Abstract: A Full Adder (FA) circuit may include a Sum Generation (SG) circuit configured to: generate at least one of an XNOR signal or an XOR signal using at least one of a first NOR signal, an AND signal, a NAND signal, and an OR signal; and generate an output SUM signal using the generated at least one of the XN signal and the X signal. The FA circuit may further include a Carry Output Generation (COG) circuit connected to the SG circuit to generate a CO signal using the first NOR signal, the AND signal, the NAND signal, and/or the OR signal.

Abstract: A method may include finetuning a generative artificial intelligence (AI) model based on training data including finite state machine (FSM) conversation flows and corresponding intents. The method may include finetuning the generative AI model by tuning a set of base weights with a single set of weights that are based on the FSM conversation flows and the intents. The method may include receiving a query corresponding to an FSM conversation flow. The method may include determining that the first query contains a first intent associated with a first state of the FSM conversation flow. The method may include generating a first response to the first query that corresponds with the first state of the first FSM conversation flow. The method may include communicating commands to a service associated with the first FSM conversation flow, the commands corresponding with actions associated with the first intent and the first state.

Abstract: A cloud platform trains a machine-learned entity matching model that generates predictions on whether a pair of electronic records refer to a same entity. In one embodiment, the entity matching model is configured as a transformer architecture. In one instance, the entity matching model is trained using a combination of a first loss and a second loss. The first loss indicates a difference between an entity matching prediction for a training instance and a respective match label for the training instance. The second loss indicates a difference between a set of named-entity recognition (NER) predictions for the training instance and the set of NER labels for the tokens of the training instance.

Abstract: A robotic system includes a mobile base assembly and a lifting assembly. The lifting assembly includes wedge blocks, each of which is pivotably connected to a front edge of the mobile base assembly and pivotable along a hinge axis. During an operation of the robotic system, at least a portion of the wedge blocks may be slid under the boxes to lift the boxes.

Abstract: A hierarchical neural network for predicting out of stock products comprises an input layer that receives data from data sources that store disparate datasets having different levels of attribute detail pertaining to products for sale in stores of a retailer. A first level of neural networks processes the data from the data sources into respective learned intermediate vector representations. A second level comprises a concatenate layer that concatenates the learned intermediate vector representations from the second level into a combined vector representation. A third level comprises a feed forward network that receives the combined vector representation and outputs to the retailer an out of stock probability indicating which store and product combinations are likely to have out of stock products over a predetermined timeframe.

Abstract: Disclosed are apparatuses, systems, and techniques that implement software-agnostic transport of messages to, from, and within managed devices. In one embodiment, a managed device has an intra-device network including a plurality of units, each unit associated with a unit controller. The managed device further includes a hub controller that receives data packet(s) jointly carrying a message from an external host. The controller identifies that the one or more first data packets are associated with a given unit and forwards the data packet(s) to the corresponding unit controller. The unit controller extracts the message from the data packet(s) and stores the message in a memory associated with the unit controller.

Abstract: A growth predictor includes a monitor, a prediction engine, and a prioritization engine. The monitor receives or generates first information of a network already identified as a candidate money laundering (ML) network by an anti-money-laundering system. The prediction engine predicts second information indicative of a growth size of the ML network at a future time based on the first information. The prediction engine executes one or more predictive models to generate the second information indicative of growth size based on the first information, which indicates one or more changes that have occurred in the candidate ML network over a past period of time. The prioritization engine determines a priority of the candidate ML network based on the second information.

Abstract: A method and system for synthetic data generation are provided that receive a schema configuration file in a synthetic data set request from a client application, create a set of worker processes to generate the synthetic data set based on the schema configuration file, upload the generated synthetic data to an analytics platform, and enable the client application to utilize the generated synthetic data in prediction models for the analytics platform.

Abstract: A robotic system configured for performing a task of loading and unloading goods. The robotic system comprises a mobile base assembly, an actuation assembly coupled to the mobile base assembly and configured to move with respect to the base assembly, and a head assembly mounted atop the actuation assembly or the mobile base assembly. The head assembly comprises one or more sensors configured to collect data related to an operation environment and the task to be performed. The robotic system further comprises a dynamic conveyor coupled to the mobile base assembly. The dynamic conveyor comprises a platform. The robotic system furthermore comprises a control unit configured to move the platform to align the platform with respect to the goods to be loaded or unloaded. Further, the robotic system comprises a manipulation arm coupled to the actuation assembly or the dynamic conveyor and configured to move to perform the task.

Abstract: Systems and methods are directed toward key distribution systems and methods. A key distribution system may include a policy table and a key table to develop different policy regions for various associated managers and then map memory address locations to table locations for the policy regions. The policy regions may be established using different parameters and then locked after activation to prevent further editing or modification after creation. When activated, users and owners may then access the associated memory addresses associated with authorized active policy regions.

