Abstract: Provided are systems and methods that leverage machine learning to perform audio editing with improved precision and flexibility. Some example systems utilize a vector-based audio editing representation to condition and control a machine-learned audio editing model. This approach allows for detailed and precise control over audio edits by encoding the edits numerically and processing the encoding with a trained model. The system can handle a variety of edits, including audio generation, removal, transformation, time-shifting, and/or enhancement, thereby providing a comprehensive tool for audio manipulation.

Abstract: Implementing a circuit design for a target integrated circuit includes receiving guide information including placement information for a circuit design from a prior implementation flow. A light incremental flow is performed on the circuit design using the guide information. The light incremental flow generates a placed and routed circuit design that re-uses at least a portion of the placement information from the prior implementation flow.

Abstract: Systems and methods for distributing policy enforcement in a network are disclosed. Embodiments may allow the distribution of enforcement of policies from firewalls to other network elements to allow the offloading of the enforcement of actions of those policies for flows to network elements, where the applicability of those policies to those flows was determined by the firewall. Moreover, embodiments may be adapted to update flows offloaded at the network elements to which they have been offloaded with updated policy actions for changed policy actions associated with those flows.

Abstract: Systems and methods described herein provide techniques for detecting subject behavior by processing video data using one or more trained models configured to detect subject behavior. The described system processes sets of frames from the video data using different trained models. The system further processes different orientations of the sets of frames. The various outputs from the different trained models and from processing the different orientations of the sets of frames may be combined to then make a final determination as to whether the subject is exhibiting a particular behavior during a particular frame.

Abstract: This disclosure relates generally to identification and analysis of regime shift. The identification and analysis of the regime shift includes regime shift identification (RSI), root cause analysis of the identified regime shift and a recommendation unit to rectify the identified regime shift. The disclosure proposes to monitor a system continuously to identify a regime shift at real-time as presence of regime shifts in any system decreases quality of process and products and makes the system less efficient. The regime shift is identified at real-time based on key performance indicators (KPIs), a set of relevant features and real time input data using machine learning techniques. Further the disclosure also proposes techniques for detecting at least one root cause for the identified regime shift and also recommends a rectification action to rectify the identified regime shift based on optimization techniques.

Abstract: A clustering model is used to partition a set of historical opportunity profiles representing historical opportunities into a plurality of opportunity profile clusters. A target opportunity profile that includes a plurality of target-opportunity parameters is obtained, and an opportunity profile cluster is identified that meets a matching criterion with the target opportunity profile. The plurality of historical opportunity profiles of the identified opportunity profile cluster are partitioned based on an outcome indicator associated respectively with each of the plurality of historical opportunity profiles, providing a first subset of historical opportunity profiles and a second subset of historical opportunity profiles. An insight generation model is applied to the target opportunity profile along with the first subset and/or the second subset to generate one or more insights for the target opportunity profile.

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.