Abstract: A gate oxide layer for a high voltage transistor is formed using methods that avoid thinning in the corners of the gate oxide layer. A recess is formed in a silicon substrate. The exposed surfaces of the recess are thermally oxidized to form a thermal oxide layer of the gate oxide layer. A high temperature oxide layer of the gate oxide layer is then formed within the exposed surfaces of the recess by chemical vapor deposition. The combination of the thermal oxide layer and the high temperature oxide layer results in a gate oxide layer that does not exhibit the double hump phenomenon in the drain current vs. gate voltage curve. The high temperature oxide layer may include a rim that extends out of the recess.

Abstract: Some embodiments relate to an integrated circuit device incorporating an etched recessed gate dielectric region. The integrated circuit device includes a substrate including a first upper surface, a gate dielectric region disposed at the first upper surface of the substrate and extending into the substrate, and a gate structure disposed over the gate dielectric region. The gate dielectric region includes a second upper surface and forms a recess extending below the second upper surface. The second upper surface includes a perimeter portion surrounding the recess. The gate structure completely covers the second upper surface of the gate dielectric region and extends into the recess.

Abstract: The invention relates to a process for preparing a berry composition from whole berries having bioactive and/or nutritional compounds of a whole berry and having improved sensory and miscibility/dispersibility properties. The process comprises the steps of providing whole berries, grinding said whole berries to obtain a whole berry suspension and micronizing said whole berry suspension. In particular, the process does not comprise any step of removal of the inherent insoluble fibers coming from the whole berries. The invention also relates to a berry composition, to oral compositions comprising such as berry composition and to the use of such compositions.

Abstract: A method and a system of molecular typing and subtyping classifier for immune-related diseases are provided. The method includes: conducting molecular typing via a clustering algorithm in a training set to obtain a plurality of subtypes stably appearing in the training set and a marker gene for each subtype; conducting enrichment analysis on marker genes for the plurality of subtypes, conducting immune cell infiltration evaluation on the plurality of subtypes, and dividing the plurality of subtypes into a plurality of subtype classes according to results of the analysis and the evaluation; comparing treatment response rates of different subtype classes through a comparison set to determine a subtype class to be identified; constructing a support vector machine model with feature genes screened and an optimal parameter combination; and determining whether immune-related disease data to be classified is the subtype class to be identified.

Abstract: Methods and systems for providing an interactive image scene graph pattern search are provided. A user is provide with an image having a plurality of selectable segmented regions therein. The user selects one or more of the segmented regions to build a query graph. Via a graph neural network, matching target graphs are retrieved that contain the query graph from a target graph database. Each matching target graph has matching target nodes that match with the query nodes of the query graph. Matching target images from an image database are associated with the matching target graphs. Embeddings of each of the query nodes and the matching target nodes are extracted. A comparison of the embeddings of each query node with the embeddings of each matching target node is performed. The user interface displays the matching target images that are associated with the matching target graphs.

Abstract: According to embodiments of the present disclosure, there are provided a method, an apparatus, a device and a storage medium for code parameter verification. In a method, a parameter verification request for target code is detected; in response to detecting a parameter verification statement, at least one code segment is extracted from the target code that matches at least one predetermined statement type of a plurality of predetermined statement types; and a verification statement for at least one parameter of the target code is generated, with a trained machine learning model, based on the at least one code segment, the verification statement being configured to verify validation of the at least one parameter, where the machine learning model is trained based on a sample code set and sample verification statement for parameters of sample code in the sample code set.

Abstract: A method for training a model for determining graph similarity is disclosed. The method comprises receiving a first graph and a second graph as training inputs, the first graph and the second graph each including nodes connected by edges. The method further comprises applying a model to the first graph and the second graph to determine (i) pairs of aligned nodes between the first graph and the second graph and (ii) a first training loss. The method further comprises generating a first augmented graph by modifying the first graph depending on the pairs of aligned nodes. The method further comprises applying the model to the first graph and the first augmented graph to determine a second training loss. The method further comprises refining the model based on the first training loss and the second training loss.

Abstract: A computer-implemented method for a machine-learning network includes receiving an input dataset, wherein the input dataset is indicative of image information, tabular information, radar information, sonar information, or sound information, sending the input dataset to the machine-learning model to output predictions associated with the input data, identifying one or more slices associated with the input dataset and the machine learning model in a first iteration, wherein each of the one or more slices include input data from the input dataset and common attributes associated with each slice, outputting an interface that includes information associated with the one or more slices and performance measurements of the one or more slices of the first iteration and subsequent iterations identifying subsequent slices, wherein the performance measurements relate to the predictions associated with the first iteration and subsequent iterations.

Abstract: A system for time series analysis using attention models is disclosed. The system may capture dependencies across different variables through input embedding and may map the order of a sample appearance to a randomized lookup table via positional encoding. The system may capture capturing dependencies within a single sequence through a self-attention mechanism and determine a range of dependency to consider for each position being analyzed. The system may obtain an attention weighting to other positions in the sequence through computation of an inner product and utilize the attention weighting to acquire a vector representation for a position and mask the sequence to enable causality. The system may employ a dense interpolation technique for encoding partial temporal ordering to obtain a single vector representation and a linear layer to obtain logits from the single vector representation. The system may use a type dependent final prediction layer.

Abstract: Various embodiments of systems and methods for attention models with random features for multi-layered graph embeddings are disclosed.

