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Relevant bibliographies by topics / Black-box Analysis

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Academic literature on the topic 'Black-box Analysis'

Author: Grafiati

Published: 4 June 2021

Last updated: 20 June 2025

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Journal articles on the topic "Black-box Analysis"

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NAILA, FIRDAUS, and MANISHA DAWRA Ms. "Requirement Analysis of Black Box Testing." International Journal of Advances in Engineering & Scientific Research 1, no. 5 (2014): 19–23. https://doi.org/10.5281/zenodo.10721079.

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<strong><em>Abstract</em></strong> <em>                        </em><em>Black Box Testing is used when code of the module is not available. Black box testing treats the system as a “black-box”, so it doesn’t explicitly use knowledge of the internal structure or code. Or in other words the Test engineer need not know the internal working of the “Black box” or application. In such situations appropriate priorities can be given to different test cases, so that the quality of software is not compromised, if testing is to be stopped prematurely. This paper briefly sketches a general strategy for black-box testing, different methods involved and the advantages and disadvantages associated with them. The study of this paper would be beneficial to both researchers and practitioners alike in having an understanding of black box testing. </em> <em> </em> <strong><em>Keywords:  </em></strong><em> Black-Box Testing, White-Box Testing, Equivalence Class Portioning, Boundary Value Analysis, Decision Table.</em>

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Leshkevich, Tatiana G. "Metaphors of the digital age and the Black Box Problem." Philosophy of Science and Technology 27, no. 1 (2022): 34–48. http://dx.doi.org/10.21146/2413-9084-2022-27-1-34-48.

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The article discusses the effects of the digital era, the driver of which is AI. The main goal is to focus on the Black Box Problem, “opacity of AI” and the possibility of Malicious Use of Artificial Intelligence. Three interconnected directions are interfaced. Firstly, in the context of the analysis of the digital age, the potential of metaphors is used, which makes it possible to describe digital transformations figuratively. Secondly, due to the growing demand for high technologies, the negative consequences of using AI are considered and a number of paradoxes of scientific and technological progress are formulated. Thirdly, the article examines the widespread practice of trust in intelligent systems, as well as the prospects for technological symbiosis. The analysis is based on the Russian and English-language literature. The author analyzes metaphors that indicate the type of modern existence – “face-to-screen” or “face-to-device” (1); features of the subject of the digital age – “content viewer” (2); the specifics of digital rationality – “knowledge rent”, digital multitasking (3). Attention is drawn to the process of “convergence of subjectivity”. The issue of malicious use of AI is discussed. The author draws conclusions about the need for “algorithmic responsibility” and expanding the field of reflective analysis aimed at studying the consequences of using AI.

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Resende, David Nunes, Xiaowen Yu, Sílvia Gaia, and David V. Gibson. "Inside the Black Box." International Journal of Information Systems and Social Change 8, no. 4 (2017): 1–22. http://dx.doi.org/10.4018/ijissc.2017100101.

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This article uses an original three-phase approach for empirical assessment and comparative evaluation of the efficiency of university technology transfer. It is based on analysis of inputs and outputs of a disclosure phase followed by a value-add phase and a final license phase, using a multidimensional framework. The objective is to find university Technology Transfer Office (TTO) efficiency and effectiveness patterns for each phase as well as overall TT processes. A network Data Envelopment Analysis (DEA) model was used to analyze and describe the complicated TT operational processes using Association of University Technology Managers data collected from 90 US university TTOs for the period 2007-2013. It was concluded that the sampled TTOs were most inefficient in their value-add (2nd) phase and that the average overall efficiency as well as the efficiency of disclosure (1st) and license (3rd) phases decreased during the last five years of data analyzed. In addition, in line with other studies, analysis supports the contention that the presence of medical school does not increase TT efficiency or effectiveness. The present research's contributions focus on three areas: (1) Analysing and modelling TTO valorization and commercialization process with a UML activity diagram to provide a clear picture of TT procedures and processes; (2) Proposing a three-phase DEA framework showing input/output indicators closely related to each phase of processes rather than a black box or separated activities; (3) Offering a strategy to conduct empirical studies on TTO's operational efficiency thereby helping to better understand future research operational problems.

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Mickelborough, Jane. "Unpacking the black box." Reviews in Clinical Gerontology 7, no. 4 (1997): 287–88. http://dx.doi.org/10.1017/s0959259897007417.

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There are two black boxes. One is Pandora's, stuff of myth and legend, and remembered for releasing into the world all its cares and troubles, leaving only hope behind in the bottom of the box. The other, more recent and less mythical, is the in-flight recorder, used to monitor the events in an aircraft's flight, for later analysis in case of difficulty. Both these boxes have their analogy when we consider the role and current state of rehabilitation research in physiotherapy.

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Kim, Byol, and Rina Foygel Barber. "Black-box tests for algorithmic stability." Information and Inference: A Journal of the IMA 12, no. 4 (2023): 2690–719. http://dx.doi.org/10.1093/imaiai/iaad039.

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Abstract Algorithmic stability is a concept from learning theory that expresses the degree to which changes to the input data (e.g. removal of a single data point) may affect the outputs of a regression algorithm. Knowing an algorithm’s stability properties is often useful for many downstream applications—for example, stability is known to lead to desirable generalization properties and predictive inference guarantees. However, many modern algorithms currently used in practice are too complex for a theoretical analysis of their stability properties, and thus we can only attempt to establish these properties through an empirical exploration of the algorithm’s behaviour on various datasets. In this work, we lay out a formal statistical framework for this kind of black-box testing without any assumptions on the algorithm or the data distribution, and establish fundamental bounds on the ability of any black-box test to identify algorithmic stability.

