Abstract: A multivariate, automated and scalable method for extracting profiles from images to quantify the effects of perturbations on biological samples. Morphological features are determined from images of treated (perturbed) and control (unperturbed) biological samples, and multivariate classification, for example, using a separating decision hyperplane, is used to separate the distribution of measured feature data into control and treated groups. This classification may be used to determine a magnitude of the effect of the particular perturbation under study. A practical application is high-throughput image-based drug screening, wherein the effects of many different compounds, each applied at different doses and for different exposure times, may be profiled to, for example, characterize compound activities and to identify dose-dependent multiphasic drug responses, or to determine and classify the biological effects of new compounds.

Abstract: Sound source separation, without permutation, using convolutional mixing independent component analysis based on a priori knowledge of the target sound source is disclosed. The target sound source can be a human speaker. The reconstruction filters used in the sound source separation take into account the a priori knowledge of the target sound source, such as an estimate the spectra of the target sound source. The filters may be generally constructed based on a speech recognition system. Matching the words of the dictionary of the speech recognition system to a reconstructed signal indicates whether proper separation has occurred. More specifically, the filters may be constructed based on a vector quantization codebook of vectors representing typical sound source patterns. Matching the vectors of the codebook to a reconstructed signal indicates whether proper separation has occurred. The vectors may be linear prediction vectors, among others.

Abstract: Sound source separation, without permutation, using convolutional mixing independent component analysis based on a priori knowledge of the target sound source is disclosed. The target sound source can be a human speaker. The reconstruction filters used in the sound source separation take into account the a priori knowledge of the target sound source, such as an estimate the spectra of the target sound source. The filters may be generally constructed based on a speech recognition system. Matching the words of the dictionary of the speech recognition system to a reconstructed signal indicates whether proper separation has occurred. More specifically, the filters may be constructed based on a vector quantization codebook of vectors representing typical sound source patterns. Matching the vectors of the codebook to a reconstructed signal indicates whether proper separation has occurred. The vectors may be linear prediction vectors, among others.

Abstract: The invention relates to methods and systems (e.g., computer systems and computer program products) for identifying and characterizing genes using microarrays. In particular, the invention provides for improved, robust methods for detecting genes through the use of microarrays to analyze the expression state of the genome. Genes which are expressed can be mapped to their respective positions in the genome, and the structure of such genes can be determined.

Abstract: Methods and apparatus for analyzing tasks performed by computer users by (i) gathering usage data, (ii) converting logged usage data into a uniform format, (iii) determining or defining task boundaries, and (iv) determining a task analysis model by “clustering” similar tasks together. The task analysis model may be used to (i) help users complete a task (such help, for example, may be in the form of a gratuitous help function), and/or (ii) to target marketing information to users based on user inputs and the task analysis model. The present invention also provides a uniform semantic network for representing different types of objects in a uniform way.

Abstract: The invention relates to methods and systems (e.g., computer systems and computer program products) for identifying and characterizing genes using microarrays. In particular, the invention provides for improved, robust methods for detecting genes through the use of microarrays to analyze the expression state of the genome. Genes which are expressed can be mapped to their respective positions in the genome, and the structure of such genes can be determined.

Abstract: Methods and apparatus for analyzing tasks performed by computer users by (i) gathering usage data, (ii) converting logged usage data into a uniform format, (iii) determining or defining task boundaries, and (iv) determining a task analysis model by “clustering” similar tasks together. The task analysis model may be used to (i) help users complete a task (such help, for example, may be in the form of a gratuitous help function), and/or (ii) to target marketing information to users based on user inputs and the task analysis model. The present invention also provides a uniform semantic network for representing different types of objects in a uniform way.

Abstract: The invention relates to methods and systems (e.g., computer systems and computer program products) for identifying and characterizing genes using microarrays. In particular, the invention provides for improved, robust methods for detecting genes through the use of microarrays to analyze the expression state of the genome. Genes which are expressed can be mapped to their respective positions in the genome, and the structure of such genes can be determined.

Abstract: Methods and apparatus for analyzing tasks performed by computer users by (i) gathering usage data, (ii) converting logged usage data into a uniform format, (iii) determining or defining task boundaries, and (iv) determining a task analysis model by “clustering” similar tasks together. The task analysis model may be used to (i) help users complete a task (such help, for example, may be in the form of a gratuitous help function), and/or (ii) to target marketing information to users based on user inputs and the task analysis model. The present invention also provides a uniform semantic network for representing different types of objects in a uniform way.

Abstract: Sound source separation, without permutation, using convolutional mixing independent component analysis based on a priori knowledge of the target sound source is disclosed. The target sound source can be a human speaker. The reconstruction filters used in the sound source separation take into account the a priori knowledge of the target sound source, such as an estimate the spectra of the target sound source. The filters may be generally constructed based on a speech recognition system. Matching the words of the dictionary of the speech recognition system to a reconstructed signal indicates whether proper separation has occurred. More specifically, the filters may be constructed based on a vector quantization codebook of vectors representing typical sound source patterns. Matching the vectors of the codebook to a reconstructed signal indicates whether proper separation has occurred. The vectors may be linear prediction vectors, among others.

Abstract: Entering and evaluating group(s) of similar dependence hypotheses, such as time dependence hypotheses, and forecasting values of a variable based on the group(s) of similar dependence hypotheses. The group(s) of similar dependence hypotheses may be used to forecast requests for an Internet resource or Internet resources having one or more particular attributes. The entered group(s) of similar dependence hypotheses may be evaluated based on known data, such as past requests for Internet resources or Internet resources of a particular type.

Abstract: Building resource (e.g., Internet content) and attribute transition probability models and using such models for pre-fetching resources, editing resource link topology, building resource link topology templates, and collaborative filtering.

Abstract: Building resource (e.g., Internet content) and attribute transition probability models and using such models for pre-fetching resources, editing resource link topology, building resource link topology templates, and collaborative filtering.

Abstract: Resource usage data is used to infer degrees of influence between users. Once such inferences are made, a directed graph representation of the users and the inferred "influence" between the users can be generated. "Influential rumormongers" can then be determined from the directed graph, for example, by using a greedy graph covering algorithm. In this way, marketing information can be targeted to "influential rumormongers" to optimize its dissemination and impact. If actual (explicit) data regarding the influence between users is known, such data may be used to refine or replace at least some edge values.

Abstract: Building resource (e.g., Internet content) and attribute transition probability models and using such models for pre-fetching resources, editing resource link topology, building resource link topology templates, and collaborative filtering.

Abstract: Building resource (e.g., Internet content) and attribute transition probability models and using such models for pre-fetching resources, editing resource link topology, building resource link topology templates, and collaborative filtering.

Abstract: A method, system and computer product for allowing efficient user interaction with digital time-based signals. User control and filter information (symbolic, procedural or a combination of both) are optimized for greatly improving calculating efficiency. First, filters are symbolically optimized. Then, a single optimized procedure is generated and compiled into an optimized procedure code. The procedure code then processes the input signal according to control information.