Abstract: Described herein are system and method for pacing information delivery to mobile devices. The method comprises, for each respective request of a first plurality of requests received during a time unit that qualifies for information delivery, predicting a respective conversion probability corresponding to a predicted probability of a mobile device associated with the respective request having at least one location event at any of one or more POIs during a time frame corresponding to the time unit. The method further comprises placing a bid for fulfilling the respective request based on the respective conversion probability and a bidding model, determining a set of predicted numbers of conversions corresponding, respectively, to a set of ranges of predicted conversion probabilities for a first number of fulfilled requests corresponding to the time unit, and adjusting the bidding model based at least on the predicted number of conversions.

Abstract: Described herein are system and method for pacing information delivery to mobile devices. The method comprises, for each respective request of a first plurality of requests received during a time unit that qualifies for information delivery, predicting a respective conversion probability corresponding to a predicted probability of a mobile device associated with the respective request having at least one location event at any of one or more POIs during a time frame corresponding to the time unit. The method further comprises placing a bid for fulfilling the respective request based on the respective conversion probability and a bidding model, determining a set of predicted numbers of conversions corresponding, respectively, to a set of ranges of predicted conversion probabilities for a first number of fulfilled requests corresponding to the time unit, and adjusting the bidding model based at least on the predicted number of conversions.

Abstract: Described herein are system and method for pacing information delivery to mobile devices. The method comprises, for each respective request of a first plurality of requests received during a time unit that qualifies for information delivery, predicting a respective conversion probability corresponding to a predicted probability of a mobile device associated with the respective request having at least one location event at any of one or more POIs during a time frame corresponding to the time unit. The method further comprises placing a bid for fulfilling the respective request based on the respective conversion probability and a bidding model, determining a set of predicted numbers of conversions corresponding, respectively, to a set of ranges of predicted conversion probabilities for a first number of fulfilled requests corresponding to the time unit, and adjusting the bidding model based at least on the predicted number of conversions.

Abstract: A system includes a machine learning module configured to train a location prediction model for an information campaign, a front-end server configured to receive and process information requests, and a prediction unit. During the information campaign, the prediction unit is configured to use the location prediction model to predict a conversion probability for any particular mobile device associated with a qualified information request received during any respective time unit. The conversion probability corresponds to a predicted probability of the particular mobile device having at least one location event at any of one or more POIs during a particular time frame. The front-end server is further configured to determine a respective target number of conversions to be achieved by the information campaign during the respective time unit, and to determine a response to the particular information request based at least in part on the conversion probability and on the respective target number of conversions.

Abstract: A system includes a machine learning module configured to train a location prediction model using features constructed from mobile device data with time stamps in a training time period, and labels extracted from mobile device data with time stamps in a training time frame. The system further includes a prediction module configured apply the prediction model to a feature set constructed using mobile device data associated with a mobile device with time stamps in a prediction time period to obtain a prediction result corresponding to the mobile device. The system further includes a calibration module configured to obtain a calibration model corresponding to an information campaign, and a calibrated prediction module configured to apply the calibration model to the prediction result to obtain a calibrated probability for the mobile device to have at least one location event at any of one or more locations associated with the information campaign during a prediction time frame.

Abstract: A system includes a machine learning module configured to train a location prediction model using features constructed from mobile device data with time stamps in a training time period, and labels extracted from mobile device data with time stamps in a training time frame. The system further includes a prediction module configured apply the prediction model to a feature set constructed using mobile device data associated with a mobile device with time stamps in a prediction time period to obtain a prediction result corresponding to the mobile device. The system further includes a calibration module configured to obtain a calibration model corresponding to an information campaign, and a calibrated prediction module configured to apply the calibration model to the prediction result to obtain a calibrated probability for the mobile device to have at least one location event at any of one or more locations associated with the information campaign during a prediction time frame.

Abstract: A system includes a machine learning module configured to train a location prediction model for an information campaign, a front-end server configured to receive and process information requests, and a prediction unit. During the information campaign, the prediction unit is configured to use the location prediction model to predict a conversion probability for any particular mobile device associated with a qualified information request received during any respective time unit. The conversion probability corresponds to a predicted probability of the particular mobile device having at least one location event at any of one or more POIs during a particular time frame. The front-end server is further configured to determine a respective target number of conversions to be achieved by the information campaign during the respective time unit, and to determine a response to the particular information request based at least in part on the conversion probability and on the respective target number of conversions.

