Microsoft

About: Microsoft is a company organization based out in Redmond, Washington, United States. It is known for research contribution in the topics: User interface & Context (language use). The organization has 49501 authors who have published 86900 publications receiving 4195429 citations. The organization is also known as: MS & MSFT.

Driving with knowledge from the physical world

Abstract: This paper presents a Cloud-based system computing customized and practically fast driving routes for an end user using (historical and real-time) traffic conditions and driver behavior. In this system, GPS-equipped taxicabs are employed as mobile sensors constantly probing the traffic rhythm of a city and taxi drivers' intelligence in choosing driving directions in the physical world. Meanwhile, a Cloud aggregates and mines the information from these taxis and other sources from the Internet, like Web maps and weather forecast. The Cloud builds a model incorporating day of the week, time of day, weather conditions, and individual driving strategies (both of the taxi drivers and of the end user for whom the route is being computed). Using this model, our system predicts the traffic conditions of a future time (when the computed route is actually driven) and performs a self-adaptive driving direction service for a particular user. This service gradually learns a user's driving behavior from the user's GPS logs and customizes the fastest route for the user with the help of the Cloud. We evaluate our service using a real-world dataset generated by over 33,000 taxis over a period of 3 months in Beijing. As a result, our service accurately estimates the travel time of a route for a user; hence finding the fastest route customized for the user.

The application of two-level attention models in deep convolutional neural network for fine-grained image classification

Abstract: Fine-grained classification is challenging because categories can only be discriminated by subtle and local differences. Variances in the pose, scale or rotation usually make the problem more difficult. Most fine-grained classification systems follow the pipeline of finding foreground object or object parts (where) to extract discriminative features (what).

Verbs and adverbs: multidimensional motion interpolation

Abstract: The article describes a system for real-time interpolated animation that addresses some of these problems. Through creating parameterized motions-which the authors call verbs parameterized by adverbs-a single authored verb produces a continuous range of subtle variations of a given motion at real-time rates. As a result, simulated figures alter their actions based on their momentary mood or in response to changes in their goals or environmental stimuli. For example, they demonstrate a walk verb that can show emotions such as happiness and sadness, and demonstrate subtle variations due to walking up or down hill while turning to the left and right. They also describe verb graphs, which act as the glue to assemble verbs and their adverbs into a runtime data structure. Verb graphs provide the means for seamless transition from verb to verb for the simulated figures within an interactive runtime system. Finally they briefly discuss the discrete event simulator that handles the runtime main loop.

The Microarray Quality Control (MAQC)-II study of common practices for the development and validation of microarray-based predictive models

Abstract: Gene expression data from microarrays are being applied to predict preclinical and clinical endpoints, but the reliability of these predictions has not been established. In the MAQC-II project, 36 independent teams analyzed six microarray data sets to generate predictive models for classifying a sample with respect to one of 13 endpoints indicative of lung or liver toxicity in rodents, or of breast cancer, multiple myeloma or neuroblastoma in humans. In total, >30,000 models were built using many combinations of analytical methods. The teams generated predictive models without knowing the biological meaning of some of the endpoints and, to mimic clinical reality, tested the models on data that had not been used for training. We found that model performance depended largely on the endpoint and team proficiency and that different approaches generated models of similar performance. The conclusions and recommendations from MAQC-II should be useful for regulatory agencies, study committees and independent investigators that evaluate methods for global gene expression analysis.