Abstract: Systems and methods for determining the value of bids placed by content providers for placement positions on a page, e.g., a web page, rendered according to a given context, for instance, the search results listing for a particular query initiated on a search engine web site, are provided. Additionally, systems and methods are provided for determining placement of content items, e.g., advertisements and/or images, on a rendered page relative to other content items on the page based upon bid value.

Abstract: Decision trees populated with classifier models are leveraged to provide enhanced spam detection utilizing separate email classifiers for each feature of an email. This provides a higher probability of spam detection through tailoring of each classifier model to facilitate in more accurately determining spam on a feature-by-feature basis. Classifiers can be constructed based on linear models such as, for example, logistic-regression models and/or support vector machines (SVM) and the like. The classifiers can also be constructed based on decision trees. “Compound features” based on internal and/or external nodes of a decision tree can be utilized to provide linear classifier models as well. Smoothing of the spam detection results can be achieved by utilizing classifier models from other nodes within the decision tree if training data is sparse. This forms a base model for branches of a decision tree that may not have received substantial training data.

Abstract: The claimed subject matter provides a system and/or a method that facilitates dynamically providing a question to ask a medical professional during an appointment. An interface can receive a portion of medical data. A counselor component can generate a question based on the portion of medical data, wherein the question is generated to elicit an answer from a medical professional during an appointment. Moreover, the counselor component can dynamically generate a second question directed toward the medical professional based upon at least one of the answer or a value of information (VOI) computation.

Abstract: Aspects of the subject matter described herein relate to predicting phenotypes. In aspects, a probabilistic predictor is used to summarize a relationship between a set of biological predictors and a phenotype. The probabilistic predictor may use a function that is selected based on the type of the phenotype (e.g., binary, multi-state, or continuous). The probabilistic predictor may use genetic and/or epigenetic information. The probabilistic predictor may be trained on a portion of the data in conjunction with predicting phenotypes in another portion of the data. The probabilistic predictor may be used for various analyses including genome-wide association analysis and gene-set enrichment analysis.

Abstract: The claimed subject matter provides systems and/or methods that determines a number of non-spurious arcs associated with a learned graphical model. The system can include devices and mechanisms that utilize learning algorithms and datasets to generate learned graphical models and graphical models associated with null permutations of the datasets, ascertaining the average number of arcs associated with the graphical models associated with null permutations of the datasets, enumerating the total number of arcs affiliated with the learned graphical model, and presenting a ratio of the average number of arcs to the total number of arcs, the ratio indicative of the number of non-spurious arcs associated the learned graphical model.

Abstract: Provided are systems and/or methods that facilitate sensing, detecting, logging, or treatment of a condition or need of a living body using a controlled parasite.

Abstract: A dependency network is created from a training data set utilizing a scalable method. A statistical model (or pattern), such as for example a Bayesian network, is then constructed to allow more convenient inferencing. The model (or pattern) is employed in lieu of the training data set for data access. The computational complexity of the method that produces the model (or pattern) is independent of the size of the original data set. The dependency network directly returns explicitly encoded data in the conditional probability distributions of the dependency network. Non-explicitly encoded data is generated via Gibbs sampling, approximated, or ignored.

Abstract: The methods/systems described herein facilitate large-scale data collection and aggregation. One exemplary system that facilitates large-scale reporting of health-related data comprises a data collection component, a database and an aggregation component. The data collection component can collect health-related data on a large-scale from a non-selected population. The database can store at least some of the health-related data. The aggregation component can facilitate automatically ascertaining at least one pattern from the health-related data at least in part by applying one or more statistical, data-mining or machine-learning techniques to the database.

Abstract: A visualization input system is provided. The system includes a visualization component that receives input gestures from a user (or users) and translates the gestures into one or more data manipulation commands. A distribution component receives the data manipulation commands and propagates data modifications across one or more databases in view of the commands. This includes a rights component that enables the data modifications to be implemented across the one or more databases.

Abstract: Provided are systems and/or methods that treat illnesses and conditions using ultrasound tuned to a resonant frequency of a target material with the assistance of computer processing. The ultrasound tuned to the resonance frequency of a target material destroys the target material without harming healthy material that surrounds the target material. A resonance frequency database can be employed to ensure that local healthy material surrounding a target has a natural resonance frequency dissimilar enough from the tuned resonance frequency.

Abstract: Architecture for detecting and removing obfuscating clutter from the subject and/or body of a message, e.g., e-mail, prior to filtering of the message, to identify junk messages commonly referred to as spam. The technique utilizes the powerful features built into an HTML rendering engine to strip the HTML instructions for all non-substantive aspects of the message. Pre-processing includes pre-rendering of the message into a final format, which final format is that which is displayed by the rendering engine to the user. The final format message is then converted to a text-only format to remove graphics, color, non-text decoration, and spacing that cannot be rendered as ASCII-style or Unicode-style characters. The result is essentially to reduce each message to its common denominator essentials so that the junk mail filter can view each message on an equal basis.

