Abstract: A computer implemented method, system and non-transitory computer-readable device for a remote deposit environment activating, on a client device, a financial application, wherein the financial application is configured to instantiate a customer interface (UI) on the client device. Upon receiving a customer request, based on interactions with the UI, the method implements an electronic deposit of a financial instrument by activating a camera on the client device to generate a live stream of image data of a field of view of at least one camera, wherein the live stream includes imagery of at least a portion of the financial instrument. The method continues by blending common pixels from the imagery to form a blended image, and extracting by an optical character recognition program one or more data fields from the blended image of the financial instrument.

Abstract: Systems and methods are described herein for leveraging offline virtual authentication tokens during periods of inaccessible network connectivity. In some aspects, after determining the system is offline, systems and methods request an authentication code from a user and determine whether the authentication code received from the user is valid. In response to determining that the authentication code is valid, systems and methods select an offline virtual authentication token from the plurality of stored offline virtual authentication tokens associated with the user account.

Abstract: Conventional methods implement Density Functional Theory (DFT) simulations on classical processors which is time consuming. The disclosure relates to method and system for implementing DFT on quantum processors. Initially, atomic coordinates of each atom of a chemical compound whose properties are to be extracted is received. Electron integrals, a core Hamiltonian, and a collocation matrix are computed from the atomic coordinates. The core Hamiltonian is diagonalized to obtain a density matrix of the chemical compound which is further updated iteratively until a convergence criteria is satisfied. At each iteration, a direct matrix is computed from which correlation exchange matrix is obtained using one or more DFT protocols. Further, the direct and correlation exchange matrix are added to get a Fock matrix which is diagonalized to obtain updated density matrix. Once the convergence criteria is satisfied, a final density matrix is obtained which is used to extract the properties.

Abstract: This disclosure relates generally to a method and system for implementing density functional theory (DFT) on quantum processors through generalized gradient approximation (GGA). Conventional methods implement DFT simulations on classical processors and state of the art methods implement DFT on a combination of classical and quantum processors. The embodiments of the present disclosure perform complex calculations of the DFT through GGA on quantum processors such as iteratively updating a density matrix by computing a direct matrix, a correlation exchange matrix, a gradient of the collocation matrix, an electronic density, an electronic density gradient, a derivative of the electronic density gradient and finally a Fock matrix at each iteration. A final density matrix is determined based on a convergence criteria for the iterative updating. These computations are performed using several quantum circuit components.

Abstract: A method for query optimization may include capturing a proposed database query input into a user interface. The method may further include providing the proposed database query to a machine-learning model. The machine-learning model may have been trained, using one or more gathered and/or simulated sets of query execution overhead data and one or more gathered and/or simulated sets of database queries, to determine a potential execution overhead of a database query and output a query execution score. The method may further include outputting, by the machine-learning model, the query execution score based on the proposed database query. The method may further include determining that the query execution score exceeds a query execution score threshold. The method may further include triggering a corrective action based on the query execution score exceeding the query execution score threshold.

Abstract: A computer-implemented method includes: receiving a request to scan a resource for detection of a vulnerability, the resource comprising one or more of: a component of an application, a code-base of the application, or a third-party library associated with the application; determining one or more matching vulnerability scanning operators among a plurality of vulnerability scanning operators based at least in part on one or more properties associated with the request; transmitting the request to the one or more matching vulnerability scanning operators, the one or more matching vulnerability scanning operators having access to the resource; receiving an initial result associated with the request from the one or more matching vulnerability scanning operators; obtaining a predetermined format for the initial result; generating a translated result by modifying the initial result based on the predetermined format; and transmitting the translated result to a source of the request.

Abstract: Private set intersection (PSI) protocols can be efficiently performed for sets of disparate sizes. A server can compute an array, such as an inverted Bloom filter or cuckoo hash table, that represents the content of the server set. A client can query the array, e.g., using a private information retrieval (PIR) protocol, to obtain information that enables the client to determine whether a particular element of the client's set is also in the server's set. By repeating the query for each element of the client's set, the client can learn the intersection.

