Abstract: A method for visualizing a macromolecule fragment pattern resulting from gel electrophoresis has steps for preparing a polyacrylamide gel in an electrophoresis apparatus, treating the polyacrylamide gel with Meldrum's acid Activated Furan (MAF) dissolved in a suitable solvent, loading a protein sample to a prepared position in the gel, and resolving the protein sample by applying an electrical potential across the gel for a period of time, separating molecular fragments of the protein into the fragment pattern in the gel. The method is characterized in that the MAF stains the molecular fragments in a manner to be discernible by human vision and by conventional analysis techniques.

Abstract: System and methods for generating deployments, such as software architectures, and identifying standards-related requirements applicable to deployments. A plain language message is received that describes a desired deployment. One or more entities are extracted from the plain language message. Based on the extracted entities, the system recommends one or more standards-related requirements relevant to the desired deployment, such as requirements for legal, privacy, and/or security standards. A selection is received from among the recommended standards-related requirements, the selected standards-related requirements being associated with a set of telecommunication resources configured to fulfill the standards-related requirements. The set of telecommunication resources can comprise microservices, data sources, and/or communication channels. The desired deployment is generated, which includes the set of telecommunication resources configured to fulfill the standards-related requirements.

Abstract: System and methods for generating deployments, such as software architectures, and identifying standards-related requirements applicable to deployments. A plain language message is received that describes a desired deployment. One or more entities are extracted from the plain language message. Based on the extracted entities, the system recommends one or more standards-related requirements relevant to the desired deployment, such as requirements for legal, privacy, and/or security standards. A selection is received from among the recommended standards-related requirements, the selected standards-related requirements being associated with a set of telecommunication resources configured to fulfill the standards-related requirements. The set of telecommunication resources can comprise microservices, data sources, and/or communication channels. The desired deployment is generated, which includes the set of telecommunication resources configured to fulfill the standards-related requirements.

Abstract: Systems, apparatuses, methods, and computer program products are disclosed for screening data instances based on a target text of a target corpus. A screening device analyzes a target corpus to generate at least two term dictionaries for the target corpus. The screening apparatus, based on a frequency of a term in the target corpus, determines a term weight for the term; for each data instance, determines term scores for the data instance and the target text based on the term weights; filters the data instances based on the term scores, to generate a short list of data instances; determines term similarity scores between each data instance of the short list and target text based on the term weights; and provides a data instance determined to likely correspond to the target text and an indication of the corresponding term similarity score(s). A term is a word or an n-gram.

Abstract: Systems, apparatuses, methods, and computer program products are disclosed for determining robustness information for an NLP model. Modification rules, such as replacement rules and/or insertion rules, are used to generate instances of modified test data based on instances of test data that comprise words and have a syntax and a semantic meaning. The instances of test data and modified test data are provided to the NLP model and the output of the NLP model is analyzed to determine output changing instances of modified test data, which are instances of modified test data yielded output from the NLP model that is different and/or not similar to the output yielded from the NLP model for the corresponding instance of test data. Robustness information for the NLP model is determined based at least in part on the output changing instances of modified test data.

Abstract: Systems, apparatuses, methods, and computer program products are disclosed for screening data instances based on a target text of a target corpus. A screening device analyzes a target corpus to generate at least two term dictionaries for the target corpus. The screening apparatus, based on a frequency of a term in the target corpus, determines a term weight for the term; for each data instance, determines term scores for the data instance and the target text based on the term weights; filters the data instances based on the term scores, to generate a short list of data instances; determines term similarity scores between each data instance of the short list and target text based on the term weights; and provides a data instance determined to likely correspond to the target text and an indication of the corresponding term similarity score(s). A term is a word or an n-gram.

Abstract: Systems, apparatuses, methods, and computer program products are disclosed for at least semi-automated generation of model documentation. In an example embodiment, a template corresponding to a model is selected. User input populating portions of a build notebook is received. The build notebook comprises a model identifier identifying the model and executable code. The build notebook is executed using an execution engine to cause an intermediary file encoded in a type-setting system to be generated. Executing the build notebook causes cross-references of elements of the model documentation to be mapped to appropriate locations within the intermediary file. The elements comprise graphical representations of results of model tests executed on the model and/or extracted or user-defined artifacts required for the model documentation.

