Abstract: Methods and systems are provided for reducing contactor freezing. In one example, a system may include a contactor comprising a set of conductors and a phase change material. The phase change material is coupled to a first conductor of the set of conductors and is not positioned between the set of conductors.

Abstract: Systems and methods for selecting items from a pool of items based on comparisons of composite embeddings are disclosed. A first initial embedding is obtained from a first database for each item in a pool of items and a second initial embedding is obtained from a second database for each item in the pool of items. The first initial embedding is generated using a first embedding model and the second initial embedding is generated using a second embedding model. A first composite embedding is generated for each item in the pool of items comprising the first initial embedding and the second initial embedding; and compare the first composite embedding for each item in the pool of items to a first anchor embedding, wherein the first anchor embedding comprises a first initial anchor embedding generated using the first embedding model and a second initial anchor embedding generated using the second embedding model.

Abstract: The present disclosure provides for the division of environmental sounds from speech sounds to extract and analyze the behaviors and activities occurring in the environment; thus expanding and improving the functionality of AI assistant devices. These environmental sounds can be securely uploaded to an electronic chart, and may be used to aid in the treatment of existing conditions or the prophylaxis/mitigation of conditions not yet experienced by a patient under observation. Accordingly, the present disclosure provides for improved functionality in assistant devices and devices linked to the AI assistant devices.

Abstract: In some implementations, the techniques described herein relate to a method including: loading, by a processor, a predictive model, the predictive model including a first set of hidden layers; loading, by the processor, a semantic model, the semantic model including a second set of hidden layers; generating, by the processor, a tenant model using the first set of hidden layers and a third set of hidden layers, the third set of hidden layers receiving, as input, an output of the first set of hidden layers; loading, by the processor, a tenanted training data set; training, by the processor, the tenant model by biasing the first set of hidden layers with the second set of hidden layers and training weights of at least the third set of hidden layers using the tenanted training data set; and building, by the processor, an embedding index using the tenant model.

Abstract: A method of managing a wireless communication network includes obtaining data regarding performance of a cell of the wireless communication network, and generating, based on the obtained data, predictions of values of a plurality of key performance indicators (KPIs) of the cell of the wireless communication network that are correlated with uplink (UL) throughput. The method includes generating a prediction that the cell will experience degraded UL throughput at a future time based on the predicted values of the KPIs, and determining, from among the plurality of KPIs, a set of candidate root cause KPIs associated with the predicted degraded UL throughput. The method further includes selecting an actuation based on the determined set of candidate KPIs, and applying the actuation to the wireless communication network.

Abstract: Techniques for enterprise data aggregation and collective insights generation are disclosed, including: receiving multiple sets of enterprise-specific customer relationship management (CRM) data, respectively, from enterprise-specific CRMs; aggregating the sets of enterprise-specific CRM data, to obtain collective CRM data; receiving a request to generate a collective insight that is applicable to a particular subset of the collective CRM data; responsive to the request, selecting a particular machine learning model from multiple machine learning models, wherein each machine learning model is configured to generate collective insights for a respective subset of the collective CRM data; generating the collective insight that is applicable to the particular subset of the collective CRM data, using the particular machine learning model.

Abstract: Techniques for filtering queries to a large language model (LLM) based on their relevance to an enterprise domain associated with the LLM involve training a machine learning model using historical LLM query data and associated relevance scores. These scores indicate how closely a query relates to the enterprise's operations. The trained model is then applied to new input queries, generating relevance scores for the input queries. Queries meeting a predetermined relevance threshold are passed to the LLM for processing. For queries falling below this threshold, remedial actions are taken instead of processing by the LLM. The techniques optimize computational resource allocation by prioritizing queries relevant to the enterprise while filtering out less pertinent ones. The techniques create a relevance-based gatekeeping mechanism for LLM query processing, enhancing efficiency and focusing the LLM's capabilities on enterprise-specific tasks.

Abstract: Systems and methods for continuous monitoring of the behavior of animals, such as small rodents, are provided. Monitoring can include video, audio, and other sensor modalities. In one embodiment, the system can include cameras, arena design, environmental sensors, and ultrasonic sensors. The system uniquely provides a continuous long-term monitoring system suitable for mouse behavioral study. Further provided is a neural network based tracker configured for use with video data acquired by the monitoring system. 3 different neural network architectures have been tested to determine their performance on genetically diverse mice under varying environmental conditions. It has been observed that that an encoder-decoder segmentation neural network achieves high accuracy and speed with minimal training data.

Abstract: In traditional systems, every time a digital twin of a component needs to be generated, behavioral as well as operational data specific to the component needs to be fetched, which has practical difficulties owing to complex nature of processes/equipment the component is associated with. The disclosure herein generally relates to building digital twins, and, more particularly, to a method and system for building digital twin by leveraging existing knowledge. The system determines extent of similarity between two components, and based on the determined extent of similarity, uses different approaches to retrain a ANN data-driven model to obtain a desired accuracy for features of the component for which the digital twin is being generated.

Abstract: Techniques for generating proprietary software code using large language models (LLMs) are disclosed. An LLM is trained on billions of words, tokens, and code segments to generate non-proprietary software code. A system uses the LLM to generate proprietary software code by generating a set of LLM prompts and a proprietary code mapping. Based on receiving an instruction to generate a set of software code, a system generates a set of LLM prompts. The system prompts the LLM to generate a set of non-proprietary software code. The system further prompts the LLM to generate a set of pseudocode from the non-proprietary software code. The system further prompts the LLM to generate proprietary software code from the pseudocode and the proprietary code mapping.

