Abstract: A system and method for visualizing large graph data using spirals are provided. The method includes (i) generating communities from an input graph data, (ii) configuring nodes in the communities in an ascending order based on a centrality measure, (iii) determining a spiral for the communities by configuring (a) a node with a highest centrality measure at a spiral center and (b) the nodes in a spiral shape based on the ascending order of the centrality measure along the spiral shape, (iv) determining a first super edge for the spiral of the communities, (v) generating a coarse graph using super nodes, the first super edge, and a second super edge, (vi) configuring, using a standard force-directed layout algorithm, corresponding spirals of the communities on the generated coarse graph based on a weight of the second super edge and attractive forces to generate the visualization of the large graph data.

Abstract: In one embodiment, a method includes determining, by a router, a common prefix pool from a transport interface associated with a transport virtual private network (VPN). The method also includes identifying, by the router, a prefix associated with a service VPN and generating, by the router, an IPv6-to-IPv6 Network Address Translation (NAT66) prefix translation using the common prefix pool and the prefix. The NAT66 prefix translation includes a predetermined prefix length. The method further includes automatically installing, by the router, the NAT66 prefix translation into a translation table.

Abstract: In one example of the present disclosure, a method for determining false positive and active indications is disclosed. The method may apply anomaly network event data to a machine learning model. The machine learning model is trained with historic and correlated anomaly network event data. The method then determines which one of the anomaly network event data is a false positive indication and which one is an active indication.

Abstract: Binding proteins comprising a VL region paired with a VH region, and a CH1 region paired with a CL region, wherein the VL region and VH region comprise opposite charged mutations to facilitate pairing, and wherein the CH1 region and CL region comprise mutations to facilitate pairing, are provided. Binding proteins comprising one or more cysteine residues engineered into the VH/VL pair to form one or more disulfide bonds, are also provided. Multispecific binding proteins, nucleic acids encoding binding proteins and multispecific binding proteins, expression vectors, host cells, pharmaceutical composition and methods of treatment administering the binding proteins or multispecific binding proteins described herein are also provided.

Abstract: Methods and server systems for computing fraud risk scores for various merchants associated with an acquirer described herein. The method performed by a server system includes accessing merchant-related transaction data including merchant-related transaction indicators associated with a merchant from a transaction database. Method includes generating a merchant-related transaction features based on the merchant-related indicators. Method includes generating via risk prediction models, for a payment transaction with the merchant, merchant health and compliance risk scores, merchant terminal risk scores, merchant chargeback risk scores, and merchant activity risk scores based on the merchant-related transaction features. Method includes facilitating transmission of a notification message to an acquirer server associated with the merchant.

Abstract: A method, a device, and a non-transitory storage medium are described in which a dynamic C-DRX provisioning service is provided. The service may disable or enable discontinuous reception on a per end device application or network slice basis. The service may invoke an end device capability procedure to modify a discontinuous reception configuration of a network. The service may store information that correlates an application or network slice to a discontinuous reception setting.

Abstract: A method and system are provided to infer and use machine learning algorithms that is applied to financial data and also to determine: (i) compliance with the applied algorithms, and (ii) whether any exceptions to and/or variations from the algorithms are within an organization's norms. A method includes: receiving financial data; applying a set of dynamic or interdependent algorithms to the received financial data to obtain a set of outcomes; and using a machine learning classifier to classify the outcomes as: (i) algorithm compliant, (ii) potentially algorithm non-compliant, or (iii) algorithm non-compliant. Additionally, the method includes generating controllership persona based actionable triggers, which on acceptance corrects the anomalies in financial data, enabling the method and system to perform as a comprehensive, contextual, consistent & machine learning powered, persona based real time controllership engine leading to reduced misstatements in reported financial data.

Abstract: A method of reducing malodor on surfaces including providing an apparatus in an environment including a surface wherein the surface has a malodor containing compound selected from: amine-containing compound and thiol-containing compound. The apparatus includes a volatile material having a volatile carbonyl containing compound. The volatile material is exposed to the environment such that the volatile carbonyl containing compound vaporises and deposits on the surface. The carbonyl containing compound undergoes a nucleophilic addition in the presence of the malodor containing compound.

Abstract: The instant disclosure relates to methods for preventing, controlling and/or inhibiting the rare events of adventitious viral infection or viral replication/amplification after reactivation of latent viral infection during cell culture in the manufacturing process of cell-based drug products, including CAR T cell drug products. Also provided are in vitro cell culture models for HHV-6 latent infection and methods of making the same.

Abstract: Described herein are methods for identifying quasispecies of genomes by clustering sequence reads for samples including the genomes based on the similarities of the sequence reads.

Abstract: According to some embodiments, systems and methods are provided, including a repository storing at least an Application Programming Interface (API) mapping table; a memory storing processor-executable program code; and a processing unit to execute the processor-executable program code to: receive an input of one or more legacy API identification elements for a legacy API; determine whether the received legacy API identification elements correspond to a standard legacy API; in a case the received legacy API identification elements do correspond to a standard legacy API, determine whether a corresponding updated API is available; in a case the corresponding updated API is available, determine whether the legacy API includes at least one extension; and in a case the legacy API does include at least one extension, generate an updated corresponding API extension, and transmit the corresponding updated API and the updated corresponding API extension to the user. Numerous other aspects are provided.