Abstract: Systems, apparatuses, methods, and computer program products are disclosed for at least semi-automated generation of model documentation. In an example embodiment, a template corresponding to a model is selected. User input populating portions of a build notebook is received. The build notebook comprises a model identifier identifying the model and executable code. The build notebook is executed using an execution engine to cause an intermediary file encoded in a type-setting system to be generated. Executing the build notebook causes cross-references of elements of the model documentation to be mapped to appropriate locations within the intermediary file. The elements comprise graphical representations of results of model tests executed on the model and/or extracted or user-defined artifacts required for the model documentation.

Abstract: Systems, apparatuses, methods, and computer program products are disclosed for training a GAMI-Tree model. An example method includes initializing an iterative prediction model and performing a required number of model training iterations. For each model training iteration, the method further includes (i) performing a required number of main-effect gradient boosting iterations of a main-effect gradient boosting routine, (ii) generating a plurality of qualified input feature pairs, and (iii) performing a required number of interaction-effect gradient boosting iterations of a interaction-effect gradient boosting routine. The method further includes generating the GAMI-Tree model based on the iterative prediction model generated by a final interaction-effect gradient boosting iteration of a final model training iteration.

Abstract: System and methods for generating a deployment that uses existing telecommunication resources, such as microservices, data sources, and/or communication channels. The deployment can comprise a digital representation of a base station deployment. A plain language message is received that describes a desired deployment of telecommunication resources. One or more entities are extracted from the plain language message. Based on the extracted entities, the system recommends one or more existing telecommunication resources for use in the desired deployment. In some implementations, recommendations are generated using a machine learning model that generates relevance scores for each of multiple existing telecommunication resources. A selection is received from among the recommended telecommunication resources, and the desired deployment is generated using the selected telecommunication resources.

Abstract: System and methods for generating deployments, such as software architectures, and identifying standards-related requirements applicable to deployments. A plain language message is received that describes a desired deployment. One or more entities are extracted from the plain language message. Based on the extracted entities, the system recommends one or more standards-related requirements relevant to the desired deployment, such as requirements for legal, privacy, and/or security standards. A selection is received from among the recommended standards-related requirements, the selected standards-related requirements being associated with a set of telecommunication resources configured to fulfill the standards-related requirements. The set of telecommunication resources can comprise microservices, data sources, and/or communication channels. The desired deployment is generated, which includes the set of telecommunication resources configured to fulfill the standards-related requirements.

Abstract: Systems, apparatuses, methods, and computer program products are disclosed for generating a predictive contribution report for an attribute using machine learning techniques. An example method includes generating an entity score for an entity using a predictive analysis machine learning model. The method further includes, in an instance the entity score fails to satisfy a determination decision threshold, selecting a reference entity from a plurality of candidate reference entities and determining a plurality of per-candidate feature contribution scores using a predictive analysis machine learning model. The method further includes generating a predictive contribution report, where the predictive contribution report includes an indication that the entity does not satisfy the determination decision threshold, and an indication of one or more candidate features associated with largest contributions to the entity score.

Abstract: Systems, apparatuses, methods, and computer program products are disclosed for generating a predictive temporal feature impact report using a feature engineering machine with attention for time series (FEATS model). An example method includes receiving an entity input data object. The method further includes determining one or more attention head scores for each feature attention head included in the FEATS model based at least in part on one or more per-temporal feature time impact scores over each time window for each temporal feature set. The method further includes generating a predictive temporal feature impact report based at least in part on at least one of the one or more attention head scores for each attention head or the one or more per-temporal feature time impact scores for each temporal feature time point as determined in each attention head.

