Abstract: In particular embodiments, a method includes accessing data streams from a renal Doppler sonograph and one or more of a heart-rate monitor, a blood-pressure monitor, a pulse oximeter, or a mood sensor monitoring a person, and generating a stress model of the person based on the data streams.

Abstract: In particular embodiments, a method includes accessing data streams from at least two of an accelerometer, a heart-rate monitor, a blood-pressure monitor, a pulse oximeter, or a mood sensor monitoring a person, analyzing data sets collected from the person when the person is engage and not engaged in a therapy, and determining a current stress factor for the therapy on the person based on the analysis.

Abstract: In particular embodiments, a method includes accessing data streams from an environmental sensor, and one or more of a mood sensor, a heart-rate monitor, a blood-pressure monitor, a pulse oximeter, or an accelerometer monitoring a person, analyzing data sets collected from the person when the person is exposed to various environmental states, and determining a current stress index of the person based on the analysis.

Abstract: In particular embodiments, a method includes accessing one or more physiological data streams, analyzing each physiological data stream in reference to a set of control parameters, and, if a data stream deviates from its control parameters, then transmitting a query to a mood sensor or a behavioral sensor.

Abstract: A method for processing access control lists using a hashing scheme includes receiving a packet identifying data and determining a fixed number of a plurality of hash tables comprising a fixed number of two or more buckets comprising a fixed number of one or more entries, such that the two or more hash tables store data in memory associated with an access control list. The method also includes searching the one or more hash tables in parallel for the identifying data using a plurality of hashing functions and returning a search result.

Abstract: In one embodiment, assigning tags to a document includes accessing the document, where the document comprises text units that include words. The following is performed for each text unit: a subset of words of a text unit is selected as candidate tags, relatedness is established among the candidate tags, and certain candidate tags are selected according to the established relatedness to yield a candidate tag set for the text unit. Relatedness between the candidate tags of each candidate tag set and the candidate tags of other candidate tag sets is determined. At least one candidate tag is assigned to the document according to the determined relatedness.

Abstract: In particular embodiments, a method includes accessing data streams from stress meters, accelerometers, and continuous glucose monitors affixed to a person's body and generating a baseline insulin-resistance model of the person based on the data streams.

Abstract: In particular embodiments, a method includes accessing data streams from stress meters, accelerometers, and continuous glucose monitors affixed to a person's body, accessing a baseline insulin-resistance model of the person, analyzing the data streams with respect to the baseline insulin-resistance model, and determining whether the data streams indicate a change in the person's insulin resistance.

Abstract: In particular embodiments, a method includes generating a 3D display of an avatar of a person, where the avatar can receive inputs identifying a type of a physiological event, a location of the physiological event in or on a person's body in three spatial dimensions, a time range of the physiological event, a quality of the physiological event, and rendering the physiological event on the avatar based on the inputs.

Abstract: In particular embodiments, a method includes accessing data streams from accelerometers or kinesthetic sensors affixed to a person's body, analyzing data sets collected from the person when the person is engaged in various activities, and determining the current musculoskeletal pathology grade of the person.

Abstract: In particular embodiments, a method includes accessing data streams from respiration sensors, accelerometers, or pulse oximeters monitoring a person's body, analyzing data sets collected from the person when the person is engaged in various activities, and determining the current dyspnea grade of the person.

Abstract: In particular embodiments, a method includes receiving and recording inputs identifying a mood of a person, a mood intensity level of the mood, an activity of the person coinciding with the mood, and time of the mood.

Abstract: In particular embodiments, a system includes a sensor affixable to a person's body, where the sensor can wirelessly receive power from a power source and wirelessly transmit sensor information to a receiver.

Abstract: In one embodiment, a method includes simulating by one or more computer systems a larger circuit to assign one or more values to one or more latch variables associated with the larger circuit, generating by the one or more computer systems one or more reduced circuits from the larger circuit according to the values assigned to the latch variables, generating by the one or more computer systems a transition relation (TR) for each reduced circuit, and generating by the one or more computer systems an initial state set for one or more instances of validation on the reduced circuits according to the TRs.

