The Experts below are selected from a list of 4134 Experts worldwide ranked by ideXlab platform
Dimitris Visvikis - One of the best experts on this subject based on the ideXlab platform.
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PET Reconstruction With Non-Negativity Constraint in Projection Space: Optimization Through Hypo-Convergence
IEEE Transactions on Medical Imaging, 2020Co-Authors: Alexandre Bousse, Matias Courdurier, Élise Émond, Brian F. Hutton, Kris Thielemans, Pablo Irarrazaval, Dimitris VisvikisAbstract:Standard positron emission tomography (PET) reconstruction techniques are based on maximum-likelihood (ML) optimization methods, such as the maximum-likelihood expectation-maximization (MLEM) algorithm and its variations. Most methodologies rely on a positivity constraint on the activity distribution image. Although this constraint is meaningful from a physical point of view, it can be a source of bias for low-count/high-background PET, which can compromise accurate quantification. Existing methods that allow for negative values in the estimated image usually utilize a modified log-likelihood, and therefore break the data statistics. In this paper, we propose to incorporate the positivity constraint on the projections only, by approximating the (penalized) log-likelihood function by an adequate sequence of objective functions that are easily maximized without constraint. This sequence is constructed such that there is hypo-convergence (a type of convergence that allows the convergence of the Maximizers under some conditions) to the original log-likelihood, hence allowing us to achieve maximization with positivity constraint on the projections using simple settings. A complete proof of convergence under weak assumptions is given. We provide results of experiments on simulated data where we compare our methodology with the alternative direction method of multipliers (ADMM) method, showing that our algorithm converges to a Maximizer, which stays in the desired feasibility set, with faster convergence than ADMM. We also show that this approach reduces the bias, as compared with MLEM images, in necrotic tumors-which are characterized by cold regions surrounded by hot structures-while reconstructing similar activity values in hot regions.
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PET Reconstruction with non-Negativity Constraint in Projection Space: Optimization Through Hypo-Convergence
IEEE Transactions on Medical Imaging, 2019Co-Authors: Alexandre Bousse, Matias Courdurier, Élise Émond, Kris Thielemans, Pablo Irarrazaval, Brian Hutton, Dimitris VisvikisAbstract:Standard positron emission tomography (PET) reconstruction techniques are based on maximum-likelihood (ML) optimization methods, such as the maximum-likelihood expectation-maximization (MLEM) algorithm and its variations. Most of these methodologies rely on a positivity constraint on the activity distribution image. Although this constraint is meaningful from a physical point of view, it can be a source of bias for low-count/high-background PET, which can compromise accurate quantification. Existing methods that allow for negative values in the estimated image usually utilize a modified log-likelihood, and therefore break the data statistics. In this work we propose to incorporate the positivity constraint on the projections only, by approximating the (penalized) log-likelihood function by an adequate sequence of objective functions that are easily maximized without constraint. This sequence is constructed such that there is hypo-convergence (a type of convergence that allows the convergence of the Maximizers under some conditions) to the original log-likelihood, hence allowing us to achieve maximization with positivity constraint on the projections using simple settings. A complete proof of convergence under weak assumptions is given. We provide results of experiments on simulated data where we compare our methodology with the alternative direction method of multipliers (ADMM) method, showing that our algorithm converges to a Maximizer which stays in the desired feasibility set, with faster convergence than ADMM. We also show that this approach reduces the bias, as compared with MLEM images, in necrotic tumors-which are characterized by cold regions surrounded by hot structures-while reconstructing similar activity values in hot regions.
Juan M Posada - One of the best experts on this subject based on the ideXlab platform.