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Lamb, Anthony R. "Computer analysis: avoiding the ‘black box’ syndrome." Proceedings of the Institution of Civil Engineers - Civil Engineering 157, no. 3 (2004): 134–39. http://dx.doi.org/10.1680/cien.2004.157.3.134.

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Lamb, Anthony R. "Computer analysis: avoiding the ‘black box’ syndrome." Civil Engineering Innovation 1, no. 2 (2007): 93–99. http://dx.doi.org/10.1680/einn.2007.1.2.93.

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Lamb, Anthony R. "Computer analysis: avoiding the ‘black box’ syndrome." Civil Engineering Innovation 1, no. 2 (2007): 93–99. http://dx.doi.org/10.1680/jciei.2007.13258.

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Birkmeyer, John D., and Jean Y. Liu. "Decision analysis models: Opening the black box." Surgery 133, no. 1 (2003): 1–4. http://dx.doi.org/10.1067/msy.2003.21.

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Rossi, Dario, Elisa Sottile, and Paolo Veglia. "Black-box analysis of Internet P2P applications." Peer-to-Peer Networking and Applications 4, no. 2 (2010): 146–64. http://dx.doi.org/10.1007/s12083-010-0072-4.

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Dissertations / Theses on the topic "Black-box Analysis"

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Arlt, Stephan [Verfasser], and Andreas [Akademischer Betreuer] Podelski. "Program analysis and black-box GUI testing = Program Analysis und Black-box GUI Testing." Freiburg : Universität, 2014. http://d-nb.info/1123479232/34.

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Mohammadi, Hossein. "Kriging-based black-box global optimization : analysis and new algorithms." Thesis, Lyon, 2016. http://www.theses.fr/2016LYSEM005/document.

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L’«Efficient Global Optimization» (EGO) est une méthode de référence pour l’optimisation globale de fonctions «boites noires» coûteuses. Elle peut cependant rencontrer quelques difficultés, comme le mauvais conditionnement des matrices de covariance des processus Gaussiens (GP) qu’elle utilise, ou encore la lenteur de sa convergence vers l’optimum global. De plus, le choix des paramètres du GP, crucial car il contrôle la famille des fonctions d’approximation utilisées, mériterait une étude plus poussée que celle qui en a été faite jusqu’à présent. Enfin, on peut se demander si l’évaluation classique des paramètres du GP est la plus appropriée à des fins d’optimisation. \\Ce travail est consacré à l'analyse et au traitement des différentes questions soulevées ci-dessus.La première partie de cette thèse contribue à une meilleure compréhension théorique et pratique de l’impact des stratégies de régularisation des processus Gaussiens, développe une nouvelle technique de régularisation, et propose des règles pratiques. Une seconde partie présente un nouvel algorithme combinant EGO et CMA-ES (ce dernier étant un algorithme d’optimisation globale et convergeant). Le nouvel algorithme, nommé EGO-CMA, utilise EGO pour une exploration initiale, puis CMA-ES pour une convergence finale. EGO-CMA améliore les performances des deux algorithmes pris séparément. Dans une troisième partie, l’effet des paramètres du processus Gaussien sur les performances de EGO est soigneusement analysé. Finalement, un nouvel algorithme EGO auto-adaptatif est présenté, dans une nouvelle approche où ces paramètres sont estimés à partir de leur influence sur l’efficacité de l’optimisation elle-même<br>The Efficient Global Optimization (EGO) is regarded as the state-of-the-art algorithm for global optimization of costly black-box functions. Nevertheless, the method has some difficulties such as the ill-conditioning of the GP covariance matrix and the slow convergence to the global optimum. The choice of the parameters of the GP is critical as it controls the functional family of surrogates used by EGO. The effect of different parameters on the performance of EGO needs further investigation. Finally, it is not clear that the way the GP is learned from data points in EGO is the most appropriate in the context of optimization. This work deals with the analysis and the treatment of these different issues. Firstly, this dissertation contributes to a better theoretical and practical understanding of the impact of regularization strategies on GPs and presents a new regularization approach based on distribution-wise GP. Moreover, practical guidelines for choosing a regularization strategy in GP regression are given. Secondly, a new optimization algorithm is introduced that combines EGO and CMA-ES which is a global but converging search. The new algorithm, called EGO-CMA, uses EGO for early exploration and then CMA-ES for final convergence. EGO-CMA improves the performance of both EGO and CMA-ES. Thirdly, the effect of GP parameters on the EGO performance is carefully analyzed. This analysis allows a deeper understanding of the influence of these parameters on the EGO iterates. Finally, a new self-adaptive EGO is presented. With the self-adaptive EGO, we introduce a novel approach for learning parameters directly from their contribution to the optimization

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Atamna, Asma. "Analysis of Randomized Adaptive Algorithms for Black-Box Continuous Constrained Optimization." Thesis, Université Paris-Saclay (ComUE), 2017. http://www.theses.fr/2017SACLS010/document.

