The Experts below are selected from a list of 8880 Experts worldwide ranked by ideXlab platform
James T Kwok - One of the best experts on this subject based on the ideXlab platform.
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mandatory Leaf Node prediction in hierarchical multilabel classification
IEEE Transactions on Neural Networks, 2014Co-Authors: James T KwokAbstract:In hierarchical classification, the output labels reside on a tree- or directed acyclic graph (DAG)-structured hierarchy. On testing, the prediction paths of a given test example may be required to end at Leaf Nodes of the label hierarchy. This is called mandatory Leaf Node prediction (MLNP) and is particularly useful, when the Leaf Nodes have much stronger semantic meaning than the internal Nodes. However, while there have been a lot of MLNP methods in hierarchical multiclass classification, performing MLNP in hierarchical multilabel classification is difficult. In this paper, we propose novel MLNP algorithms that consider the global label hierarchy structure. We show that the joint posterior probability over all the Node labels can be efficiently maximized by dynamic programming for label trees, or greedy algorithm for label DAGs. In addition, both algorithms can be further extended for the minimization of the expected symmetric loss. Experiments are performed on real-world MLNP data sets with label trees and label DAGs. The proposed method consistently outperforms other hierarchical and flat multilabel classification methods.
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mandatory Leaf Node prediction in hierarchical multilabel classification
Neural Information Processing Systems, 2012Co-Authors: James T KwokAbstract:In hierarchical classification, the prediction paths may be required to always end at Leaf Nodes. This is called mandatory Leaf Node prediction (MLNP) and is particularly useful when the Leaf Nodes have much stronger semantic meaning than the internal Nodes. However, while there have been a lot of MLNP methods in hierarchical multiclass classification, performing MLNP in hierarchical multilabel classification is much more difficult. In this paper, we propose a novel MLNP algorithm that (i) considers the global hierarchy structure; and (ii) can be used on hierarchies of both trees and DAGs. We show that one can efficiently maximize the joint posterior probability of all the Node labels by a simple greedy algorithm. Moreover, this can be further extended to the minimization of the expected symmetric loss. Experiments are performed on a number of real-world data sets with tree- and DAG-structured label hierarchies. The proposed method consistently outperforms other hierarchical and flat multilabel classification methods.
Alexander Reshetov - One of the best experts on this subject based on the ideXlab platform.
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faster ray packets triangle intersection through vertex culling
2007 IEEE Symposium on Interactive Ray Tracing, 2007Co-Authors: Alexander ReshetovAbstract:Acceleration structures are used in ray tracing to sharply reduce number of ray-triangle intersection tests at the expense of traversing such structures. Bigger structures eliminate more tests, but their traversal becomes less efficient, especially for ray packets, for which number of inactive rays increases at the lower levels of the acceleration structures. For dynamic scenes, building or updating acceleration structures is one of the major performance impediments. We propose a new way to reduce the total number of tests by creating a special transient frustum every time a Leaf is traversed by a packet of rays. This frustum contains intersections of active rays with a Leaf Node and eliminates over 90% of all potential tests. It allows a tenfold reduction in size of acceleration structure whilst still achieving a better performance.
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faster ray packets triangle intersection through vertex culling
International Conference on Computer Graphics and Interactive Techniques, 2007Co-Authors: Alexander ReshetovAbstract:To eliminate unnecessary ray packet-triangle intersection tests, we check for separation of two convex objects: a triangle and a frustum containing intersections of rays with a Leaf Node of an acceleration structure. We show a performance improvement that is proportional to the ratio of lengths of average Leaf edge to triangle edge, which opens new possibilities for creation of better acceleration structures.
Gregory Provan - One of the best experts on this subject based on the ideXlab platform.
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query dags a practical paradigm for implementing belief network inference
arXiv: Artificial Intelligence, 2014Co-Authors: Adnan Darwiche, Gregory ProvanAbstract:We describe a new paradigm for implementing inference in belief networks, which relies on compiling a belief network into an arithmetic expression called a Query DAG (Q-DAG). Each non-Leaf Node of a Q-DAG represents a numeric operation, a number, or a symbol for evidence. Each Leaf Node of a Q-DAG represents the answer to a network query, that is, the probability of some event of interest. It appears that Q-DAGs can be generated using any of the algorithms for exact inference in belief networks --- we show how they can be generated using clustering and conditioning algorithms. The time and space complexity of a Q-DAG generation algorithm is no worse than the time complexity of the inference algorithm on which it is based; that of a Q-DAG on-line evaluation algorithm is linear in the size of the Q-DAG, and such inference amounts to a standard evaluation of the arithmetic expression it represents. The main value of Q-DAGs is in reducing the software and hardware resources required to utilize belief networks in on-line, real-world applications. The proposed framework also facilitates the development of on-line inference on different software and hardware platforms, given the simplicity of the Q-DAG evaluation algorithm. This paper describes this new paradigm for probabilistic inference, explaining how it works, its uses, and outlines some of the research directions that it leads to.
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query dags a practical paradigm for implementing belief network inference
Uncertainty in Artificial Intelligence, 1996Co-Authors: Adnan Darwiche, Gregory ProvanAbstract:We describe a new paradigm for implementing inference in belief networks, which consists of two steps: (1) compiling a belief network into an arithmetic expression called a Query DAG (Q-DAG); and (2) answering queries using a simple evaluation algorithm. Each non-Leaf Node of a Q-DAG represents a numeric operation, a number, or a symbol for evidence. Each Leaf Node of a Q-DAG represents the answer to a network query, that is, the probability of some event of interest. It appears that Q-DAGs can be generated using any of the standard algorithms for exact inference in belief networks -- we show how they can be generated using the clustering algorithm. The time and space complexity of a Q-DAG generation algorithm is no worse than the time complexity of the inference algorithm on which it is based. The complexity of a Q-DAG evaluation algorithm is linear in the size of the Q-DAG, and such inference amounts to a standard evaluation of the arithmetic expression it represents. The main value of Q-DAGs is in reducing the software and hardware resources required to utilize belief networks in on-line, real-world applications. The proposed framework also facilitates the development of on-line inference on different software and hardware platforms due to the simplicity of the Q-DAG evaluation algorithm.
