Abstract: The present application provides an organic light emitting diode (OLED) display panel and a method of manufacturing the same, and a display terminal. The OLED display panel includes an array substrate, a protective layer and a cathode. The array substrate includes a first auxiliary electrode, a second auxiliary electrode, and a third auxiliary electrode. The third auxiliary electrode is connected to the first auxiliary electrode and the second auxiliary electrode. The protective layer is provided with an undercut opening exposing a portion of the third auxiliary electrode. The cathode extends into the undercut opening and is connected to the third auxiliary electrode.

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 preparation method for a silver (Ag) and graphitic carbon nitride (g-C3N4) co-modified zinc oxide (ZnO) nanocomposite material using a polymer network gel method includes: dispersing zinc oxide, bulk graphitic carbon nitride, and a soluble silver salt in water to obtain a first solution; adding glucose, a complexing agent, a polymer monomer, and a cross-linking agent into the first solution to obtain a second solution; performing a heating reaction on the second solution to obtain a three-dimensional network wet gel; drying the three-dimensional network wet gel to obtain a dry gel, and calcining the dry gel to obtain the Ag and g-C3N4 co-modified ZnO nanocomposite material. The preparation method has advantages of low cost, short period and simple steps; and the prepared nanocomposite material can be simultaneously applied to photocatalytic degradation of organic dye pollutants and photoexcitation detection of nitrogen dioxide gas at room temperature.

Abstract: The present disclosure provides a neural network-based method for calibration and localization of an indoor inspection robot. The method includes the following steps: presetting positions for N label signal sources capable of transmitting radio frequency (RF) signals; computing an actual path of the robot according to numbers of signal labels received at different moments; computing positional information moved by the robot at a tth moment, and computing a predicted path at the tth moment according to the positional information; establishing an odometry error model with the neural network and training the odometry error model; and inputting the predicted path at the tth moment to a well-trained odometry error model to obtain an optimized predicted path. The present disclosure maximizes the localization accuracy for the indoor robot by minimizing the error of the odometer with the odometry calibration method.

Abstract: An optoelectrical assembly includes a voltage conversion circuit, an optoelectrical semiconductor device, an optoelectrical detection circuit, and a controller. The voltage conversion circuit provides a bias voltage to the optoelectrical semiconductor device, and adjusts, by changing the bias voltage, an output optical power. A differential resistance value (Rdiff) of the optoelectrical semiconductor device within a range of a target optical power satisfies 0.1 ohm (?)?Rdiff?50?, and the differential resistance value is a ratio of a voltage variation to a current variation corresponding to the voltage variation. The optoelectrical detection circuit detects the output optical power, and outputs a detection signal to the controller. The controller determines a control signal based on the detection signal, and outputs the control signal to the voltage conversion circuit, where the control signal is used to adjust the bias voltage.

Abstract: A back roller includes a roller body and a wear-resistant layer. The roller body has an axis and a roll surface disposed around the axis. The wear-resistant layer covers the roll surface. Vickers hardness (H) of the wear-resistant layer is 800 HV?H?1400 HV. The roll surface is covered by the wear-resistant layer, and the Vickers hardness (H) of the wear-resistant layer is controlled to be between 800 HV and 1400 HV, so that the roll surface is effectively protected, and wear resistance of the roll surface is improved. In this way, the wear-resistant layer is in direct contact with a material during operation, thereby effectively reducing an abrasion loss on the roll surface and extending the service life of the roll surface.

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: 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: 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: 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 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 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: Abnormal motions are detected in sensor data collected with respect to performance of repetitive human activities. An autoencoder network model is trained based on a set of standard activity. Repetitive activity is extracted from sensor data. A first score is generated indicative of distance of a repetition of the repetitive activity from the standard activity. The repetitive activity is used to retrain the autoencoder network model, using weights of the autoencoder network model as initial values, the weights being based on the training of the autoencoder network model using the set of standard activity. A second score is generated indicative of whether the repetition is an outlier as compared to other repetitions of the repetitive activity. A final score is generated based on a weighting of the first score and the second score.

Abstract: Motion windows are generated from a query activity sequence. For each of the motion windows in the query activity sequence, a corresponding motion window in the reference activity sequence is found. One or more difference calculations are performed between the motion windows of the query activity sequence and the corresponding motion windows in the reference activity sequence based on at least one criterion associated with physical meaning. Abnormality of the motion windows is determined based on the one or more difference calculations. A standardized evaluation result of the query activity sequence is output based on the detected abnormal motion windows in the query activity sequence.