Abstract: The invention is within the general field of electroporation. In particular, the invention is within the general field of endoscopic electroporation and relates to a bipolar electrode suitable for endoscopic use, i.e. an electrode assembly that can be inserted in a resectoscope and deployed so as to treat e.g. internal organs tumors, such as bladder, rectum or esophagus.

Abstract: A system includes a processor and a memory device communicatively coupled to the processor. The system also includes a database communicatively coupled to the processor. The database is configured to store a first plurality of prediction sets. Each prediction set is associated with a respective individual within a first population. Each prediction set comprises a plurality of prediction results, and each prediction result corresponds to a selected one of a plurality of features. The processor is configured to receive a request for a distribution value associated with a selected feature and one or more parameters. The distribution value indicate how often the selected feature appears in a second population of individuals, the second population being defined by the one or more parameters.

Abstract: A method for facilitating an online continuous multimedia contest is described. The method includes receiving a multimedia content entry corresponding to a predefined category of the online continuous multimedia contest. The method further comprises associating the received multimedia content entry with an event identifier by determining an event identifier corresponding to an event associated with the predefined category of the online continuous multimedia contest, determining whether a quantity of multimedia content entries associated with the determined event identifier meets a first predefined threshold value, creating another event identifier when the quantity of multimedia content entries associated with the determined event identifier meets or exceeds the first predefined threshold value, and associating the received multimedia content entry with the other event identifier.

Abstract: A method for facilitating an online continuous multimedia contest is described. The method includes receiving a multimedia content entry corresponding to a predefined category of the online continuous multimedia contest. The method further comprises associating the received multimedia content entry with an event identifier by determining an event identifier corresponding to an event associated with the predefined category of the online continuous multimedia contest, determining whether a quantity of multimedia content entries associated with the determined event identifier meets a first predefined threshold value, creating another event identifier when the quantity of multimedia content entries associated with the determined event identifier meets or exceeds the first predefined threshold value, and associating the received multimedia content entry with the other event identifier.

Abstract: One example method of operation may include identifying a number of features associated with information of one or more entities, accessing a probability distribution store comprising defined numerical ranges as potential possibilities for being paired with the features of the one or more entities, determining first probability distributions for each of the defined numerical ranges indicating probabilities that each defined numerical range is assigned to each entity having one or more of the features, determining second probability distributions for each of the defined numerical ranges indicating probabilities that each defined numerical range is assigned to each entity having one or more additional features, determining a merged probability distribution based on the first probability distributions and the second probability distributions, determining and storing one or more prediction sets based on the merged probability distribution, selecting one or more content items to display on a device interface based

Abstract: An individual having a plurality of first features and a second characteristic is identified. A plurality of second features associated with a second characteristic is determined. For each first feature among the plurality of first features, a respective probability distribution indicating, for each respective second feature, a probability that a person having the respective second feature has the first feature, is determined, thereby generating a plurality of probability distributions. A probabilistic classifier is used to combine the plurality of probability distributions, thereby generating a merged probability distribution. A Monte Carlo method is used to generate a prediction set based on the merged probability distribution, the prediction set including a plurality of prediction values for the second characteristic of the individual, each respective prediction value being associated with one of the plurality of second features. The prediction set is stored in a memory.

Abstract: The invention is within the general field of electroporation. In particular, the invention is within the general field of endoscopic electroporation and relates to a bipolar electrode suitable for endoscopic use, i.e. an electrode assembly that can be inserted in a resectoscope and deployed so as to treat e.g. internal organs tumors, such as bladder, rectum or esophagus.

Abstract: One example method of operation may include identifying a number of features associated with information of one or more entities, accessing a probability distribution store comprising defined numerical ranges as potential possibilities for being paired with the features of the one or more entities, determining first probability distributions for each of the defined numerical ranges indicating probabilities that each defined numerical range is assigned to each entity having one or more of the features, determining second probability distributions for each of the defined numerical ranges indicating probabilities that each defined numerical range is assigned to each entity having one or more additional features, determining a merged probability distribution based on the first probability distributions and the second probability distributions, determining and storing one or more prediction sets based on the merged probability distribution, selecting one or more content items to display on a device interface based

Abstract: An individual having a plurality of first features and a second characteristic is identified. A plurality of second features associated with a second characteristic is determined. For each first feature among the plurality of first features, a respective probability distribution indicating, for each respective second feature, a probability that a person having the respective second feature has the first feature, is determined, thereby generating a plurality of probability distributions. A probabilistic classifier is used to combine the plurality of probability distributions, thereby generating a merged probability distribution. A Monte Carlo method is used to generate a prediction set based on the merged probability distribution, the prediction set including a plurality of prediction values for the second characteristic of the individual, each respective prediction value being associated with one of the plurality of second features. The prediction set is stored in a memory.

Abstract: Embodiments described herein are directed systems and methods that include the actions of receiving sensor data from a sensor configured to monitor a resource and analyzing the sensor data to identify one or more determined activities of the resource. A determined activity is selected, based on criteria, for a collection of additional information, and a query is generated for the collection of the additional information to reduce an error potential of the determined activity. The query is transmitted and results are received. Based on the results, the determined activity is updated, then stored with a timestamp as an activity data object in an activity data store. A record is generated that comprises the determined activity.

