Abstract: Techniques are provided that include obtaining a Document Object Model of an HTML document, such as a web page of a publisher. Elements of the Document object model may be identified that are associated with native advertisement placement candidate containers. Based at least in part on analysis associated with the Document Object Model, and utilizing at least some of the identified elements, one or more native advertisement placement candidate containers may be determined. Some techniques may utilize, in the analysis, identification of one or more secondary source HTML documents associated with a primary HTML document, and may identify, within the secondary source HTML document(s), content associated with native advertisement placement candidate containers of the primary HTML document(s).

Abstract: Techniques are provided that include obtaining a Document Object Model of an HTML document, such as a web page of a publisher. Elements of the Document object model may be identified that are associated with native advertisement placement candidate containers. Based at least in part on analysis associated with the Document Object Model, and utilizing at least some of the identified elements, one or more native advertisement placement candidate containers may be determined. With some techniques, the analysis includes identifying a deepest set of nodes in the Document Object Model, corresponding to a particular tag sequence, whose cardinality is no less than a cardinality of a set of nodes in the Document Object Model, corresponding to a particular tag sequence, corresponding to the identified elements. Some techniques may utilize XPaths in the analysis.

Abstract: Techniques are provided that include obtaining a Document Object Model of an HTML document. A configuration XPath is obtained identifying, as of a configuration time, a node within the Document Object Model as a native advertisement placement candidate container. Additional information is obtained regarding native advertisement placement candidate containers within the Document Object Model, and an injection XPath, identifying a node within the Document Object Model as a native advertisement placement candidate container, is generated at an injection time, subsequent to the configuration time, based on the configuration XPath and the additional information. The additional information may include information regarding at least one of a title element, description element and image element identified by a user as being associated with native advertisement placement candidate containers. Generating the injection XPath may include removing a numerical identifier from one or more tags of the configuration XPath.

Abstract: According to certain embodiments, a set of samples of sensor data is accessed. The set of samples records medical measurements taken by one or more medical sensors. A characteristic function is generated from the set of samples. The characteristic function indicates whether a given sample is a member of the set of samples. One or more samples of the set of samples that are associated with a given medical annotation are identified according to the characteristic function.

Abstract: In one embodiment, a set of target search terms for a search is received. Candidate terms are selected, where a candidate term is selected to reduce an ontology space of the search. The candidate terms are sent to a computer to recommend the candidate terms as search terms. In another embodiment, a document stored in one or more tangible media is accessed. A set of target tags for the document is received. Terms are selected, where a term is selected to reduce an ontology space of the document. The terms are sent to a computer to recommend the terms as tags.

Abstract: In particular embodiments, a method includes accessing a first binary decision diagram (BDD) representing data streams from sensors, selecting portions from the first BDD, constructing a second BDD representing the selected portions and a third BDD representing the non-selected portions, determining sizes of the first, second, and third BDDs, and if the size of the first BDD is less than a sum of the sizes of the second and third BDDs, then storing the first BDD, else storing the second and third BDDs.

Abstract: A method of operating an image-sensing device includes detecting a trigger. The method also includes, in response to detecting the trigger, changing an operating mode of the image-sensing device from a first operating mode having a first spatial resolution and a first temporal resolution to a second operating mode having a second spatial resolution and a second temporal resolution. The second spatial resolution may be lower than the first spatial resolution and the second temporal resolution may be higher than the first temporal resolution. The method may also include capturing video data in the second operating mode, the video data having the second spatial resolution and the second temporal resolution.

Abstract: According to certain embodiments, a set of samples of sensor data is accessed. The set of samples records environmental measurements taken by one or more environmental sensors. A characteristic function is generated from the set of samples. The characteristic function indicates whether a given sample is a member of the set of samples. One or more samples of the set of samples that are associated with a given environmental annotation are identified according to the characteristic function.

Abstract: In one embodiment, a dynamic graph having a plurality of nodes is modeled with a Binary Decision Diagram (BDD). Each pair of nodes in the dynamic graph is modeled using a characteristic function, g({right arrow over (t)};{right arrow over (a)};{right arrow over (b)}), where: {right arrow over (t)} denotes a time; {right arrow over (a)} denotes a first node identifier; {right arrow over (b)} denotes a second node identifier; and g evaluates to 1 (or TRUE) if and only if an edge exists and connects nodes {right arrow over (a)} and {right arrow over (b)} at time {right arrow over (t)}. The BDD is a combination of all the characteristic functions corresponding to all unique pairs of nodes in the dynamic graph.