Abstract: System and methods for generating a deployment, such as a software architecture, using existing telecommunication resources, such as microservices, data sources, and/or communication channels. A plain language message is received that describes requirements of a desired deployment. One or more entities are extracted from the plain language message. Based on the extracted entities, the system recommends one or more existing telecommunication resources for use in the desired deployment. In some implementations, recommendations are generated using a machine learning model that generates relevance scores for each of multiple existing telecommunication resources. A selection is received from among the recommended telecommunication resources, and the desired deployment is generated using the selected telecommunication resources.

Abstract: Systems, apparatuses, methods, and computer program products are disclosed for determining robustness information for an NLP model. Modification rules, such as replacement rules and/or insertion rules, are used to generate instances of modified test data based on instances of test data that comprise words and have a syntax and a semantic meaning. The instances of test data and modified test data are provided to the NLP model and the output of the NLP model is analyzed to determine output changing instances of modified test data, which are instances of modified test data yielded output from the NLP model that is different and/or not similar to the output yielded from the NLP model for the corresponding instance of test data. Robustness information for the NLP model is determined based at least in part on the output changing instances of modified test data.

Abstract: Systems, apparatuses, methods, and computer program products are disclosed for determining robustness information for an NLP model. Modification rules, such as replacement rules and/or insertion rules, are used to generate instances of modified test data based on instances of test data that comprise words and have a syntax and a semantic meaning. The instances of test data and modified test data are provided to the NLP model and the output of the NLP model is analyzed to determine output changing instances of modified test data, which are instances of modified test data yielded output from the NLP model that is different and/or not similar to the output yielded from the NLP model for the corresponding instance of test data. Robustness information for the NLP model is determined based at least in part on the output changing instances of modified test data. White and/or black box attacks may be performed.

Abstract: A method for visualizing a macromolecule fragment pattern resulting from gel electrophoresis has steps for preparing a polyacrylamide gel in an electrophoresis apparatus, treating the polyacrylamide gel with Meldrum's acid Activated Furan (MAF) dissolved in a suitable solvent, loading a protein sample to a prepared position in the gel, and resolving the protein sample by applying an electrical potential across the gel for a period of time, separating molecular fragments of the protein into the fragment pattern in the gel. The method is characterized in that the MAF stains the molecular fragments in a manner to be discernible by human vision and by conventional analysis techniques.

Abstract: Systems, apparatuses, methods, and computer program products are disclosed for screening data instances based on a target text of a target corpus. A screening device analyzes a target corpus to generate at least two term dictionaries for the target corpus. The screening apparatus, based on a frequency of a term in the target corpus, determines a term weight for the term; for each data instance, determines term scores for the data instance and the target text based on the term weights; filters the data instances based on the term scores, to generate a short list of data instances; determines term similarity scores between each data instance of the short list and target text based on the term weights; and provides a data instance determined to likely correspond to the target text and an indication of the corresponding term similarity score(s). A term is a word or an n-gram.

Abstract: Systems, apparatuses, methods, and computer program products are disclosed for at least semi-automated generation of model documentation. In an example embodiment, a template corresponding to a model is selected. User input populating portions of a build notebook is received. The build notebook comprises a model identifier identifying the model and executable code. The build notebook is executed using an execution engine to cause an intermediary file encoded in a type-setting system to be generated. Executing the build notebook causes cross-references of elements of the model documentation to be mapped to appropriate locations within the intermediary file. The elements comprise graphical representations of results of model tests executed on the model and/or extracted or user-defined artifacts required for the model documentation.

Abstract: Systems, apparatuses, methods, and computer program products are disclosed for at least semi-automated generation of model documentation. In an example embodiment, a template corresponding to a model is selected. User input populating portions of a build notebook is received. The build notebook comprises a model identifier identifying the model and executable code. The build notebook is executed using an execution engine to cause an intermediary file encoded in a type-setting system to be generated. Executing the build notebook causes cross-references of elements of the model documentation to be mapped to appropriate locations within the intermediary file. The elements comprise graphical representations of results of model tests executed on the model and/or extracted or user-defined artifacts required for the model documentation.