Abstract: An anti-microbial composition, comprising a chelator (such as EDTA and its salts), and a transport enhancer (such as Methyl Sufonyl Methane; MSM) is provided. Together, the combination of the two substances unexpectedly and beneficially inhibits bacterial or fungal biofilms when administered to an area of microbial infection. Preferred formulations include spray, lotion, solution, gel, cream, ointment, soap, deodorant, surgical rinse, or dental rinse.

Abstract: Methods and formulations are provided for the treatment of localized edema, particularly localized edema resulting from chronic venous insufficiency. A metal ion sequestrant is topically administered to a subject afflicted with localized edema in combination with a permeation enhancer selected from methylsulfonylmethane and a combination of methylsulfonylmethane and dimethylsulfoxide. Topically administrable formulations for use in the aforementioned method are also provided.

Abstract: Oral formulations are provided for prevention and treatment of adverse oral conditions such as gingivitis, periodontal disease, removal of calculus to improve dental hygiene, and control of dental plaque and biofilm. Use of the formulations for prevention and treatment of other adverse oral conditions including inflammation and oxidative and/or free radical damage within the oral cavity are provided. Treatable conditions may relate to other conditions or diseases, including diabetes, AIDS and cancer. Oral formulations containing a biocompatible chelating agent, and a permeation enhancer such as methylsulfonylmethane (MSM) are disclosed. Components of the formulations are multifunctional and Generally Regarded As Safe.

Abstract: A method for treatment of adverse biological conditions is provided, wherein a biologically active agent such as a macromolecular biomolecule, e.g., a nucleic acid or a peptidic compound, is administered to a subject in need of such treatment in a formulation containing a transport enhancer having the structure of formula (I) wherein Q, R1, and R2 are as defined herein. Methylsulfonylmethane (MSM) is a representative and preferred transport enhancer. Formulations are also provided.

Abstract: A system, method, and computer program product for presenting items within a list of items based on an individual user profile, a social network of friends of the user or a mood of the user. The system generates the individual profile of the user, determines passive friends of the user from the social network of the user and also determines the mood of the user. The user has a choice to receive an ordered list of items based on the individual user profile, the social network or the mood of the user.

Abstract: Methods and formulations are provided for the treatment of localized edema, particularly localized edema resulting from chronic venous insufficiency. A metal ion sequestrant is topically administered to a subject afflicted with localized edema in combination with a permeation enhancer selected from methylsulfonylmethane and a combination of methylsulfonylmethane and dimethylsulfoxide. Topically administrable formulations for use in the aforementioned method are also provided.

Abstract: Methods and compositions are provided for the treatment of inflammation and disorders, diseases, and adverse conditions, i.e., pathologies, caused by or otherwise associated with inflammatory processes. A metal ion sequestering agent that directly or indirectly exerts an anti-inflammatory effect is administered to a subject in combination with a sequestration inactivating moiety that facilitates transport of the metal ion sequestering agent through biological membranes. The sequestration inactivating moiety also inactivates the metal ion sequestering agent until association between the two components is cleaved in vivo to release the active sequestering agent. Compositions containing a metal ion sequestering agent and a sequestration inactivating moiety are also provided; the compositions optionally contain an added anti-inflammatory agent.

Abstract: The present system relates to a method for indexing a document comprising the acts of retrieving in the content of the document true landmarks associated with said content, selecting one true landmark as representing the document, indexing the document to include a reference to the selected true landmark, wherein the retrieving act further comprises the act of identifying landmarks through the comparison of the content of the document to a landmark database, and, retaining only true landmarks among the identified landmark using a natural language approach, and wherein the selecting act further comprising the acts of assigning a weight to each true landmark based on at least one criterion, selecting the true landmark that represents the document based on the assigned weights.

Abstract: A system, method, and computer program product for presenting items within a list of items based on an individual user profile, a social network of friends of the user or a mood of the user. The system generates the individual profile of the user, determines passive friends of the user from the social network of the user and also determines the mood of the user. The user has a choice to receive an ordered list of items based on the individual user profile, the social network or the mood of the user.