The latest advancements in Augmented Reality (AR) and Diminished Reality (DR) have allowed the development of many consumer-oriented applications (such as sales and driving aid, or education). To increase the realism in rendering, estimating the illumination in the scene is a key element. A lot of works tackle this problem but rarely discuss the color of the reconstructed illumination. The Dichromatic Model indicates that the specular component is not affected in color by the texture underneath and holds the light source's color. Though theoretically sound, in practice consumer cameras are subject to nonlinear behaviors which change RGB ratios and create inconsistencies when estimating the illumination. In this paper, we study the conditioning and limits of inverting local illumination models while relying on the Dichromatic Model. We show that the reconstructed specular component has an inconsistent color because it changes depending on the surface's colors.

Content distribution networks (CDNs) using storage clouds have recently started to emerge. Compared to traditional CDNs, storage cloud-based CDNs have the advantage of cost effectively offering hosting services to Web content providers without owning infrastructure. However, existing work on replica placement in CDNs does not readily apply in the cloud. In this paper, we investigated the joint problem of building distribution paths and placing Web server replicas in cloud CDNs to minimize the cost incurred on the CDN providers while satisfying QoS requirements for user requests. We formulate the cost optimization problem with accurate cost models and QoS requirements and show that the monthly cost can be as low as 2.62 US Dollars for a small Web site. We develop a suite of offline, online-static and online-dynamic heuristic algorithms that take as input network topology and work load information such as user location and request rates. We then evaluate the heuristics via Web trace-based simulation, and show that our heuristics behave very close to optimal under various network conditions.

In many disciplines success depends onaccess to scarce resources, such as unique instruments. Researchon computer-supported cooperative work has contributed to the development of technologies, such as collaboratories, that broaden access to scarce scientific and engineering resources. While broader access is often applauded, little attention has been focused on the problem of equitable and efficient resource allocation in the face of increased demand created through collaboratory use. This paper, then, uses the framework of mechanism design to evaluate different resource allocation schemes (RAD, Vickery, and knapsack). Experimental results show that knapsack achieves a more equitable distribution of resources than RAD or Vickery, but that RAD and Vickery are both more efficient than knapsack. The findings highlight the need for systematic exploration of allocation mechanisms within collaboratories, where simple optimization (e.g., knapsack) is likely to produce a sub-optimal match of resources to needs. More generally, the findings illustrate the utility of experimental economic approaches in understanding issues that emerge in large-scale collaborations, such as entire scientific and engineering communities.

UNLABELLED: BioNetBuilder is an open-source client-server Cytoscape plugin that offers a user-friendly interface to create biological networks integrated from several databases. Users can create networks for approximately 1500 organisms, including common model organisms and human. Currently supported databases include: DIP, BIND, Prolinks, KEGG, HPRD, The BioGrid and GO, among others. The BioNetBuilder plugin client is available as a Java Webstart, providing a platform-independent network interface to these public databases. AVAILABILITY: http://err.bio.nyu.edu/cytoscape/bionetbuilder/

Capturing the human hand motion from video involves the estimation of the rigid global hand pose as well as the nonrigid finger articulation. The complexity induced by the high degrees of freedom of the articulated hand challenges many visual tracking techniques. For example, the particle filtering technique is plagued by the demanding requirement of a huge number of particles and the phenomenon of particle degeneracy. This paper presents a novel approach to tracking the articulated hand in video by learning and integrating natural hand motion priors. To cope with the finger articulation, this paper proposes a powerful sequential Monte Carlo tracking algorithm based on importance sampling techniques, where the importance function is based on an initial manifold model of the articulation configuration space learned from motion-captured data. In addition, this paper presents a divide-and-conquer strategy that decouples the hand poses and finger articulations and integrates them in an iterative framework to reduce the complexity of the problem. Our experiments show that this approach is effective and efficient for tracking the articulated hand. This approach can be extended to track other articulated targets.

In a CDMA2000 network, a mobile node (MN) gets packet data service by establishing a PPP session with a packet data serving node (PDSN). The PDSN acts as the muter for the IP packets transported over the PPP session. A packet control function (PCF) sits between the radio access network and the packet network, selects the PDSN for the MN during session setup, and relays the PPP frames between the MN and the PDSN during the session. The PCF selects a PDSN based on the MN's device identity. Due to this, the data services that a mobile user subscribes become tightly coupled with the device that the user registers with the service provider. Because the PCF selects a PDSN based only on mobile device identity, it is unable to select the "best" PDSN to support the services subscribed by a specific user. This work presents the design and implementation of an entity called radio-packet session redirector (RPSR) that works within the current standard and alleviates this shortcoming. The RPSR intercepts a PPP session, parses the user identity, selects a PDSN based on the user identity, creates a PPP session with the selected PDSN and splices the sessions together so that PPP frames can be seamlessly forwarded between the MN and the PDSN with very little overhead. We identify a number of services that RPSR enables in the network. We describe a prototype stand-alone device implementation of the RPSR in the Linux kernel and present performance results.

Capturing human hand motion through visual input is a challenging problem that involves the estimation of both global hand pose as well as the local finger articulation. This is a difficult task that requires a search in a high dimensional space due to the high degrees of freedom that fingers exhibit and the self occlusions caused by global hand motion. We propose a divide and conquer approach to estimate both global and local hand motion. The hand pose is determined from the palm using Iterative closed point (ICP) algorithm and factorization method. By incorporating natural hand motion constraints, we propose an efficient tracking algorithm based on a sequential Monte Carlo technique for tracking finger motion. Finally, the iteration step between the pose estimation and finger articulation tracking is performed in an EM fashion to obtain an accurate configuration estimation. Our experiments show that our approach is accurate and robust for natural hand movements.