Abstract: A system for decentralized federated learning is provided. The system comprises agents and aggregators coupled to a communication network. Each agent comprises a data collector collecting raw data; a memory storing the collected raw data and a local machine learning model; and a processor training the local machine learning model. Each aggregator comprises a model collector collecting the local machine learning models; a memory storing the collected local machine learning models; and a processor creating a cluster machine learning model from the local machine learning models. The aggregators communicate with each other and exchange the cluster machine learning models to create a semi-global machine learning model. Each of the aggregators sends the semi-global machine learning model to the associated agents. Each of the agents updates the local machine learning model with the semi-global machine learning model.

Abstract: A model selection method includes: receiving a request to train or validate a plurality of models where the request includes training data and one or more trigger conditions; obtaining the models from a model catalog and training the models using the training data to obtain results for each of the one or more trigger conditions; and selecting, based on the results of each of the one or more trigger conditions and from among the models, a best model to be pushed to production.

Abstract: As described herein, various embodiments of the present invention use an output space refinement machine learning model to filter C of the B candidate predictive associations that are referred to herein as dynamically-preselected candidate predictive associations for each prediction input data object, with every prediction input data object being associated with a different subset of the dynamically-preselected candidate predictive associations, where C is less than B and is in some embodiments typically much less than B. As described in greater detail below, this approach reduces the number of computational operations that need to be performed by a final classification machine learning model (referred to herein as a variable-output-space prediction machine learning model), and leads to substantial computational efficiency advantages relative to naïve implementations.

Abstract: A topic mapping system generates customized mapping schemas for multiple topic sets. The topic mapping system generates document clusters that represent groups of digital documents. The topic mapping system also generates, for each topic set, a document-topic mapping data object (“DTM data object”) that describes a customized mapping schema of the document clusters to labels in the topic set. The topic mapping system identifies customized groups of documents for responding to multiple requests that have a particular keyword. For each request, the topic mapping system identifies a particular topic set and DTM data object associated with a computing system that provided the request. Based on the keyword, the topic mapping system identifies documents that are categorized according to the customized mapping schema in the DTM data object. The topic mapping system can provide customized groups of documents to respective computing systems that provided the multiple requests.

Abstract: A system and method perform near real time scraping of a web page to generate a modified web page. The method includes receiving a uniform resource locator (URL) request directed at a web server; detecting in the URL request a textual resource and a non-textual resource; generating a request for the textual resource over a first network path including a first determined latency; generating a request for the non-textual resource over a second network path including a second determined latency, which is greater than the first determined latency; receiving the textual resource and the non-textual resource; scraping data from the textual resource; and generating a modified web page based on the scraped data, the textual resource, and the non-textual resource.

Abstract: A first user-device comprises a first directional-network module; a first display screen; and at least one first processor and first memory. A second user-device comprises a second directional-network module; a second display screen; and at least one second processor and second memory. The first user device is configured to execute a social-connection application; determine that the second user-device is also executing the social-connection application; determine a distance and a first direction; display a first GUI of the social-connection application; receive, a first user input from a first user to request a connection to the second user; send, to the second user-device using the first directional-network module, a connection-request message; receive, through the first directional-network module, a connection-acceptance message; and display, through the GUI on the first display screen, an indication that the second user has accepted the request for the connection.

Abstract: A computing system calculates, by using an inspector model, whether or not the plurality of pieces of training data are located in a vicinity of the decision boundary, acquires a first proportion of the training data, calculates, by using the inspector model, whether or not a plurality of pieces of operation data associated with one of correct answer labels out of the plurality of correct answer labels are located in a vicinity of the decision boundary, and acquires a second proportion of the operation data located in the vicinity of the decision boundary out of all of the pieces of operation data and detects, based on the first proportion and the second proportion, a change in the output result of the machine learning model caused by a temporal change in a tendency of the operation data.

Abstract: Techniques may include displaying a user interface for a digital pathology platform and displaying a slide panel on the user interface. Displaying the slide panel may include displaying a thumbnail of a digitized image of a specimen, and displaying one or more interactive icons associated with the slide panel. The one or more interactive icons may indicate status associated with the slide panel.

Abstract: In a method an orchestrator of a computing system determines that results of Machine Learning model computations are available and dispatches a worker to perform model computations that include computing gradients of the results. The orchestrator determines that a set of gradients of the results is available and dispatches a gradient worker to compute a sum of the gradients. The orchestrator determines that a second set of gradients of the results is available and dispatches a second gradient worker to compute a sum of the second set of gradients. The orchestrator determines that the sums of the first and second gradients are available and dispatches a third gradient worker to compute synchronized gradients. The gradient workers compute the sums and synchronized gradients concurrent with training workers computing additional model computations results and/or gradients. A computer program product can include the method and a computing system can include the orchestrator.

Abstract: Embodiments may include novel techniques for training and using an adversarial autoencoder for multi-source domain functions. For example, a method may comprise training an adversarial encoder comprising an encoder and a decoder by simultaneously training the encoder and the decoder, using data comprising a plurality of datasets, the data having labels based on an origin class and a dataset number, training the encoder to act as a generator to generate codewords based on the data for a generative adversarial network including the generator and a discriminator by training the generator to cause the discriminator to predict random labels for a plurality of data samples of each class and training the generator using the predicted random labels to generate codewords that relate to the origin class, and classifying new data samples using the trained adversarial encoder and generator, and the discriminator.

