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- Resource Type:
- Article
- Creator:
- Bose, Prosenjit, Overmars, M., Wilfong, G., Toussaint, G., Garcia-Lopez, J., Zhu, B., Asberg, B., and Blanco, G.
- Abstract:
- We study the feasibility of design for a layer-deposition manufacturing process called stereolithography which works by controlling a vertical laser beam which when targeted on a photocurable liquid causes the liquid to harden. In order to understand the power as well as the limitations of this manufacturing process better, we define a mathematical model of stereolithography (referred to as vertical stereolithography) and analyze the class of objects that can be constructed under the assumptions of the model. Given an object (modeled as a polygon or a polyhedron), we give algorithms that decide in O(n) time (where n is the number of vertices in the polygon or polyhedron) whether or not the object can be constructed by vertical stereolithography. If the answer is in the affirmative, the algorithm reports a description of all the orientations in which the object can be made. We also show that the objects built with vertical stereolithography are precisely those that can be made with a 3-axis NC machine. We then define a more flexible model that more accurately reflects the actual capabilities of stereolithography (referred to as variable-angle stereolithography) and again study the class of feasible objects for this model. We give an O(n)-time algorithm for polygons and O(n log n)- as well as O(n)-time algorithms for polyhedra. We show that objects formed with variable-angle stereolithography can also be constructed using another manufacturing process known as gravity casting. Furthermore, we show that the polyhedral objects formed by vertical stereolithography are closely related to polyhedral terrains which are important structures in geographic information systems (GIS) and computational geometry. In fact, an object built with variable-angle stereolithography resembles a terrain with overhangs, thus initiating the study of more realistic terrains than the standard ones considered in geographic information systems. Finally, we relate our results to the area of grasping in robotics by showing that the polygonal and polyhedral objects that can be built by vertical stereolithography can be clamped by parallel jaw grippers with any positive-sized gripper.
- Date Created:
- 1997-01-01
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- Resource Type:
- Article
- Creator:
- Sack, Jörg-Rüdiger, Maheshwari, Anil, and Lingas, A.
- Abstract:
- We provide optimal parallel solutions to several link-distance problems set in trapezoided rectilinear polygons. All our main parallel algorithms are deterministic and designed to run on the exclusive read exclusive write parallel random access machine (EREW PRAM). Let P be a trapezoided rectilinear simple polygon with n vertices. In O(log n) time using O(n/log n) processors we can optimally compute: 1. Minimum réctilinear link paths, or shortest paths in the L1 metric from any point in P to all vertices of P. 2. Minimum rectilinear link paths from any segment inside P to all vertices of P. 3. The rectilinear window (histogram) partition of P. 4. Both covering radii and vertex intervals for any diagonal of P. 5. A data structure to support rectilinear link-distance queries between any two points in P (queries can be answered optimally in O(log n) time by uniprocessor). Our solution to 5 is based on a new linear-time sequential algorithm for this problem which is also provided here. This improves on the previously best-known sequential algorithm for this problem which used O(n log n) time and space.5 We develop techniques for solving link-distance problems in parallel which are expected to find applications in the design of other parallel computational geometry algorithms. We employ these parallel techniques, for example, to compute (on a CREW PRAM) optimally the link diameter, the link center, and the central diagonal of a rectilinear polygon.
- Date Created:
- 1995-09-01
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- Resource Type:
- Conference Proceeding
- Creator:
- Peng, Mengfei, Shi, Wei, Croft, William Lee, and Corriveau, Jean-Pierre
- Abstract:
- New threats to networks are constantly arising. This justifies protecting network assets and mitigating the risk associated with attacks. In a distributed environment, researchers aim, in particular, at eliminating faulty network entities. More specifically, much research has been conducted on locating a single static black hole, which is defined as a network site whose existence is known a priori and that disposes of any incoming data without leaving any trace of this occurrence. However, the prevalence of faulty nodes requires an algorithm able to (a) identify faulty nodes that can be repaired without human intervention and (b) locate black holes, which are taken to be faulty nodes whose repair does require human intervention. In this paper, we consider a specific attack model that involves multiple faulty nodes that can be repaired by mobile software agents, as well as a virus v that can infect a previously repaired faulty node and turn it into a black hole. We refer to the task of repairing multiple faulty nodes and pointing out the location of the black hole as the Faulty Node Repair and Dynamically Spawned Black Hole Search. Wefirst analyze the attack model we put forth. We then explain (a) how to identify whether a node is either (1) a normal node or (2) a repairable faulty node or (3) the black hole that has been infected by virus v during the search/repair process and, (b) how to perform the correct relevant actions. These two steps constitute a complex task, which, we explain, significantly differs from the traditional Black Hole Search. We continue by proposing an algorithm to solve this problem in an asynchronous ring network with only one whiteboard (which resides in a node called the homebase). We prove the correctness of our solution and analyze its complexity by both theoretical analysis and experiment evaluation. We conclude that, using our proposed algorithm, b + 4 agents can repair all faulty nodes and locate the black hole infected by a virus v within finite time. Our algorithm works even when the number of faulty nodes b is unknown a priori.
