Abstract: Systems, methods, and media are provided for the selective indication of the presence of off-screen objects in the shared gaming environment of a multiplayer game played on a mobile device. The shared gaming environment can include a game space and a plurality of objects located at various places of the game space. The player may view a portion of the game space via a screen view presented by the mobile device. The screen view can be bordered by a frame area that includes a portion of the game space that is outside of the screen view. Based on a predetermined rule set, the presence of an object that is located within the frame area can be selectively indicated within the screen view by a visual indicator, such as an icon. The visual indicator can be assembled from a library of elements to visually communicate contextually relevant information to the player.

Abstract: A device for removing material from a vasculature includes a rotation subassembly having a first central axis and a first gear, a valve subassembly having the first central axis and being adjacent to the rotation subassembly, and an actuation system having a second central axis and a second gear. The rotation subassembly is coupled to a catheter to rotate the catheter. The valve subassembly is configured to selectively allow flow of the material from the catheter to the device. The actuation system configured to actuate rotation of the rotation subassembly. The first central axis and the second central axis are parallel.

Abstract: Various embodiments of the present disclosure describe data evaluation techniques that leverage a graph-based machine learning model to evaluate a knowledge graph. The techniques include using a target graph model to generate a predictive representation for a graph node of a graph training dataset. The techniques include using a feature prediction model to generate predicted feature values for the graph node based on the predictive representation. The techniques include generating a data evaluation score for the graph training dataset based on the predicted feature values. The techniques include using the target graph model to generate a predictive output for the graph node based on the predictive representation and then generating an evaluation output for the target graph model based on the evaluation score and the predictive output.

Abstract: Various embodiments of the present disclosure describe holistic machine learning model evaluation techniques. The techniques include determining a holistic evaluation vector for a target machine learning model based on a plurality of evaluation scores for the target machine learning model. The plurality of evaluation scores may include a data evaluation score corresponding to a training dataset for the target machine learning model, a model evaluation score corresponding to one or more performance metrics for the target machine learning model, and a decision evaluation score corresponding to an output class of the target machine learning model. A holistic evaluation score for the target machine learning model may be determined from the holistic evaluation vector or a plurality of evaluation scores. An informed evaluation output is provided based on the holistic vector or score.

Abstract: Various embodiments of the present disclosure describe machine learning monitoring and retraining techniques for automatically triggering model retraining based on evaluation scores. The techniques include receiving a request to process an input data object with a target machine learning model that is previously trained using an at least partially synthetic training dataset. The techniques include identifying a synthetic data object from the training dataset that corresponds to the input data object and, in response, modifying a holistic evaluation score for the model, initiating the performance of a labeling process for assigning a ground truth label to the input data object, and augmenting a supplemental training dataset with the input data object and the ground truth label. In the event that the holistic evaluation score decreased beyond a threshold, the model may be retrained with the supplemental training dataset.

Abstract: Systems and methods are disclosed for generating a personalized care path for a patient. The method includes receiving, by one or more processors, relevant data associated with the patient from a plurality of data sources. The relevant data includes demographic data and medical data associated with the patient. The one or more processors using a graph convolutional neural network-based model determine the personalized care path for the patient based on the relevant data associated with the patient. The graph convolutional neural network-based model is trained based on a plurality of care paths of a plurality of patients represented by a patient-bucket-procedure (PBP) graph. The one or more processors provide data associated with the determined personalized care path for the patient to a device associated with a user.

Abstract: Systems, methods, and media are provided for the selective indication of the presence of off-screen objects in the shared gaming environment of a multiplayer game played on a mobile device. The shared gaming environment can include a game space and a plurality of objects located at various places of the game space. The player may view a portion of the game space via a screen view presented by the mobile device. The screen view can be bordered by a frame area that includes a portion of the game space that is outside of the screen view. Based on a predetermined rule set, the presence of an object that is located within the frame area can be selectively indicated within the screen view by a visual indicator, such as an icon. The visual indicator can be assembled from a library of elements to visually communicate contextually relevant information to the player.

Abstract: There is a need for solutions that classification solutions in hierarchical prediction domains. This need can be addressed by, for example, performing one or more online machine learning, co-occurrence analysis machine learning, structured fusion machine learning, and unstructured fusion machine learning. In one example, structured predictions inputs are processed in accordance with an online machine learning analysis to generate structurally hierarchical predictions and in accordance with a co-occurrence analysis machine learning analysis to generate structurally non-hierarchical predictions. Then, the structurally hierarchical predictions and the structurally non-hierarchical predictions in accordance with processed by a structured fusion model to generate structure-based predictions. Afterward, the structure-based predictions and non-structure-based predictions are processed in accordance with an unstructured fusion model to generate one or more unstructured-fused predictions.

Abstract: A chute rotation control system for a snow thrower is disclosed. One example embodiment is a chute rotation control system configured to rotate a chute of a snow thrower, comprising: a chute rotation motor coupled to the chute via one or more gears, wherein the chute rotation motor is configured to alternately rotate the chute clockwise and counterclockwise; a left chute control configured to receive one or more left user inputs and to generate a left output signal; a right chute control configured to receive one or more right user inputs and to generate a right output signal; and a motor controller configured to receive the left input signal and the right input signal, to cause the chute rotation motor to rotate the chute counterclockwise in response to the left input, and to cause the chute rotation motor to rotate the chute clockwise in response to the right input signal.

