Abstract: Disclosed are cathode active material (CAM) coated with a lithium carbonate doped with lithium borate with a formula of Li2+xC1?xBxO3 wherein 0<x<0.5 and a preparation method therefor. Also disclosed is a cathode layer comprising the coated CAM in the form of particles. In one embodiment, an all-solid-state battery comprising the cathode layer exhibits improved stability and cycling performance.

Abstract: A solid-state polymer electrolyte for lithium batteries is provided. The solid-state polymer electrolyte includes a solid-state network polymer electrolyte, formed by a modified poly(ethylene oxide) polymer material and an epoxy alkane crosslinking agent to form a poly(ethylene oxide) network structure, wherein the modified poly(ethylene oxide) high molecular weight material has an amino end, and the solid-state network polymer electrolyte comprises a lithium salt, The ratio of ethylene oxide (EO) to lithium (Li) in the solid-state network polymer electrolyte is 1:1 to 3:1, and a poly(vinylidene fluoride)-based polymer is doped in the solid-state network polymer electrolyte.

Abstract: Using a first dataset of labeled data, a model is trained by adjusting a feature extractor parameter, a classifier parameter, and a discriminator parameter of the model. Using the discriminator parameter and a parametric function of the feature extractor parameter, a plurality of samples of a dataset of unlabeled data is scored. A subset of the scored plurality of samples is selected for labeling. Responsive to receiving a label of each of the selected subset of the scored plurality of samples, the first dataset of labeled data is augmented with the selected subset of the scored plurality of samples and the label of each of the selected subset of the scored plurality of samples. Using the augmented dataset of labeled data, the model is retrained. The retraining comprises further adjusting the feature extractor parameter, the classifier parameter, and the discriminator parameter of the model.

Abstract: Systems/techniques that facilitate improved spinal hardware rendering are provided. In various embodiments, a system can access a medical imaging voxel array depicting a spine of a medical patient. In various aspects, the system can determine whether the medical imaging voxel array depicts a set of surgical hardware inserted in the spine of the medical patient. In various instances, the system can, in response to a determination that the medical imaging voxel array depicts the set of surgical hardware, localize a surface that sagittally bisects the spine of the medical patient. In various cases, the system can render, on an electronic display, a butterfly-view of the medical imaging voxel array, wherein the butterfly-view can be hinged about the localized surface.

Abstract: In one embodiment according to the present invention, a stent is described having a generally cylindrical body formed from a single woven nitinol wire. The distal and proximal ends of the stent include a plurality of loops, some of which include marker members used for visualizing the position of the stent. In another embodiment, the previously described stent includes an inner flow diverting layer.

Abstract: An accessory tool assembly adapted to couple with a hose of an extraction cleaner including a suction source includes a tool head and at least one fluid distributor for supplying and directing a cleaning fluid therethrough. The tool head includes a front end and a rear end opposed to the front end, wherein a longitudinal direction extends through the front and rear ends and a transverse direction extends perpendicular to the longitudinal direction. The tool head includes a working surface on an underside of the front end that defines a working surface edge. A plurality of agitators extend from the working surface adjacent to at least a portion of the working surface edge and define a first interior area therewithin. An extraction nozzle is positioned in the first interior area and defines at least a portion of a suction air inlet.

Abstract: Bias supplies and bias control methods are disclosed. One method comprises applying an asymmetric periodic voltage waveform and providing a corresponding current waveform at an output node relative to a return node; receiving a signal to change from a current state of the asymmetric periodic voltage waveform to a next state of the asymmetric periodic voltage waveform; and adjusting, during a transition from the current state to the next state, at least one portion of the asymmetric periodic voltage waveform and simultaneously adjusting a fundamental frequency of the asymmetric periodic voltage waveform to settle at the next state.

Abstract: A maintenance solution pipeline is automatically selected from a plurality of maintenance solution pipelines, based on obtained information. The maintenance solution pipeline is to be used in providing a physical asset maintenance solution for a plurality of physical assets. Code and model rendering for the maintenance solution pipeline automatically selected is initiated. Output from an artificial intelligence process is obtained. The output includes an automatically generated risk estimation relating to one or more conditions of at least one physical asset of the plurality of physical assets. Code and model rendering for the maintenance solution pipeline is re-initiated, based on the output from the artificial intelligence process. The maintenance solution pipeline automatically selected is reused.

Abstract: In aspects of the disclosure, a method comprises training, by a computing system, a dynamics model of a cooperative multi-agent reinforcement learning (c-MARL) environment. The method further comprises processing, by the computing system, a perturbation optimizer to generate a state perturbation of the c-MARL environment, based on the dynamics model. The method further comprises selecting one or more agents of the c-MARL system as having enhanced vulnerability. The method further comprises attacking, by the computing system, the c-MARL system based on the state perturbation and the selected one or more agents.

