2644615
3D finite element mesh generation of complicated tooth model based on CT slices
An interactive three-dimensional finite element generation method is presented for modelling a multi-connected teeth and mandible structure. The tetrahedron is chosen as the basic element type due to its rigorous adaptability to structures with geometric complexities. The mesh generation is implemented by allocating two quadrangles in adjacent CT image slices to form a set of tetrahedrons. By examining all the possible allocations and their degradations, an algorithm is developed for interactive mesh generation, resulting in a series of tetrahedrons consistent with all the others without overlapping and spacing. The developed system was applied to a tooth-mandibular structure, generating a complicated 3D FEM model consisting of 4762 nodes and 18,534 tetrahedral elements with nine different materials. This 3D model was successfully used to evaluate different tooth restoration strategies, which proved the viability and effectiveness of the proposed method.
17440351
Instrumentation and experiment design for in-vitro interface temperature measurement during the insertion of an orthopaedic implant
The metal implants have been widely used for the human skeleton in dental, maxillofacial and orthopedic treatments for many years. The main function of the implant is to reinforce the fractured bone or provide a direct attachment for a prosthetic limb. The mechanical based orthopedic fixation normally uses a self-tapping implant to provide connectivity between fractured bones. In such a system, the integration between bone and implant is normally based on a biology process called Osseointegration. However, the implantation process courses temperature elevation at the bone-implant interface. Clinically the high interfacial temperature affects regeneration of bone tissue and ultimately the osseointegration between implant and bone. In order to investigate the temperature elevation at the bone-implant interface, the authors developed a designated instrument system and special self-tapping implant with K type thermocouples placed close to the outer surface inside the implant. The experiments were carried out using re-hydrated human femurs while the implant was screwed into the femur. In this study, the temperature elevation was investigated using different implant diameters, insertion distances and speeds. The experiments were performed at the operation room temperature 21degC and the recorded peak temperature was 47.2degC. The result showed that there was a considerable temperature increase at the bone-implant interface while a self-tapping implant was inserted.
394877
Exploration and Exploitation in Adaptive Filtering Based on Bayesian Active Learning
In the task of adaptive information filtering, a system receives a stream of documents but delivers only those that match a person's information need. As the system filters it also refines its knowledge about the user's information needs based on relevance feedback from the user. Delivering a document thus has two effects: i) it satisfies the user's information need immediately, and ii) it helps the system better satisfy the user in the future by improving its model of the user's information need. The traditional approach to adaptive information filtering fails to recognize and model this second effect. 
 
This paper proposes utility divergence as the measure of model quality. Unlike the model quality measures used in most active learning methods, utility divergence is represented on the same scale as the filtering system's target utility function. Thus it is meaningful to combine the expected immediate utility with the model quality, and to quantitatively manage the trade-off between exploitation and exploration. The proposed algorithm is implemented for setting the filtering system's dissemination threshold, a major problem for adaptive filtering systems. Experiments with TREC-9 and TREC-10 filtering data demonstrate that the proposed method is effective.
1723065
An approach for realizing privacy-preserving web-based services
The past decade has witnessed a phenomenal growth in the number of users who routinely use the web to obtain information, conduct research, or carry out financial transactions. While the ability of the web to provide customized information and financial services has boosted personal and business productivity, it has raised significant concerns regarding an individual’s privacy. The privacy concerns are exacerbated further with the advent of more complex services that involve multiple service providers, as it raises the potential of personal information being shared across these providers in ways that weren’t intended by the owner of the information. To illustrate the increased privacy concerns raised by composite web-based services, consider a web-based service that allows an individual to research and order over-the-counter medication (or health supplements) at a low cost, while ensuring that there are no adverse interactions based on the individuals current prescription. Such a service will require access to the individual’s personal medical history, and will also require interaction with other entities such as web-based medical references, discount pharmacies and creditcard companies. Users may not want their personal health histories to be shared with discount pharmacies or credit card companies. Similarly, they may not want their credit card information to be shared with providers of free on-line information. Web-based service providers have long recognized the importance of addressing user privacy concerns, and taken steps to state their policies regarding the storage and use of personal user information. Unfortunately, since these policies are stated in English, they are imprecise and ambiguous, posing problems in accountability and enforceability, as well as in checking compatibility between consumer and provider policies. Indeed, the verbose nature of these policies discourage most users from even reading them! To address the above problem, we present a new approach where the consumers as well as providers can express their privacy concerns in a formal way. Specifically, consumers express their requirements in the form of policies, while providers specify their use of consumer data using models. Our approach automates compatibility checking between policies and models. If there is an incompatibility, the consumer is informed how she can refine her policies in order to use the service. If she does not want to change her policies in any way, the approach passes on additional privacy requirements to the provider. Service access can continue in case
9167227
A hierarchical naive Bayes mixture model for name disambiguation in author citations
Because of name variations, an author may have multiple names and multiple authors may share the same name. Such name ambiguity affects the performance of document retrieval, web search, database integration, and may cause improper attribution to authors. This paper presents a hierarchical naive Bayes mixture model, an unsupervised learning approach, for name disambiguation in author citations. This method partitions a collection of citations1 into clusters, with each cluster containing only citations authored by the same author, thus disambiguating authorship in citations to induce author name identities. Three types of citation features are used: co-author names, paper title words, and journal or proceeding title words. The approach is illustrated with 16 name datasets that are constructed based on the publication lists collected from author homepages and DBLP computer science bibliography.
15478948
Efficient Video Object Segmentation by Graph-Cut
Segmentation of video objects from background is a popular computer vision topic and has many important applications. Most existing methods are either computationally expensive or requiring manual initialization, static cameras, and/or rigid scenes. In a previous work, we proposed a joint spatio-temporal linear regression algorithm to automatically cluster the sparse edge/corner pixels in each video frame and obtain two motion models for the object and background respectively. To label the rest pixels for object segmentation, in this paper, we propose to model the Optical-Flow residual error, color intensity residual error and temporal label consistency features, as well as color/edge orientation consistency constrains, in a graph, and apply the Graph-Cut algorithm to minimize the energy of the graph to obtain an optimal segmentation of the two motion layers boundaries. Finally the object layer is identified from the two using simple heuristics. Experimental segmentation result with videos taken by webcams is promising.
2062974
A Conditional Random Field Model for Video Super-resolution
In this paper, we propose a learning-based method for video super-resolution. There are two main contributions of the proposed method. First, information from cameras with different spatial-temporal resolutions is combined in our framework. This is achieved by constructing training dictionary using the high resolution images captured by still camera and the low resolution video is enhanced via searching in this customized database. Second, we enforce the spatio-temporal constraints using the conditional random field (CRF) and the problem of video super-resolution is posed as finding the high resolution video that maximizes the conditional probability. We apply the algorithm to video sequences taken from different scenes using cameras with different qualities and promising results are presented