Abstract: Existing AutoML approaches fail to capture human intuition and domain alignment as part of the ML model development. Embodiments of the present disclosure provide method and system comprising Theta-DecNN for AI Model Refinement. Theta-DecNN applies three level process for model building by integrating and optimizing a human intuition factor (?), thereafter aligning the AI model to a domain of interest. Theta-DecNN utilizes iterative feedback mechanisms at each level to extract the human intuition form input task, further optimize the human intuition factor based on Historical tuning data, live performance metrics to obtain a trained AI model for the task. At the third level the train AI model is finetuned to align with domain of the task based on domain specific function, which is optimized by tuning hyperparameter to generate human intuition based domain optimized AI model enabling outputs that are accurate, and contextually and intuitively aligned with complex human thought processes.

Abstract: Traditional machine learning operations utilize high-precision numerical formats that lead to computational overhead and memory usage. Hence, it is imperative that precision of these computations needs to be reduced without compromising accuracy of machine learning (ML) models. Present disclosure provides systems for optimizing computational efficiency and performance of machine learning (ML) models. ML model consisting of model states in an original precision is fed as input, which are analyzed to determine (i) how to best divide model states into smaller chunks for efficient parallel processing, and (ii) an optimal bit-length for offloading of model states to a target precision and obtain offloaded data. The offloaded data is transferred to computing node which performs broadcast operations to obtain result(s). The results are integrated into original precision based on a mapping of subset of partitioned model states to the offloaded data to obtain a validated integrated result.

Abstract: Disclosed hereing are methods of diagnosing, and treating and/or preventing CD55-deficiency, hyperactivation of complement, angiopathic thrombosis and protein-losing enteropathy (CHAPLE). The method of diagnosing includes: providing a sample from a patient; performing an assay detecting at least one of at least one mutation in a DNA sequence of a CD55 gene, at least one mutation in a RNA sequence of a CD55 transcript, at least one mutation in a DNA sequence of a CD55 complementary-DNA (cDNA), CD55 protein, CD55 protein binding, complement deposition or combination thereof, and diagnosing the patient with CHAPLE. The method of treating and/or preventing at least one symptom of CHAPLE includes: administering an effective amount of a composition comprising at least one complement inhibitor to a subject in need thereof, wherein the composition is effective in treating or preventing at least on symptom of CHAPLE. The disclosure further relates to compositions effective at treating and/or preventing CHAPLE.

Abstract: Described herein are techniques a systems for maintaining a system of record for one or more contactless cards. A computing apparatus is configured to generate a first unique identifier for a contactless card associated with a user account. The apparatus is further caused to generate a message to update a database with a new database entry, the message including the first unique identifier and identity information for the user account. Next, the apparatus is caused to send the message to the database to add the new entry therein, each entry in the database including a recorded unique identifier associated with a corresponding contactless card, each contactless card being associated with one of a plurality of user accounts. The apparatus is further caused to send the first unique identifier to a personalization server to store the first unique identifier in a memory of the first contactless card.

Abstract: Black-Box Optimization (BBO) approaches have found optimal policies for systems that interact with environments with no analytical representation and that are complex in nature. However, such approaches are overlooked in AI tasks related aspects. Present disclosure provides systems and methods for optimizing the performance of artificial intelligence (AI) agents. The AI agents are generated based on NLP data and tasks received as inputs to the system. The AI agents are then evaluated and selected based on associated aggregated interpretability score. At least a subset of AI agents is mapped to corresponding tasks based on the associated aggregated interpretability score and by meta-learning techniques. The AI agents deployed to perform the mapped tasks are then optimized based on a dynamic prediction of an effectiveness of one or more associated metric weightings, thus obtaining an optimized chain of AI agents.

Abstract: Ideation phase of research life cycle is challenging for researchers. The present disclosure provides a system and method for ideation of research proposal using a large language models (LLM) agent-based architecture. Ideation process is emulated using the LLM agent-based architecture having a colleague persona and mentor personas to execute a motivation validation and method synthesis. The motivation validation and the method synthesis engage users in an interactive manner to develop a research proposal document. The research proposal document comprises a validated motivation and a set of plausible solutions addressing a research problem based on a plurality of tasks performed by agents of the LLM agent-based architecture.