Abstract: Conventionally, design methodologies employed deep learning model based on physics which considers only real (permittivity) term and ignores the imaginary (conductivity) term in complex loss function which fail to help in design of complex structures and limit their applications to scenarios such as array of metamaterial structures. Present application provides systems and method implement apply a Physics-Informed Neural Network (PINN) for inversely calculating the effective material parameters of a multi-dimensional metamaterial from its scattered field(s). By employing a loss function based on the Helmholtz wave equation, performance of a metamaterial is modeled by the system the dependance of resonant behavior on the homogenized electric permittivity distribution profile generated by the PINN is demonstrated.

Abstract: A method is provided that includes displaying packages for a semiconductor product in a graphical user interface (GUI), by at least one processor, wherein each package includes design resources for a respective design use case for the semiconductor product, and displaying, by the at least one processor, design resources included in a package of the packages in the GUI responsive to user selection of the package.

Abstract: An improved electronic communication system schedules transmission of electronic communications based on a predicted open time and click time. The open and click times are predicted from a machine learning model that is trained to optimize for both tasks. Additionally, when training the machine learning model, the loss used for adjusting the system to achieve a desired accuracy may be a biased loss determined from a function that penalizes overpredicting the open time. As such, the loss value may be determined by different set of rules depending on whether the predicted time is greater than the actual time or not.

Abstract: Disclosed in the embodiments of the present disclosure are a method and a device for constructing decision trees. A particular embodiment of the method comprises: sending, to at least one client, a request for acquiring statistical information of attribute information of a target category; receiving the statistical information of attribute information of the target category of samples stored by the clients; generating split point information according to the statistical information of attribute information of the target category of samples respectively stored by the clients, and sending the split point information to the at least one client.

Abstract: A technique to compute statistics of data elements include serially inputting the data elements into a compute channel. The compute channel can generate a first running mean and a first running variance associated with data elements of the vector having odd sequence indices, and a second running mean and a second running variance associated with data elements of the vector having even sequence indices. Subsequent to serially inputting data elements into the compute channel, the first running mean and the second running mean are aggregated to generate a mean associated with the data elements of the vector, and the first running variance and the second running variance are aggregated to generate a variance associated with the data elements of the vector.

Abstract: A neural network system for retraining operational neural networks using a synthetic data set generated by a synthetic data generator neural network is provided. The synthetic data generator network comprises an input layer for receiving an input data set; an output layer for outputting the synthetic data set; and a loss function for receiving from each operational network a value of a medical metric. The operational networks each comprise an input layer for receiving the synthetic data set; and an output layer for outputting the value of the medical metric. The synthetic data generator network is trained for generating the synthetic data set based on the loss function comprising a difference of the values of the medical metric. Each operational network is retrained using the synthetic data set.

Abstract: A computer-implemented method for creating a new document space for a content collaboration system is disclosed. The method includes displaying a space-generation graphical user interface on a client device. The space-generation graphical user interface is displayed in response to a request for a new document space creation, and includes a first user input region, a second user input region, and a space-creation control. The method includes, in response to a user selection of the space-creation control, generating the new document space having a set of predefined space settings determined in accordance with a user selection of a particular selectable tab associated with a particular new-space type, generating space content having a space title determined in accordance with a proposed space title in the second user input region; and generating a new space path using a unique space key. The unique space key is generated based on the proposed space title.

Abstract: Methods, systems, apparatuses, devices, and computer program products are described. A group-based communication system may support automatic layout updates for a document space. If a user adds an object into the document space, the system may generate a card representing the object. The card may include a link to a resource external to the document space and a preview image for the object, where the preview image may be rendered based on unfurling the link. The system may update a layout of the document space to include the generated card and may send, for display in a user interface (UI) of a user device, the updated layout. In some examples, a user may select to reposition the card within the UI (e.g., by dragging-and-dropping the card within the document space). The system may further update the layout of the document space based on the repositioning of the card.

Abstract: In some embodiments, an electronic device expands an item of content in accordance with detection of a user's gaze. In some embodiments, an electronic device scrolls text of a content item in accordance with a determination that the user is reading the content item. In some embodiments, an electronic device navigates between user interfaces in accordance with detection of movement of the user's head and detection of the user's gaze. In some embodiments, an electronic device displays augmented content related to a portion of content in accordance with detection of movement of the user's head and detection of the user's gaze in accordance with some embodiments.

Abstract: A method for extracting an explanation from a solution of a global optimization problem derived via an interval analysis is provided. In the context of a global optimization problem characterized by a model, a set of model parameters, and an objective function delimiting relationships between and among the set of model parameters, each model parameter in the set of model parameters corresponding to a unique solution interval of a set of solution intervals, a ranked order of significance for a solution interval in the set of solution intervals as a function of first and second endpoints of the solution interval is derived. Thereafter, an explanation to the solution of the global optimization problem is provided, the explanation embodied in a list or an index that effectively sorts model parameters in the set of model parameters as a function of the ranked order of significance derived for a model parameter's corresponding solution interval.