Abstract: The present disclosure relates generally to the generation and deployment of a machine learning-enabled decision engine (MLDE). The MLDE includes decision options that are composed of a discrete list of selectable options. Further, the MLDE includes data inputs that can be used to influence decisions made by the machine learning models of the MLDE. Controls are applied to the MLDE to overlay and bound the decisioning within guidelines established by an operator of the MLDE. Once the MLDE is established, the MLDE is validated and deployed for use by software applications to make decisions.

Abstract: The technology disclosed relates to a system and method of exporting learned features between federated endpoints whose learning is confined to respective training datasets. The system includes logic to access a first training dataset to train a first federated endpoint and a second training dataset to train a second federated endpoint. The first and second training datasets have first and second sample sets that share one or more shared sample features. The shared sample features are common between the first and second sample sets. The system includes logic to train a first generator on the first federated endpoint. The system includes logic to use the first trained generator for a second inference on a second performance task executed on the second federated endpoint.

Abstract: This invention provides a set of biological markers that are useful for detecting cancer. This invention further provides methods of using those biological markers for the diagnosis, prognosis, or monitoring of cancer.

Abstract: A swimmer surveillance system configured to detect swimmers in a water body that are in a state of distress, or in a danger of drowning, and provide an alarm signal for prompting assistance to the swimmers in distress or in a danger of drowning, the system including: a processor; at least one swimmer sensor signally connected to the processor, and configured to attach to a swimmer, acquire data relating to an at least one physiological condition of the swimmer, and transmit the data to the processor; at least one image acquiring device signally connected to the processor, and configured to acquire images of the swimmers in the water body, and transmit visual data of the acquired images to the processor; and at least one alarming device signally connected to the processor, and configured to receive a signal from the processor and in response provide an alarm.

Abstract: A method for extracting semantic hashtags representing topics in one or more domain-specific documents, each topic relevant to achieving a goal of a domain-specific entity includes a processor executing a routine to split a domain-specific document into data objects, the data objects comprising sentences and paragraphs, using grammar rules specific to the domain-specific entity; applying an unsupervised learning model to classify the data objects as noisy and non-noisy for the domain-specific entity; discarding the noisy data objects; applying a supervised learning model to identify, based on a pre-defined set of intents, an intent of each non-noisy data object; tagging each non-noisy data object with its intent; applying the intent to an ontology graph base to identify a corresponding semantic hashtag; annotating each non-noisy data object with its semantic hashtag; and using one or more annotated non-noisy data objects, generating, for the domain-specific entity, a recommended action for achieving the goal.

Abstract: One or more structural equations modeling a physical process over time may be sampled using simulated parameter values to generate input data signal values. A noise generator may be applied to the input data signal values to generate noise values. The noise values and the input data signal values may be combined to determined noisy data signal values. These noisy data signal values may in turn be used in combination with one or more states to train a prediction model.

Abstract: Techniques and mechanisms described herein provide automated processes for integrating supervised and unsupervised classification results of a test data observation with training data observations in a feature space. Novelty of the test data observation relative to the feature space may be measured using one or more distance metrics. Novelty of a test data observation may be further refined by comparison to a confusion matrix segment determined based on a supervised model. Based on the novelty information, the supervised and/or unsupervised models may be updated, for instance via incremental or batch training.

Abstract: Systems and methods for implementing tensor query-based vector search operations for multi-dimensional sample datasets of tensors are disclosed. The solution can utilize one or more processors coupled to memory to identify a query for a multi-dimensional sample dataset. The query can indicate an operation to search embeddings in the plurality of tensors of a plurality of samples of the dataset. Each sample can have a respective tensor of the plurality of tensors comprising one or more embeddings of the respective sample. The one or more processors can execute the query to generate an output dataset comprising a subset of samples of the plurality of samples. The subset of samples can be identified based on the operation and the respective one or more embeddings of each tensor of the subset of samples. The one or more processors can provide the output dataset.

Abstract: Systems and methods for executing queries on tensor datasets are disclosed. A system can identify a query for a multi-dimensional sample dataset. Each sample of the multi-dimensional sample dataset can include one or more tensors. Each tensor of the one or more tensors can be associated with a respective identifier that is common to each sample of the multi-dimensional sample dataset. The query specifying a first identifier of a first tensor of the multi-dimensional sample dataset and a first range of a first dimension of the first tensor, or one or more operations such as sampling, grouping, ungrouping, or transformation operations, to perform on the first tensor of the multi-dimensional sample dataset. The system can parse the query, and execute the query to generate query results. The system can provide the query results as output.

Abstract: In a training phase, training data may be used to train a supervised machine learning prediction model and an unsupervised machine learning segmentation model. Then, in a testing phase, the supervised machine learning prediction model may be used to predict a target outcome for a test data observation. Also, the unsupervised machine learning segmentation model may be used to evaluate the novelty of the test data observation relative to the training data.

