Abstract: Systems and method for performing a medical imaging analysis task for making a clinical decision are provided. One or more input medical images of a patient are received. A medical imaging analysis task is performed from the one or more input medical images using a machine learning based network. The machine learning based network generates a probability score associated with the medical imaging analysis task. An uncertainty measure associated with the probability score is determined. A clinical decision is made based on the probability score and the uncertainty measure.

Abstract: In certain embodiments, a method includes recursively performing a procedure that includes using an allowed set of object identifiers and a hash function to update a bit array, using a disallowed set of object identifiers and the hash function to further update the bit array where collisions occur, repeating the process with a new allowed set that includes object identifiers from the original allowed set that collided with the disallowed set and a new hash function, until reaching a round where no collisions occurred, generating a data structure that includes the bit arrays created during each recursive round, and compressing the data structure.

Abstract: An example method may include blocks to initiate a network performance analysis on the enterprise network; and receive a translated recording from a backend computing device to be executed as part of the network performance analysis. Additional blocks may add the translated recording to a set of tests in a queue to be executed on the enterprise network; and execute a primary set of low-level instructions of the translated recording using a headless browser. Further blocks may in response to a failed result of the primary set of low-level instructions, execute an alternative set of low-level instructions using the headless browser. Furthermore, blocks may, record, in an activity log, results of executing the translated recording, including at least a total execution time of the executed low-level instructions; and upload the activity log to the backend computing device.

Abstract: A computer-implemented method for designing a 3D robot body model representing a robot body formed in one or more materials. The method comprises obtaining an objective function based on predetermined parameters quantifying a motion metric of the robot. The predetermined parameters include a plurality of voxels forming a gridding of a 3D space, one or more parameters related to the one or more materials, and an actuation function which represents an actuation signal. The design variables include a distribution of density values over the plurality of voxels, and a distribution of actuation coefficients over the plurality voxels. The method further comprises exploring the design variables so as to perform a gradient-based optimization of the objective function, thereby obtaining an optimal continuous value of the design variables, and determining a 3D robot body model based on the optimal continuous value of the design variables.

Abstract: An example method may include blocks to initiate a network performance analysis on the enterprise network; and receive a translated recording from a backend computing device to be executed as part of the network performance analysis. Additional blocks may add the translated recording to a set of tests in a queue to be executed on the enterprise network; and execute a primary set of low-level instructions of the translated recording using a headless browser. Further blocks may in response to a failed result of the primary set of low-level instructions, execute an alternative set of low-level instructions using the headless browser. Furthermore, blocks may, record, in an activity log, results of executing the translated recording, including at least a total execution time of the executed low-level instructions; and upload the activity log to the backend computing device.

Abstract: The disclosure notably relates to a computer-implemented method for designing a modeled object. The method includes obtaining a finite element mesh, data associated to the finite element mesh and a non-uniform distribution of one or more local quantity constraints. The data associated to the finite element mesh include forces, boundary conditions, parameters, and a global quantity constraint. The method also comprises performing a topology optimization based on the finite element mesh, the data associated to the finite element mesh, and the non-uniform distribution. The method improves the design of a modeled object representing a mechanical part by topology optimization.

Abstract: An example method may include blocks to initiate a network performance analysis on the enterprise network; and receive a translated recording from a backend computing device to be executed as part of the network performance analysis. Additional blocks may add the translated recording to a set of tests in a queue to be executed on the enterprise network; and execute a primary set of low-level instructions of the translated recording using a headless browser. Further blocks may in response to a failed result of the primary set of low-level instructions, execute an alternative set of low-level instructions using the headless browser. Furthermore, blocks may, record, in an activity log, results of executing the translated recording, including at least a total execution time of the executed low-level instructions; and upload the activity log to the backend computing device.

Abstract: An example method may include blocks to initiate a network performance analysis on the enterprise network; and receive a translated recording from a backend computing device to be executed as part of the network performance analysis. Additional blocks may add the translated recording to a set of tests in a queue to be executed on the enterprise network; and execute a primary set of low-level instructions of the translated recording using a headless browser. Further blocks may in response to a failed result of the primary set of low-level instructions, execute an alternative set of low-level instructions using the headless browser. Furthermore, blocks may, record, in an activity log, results of executing the translated recording, including at least a total execution time of the executed low-level instructions; and upload the activity log to the backend computing device.

Abstract: A discrete geometrical pattern library guides a method for design optimization of a finite element model in a computer aided design (CAD) environment. Boundary conditions are applied to the finite element model, design variables for the bounded finite element model are initialized, and an objective function for the finite element model is evaluated. A gradient of the objective function is evaluated with respect to the design variables, an appearance constraint function is evaluated for the finite element model, and a gradient of the appearance constraint function is evaluated with respect to the design variables. The design variables are updated using a mathematical programming, and a convergence in the design optimization is detected, producing a converged design optimization of the finite element model is produced.

Abstract: Systems and method for performing a medical imaging analysis task for making a clinical decision are provided. One or more input medical images of a patient are received. A medical imaging analysis task is performed from the one or more input medical images using a machine learning based network. The machine learning based network generates a probability score associated with the medical imaging analysis task. An uncertainty measure associated with the probability score is determined. A clinical decision is made based on the probability score and the uncertainty measure.

Abstract: Embodiments automatically determine optimized designs for manufacturing real-world objects. An embodiment begins with defining a finite element model comprised of a plurality of elements that represents a real-world object. Next, equilibriums and design responses of the object in response boundary conditions are determined, which includes calculating a local volume constraint for a given element of the finite element model. Then, design response sensitivities of the object in response to the boundary conditions are determined, which includes differentiating the calculated local volume constraint to determine sensitivity of a sizing design variable.

Abstract: Embodiments automatically determine optimized designs for manufacturing real-world objects. An embodiment begins with defining a finite element model comprised of a plurality of elements that represents a real-world object. Next, equilibriums and design responses of the object in response boundary conditions are determined, which includes calculating a local volume constraint for a given element of the finite element model. Then, design response sensitivities of the object in response to the boundary conditions are determined, which includes differentiating the calculated local volume constraint to determine sensitivity of a sizing design variable.

Abstract: The disclosure notably relates to a computer-implemented method for designing a modeled object. The method includes obtaining a finite element mesh, data associated to the finite element mesh and a non-uniform distribution of one or more local quantity constraints. The data associated to the finite element mesh include forces, boundary conditions, parameters, and a global quantity constraint. The method also comprises performing a topology optimization based on the finite element mesh, the data associated to the finite element mesh, and the non-uniform distribution. The method improves the design of a modeled object representing a mechanical part by topology optimization.