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On s'intéresse à l'étude d'algorithmes stochastiques pour l'optimisation numérique boîte-noire. Dans la première partie de cette thèse, on présente une méthodologie pour évaluer efficacement des stratégies d'adaptation du step-size dans le cas de l'optimisation boîte-noire sans contraintes. Le step-size est un paramètre important dans les algorithmes évolutionnaires tels que les stratégies d'évolution; il contrôle la diversité de la population et, de ce fait, joue un rôle déterminant dans la convergence de l'algorithme. On présente aussi les résultats empiriques de la comparaison de trois méthodes d'adaptation du step-size. Ces algorithmes sont testés sur le testbed BBOB (black-box optimization benchmarking) de la plateforme COCO (comparing continuous optimisers). Dans la deuxième partie de cette thèse, sont présentées nos contributions dans le domaine de l'optimisation boîte-noire avec contraintes. On analyse la convergence linéaire d'algorithmes stochastiques adaptatifs pour l'optimisation sous contraintes dans le cas de contraintes linéaires, gérées avec une approche Lagrangien augmenté adaptative. Pour ce faire, on étend l'analyse par chaines de Markov faite dans le cas d'optimisation sans contraintes au cas avec contraintes: pour chaque algorithme étudié, on exhibe une classe de fonctions pour laquelle il existe une chaine de Markov homogène telle que la stabilité de cette dernière implique la convergence linéaire de l'algorithme. La convergence linéaire est déduite en appliquant une loi des grands nombres pour les chaines de Markov, sous l'hypothèse de la stabilité. Dans notre cas, la stabilité est validée empiriquement<br>We investigate various aspects of adaptive randomized (or stochastic) algorithms for both constrained and unconstrained black-box continuous optimization. The first part of this thesis focuses on step-size adaptation in unconstrained optimization. We first present a methodology for assessing efficiently a step-size adaptation mechanism that consists in testing a given algorithm on a minimal set of functions, each reflecting a particular difficulty that an efficient step-size adaptation algorithm should overcome. We then benchmark two step-size adaptation mechanisms on the well-known BBOB noiseless testbed and compare their performance to the one of the state-of-the-art evolution strategy (ES), CMA-ES, with cumulative step-size adaptation. In the second part of this thesis, we investigate linear convergence of a (1 + 1)-ES and a general step-size adaptive randomized algorithm on a linearly constrained optimization problem, where an adaptive augmented Lagrangian approach is used to handle the constraints. To that end, we extend the Markov chain approach used to analyze randomized algorithms for unconstrained optimization to the constrained case. We prove that when the augmented Lagrangian associated to the problem, centered at the optimum and the corresponding Lagrange multipliers, is positive homogeneous of degree 2, then for algorithms enjoying some invariance properties, there exists an underlying homogeneous Markov chain whose stability (typically positivity and Harris-recurrence) leads to linear convergence to both the optimum and the corresponding Lagrange multipliers. We deduce linear convergence under the aforementioned stability assumptions by applying a law of large numbers for Markov chains. We also present a general framework to design an augmented-Lagrangian-based adaptive randomized algorithm for constrained optimization, from an adaptive randomized algorithm for unconstrained optimization

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ROSSIGNOLI, DOMENICO. "DEMOCRACY, INSTITUTIONS AND GROWTH: EXPLORING THE BLACK BOX." Doctoral thesis, Università Cattolica del Sacro Cuore, 2013. http://hdl.handle.net/10280/1870.

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La letteratura economica e politologica evidenzia un ampio consenso sull’esistenza di un effetto positivo sulla crescita di lungo periodo da parte di diritti di proprietà, stato di diritto e, in generale, istituzioni economiche. Contestualmente, il rapporto tra democrazia e crescita rimane teoricamente poco chiaro mentre l'evidenza empirica è in gran parte inconcludente. Questo studio cerca di riconciliare i fatti stilizzati su crescita e democrazia qui evidenziati, che dimostrano l'esistenza di un "successo sinergico" negli ultimi trent'anni, con la teoria esistente e l’evidenza empirica. Dopo aver dettagliatamente scandagliato la letteratura esistente, questo studio suggerisce che l’effetto della democrazia sulla crescita di lungo periodo sia indiretto, mediato dalle istituzioni. Per testare questa ipotesi si propone un modello di analisi originale, applicato ad un panel di 194 paesi osservati nel periodo 1961-2010, utilizzando lo stimatore System-GMM e una vasta gamma di controlli. I risultati dell’analisi suggeriscono che la democrazia è positivamente correlata a istituzioni “più favorevoli” alla crescita economica, in particolare diritti di proprietà e stato di diritto. Inoltre, l’evidenza empirica supporta la tesi di un effetto indiretto complessivamente positivo della democrazia sulla crescita. Infine, si propone uno sviluppo ulteriore dell’analisi, concentrato sulle determinanti della democrazia, ricercando possibili concause nell’interazione con i processi economici.<br>Economic and political science literature show a wide consensus about the positive effect of property rights, contract enforcing arrangements and, more generally, economic institutions to long-run growth. Conversely, the linkage between democracy and growth remains unclear and not conclusively supported by empirical research. This work is an attempt to reconcile the stylized facts about democracy and growth –evidencing a long-run “synergic success” between the two terms – with theoretical and empirical literature. After thoroughly surveying the relevant literature on the topic, this study claims that the effect of democracy on long-run growth is indirect, channeled by the means of institutions. To test this hypothesis, the thesis provides an original analytical framework which is applied to a panel of 194 countries over the period 1961-2010, adopting a System-GMM estimation technique and a wide range of robustness controls. The results suggest that democracy is positively related to “better” (namely more growth-enhancing) institutions, especially with respect to economic institutions and rule of law. Hence, the findings suggest that the overall effect on growth is positive, indirect and channeled by institutions. However, since the results are not completely conclusive, a further investigation is suggested, on further determinants of democracy, potentially affecting its pro-growth effect.