Abstract: The present disclosure provides for the division of environmental sounds from speech sounds to extract and analyze the behaviors and activities occurring in the environment; thus expanding and improving the functionality of AI assistant devices. These environmental sounds can be securely uploaded to an electronic chart, and may be used to aid in the treatment of existing conditions or the prophylaxis/mitigation of conditions not yet experienced by a patient under observation. Accordingly, the present disclosure provides for improved functionality in assistant devices and devices linked to the AI assistant devices.

Abstract: The present disclosure provides a system and a method for managing messages in a network. The system generates cell-identification (ID) barring in networks for restricting users (102) from sending and receiving messages in the network. Further, the system provides an efficient way to handle a logical barring of the devices from sending and receiving the messages. The system facilitates the barring of selective users in a defined cell-ID of a public land mobile network (PLMN), and therefore, provides flexibility while managing messages in the network. The system verifies if a mobile originating/mobile terminating (MO/MT) message is barred in the defined cell-ID in the PLMN and processes the MO/MT message based on the verification.

Abstract: Techniques for filtering queries to a large language model (LLM) based on their relevance to an enterprise domain associated with the LLM involve training a machine learning model using historical LLM query data and associated relevance scores. These scores indicate how closely a query relates to the enterprise's operations. The trained model is then applied to new input queries, generating relevance scores for the input queries. Queries meeting a predetermined relevance threshold are passed to the LLM for processing. For queries falling below this threshold, remedial actions are taken instead of processing by the LLM. The techniques optimize computational resource allocation by prioritizing queries relevant to the enterprise while filtering out less pertinent ones. The techniques create a relevance-based gatekeeping mechanism for LLM query processing, enhancing efficiency and focusing the LLM's capabilities on enterprise-specific tasks.

Abstract: This disclosure relates generally to method and system to monitor and control continuous ultrafiltration (UF) process units. In real time, continuous operation of UF to handle variating concentration in feed stream is tedious and complex. The UF plant system receives a plurality of input data configured to UF process units and from the real time data outliers are removed and missing values are imputed. The prediction module predicts a volumetric concentration factor (VCF) value and a throughput value by selecting a model from a model repository. The optimization module optimizes the VCF value, and the throughput value based on a plurality of optimal variables recommended for a given feed concentration. The UF plant system controls the VCF value and the throughput value for a predefined period of a prediction horizon based on a plurality of trajectory profiles recommended for the feed flow rate, the pressure data, and a feed concentration.

Abstract: Certain aspects of the present disclosure provide techniques for predicting wound management treatment resources. This includes determining characteristics of a wound for a patient based on an image of the wound, including detecting the characteristics based on analyzing the image using a first machine learning (ML) model trained to detect wound characteristics from a captured image. The techniques further include predicting at least one of: (i) treatment resources or (ii) a treatment facility for treating the wound, including providing to a second trained ML model characteristics of the wound, patient medical data for the patient, and treatment facility data describing a plurality of available treatment facilities.

Abstract: A method includes: determining a wall temperature of a wall enclosing a lumen of a flow line; determining a density, a viscosity, a thermal conductivity, a thermal capacity, and a pressure differential of a medium to be measured flowing in the line; determining a characteristic number value for the medium, which characterizes a heating of the medium flowing in the line as a result of dissipation and is a function of an Eckert number, a Prandtl number, and a pressure loss coefficient of the line as well as line-specific first, second and third exponents; and determining a temperature of the medium using the characteristic number value and the wall temperature. A measuring system for the method includes: a temperature sensor thermally coupled to a lateral surface of the wall and configured to generate a temperature measurement signal; and an operating electronic system electrically connected to the temperature sensor.

Abstract: In some implementations, the techniques described herein relate to a method including: loading, by a processor, a predictive model, the predictive model including a first set of hidden layers; loading, by the processor, a semantic model, the semantic model including a second set of hidden layers; generating, by the processor, a tenant model using the first set of hidden layers and a third set of hidden layers, the third set of hidden layers receiving, as input, an output of the first set of hidden layers; loading, by the processor, a tenanted training data set; training, by the processor, the tenant model by biasing the first set of hidden layers with the second set of hidden layers and training weights of at least the third set of hidden layers using the tenanted training data set; and building, by the processor, an embedding index using the tenant model.

Abstract: A processing device identifies a first clock tree topology for a circuit design, the first clock tree topology having a threshold feedthrough count and a first timing solution. The processing device further identifies one or more additional clock tree topologies for the circuit design, each of the one or more additional clock tree topologies having a different respective feedthrough count that is less than the threshold feedthrough count, and each of the one or more additional clock tree topologies comprising a respective timing solution. In addition, the processing device receives a selection of at least one of the first clock tree topology or the one or more additional clock tree topologies, and generates the circuit design according to the selection.

Abstract: The present disclosure relates generally to compounds useful in treatment of conditions associated with Checkpoint kinase (CHK), particularly CHK-1 enzymes. Specifically, the present invention discloses compound of formula (IA), which exhibits inhibitory activity against CHK-1 enzymes. Methods of treating conditions associated with excessive activity of CHK-1 enzymes with such compounds is disclosed. Uses thereof, pharmaceutical compositions, kits and method of synthesis also disclosed.

Abstract: Techniques for runtime customization and management of user interfaces for enterprise applications are disclosed. In some embodiments, the techniques include tools, systems, and processes for building frontend, technology-independent user interfaces. A system may consume user input files that are language dependent and generate a metadata file that is language independent. The metadata file may be updated to customize or otherwise modify a user interface of an application at runtime. An event signal may be raised to notify the application when a new user interface is available. In response to detecting the signal, the application may refresh the user interface, including the application page being currently viewed, to modify the set of user interface components that are rendered and displayed to the end user. The system may store metadata for multiple versions of the user interface to allow users to quickly roll back to prior versions of a user interface.