Abstract: A predetermined number of temporary terms are obtained that have the highest differential affinity to each of a number of candidate terms. Each temporary term and the associated differential affinity is placed into a set of temporary terms. An average differential affinity is calculated for each temporary term of the set of temporary terms, the average differential affinity representing an average of differential affinities from the each temporary term to every term of the initial set of terms. One or more terms with an average differential affinity that fails to satisfy a predetermined threshold are removed from the temporary set. One or more terms of the temporary set with differential affinities above the threshold are placed into the set of candidate terms. One or more terms of the set of candidate terms are selected and output to a user.

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: According to one embodiment, generating a dictionary and determining a co-occurrence context includes accessing a domain corpus comprising articles. Each article corresponds to a particular topic and comprises one or more terms that link to one or more other articles corresponding to one or more other topics. Each topic is designated as a term to yield a dictionary. A co-occurrence context is defined for the domain corpus. At least two terms appearing in the co-occurrence context are considered co-occurring. Co-occurrences among the terms are calculated according to the co-occurrence context.

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, 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; 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; 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: 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: 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 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, where n?2, wherein: the BDD comprises n layers corresponding to the n variables, respectively; and the BDD has a first variable order where each variable i is at layer i for 1?i?n; and reorders the n variables of the BDD according to a second variable order denoted as ?(i), where each variable i is at layer ?(i) for 1?i?n, by iteratively and alternatingly swapping one or more first disjoint pairs of consecutive layers during each odd iteration and swapping one or more second disjoint pairs of consecutive layers during each even iteration, until the second variable order is achieved, wherein during each iteration, two consecutive layers are swapped only if a current order of two variables at the two consecutive layers differs from an order of the two variables specified by the second variable order.

Abstract: According to certain embodiments, facilitating display of terms includes facilitating display of a graphical user interface. One or more first input terms entered into a user entry window of the graphical user interface are received. One or more first output terms related to the first input terms are determined. Display of a first graphical cloud comprising the first output terms is facilitated. The first input terms are modified to yield one or more second input terms. One or more second output terms related to the second input terms are determined. Display of a second graphical cloud comprising the second output terms is facilitated.

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: 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: 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: 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: 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 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: According to certain embodiments, a characteristic function representing data is received. A first hash code calculated from the characteristic function is received. The characteristic function is transformed to an arithmetic function. A second hash code is calculated from the arithmetic function. If the first hash code equals the second hash code, the data is designated as valid; otherwise, the data is designated as invalid.