Abstract: Systems and methods for determining the value of bids placed by content providers for placement positions on a page, e.g., a web page, rendered according to a given context, for instance, the search results listing for a particular query initiated on a search engine web site, are provided. Additionally, systems and methods are provided for determining placement of content items, e.g., advertisements and/or images, on a rendered page relative to other content items on the page based upon bid value.

Abstract: Methods and systems are disclosed for learning a regression decision graph model using a Bayesian model selection approach. In a disclosed aspect, the model structure and/or model parameters can be learned using a greedy search algorithm applied to grow the model so long as the model improves. This approach enables construction of a decision graph having a model structure that includes a plurality of leaves, at least one of which includes a non-trivial linear regression. The resulting model thus can be employed for forecasting, such as for time series data, which can include single or multi-step forecasting.

Abstract: A tool for providing health and/or wellness services is described herein. Not necessarily clean or unclean data about a plurality of self-selected or non-selected or unselected subjects is received. The data can be aggregated and mined at least in part by employing a statistical algorithm, a data-mining algorithm and/or a machine-learning algorithm. The data can be further employed to provide health and/or wellness services to participants.

Abstract: Systems and methods are provided for the identification of genotype-phenotype associations in genome-wide association (GWA) studies. In an illustrative implementation, a data correlation environment comprises a population structure engine and at least one instruction set to instruct the population structure engine to process pedigree or population genetic data to generate a population structure sub-model according to a selected graphical model-based data correlation paradigm. Illustratively, the parameter of the resulting generalized linear mixed model can be learned using a variational approximation.

Abstract: The invention relates to a system for filtering messages—the system includes a seed filter having associated therewith a false positive rate and a false negative rate. A new filter is also provided for filtering the messages, the new filter is evaluated according to the false positive rate and the false negative rate of the seed filter, the data used to determine the false positive rate and the false negative rate of the seed filter are utilized to determine a new false positive rate and a new false negative rate of the new filter as a function of threshold. The new filter is employed in lieu of the seed filter if a threshold exists for the new filter such that the new false positive rate and new false negative rate are together considered better than the false positive and the false negative rate of the seed filter.

Abstract: The present invention provides collaborative filtering systems and methods employing statistical smoothing to provide quickly creatable models that can efficiently predict probability that a user likes an item and/or similarities between items. Smoothing is accomplished by utilizing statistical methods such as support cutoff, single and multiple prior on counts, and prior on measure of association and the like. By improving model-based collaborative filtering with such techniques, performance is increased with regard to product-to-product recommendations. The present invention also provides improvements over systems based on dependency nets (DN) in both areas of quality of recommendations and speed of model creation. It can also be complementary to DN to improve the value of an existing collaborative filtering system's overall efficiency. It is also employable with low frequency user preference data.

Abstract: A predictive model analysis system comprises a receiver component that receives predictive samples created by way of forward sampling. An analysis component analyzes a plurality of the received predictive samples and automatically determines whether a predictive model is reliable at a time range associated with the plurality of predictive sample, wherein the determination is made based at least in part upon an estimated norm associated with a forward sampling operator.

Abstract: An indirect calorimeter estimates nutritional caloric intake by periodically monitoring weight and sensing physical exercise (i.e., physiological data and/or motion data related to physical exertion), which can then be used in a calorimetry model derived from regression analysis of a population (e.g., linear regression, feed-forward neural network, Gaussian process, boosted regression tree, etc.). A strap-on user device for tracking exercise can detect one or more of heart rate, body temperature, skin resistance, motion/acceleration sensing (e.g., pedometer, accelerometer), velocity sensing (e.g., global positioning system (GPS)), and an intelligent, integrated exercise machine (e.g., treadmill, exercise bike, etc.). To gain further fidelity, the user can fine-tune the estimate by undergoing a journal-based routine for a relatively short period of time or clinical calorimetry measurement (e.g., respiratory calorimeter), thereby providing a baseline for resting or exercising metabolic rate.

Abstract: The present invention utilizes a cross-prediction scheme to predict values of discrete and continuous time observation data, wherein conditional variance of each continuous time tube variable is fixed to a small positive value. By allowing cross-predictions in an ARMA based model, values of continuous and discrete observations in a time series are accurately predicted. The present invention accomplishes this by extending an ARMA model such that a first time series “tube” is utilized to facilitate or “cross-predict” values in a second time series tube to form an “ARMAxp” model. In general, in the ARMAxp model, the distribution of each continuous variable is a decision graph having splits only on discrete variables and having linear regressions with continuous regressors at all leaves, and the distribution of each discrete variable is a decision graph having splits only on discrete variables and having additional distributions at all leaves.