Abstract: Various technologies described herein pertain to automated data splitting using predictive program synthesis. Input-only examples for splitting an input column of an input data set can be received. The input-only examples can include example entries from the input column of the input data set to be split into multiple output columns without specification of how the example entries are to be split into multiple output columns. Further, a program for splitting the input column of the input data set into the multiple output columns can be synthesized based on the input-only examples. The program can be synthesized, given the input-only examples, in a domain-specific language (DSL) for splitting an entry into a tuple of fields utilizing a predictive program synthesis algorithm. Moreover, the program can be executed on the input data set to split the input column of the input data set into the multiple output columns.

Abstract: Embodiments use a multi-modal approach to generate software programs that match a solution program description. The solution program description may include natural language, input-output examples, partial source code, desired operators, or other hints. Some embodiments use optimized prompts to a pre-trained language model to obtain initial candidate programs. Maximal program components are extracted and then recombined variously using component-based synthesis. Beam search reduces a solution program search space by discarding some candidates from a given synthesis iteration. Relevance metrics, string similarity metrics, operator frequency distributions, token rareness scores, and other optimizations may be employed. By virtue of optimizations and the multi-modal approach, a solution program may be obtained after fewer iterations than by use of a language model alone.

Abstract: Various technologies described herein pertain to automated data splitting using predictive program synthesis. Input-only examples for splitting an input column of an input data set can be received. The input-only examples can include example entries from the input column of the input data set to be split into multiple output columns without specification of how the example entries are to be split into multiple output columns. Further, a program for splitting the input column of the input data set into the multiple output columns can be synthesized based on the input-only examples. The program can be synthesized, given the input-only examples, in a domain-specific language (DSL) for splitting an entry into a tuple of fields utilizing a predictive program synthesis algorithm. Moreover, the program can be executed on the input data set to split the input column of the input data set into the multiple output columns.

Abstract: Generating a template based on source code examples includes identifying a set of related files within a source code project, and identifying a set of textual content portions that are each at least partially repeated across a subset of the related files. A set of templates is generated, each comprising at least one textual content portion from the set of textual content portions. Each template is associated with a set of selection criteria. The set of templates is exposed for automated consumption within a source code editor, based on the set of selection criteria associated with each template. Consuming a template includes identifying a user input indicating creation of a source code block or file within the source code editor, identifying attribute(s) of the source code block or file, and using the attribute(s) to identify a selection criterion associated with a particular template. The particular template is automatically presented.

Abstract: Pasting content from a clipboard buffer as structured tabular data. A computer system determines a data type of content within a clipboard buffer. Based on the data type of the content, the computer system identifies a tabular pattern analysis technique to apply to the content. Based on applying the tabular pattern analysis technique to the content, the computer system identifies a portion of tabular content within the content. Using a clipboard application programming interface, the computer system presents the portion of tabular content to an application as paste data that is structured as a set of rows and a set of columns.

Abstract: Embodiments use a multi-modal approach to generate software programs that match a solution program description. The solution program description may include natural language, input-output examples, partial source code, desired operators, or other hints. Some embodiments use optimized prompts to a pre-trained language model to obtain initial candidate programs. Maximal program components are extracted and then recombined variously using component-based synthesis. Beam search reduces a solution program search space by discarding some candidates from a given synthesis iteration. Relevance metrics, string similarity metrics, operator frequency distributions, token rareness scores, and other optimizations may be employed. By virtue of optimizations and the multi-modal approach, a solution program may be obtained after fewer iterations than by use of a language model alone.

Abstract: Embodiments automate several aspects of document copy-paste updates. An enhanced editor submits context, such as a copied section, pasted section, nearby text, or parser information, to an automatic suggestion generator. The editor gets back a suggestion for automatically changing the pasted section, thus helping users avoid tedium and errors. For instance, string substitutions begun by the user can be automatically and easily completed within the pasted section. Refactoring between variable declarations and parameter lists is detected and completed on request. Situation-specific transforms based on code synthesis, word associations, temporal edit patterns, anchor target lists, regular expressions, or autocompletion are offered. Suggestions are given inside the user's current workflow to avoid breaks in focus. Suggestions can be refined automatically in response to implicit or explicit user feedback. Users are warned of unedited pasted sections. Code review is aided by highlighting pasted sections.

Abstract: An indication to migrate requested data objects stored in a source database environment to a destination database environment is received. Some of data objects have many-to-one relationships with other data objects in the source database environment. At least one snapshot file generated by the source database environment is transferred to a destination database environment in bulk transfer mode. Subsequent incoming data received during bulk transfer mode, after the indication is stored in a temporary table. Upon completion of migration of the requested data objects the system transitions from bulk transfer mode to streaming mode. The subsequent incoming data from the temporary table is transferred to the destination database environment in response to the transition to streaming mode. Additional data received after the temporary table is empty is transferred from the source database environment to the destination database environment without use of the temporary table.