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ROSSIGNOLI, DOMENICO. "DEMOCRACY, INSTITUTIONS AND GROWTH: EXPLORING THE BLACK BOX." Doctoral thesis, Università Cattolica del Sacro Cuore, 2013. http://hdl.handle.net/10280/1870.

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La letteratura economica e politologica evidenzia un ampio consenso sull’esistenza di un effetto positivo sulla crescita di lungo periodo da parte di diritti di proprietà, stato di diritto e, in generale, istituzioni economiche. Contestualmente, il rapporto tra democrazia e crescita rimane teoricamente poco chiaro mentre l'evidenza empirica è in gran parte inconcludente. Questo studio cerca di riconciliare i fatti stilizzati su crescita e democrazia qui evidenziati, che dimostrano l'esistenza di un "successo sinergico" negli ultimi trent'anni, con la teoria esistente e l’evidenza empirica. Dopo aver dettagliatamente scandagliato la letteratura esistente, questo studio suggerisce che l’effetto della democrazia sulla crescita di lungo periodo sia indiretto, mediato dalle istituzioni. Per testare questa ipotesi si propone un modello di analisi originale, applicato ad un panel di 194 paesi osservati nel periodo 1961-2010, utilizzando lo stimatore System-GMM e una vasta gamma di controlli. I risultati dell’analisi suggeriscono che la democrazia è positivamente correlata a istituzioni “più favorevoli” alla crescita economica, in particolare diritti di proprietà e stato di diritto. Inoltre, l’evidenza empirica supporta la tesi di un effetto indiretto complessivamente positivo della democrazia sulla crescita. Infine, si propone uno sviluppo ulteriore dell’analisi, concentrato sulle determinanti della democrazia, ricercando possibili concause nell’interazione con i processi economici.<br>Economic and political science literature show a wide consensus about the positive effect of property rights, contract enforcing arrangements and, more generally, economic institutions to long-run growth. Conversely, the linkage between democracy and growth remains unclear and not conclusively supported by empirical research. This work is an attempt to reconcile the stylized facts about democracy and growth –evidencing a long-run “synergic success” between the two terms – with theoretical and empirical literature. After thoroughly surveying the relevant literature on the topic, this study claims that the effect of democracy on long-run growth is indirect, channeled by the means of institutions. To test this hypothesis, the thesis provides an original analytical framework which is applied to a panel of 194 countries over the period 1961-2010, adopting a System-GMM estimation technique and a wide range of robustness controls. The results suggest that democracy is positively related to “better” (namely more growth-enhancing) institutions, especially with respect to economic institutions and rule of law. Hence, the findings suggest that the overall effect on growth is positive, indirect and channeled by institutions. However, since the results are not completely conclusive, a further investigation is suggested, on further determinants of democracy, potentially affecting its pro-growth effect.

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Karcher, Cody Jacob. "A heuristic for including black box analysis tools into a geometric programming formulation." Thesis, Massachusetts Institute of Technology, 2017. http://hdl.handle.net/1721.1/112465.

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Thesis: S.M., Massachusetts Institute of Technology, Department of Aeronautics and Astronautics, 2017.<br>Cataloged from PDF version of thesis.<br>Includes bibliographical references (pages 79-82).<br>Recently, geometric programming has been proposed as a powerful tool for enhancing aircraft conceptual design. While geometric programming has shown promise in early studies, current formulations preclude the designer from using black box analysis codes which are prolific in the aircraft design community. Previous work has shown the ability to fit data from these black box codes prior to the optimization run, however, this is often a time consuming and computationally expensive process that does not scale well to higher dimensional black boxes. Based upon existing iterative optimization methods, we propose a heuristic for including black box analysis codes in a geometric programming framework by utilizing sequential geometric programming (SGP). We demonstrate a heuristic SGP method and apply it to a solar powered aircraft using a black boxed GP compatible profile drag function. Using this heuristic algorithm, we achieve less than a 1% difference in the objective function between a direct implementation of the constraint and a black box implementation of the constraint.<br>by Cody Jacob Karcher.<br>S.M.

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Eskandari, Aram, and Benjamin Tellström. "Analysis of the Performance Impact of Black-box Randomization for 7 Sorting Algorithms." Thesis, KTH, Skolan för teknikvetenskap (SCI), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-231089.

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Can black-box randomization change the performance of algorithms? The problem of worst-case behaviour in algorithms is difficult to handle, black-box randomization is one method that has not been rigorously tested. If it could be used to mitigate worst-case behaviour for our chosen algorithms, black-box randomization should be seriously considered for active usage in more algorithms. We have found variables that can be put through a black-box randomizer while our algorithm still gives correct output. These variables have been disturbed and a qualitative manual analysis has been done to observe the performance impact during black-box randomization. This analysis was done for 7 different sorting algorithms using Java openJDK 8. Our results show signs of improvement after black-box randomization, however our experiments showed a clear uncertainty when con- ducting time measurements for sorting algorithms.<br>Kan svartlåde-slumpning förändra prestandan hos algoritmer? Problemet med värsta-fall beteende hos algoritmer är svårt att hantera, svartlåde-slumpning är en metod som inte testast rigoröst än. Om det kan utnyttjas för att mildra värsta-fall beteende för våra utval- da algoritmer, bör svartlåde-slumpning beaktas för aktiv användning i fler algoritmer. Vi har funnit variabler som kan köras igenom svartlåde-slumpning samtidigt som vår algoritm ger korrekt utmatning. Dessa variabler har blivit utsatta för små störningar och en kvalitativ manuell ana- lys har gjorts för att observera huruvida prestandan förändrats under svartlåde-slumpning. Denna analys har gjorts för 7 sorteringsalgoritmer med hjälp av Java openJDK 8. Våra resultat visar tecken på förbättring efter svartlåde-slumpning, men våra experiment visade en klar osäkerhet när man utför tidsmätningar på sorteringsalgoritmer.