Abstract: A system and method for monitoring performance of a repeated activity is described. The system comprises a motion sensing system and a processing system. The motion sensing system includes sensors configured to measure or track motions corresponding to a repeated activity. The processing system is configured to process motion data received from the motion sensing system to recognize and measure cycle durations in the repeated activity. In contrast to the conventional systems and methods, which may work for repeated activities having a high level of standardization, the system advantageously enables recognition and monitoring of cycle durations for a repeated activity, even when significant abnormal motions are present in each cycle. Thus, the system can be utilized in a significantly broader set of applications, compared conventional systems and methods.

Abstract: A system for time series analysis using attention models is disclosed. The system may capture dependencies across different variables through input embedding and may map the order of a sample appearance to a randomized lookup table via positional encoding. The system may capture capturing dependencies within a single sequence through a self-attention mechanism and determine a range of dependency to consider for each position being analyzed. The system may obtain an attention weighting to other positions in the sequence through computation of an inner product and utilize the attention weighting to acquire a vector representation for a position and mask the sequence to enable causality. The system may employ a dense interpolation technique for encoding partial temporal ordering to obtain a single vector representation and a linear layer to obtain logits from the single vector representation. The system may use a type dependent final prediction layer.

Abstract: Systems and methods are disclosed for identifying target graphs that have nodes or neighborhoods of nodes (sub-graphs) that correspond with an input query graph. A visual analytics system supports human-in-the-loop, example-based subgraph pattern search utilizing a database of target graphs. Users can interactively select a pattern of nodes of interest. Graph neural networks encode topological and node attributes in a graph as fixed length latent vector representations such that subgraph matching can be performed in the latent space. Once matching target graphs are identified as corresponding to the query graph, one-to-one node correspondence between the query graph and the matching target graphs.

Abstract: Methods and systems for providing an interactive image scene graph pattern search are provided. A user is provide with an image having a plurality of selectable segmented regions therein. The user selects one or more of the segmented regions to build a query graph. Via a graph neural network, matching target graphs are retrieved that contain the query graph from a target graph database. Each matching target graph has matching target nodes that match with the query nodes of the query graph. Matching target images from an image database are associated with the matching target graphs. Embeddings of each of the query nodes and the matching target nodes are extracted. A comparison of the embeddings of each query node with the embeddings of each matching target node is performed. The user interface displays the matching target images that are associated with the matching target graphs.

Abstract: A method including receiving an input data set. The input data set can include one of a feature domain set or a kernel matrix. The method also can include constructing dense embeddings using: (i) Nyström approximations on the input data set when the input data set comprises the kernel matrix, and (ii) clustered Nyström approximations on the input data set when the input data set comprises the feature domain set. The method additionally can include performing representation learning on each of the dense embeddings using a multi-layer fully-connected network for each of the dense embeddings to generate latent representations corresponding to each of the dense embeddings. The method further can include applying a fusion layer to the latent representations corresponding to the dense embeddings to generate a combined representation. The method additionally can include performing classification on the combined representation. Other embodiments of related systems and methods are also disclosed.

Abstract: A system and method for domain adaptation involves a first domain and a second domain. A machine learning system is trained with first sensor data and first label data of the first domain. Second sensor data of a second domain is obtained. Second label data is generated via the machine learning system based on the second sensor data. Inter-domain sensor data is generated by interpolating the first sensor data of the first domain with respect to the second sensor data of the second domain. Inter-domain label data is generated by interpolating first label data of the first domain with respect to second label data of the second domain. The machine learning system is operable to generate inter-domain output data in response to the inter-domain sensor data. Inter-domain loss data is generated based on the inter-domain output data with respect to the inter-domain label data. Parameters of the machine learning system are updated upon optimizing final loss data that includes at least the inter-domain loss data.

Abstract: Various embodiments of systems and methods for attention models with random features for multi-layered graph embeddings are disclosed.

Abstract: Few-shot learning of repetitive human tasks is performed. Sliding window-based temporal segmentation is performed of sensor data for a plurality of cycles of a repetitive task. Motion alignment is performed of the plurality of cycles, the motion alignment mapping portions of the plurality of cycles to corresponding portions of other of the plurality of cycles. Categories are constructed for each of the corresponding portions of the plurality of cycles according to the motion alignment. Meta-training is performed to teach a model according to data sampled from a labeled set of human motions and the categories for each of the corresponding portions, the model utilizing a bidirectional long short-term memory (LSTM) network to account for length variation between the plurality of cycles. The model is used to perform temporal segmentation on a data stream of sensor data in real time for predicting motion windows within the data stream.

Abstract: A system and method for monitoring a walking activity are disclosed, which have three major components: a pre-processing phase, a step detection phase, and a filtering and post-processing phase. In the preprocessing phase, recorded motion data is received, reoriented with respect to gravity, and low-pass filtered. Next, in the step detection phase, walking step candidates are detected from vertical acceleration peaks and valleys resulting from heel strikes. Finally, in the filtering and post-processing phase, false positive steps are filtered out using a composite of criteria, including time, similarity, and horizontal motion variation. The method 200 is advantageously able to detect most walking activities with accurate time boundaries, while maintaining very low false positive rate.

Abstract: A system and method for monitoring performance of a repeated activity is described. The system comprises a motion sensing system and a processing system. The motion sensing system includes sensors configured to measure or track motions corresponding to a repeated activity. The processing system is configured to process motion data received from the motion sensing system to recognize and measure cycle durations in the repeated activity. In contrast to the conventional systems and methods, which may work for repeated activities having a high level of standardization, the system advantageously enables recognition and monitoring of cycle durations for a repeated activity, even when significant abnormal motions are present in each cycle. Thus, the system can be utilized in a significantly broader set of applications, compared conventional systems and methods.