Abstract: A computer-implemented method includes converting tabular data to a text representation, generating metadata associated with the text representation of the tabular data, outputting one or more natural language data descriptions indicative of the tabular data in response to utilizing a large language model (LLM) and zero-shot prompting of the metadata and text representation of the tabular data, outputting one or more summaries utilizing the LLM and appending a prompt on the one or more natural language data descriptions, selecting a single summary of the one or more summaries in response to the single summary having a smallest validation rate, receiving a query associated with the tabular data, outputting one or more predictions associated with the query, and in response to meeting a convergence threshold with the one or more predictions generated from the one or more iterations, output a final prediction associated with the query.

Abstract: A method that includes obtaining, from one or more stations, embedding vectors that embed features of the stations, obtaining measurement vectors and associated measurement names, generating a text array of the measurement names utilizing a language model, concatenating the text array and the measurement vector at one or more cross-attention modules configured to encode one or more measurement arrays to one or more latent embedding vectors, generating one or more latent embedding vectors associated with the measurement vector and corresponding measurement names via the cross-attention module and a fixed-size station embedding vector, outputting the latent embeddings; generating a query vector; generating key vectors value vectors utilizing a latent embedding vector; decoding the latent vectors utilizing the key vector and value vector; utilizing the cross attention modules and query vectors, decoding the latent embedding vectors; and output a predication.

Abstract: A computer-implemented method for training a deep state space model using machine learning. The deep state space model includes a generative model and a multi-modal inference model. The generative model includes a transition model, and an emission model. The method includes: a) receiving a training data set comprising a sequence of observation vectors. For a plurality of observation vectors, the method iterates between b), c), and d) in sequence: b) inferring, using the multi-modal inference model, a current latent state of the generative model; c) constructing, using the multi-modal inference model, a posterior approximation of the current latent state as a mixture density network. For a plurality of observation vectors comprised in the sequence of observation vectors, d) decoding, using the emission model, the plurality of approximated latent state vectors to provide a plurality of synthetic observations; and e) outputting the trained deep state space model.

Abstract: A computer-implemented system and method relate to anomaly detection. A first source image is obtained from a first image set and a second source image is obtained from a second image set of an in-distribution image dataset. A diffusion model generates a modified image using the first source image and the second source image. A non-anomalous label is automatically generated for the first source image. The non-anomalous label is also generated for the second source image. An anomalous label is generated for the modified image. A training dataset is created. The training dataset includes at least the first source image with the non-anomalous label, the second source image with the non-anomalous label, and the modified image with the anomalous label. A machine learning model is trained or fine-tuned using the training dataset. The machine learning model being configured to perform a task of anomaly detection.

Abstract: A computer-implemented system and method relate to anomaly detection. Latent code of a source image is obtained. The latent code is designated as a target image. Source embedding data is generated form the source image. Text data, which is of a different domain than that of the source image, is obtained. Text embedding data is generated from the text data. Additional embedding data is generated using the source embedding data and the text embedding data. The additional embedding data provides guidance for modifying the source image. A modified image is generated via an iterative process that includes at least one iteration, where each iteration includes at least (i) encoding the target image to generate target embedding data, (ii) generating updated embedding data by combining the target embedding data and the additional embedding data, (iii) decoding the updated embedding data to generate a new image, and (iv) assigning the new image as the target image and the modified image.

Abstract: A method of generating text-driven prompts and class prediction probabilities using a vision-language model (VLM) includes receiving candidate class names associated with a plurality of candidate classes for images, generating class text tokens based on a text description of the candidate class names, and generating a plurality of context prompt vectors using a prompt generator. The context prompt vectors define context information associated with an image classification task to be performed by the VLM. The method further includes generating prompts for each of the plurality of candidate classes by appending respective class text tokens to the context prompt vectors for each of the plurality of candidate classes, and, using the VLM, generating and outputting a class prediction probability for a sample image based on the plurality of context prompt vectors.

Abstract: Disclosed embodiments include methods for evaluating augmented training elements. The augmented training elements may be generated using different augmentation techniques. Disclosed embodiments may generate training set useful for training a plurality of different machine-learning models.

Abstract: A method for processing comments includes: displaying a plurality of image-generating entries on a comment editing panel of a comment object, wherein the plurality of image-generating entries is configured to generate comment images for commenting the comment object by triggering different image-generating networks, each image-generating entry corresponds to an image-generating network, and the image-generating entry is configured to generate a comment image by triggering the image-generating network corresponding to the image-generating network.

Abstract: Disclosed is a method for processing comments. A post-operation region corresponding to a current resource is displayed, wherein the post-operation region is configured to post comment information and includes an image generation component. An image set is displayed in the post-operation region in response to triggering the image generation component in the case that the post-operation region includes source comment information, wherein the image set is generated based on the source comment information.

Abstract: Methods and systems for training neural networks with federated learning. Server-maintained machine learning models are sent from a server to a plurality of clients, yielding local machine learning models. At each client, the local machine learning models are trained with locally-stored data, stored locally at that respective client. Respective losses are determined and weights updated for each of the local machine learning models. Updated weights are transferred to the server for updating of the server-maintained machine learning models for training of those models. If one of the clients is disconnected or otherwise unable to receive the server-maintained models, that disconnected client can connect to neighboring clients, receiving the models from those neighboring clients, and training those models with the disconnected clients own locally-stored data.