Abstract: A device configured to monitor blood pressure includes an audio sensor, an optical sensor, and a processing device. The audio sensor is configured to generate an audio data signal representing a heartbeat of a subject over time. The optical sensor is configured to generate an optical data signal representing blood volume within a blood vessel of the subject over time. The processing device is configured to calculate a pulse wave transit time (PWTT) based on the audio data signal and the optical data signal. The processing device is also configured to calculate a blood pressure of the subject based on the PWTT.

Abstract: According to certain embodiments, a search query for a search of samples of sensor data is received. The search query indicates one or more requested values of one or more data parameters. The samples are represented by a characteristic function indicating whether a given binary representation represents a sample. A query function representing the one or more requested values is formulated. The query function and the characteristic function are used to identify one or more samples that have the one or more requested values.

Abstract: In particular embodiments, a method includes accessing a first binary decision diagram (BDD) representing a data stream from a first sensor and a second BDD representing a data stream from a second sensor, determining whether the first sensor data and the second sensor data correlate by: constructing a third BDD by performing an OR operation between the first and second BDDs, determining sizes of the first, second, and third BDDs, and comparing the third size to the sum of the first size and the second size; where the degree that the first sensor data and the second sensor data correlate is indicated by the amount that the third size is less than the sum of the first size and the second size.

Abstract: In one embodiment, a method includes receiving first input from a first quick-point controller in a keypad that comprises a plurality of keys; receiving second input from a second quick-point controller in the keypad; and rendering a multi-touch gesture on a display associated with the keypad based on the first and second input.

Abstract: In one embodiment, a first computing device selects k first variable orders for a Binary Decision Diagram (BDD) representing a function having n variables, where n?4 and k?2. The first computing device distributes the k first variable orders among a plurality of second computing devices. For each of the k first variable orders distributed to one of the second computing devices, the first computing device receives, from the one second computing device, a local best variable order among the first variable order and one or more second variable orders derived from the first variable order. The first computing device determines a global best variable order for the BDD among all local best variable orders received from the second computing devices.

Abstract: According to certain embodiments, a set of samples of sensor data is accessed. The set of samples records measurements taken by one or more sensors. Each sample is represented as a minterm to yield a set of minterms. A characteristic function is generated from the set of minterms. The characteristic function indicates whether a given minterm is a member of the set of minterms.

Abstract: In particular embodiments, a method includes receiving data sets, constructing a first binary decision diagram (BDD) representing the data sets, iteratively adding data from the data sets to the first BDD until a compression rate of the first BDD reaches a threshold compression rate, constructing a second BDD representing data from the data sets received after the compression rate of the first BDD equals a threshold compression rate, and iteratively adding data from the data sets to the second BDD.

Abstract: In particular embodiments, a method includes receiving from a remote system a binary decision diagram (BDD) representing data streams from sensors, an input, and a first hash code, transforming the received BDD to a second arithmetic function by performing the arithmetic transformation on the received BDD, calculating a second hash code from the second arithmetic function and the input, and if the first hash code equals the second hash code, then indicating that the received BDD is uncorrupted data, else indicating that the received BDD is corrupted data.

Abstract: A method of determining stress includes receiving a data signal including multiple consecutive RR intervals of a subject. The method may also include calculating heart rate variability (HRV) data for the subject from the data signal. The method may also include calculating an RR integral average (RRIA) from the HRV data, the RRIA indicating a stress level of the subject.

Abstract: In particular embodiments, a method includes receiving a query for data in data sets that are within a specified range, constructing a first binary decision diagram (BDD) representing the specified range, and constructing a third BDD representing the data in the specified range by performing an AND operation between the first BDD and a second BDD representing the data sets.

Abstract: In particular embodiments, a method includes accessing an original data stream from a sensor, associating a timestamp with each of the samples in the data stream based on a system clock, and recording the original data stream with the associated timestamps.