Abstract: A computer implemented method generates a graph model for performing a data processing operation associated with an accounting task. Training instruction data is received from an interface identifying a subset of nodes and edges, the edges interconnecting the nodes, from a network graph representing a plurality of relationships, and an output variable associated with the accounting task for the trained graph model to predict. A subset of nodes and edges are retrieved from the network graph. One or more accounting data records associated with one or more of the subset of nodes and edges are retrieved. A training graph is generated with the retrieved subset of nodes and edges supplemented by the accounting data. A graph model is trained to predict the output variable using the training graph. The training graph is deployed to a system for performing the data processing operation associated with the accounting task.

Abstract: Currently task automation is in its initial stage. Understanding task complexity/requirements, effectiveness/efficiency of automation using best models to automate the task and performance of such task automation approaches are being explored. A method and system for selection-optimization-fine-tuning of Artificial Intelligence (AI) models for nuanced task by analyzing the nuanced task using Logic and Rule Integrated Decoder Neural Network (LRI-DecNN) is disclosed. The system performs tasks analysis using the LRI-DecNN, AI augmentation suitability check and constructs agent chain of subtasks using meta-mixture technique tailored to specific enterprise needs. Thereafter, the most appropriate generative AI model is selected and fine-tuned for the task at hand using Blackbox estimation. The task is executed and monitored for optimal performance.

Abstract: Expansion cones and methods of using expansion cones with expandable liner hangers. An example expansion cone includes a detachable support coupled to a mandrel. The detachable support is configured to contact an interior surface of the expandable liner hanger during expansion of the liner hanger. The detachable support is further configured to detach after the liner hanger is expanded thereby producing a detached support. The detached support remains in contact with the interior surface of the expandable liner hanger after expansion of the expandable liner hanger and removal of the mandrel.

Abstract: Existing approaches in this domain are targeted to identifying specific anomalies, and there is no system available that determines implant health comprehensively by considering various aspects that may represent health of the implants. The method and system disclosed herein provide an implant health monitoring approach in which by processing an X-ray image received as input, a system identifies location of an implant in body of a subject, and identifies manufacturer and subtype details of the implant. Further, an implant size and implant fixation details are determined. Further, anomaly detection is performed to determine presence of one or more anomalies in the implant, based on the determined manufacturer and subtype details, the determined location of the implant, a determined region around the implant, the implant fixation details, and the implant size information, and accordingly the implant is classified as one of Faulty, Healthy, and Probable faulty.

Abstract: Training of Artificial Intelligence (AI) data models requires huge amount of data to be processed, which requires a huge amount of resources to be allocated for the data processing. For the same reason, time involved in training of the AI data model also substantially increases, which is a challenge existing AI data model training approaches fail to address. Embodiments disclosed herein provide a method and system for quantum computing based pre-training of AI data models. In this quantum computing based approach, the system, by means of achieving synchronization across various quantum states and further by optimizing activation functions being used, pre-trains the AI data model in iterations, till convergence with a determined global minima is achieved.

Abstract: The present application provides tobacco inbred plants dS1746, dS1746MS, dS1564 and dS1564MS. The present application also provides parts of such plants and products made from those parts. The present application also includes progeny of the provided plants including hybrids.

Abstract: Methods and systems for intercepting convergent data streams. In some aspects, the system may, in response to receiving a first data stream including a plurality of user activities executed by a user, generate a first projected future data stream. The first projected future data stream can include potential future activities for the user. The system may process the first projected future data stream to determine a first cluster to associate with the user. Furthermore, the system may determine whether the first cluster is a type of intervention cluster requiring direction to the user for future activity. In response to determining that the first cluster is a type of intervention cluster, the system may generate a notification to the user including one or more user activities to execute to exit the first cluster. This allows the system to prevent or intercept undesired activities executed by users.