Abstract: There is a need for solutions that classification solutions in hierarchical prediction domains. In one embodiment, this need can be addressed by, for example, performing one or more online machine learning, co-occurrence analysis machine learning, structured fusion machine learning, and/or unstructured fusion machine learning. In one particular example, structured predictions inputs are processed in accordance with an online machine learning analysis to generate structurally hierarchical predictions and in accordance with a co-occurrence analysis machine learning analysis to generate structurally non-hierarchical predictions. Then, the structurally hierarchical predictions and the structurally non-hierarchical predictions in accordance with processed by a structured fusion model to generate structure-based predictions.

Abstract: Devices and methods may allow for the removal of material from a remote location in the vasculature. In an example of such a remote location, the device may be used in the vasculature of a lower extremity in combination with an external cuff. The external cuff may create a dam preventing material from flowing throughout the body. With the external cuff in place, the device of the present disclosure may be utilized to suction the material from the vasculature while rotating the catheter to assist in the removal of the material.

Abstract: The present invention relates to herbicidal) active pyridyl-/pyrimidyl-pyrazine derivatives, as well as to processes and intermediates used for the preparation of such derivatives. The invention further extends to herbicidal compositions comprising such derivatives, as well as to the use of such compounds and compositions in controlling undesirable plant growth: in particular the use in controlling weeds, in crops of useful plants.

Abstract: The present invention relates to herbicidal) active pyridyl-/pyrimidyl-pyridine derivatives, as well as to processes and intermediates used for the preparation of such derivatives. The invention further extends to herbicidal compositions comprising such derivatives, as well as to the use of such compounds and compositions in controlling undesirable plant growth: in particular the use in controlling weeds, in crops of useful plants.

Abstract: Devices and methods may allow for the removal of material from a remote location in the vasculature. In an example of such a remote location, the device may be used in the vasculature of a lower extremity in combination with an external cuff. The external cuff may create a dam preventing material from flowing throughout the body. With the external cuff in place, the device of the present disclosure may be utilized to suction the material from the vasculature while rotating the catheter to assist in the removal of the material.

Abstract: There is a need for solutions that classification solutions in hierarchical prediction domains. In one embodiment, this need can be addressed by, for example, performing one or more online machine learning, co-occurrence analysis machine learning, structured fusion machine learning, and/or unstructured fusion machine learning. In one particular example, structured predictions inputs are processed in accordance with an online machine learning analysis to generate structurally hierarchical predictions and in accordance with a co-occurrence analysis machine learning analysis to generate structurally non-hierarchical predictions. Then, the structurally hierarchical predictions and the structurally non-hierarchical predictions in accordance with processed by a structured fusion model to generate structure-based predictions.

Abstract: A surgical training model can have features for training surgical suturing techniques. The training model can be formed as a sheet of simulated tissue having at least one cut with markings arranged on either side of the cut. The markings can be formed of a first layer of resilient simulated tissue material having a color that contrasts with a color of the remainder of the sheet of simulated tissue material. The sheet of simulated tissue material can have several cuts having different configurations and orientations to facilitate suturing training for a variety of tissue orientations. The sheet of simulated tissue material can further include holes positioned to be mounted to a base of a surgical training system. The sheet of simulated tissue material can be manufactured by molding a marking layer and casting a tissue layer over the marking layer.

Abstract: There is a need for solutions that classification solutions in hierarchical prediction domains. This need can be addressed by, for example, performing one or more online machine learning, co-occurrence analysis machine learning, structured fusion machine learning, and unstructured fusion machine learning. In one example, structured predictions inputs are processed in accordance with an online machine learning analysis to generate structurally hierarchical predictions and in accordance with a co-occurrence analysis machine learning analysis to generate structurally non-hierarchical predictions. Then, the structurally hierarchical predictions and the structurally non-hierarchical predictions in accordance with processed by a structured fusion model to generate structure-based predictions. Afterward, the structure-based predictions and non-structure-based predictions are processed in accordance with an unstructured fusion model to generate one or more unstructured-fused predictions.

Abstract: A surgical training model can have features for training surgical suturing techniques. The training model can be formed as a sheet of simulated tissue having at least one cut with markings arranged on either side of the cut. The markings can be formed of a first layer of resilient simulated tissue material having a color that contrasts with a color of the remainder of the sheet of simulated tissue material. The sheet of simulated tissue material can have several cuts having different configurations and orientations to facilitate suturing training for a variety of tissue orientations. The sheet of simulated tissue material can further include holes positioned to be mounted to a base of a surgical training system. The sheet of simulated tissue material can be manufactured by molding a marking layer and casting a tissue layer over the marking layer.

Abstract: Systems, methods, and media are provided for the selective indication of the presence of off-screen objects in the shared gaming environment of a multiplayer game played on a mobile device. The shared gaming environment can include a game space and a plurality of objects located at various places of the game space. The player may view a portion of the game space via a screen view presented by the mobile device. The screen view can be bordered by a frame area that includes a portion of the game space that is outside of the screen view. Based on a predetermined rule set, the presence of an object that is located within the frame area can be selectively indicated within the screen view by a visual indicator, such as an icon. The visual indicator can be assembled from a library of elements to visually communicate contextually relevant information to the player.

Abstract: Devices and methods may allow for the removal of material from a remote location in the vasculature. In an example of such a remote location, the device may be used in the vasculature of a lower extremity in combination with an external cuff. The external cuff may create a dam preventing material from flowing throughout the body. With the external cuff in place, the device of the present disclosure may be utilized to suction the material from the vasculature while rotating the catheter to assist in the removal of the material.