- Date Created:
- 2017-01-01
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- Resource Type:
- Conference Proceeding
- Creator:
- Guo, Yuhong and Li, Xin
- Abstract:
- Semantic scene classification is a challenging problem in computer vision. In this paper, we present a novel multi-level active learning approach to reduce the human annotation effort for training robust scene classification models. Different from most existing active learning methods that can only query labels for selected instances at the target categorization level, i.e., the scene class level, our approach establishes a semantic framework that predicts scene labels based on a latent object-based semantic representation of images, and is capable to query labels at two different levels, the target scene class level (abstractive high level) and the latent object class level (semantic middle level). Specifically, we develop an adaptive active learning strategy to perform multi-level label query, which maintains the default label query at the target scene class level, but switches to the latent object class level whenever an "unexpected" target class label is returned by the labeler. We conduct experiments on two standard scene classification datasets to investigate the efficacy of the proposed approach. Our empirical results show the proposed adaptive multi-level active learning approach can outperform both baseline active learning methods and a state-of-the-art multi-level active learning method.
- Date Created:
- 2014-01-01
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- Resource Type:
- Conference Proceeding
- Creator:
- Oommen, B. John and Polk, Spencer
- Abstract:
- The field of game playing is a particularly well-studied area within the context of AI, leading to the development of powerful techniques, such as the alpha-beta search, capable of achieving competitive game play against an intelligent opponent. It is well known that tree pruning strategies, such as alpha-beta, benefit strongly from proper move ordering, that is, searching the best element first. Inspired by the formerly unrelated field of Adaptive Data Structures (ADSs), we have previously introduced the History-ADS technique, which employs an adaptive list to achieve effective and dynamic move ordering, in a domain independent fashion, and found that it performs well in a wide range of cases. However, previous work did not compare the performance of the History-ADS heuristic to any established move ordering strategy. In an attempt to address this problem, we present here a comparison to two well-known, acclaimed strategies, which operate on a similar philosophy to the History-ADS, the History Heuristic, and the Killer Moves technique. We find that, in a wide range of two-player and multi-player games, at various points in the game’s progression, the History-ADS performs at least as well as these strategies, and, in fact, outperforms them in the majority of cases.
- Date Created:
- 2016-01-01
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- Resource Type:
- Conference Proceeding
- Creator:
- Oommen, B. John and Astudillo, César A.
- Abstract:
- We present a method that employs a tree-based Neural Network (NN) for performing classification. The novel mechanism, apart from incorporating the information provided by unlabeled and labeled instances, re-arranges the nodes of the tree as per the laws of Adaptive Data Structures (ADSs). Particularly, we investigate the Pattern Recognition (PR) capabilities of the Tree-Based Topology-Oriented SOM (TTOSOM) when Conditional Rotations (CONROT) [8] are incorporated into the learning scheme. The learning methodology inherits all the properties of the TTOSOM-based classifier designed in [4]. However, we now augment it with the property that frequently accessed nodes are moved closer to the root of the tree. Our experimental results show that on average, the classification capabilities of our proposed strategy are reasonably comparable to those obtained by some of the state-of-the-art classification schemes that only use labeled instances during the training phase. The experiments also show that improved levels of accuracy can be obtained by imposing trees with a larger number of nodes.
