Abstract: Systems and methods for retrieving similar multivariate time series segments are provided. The systems and methods include extracting a long feature vector and a short feature vector from a time series segment, converting the long feature vector into a long binary code, and converting the short feature vector into a short binary code. The systems and methods further include obtaining a subset of long binary codes from a binary dictionary storing dictionary long codes based on the short binary codes, and calculating similarity measure for each pair of the long feature vector with each dictionary long code. The systems and methods further include identifying a predetermined number of dictionary long codes having the similarity measures indicting a closest relationship between the long binary codes and dictionary long codes, and retrieving a predetermined number of time series segments associated with the predetermined number of dictionary long codes.

Abstract: A computer-implemented method for employing a graph-based log anomaly detection framework to detect relational anomalies in system logs is provided. The method includes collecting log events from systems or applications or sensors or instruments, constructing dynamic graphs to describe relationships among the log events and log fields by using a sliding window with a fixed time interval to snapshot a batch of the log events, capturing sequential patterns by employing temporal-attentive transformers to learn temporal dependencies within the sequential patterns, and detecting anomalous patterns in the log events based on relationships between the log events and temporal context determined from the temporal-attentive transformers.

Abstract: Methods and systems for root cause analysis include combining system logs and system metrics into time-series data. Individual root cause analysis is performed to determine individual causal scores for respective system entities. Topological root cause analysis is performed to capture topological patterns of system anomalies. The individual causal scores and the topological patterns are integrated by a weighted sum. A corrective action is performed on an entity identified based on the weighted sum.

Abstract: A computer implemented method is provided. The method includes jointly encoding, by a dual-channel feature extractor, a current time series segment with corresponding static statuses into a compact feature. The method further includes converting, by a binary code extractor, the compact feature into a binary code. The method also includes computing distances between the binary code and all binary codes stored in a binary code database. The method additionally includes retrieving the top relevant multivariate time series segments based on the distances.

Abstract: Methods and systems include annotating a set of training data to indicate tokens that are sensitive. Instructions are generated based on the training data, including original token sequences and respective substituted token sequences. A language model is fine-tuned using the instructions with a penalty-based loss function to generate a privacy-protected language model.

Abstract: Systems and methods for predicting an occurrence of a medical event for a patient using a trained neural network. Historical patient data is preprocessed to generate normalized training samples, and the normalized training samples are sent to a personalized deep convolutional neural network for model pretraining and updating of model parameters. The pretrained model is stored in a remote server for utilization by a local machine for personalization during a preparation time period for a medical treatment. A normalized finetuning set is generated as output, and the model parameters are iteratively finetuned. A personal prediction score for future medical events is generated, and an operation of a medical treatment device is controlled responsive to the prediction score.

Abstract: The present disclosure relates to a heterologous recombinant baculovirus (rBV) expression system for the production of foreign heterologous proteins in insect cells. This system comprises a recombinant baculovirus backbone within a genome with a deletion in the cathepsin gene into which foreign gene cassettes can be integrated, and an insect cell that can be infected by the ?v-cath-rBV, and in which the foreign proteins and/or viral vectors or particles are expressed.

Abstract: Methods and systems include inferring a first prediction and an explanation based on a time series input and a text input using a multi-modal prototype-based encoder. A second prediction is inferred based on the text input and the explanation using a large language model. The first prediction and the second prediction are fused to generate a fused prediction. A reflection is generated based on the fused prediction, the second prediction, and the text input. The text input is refined based on the reflection.

Abstract: Systems and methods for detecting machine-generated texts by detecting motifs. The system and method including tokenizing documents into token sequences, generating at least one graph where tokens and documents are nodes, wherein the tokens are one-hot features and the documents are zero features, and wherein the graph has at least two types of connections, at least one type connects documents to tokens that the documents contain, and at least one other type links tokens that frequently appear together across all documents, classifying each node in the at least one graph using a graph neural network (GNN), and generating an optimal explanation subgraph from the classified graph, wherein the optimal explanation subgraph represents a motif. The optimal explanation subgraph can later be used to support artificial intelligence (AI) agent decision-making.

