Abstract: Removal of PII is provided. Sensor data is captured using sensors of a vehicle. Object detection is performed on the sensor data to create a sematic labeling of objects in the sensor data. A model is utilized to classify regions of the sensor data with a public or private labeling according to the sematic labeling and a PII filter corresponding to a jurisdiction of a current location of the vehicle. The sensor data is utilized in accordance with the public or private labeling.

Abstract: Embodiments of an adapter, a system including the adapter, and methods of using the adapter may be for mixing a first substance and a second substance. The adapter may include a body having a first port configured to connect with a first container to receive a first substance, a second port configured to connect with a second container to receive a second substance, a third port configured to connect with a recipient container to output a mixture of the first substance and the second substance, and a mixing chamber having a first portion in communication with the first port and the second port and a second portion in communication with the third port. The adapter may further include a mixing pin inserted into the mixing chamber. In some embodiments, the mixing pin at least partially defines at least one microfluidic path for mixing the first substance and the second substance. In some embodiments, at least one of the first channel and the second channel are oriented offset of a central plane of the body.

Abstract: A vapor delivery system and method is provided that includes a number of features. In one embodiment, a method comprises inserting a vapor delivery needle into tissue of a patient, activating a flow of vapor from a vapor generator through vapor delivery ports of the vapor delivery needle to cause condensed liquid to exit vapor delivery ports of the vapor delivery needle, generating vapor in the vapor generator, delivering a dose of vapor through the vapor delivery ports of the vapor delivery needle into the tissue, and after the dose of vapor is delivered, re-activating the flow of vapor from the vapor generator through the vapor delivery ports of the vapor delivery needle to prevent a vacuum from forming in the vapor delivery needle.

Abstract: Raw sensor data from a device of a user is collected. The raw data does not include location data for the device. The data is preprocessed to identify driving trips in which the user had the device. The trip data is provided to machine-learning models (MLMs) as input and the MLMs provide as output predictions for each trip's estimated speeds, estimated number of hard brakes and a degree of each hard brake, and estimated degrees of distracted driving. A duration, time of day, and calendar date of each trip is identified. The predictions, duration, time of day, and calendar date are modified into four factor values. The four modified factor values are combined into an overall driver characteristic value and provided to a network service associated with the user for purposes of providing or modifying services provided to the user through the network service based on the overall characteristic value.

Abstract: A vapor delivery needle is provided that may include any of a number of features. One feature of the energy delivery probe is that it can apply condensable vapor energy to tissue, such as a prostrate, to shrink, damage, or denature tissues of the prostate. Methods associated with use of the energy delivery probe are also covered.

Abstract: A method for coating a surface, including the steps: a) applying a first layer of a moisture-curable composition onto a surface, b) optionally letting the applied moisture-curable composition cure into a dried first layer, c) applying a second layer of the moisture-curable composition onto the optionally dried first layer, wherein the moisture-curable composition includes: between 20 and 50 wt.-%, preferably between 25 and 45 wt.-%, based on the total weight of the composition, of at least one organic polymer which is liquid at room temperature and contains reactive silane groups, and between 10 and 30 wt.-%, preferably between 15 and 25 wt.-%, based on the total weight of the composition, of at least one polyether PE having between 2 and 6 ether oxygen atoms and is free of hydroxyl groups; and at least one curing catalyst for reactive silane groups. The method yields a multilayer coating with high intercoat adhesion.

Abstract: An apparatus includes a memory and processor. The memory stores document categories, text generated from an image a physical document page, and a machine learning algorithm. The machine learning algorithm is configured to extract features associated with natural language processing and features associated with the text. The machine learning algorithm is also configured to generate a feature vector that includes the first and second pluralities of features, and to generate, based on the feature vector, a set of probabilities, each of which is associated with a document category and indicates a probability that the physical document from which the text was generated belongs to that document category. The processor applies the machine learning algorithm to the text, to generate the set of probabilities, identifies a largest probability, and assigns the image to the associated document category.

Abstract: There is a need for more effective and efficient predictive data analysis. This need can be addressed by, for example, solutions for performing cross-temporal predictive data analysis. In one example, a method includes determining a time-adjusted encoding for each temporal unit of a group of temporal units, processing each time-adjusted encoding using a cross-temporal encoding machine learning model to generate a cross-temporal encoding of the group of temporal units, and performing one or more prediction-based actions based at least in part on the cross-temporal encoding.

Abstract: Various embodiments of the present invention provide methods, apparatus, systems, computing devices, computing entities, and/or the like for converting a multilabel classification model into a sequence of a plurality of binary classification models based on a plurality of label subgroups associated with the multilabel classification model, where the label subgroups comprise an optimal subgroup size, the optimal subgroup size is generated by optimizing an optimization measure defined by a subgroup size variable and a total inner group correlation measure, and identifying label membership to a particular subgroup by using a mixed integer linear program model.

Abstract: Various embodiments of the present invention provide methods, apparatus, systems, computing devices, computing entities, and/or the like for performing predictive data analysis using semi-structured input data. Certain embodiments of the present invention utilize systems, methods, and computer program products that perform predictive data analysis using semi-structured input data using at least one of techniques using inferred codified fields and temporally-arranged codified fields.

