15852893
The swisshouse: an inhabitable interface for connecting nations
We present the Swisshouse, a novel type of "inhabitable interface" that supports direct and indirect communication and cultural awareness of habitants in different nations. The Swisshouse allows unsophisticated users to collaborate and be aware of each other over distance. By carefully sculpting the space and choreographing interactive elements in the space, the Swisshouse allows users to instantly separate, combine, and customize environments for specific collaborative activities. By providing visual representations of remote users and places, the Swisshouse provides awareness of the culture, presence and identities of virtual participants. And by tagging physical users with RFID tags, the Swisshouse makes participants aware of the social interaction occurring in the physical space. The combination of these primitives provides a single consistent inhabitable interface that integrates architectural and user interface design thinking, hardware and software design. To illustrate the power of this convergent approach, we describe several examples of real-life applications including a remote lecture, a brainstorming session, a business meeting, and an exhibition.
2569105
Implicit brushing and target snapping: data exploration and sense-making on large displays
During grouping tasks for data exploration and sense-making, the criteria are normally not well-defined. When users are bringing together data objects thought to be similar in some way, implicit brushing continually detects for groups on the freeform workspace, analyzes the groups' text content or metadata, and draws attention to related data by displaying visual hints and animation. This provides helpful tips for further grouping, group meaning refinement and structure discovery. The sense-making process is further enhanced by retrieving relevant information from a database or network during the brushing. Closely related to implicit brushing, target snapping provides a useful means to move a data object to one of its related groups on a large display. Natural dynamics and smooth animations also help to prevent distractions and allow users to concentrate on the grouping and thinking tasks. Two different prototype applications, note grouping for brainstorming and photo browsing, demonstrate the general applicability of the technique.
2348760
Adaptive live video streaming by priority drop
In this paper we explore the use of priority progress streaming (PPS) for video surveillance applications. PPS is an adaptive streaming technique for the delivery of continuous media over variable bit-rate channels. It is based on the simple idea of reordering media components within a time window into priority order before transmission. The main concern when using PPS for live video streaming is the time delay introduced by reordering. In this paper we describe how PPS can be extended to support live streaming and show that the delay inherent in the approach can be tuned to satisfy a wide range of latency constraints while supporting fine-grain adaptation.
2129354
Visualization of neuronal fiber connections from DT-MRI with global optimization
Diffusion Tensor MRI (DT-MRI) provides valuable 3D data describing diffusion characteristics of water molecules in the human brain. From DT-MRI, it is hoped that neuronal fiber connections among cortical regions can be reliably extracted and adequately interpreted. To achieve this goal, several significant challenges persist. In this paper, by means of dynamic programming we have developed a global fiber reconstruction algorithm enabling efficient visualization of neuronal connections queried by both the start and end points on the fly. Besides an inherent ability to handle noisy datasets, our algorithm also naturally addresses situations where neuronal fibers branch or cross each other. We demonstrate the efficacy of our approach with visualization of neuronal connections among activated brain cortical regions detected by functional MRI (fMRI).
16721854
Learning from labeled and unlabeled data on a directed graph
We propose a general framework for learning from labeled and unlabeled data on a directed graph in which the structure of the graph including the directionality of the edges is considered. The time complexity of the algorithm derived from this framework is nearly linear due to recently developed numerical techniques. In the absence of labeled instances, this framework can be utilized as a spectral clustering method for directed graphs, which generalizes the spectral clustering approach for undirected graphs. We have applied our framework to real-world web classification problems and obtained encouraging results.
28276441
A Programmable BIST Core for Embedded DRAM
The programmable BIST design presented here supports various test modes using a simple controller. With the March C algorithm, the BIST circuit's overhead is under 1.3% for a 1-Mbit DRAM and under 0.3% for a 16-Mbit DRAM. The BIST design presented for embedded DRAM supports built-in self-diagnosis by feeding error information to the external tester. Moreover, using a specific test sequence, it can test for critical timing faults, reducing tester time for ac parametric test. The design supports wafer test, pre-burn-in test, burn-in, and final test. It is field-programmable; the user can program test algorithms using predetermined test elements (such as march elements, surround test elements, and refresh modes). The user can optimize the hardware for a specific embedded DRAM with a set of predetermined test elements. Our design is different from the microprogram-controlled BIST described by J. Dreibelbis et al. (1998) which has greater flexibility but higher overhead. Because our design begins at the register-transfer language level, test element insertion (for higher test coverage) and deletion (for lower hardware overhead are relatively easy.
8770859
K-relevance: a spectrum of relevance for data sources impacting a query
Applications ranging from grid management to sensor nets to web-based information integration and extraction can be viewed as receiving data from some number of autonomous remote data sources and then answering queries over this collected data. In such environments it is helpful to inform users which data sources are "relevant" to their query results. It is not immediately obvious what "relevant" should mean in this context, as different users will have different requirements. In this paper, rather than proposing a single definition of relevance, we propose a spectrum of definitions, which we term "k-relevance", for k ≥ 0. We give algorithms for identifying k-relevant data sources for relational queries and explore their efficiency both analytically and experimentally. Finally, we explore the impact of integrity constraints (including dependencies) and materialized views on the problem of computing and maintaining relevant data sources.