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contributions of leaf photosynthetic capacity leaf angle and self shading to the maximization of net photosynthesis in acer saccharum a modelling assessment
Annals of Botany, 2012Co-Authors: Juan M Posada, Risto Sievanen, Christian Messier, Jari Perttunen, Eero Nikinmaa, Martin J LechowiczAbstract:† Background and Aims Plants are expected to maximize their net photosynthetic gains and efficiently use available resources, but the fundamental principles governing trade-offs in suites of traits related to resourceuse optimization remain uncertain. This study investigated whether Acer saccharum (sugar maple) saplings could maximize their net photosynthetic gains through a combination of crown structure and foliar characteristics that let all leaves maximize their photosynthetic light-use efficiency (1). † Methods A functional ‐structural model, LIGNUM, was used to simulate individuals of different leaf area index (LAIind) together with a genetic algorithm to find distributions of leaf angle (LA) and leaf photosynthetic capacity (Amax) that maximized net carbon gain at the whole-plant level. Saplings grown in either the open or in a forest gap were simulated with Amax either unconstrained or constrained to an upper value consistent with reported values for Amax in A. saccharum. † Key Results It was found that total net photosynthetic gain was highest when whole-plant PPFD absorption and leaf 1 were simultaneously maximized. Maximization of 1 required simultaneous adjustments in LA and Amax along gradients of PPFD in the plants. When Amax was constrained to a maximum, plants growing in the open maximized their PPFD absorption but not 1 because PPFD incident on leaves was higher than the PPFD at which 1max was attainable. Average leaf 1 in constrained plants nonetheless improved with increasing LAIind because of an increase in self-shading. † Conclusions It is concluded that there are selective pressures for plants to simultaneously maximize both PPFD absorption at the scale of the whole individual and 1 at the scale of leaves, which requires a highly integrated response between LA, Amax and LAIind. The results also suggest that to maximize 1 plants have evolved mechanisms that co-ordinate the LA and Amax of individual leaves with PPFD availability.
David G Rand - One of the best experts on this subject based on the ideXlab platform.
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cooperation fast and slow meta analytic evidence for a theory of social heuristics and self interested deliberation
Psychological Science, 2016Co-Authors: David G RandAbstract:Does cooperating require the inhibition of selfish urges? Or does "rational" self-interest constrain cooperative impulses? I investigated the role of intuition and deliberation in cooperation by meta-analyzing 67 studies in which cognitive-processing manipulations were applied to economic cooperation games (total N = 17,647; no indication of publication bias using Egger's test, Begg's test, or p-curve). My meta-analysis was guided by the social heuristics hypothesis, which proposes that intuition favors behavior that typically maximizes payoffs, whereas deliberation favors behavior that maximizes one's payoff in the current situation. Therefore, this theory predicts that deliberation will undermine pure cooperation (i.e., cooperation in settings where there are few future consequences for one's actions, such that cooperating is not in one's self-interest) but not strategic cooperation (i.e., cooperation in settings where cooperating can maximize one's payoff). As predicted, the meta-analysis revealed 17.3% more pure cooperation when intuition was promoted over deliberation, but no significant difference in strategic cooperation between more intuitive and more deliberative conditions.
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cooperation fast and slow meta analytic evidence for a theory of social heuristics and self interested deliberation
Social Science Research Network, 2016Co-Authors: David G RandAbstract:Does cooperating require the inhibition of selfish urges? Or does “rational” self-interest constrain cooperative impulses? I investigated the role of intuition and deliberation in cooperation by meta-analyzing 67 studies in which cognitive-processing manipulations were applied to economic cooperation games (total N = 17,647; no indication of publication bias using Egger’s test, Begg’s test, or p-curve). My meta-analysis was guided by the Social Heuristics Hypothesis, which proposes that intuition favors behavior that typically maximizes payoffs, whereas deliberation favors behavior that maximizes one’s payoff in the current situation. Therefore, this theory predicts that deliberation will undermine pure cooperation (i.e., cooperation in settings where there are few future consequences for one’s actions, such that cooperating is not in one’s self-interest) but not strategic cooperation (i.e., cooperation in settings where cooperating can maximize one’s payoff). As predicted, the meta-analysis revealed 17.3% more pure cooperation when intuition was promoted relative to deliberation, but no significant difference in strategic cooperation between more intuitive and more deliberative conditions.
Johannes Rauh - One of the best experts on this subject based on the ideXlab platform.
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maximization of the information divergence from an exponential family and criticality
International Symposium on Information Theory, 2011Co-Authors: Frantisek Matus, Johannes RauhAbstract:The problem to maximize the information divergence from an exponential family is compared to the maximization of an entropy-like quantity over the boundary of a polytope. First-order conditions on directional derivatives define critical sets for the two problems. The bijection between the sets of global Maximizers in the two problems found earlier is extended here to bijections between the sets of local Maximizers and the critical sets. This is based on new inequalities relating the maximized quantities and a reformulation of the first order criticality conditions for the second problem.