Abstract: Aspects relate to recommending ETL workflows for performing specific tasks based on user inputs related to a predetermined business domain while complying with policy and access conditions. Provided is an ETL workflow recommendation device including a knowledge database for storing an overall knowledge graph including an ETL knowledge graph that at least indicates ETL information about a predetermined business domain in a graph format, an input management unit that receives a user input related to the predetermined business domain and converts the user input into a graph query for searching the overall knowledge graph, an ETL recommendation unit that searches the overall knowledge graph using the graph query and generates ETL workflow candidates with respect to the user input; and an output management unit that evaluates the ETL workflow candidates and outputs an ETL workflow report indicating a recommended ETL workflow.

Abstract: The present disclosure describes a system and method for applying machine learning to analyze the sustainability of products and using scoring based on the analysis to curate product recommendations for customers and feedback for product producers. The system and method incorporate product feature (e.g., sustainability) data, including historical data, from multiple sources, and user preferences to generate customized feature (e.g., sustainability) scores.

Abstract: Systems and methods described herein use group segmentation of applications (referred to herein as “slicing classes”), based on common service requirements, to allow carriers to assign UE traffic to network slices. A UE stores slicing class definitions for use with UE Route Selection Policies (URSP). Each of the slicing class definitions include network quality of service (QoS) characteristics that are applicable to network traffic for multiple software applications. The UE maps the software applications to corresponding slicing class identifiers associated with the slicing class definitions. The UE also stores URSP rules for associating a slicing class identifier with a network slice identifier. A UE operating system sends, to a modem, one of the slicing class identifiers for an application that is requesting attachment to a wireless network. The modem sends a registration request with a selected network slice identifier based on the received slicing class identifier and the URSP rules.

Abstract: In the world of digital advertising, optimally allocating an advertisement campaign within a fixed pre-defined budget for an advertising duration aimed at maximizing number of conversions is very important for an advertiser. Embodiments of present disclosure provides a robust and easily generalizable method of optimal allocation of advertisement campaign by formulating it as a constrained Markov Decision Process (MDP) defined by agent state comprising user state and advertiser state, action space comprising a plurality of ad campaigns, state transition routine and a cumulative reward model which rewards maximum total conversions in an advertising duration. The cumulative reward model is trained in conjunction with a deep Q-network for solving the MDP to optimally allocate advertisement campaign for an advertising duration within a constrained budget.

Abstract: Methods and server systems for detecting fraudulent transactions are described herein. Method performed by server system includes accessing base graph including plurality of nodes further including plurality of labeled nodes and unlabeled nodes. Method includes assigning via Graph Neural Network (GNN) model, fraudulent label or non-fraudulent label to each unlabeled node based on the base graph. This assigning process includes generating plurality of sub-graphs based on splitting the base graph and filtering these sub-graphs via Siamese Neural Network model based on pre-defined threshold values. Then, the GNN model generates plurality of sets of embeddings based on plurality of filtered sub-graphs. Further, aggregated node embedding is generated for each node and then, final node representation for each node is generated via dense layer of GNN model. Then, fraudulent label or the non-fraudulent label is assigned to each unlabeled node of plurality of unlabeled nodes based on final node representation.

Abstract: Siamese neural networks (SNN) are configured to detect differences between financial transactions for multiple financial institutions and transactions for a target party. A first neural network of the SNN tracks transactions (target transactions) for a particular customer or financial institution over time and provides a target output vector. Similarly, a second neural network of the SNN tracks transactions (baseline transactions) for all or a plurality of financial institutions (e.g., within a region) over the same period of time and provides a baseline output vector. The transactions for all or a plurality of financial institutions act as a baseline of transactions against which potentially fraudulent or money laundering activity may be compared. Because Siamese neural networks account for temporal changes based on the baseline of transactions, sudden changes in target transactions will only trigger an alarm if such changes (e.g., deviations or drifts) are relative to a baseline of transactions.

Abstract: The present invention relates to method of using spectroscopy for real time measuring of concentration of desired product and using measured data for monitoring and control of chromatography. It develops a method and system for measuring real-time concentration of clarified harvest and that of flow through of loading step of the chromatography and using measured data for determining breakthrough in real-time. The two modes of operation are used viz. first mode (Part A) uses a single near infrared spectroscopy (NIR) flow cell prior to the continuous chromatography column to ensure optimal loading in each cycle based on dynamic binding capacity studies carried out previously with the desired Protein A resin and second mode (Part B) uses two near infrared spectroscopy (NIR) flow cells, one before and one after the column, to detect the breakthrough curve (from 1% breakthrough onwards).

Abstract: In one embodiment, a method includes determining, by a router, a common prefix pool from a transport interface associated with a transport virtual private network (VPN). The method also includes identifying, by the router, a prefix associated with a service VPN and generating, by the router, an IPv6-to-IPv6 Network Address Translation (NAT66) prefix translation using the common prefix pool and the prefix. The NAT66 prefix translation includes a predetermined prefix length. The method further includes automatically installing, by the router, the NAT66 prefix translation into a translation table.

Abstract: The present disclosure provides techniques and solutions for enhancing or customizing the functionality of software applications, such as “apps.” More particularly, the present disclosure provides for grouping app extensions into groups, such as groups representing a particular semantic domain. A group of extensions can be applied to one or more apps. Applying extensions as a group can save development time and effort, as can the ability to apply a given extension group to multiple apps, either at the same time or over a particular time period. Further disclosed aspects assist users in implementing extensions with an extension group, such as assisting in determining where in a user interface particular extensions should appear. Once an extension group has been selected to be applied to an app, programmatic logic can cause the extensions to be automatically implemented in a computing artifact/object used by a particular extended app.