Abstract: A contact lens for shaping a cornea of an eye of a patient to treat high cylinder astigmatism may include a peripheral portion, an alignment portion, a treatment portion, a reverse portion, and a staining feature. The peripheral portion may cause migration of epithelial cells from a periphery of a cornea of the eye of the patient towards a center of the cornea of the eye of the patient. The alignment portion may align the contact lens on the cornea. The treatment portion may cause migration of the epithelial cells from the center of the cornea towards the periphery of the cornea. The reverse portion may relieve pressure caused by the migration of the epithelial cells. The staining feature may contact the cornea, thereby reshaping the cornea to treat high cylinder astigmatism. The staining feature may apply a ribbon-shaped staining pattern to the cornea across the contact lens.

Abstract: Select an initial weight vector for a convex optimization sub-problem associated with a neural network having a non-convex network architecture loss surface. With at least one processor, approximate a solution to the convex optimization sub-problem that obtains a search direction, to learn a common classifier from training data. With the at least one processor, update the initial weight vector by subtracting the approximate solution to the convex optimization sub-problem times a first learning rate. With the at least one processor, repeat the approximating and updating steps, for a plurality of iterations, with the updated weight vector from a given one of the iterations taken as the initial weight vector for a next one of the iterations, to obtain a final weight vector for the neural network, until convergence to a global minimum is achieved, to implement the common classifier.

Abstract: Methods, systems, and computer program products for a decision-improvement framework are provided herein. A computer-implemented method includes obtaining regression functions that predict an output of processes of a physical system based on inputs received at each process; automatically generating one or more constraints and one or more objective functions for a model for the physical system based on the regression functions and a representation of the physical system, where the representation specifies relationships between at least a portion of the processes; identifying a set of parameter values for controlling the physical system based on the model; generating a score, for the set of parameter values, based on a predicted improvement of the physical system relative to historical performance of the physical system; and in response to the generated score satisfying a threshold, causing the physical system to be configured in accordance with the set of parameter values.

Abstract: An elevator of a medical device may comprise: a plurality of segments joined together by at least one hinge, and a control element coupled to at least one segment of the plurality of segments. A first segment of the plurality of segments may be a discrete structure. Proximal or distal movement of the control element may be configured to move the at least one segment of the plurality of segments relative to another of the plurality of segments.

Abstract: In example aspects of this disclosure, a method includes generating, by one or more computing devices, a parametric model that expresses condition states for each of a plurality of assets, and the probability of the assets transitioning between the condition states; generating, by the one or more computing devices, stochastic degradation predictions of a group of the assets, based on the condition states and the probability of transitioning between the condition states for at least some of the assets; and generating, by the one or more computing devices, a maintenance schedule based on: the stochastic degradation predictions of the group of the assets, costs of corrective maintenance for assets in a failed state, and costs of scheduled maintenance for the assets.

Abstract: A computer implemented method for certifying robustness of image classification in a neural network is provided. The method includes initializing a neural network model. The neural network model includes a problem space and a decision boundary. A processor receives a data set of images, image labels, and a perturbation schedule. Images are drawn from the data set in the problem space. A distance from the decision boundary is determined for the images in the problem space. A re-weighting value is applied to the images. A modified perturbation magnitude is applied to the images. A total loss function for the images in the problem space is determined using the re-weighting value. A confidence level of the classification of the images in the data set is evaluated for certifiable robustness.

Abstract: A handle of an endoscope may comprise a body portion that defines a first chamber having a first feature configured to support at least a first component of a steering assembly, and a second chamber having a second feature configured to support at least one fluidic component or electronic component.

Abstract: Bias supplies and bias control methods are disclosed. One method comprises applying an asymmetric periodic voltage waveform and providing a corresponding current waveform at an output node relative to a return node; receiving a signal to change from a current state of the asymmetric periodic voltage waveform to a next state of the asymmetric periodic voltage waveform; and adjusting, during a transition from the current state to the next state, at least one portion of the asymmetric periodic voltage waveform and simultaneously adjusting a fundamental frequency of the asymmetric periodic voltage waveform to settle at the next state.

Abstract: An extraction cleaner includes a portable extractor and a base adapted to be moved to perform a cleaning operation on a floor surface when the portable extractor is operably mounted to the base. The portable extractor includes a recovery tank assembly provided on the housing adjacent to the recovery tank assembly.

Abstract: A surface cleaning apparatus having a suction source defining a portion of a working air path and a fluid delivery system includes at least one fluid distributor, a fluid supply path, a first pump in the fluid supply path, and a second pump in the fluid supply path. The pumps are operable to effect dispensing cleaning fluid at different volumetric flow rates.