120,751 research outputs found

    Ultrasonic Computed Tomography

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    Ultrasonic Computed Tomography (UCT) is a full digital imaging technique, which consists in numerically solving the inverse scattering problem associated to the forward scattering problem describing the interaction of ultrasonic waves with inhomogeneous media. For weakly inhomogeneous media such as soft tissues, various approximations of the solution of the forward problem (straight ray approximation, Born approximation...), leading to easy-to-implement approximations of the inverse scattering problem (back-projection or back-propagation algorithms) can be used. In the case of highly heterogeneous media such as bone surrounded by soft tissues, such approximations are no more valid. We present here two non-linear inversion schemes based on high-order approximations. These methods are conceived like the prolongation of the methods implemented in the weakly inhomogeneous case for soft tissues. The results show the feasibility of this UCT approach to bones and its potential to perform measurements in vivo

    An Optimization of University Course Timetabling using Case-Based Reasoning and Graph Coloring

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    ABSTRAKSI: University Course Timetabling (UCT) has a very challenging task to satisfy a set of stated objectives for students, lecturers, courses, rooms, and times to the highest possible extent. These objectives including the constraints must be assigned into the timeslots. This study attempts to solve the UCT problem by combining a Case-Based Reasoning (CBR) method and a Graph Coloring result. These two methods will solve satisfy the corresponding constraints, CBR to satisfy the soft-constraints and Graph Coloring to satisfy the hard-constraint. Combining these two methods has been implemented in this UCT system, which is an automated timetabling system to provide the timetable with an optimal solution.Kata Kunci : University Course Timetabling (UCT), hard-constraint, soft-constraint, Graph Coloring, Case-Based Reasoning (CBR), Optimal, timetabling.ABSTRACT: Permasalahan Penjadwalan Kuliah merupakan sebuah permasalahan yang kompleks dan menarik untuk diselesaikan karena harus dapat memenuhi sejumlah kebutuhan dari objek perkuliahan seperti mahasiswa, dosen, mata kuliah, ruang dan waktu dengan tingkat kepuasan setinggi mungkin. Adapun kebutuhan-kebutuhan tersebut memiliki sejumlah batasan untuk dapat dialokasikan ke dalam timeslot. Penelitian ini berusaha memecahkan permasalahan penjadwalan kuliah dengan mengkombinasikan metode Penalaran Berbasis Kasus atau Case-Based Reasoning (CBR) dan Pewarnaan Graf. Kedua metode ini digunakan sesuai dengan batasan atau constraint yang akan dipenuhi, CBR dipakai untuk memenuhi soft-constraints dan Pewarnaan Graf dipakai untuk memenuhi hard-constraint. Kombinasi kedua metode ini diimplementasikan dalam sistem penjadwalan kuliah, menjadi sebuah sistem penjadwalan kuliah otomatis dengan hasil berupa sebuah jadwal kuliah yang optimal.Keyword: Permasalahan Penjadwalan kuliah, hard-constraint, soft-constraint, Pewarnaan Graf, Case-Based Reasoning (CBR), Optimal, Penjadwalan Kuliah

    Crossover from Isotropic to Directed Percolation

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    Percolation clusters are probably the simplest example for scale--invariant structures which either are governed by isotropic scaling--laws (``self--similarity'') or --- as in the case of directed percolation --- may display anisotropic scaling behavior (``self--affinity''). Taking advantage of the fact that both isotropic and directed bond percolation (with one preferred direction) may be mapped onto corresponding variants of (Reggeon) field theory, we discuss the crossover between self--similar and self--affine scaling. This has been a long--standing and yet unsolved problem because it is accompanied by different upper critical dimensions: dcI=6d_c^{\rm I} = 6 for isotropic, and dcD=5d_c^{\rm D} = 5 for directed percolation, respectively. Using a generalized subtraction scheme we show that this crossover may nevertheless be treated consistently within the framework of renormalization group theory. We identify the corresponding crossover exponent, and calculate effective exponents for different length scales and the pair correlation function to one--loop order. Thus we are able to predict at which characteristic anisotropy scale the crossover should occur. The results are subject to direct tests by both computer simulations and experiment. We emphasize the broad range of applicability of the proposed method.Comment: 19 pages, written in RevTeX, 12 figures available upon request (from [email protected] or [email protected]), EF/UCT--94/2, to be published in Phys. Rev. E (May 1994