Abstract: The disclosed embodiments include computer-implemented apparatuses and processes that dynamically predict future occurrences of events using adaptively trained machine-learning or artificial-intelligence processes. For example, an apparatus may generate an input dataset based on first interaction data associated with a prior temporal interval, and may apply an adaptively trained, gradient-boosted, decision-tree process to the input dataset. Based on the application of the adaptively trained, gradient-boosted, decision-tree process to the input dataset, the apparatus may generate output data representative of a predicted likelihood of an occurrence of an event during a future temporal interval, which may be separated from the prior temporal interval by a corresponding buffer interval. The apparatus may also transmit a portion of the generated output data to a computing system, and the computing system may be configured to generate or modify second interaction data based on the portion of the output data.

Abstract: Various technologies described herein pertain to interactive data splitting. A program for splitting an input column of an input data set into multiple output columns can be synthesized based on input-only examples. The program can further be generated based on various user input; thus, the user input can guide the synthesis of the program. Moreover, the program can be executed on the input data set to split the input column of the input data set into the multiple output columns.

Abstract: Systems, methods, and computer-executable instructions for synthesizing a program for a task. A domain specific language that includes a disjunctive symbol and an input/output example for the task are received. A program for the task is synthesized based on the input/output example and the domain specific language. Sub-programs for an instance of the disjunctive symbol are synthesized, A set of feature calculators and target feature values are determined. The program for the task is returned. The program includes the sub-programs, the set of feature calculators and the target feature values.

Abstract: Systems, methods, and computer-executable instructions for synthesizing a program for a task. A domain specific language that includes a disjunctive symbol and an input/output example for the task are received. A program for the task is synthesized based on the input/output example and the domain specific language. Sub-programs for an instance of the disjunctive symbol are synthesized, A set of feature calculators and target feature values are determined. The program for the task is returned. The program includes the sub-programs, the set of feature calculators and the target feature values.

Abstract: Various technologies described herein pertain to automated data splitting using predictive program synthesis. Input-only examples for splitting an input column of an input data set can be received. The input-only examples can include example entries from the input column of the input data set to be split into multiple output columns without specification of how the example entries are to be split into multiple output columns. Further, a program for splitting the input column of the input data set into the multiple output columns can be synthesized based on the input-only examples. The program can be synthesized, given the input-only examples, in a domain-specific language (DSL) for splitting an entry into a tuple of fields utilizing a predictive program synthesis algorithm. Moreover, the program can be executed on the input data set to split the input column of the input data set into the multiple output columns.

Abstract: Various technologies described herein pertain to interactive data splitting. A program for splitting an input column of an input data set into multiple output columns can be synthesized based on input-only examples. The program can further be generated based on various user input; thus, the user input can guide the synthesis of the program. Moreover, the program can be executed on the input data set to split the input column of the input data set into the multiple output columns.

Abstract: Predicting user responses to items is useful in many application domains, such as personalized information retrieval and recommendation systems. In an embodiment a contacts service identifies contacts of a target user and predictions are elicited from the contacts about the target user's response to an item. In various examples, the predictions are combined taking into account weights of the contacts to produce a prediction of the target user's response. For example, the response may be one or more of: a numerical rating, a word or phrase describing the targets user's opinion of the item and a word or phrase stating a reason that the target user holds the opinion. In examples, accuracy of the predictions is calculated after observing the target user's actual response. The accuracy may be used to calculate and display scores and rankings of the contact's prediction abilities and to update the weights of the contacts.

Abstract: A variety of methods and arrangements for sharing medical data are described. In one aspect, one or more data streams are received from one or more medical imaging/sensing or other types of devices. Frames are obtained from the streams. In some embodiments, particular frames and/or parts of frames are selectively encrypted. The frames are transmitted to a remote device, where they are rendered and/or displayed at the remote device. In various embodiments, the frames of different streams are synchronized.

Abstract: A variety of methods and arrangements for sharing medical data are described. In one aspect, one or more data streams are received from one or more medical imaging/sensing or other types of devices. Frames are obtained from the streams. In some embodiments, particular frames and/or parts of frames are selectively encrypted. The frames are transmitted to a remote device, where they are rendered and/or displayed at the remote device. In various embodiments, the frames of different streams are synchronized.

Abstract: A variety of methods and arrangements for sharing medical data are described. In one aspect, one or more data streams are received from one or more medical imaging/sensing or other types of devices. Frames are obtained from the streams. In some embodiments, particular frames and/or parts of frames are selectively encrypted. The frames are transmitted to a remote device, where they are rendered and/or displayed at the remote device. In various embodiments, the frames of different streams are synchronized.

Abstract: A system and method for managing an avatar is provided. The method comprises receiving an initial input from one or more communication interfaces, processing the initial input using one or more processors, determining one or more initial characteristics based on processing the initial input and providing instructions to generate and display the avatar with one or more initial avatar characteristics in response to the one or more initial characteristics.

Abstract: Predicting user responses to items is useful in many application domains, such as personalized information retrieval and recommendation systems. In an embodiment a contacts service identifies contacts of a target user and predictions are elicited from the contacts about the target user's response to an item. In various examples, the predictions are combined taking into account weights of the contacts to produce a prediction of the target user's response. For example, the response may be one or more of: a numerical rating, a word or phrase describing the targets user's opinion of the item and a word or phrase stating a reason that the target user holds the opinion. In examples, accuracy of the predictions is calculated after observing the target user's actual response. The accuracy may be used to calculate and display scores and rankings of the contact's prediction abilities and to update the weights of the contacts.