Abstract: Systems and methods for optimizing key performance indicators (KPIs) using adversarial imitation deep learning include processing sensor data received from sensors to remove irrelevant data based on correlation to a final KPI and generating, using a policy generator network with a transformer-based architecture, an optimal sequence of actions based on the processed sensor data. A discriminator network is employed to differentiate between the generated action sequences and real-world high performance sequences employing. Final KPI results are estimated based on the generated action sequences using a performance prediction network. The generated action sequences are applied to the process to optimize the KPI in real-time.

Abstract: Systems and methods for an agent-based carbon emission reduction system. A carbon product of a supply chain system can be limited below a carbon product threshold by performing a corrective action to monitored entities based on a calculated carbon emission. The carbon emission can be calculated based on carbon-relevant data and a calculation route by utilizing an agent-based simulation model that simulates a learned relationship between a supply chain system and the carbon-relevant data. The calculation route can be determined based on the carbon-relevant data based on a relevance of a carbon product contribution of monitored entities to a goal of the monitored entities. Carbon-relevant data can be extracted from the monitored entities.

Abstract: Systems and methods are provided for classifying components include monitoring sensors to collect sensor data related to a state of a plurality of components; processing, by a computing system, the sensor data to generate an action sequence using a transformer-based policy network for each of the components. A risk score is generated for the action sequence using a Generative Adversarial Network (GAN), wherein the GAN includes a generator for generating action sequences and a discriminator to distinguish low-risk action sequences in accordance with a threshold. The low-risk action sequences are associated with components in the plurality of components based on the risk score. A status of the low-risk action sequences is communicated to the components.

Abstract: Systems and methods train a transformer-based policy network and Generative Adversarial Network (GAN) by initializing a transformer-based policy network to model action sequences by encoding temporal dependencies within sensor data. Multi-head self-attention mechanisms process sequential sensor inputs by being pre-trained on a labeled dataset having sensor data from known low-risk action sequences. A generator within the GAN is trained to produce generated action sequences, which mimic behavior of low-risk action sequences. A discriminator within the GAN is concurrently trained to differentiate between action sequences derived from the labeled dataset and synthetic action sequences produced by the generator. A feedback loop is employed to adjust parameters to produce sequences indistinguishable from real low-risk action sequences.

Abstract: Systems and methods for event prediction include converting event information into categorical time series data for a plurality of properties; determining relationships between pairs of properties of the plurality of properties based on a plurality of data types. A likelihood of an event occurring is predicted during a future period by summing over a Hawkes process for the pairs of properties, the Hawkes process taking as input the relationships between the pairs of properties and comparing the likelihood to an anomaly threshold that is based on a range of normal intensity values for transition sets based on the categorical time series data; and performing an action responsive to a determination that the likelihood exceeds the anomaly threshold.

Abstract: Systems and methods for pre-processing time series data include assigning transition events from categorical time series data into a list of transition sets that each include transitions from a respective first category to a respective second category and determining a mean duration and standard deviation, for each transition set, of the respective first category before the transition to the respective second category. A ratio is compared between the mean duration and the standard deviation to a threshold value to identify noisy transition sets; removing noisy transition sets from the list of transition sets to output de-noised transition sets. A probability of an event occurrence is predicted using the de-noised transition sets, and an action is performed responsive to the probability.

Abstract: Systems and methods include converting historical data into categorical time series data and de-noising the categorical time series data by removing noisy transitions sets according to a coefficient of variation. A likelihood of a category transition is determined based on historical events using a Hawkes process to generate a relationship graph. Relationships between pairs of nodes are determined using the relationship graph, where the relationships indicate a degree of correlation between the nodes based on de-noised categorical time-series data. An anomaly threshold is determined based on anomaly scores for a validation dataset using the relationship graph, wherein a likelihood output of the Hawkes process that exceeds the anomaly threshold indicates an anomaly.

Abstract: Systems and methods for creating a model include converting historical data into categorical time series data; de-noising the categorical time series data by organizing events into transition sets and removing noisy transitions sets according to a coefficient of variation. A relationship graph is generated that determines relationships between pairs of nodes, where the nodes relate to respective data sources and where the relationships indicate a degree of correlation between nodes based on the de-noised categorical time-series data, using a Hawkes process that determines a likelihood of a category transition based on historical events. An anomaly threshold is determined based on anomaly scores for a validation dataset using the relationship graph, wherein a likelihood output of the Hawkes process that exceeds the anomaly threshold indicates an anomaly.

Abstract: Systems and methods for training a time-series-language (TSLa) model adapted for domain-specific tasks. An encoder-decoder neural network can be trained to tokenize time-series data to obtain a discrete-to-language embedding space. The TSLa model can learn a linear mapping function by concatenating token embeddings from the discrete-to-language embedding space with positional encoding to obtain mixed-modality token sequences. Token augmentation can transform the tokens from the mixed-modality token sequences with to obtain augmented tokens. The augmented tokens can train the TSLa model using a computed token likelihood to predict next tokens for the mixed-modality token sequences to obtain a trained TSLa model. A domain-specific dataset can fine-tune the trained TSLa model to adapt the trained TSLa model to perform a domain-specific task.

Abstract: Systems and methods for data fusion and analysis of vehicle sensor data, including receiving a multiple modality input data stream from a plurality of different types of vehicle sensors, determining latent features by extracting modality-specific features from the input data stream, and aligning a distribution of the latent features of different modalities by feature-level data fusion. Classification probabilities can be determined for the latent features using a fused modality scene classifier. A tree-organized neural network can be trained to determine path probabilities and issue driving pattern judgments, with the tree-organized neural network including a soft tree model and a hard decision leaf. One or more driving pattern judgments can be issued based on a probability of possible driving patterns derived from the modality-specific features.