Abstract: Embodiments of the present disclosure provide systems and methods for training a machine-learning model for predicting emotions from received media data. Methods according to the present disclosure include displaying a user interface. The user interface includes a predefined media content, a plurality of predefined emotion tags, and a user interface control for controlling a recording of the user imitating the predefined media content. Methods can further include receiving, from a user, a selection of one or more emotion tags from the plurality of predefined emotion tags, receiving the recording of the user imitating the predefined media content, storing the recording in association with the selected one or more emotion tags, and training, based on the recording, the machine-learning model configured to receive input media data and predict an emotion based on the input media data.

Abstract: Embodiments presented herein describe a method for processing streams of data of one or more networked computer systems. According to one embodiment of the present disclosure, an ordered stream of normalized vectors corresponding to information security data obtained from one or more sensors monitoring a computer network is received. A neuro-linguistic model of the information security data is generated by clustering the ordered stream of vectors and assigning a letter to each cluster, outputting an ordered sequence of letters based on a mapping of the ordered stream of normalized vectors to the clusters, building a dictionary of words from of the ordered output of letters, outputting an ordered stream of words based on the ordered output of letters, and generating a plurality of phrases based on the ordered output of words.

Abstract: Embodiments are directed to security analysis agents. Events associated with a computing environment may be provided. Prompt fragments may be determined based on the events. A prompt may be generated for a large language model (LLM) based on a prompt template and the prompt fragments such that the prompt fragments may be included in the prompt and provided to the LLM. Actions for evaluating the events may be determined based on the LLM response. These actions may be executed to evaluate the events. Portions of the response that correspond to the prompt fragments may be determined. A performance score may be determined for each prompt fragment based on its corresponding portion of the response such that the prompt may be modified to exclude a portion of the prompt fragments that have a performance score less than a threshold value.

Abstract: Embodiments of the present disclosure provide systems and methods for training a machine-learning model for predicting emotions from received media data. Methods according to the present disclosure include displaying a user interface. The user interface includes a predefined media content, a plurality of predefined emotion tags, and a user interface control for controlling a recording of the user imitating the predefined media content. Methods can further include receiving, from a user, a selection of one or more emotion tags from the plurality of predefined emotion tags, receiving the recording of the user imitating the predefined media content, storing the recording in association with the selected one or more emotion tags, and training, based on the recording, the machine-learning model configured to receive input media data and predict an emotion based on the input media data.

Abstract: Techniques are disclosed for generating a syntax for a neuro-linguistic model of input data obtained from one or more sources. A stream of words of a dictionary built from a sequence of symbols are received. The symbols are generated from an ordered stream of normalized vectors generated from input data. Statistics for combinations of words co-occurring in the stream are evaluated. The statistics includes a frequency upon which the combinations of words co-occur. A model of combinations of words based on the evaluated statistics is updated. The model identifies statistically relevant words. A connected graph is generated. Each node in the connected graph represents one of the words in the stream. Edges connecting the nodes represent a probabilistic relationship between words in the stream. Phrases are identified based on the connected graph.

Abstract: Described herein is a gearbox for an electric vehicle drivetrain unit comprising a gearbox enclosure, a support rod, a first piston, a second piston, and various hydraulic fluid passages. The support rod extends into the gearbox enclosure and at least partially protrudes/extends into the first and second pistons. The first piston, second piston, and support rod are fluidically connected with the hydraulic fluid passages such that when the gearbox is in a neutral gear, pressurized hydraulic fluid forces the first piston and the second piston against the support rod. When the gearbox is in a first gear, pressurized hydraulic fluid forces the support rod against the gearbox enclosure either directly or through the first piston. When the gearbox is in a second gear, pressurized hydraulic fluid forces the support rod against the gearbox enclosure directly or through the second piston. Also described are electric vehicles and shifting methods.

Abstract: In an embodiment, the systems and methods discussed herein are related to generating, via a processor, a Markov Distribution Problem (MDP), the MDP including a state space, an action space, a transition function, a reward function, and a discount factor. A reinforcement learning (RL) model is applied, via the processor, to the MDP to generate a RL agent. An input data associated with a first user is received at the RL agent. At least one counterfactual explanation (CFE) is generated via the processor and by the RL agent and based on the input data. A representation of the at least one CFE and at least one recommended remedial action is caused to transmit, via the processor, to at least one of a compute device of the first user or a compute device of a second user different from and associated with the first user.

Abstract: Improved calibration of a vehicle sensor based on static objects detected within an environment being traversed by the vehicle is disclosed. A first sensor such as a LiDAR can be calibrated to a global coordinate system via a second pre-calibrated sensor such as a GPS IMU. Static objects present in the environment are detected such as signage. Point cloud data representative of the static objects are captured by the first sensor and a first transformation matrix for performing a transformation from a local coordinate system of the first sensor to a local coordinate system of the second sensor is iteratively redetermined until a desired calibration accuracy is achieved. Transformation to the global coordinate system is then achieved via application of the first transformation matrix followed by application of a second known transformation matrix to transition from the local coordinate system of the second pre-calibrated sensor to the global coordinate system.