Abstract: In one embodiment, generating an ontology includes accessing an inverted index that comprises inverted index lists for words of a language. An inverted index list corresponding to a word indicates pages that include the word. A word pair comprises a first word and a second word. A first inverted index list and a second inverted index list are searched, where the first inverted index list corresponds to the first word and the second inverted index list corresponds to the second word. An affinity between the first word and the second word is calculated according to the first inverted index list and the second inverted index list. The affinity describes a quantitative relationship between the first word and the second word. The affinity is recorded in an affinity matrix, and the affinity matrix is reported.

Abstract: One embodiment accesses a binary decision diagram (BDD) representing a function having n variables; constructs one group of one ordered set of the n variables; recursively constructs one or more new groups of one or more ordered sets of one or more variables, replacing existing groups of one or more ordered sets of one or more variables, until each existing group comprises one or more ordered sets of k variables or less, where 1?k<n; for each of the one or more existing groups, determines a locally optimum variable order that yields a smallest size among a plurality of different variable orders of the BDD obtained within the group; and selects from one or more locally optimum variable orders corresponding to the one or more existing groups an optimum variable order of the BDD that yields a smallest size among the one or more existing locally optimum variable orders.

Abstract: One embodiment accesses a binary decision diagram (BDD) representing a function having n variables, where n?2, wherein the BDD comprises it layers corresponding to the n variables, respectively; and reorders the n variables of the BDD by iteratively and alternating reordering a plurality of disjoint sets of k consecutive layers in parallel, where 1<k<n, until a size of the BDD has reached a desired threshold, wherein each iteration comprises: divides the n layers into the plurality of disjoint sets of k consecutive layers, wherein the plurality of disjoint sets of k consecutive layers formed during a current iteration differs from the plurality of disjoint sets of k consecutive layers formed during a previous iteration immediately preceding the current iteration; and concurrently reorders the plurality sets of k consecutive layers.

Abstract: One embodiment accesses a binary decision diagram (BDD) representing a function having n variables; and reorders the n variables of the BDD by iteratively moving k variables of the n variables to their locally optimum layers, until a size of the BDD has reached a desired threshold, wherein each iteration comprises: selects from the n layers k layers that currently have the k largest sizes among the n layers, wherein the k variables are currently positioned at the k layers; iteratively and concurrently moves the k variables to different layers of the BDD until each of the k variables has been at all the n layers to determine a locally optimum layer for each of the k variables, wherein the locally optimum layer of a variable during each iteration is one of the n layers that currently yields a smallest size among the n layers with the variable at each of the n layers; and concurrently moves the k variables to their respective locally optimum layers.

Abstract: One embodiment accesses a binary decision diagram (BDD) representing a function having 4 variables, variables 1, 2, 3, and 4, wherein the BDD comprises 4 layers, layers 1, 2, 3, and 4, corresponding to the 4 variables, respectively; determines an optimum variable order of the BDD by performing at most 19 layer swaps during at most 13 iterations, wherein each of 6 iterations of the at most 13 iterations comprises: determines a first size of the layers 1 and 2 and a first size of the layers 3 and 4; swaps the layers 1 and 2 and the layers 3 and 4 concurrently; determines a second size of the layers 1 and 2 and a second size of the layers 3 and 4; and determines 1 variable order among 4 variable orders obtained during the iteration that yields a smallest size among the 4 variable orders obtained during the iteration.

Abstract: One embodiment accesses a binary decision diagram (BDD) representing a function having n variables, where n?2, wherein the BDD comprises n layers corresponding to the n variables, respectively; separates the n variables into n ! 2 ? n / 2 ? groups, wherein each group comprises ? n 2 ? ordered sets, and each set in each group comprises 1 or 2 variables; for each of the n ! 2 ? n / 2 ? groups, determines a locally optimum variable order that yields a smallest size among 2?n/2? different variable orders of the BDD obtained within the group; and selects from n ! 2 ? n / 2 ? locally optimum variable orders corresponding to the n ! 2 ? n / 2 ? groups an optimum variable order of the BDD that yields a smallest size among the n ! 2 ? n / 2 ? locally optimum variable orders.