Abstract: Systems and methods for log representation learning for automated system maintenance. An optimized parser can transform collected system logs into log templates. A tokenizer can tokenize the log templates partitioned into time windows to obtain log template tokens. The log template tokens can train a language model (LM) with deep learning to obtain a trained LM. The trained LM can detect anomalies from system logs to obtain detected anomalies. A corrective action can be performed on a monitored entity based on the detected anomalies.

Abstract: A computer-implemented method for multi-model representation learning is provided. The method includes encoding, by a trained time series (TS) encoder, an input TS segment into a TS-shared latent representation and a TS-private latent representation. The method further includes generating, by a trained text generator, a natural language text that explains the input TS segment, responsive to the TS-shared latent representation, the TS-private latent representation, and a text-private latent representation.

Abstract: A method for early warning is provided. The method clusters normal historical data of normal cars into groups based on the car subsystem to which they belong. The method extracts (i) features based on group membership and (ii) feature correlations based on correlation graphs formed from the groups. The method trains an Auto-Encoder and Auto Decoder (AE&AD) model based on the features and the feature correlations to reconstruct the normal historical data with minimum reconstruction errors. The method reconstructs, using the trained AE&AD model, historical data of specific car fault types with reconstruction errors, normalizes the reconstruction errors, and selects features of the car faults with a top k large errors as fault signatures. The method reconstructs streaming data of monitored cars using the trained AE&AD model to determine streaming reconstruction errors, comparing the streaming reconstruction errors with the fault signatures to predict and provide alerts for impending known faults.

Abstract: Systems and methods are provided for adapting a model trained from multiple source time-series domains to a target time-series domain, including integrating input data from source time-series domains to pretrain a model with a set of domain-invariant representations, fine-tuning the model by learning prompts specific to each source time-series domain using data from the source time-series domains, and applying instance normalization and segmenting the time-series data into subseries-level normalized patches for the target time-series domain. The normalized patches are fed into a transformer encoder to generate high-dimensional representations of the normalized patches, and a limited number of samples from the target time-series domain are utilized to learn the prompt specific to the target domain.

Abstract: Systems and methods are provided for incident analysis in Cyber-Physical Systems (CPS) using a Temporal Graph-based Incident Analysis System (TGIAS) and/or Transition Based Categorical Anomaly Detection (TCAD). Dynamically gathered multimodal data from a distributed network of sensors across the CPS are preprocessed to identify abnormal sensor readings indicative of potential incidents, and a multi-layered incident timeline graph, representing abnormal sensor readings, relationships to specific CPS components, and temporal sequencing of events is constructed. Severity scores are calculated, and severity rankings are assigned to identified anomalies based on a composite index including impact on CPS operation, comparison with historical incident data, and predictive risk assessments.

Abstract: Systems and methods are provided for incident analysis in Cyber-Physical Systems (CPS) using a Temporal Graph-based Incident Analysis System (TGIAS) and/or Transition Based Categorical Anomaly Detection (TCAD). Dynamically gathered multimodal data from a distributed network of sensors across the CPS are preprocessed to identify abnormal sensor readings indicative of potential incidents, and a multi-layered incident timeline graph, representing abnormal sensor readings, relationships to specific CPS components, and temporal sequencing of events is constructed. Severity scores are calculated, and severity rankings are assigned to identified anomalies based on a composite index including impact on CPS operation, comparison with historical incident data, and predictive risk assessments.

Abstract: Methods and systems for anomaly detection include encoding a time series with a time series encoder and encoding an event sequence with an event sequence encoder. A latent code is generated from outputs of the time series encoder and the event sequence encoder. The time series is reconstructed from the latent code using a time series decoder. The event sequence is reconstructed from the latent code using an event sequence decoder. An anomaly score is determined based on a reconstruction loss of the reconstructed time series and a reconstruction loss of the reconstructed event sequence. An action is performed responsive to the anomaly score.

Abstract: Methods and systems for anomaly detection include encoding a multivariate time series and a multi-type event sequence using respective transformers and an aggregation network to generate a feature vector. Anomaly detection is performed using the feature vector to identify an anomaly within a system. A corrective action is performed responsive to the anomaly to correct or mitigate an effect of the anomaly. The detected anomaly can be used in a healthcare context to support decision making by medical professionals with respect to the treatment of a patient. The encoding may include machine learning models to implement the transformers and the aggregation network using deep learning.

Abstract: Methods and systems for event prediction include encoding a multivariate time series and a multi-type event sequence using respective transformers and an aggregation network to generate a feature vector. Event prediction is performed using the feature vector to identify a next event to occur within a system. A corrective action is performed responsive to the next event to prevent or mitigate an effect of the next event. The predicted next event can be used in a healthcare context to support decision making by medical professionals with respect to the treatment of a patient. The encoding may include machine learning models to implement the transformers and the aggregation network using deep learning.

Abstract: Methods and systems for defect detection include determining a first residual score by comparing a first predicted system state, determined according to previously measured environment data, to an actual system state. A second residual score is determined by comparing a second predicted system state, determined according to previously measured system state data, to the actual system state. A defect score is generated based on a difference between the first residual score and the second residual score. An automatic action is performed responsive to a determination that the defect score indicates a defect in system behavior.