- Date Created:
- 2015-01-01
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- Resource Type:
- Conference Proceeding
- Creator:
- Yazidi, Anis, Oommen, B. John, and Hammer, Hugo Lewi
- Abstract:
- The problem of clustering, or unsupervised classification, has been solved by a myriad of techniques, all of which depend, either directly or implicitly, on the Bayesian principle of optimal classification. To be more specific, within a Bayesian paradigm, if one is to compare the testing sample with only a single point in the feature space from each class, the optimal Bayesian strategy would be to achieve this based on the distance from the corresponding means or central points in the respective distributions. When this principle is applied in clustering, one would assign an unassigned sample into the cluster whose mean is the closest, and this can be done in either a bottom-up or a top-down manner. This paper pioneers a clustering achieved in an “Anti-Bayesian” manner, and is based on the breakthrough classification paradigm pioneered by Oommen et al. The latter relies on a radically different approach for classifying data points based on the non-central quantiles of the distributions. Surprisingly and counter-intuitively, this turns out to work equally or close-to-equally well to an optimal supervised Bayesian scheme, which thus begs the natural extension to the unexplored arena of clustering. Our algorithm can be seen as the Anti-Bayesian counter-part of the wellknown k-means algorithm (The fundamental Anti-Bayesian paradigm need not just be used to the k-means principle. Rather, we hypothesize that it can be adapted to any of the scores of techniques that is indirectly based on the Bayesian paradigm.), where we assign points to clusters using quantiles rather than the clusters’ centroids. Extensive experimentation (This paper contains the prima facie results of experiments done on one and two-dimensional data. The extensions to multi-dimensional data are not included in the interest of space, and would use the corresponding multi-dimensional Anti-Na¨ıve-Bayes classification rules given in [1].) demonstrates that our Anti-Bayesian clustering converges fast and with precision results competitive to a k-means clustering.
- Date Created:
- 2015-01-01
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- Resource Type:
- Conference Proceeding
- Creator:
- Tavasoli, Hanane, Oommen, B. John, and Yazidi, Anis
- Abstract:
- In this paper, we propose a novel online classifier for complex data streams which are generated from non-stationary stochastic properties. Instead of using a single training model and counters to keep important data statistics, the introduced online classifier scheme provides a real-time self-adjusting learning model. The learning model utilizes the multiplication-based update algorithm of the Stochastic Learning Weak Estimator (SLWE) at each time instant as a new labeled instance arrives. In this way, the data statistics are updated every time a new element is inserted, without requiring that we have to rebuild its model when changes occur in the data distributions. Finally, and most importantly, the model operates with the understanding that the correct classes of previously-classified patterns become available at a later juncture subsequent to some time instances, thus requiring us to update the training set and the training model. The results obtained from rigorous empirical analysis on multinomial distributions, is remarkable. Indeed, it demonstrates the applicability of our method on synthetic datasets, and proves the advantages of the introduced scheme.
- Date Created:
- 2016-01-01
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- Resource Type:
- Conference Proceeding
- Creator:
- Polk, Spencer and Oommen, B. John
- Abstract:
- This paper pioneers the avenue of enhancing a well-known paradigm in game playing, namely the use of History-based heuristics, with a totally-unrelated area of computer science, the field of Adaptive Data Structures (ADSs). It is a well-known fact that highly-regarded game playing strategies, such as alpha-beta search, benefit strongly from proper move ordering, and from this perspective, the History heuristic is, probably, one of the most acclaimed techniques used to achieve AI-based game playing. Recently, the authors of this present paper have shown that techniques derived from the field of ADSs, which are concerned with query optimization in a data structure, can be applied to move ordering in multi-player games. This was accomplished by ranking opponent threat levels. The work presented in this paper seeks to extend the utility of ADS-based techniques to two-player and multi-player games, through the development of a new move ordering strategy that incorporates the historical advantages of the moves. The resultant technique, the History-ADS heuristic, has been found to produce substantial (i.e, even up to 70%) savings in a variety of two-player and multi-player games, at varying ply depths, and at both initial and midgame board states. As far as we know, results of this nature have not been reported in the literature before.
- Date Created:
- 2015-01-01
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- Resource Type:
- Conference Proceeding
- Creator:
- Labiche, Yvan and Barros, Márcio
- Date Created:
- 2015-01-01