    Inferring Dynamic User Interests in Streams of Short Texts for User Clustering

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    User clustering has been studied from different angles. In order to identify shared interests, behavior-based methods consider similar browsing or search patterns of users, whereas content-based methods use information from the contents of the documents visited by the users. So far, content-based user clustering has mostly focused on static sets of relatively long documents. Given the dynamic nature of social media, there is a need to dynamically cluster users in the context of streams of short texts. User clustering in this setting is more challenging than in the case of long documents, as it is difficult to capture the users’ dynamic topic distributions in sparse data settings. To address this problem, we propose a dynamic user clustering topic model (UCT). UCT adaptively tracks changes of each user’s time-varying topic distributions based both on the short texts the user posts during a given time period and on previously estimated distributions. To infer changes, we propose a Gibbs sampling algorithm where a set of word pairs from each user is constructed for sampling. UCT can be used in two ways: (1) as a short-term dependency model that infers a user’s current topic distribution based on the user’s topic distributions during the previous time period only, and (2) as a long-term dependency model that infers a user’s current topic distributions based on the user’s topic distributions during multiple time periods in the past. The clustering results are explainable and human-understandable, in contrast to many other clustering algorithms. For evaluation purposes, we work with a dataset consisting of users and tweets from each user. Experimental results demonstrate the effectiveness of our proposed short-term and long-term dependency user clustering models compared to state-of-the-art baselines

    Bandit-Aided Boosting

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    In this paper we apply multi-armed bandits (MABs) to accelerate ADABOOST. ADABOOST constructs a strong classifier in a stepwise fashion by selecting simple base classifiers and using their weighted "vote" to determine the final classification. We model this stepwise base classifier selection as a sequential decision problem, and optimize it with MABs. Each arm represent a subset of the base classifier set. The MAB gradually learns the "utility" of the subsets, and selects one of the subsets in each iteration. ADABOOST then searches only this subset instead of optimizing the base classifier over the whole space. The reward is defined as a function of the accuracy of the base classifier. We investigate how the MAB algorithms (UCB, UCT) can be applied in the case of boosted stumps, trees, and products of base classifiers. On benchmark datasets, our bandit-based approach achieves only slightly worse test errors than the standard boosted learners for a computational cost that is an order of magnitude smaller than with standard ADABOOST

    On the Divergence-Free Condition in Godunov-Type Schemes for Ideal Magnetohydrodynamics: the Upwind Constrained Transport Method

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    We present a general framework to design Godunov-type schemes for multidimensional ideal magnetohydrodynamic (MHD) systems, having the divergence-free relation and the related properties of the magnetic field B as built-in conditions. Our approach mostly relies on the 'Constrained Transport' (CT) discretization technique for the magnetic field components, originally developed for the linear induction equation, which assures div(B)=0 and its preservation in time to within machine accuracy in a finite-volume setting. We show that the CT formalism, when fully exploited, can be used as a general guideline to design the reconstruction procedures of the B vector field, to adapt standard upwind procedures for the momentum and energy equations, avoiding the onset of numerical monopoles of O(1) size, and to formulate approximate Riemann solvers for the induction equation. This general framework will be named here 'Upwind Constrained Transport' (UCT). To demonstrate the versatility of our method, we apply it to a variety of schemes, which are finally validated numerically and compared: a novel implementation for the MHD case of the second order Roe-type positive scheme by Liu and Lax (J. Comp. Fluid Dynam. 5, 133, 1996), and both the second and third order versions of a central-type MHD scheme presented by Londrillo and Del Zanna (Astrophys. J. 530, 508, 2000), where the basic UCT strategies have been first outlined
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