Abstract: A robotic system includes a mobile base assembly and a lifting assembly. The lifting assembly includes wedge blocks, each of which is pivotably connected to a front edge of the mobile base assembly and pivotable along a hinge axis. During an operation of the robotic system, at least a portion of the wedge blocks may be slid under the boxes to lift the boxes.

Abstract: Methods, systems, apparatuses, devices, and computer program products are described. A system may support a machine learning model for legal clause extraction. The machine learning model may receive, as an input, at least a portion of a document and may output an indication of one or more legal clauses included in the document. To train the model, the system may receive a document and an indication of ground truths (e.g., legal clauses) for the document. The system may determine one-to-one mappings between the legal clauses indicated by the ground truths and the legal clauses indicated by the output of the machine learning model. The system may perform a longest common substring analysis on the one-to-one mappings to determine an accuracy of the machine learning model and may iteratively update the model based on the analysis.

Abstract: Methods, apparatuses, systems, and computer-program products are disclosed. For example, a system may receive, via a cloud-based platform, first user input including a request for generation of the output data object. The system may generate a prompt based on the first user input and a prompt appendix that defines a response format for a plurality of responses to the prompt that are to be generated by a large language model (LLM). The system may transmit the prompt to the LLM and may receive, from the LLM, the plurality of responses formatted in the response format. The system may generate the output data object that comprises the plurality of responses.

Abstract: Architectures and techniques are described that can translate permissions from a first storage system to a second storage system. Such can be applied during a data migration operation that migrate storage data from the first storage system to the second storage system. A directory tree structure of the storage data can be explored to identify one or more target directories that can be used to leverage an inheritance property. Permissions can be translated based on the identification of the target directories.

Abstract: Architectures and techniques are described that can translate permissions from a first storage system to a second storage system. Such can be applied during a data migration operation that migrate storage data from the first storage system to the second storage system. A directory tree structure of the storage data can be explored to identify one or more target directories that can be used to leverage an inheritance property. Permissions can be translated based on the identification of the target directories.

Abstract: A robotic system configured for performing a task of loading and unloading goods. The robotic system comprises a mobile base assembly, an actuation assembly coupled to the mobile base assembly and configured to move with respect to the base assembly, and a head assembly mounted atop the actuation assembly or the mobile base assembly. The head assembly comprises one or more sensors configured to collect data related to an operation environment and the task to be performed. The robotic system further comprises a dynamic conveyor coupled to the mobile base assembly. The dynamic conveyor comprises a platform. The robotic system furthermore comprises a control unit configured to move the platform to align the platform with respect to the goods to be loaded or unloaded. Further, the robotic system comprises a manipulation arm coupled to the actuation assembly or the dynamic conveyor and configured to move to perform the task.

Abstract: A robotic system configured for performing a task of loading and unloading goods. The robotic system comprises a mobile base assembly, an actuation assembly coupled to the mobile base assembly and configured to move with respect to the base assembly, and a head assembly mounted atop the actuation assembly or the mobile base assembly. The head assembly comprises one or more sensors configured to collect data related to an operation environment and the task to be performed. The robotic system further comprises a dynamic conveyor coupled to the mobile base assembly. The dynamic conveyor comprises a platform. The robotic system furthermore comprises a control unit configured to move the platform to align the platform with respect to the goods to be loaded or unloaded. Further, the robotic system comprises a manipulation arm coupled to the actuation assembly or the dynamic conveyor and configured to move to perform the task.

Abstract: A method or a system for predicting a likelihood of an occurrence of a transaction. The system accesses a graph including multiple nodes and multiple edges linking the nodes. The multiple nodes include a first type of nodes representing a first type of entities an a second type of nodes representing a second type of entities. The system extract a set of node features for each node, and a set of edge features for each edge. For an edge connecting a first node of the first type and a second node of the second type, the system generates a set of edge embeddings based in part on the node features and edge features, and computes a score based in part on the set of edge embeddings. The score indicates a likelihood of an occurrence of a transaction between the first node and the second node.

Abstract: A method for using a neural network to generate node embeddings and edge embeddings for graphs. The neural network has K layers. The graph includes multiple nodes and edges linking the multiple nodes. The method includes determining a set of node features for the multiple nodes, and determining a set of edge features for the multiple edges. A first layer of the neural network is applied to the node features and the edge features to output a first set of node embeddings and a first set of edge embeddings. A k-th layer of the neural network is applied to (k?1)th set of node embeddings and (k?1)th set of edge embeddings to output a k-th set of node embeddings and a k-th set of edge embeddings, where the (k?1)th set of node embeddings and (k?1)th set of edge embeddings are output from (k?1)th layer of neural network.