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Brown, Morgan Lorene. "Of Mice and Men: The Development and Analysis of a Black Box Production." Kent State University Honors College / OhioLINK, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=ksuhonors1575839244848729.

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Menguy, Grégoire. "Black-box code analysis for reverse engineering through constraint acquisition and program synthesis." Electronic Thesis or Diss., université Paris-Saclay, 2023. http://www.theses.fr/2023UPASG023.

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Les logiciels sont de plus en plus grands et complexes. Ainsi, certaines tâches comme le test et la vérification de code, ou la compréhension de code, sont de plus en plus difficiles à réaliser pour un humain. D'où la nécessité de développer des méthodes d'analyse automatique. Celles-ci sont usuellement en boîte blanche, utilisant la syntaxe du code pour déduire ses propriétés. Elles sont très efficaces mais présentent certaines limitations: le code source est nécessaire, la taille et la complexité syntaxique du code (accentuée par des optimisations et de l'obfuscation) impactent leur efficacité. Cette thèse explore comment les méthodes en boîte noire peuvent inférer des propriétés utiles pour la rétro-ingénierie. Nous étudions, tout d'abord, l'inférence de contrat de fonction qui tente d'apprendre sur quelles entrées une fonction peut être exécutée pour obtenir les sorties souhaitées. Nous adaptons l'acquisition de contraintes, en résolvant une de ses principales limitations: la dépendance à un être humain. En ressort PreCA, la première approche totalement boîte noire offrant des garanties claires de correction. PreCA est ainsi particulièrement approprié pour l'aide au développement. Nous étudions ensuite la déobfuscation, qui vise à simplifier du code obfusqué. Nous proposons Xyntia qui synthétise, via des S-métaheuristiques, une version compréhensible de blocs de code. Xyntia est plus rapide et robuste que l'état de l'art. De plus, nous proposons les deux premières protections contre la déobfuscation en boîte noire<br>Software always becomes larger and more complex, making crucial tasks like code testing, verification, or code understanding highly difficult for humans. Hence the need for methods to reason about code automatically. These are usually white-box, and use the code syntax to deduce its properties. While they have proven very powerful, they also show limitations: they need the source code, the code size and the data structures' complexity degrade their efficiency, they are highly impacted by syntactic code complexity amplified by optimizations obfuscations. This thesis explores how black-box code analysis can infer valuable properties for reverse engineering through data-driven learning. First, we consider the function contracts inference problem, which aims to infer over which inputs a code function can be executed to get good behaviors only. We extend the constraint acquisition learning framework, notably solving one of its major flaws: the dependency on a human user. It leads to PreCA, the first black-box approach enjoying clear theoretical guarantees. It makes PreCA especially suitable for development uses. Second, we consider the deobfuscation problem, which aims to simplify obfuscated code. Our proposal, Xyntia, synthesizes code block semantics through S-metaheuristics to offer an understandable version of the code. Xyntia significantly improves the state-of-the-art in terms of robustness and speed. In addition, we propose the two first protections efficient against black-box deobfuscation

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Jankovic, Anja. "Towards Online Landscape-Aware Algorithm Selection in Numerical Black-Box Optimization." Electronic Thesis or Diss., Sorbonne université, 2021. http://www.theses.fr/2021SORUS302.

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Les algorithmes d'optimisation de boîte noire (BBOA) sont conçus pour des scénarios où les formulations exactes de problèmes sont inexistantes, inaccessibles, ou trop complexes pour la résolution analytique. Les BBOA sont le seul moyen de trouver une bonne solution à un tel problème. En raison de leur applicabilité générale, les BBOA présentent des comportements différents lors de l'optimisation de différents types de problèmes. Cela donne un problème de méta-optimisation consistant à choisir l'algorithme le mieux adapté à un problème particulier, appelé problème de sélection d'algorithmes (AS). La vision d'automatiser cette sélection a vite gagné du terrain dans la communauté. Un moyen important de le faire est l'AS tenant compte du paysage, où le choix de l'algorithme est basé sur la prédiction de ses performances via des représentations numériques d'instances de problèmes appelées caractéristiques. Un défi clé auquel l'AS tenant compte du paysage est confrontée est le coût de calcul de l'extraction des caractéristiques, une étape qui précède l'optimisation. Dans cette thèse, nous proposons une approche d'AS tenant compte du paysage basée sur la trajectoire de recherche qui intègre cette étape d'extraction dans celle d'optimisation. Nous montrons que les caractéristiques calculées à l'aide de la trajectoire conduisent à des prédictions robustes et fiables des performances des algorithmes, et à de puissants modèles d'AS construits dessus. Nous présentons aussi plusieurs analyses préparatoires, y compris une perspective de combinaison de 2 stratégies de régression complémentaires qui surpasse des modèles classiques de régression simple et amplifie la qualité du sélecteur<br>Black-box optimization algorithms (BBOAs) are conceived for settings in which exact problem formulations are non-existent, inaccessible, or too complex for an analytical solution. BBOAs are essentially the only means of finding a good solution to such problems. Due to their general applicability, BBOAs can exhibit different behaviors when optimizing different types of problems. This yields a meta-optimization problem of choosing the best suited algorithm for a particular problem, called the algorithm selection (AS) problem. By reason of inherent human bias and limited expert knowledge, the vision of automating the selection process has quickly gained traction in the community. One prominent way of doing so is via so-called landscape-aware AS, where the choice of the algorithm is based on predicting its performance by means of numerical problem instance representations called features. A key challenge that landscape-aware AS faces is the computational overhead of extracting the features, a step typically designed to precede the actual optimization. In this thesis, we propose a novel trajectory-based landscape-aware AS approach which incorporates the feature extraction step within the optimization process. We show that the features computed using the search trajectory samples lead to robust and reliable predictions of algorithm performance, and to powerful algorithm selection models built atop. We also present several preparatory analyses, including a novel perspective of combining two complementary regression strategies that outperforms any of the classical, single regression models, to amplify the quality of the final selector

