Abstract: A computing system for interactive prompting for a supply chain includes processing circuitry that constructs a knowledge graph based ontologies from a plurality of data sources, the ontologies being related to a product. In a turn-based dialog session, the processing circuitry receives a prompt for the product, identifies at least one ontology-level node in a first layer of the knowledge graph, and generates one or more sub-questions. The processing circuitry outputs the sub-questions via a large language model, receives responses to the sub-questions, identifies one or more second-level nodes in a second, middle layer of the knowledge graph based on the responses, and performs a multi-hop query to identify one or more instance-level nodes in the third layer of the knowledge graph. The processing circuitry outputs, via the large language model, text data corresponding to the instance-level nodes as an answer to the prompt.

Abstract: The techniques disclosed herein enable systems to enable multi-market optimization of renewable energies using data-driven models. To achieve this, a model retrieves a current state from a resource generation system and associated resource markets. The model can then compute a policy based on the state as well physical and technical constraints. The policy defines various actions that direct operation of the resource generation system such as resource production and dispatch to markets. Applying the policy to the resource generation results in a modified state which the model extracts along with a measure of optimality which quantifies the success of the policy. Based on these metrics, the model can generate an updated iteration of the policy defining a different set of actions. In this way, the model can gradually develop an optimal policy for controlling the resource generation system.

Abstract: A method of operation for a compute system comprising: monitoring a phone sensor array to detect a trigger; calculating a position of a cell phone based on the trigger and sensor data from the phone sensor array; predicting a driver distraction event by analyzing the sensor data and the position of the cell phone; compiling a driver distraction evaluation based on the driver distraction event and a sensor data packet from an in-vehicle sensor array; generating a distraction rating for display on a device based on the driver distraction evaluation.

Abstract: The present disclosure relates to methods and systems that preserve privacy in a secure multi-party computation (MPC) framework in multi-agent reinforcement learning (MARL). The methods and systems use a secure MPC framework that allows for direct computation on encrypted data and enables parties to learn from others while keeping their own information private. The methods and systems provide a learning mechanism that carries out floating point operations in a privacy-preserving manner.

Abstract: A traceability system for a bulk commodity supply chain is provided. The system includes a tracking device, a location determination subsystem, and at least one computing device having at least one processor. The location determination subsystem is configured to determine positional information of the tracking device while placed in a bulk commodity traveling along the bulk commodity supply chain. The processor receives the positional information from the location subsystem, extracts positional values from the positional information, and processes the positional values to identify motion primitives. A modeling tool is applied to the identified motion primitives to produce a positional path of the tracking device, which is output, for example, via a user interface. The positional path represents travel of the bulk commodity along the supply chain.

Abstract: This document relates to accurate quantitative predictions relating to various systems of interest. One example can obtain temporal data relating to a system from a first source and obtain complex events that can affect the system from a second source. The example can train a model iteratively using generative networks that correlate the temporal data from the first source and the complex events from the second source. The example can employ a temporal sequential encoder to control predictions for future temporal data utilizing the trained model.

Abstract: A computing system for achieving traceability in a food commodity supply chain is provided, including a server computing device configured to receive a message indicating an optical code associated with a unit of a food product has been scanned by a camera-equipped computing device at a location, in which the message includes a product unit-specific identifier encoded in the optical code. The server computing device is further configured to identify in a database, a merchant record including the product unit-specific identifier and a merchant identifier of a merchant at the location. The server computing device is further configured to trace a supply chain path of the unit of the food product through additional records in the database that are linked to the product unit-specific identifier, and output supply chain derived information on the unit of the food product based on the traced supply chain path.

Abstract: Generally, the method provides an interface for clinical research organizations to estimate, in real-time, an eligible patient population for a clinical study, as well as a point-of-care interface that can alert patients of their eligibility in a clinical study. Thus, the method provides improved identification of clinical cohorts and can quickly match patients with clinical studies that fit their medical needs.

Abstract: Aspects of the disclosure are directed to sensing integrated circuit (IC) Back End Of Line (BEOL) process corners. In one aspect, an apparatus for sensing IC BEOL process corners includes a ring oscillator including a plurality of ring oscillator stages configured to generate an output waveform with a frequency state; and a shield net circuit including a plurality of shield net stages corresponding to the plurality of ring oscillator stages, the shield net circuit having a toggle input. And, a method includes generating an output waveform with a frequency state using a ring oscillator that includes a plurality of ring oscillator stages; modifying a plurality of ring oscillator stage time delays through a coupling between a plurality of shield net stages and the plurality of ring oscillator stages; and selecting the frequency state using a toggle input of a shield net circuit which includes the plurality of shield net stages.

Abstract: The techniques disclosed herein enable systems to optimize generation and dispatch of renewable energies using data-driven models. In many contexts, a renewable energy system is collocated with a local consumer such as a datacenter, a smart building, and so forth. The objective of the renewable energy system is to meet local power needs while participating in various energy markets of differing trading frequencies. To optimally manage the renewable energy system, a data-driven model is configured to analyze current conditions and generate policies to control renewable energy system operations. For instance, the model can retrieve current market prices, generation capacity, costs associated with generating energy, and so forth. Based on the collected information, the model can generate a policy that maximizes revenue obtained by the renewable energy system while meeting local demand. Through many iterations, the model can determine a realistically optimal policy for managing the renewable energy system.