Abstract: An individual having a plurality of first features and a second characteristic is identified. A plurality of second features associated with a second characteristic is determined. For each first feature among the plurality of first features, a respective probability distribution indicating, for each respective second feature, a probability that a person having the respective second feature has the first feature, is determined, thereby generating a plurality of probability distributions. A probabilistic classifier is used to combine the plurality of probability distributions, thereby generating a merged probability distribution. A Monte Carlo method is used to generate a prediction set based on the merged probability distribution, the prediction set including a plurality of prediction values for the second characteristic of the individual, each respective prediction value being associated with one of the plurality of second features. The prediction set is stored in a memory.

Abstract: An individual having a plurality of first features and a second characteristic is identified. A plurality of second features associated with a second characteristic is determined. For each first feature among the plurality of first features, a respective probability distribution indicating, for each respective second feature, a probability that a person having the respective second feature has the first feature, is determined, thereby generating a plurality of probability distributions. A probabilistic classifier is used to combine the plurality of probability distributions, thereby generating a merged probability distribution. A Monte Carlo method is used to generate a prediction set based on the merged probability distribution, the prediction set including a plurality of prediction values for the second characteristic of the individual, each respective prediction value being associated with one of the plurality of second features. The prediction set is stored in a memory.

Abstract: A system includes a processor and a memory device communicatively coupled to the processor. The system also includes a database communicatively coupled to the processor. The database is configured to store a first plurality of prediction sets. Each prediction set is associated with a respective individual within a first population. Each prediction set comprises a plurality of prediction results, and each prediction result corresponds to a selected one of a plurality of features. The processor is configured to receive a request for a distribution value associated with a selected feature and one or more parameters. The distribution value indicate how often the selected feature appears in a second population of individuals, the second population being defined by the one or more parameters.

Abstract: An individual having a plurality of first features and a second characteristic is identified. A plurality of second features associated with a second characteristic is determined. For each first feature among the plurality of first features, a respective probability distribution indicating, for each respective second feature, a probability that a person having the respective second feature has the first feature, is determined, thereby generating a plurality of probability distributions. A probabilistic classifier is used to combine the plurality of probability distributions, thereby generating a merged probability distribution. A Monte Carlo method is used to generate a prediction set based on the merged probability distribution, the prediction set including a plurality of prediction values for the second characteristic of the individual, each respective prediction value being associated with one of the plurality of second features. The prediction set is stored in a memory.

Abstract: An individual having a plurality of first features and a second characteristic is identified. A plurality of second features associated with a second characteristic is determined. For each first feature among the plurality of first features, a respective probability distribution indicating, for each respective second feature, a probability that a person having the respective second feature has the first feature, is determined, thereby generating a plurality of probability distributions. A probabilistic classifier is used to combine the plurality of probability distributions, thereby generating a merged probability distribution. A Monte Carlo method is used to generate a prediction set based on the merged probability distribution, the prediction set including a plurality of prediction values for the second characteristic of the individual, each respective prediction value being associated with one of the plurality of second features. The prediction set is stored in a memory.

Abstract: A system includes a plurality of large screen display devices located at a venue, and a storage adapted to store one or more items of social media content having respective content tags. The system also includes one or more processors adapted to select a plurality of items of social media content having content tags matching one or more predetermined criteria, cause first items selected from the plurality of items of social media content to be displayed, during a live event occurring at the venue, on each of the plurality of large screen display devices, select a second item having a particular content tag from the first plurality of items of social media content, identify an advertisement image having an advertisement campaign tag that matches the particular content tag of the second item, and cause the second item and the advertisement image to be displayed, at a selected time during the live event, simultaneously on each of the plurality of large screen display devices.

Abstract: An individual having a plurality of first features and a second characteristic is identified. A plurality of second features associated with a second characteristic is determined. For each first feature among the plurality of first features, a respective probability distribution indicating, for each respective second feature, a probability that a person having the respective second feature has the first feature, is determined, thereby generating a plurality of probability distributions. A probabilistic classifier is used to combine the plurality of probability distributions, thereby generating a merged probability distribution. A Monte Carlo method is used to generate a prediction set based on the merged probability distribution, the prediction set including a plurality of prediction values for the second characteristic of the individual, each respective prediction value being associated with one of the plurality of second features. The prediction set is stored in a memory.

Abstract: Methods, systems, and apparatus are disclosed which include filtering social media data for content resources that match one or more search terms, generating a feed based on the filtering, associating a content resource tag with each content resource, associating the feed with a first section of a layout of a content display, associating an advertisement campaign with a second section of the layout, providing the layout to be displayed on a display device, providing the content resources from the feed to be displayed in the first section of the layout, determining that one of the content resources currently displayed in the first section of the layout is associated with a content resource tag that matches one of the campaign tags of the advertisement campaign; and providing the advertisement image to be displayed in the second section of the layout.

Abstract: A base for holding a wind turbine tower is disclosed. The base includes individual shaft piles, a base cap and anchoring units. The base cap includes reinforcement inserts and is connected to each of the individual shaft piles. The anchoring units are embedded in the base cap and extend in an anchoring direction from the base cap at anchoring locations for anchoring the wind turbine tower to the base cap. Each of the reinforcement inserts extend in a radial direction relative to a center axis of the wind turbine tower. At least one of the plurality of reinforcement inserts, at a radius of the center axis adjacent to an anchoring location of a respective anchoring unit, has a portion of the respective reinforcement insert extending along the anchoring direction.