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Books on the topic "Black-box Analysis"

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Narang, Rishi K. Inside the Black Box. John Wiley & Sons, Ltd., 2009.

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Thomas, Phelps. Black Box Pop: Analysen populärer Musik. Transcript, 2012.

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K, Iyer Ravishankar, and United States. National Aeronautics and Space Administration., eds. Analysis of large system black box verification test data. Coordinated Science Laboratory, College of Engineering, University of Illnois at Urbana-Champaign, 1993.

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Kleinbaum, Adam M. Inside the black box of the corporate staff: An exploratory analysis through the lens of e-mail networks. Harvard Business School, 2011.

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Bibel, G. D. Beyond the black box: The forensics of airplane crashes. Johns Hopkins University Press, 2008.

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Inside the Fft Black Box. Chapman & Hall/CRC, 2001.

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Helms, Dietrich, and Thomas Phleps (verst ). Black Box Pop: Analysen Populärer Musik. Transcript Verlag, 2014.

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Black Box Pop: Analysen Popularer Musik. transcript Verlag, 2014.

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Martynenko, Sergey I. Robust Multigrid Technique: For Black-Box Software. de Gruyter GmbH, Walter, 2017.

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Martynenko, Sergey I. Robust Multigrid Technique: For Black-Box Software. de Gruyter GmbH, Walter, 2017.

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Book chapters on the topic "Black-box Analysis"

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Laski, Janusz, and William Stanley. "Prepare Test for Any Implementation: Black-Box Testing." In Software Verification and Analysis. Springer London, 2009. http://dx.doi.org/10.1007/978-1-84882-240-5_4.

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Vinokurov, Dmitry, and Maxim Buzdalov. "Towards Fixed-Target Black-Box Complexity Analysis." In Lecture Notes in Computer Science. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-14721-0_42.

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Bornot, Sebastien, Remi Morin, Peter Niebert, and Sarah Zennou. "Black Box Unfolding with Local First Search." In Tools and Algorithms for the Construction and Analysis of Systems. Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/3-540-46002-0_27.

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Jager, Tibor. "Analysis of Cryptographic Assumptions in the Generic Ring Model." In Black-Box Models of Computation in Cryptology. Vieweg+Teubner Verlag, 2012. http://dx.doi.org/10.1007/978-3-8348-1990-1_4.

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Waga, Masaki, Ezequiel Castellano, Sasinee Pruekprasert, Stefan Klikovits, Toru Takisaka, and Ichiro Hasuo. "Dynamic Shielding for Reinforcement Learning in Black-Box Environments." In Automated Technology for Verification and Analysis. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-19992-9_2.

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Movin, Maria, Guilherme Dinis Junior, Jaakko Hollmén, and Panagiotis Papapetrou. "Explaining Black Box Reinforcement Learning Agents Through Counterfactual Policies." In Advances in Intelligent Data Analysis XXI. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-30047-9_25.

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Fukuyama, Hirofumi, and William L. Weber. "Measuring Bank Performance: From Static Black Box to Dynamic Network Models." In Handbook of Operations Analytics Using Data Envelopment Analysis. Springer US, 2016. http://dx.doi.org/10.1007/978-1-4899-7705-2_10.

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Shalileh, Soroosh. "Classification Using Marginalized Maximum Likelihood Estimation and Black-Box Variational Inference." In Data Analysis and Optimization. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-31654-8_21.

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Zhao, Mingyi, and Peng Liu. "Empirical Analysis and Modeling of Black-Box Mutational Fuzzing." In Lecture Notes in Computer Science. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-30806-7_11.

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Karbasizadeh, Parsa, Fathiyeh Faghih, and Pouria Golshanrad. "Effective Black Box Testing of Sentiment Analysis Classification Networks." In Lecture Notes in Business Information Processing. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-89277-6_3.

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Conference papers on the topic "Black-box Analysis"

1

Fygenson, Racquel, Kazi Jawad, Isabel Li, et al. "Opening the Black Box of 3D Reconstruction Error Analysis with VECTOR." In 2024 IEEE Visualization and Visual Analytics (VIS). IEEE, 2024. https://doi.org/10.1109/vis55277.2024.00065.