Abstract: A tracking system for a food commodity supply chain includes a tracking device and a computing device. The tracking device is mounted to a conveyance structure that is configured to receive a unit load of a food commodity. The tracking device includes a sensor to track an environmental condition of an environment of the tracking device while the tracking device is traveling along the food commodity supply chain. The computing device is configured to receive an environmental value of the environmental condition sensed by the sensor, process the environmental value to determine whether the environmental condition is within a predetermined environmental range, and transmit an alert when the environmental condition falls outside the predetermined environmental range. The alert includes a suggested interventive action based on the environmental condition that falls outside the predetermined environmental range.

Abstract: A computing system configured to execute a predictive program is provided. The predictive program, in a run-time phase, receives a current value for a remotely sourced forecast as run-time input into an artificial intelligence model. The artificial intelligence model has been trained on training data including a time series of locally sourced measurements for a parameter and a time series of remotely sourced forecast data for the parameter. The predictive program outputs a predicted forecast offset between the current value of a remotely sourced forecast and a future locally sourced measurement for the parameter. The predictive program outputs from the artificial intelligence model a predicted forecast offset based on the run-time input.

Abstract: The techniques disclosed herein enable systems to enable multi-market optimization of renewable energies using data-driven models. To achieve this, a model retrieves a current state from a resource generation system and associated resource markets. The model can then compute a policy based on the state as well physical and technical constraints. The policy defines various actions that direct operation of the resource generation system such as resource production and dispatch to markets. Applying the policy to the resource generation results in a modified state which the model extracts along with a measure of optimality which quantifies the success of the policy. Based on these metrics, the model can generate an updated iteration of the policy defining a different set of actions. In this way, the model can gradually develop an optimal policy for controlling the resource generation system.

Abstract: A traceability system for a bulk commodity supply chain is provided. The system includes a tracking device, a location determination subsystem, and at least one computing device having at least one processor. The location determination subsystem is configured to determine positional information of the tracking device while placed in a bulk commodity traveling along the bulk commodity supply chain. The processor receives the positional information from the location subsystem, extracts positional values from the positional information, and processes the positional values to identify motion primitives. A modeling tool is applied to the identified motion primitives to produce a positional path of the tracking device, which is output, for example, via a user interface. The positional path represents travel of the bulk commodity along the supply chain.

Abstract: A computing system including a processor configured to receive training data including, for each of a plurality of training timesteps, training forecast states associated with respective training-phase agents included in a training supply chain graph. The processor may train a reinforcement learning simulation of the training supply chain graph using the training data via policy gradient reinforcement learning. At each training timestep, the training forecast states may be shared between simulations of the training-phase agents during training. The processor may receive runtime forecast states associated with respective runtime agents included in a runtime supply chain graph. For a runtime agent, at the trained reinforcement learning simulation, the processor may generate a respective runtime action output associated with a corresponding runtime forecast state of the runtime agent based at least in part on the runtime forecast states. The processor may output the runtime action output.

Abstract: Synthetic molecular tags are placed on an item at various points in a supply chain to create a molecular record of movement through the supply chain. Associations between each unique synthetic molecular tag and individual locations in the supply chain are stored in an electronic record which may be maintained in the cloud. The synthetic molecular tags are collected from the item and sequenced to determine movement of the item through the supply chain by reference to the electronic record. The synthetic molecular tags can be used for identifying recalled items based on locations in the supply chain associated with a recall. The synthetic molecular tags may be polynucleotides such as deoxyribose nucleic acid (DNA). The item may be any type of item including food.

Abstract: A computing system configured to execute a predictive program is provided. The predictive program, in a run-time phase, receives a current value for a remotely sourced forecast as run-time input into an artificial intelligence model. The artificial intelligence model has been trained on training data including a time series of locally sourced measurements for a parameter and a time series of remotely sourced forecast data for the parameter. The predictive program outputs a predicted forecast offset between the current value of a remotely sourced forecast and a future locally sourced measurement for the parameter. The predictive program outputs from the artificial intelligence model a predicted forecast offset based on the run-time input.

Abstract: A method for pollutant sensor placement is described. Data about environmental characteristics across a geographic region is received from a plurality of environmental sensors. The geographic region includes one or more pollutant sources that emit a pollutant. The received data is transformed from one or more of the plurality of environmental sensors into common data having a common grid across the geographic region. The geographic region is divided into a plurality of sub-regions based on the common data. Locations within the geographic region are determined for placement of pollutant sensors based on estimated dispersion of the pollutant through the plurality of sub-regions.

Abstract: A system for fusion of multimodal receives a spatial input and a temporal input, wherein the spatial input comprises spatial data having spatial embeddings and the temporal input comprises temporal data having temporal embeddings. The spatial embeddings and the temporal embeddings have different time dimensions. A spatial data output with the spatial embeddings having a same time dimension as the temporal embeddings is generated from the spatial data based on a spatial perception model. The spatial perception model is pre-trained. A temporal data output is generated from the temporal data based on a temporal model. The spatial data output and the temporal data output are combined into an output representing dependencies between the spatial input and the temporal input using a fusion model. A desired target variable is obtained from the output and one of an estimated or predicted value is generated based on the desired target value.

Abstract: Aspects of the disclosure are directed to sensing integrated circuit (IC) Back End Of Line (BEOL) process corners. In one aspect, an apparatus for sensing IC BEOL process corners includes a ring oscillator including a plurality of ring oscillator stages configured to generate an output waveform with a frequency state; and a shield net circuit including a plurality of shield net stages corresponding to the plurality of ring oscillator stages, the shield net circuit having a toggle input. And, a method includes generating an output waveform with a frequency state using a ring oscillator that includes a plurality of ring oscillator stages; modifying a plurality of ring oscillator stage time delays through a coupling between a plurality of shield net stages and the plurality of ring oscillator stages; and selecting the frequency state using a toggle input of a shield net circuit which includes the plurality of shield net stages.