Abstract: According to an aspect of the present invention, a system method is provided for solution decomposition, the system and method comprising in response to a query, decomposing a solution using a recursive subprocess including generating complete solutions using a policy until there are enough complete solutions to determine a reward model, selecting at least one target step of the complete solutions based on the reward model, partitioning the at least one target step into at least two segments based on a predefined partition fraction, wherein for each segment, a priority metric is estimated, wherein the at least one target step is a first step or a segment of a previously partitioned step, appending the partitioned segment with a lower priority metric, determining whether an end condition has been met, and ending the recursive subprocess if the end condition is met; and outputting a decomposition.

Abstract: Methods and systems include training codebook parameters to encode knowledge. Parameters of a layer of a pre-trained large language model (LLM) are replaced with the trained codebook parameters. An output is generated using the LLM with the trained codebook parameters, based on an input query.

Abstract: Systems and methods for Evidence-based Sound Event Early Detection is provided. The system/method includes parsing collected labeled audio corpus data and real time audio streaming data utilizing mel-spectrogram, encoding features of the parsed mel-spectrograms using a trained neural network, and generating a final predicted result for a sound event based on the belief, disbelief and uncertainty outputs from the encoded mel-spectrograms.

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: A computer-implemented method for meta-learning is provided. The method includes receiving a training time series and labels corresponding to some of the training time series. The method further includes optimizing time series augmentations of the training time series using a time series augmentation selection process performed by a meta learner to obtain a selected augmentation from a plurality of candidate augmentations. The method also includes training a time series encoder with contrastive loss using the selected augmentation to obtain a learned time series encoder. The method additionally includes learning, by the learned time series encoder, a vector representation of another time series. The method further includes performing, by the learned time series encoder, a downstream task of label classification for at least a portion of the other time series.

Abstract: Systems and methods for multi-agent causal discovery. In an embodiment, the system and method may include generating an initial causal graph, prompting a first AI agent to generate contextual data using metadata from the initial causal graph, prompting a second AI agent to generate causal constraints using the initial causal graph and the generated contextual data, wherein the second AI agent includes a prompt builder, and generating a refined causal graph using the generated causal constraints.

Abstract: Methods and systems for training and deploying a neural network mode include training a modular encoder model using training data collected from heterogeneous system types. The modular encoder model includes layers of neural network blocks and a selectively enabled connections between neural network blocks of adjacent layers. Each neural network block includes neural network layers. The modular encoder model is deployed to a system corresponding to one of the heterogeneous system types.

Abstract: Systems and methods for detecting artificial intelligence (AI) generated computer code. Lines of code can be masked from a candidate code to obtain perturbed codes. Missing code can be generated from the perturbed codes by employing an AI code generator model to obtain machine-filled codes. Probabilities of the candidate code probability and the machine-filled codes as AI-generated can be predicted by employing a surrogate model. The candidate code can be distinguished as AI-generated by comparing the probabilities against a detection threshold to obtain detection results.

Abstract: Methods and systems include embedding an input query, including contextual information. Performance and cost of executing the input query are predicted on each of a set of language models. The prediction is performed using a multi-armed bandit approach with each of the language models being represented by a respective arm. The input query is executed on a selected model that has a best balance of performance and cost.

Abstract: Systems and methods for detecting artificial intelligence (AI) generated text. A candidate text can be truncated to obtain a prefix text and a remainder text by employing a truncation module. Regenerated model texts can be regenerated by utilizing the prefix text by employing an AI text generation model. Detection results can be predicted by comparing n-gram similarities of the regenerated model texts and the remainder text. The candidate text can be distinguished as AI generated text by providing explanation texts based on the detection results.

Abstract: Systems and methods for detecting artificial intelligence (AI) generated computer code. Lines of code can be masked from a candidate code to obtain perturbed codes. Missing code can be generated from the perturbed codes by employing an AI code generator model to obtain machine-filled codes. Probabilities of the candidate code probability and the machine-filled codes as AI-generated can be predicted by employing a surrogate model. The candidate code can be distinguished as AI-generated by comparing the probabilities against a detection threshold to obtain detection results.