Abstract: Methods and systems for federated learning in a machine learning environment are disclosed. At least portions of a plurality of server-maintained machine learning models are sent from a server to a plurality of clients, yielding a plurality of local machine learning models. At each client, the plurality of local machine learning models are trained with locally-stored data that is stored locally at that respective client. A respective loss for each of the plurality of local machine learning models is determined, and respective weights for each of the plurality of local machine learning models are updated. The respective updated weights from each client are transferred to the server without transferring the locally-stored data of the clients. At the server, the plurality of server-maintained machine learning models are trained with the updated weights sent from each of the clients.

Abstract: A computer-implemented method includes converting tabular data to a text representation, generating metadata associated with the text representation of the tabular data, outputting one or more natural language data descriptions indicative of the tabular data in response to utilizing a large language model (LLM) and zero-shot prompting of the metadata and text representation of the tabular data, outputting one or more summaries utilizing the LLM and appending a prompt on the one or more natural language data descriptions, selecting a single summary of the one or more summaries in response to the single summary having a smallest validation rate, receiving a query associated with the tabular data, outputting one or more predictions associated with the query, and in response to meeting a convergence threshold with the one or more predictions generated from the one or more iterations, output a final prediction associated with the query.

Abstract: A simple and highly effective zero-shot anomaly detection approach is disclosed. The approach is compatible with a variety of established anomaly detection methods. The approach relies on training an anomaly detector, such as a neural network, on a meta-set in combination with batch normalization.

Abstract: A computer-implemented method of anomaly detection associated with graphical data includes receiving as input one or more input data sets, wherein the input data sets includes one or more graphs, utilizing a plurality of graph neural networks (GNNs) to identify an aggregate loss including a first loss and second loss associated with the input data set, wherein the aggregate loss is associated with embedding's of the GNNs, and outputting a notification associated with an anomaly detection when the first and second loss exceeds an anomaly-detection threshold.

Abstract: A computer-implemented method of anomaly detection associated with graphical data includes receiving as input one or more input data sets, wherein the input data sets includes one or more graphs, utilizing a plurality of graph neural networks (GNNs) to identify an aggregate loss including a first loss and second loss associated with the input data set, wherein the aggregate loss is associated with embedding's of the GNNs, and outputting a notification associated with an anomaly detection when the first and second loss exceeds an anomaly-detection threshold.

Abstract: A device control system includes a controller. The controller may be configured to, receive a data set of N samples that includes normal and unlabeled unidentified anomalous data samples, process, via a model, the data set to produce an anomaly score associated with each sample in the data set, rank the normal and anomalous data samples according to the anomaly score associated with each data sample to produce a ranked order, label a fraction ? of the N samples that have the highest scores with an anomaly label and the remaining samples with a normal label, retrain the model using all N samples, the labels, and a joint loss function, repeat the process, rank, label, and retrain steps until the ranked order and labels for all of the N samples do not change, and operate the device control system based on the trained model.

Abstract: A method of controlling a device includes receiving data from a first sensor, encoding, via parameters of an encoder, the data to obtain a latent observation (wt) for the data and an uncertainty vector (?wt) for the latent observation, processing the latent observation with a recurrent neural network to obtain a switching variable (st) which determines weights (?t) of a locally linear Kalman filter, processing the latent observation and the uncertainty vector with said locally linear Kalman filter to obtain updated mean of latent representation (?zt) and covariance of latent representation (?zt) of the Kalman filter, decoding the latent representation to obtain mean (?xt) and covariance of a reconstruction of the data (?xt) and outputting the reconstruction at a time t.

Abstract: An optical transmitting apparatus is disclosed, in the apparatus, an array light source include M*N light sources, and an included angle between any column of light sources in the N columns of light sources and any row of light sources in the M rows of light sources is a preset angle. The array light source is located on a first side of a collimating lens, a plane on which the array light source is located is perpendicular to an optical axis of the collimating lens, and a distance between the plane on which the array light source is located and a center point of the collimating lens is a focal length of the collimating lens. An rotatable scanning mirror is located on a second side of the collimating lens, and a center point of a reflective surface of the scanning mirror is on the optical axis of the collimating lens.

Abstract: A anomalous region detection method includes receiving time series data being grouped in patches, encoding, via parameters of an encoder, the data to obtain local latent representations, determining a representation loss from the local latent representations, transforming the local latent representations associated with each patch, via at least two local neural transformations, to a series of diverse transformed vector representations, determining a dynamic deterministic contrastive loss (DDCL) from the series of diverse transformed vector representations, combining the representation loss and the DDCL to obtain updated parameters, updating the parameters of the encoder with the updated parameters, scoring each of the series of the diverse transformed vector representations, via the DDCL, to obtain a diverse semantic requirement score associated with each patch, smoothing the diverse semantic requirement score to obtain a loss region, masking data associated with the loss region to obtain verified data, and o