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finding the Maximizers of the information divergence from an exponential family
arXiv: Information Theory, 2009Co-Authors: Johannes RauhAbstract:This paper investigates Maximizers of the information divergence from an exponential family $E$. It is shown that the $rI$-projection of a Maximizer $P$ to $E$ is a convex combination of $P$ and a probability measure $P_-$ with disjoint support and the same value of the sufficient statistics $A$. This observation can be used to transform the original problem of maximizing $D(\cdot||E)$ over the set of all probability measures into the maximization of a function $\Dbar$ over a convex subset of $\ker A$. The global Maximizers of both problems correspond to each other. Furthermore, finding all local Maximizers of $\Dbar$ yields all local Maximizers of $D(\cdot||E)$. This paper also proposes two algorithms to find the Maximizers of $\Dbar$ and applies them to two examples, where the Maximizers of $D(\cdot||E)$ were not known before.
Alexandre Bousse - One of the best experts on this subject based on the ideXlab platform.
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PET Reconstruction With Non-Negativity Constraint in Projection Space: Optimization Through Hypo-Convergence
IEEE Transactions on Medical Imaging, 2020Co-Authors: Alexandre Bousse, Matias Courdurier, Élise Émond, Brian F. Hutton, Kris Thielemans, Pablo Irarrazaval, Dimitris VisvikisAbstract:Standard positron emission tomography (PET) reconstruction techniques are based on maximum-likelihood (ML) optimization methods, such as the maximum-likelihood expectation-maximization (MLEM) algorithm and its variations. Most methodologies rely on a positivity constraint on the activity distribution image. Although this constraint is meaningful from a physical point of view, it can be a source of bias for low-count/high-background PET, which can compromise accurate quantification. Existing methods that allow for negative values in the estimated image usually utilize a modified log-likelihood, and therefore break the data statistics. In this paper, we propose to incorporate the positivity constraint on the projections only, by approximating the (penalized) log-likelihood function by an adequate sequence of objective functions that are easily maximized without constraint. This sequence is constructed such that there is hypo-convergence (a type of convergence that allows the convergence of the Maximizers under some conditions) to the original log-likelihood, hence allowing us to achieve maximization with positivity constraint on the projections using simple settings. A complete proof of convergence under weak assumptions is given. We provide results of experiments on simulated data where we compare our methodology with the alternative direction method of multipliers (ADMM) method, showing that our algorithm converges to a Maximizer, which stays in the desired feasibility set, with faster convergence than ADMM. We also show that this approach reduces the bias, as compared with MLEM images, in necrotic tumors-which are characterized by cold regions surrounded by hot structures-while reconstructing similar activity values in hot regions.
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PET Reconstruction with non-Negativity Constraint in Projection Space: Optimization Through Hypo-Convergence
IEEE Transactions on Medical Imaging, 2019Co-Authors: Alexandre Bousse, Matias Courdurier, Élise Émond, Kris Thielemans, Pablo Irarrazaval, Brian Hutton, Dimitris VisvikisAbstract:Standard positron emission tomography (PET) reconstruction techniques are based on maximum-likelihood (ML) optimization methods, such as the maximum-likelihood expectation-maximization (MLEM) algorithm and its variations. Most of these methodologies rely on a positivity constraint on the activity distribution image. Although this constraint is meaningful from a physical point of view, it can be a source of bias for low-count/high-background PET, which can compromise accurate quantification. Existing methods that allow for negative values in the estimated image usually utilize a modified log-likelihood, and therefore break the data statistics. In this work we propose to incorporate the positivity constraint on the projections only, by approximating the (penalized) log-likelihood function by an adequate sequence of objective functions that are easily maximized without constraint. This sequence is constructed such that there is hypo-convergence (a type of convergence that allows the convergence of the Maximizers under some conditions) to the original log-likelihood, hence allowing us to achieve maximization with positivity constraint on the projections using simple settings. A complete proof of convergence under weak assumptions is given. We provide results of experiments on simulated data where we compare our methodology with the alternative direction method of multipliers (ADMM) method, showing that our algorithm converges to a Maximizer which stays in the desired feasibility set, with faster convergence than ADMM. We also show that this approach reduces the bias, as compared with MLEM images, in necrotic tumors-which are characterized by cold regions surrounded by hot structures-while reconstructing similar activity values in hot regions.