Abstract: A computer-implemented method for ordinal prediction is provided. The method includes encoding time series data with a temporal encoder to obtain latent space representations. The method includes optimizing the temporal encoder using semi-supervised learning to distinguish different classes in the labeled space using labeled data, and augment the latent space representations using unlabeled training data, to obtain semi-supervised representations. The method further includes discarding a linear layer after the temporal encoder and fixing the temporal encoder. The method also includes training k-1 binary classifiers on top of the semi-supervised representations to obtain k-1 binary predictions. The method additionally includes identifying and correcting inconsistent ones of the k-1 binary predictions by matching the inconsistent ones to consistent ones of the k-1 binary predictions. The method further includes aggregating the k-1 binary predictions to obtain an ordinal prediction.

Abstract: A quantum dot composite structure and a method for forming the same are provided. The quantum dot composite structure includes: a glass particle including a glass matrix and a plurality of quantum dots located in the glass matrix, wherein at least one of the plurality of quantum dots includes an exposed surface in the glass matrix; and an inorganic protective layer disposed on the glass particle and covering the exposed surface.

Abstract: A computer-implemented method for ordinal prediction is provided. The method includes encoding time series data with a temporal encoder to obtain latent space representations. The method includes optimizing the temporal encoder using semi-supervised learning to distinguish different classes in the labeled space using labeled data, and augment the latent space representations using unlabeled training data, to obtain semi-supervised representations. The method further includes discarding a linear layer after the temporal encoder and fixing the temporal encoder. The method also includes training k?1 binary classifiers on top of the semi-supervised representations to obtain k?1 binary predictions. The method additionally includes identifying and correcting inconsistent ones of the k?1 binary predictions by matching the inconsistent ones to consistent ones of the k?1 binary predictions. The method further includes aggregating the k?1 binary predictions to obtain an ordinal prediction.

Abstract: Disclosed herein are systems (e.g., moving bed redox systems) and methods for supplying thermal energy to an endothermic chemical process.

Abstract: An information processing method and apparatus, a terminal, and a storage medium. The information processing method comprises: determining first content in response to a first operation event of a first control in a first document (S11); and adding the first content to the first document on the basis of content information and type information of the first content (S12). The type information comprises first type information and/or second type information, the second type information having an association with the first type information. In the described method, first content can be added to a first document according to content information and type information of the first content, so as to distinguish different ways of adding the first content.

Abstract: In certain aspects, a wireless multimedia apparatus and an operation method thereof are disclosed. The wireless multimedia apparatus includes a system on chip (SoC), including a memory configured to store code and a processor coupled to the memory. When the code is executed, the processor is configured to determine that a router is available and connected to a multimedia provider. The processor is configured to connect to the router based on configuration information associated with the router and receive multimedia data from the multimedia provider via the router. The processor is configured to determine whether the router is still available. Responsive to the router being unavailable, the processor is configured to turn on a softAP based on the configuration information associated with the router to connect to the multimedia provider via the softAP. The processor is configured to receive the multimedia data from the multimedia provider via the softAP.

Abstract: Methods and systems for training a model include determining class prototypes of time series samples from a training dataset. A task corresponding to the time series samples is encoded using the class prototypes and a task-level configuration. A likelihood value is determined based on outputs of a time series density model, a task-class distance from a task embedding model, and a task density model. Parameters of the time series density model, the task embedding model, and the task density model are adjusted responsive to the likelihood value.

Abstract: Techniques, systems, and devices are described for providing a computational frame for estimating high-dimensional stochastic behaviors. In one exemplary aspect, a method for performing numerical estimation includes receiving a set of measurements of a stochastic behavior. The set of correlated measurements follows a non-standard probability distribution and is non-linearly correlated. Also, a non-linear relationship exists between a set of system variables that describes the stochastic behavior and a corresponding set of measurements. The method includes determining, based on the set of measurements, a numerical model of the stochastic behavior. The numerical model comprises a feature space comprising non-correlated features corresponding to the stochastic behavior. The non-correlated features have a dimensionality of M and the set of measurements has a dimensionality of N, M being smaller than N.

Abstract: A computer-implemented method for locating root causes is provided. The method includes detecting a trigger point from entity metrics data and key performance indicator (KPI) data, generating a learned causal graph by fusing a state-invariant causal graph with a state-dependent causal graph, and locating the root causes by employing a random walk-based technique to estimate a probability score for each of the entity metrics data by starting from a KPI node.

Abstract: A computer-implemented method for identifying root cause failure and fault events is provided. The method includes detecting a trigger point, converting, via an encoder, previous system state data, new batch data in a next system state, and a causal graph to system state-invariant embeddings and system state-dependent embeddings, generating a learned causal graph, via a graph generation layer, by integrating state-invariant and state-dependent information, and predicting, by a prediction layer, future time-series data on the learned causal graph.