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Cholke, Puja, Parth Kalani, Saniya Patil, and Parth Petkar. "Employing Black Box Mechanism for Investigation and Analysis of Road Accidents." In 2024 5th International Conference on Electronics and Sustainable Communication Systems (ICESC). IEEE, 2024. http://dx.doi.org/10.1109/icesc60852.2024.10690022.

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Siebenkotten, Dario, Manuel Marschall, Arne Hoehl, Bernd Kästner, and Shuhei Amakawa. "Nanoscale Permittivity Analysis in s-SNOM using a Black-Box Calibration Method." In 2024 49th International Conference on Infrared, Millimeter, and Terahertz Waves (IRMMW-THz). IEEE, 2024. http://dx.doi.org/10.1109/irmmw-thz60956.2024.10697669.

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Rodríguez-Lois, Elena, and Fernando Pérez-González. "Collusion-resistant Black-box Watermarking in Federated Learning through Weight Relevance Analysis." In ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2025. https://doi.org/10.1109/icassp49660.2025.10888286.

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Zhao, Zhengnan, Xiao Chen, Wenjuan Du, Bei Zhao, and Hao Chen. "Black-Box Based Grid-Connected Stability Analysis Method for Grid-Following Inverters." In 2024 3rd Asian Conference on Frontiers of Power and Energy (ACFPE). IEEE, 2024. https://doi.org/10.1109/acfpe63443.2024.10801056.

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Mohammed, Nabil, Weihua Zhou, Behrooz Bahrani, David Hill, and Frede Blaabjerg. "PRBS-Based Impedance Measurement Tool for Stability Analysis of Black-Box EMT Models in PSCAD." In 2024 4th International Conference on Emerging Smart Technologies and Applications (eSmarTA). IEEE, 2024. http://dx.doi.org/10.1109/esmarta62850.2024.10638870.

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Rashid, Kashif, William Bailey, and Benoı̂t Couët. "Large-scale expensive black-box function optimization." In NUMERICAL ANALYSIS AND APPLIED MATHEMATICS ICNAAM 2012: International Conference of Numerical Analysis and Applied Mathematics. AIP, 2012. http://dx.doi.org/10.1063/1.4756351.

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Mery, Domingo. "True Black-Box Explanation in Facial Analysis." In 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). IEEE, 2022. http://dx.doi.org/10.1109/cvprw56347.2022.00166.

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Prasad, Monisha J., S. Arundathi, Nayana Anil, Harshikha, and B. S. Kariyappa. "Automobile black box system for accident analysis." In 2014 International Conference on Advances in Electronics, Computers and Communications (ICAECC). IEEE, 2014. http://dx.doi.org/10.1109/icaecc.2014.7002430.

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Jiang, Mingyue, Chengjian Tang, Xiao-Yi Zhang, Yangyang Zhao, and Zuohua Ding. "Automated Image Reduction for Explaining Black-box Classifiers." In 2023 IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER). IEEE, 2023. http://dx.doi.org/10.1109/saner56733.2023.00042.

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Reports on the topic "Black-box Analysis"

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Canto, Patricia, ed. From Plan to Process: Exploring the Leadership Implications of RIS3. Universidad de Deusto, 2015. http://dx.doi.org/10.18543/yxzh8997.

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Regional innovation strategies for smart specialisation (RIS3) are currently receiving much attention in the face of the European Commission’s requirement that all regions develop smart specialisation strategies in order to receive structural funds linked to innovation. Yet the entrepreneurial discovery process that is at the core of the smart specialisation concept remains a black box. The paper looks inside this black box through an exploration of leadership requirements. A review of literature on place leadership and territorial strategy identifies three key considerations for leadership if RIS3 are to result in coherent and successful place based strategies for STI investment: a dualism in reference frame; a mix and rotation of leaderships; and the likely different sources of leaders. These are brought together with analysis of the distinct RIS3 processes in two neighbouring Spanish regions – the Basque Country and Navarre - to highlight a series of implications in terms of the context in which RIS3 develop, the required shift from plans to processes, and the required leadership mix.

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Haney, Richard H., Eric Darve, Mohammad P. Ansari, Rohit Pataki, AmirHossein AminFar, and Dale Shires. Analysis and Implementation of Particle-to-Particle (P2P) Graphics Processor Unit (GPU) Kernel for Black-Box Adaptive Fast Multipole Method. Defense Technical Information Center, 2015. http://dx.doi.org/10.21236/ada625090.

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Chronopoulos, Ilias, Katerina Chrysikou, George Kapetanios, James Mitchell, and Aristeidis Raftapostolos. Deep Neural Network Estimation in Panel Data Models. Federal Reserve Bank of Cleveland, 2023. http://dx.doi.org/10.26509/frbc-wp-202315.

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In this paper we study neural networks and their approximating power in panel data models. We provide asymptotic guarantees on deep feed-forward neural network estimation of the conditional mean, building on the work of Farrell et al. (2021), and explore latent patterns in the cross-section. We use the proposed estimators to forecast the progression of new COVID-19 cases across the G7 countries during the pandemic. We find significant forecasting gains over both linear panel and nonlinear time-series models. Containment or lockdown policies, as instigated at the national level by governments, are found to have out-of-sample predictive power for new COVID-19 cases. We illustrate how the use of partial derivatives can help open the "black box" of neural networks and facilitate semi-structural analysis: school and workplace closures are found to have been effective policies at restricting the progression of the pandemic across the G7 countries. But our methods illustrate significant heterogeneity and time variation in the effectiveness of specific containment policies.

