The Experts below are selected from a list of 452586 Experts worldwide ranked by ideXlab platform
Arthur E Stillman - One of the best experts on this subject based on the ideXlab platform.
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appropriate use criteriaacr appropriateness criteria chronic chest pain High Probability of coronary artery disease
Journal of The American College of Radiology, 2017Co-Authors: Scott Akers, Vandan Panchal, Garth M Beache, Richard K J Brown, Brian B Ghoshhajra, Bruce S Greenberg, Joe Y Hsu, Gregory Kicska, James K Min, Arthur E StillmanAbstract:In patients with chronic chest pain in the setting of High Probability of coronary artery disease (CAD), imaging has major and diverse roles. First, imaging is valuable in determining and documenting the presence, extent, and severity of myocardial ischemia, hibernation, scarring, and/or the presence, site, and severity of obstructive coronary lesions. Second, imaging findings are important in determining the course of management of patients with suspected chronic myocardial ischemia and better defining those patients best suited for medical therapy, angioplasty/stenting, or surgery. Third, imaging is also necessary to determine the long-term prognosis and likely benefit from various therapeutic options by evaluating ventricular function, diastolic relaxation, and end-systolic volume. Imaging studies are also required to demonstrate other abnormalities, such as congenital/acquired coronary anomalies and severe left ventricular hypertrophy, that can produce angina in the absence of symptomatic coronary obstructive disease due to atherosclerosis. Clinical risk assessment is necessary to determine the pretest Probability of CAD. Multiple methods are available to categorize patients as low, medium, or High risk for developing CAD. The American College of Radiology Appropriateness Criteria are evidence-based guidelines for specific clinical conditions that are reviewed annually by a multidisciplinary expert panel. The guideline development and revision include an extensive analysis of current medical literature from peer reviewed journals and the application of well-established methodologies (RAND/UCLA Appropriateness Method and Grading of Recommendations Assessment, Development, and Evaluation or GRADE) to rate the appropriateness of imaging and treatment procedures for specific clinical scenarios. In those instances where evidence is lacking or equivocal, expert opinion may supplement the available evidence to recommend imaging or treatment.
Udo Hoffman - One of the best experts on this subject based on the ideXlab platform.
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acr appropriateness criteria chronic chest pain High Probability of coronary artery disease
Journal of The American College of Radiology, 2011Co-Authors: James P Earls, Richard D White, Pamela K Woodard, Suhny Abbara, Michael K Atalay, Jeffrey J Carr, Linda B Haramati, Robert C Hendel, Udo HoffmanAbstract:Imaging is valuable in determining the presence, extent, and severity of myocardial ischemia and the severity of obstructive coronary lesions in patients with chronic chest pain in the setting of High Probability of coronary artery disease. Imaging is critical for defining patients best suited for medical therapy or intervention, and findings can be used to predict long-term prognosis and the likely benefit from various therapeutic options. Chest radiography, radionuclide single photon-emission CT, radionuclide ventriculography, and conventional coronary angiography are the imaging modalities historically used in evaluating suspected chronic myocardial ischemia. Stress echocardiography, PET, cardiac MRI, and multidetector cardiac CT have all been more recently shown to be valuable in the evaluation of ischemic heart disease. Other imaging techniques may be helpful in those patients who do not present with signs classic for angina pectoris or in those patients who do not respond as expected to standard management. The ACR Appropriateness Criteria ® are evidence-based guidelines for specific clinical conditions that are reviewed every 2 years by a multidisciplinary expert panel. The guideline development and review include an extensive analysis of current medical literature from peer-reviewed journals and the application of a well-established consensus methodology (modified Delphi) to rate the appropriateness of imaging and treatment procedures by the panel. In those instances in which evidence is lacking or not definitive, expert opinion may be used to recommend imaging or treatment.
Robert C Hendel - One of the best experts on this subject based on the ideXlab platform.
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acr appropriateness criteria chronic chest pain High Probability of coronary artery disease
Journal of The American College of Radiology, 2011Co-Authors: James P Earls, Richard D White, Pamela K Woodard, Suhny Abbara, Michael K Atalay, Jeffrey J Carr, Linda B Haramati, Robert C Hendel, Udo HoffmanAbstract:Imaging is valuable in determining the presence, extent, and severity of myocardial ischemia and the severity of obstructive coronary lesions in patients with chronic chest pain in the setting of High Probability of coronary artery disease. Imaging is critical for defining patients best suited for medical therapy or intervention, and findings can be used to predict long-term prognosis and the likely benefit from various therapeutic options. Chest radiography, radionuclide single photon-emission CT, radionuclide ventriculography, and conventional coronary angiography are the imaging modalities historically used in evaluating suspected chronic myocardial ischemia. Stress echocardiography, PET, cardiac MRI, and multidetector cardiac CT have all been more recently shown to be valuable in the evaluation of ischemic heart disease. Other imaging techniques may be helpful in those patients who do not present with signs classic for angina pectoris or in those patients who do not respond as expected to standard management. The ACR Appropriateness Criteria ® are evidence-based guidelines for specific clinical conditions that are reviewed every 2 years by a multidisciplinary expert panel. The guideline development and review include an extensive analysis of current medical literature from peer-reviewed journals and the application of a well-established consensus methodology (modified Delphi) to rate the appropriateness of imaging and treatment procedures by the panel. In those instances in which evidence is lacking or not definitive, expert opinion may be used to recommend imaging or treatment.
Benjamin G Zorn - One of the best experts on this subject based on the ideXlab platform.
