Abstract: A proactive networking system and method is disclosed. The network anticipates the user demands in advance and utilizes this predictive ability to reduce the peak to average ratio of the wireless traffic and yield significant savings in the required resources to guarantee certain Quality of Service (QoS) metrics. The system and method focuses on the existing cellular architecture and involves the design and analysis of learning algorithms, predictive resource allocation strategies, and incentive techniques to maximize the efficiency of proactive cellular networks. The system and method further involve proactive peer-to-peer (P2P) overlaying, which leverages the spatial and social structure of the network. Machine learning techniques are applied to find the optimal tradeoff between predictions that result in content being retrieved that the user ultimately never requests, and requests that are not anticipated in a timely manner.

Abstract: In one embodiment, a method includes receiving a user request for a summarization of a particular type of content objects from a client system associated with a first user, determining one or more modalities associated with the user request, selecting a plurality of content objects of the particular type based on a user profile of the first user, wherein the user profile comprises one or more confidence scores associated with one or more subjects associated with the first user, respectively, and wherein the plurality of content objects are selected based on the one or more confidence scores, generating a summary of each content object based on the user profile and the determined modalities, and sending, to the client system in response to the user request, instructions for presenting the summaries of the plurality of content objects, wherein the summaries are presented via one or more of the determined modalities.

Abstract: A proactive networking system and method is disclosed. The network anticipates the user demands in advance and utilizes this predictive ability to reduce the peak to average ratio of the wireless traffic and yield significant savings in the required resources to guarantee certain Quality of Service (QoS) metrics. The system and method focuses on the existing cellular architecture and involves the design and analysis of learning algorithms, predictive resource allocation strategies, and incentive techniques to maximize the efficiency of proactive cellular networks. The system and method further involve proactive peer-to-peer (P2P) overlaying, which leverages the spatial and social structure of the network. Machine learning techniques are applied to find the optimal tradeoff between predictions that result in content being retrieved that the user ultimately never requests, and requests that are not anticipated in a timely manner.

Abstract: A proactive networking system and method is disclosed. The network anticipates the user demands in advance and utilizes this predictive ability to reduce the peak to average ratio of the wireless traffic and yield significant savings in the required resources to guarantee certain Quality of Service (QoS) metrics. The system and method focuses on the existing cellular architecture and involves the design and analysis of learning algorithms, predictive resource allocation strategies, and incentive techniques to maximize the efficiency of proactive cellular networks. The system and method further involve proactive peer-to-peer (P2P) overlaying, which leverages the spatial and social structure of the network. Machine learning techniques are applied to find the optimal tradeoff between predictions that result in content being retrieved that the user ultimately never requests, and requests that are not anticipated in a timely manner.

Abstract: An admissions funnel process software application recommends a candidate to a school based on the admission cycle timeline of the school. The admission closing tool receives an admission schedule for a school that includes a set of admission goals for the school such as a desired number of students at each stage of an admission cycle. A candidate profile is compiled for each of a plurality of candidates. For each candidate, the admissions funnel process software application predicts a stage in the admission cycle that the candidate will be, at a future time. For each stage of an admission cycle, the candidates are aggregated by their predicted stages to obtain a predicted admissions cycle for the school. The aggregated number of candidates is compared to the desired number of candidates for that stage. Responsive to the comparing, one or more candidates are selected as leads, recommended and sent to the school.

Abstract: An admission cycle progress tracking system and method updates candidate ranking of a candidate at a particular school based on its advancement at a different school. The admission cycle progress tracker receives a candidate qualifier of a candidate at a first school. The candidate qualifier indicates the candidate's progress in the admission cycle of the first school. The admission cycle progress tracker receives a candidate qualifier of a candidate at a second school. The candidate qualifier indicates the candidate's progress in the admission cycle of the second school. Further, the candidate's advancement within the admission cycle of the first school is determined based on the candidate qualifier. Responsive to the determination of advancement at the first school, the ranking and the candidate qualifier of the candidate are updated at the second or other schools on which the candidate is listed.

Abstract: A lead matching system selects and ranks candidate leads for school admissions officers. The system records personal and academic information for each candidate as well as expressions of interest from that candidate toward one or more schools. The system also records information describing each school, such as its academic profile, location, class size, athletic program quality, and so on. The system also records each school's immediate demographic preferences for an incoming class of students. The system analyzes candidate and school information to generate a set of scored candidate leads. The system then identifies a subset of leads for each school based on its demographic preferences, and ranks the leads. The system provides a subset of ranked leads to each school.

Abstract: A proactive networking system and method is disclosed. The network anticipates the user demands in advance and utilizes this predictive ability to reduce the peak to average ratio of the wireless traffic and yield significant savings in the required resources to guarantee certain Quality of Service (QoS) metrics. The system and method focuses on the existing cellular architecture and involves the design and analysis of learning algorithms, predictive resource allocation strategies, and incentive techniques to maximize the efficiency of proactive cellular networks. The system and method further involve proactive peer-to-peer (P2P) overlaying, which leverages the spatial and social structure of the network. Machine learning techniques are applied to find the optimal tradeoff between predictions that result in content being retrieved that the user ultimately never requests, and requests that are not anticipated in a timely manner.

Abstract: A proactive networking system and method is disclosed. The network anticipates the user demands in advance and utilizes this predictive ability to reduce the peak to average ratio of the wireless traffic and yield significant savings in the required resources to guarantee certain Quality of Service (QoS) metrics. The system and method focuses on the existing cellular architecture and involves the design and analysis of learning algorithms, predictive resource allocation strategies, and incentive techniques to maximize the efficiency of proactive cellular networks. The system and method further involve proactive peer-to-peer (P2P) overlaying, which leverages the spatial and social structure of the network. Machine learning techniques are applied to find the optimal tradeoff between predictions that result in content being retrieved that the user ultimately never requests, and requests that are not anticipated in a timely manner.

Abstract: A computer-implemented method to determine a robust wrapper includes developing a model indicative of the temporal history of a document, such as a web document written in a markup language. Based on the developed model, robustness characteristics are determined for a plurality of different wrappers representing associated paths to the data item in a representation of the document. Based on a result of the determining operation, a result wrapper of the plurality of wrappers is provided. The result wrapper has a desired robustness characteristic.

Abstract: A proactive networking system and method is disclosed. The network anticipates the user demands in advance and utilizes this predictive ability to reduce the peak to average ratio of the wireless traffic and yield significant savings in the required resources to guarantee certain Quality of Service (QoS) metrics. The system and method focuses on the existing cellular architecture and involves the design and analysis of learning algorithms, predictive resource allocation strategies, and incentive techniques to maximize the efficiency of proactive cellular networks. The system and method further involve proactive peer-to-peer (P2P) overlaying, which leverages the spatial and social structure of the network. Machine learning techniques are applied to find the optimal tradeoff between predictions that result in content being retrieved that the user ultimately never requests, and requests that are not anticipated in a timely manner.

Abstract: A computer-implemented method to determine a robust wrapper includes developing a model indicative of the temporal history of a document, such as a web document written in a markup language. Based on the developed model, robustness characteristics are determined for a plurality of different wrappers representing associated paths to the data item in a representation of the document. Based on a result of the determining operation, a result wrapper of the plurality of wrappers is provided. The result wrapper has a desired robustness characteristic.