Abstract: In particular embodiments, a method includes accessing first binary decision diagrams (BDDs) representing data streams from sensors, selecting portions from the first BDDs based on ease-of-analysis, and constructing a second BDD by performing an OR operation between the selected portions of the first BDDs.

Abstract: A method includes receiving input data comprising a plurality of bits and processing an access control list into an ESOP expression comprising a plurality of product terms. The method also includes storing a plurality of bits associated with the plurality of product terms in a TCAM comprising a plurality of rows and comparing the plurality of bits associated with the input data to the plurality of bits associated with the product terms stored in each row of the plurality of rows, such that each row of the TCAM outputs a plurality of signals, such that each of the plurality of signals indicate a match or no match for each bit stored in the selected row. The method includes receiving the plurality of signals from the plurality of rows by an ESOP evaluator and outputting an address associated with a selected row from the plurality of rows of the TCAM.

Abstract: A directed graph comprises a plurality of nodes including a root node and a non-root node. Each of the plurality of nodes has a plurality of directed edges. The directed graph is traversed, with the plurality of nodes being visited according to an order of the directed edges of each of the plurality of nodes. The directed edge that leads to the non-root node is discarded when the non-root node is visited for the first time during the traversal of the directed graph.

Abstract: A mobile device includes a first camera and a second camera to determine Pulse Wave Transit Time. A first pulse at a first location on a user's body is detected using the first camera of the mobile device. A second pulse at a second location on the user's body is detected using the second camera of the mobile device. A corresponding pair of first pulse peak and second pulse peak is extracted from the first pulse and the second pulse, respectively. A time difference between the corresponding first pulse peak and second pulse peak is computed.

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; a denotes {right arrow over (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: 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, one or more sets of model samples of model sensor data are accessed. Each set comprises one or more model samples corresponding to an annotation of one or more annotations. The following are performed for each set to yield one or more annotated model characteristic functions: represent each model sample of the each set as a model minterm to yield a set of model minterms; generate a model characteristic function from the set of model minterms, the model characteristic function indicating whether a given minterm is a member of the set of model minterms; and annotate the model characteristic function to yield an annotated model characteristic function. A general model characteristic function is generated from the one or more annotated model characteristic functions.

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: In particular embodiments, a method includes accessing data streams from an accelerometer and one or more of 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 engaged in various activities, and determining a current stress index of the person based on the analysis.

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 a first binary decision diagram (BDD) representing data streams from sensors, accessing a second BDD representing health states associated with specified data ranges, and constructing a third BDD by performing an AND operation between the first and second BDDs.

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; and reorders the n variables of the BDD by iteratively and alternating reordering k consecutive layers, where 1<k?n, until a size of the BDD has reached a desired threshold, wherein each iteration comprises: selects k consecutive layers from the n layers, wherein the k consecutive layers selected during a current iteration differ from the k consecutive layers selected during a previous iteration immediately preceding the current iteration; determines a locally optimum variable order of the k consecutive layers; and reorders the k consecutive layers according to the locally optimum variable order of the k consecutive layers.

Abstract: A system and method for running multiple image computation approaches in parallel and selecting an optimal one of the image computation approaches for verification use based on efficiency of individual approaches. The method automatically determines an optimal approach with respect to modifications of parameters that can affect efficiency of the verification.

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.

Abstract: According to certain embodiments, a first Boolean function and a second Boolean function are received. The first Boolean function represents a first data set, and the second Boolean function represents a second data set. The first Boolean function and the second Boolean function are transformed to a first arithmetic function and a second arithmetic function, respectively. A first hash code and a second hash code are calculated from the first arithmetic function and the second arithmetic function, respectively. If the first hash code equals the second hash code, the first Boolean function and the second Boolean function are designated as equivalent; otherwise, the first Boolean function and the second Boolean function are designated as not equivalent.

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 n variables, where n?2, wherein the BDD comprises n 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: According to certain embodiments, a first characteristic function representing a first set of samples and a second characteristic function representing a second set of samples are generated. The first characteristic function and the second characteristic function are transformed to a first arithmetic function and a second arithmetic function, respectively. A first hash code and a second hash code are calculated from the first arithmetic function and the second arithmetic function, respectively. If the first hash code equals the second hash code, the first set of samples and the second set of samples are designated as equivalent; otherwise, the first set of samples and the second set of samples are designated as not equivalent.

Abstract: According to one embodiment, generating a domain corpus includes accessing a knowledge base. The knowledge base comprises a set of articles. Each article corresponds to a particular topic and comprises one or more terms that link to other articles corresponding to other topics. A first set of first articles is selected from the knowledge base for a domain corpus. A second set of second articles related to the first set of first articles is identified. The second set of second articles is selected from the knowledge base for the domain corpus. The domain corpus is made available to access.

Abstract: A directed graph comprises a plurality of nodes including a root node and a non-root node. Each of the plurality of nodes has a plurality of directed edges. The directed graph is traversed, with the plurality of nodes being visited according to an order of the directed edges of each of the plurality of nodes. The directed edge that leads to the non-root node is discarded when the non-root node is visited for the first time during the traversal of the directed graph.