain responses.

Abstract: An architecture and processes enable computer learning and developing an understanding of arbitrary natural language text through collaboration with humans in the context of joint problem solving. The architecture ingests the text and then syntactically and semantically processes the text to infer an initial understanding of the text. The initial understanding is captured in a story model of semantic and frame structures. The story model is then tested through computer generated questions that are posed to humans through interactive dialog sessions. The knowledge gleaned from the humans is used to update the story model as well as the computing system's current world model of understanding. The process is repeated for multiple stories over time, enabling the computing system to grow in knowledge and thereby understand stories of increasingly higher reading comprehension levels.

Abstract: An architecture and processes enable computer learning and developing an understanding of arbitrary natural language text through collaboration with humans in the context of joint problem solving. The architecture ingests the text and then syntactically and semantically processes the text to infer an initial understanding of the text. The initial understanding is captured in a story model of semantic and frame structures. The story model is then tested through computer generated questions that are posed to humans through interactive dialog sessions. The knowledge gleaned from the humans is used to update the story model as well as the computing system's current world model of understanding. The process is repeated for multiple stories over time, enabling the computing system to grow in knowledge and thereby understand stories of increasingly higher reading comprehension levels.