Abstract: A computer-implemented method for employing a graph-based adaptive domain generation framework is provided. The method includes, in a training phase, performing domain prototypical network training on source domains, constructing an autoencoding domain relation graph by applying a graph autoencoder to produce domain node embeddings, and performing, via a domain-adaptive classifier, domain-adaptive classifier training to make an informed decision. The method further includes, in a testing phase, given testing samples from a new source domain, computing a prototype by using a pretrained domain prototypical network, inferring node embedding, and making a prediction by the domain-adaptive classifier based on the domain node embeddings.

Abstract: A computer-implemented method for detecting trigger points to identify root cause failure and fault events is provided. The method includes collecting, by a monitoring agent, entity metrics data and system key performance indicator (KPI) data, integrating the entity metrics data and the KPI data, constructing an initial system state space, detecting system state changes by calculating a distance between current batch data and an initial state, and dividing a system status into different states.

Abstract: An LDMOS transistor and a method for manufacturing the same are provided. The method includes: forming an epitaxial layer on a substrate, forming a gate structure on an upper surface of the epitaxial layer, forming a body region and a drift region in the epitaxial layer, forming a source region in the body region, forming a first insulating layer on the gate structure and an upper surface of the epitaxial layer and, forming a shield conductor layer on the first insulating layer, forming a second insulating layer covering the shield conductor layer, forming a first conductive path, to connect the source region with the substrate, and forming a drain region in the drift region. By forming the first conductive path which connects the source region with the substrate, the size of the LDMOS transistor and the resistance can be reduced.

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: A computer-implemented method for ordinal prediction is provided. The method includes encoding time series data with a temporal encoder to obtain latent space representations. The method includes optimizing the temporal encoder using semi-supervised learning to distinguish different classes in the labeled space using labeled data, and augment the latent space representations using unlabeled training data, to obtain semi-supervised representations. The method further includes discarding a linear layer after the temporal encoder and fixing the temporal encoder. The method also includes training k?1 binary classifiers on top of the semi-supervised representations to obtain k?1 binary predictions. The method additionally includes identifying and correcting inconsistent ones of the k?1 binary predictions by matching the inconsistent ones to consistent ones of the k?1 binary predictions. The method further includes aggregating the k?1 binary predictions to obtain an ordinal prediction.

Abstract: Methods and systems for training a model include collecting unlabeled training data during operation of a device. A model is adapted to operational conditions of the device using the unlabeled training data. The model includes a shared encoder that is trained on labeled training data from multiple devices and further includes a device-specific decoder that is trained on labeled training data corresponding to the device.

Abstract: A computer-implemented method for ordinal classification of input data is provided. The method includes learning, by an encoder neural network, compact neural representations of the input data. The method further includes freezing the encoder neural network for downstream tasks. The method also includes training, by a hardware processor, K?1 ordinal classifiers on top of the compact neural representations to obtained trained K?1 ordinal classifiers. The method additionally includes generating, by the hardware processor, a predicted ordinal label by aggregating the trained K?1 ordinal classifiers.

Abstract: A method is provided for training a hierarchical graph neural network. The method includes using a time series generated by each of a plurality of nodes to train a graph neural network to generate a causal graph, and identifying interdependent causal networks that depict hierarchical causal links from low-level nodes to high-level nodes to the system key performance indicator (KPI). The method further includes simulating causal relations between entities by aggregating embeddings from neighbors in each layer, and generating output embeddings for entity metrics prediction and between-level aggregation.

Abstract: An LDMOS transistor and a method for manufacturing the same are provided. The method includes: forming an epitaxial layer on a substrate, forming a gate structure on an upper surface of the epitaxial layer, forming a body region and a drift region in the epitaxial layer, forming a source region in the body region, forming a first insulating layer on the gate structure and an upper surface of the epitaxial layer and, forming a shield conductor layer on the first insulating layer, forming a second insulating layer covering the shield conductor layer, forming a first conductive path, to connect the source region with the substrate, and forming a drain region in the drift region. By forming the first conductive path which connects the source region with the substrate, the size of the LDMOS transistor and the resistance can be reduced.

Abstract: An LDMOS transistor and a method for manufacturing the same are provided. The method includes: forming an epitaxial layer on a substrate, forming a gate structure on an upper surface of the epitaxial layer, forming a body region and a drift region in the epitaxial layer, forming a source region in the body region, forming a first insulating layer on the gate structure and an upper surface of the epitaxial layer and, forming a shield conductor layer on the first insulating layer, forming a second insulating layer covering the shield conductor layer, forming a first conductive path, to connect the source region with the substrate, and forming a drain region in the drift region. By forming the first conductive path which connects the source region with the substrate, the size of the LDMOS transistor and the resistance can be reduced.