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exterminator automatically correcting memory errors with High Probability
Communications of The ACM, 2008Co-Authors: Gene Novark, Emery D Berger, Benjamin G ZornAbstract:Programs written in C and C++ are susceptible to memory errors, including buffer overflows and dangling pointers. These errors, which can lead to crashes, erroneous execution, and security vulnerabilities, are notoriously costly to repair. Tracking down their location in the source code is difficult, even when the full memory state of the program is available. Once the errors are finally found, fixing them remains challenging: even for critical security-sensitive bugs, the average time between initial reports and the issuance of a patch is nearly 1 month. We present Exterminator, a system that automatically corrects heap-based memory errors without programmer intervention. Exterminator exploits randomization to pinpoint errors with High precision. From this information, Exterminator derives runtime patches that fix these errors both in current and subsequent executions. In addition, Exterminator enables collaborative bug correction by merging patches generated by multiple users. We present analytical and empirical results that demonstrate Exterminator's effectiveness at detecting and correcting both injected and real faults.
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exterminator automatically correcting memory errors with High Probability
Programming Language Design and Implementation, 2007Co-Authors: Gene Novark, Emery D Berger, Benjamin G ZornAbstract:Programs written in C and C++ are susceptible to memory errors, including buffer overflows and dangling pointers. These errors, whichcan lead to crashes, erroneous execution, and security vulnerabilities, are notoriously costly to repair. Tracking down their location in the source code is difficult, even when the full memory state of the program is available. Once the errors are finally found, fixing them remains challenging: even for critical security-sensitive bugs, the average time between initial reports and the issuance of a patch is nearly one month.We present Exterminator, a system that automatically correct sheap-based memory errors without programmer intervention. Exterminator exploits randomization to pinpoint errors with High precision. From this information, Exterminator derives runtime patches that fix these errors both in current and subsequent executions. In addition, Exterminator enables collaborative bug correction by merging patches generated by multiple users. We present analytical and empirical results that demonstrate Exterminator's effectiveness at detecting and correcting both injected and real faults.
Jan Vondrak - One of the best experts on this subject based on the ideXlab platform.
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High Probability generalization bounds for uniformly stable algorithms with nearly optimal rate
arXiv: Learning, 2019Co-Authors: Vitaly Feldman, Jan VondrakAbstract:Algorithmic stability is a classical approach to understanding and analysis of the generalization error of learning algorithms. A notable weakness of most stability-based generalization bounds is that they hold only in expectation. Generalization with High Probability has been established in a landmark paper of Bousquet and Elisseeff (2002) albeit at the expense of an additional $\sqrt{n}$ factor in the bound. Specifically, their bound on the estimation error of any $\gamma$-uniformly stable learning algorithm on $n$ samples and range in $[0,1]$ is $O(\gamma \sqrt{n \log(1/\delta)} + \sqrt{\log(1/\delta)/n})$ with Probability $\geq 1-\delta$. The $\sqrt{n}$ overhead makes the bound vacuous in the common settings where $\gamma \geq 1/\sqrt{n}$. A stronger bound was recently proved by the authors (Feldman and Vondrak, 2018) that reduces the overhead to at most $O(n^{1/4})$. Still, both of these results give optimal generalization bounds only when $\gamma = O(1/n)$. We prove a nearly tight bound of $O(\gamma \log(n)\log(n/\delta) + \sqrt{\log(1/\delta)/n})$ on the estimation error of any $\gamma$-uniformly stable algorithm. It implies that for algorithms that are uniformly stable with $\gamma = O(1/\sqrt{n})$, estimation error is essentially the same as the sampling error. Our result leads to the first High-Probability generalization bounds for multi-pass stochastic gradient descent and regularized ERM for stochastic convex problems with nearly optimal rate --- resolving open problems in prior work. Our proof technique is new and we introduce several analysis tools that might find additional applications.
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covering minimum spanning trees of random subgraphs
Random Structures and Algorithms, 2006Co-Authors: Michel X Goemans, Jan VondrakAbstract:We consider the problem of finding a sparse set of edges containing the minimum spanning tree (MST) of a random subgraph of G with High Probability. The two random models that we consider are subgraphs induced by a random subset of vertices, each vertex included independently with Probability p, and subgraphs generated as a random subset of edges, each edge with Probability p.Let n denote the number of vertices, choose p ∈ (0, 1) possibly depending on n, and let b = 1/(1 - p). We show that in both random models, for any weighted graph G, there is a set of edges Q of cardinality O(n logbn) that contains the minimum spanning tree of a random subgraph of G with High Probability. This result is asymptotically optimal. As a consequence, we also give a bound of O(kn) on the size of the union of all minimum spanning trees of G with some k vertices (or edges) removed. More generally, we show a bound of O(n logbn) on the size of a covering set in a matroid of rank n, which contains the minimum-weight basis of a random subset with High Probability. Also, we give a randomized algorithm that calls an MST subroutine only a polylogarithmic number of times and finds the covering set with High Probability. © 2005 Wiley Periodicals, Inc. Random Struct. Alg., 2006
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covering minimum spanning trees of random subgraphs
Symposium on Discrete Algorithms, 2004Co-Authors: Michel X Goemans, Jan VondrakAbstract:We consider the problem of covering the minimum spanning tree (MST) of a random subgraph of G by a sparse set of edges, with High Probability. The two random models that we consider are subgraphs induced by a random subset of vertices, each vertex included independently with Probability p, and subgraphs generated as a random subset of edges, each edge with Probability p.Let n be the number of vertices in G. We show that in both cases, there is a covering set Q of cardinality O(n logb n) where b = 1/(1 -- p) (and p is possibly a function of n) and this is asymptotically optimal. More generally, we show a similar bound on the covering set in a matroid, which contains the minimum-weight basis of a random subset with High Probability. Also, we give a randomized algorithm which calls an MST subroutine only a polylogarithmic number of times, and finds the covering set with High Probability.