John C Steffens - One of the best experts on this subject based on the ideXlab platform.
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systemic wound induction of potato solanum tuberosum polyphenol oxidase
Phytochemistry, 1995Co-Authors: Piyada Thipyapong, Michelle D Hunt, John C SteffensAbstract:Abstract Plant polyphenol oxidases (PPOs) have long been reported to be inducible upon biotic or abiotic wounding. However, observations of inducible PPO activity are frequently confounded by failure to distinguish PPO induction from loss of PPO latency, or by failure to distinguish PPO activity from peroxidase activity. We report the systemic induction of PPO activity, and increased steady-state levels of PPOs and PPO mRNA in potato ( Solanum tuberosum L.) in response to wounding. During normal growth and development, PPO is present throughout potato Leaf maturation, from the apical Leaf Node through Node 11. In contrast, PPO mRNA is only detectable in apical Leaf Nodes 1–3. Wounding of potato Leaflets at Nodes 6–8 results in 1.7-fold increase in PPO activity in apical Leaf Nodes 1–4 within 48 hr after wounding. The increases in PPO activity are accompanied by comparable increases in PPOs and PPO-specific mRNA. No PPO induction is observed in either Leaf Nodes 5 or 8. These results suggest that only those tissues which are developmentally competent to express PPO mRNA are capable of responding to the systemic wounding signal by increased accumulation of PPO mRNA.
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cdna cloning and expression of potato polyphenol oxidase
Plant Molecular Biology, 1993Co-Authors: Michelle D Hunt, Nancy T Eannetta, Haifeng Yu, Sally M Newman, John C SteffensAbstract:Polyphenol oxidases (PPOs) of plants are copper metalloproteins which catalyze the oxidation of mono- and o-diphenols to o-diquinones. Although PPOs are believed to be primarily responsible for the deleterious browning of many fruit and vegetable crops and are thought to be involved in plant-pest interactions, direct evidence for these roles is lacking. We report the cloning of two PPO cDNAs from Solanum tuberosum leaves. These cDNAs exhibit 97% and 98% sequence similarity at the DNA and deduced amino acid levels, respectively. Putative copper-binding regions of both cDNAs are very similar to those of mammalian, bacterial and Neurospora tyrosinases. Both Leaf PPO cDNAs appear to encode polypeptides which are processed to a mature molecular weight of 57000. In potato leaves, petioles, roots, and flowers, PPO is encoded by ca. 2 kb transcripts. Leaf PPO mRNA is developmentally regulated and only detectable in young foliage. In contrast, the protein profile of immunologically detectable PPO remains constant from the apical Node through the eleventh Leaf Node.
Adnan Darwiche - One of the best experts on this subject based on the ideXlab platform.
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query dags a practical paradigm for implementing belief network inference
arXiv: Artificial Intelligence, 2014Co-Authors: Adnan Darwiche, Gregory ProvanAbstract:We describe a new paradigm for implementing inference in belief networks, which relies on compiling a belief network into an arithmetic expression called a Query DAG (Q-DAG). Each non-Leaf Node of a Q-DAG represents a numeric operation, a number, or a symbol for evidence. Each Leaf Node of a Q-DAG represents the answer to a network query, that is, the probability of some event of interest. It appears that Q-DAGs can be generated using any of the algorithms for exact inference in belief networks --- we show how they can be generated using clustering and conditioning algorithms. The time and space complexity of a Q-DAG generation algorithm is no worse than the time complexity of the inference algorithm on which it is based; that of a Q-DAG on-line evaluation algorithm is linear in the size of the Q-DAG, and such inference amounts to a standard evaluation of the arithmetic expression it represents. The main value of Q-DAGs is in reducing the software and hardware resources required to utilize belief networks in on-line, real-world applications. The proposed framework also facilitates the development of on-line inference on different software and hardware platforms, given the simplicity of the Q-DAG evaluation algorithm. This paper describes this new paradigm for probabilistic inference, explaining how it works, its uses, and outlines some of the research directions that it leads to.
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query dags a practical paradigm for implementing belief network inference
Uncertainty in Artificial Intelligence, 1996Co-Authors: Adnan Darwiche, Gregory ProvanAbstract:We describe a new paradigm for implementing inference in belief networks, which consists of two steps: (1) compiling a belief network into an arithmetic expression called a Query DAG (Q-DAG); and (2) answering queries using a simple evaluation algorithm. Each non-Leaf Node of a Q-DAG represents a numeric operation, a number, or a symbol for evidence. Each Leaf Node of a Q-DAG represents the answer to a network query, that is, the probability of some event of interest. It appears that Q-DAGs can be generated using any of the standard algorithms for exact inference in belief networks -- we show how they can be generated using the clustering algorithm. The time and space complexity of a Q-DAG generation algorithm is no worse than the time complexity of the inference algorithm on which it is based. The complexity of a Q-DAG evaluation algorithm is linear in the size of the Q-DAG, and such inference amounts to a standard evaluation of the arithmetic expression it represents. The main value of Q-DAGs is in reducing the software and hardware resources required to utilize belief networks in on-line, real-world applications. The proposed framework also facilitates the development of on-line inference on different software and hardware platforms due to the simplicity of the Q-DAG evaluation algorithm.