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Schaffer, Arthur A., and Jocelyn Rose. Understanding Cuticle Development in Tomato through the Study of Novel Germplasm with Malformed Cuticles. United States Department of Agriculture, 2013. http://dx.doi.org/10.32747/2013.7593401.bard.

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Plant cuticle development and metabolism are still poorly understood, partly due to the chemical complexity of the cuticular layer. The overall research objective was to broaden and deepen our understanding of tomato fruit cuticle development by analyzing novel germplasm with cuticular malformations and by studying the transcriptome and proteome of the fruit epidermal tissues, as strategies to overcome the challenges posed by the recalcitrance of the biological system. During the project we succeeded in identifying two genes with major impact on cuticle development. One of these encoded the first cutin synthase to be identified in plants, a metabolic step that had been a black box in cutin synthesis. In addition genes controlling the triterpenoid components of the cuticle were identified and, most interestingly, genetic variability for this component was identified among the wild tomato species germplasm. Additional germplasm was developed based on interspecific crosses that will allow for the future characterization of modifier genes that interact with the microfissuring gene (CWP) to promote or inhibit fruit cracking. One of the major accomplishments of the joint project was the integrated transcriptomic and proteomic analysis of the fruit cuticle and underlying tissues which allows for the identification of the pericarp cell layers responsible for the extracellular, cuticle-localized protein component. The results of the project have expanded our understanding of tomato fruit cuticle development and its genetic control. In addition, germplasm developed will be useful in developing tomato varieties resistant to cracking, on the one hand, and varieties useful for the dehydration industry on the other.

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Gerberding, Seth. Black Box Equations of State: Creating Semi-analytic Solutions to the Noh Problem and Verifying Equation of State Interfaces. Office of Scientific and Technical Information (OSTI), 2024. http://dx.doi.org/10.2172/2433996.

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Or, Etti, Tai-Ping Sun, Amnon Lichter, and Avichai Perl. Characterization and Manipulation of the Primary Components in Gibberellin Signaling in the Grape Berry. United States Department of Agriculture, 2010. http://dx.doi.org/10.32747/2010.7592649.bard.

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Seedless cultivars dominate the table grape industry. In these cultivars it is mandatory to apply gibberellin (GA) to stimulate berry development to a commercially acceptable size. These cultivars differ in their sensitivity to GA application, and it frequently results in adverse effects such as decreased bud fertility and increased fruit drop. Our long term goals are to (1) understand the molecular basis for the differential sensitivity and identify markers for selection of sensitive cultivars (2) to develop new strategies for targeted manipulation of the grape berry response to GA that will eliminate the need in GA application and the undesirable effects of GA on the vine, while maintaining its desirable effects on the berry. Both strategies are expected to reduce production cost and meet growing consumer demand for reduced use of chemicals. This approach relies on a comprehensive characterization of the central components in the GA signaling cascade in the berry. Several key components in the GA signaling pathway were identified in Arabidopsis and rice, including the GA receptors, GID1s, and a family of DELLA proteins that are the major negative regulators of the GA response. GA activates its response pathway by binding to GID1s, which then target DELLAs for degradation via interaction with SLY, a DELLA specific F-box protein. In grape, only one DELLA gene was characterized prior to this study, which plays a major role in inhibiting GA-promoted stem growth and GA-repressed floral induction but it does not regulate fruit growth. Therefore, we speculated that other DELLA family member(s) may control GA responses in berry, and their identification and manipulation may result in GA-independent berry growth. In the current study we isolated two additional VvDELLA family members, two VvGID1 genes and two VvSLY genes. Arabidopsis anti-AtRGA polyclonal antibodies recognized all three purified VvDELLA proteins, but its interaction with VvDELLA3 was weaker. Overexpression of the VvDELLAs, the VvGID1s, and the VvSLYs in the Arabidopsis mutants ga1-3/rga-24, gid1a-2/1c-2 and sly1-10, respectively, rescued the various mutant phenotypes. In vitro GAdependent physical interaction was shown between the VvDELLAs and the VvGID1s, and GAindependent interaction was shown between the VvDELLAs and VvSLYs. Interestingly, VvDELLA3 did not interact with VvGID1b. Together, the results indicate that the identified grape homologs serve as functional DELLA repressors, receptors and DELLA-interacting F-box proteins. Expression analyses revealed that (1) VvDELLA2 was expressed in all the analyzed tissues and was the most abundant (2) VvDELLA1 was low expressed in berries, confirming former study (3) Except in carpels and very young berries, VvDELLA3 levels were the lowest in most tissues. (4) Expression of both VvGID1s was detected in all the grape tissues, but VvGID1b transcript levels were significantly higher than VvGID1a. (5) In general, both VvDELLAs and VvGID1s transcripts levels increased as tissues aged. Unfertilized and recently fertilized carpels did not follow this trend, suggesting different regulatory mechanism of GA signaling in these stages. Characterization of the response to GA of various organs in three seedless cultivars revealed differential response of the berries and rachis. Interestingly, VvDELLA3 transcript levels in the GA-unresponsive berries of cv. Spring blush were significantly higher compared to their levels in the highly responsive berries of cv. Black finger. Assuming that VvDELLA2 and VvDELLA3 are regulating berry size, constructs carrying potential dominant mutations in each gene were created. Furthermore, constitutive silencing of these genes by mIR is underway, to reveal the effect of each gene on the berry phenotype.

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