Abstract: The use of a combination of carbon nanotubes and at least one zinc oxide, more particularly an aluminium-doped zinc oxide, for producing a transparent, electrically conductive epoxy resin coating, and an electrostatic dissipative floor system which is sealed with the clear, electrically conductive epoxy resin coating that permits aesthetically attractive, transparently sealed, electrostatic dissipative epoxy resin floor systems, onto which more particularly conductive silica sand is scattered, the floor systems thus being particularly slip-resistant, wherein the structure and color of the sand is well visible through the transparent seal.

Abstract: A method comprises: obtaining a current initial image generated by an image generator of an imaging device based on current input signals of sensors of the imaging device; and applying a transformation model to the current initial image to generate a current transformed image, wherein the transformation model is a machine-learning model that has been trained to generate transformed images that more closely resemble reference images generated by a reference image generator.

Abstract: An apparatus includes a memory and processor. The memory stores document categories, text generated from an image a physical document page, and a machine learning algorithm. The text includes errors associated with noise in the image. The machine learning algorithm is configured to extract features associated with natural language processing and features associated with the errors from the text. The machine learning algorithm is also configured to generate a feature vector that includes the first and second pluralities of features, and to generate, based on the feature vector, a set of probabilities, each of which is associated with a document category and indicates a probability that the physical document from which the text was generated belongs to that document category. The processor applies the machine learning algorithm to the text, to generate the set of probabilities, identifies a largest probability, and assigns the image to the associated document category.

Abstract: There is a need for more effective and efficient constrained-optimization-based operational load balancing. In one example, a method comprises determining constraint-satisfying operator-unit mapping arrangements that satisfy an operator unity constraint and an operator capacity constraint; for each constraint-satisfying operator-unit mapping arrangement, determining an arrangement utility measure; processing each arrangement utility measure using an optimization-based ensemble machine learning model that is configured to determine an optimal operator-unit mapping arrangement of the plurality of constraint-satisfying operator-unit mapping arrangements; and initiating the performance of one or more operational load balancing operations based on the optimal operator-unit mapping arrangement.

Abstract: An apparatus includes a memory and processor. The memory stores OCR and NLP algorithms. The processor receives an image of a physical document page and executes the OCR algorithm to convert the image into text. The processor identifies errors in the text, which are associated with noise in the image. The processor generates a feature vector that includes features obtained by executing the NLP algorithm on the text, and features associated with the identified errors in the text. The processor uses the feature vector to assign the image to a document category. Documents assigned to the document category share one or more characteristics, and the feature vector is associated with a probability greater than a threshold that the physical document associated with the image includes those characteristics. The processor then stores the image in a database as a page of an electronic document belonging to the assigned document category.

Abstract: There is a need for solutions for more efficient predictive data analysis systems. This need can be addressed, for example, by a system configured to receive temporal inferences for a predictive task, where each temporal inference is associated with a temporal benchmark and the temporal benchmarks include a base temporal benchmark and supplemental temporal benchmarks; generate a cross-temporal prediction for the predictive task by applying one or more cross-temporal probabilistic updates to the base temporal inference, where each cross-temporal probabilistic update is associated with a supplemental temporal benchmark; and display the cross-temporal prediction using a cross-temporal prediction interface.

Abstract: Methods, apparatuses, systems, computing devices, and/or the like are provided. An example method may include generating a plurality of encoded input data objects associated with a measurement device; generating, using at least a bidirectional Recurrent Neural Networks (RNN) machine learning model, a predictive performance data object associated with the measurement device and a plurality of predictive weight data objects associated with the predictive performance data object, and performing one or more prediction-based actions based at least in part on the predictive performance data object or the plurality of predictive weight data objects.

Abstract: Various embodiments of the present invention provide methods, apparatus, systems, computing devices, computing entities, and/or the like for perform predictive data analysis operations. For example, certain embodiments of the present invention utilize systems, methods, and computer program products that perform predictive data analysis operations by using a cross-temporal encoding machine learning model, such as a cross-temporal encoding machine learning model that is generated by using a target intervention classification machine learning model to map outputs of the cross-temporal encoding machine learning model to historical target intervention labels, thus enabling supervised training of the cross-temporal encoding machine learning without the need for ground-truth data corresponding to the output of the cross-temporal encoding machine learning model.

Abstract: Various embodiments provide methods, apparatus, systems, computing entities, and/or the like, providing a temporal disease risk profile describing a likelihood of disease onset over time for an individual in a dynamically interpretable manner. Interpretability of the temporal disease risk profile is enabled by providing additional and contextual information, such as weight distributions of various health indicators, factors, and features.

Abstract: There is a need for more effective and efficient constrained-optimization-based operational load balancing. In one example, a method comprises determining constraint-satisfying operator-unit mapping arrangements that satisfy an operator unity constraint and an operator capacity constraint; for each constraint-satisfying operator-unit mapping arrangement, determining an arrangement utility measure; processing each arrangement utility measure using an optimization-based ensemble machine learning model that is configured to determine an optimal operator-unit mapping arrangement of the plurality of constraint-satisfying operator-unit mapping arrangements; and initiating the performance of one or more operational load balancing operations based on the optimal operator-unit mapping arrangement.