Sergey Brin

Bio: Sergey Brin is an academic researcher from Google. The author has contributed to research in topics: Web search query & Web query classification. The author has an hindex of 16, co-authored 23 publications receiving 29096 citations. Previous affiliations of Sergey Brin include Stanford University.

Laser alignment of binocular head mounted display

Abstract: A binocular head mounted display includes a frame, right and left displays, an alignment sensor, and a control system. The right and left displays display right and left images to a user and are mounted to the frame. The alignment sensor includes a first laser source mounted proximate to one of the right or left displays and a first photo-detector array mounted opposite the first laser source and proximate to an opposite one of the right or left displays. The first alignment sensor is mounted to measure misalignment between the right and left displays due to deformation of the frame about one or more rotational axes and to generate a signal that is indicative of the misalignment. The control system is coupled to the alignment sensor to receive the signal and to calculate the misalignment based at least in part upon the signal.

Seeing with your hand

Abstract: The disclosure describes methods and systems for gathering and conveying information, for example, such as with a hand of a user. In one embodiment, the method may include using a detector to record a series of images of an environment and detecting a predetermined motion by comparing two or more images in the series. The method may include selecting a function based on the predetermined motion and triggering the function. In another embodiment, the method may include using a first detector to record a first series of images of an environment and using a second detector to record a second series of images of an environment. The method may include detecting a predetermined relative motion by comparing one or more images from the first series with one or more images from the second series, and selecting and/or triggering a function based on the predetermined relative motion.

Image Capture Technique

Abstract: This disclosure relates to winking to capture image data using an image capture device that is associated with a head-mountable device (HMD). An illustrative method includes detecting a wink gesture at an HMD. The method also includes causing an image capture device to capture image data, in response to detecting the wink gesture at the HMD.

Experience sharing for conveying communication status

Abstract: Exemplary methods and systems relate to the handling of a communication request by a wearable computing device. For example, a wearable computing device may respond to communication requests by sharing real-time media that portrays the device's surroundings and the activity of a user associated with the device. The wearable device or a server system communicatively linked to the wearable device may perform the reception and transmission functions. This process may proceed automatically or as a result of user-input and may additionally involve procedures for accepting or rejecting the communication request.

Hands-Free Selection Using a Ring-Based User-Interface

Abstract: Methods and devices for providing a user-interface are disclosed. In one embodiment, the method comprises receiving data corresponding to a first position of a wearable computing device and responsively causing the wearable computing device to provide a user-interface. The user-interfaces comprises a view region and a menu, where the view region substantially fills a field of view of the wearable computing device and the menu is not fully visible in the view region. The method further comprises receiving data indicating a selection of an item present in the view region and causing an indicator to be displayed in the view region, wherein the indicator changes incrementally over a length of time. When the length of time has passed, the method comprises responsively causing the wearable computing device to select the item.

Data Mining: Concepts and Techniques

Abstract: The increasing volume of data in modern business and science calls for more complex and sophisticated tools. Although advances in data mining technology have made extensive data collection much easier, it's still always evolving and there is a constant need for new techniques and tools that can help us transform this data into useful information and knowledge. Since the previous edition's publication, great advances have been made in the field of data mining. Not only does the third of edition of Data Mining: Concepts and Techniques continue the tradition of equipping you with an understanding and application of the theory and practice of discovering patterns hidden in large data sets, it also focuses on new, important topics in the field: data warehouses and data cube technology, mining stream, mining social networks, and mining spatial, multimedia and other complex data. Each chapter is a stand-alone guide to a critical topic, presenting proven algorithms and sound implementations ready to be used directly or with strategic modification against live data. This is the resource you need if you want to apply today's most powerful data mining techniques to meet real business challenges. * Presents dozens of algorithms and implementation examples, all in pseudo-code and suitable for use in real-world, large-scale data mining projects. * Addresses advanced topics such as mining object-relational databases, spatial databases, multimedia databases, time-series databases, text databases, the World Wide Web, and applications in several fields. *Provides a comprehensive, practical look at the concepts and techniques you need to get the most out of real business data

Data Mining: Practical Machine Learning Tools and Techniques

Abstract: Data Mining: Practical Machine Learning Tools and Techniques offers a thorough grounding in machine learning concepts as well as practical advice on applying machine learning tools and techniques in real-world data mining situations. This highly anticipated third edition of the most acclaimed work on data mining and machine learning will teach you everything you need to know about preparing inputs, interpreting outputs, evaluating results, and the algorithmic methods at the heart of successful data mining. Thorough updates reflect the technical changes and modernizations that have taken place in the field since the last edition, including new material on Data Transformations, Ensemble Learning, Massive Data Sets, Multi-instance Learning, plus a new version of the popular Weka machine learning software developed by the authors. Witten, Frank, and Hall include both tried-and-true techniques of today as well as methods at the leading edge of contemporary research. *Provides a thorough grounding in machine learning concepts as well as practical advice on applying the tools and techniques to your data mining projects *Offers concrete tips and techniques for performance improvement that work by transforming the input or output in machine learning methods *Includes downloadable Weka software toolkit, a collection of machine learning algorithms for data mining tasks-in an updated, interactive interface. Algorithms in toolkit cover: data pre-processing, classification, regression, clustering, association rules, visualization

The Structure and Function of Complex Networks

Abstract: Inspired by empirical studies of networked systems such as the Internet, social networks, and biological networks, researchers have in recent years developed a variety of techniques and models to help us understand or predict the behavior of these systems. Here we review developments in this field, including such concepts as the small-world effect, degree distributions, clustering, network correlations, random graph models, models of network growth and preferential attachment, and dynamical processes taking place on networks.

The PageRank Citation Ranking : Bringing Order to the Web

Abstract: The importance of a Web page is an inherently subjective matter, which depends on the readers interests, knowledge and attitudes. But there is still much that can be said objectively about the relative importance of Web pages. This paper describes PageRank, a mathod for rating Web pages objectively and mechanically, effectively measuring the human interest and attention devoted to them. We compare PageRank to an idealized random Web surfer. We show how to efficiently compute PageRank for large numbers of pages. And, we show how to apply PageRank to search and to user navigation.

Community detection in graphs

Abstract: The modern science of networks has brought significant advances to our understanding of complex systems. One of the most relevant features of graphs representing real systems is community structure, or clustering, i. e. the organization of vertices in clusters, with many edges joining vertices of the same cluster and comparatively few edges joining vertices of different clusters. Such clusters, or communities, can be considered as fairly independent compartments of a graph, playing a similar role like, e. g., the tissues or the organs in the human body. Detecting communities is of great importance in sociology, biology and computer science, disciplines where systems are often represented as graphs. This problem is very hard and not yet satisfactorily solved, despite the huge effort of a large interdisciplinary community of scientists working on it over the past few years. We will attempt a thorough exposition of the topic, from the definition of the main elements of the problem, to the presentation of most methods developed, with a special focus on techniques designed by statistical physicists, from the discussion of crucial issues like the significance of clustering and how methods should be tested and compared against each other, to the description of applications to real networks.