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Author

Tadeusz Strzemecki

Bio: Tadeusz Strzemecki is an academic researcher from Fordham University. The author has contributed to research in topics: Implicant & Canonical normal form. The author has an hindex of 2, co-authored 2 publications receiving 62 citations.

Papers
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Journal ArticleDOI
TL;DR: It is shown that all presented algorithms are polynomial in the number of minterms occurring in the canonical disjunctive normal form representation of a Boolean function.

54 citations

Book ChapterDOI
25 Mar 2006
TL;DR: This paper proposes a widening that can be used to both constrain the size of an ROBDD and also ensure that the number of times that it is weakened is bounded by some given constant.
Abstract: Despite the ubiquity of ROBDDs in program analysis, and extensive literature on ROBDD minimisation, there is a dearth of work on approximating ROBDDs. The need for approximation arises because many ROBDD operations result in an ROBDD whose size is quadratic in the size of the inputs. Furthermore, if ROBDDs are used in abstract interpretation, the running time of the analysis is related not only to the complexity of the individual ROBDD operations but also the number of operations applied. The number of operations is, in turn, constrained by the number of times a Boolean function can be weakened before stability is achieved. This paper proposes a widening that can be used to both constrain the size of an ROBDD and also ensure that the number of times that it is weakened is bounded by some given constant. The widening can be used to either systematically approximate from above (i.e. derive a weaker function) or below (i.e. infer a stronger function).

10 citations


Cited by
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Journal Article
TL;DR: A novel network control framework that integrates the structural and functional information available for intracellular networks to predict control targets is developed and it is found that the predicted control targets are effective in a broad dynamic framework.
Abstract: Identifying control strategies for biological networks is paramount for practical applications that involve reprogramming a cell’s fate, such as disease therapeutics and stem cell reprogramming. Here we develop a novel network control framework that integrates the structural and functional information available for intracellular networks to predict control targets. Formulated in a logical dynamic scheme, our approach drives any initial state to the target state with 100% effectiveness and needs to be applied only transiently for the network to reach and stay in the desired state. We illustrate our method’s potential to find intervention targets for cancer treatment and cell differentiation by applying it to a leukemia signaling network and to the network controlling the differentiation of helper T cells. We find that the predicted control targets are effective in a broad dynamic framework. Moreover, several of the predicted interventions are supported by experiments.

145 citations

Journal ArticleDOI
TL;DR: In this paper, a novel network control framework that integrates structural and functional information available for intracellular networks to predict control targets is developed, which can drive any initial state to the target state with 100% effectiveness and needs to be applied only transiently for the network to reach and stay in the desired state.
Abstract: Identifying control strategies for biological networks is paramount for practical applications that involve reprogramming a cell’s fate, such as disease therapeutics and stem cell reprogramming. Here we develop a novel network control framework that integrates the structural and functional information available for intracellular networks to predict control targets. Formulated in a logical dynamic scheme, our approach drives any initial state to the target state with 100% effectiveness and needs to be applied only transiently for the network to reach and stay in the desired state. We illustrate our method’s potential to find intervention targets for cancer treatment and cell differentiation by applying it to a leukemia signaling network and to the network controlling the differentiation of helper T cells. We find that the predicted control targets are effective in a broad dynamic framework. Moreover, several of the predicted interventions are supported by experiments.

144 citations

Book ChapterDOI
Pierre Marquis1
01 Jan 2000
TL;DR: In this section, the notion of consequence finding is introduced and motivated in informal terms and the scope of the chapter and its organization are successively pointed out.
Abstract: In this section, the notion of consequence finding is introduced and motivated in informal terms. Then, the scope of the chapter and its organization are successively pointed out.

127 citations

Journal ArticleDOI
TL;DR: It is proved that the Minimum Equivalent DNF problem is ?

112 citations

Journal ArticleDOI
TL;DR: This paper surveys progress in the field with self-contained expositions of fundamental early results, an account of the recent advances, and some new classifications, as well as a discussion of the major remaining open problems in the complexity of logic minimization.
Abstract: The complexity of two-level logic minimization is a topic of interest to both computer-aided design (CAD) specialists and computer science theoreticians. In the logic synthesis community, two-level logic minimization forms the foundation for more complex optimization procedures that have significant real-world impact. At the same time, the computational complexity of two-level logic minimization has posed challenges since the beginning of the field in the 1960s; indeed, some central questions have been resolved only within the last few years, and others remain open. This recent activity has classified some logic optimization problems of high practical relevance, such as finding the minimal sum-of-products (SOP) form and maximal term expansion and reduction. This paper surveys progress in the field with self-contained expositions of fundamental early results, an account of the recent advances, and some new classifications. It includes an introduction to the relevant concepts and terminology from computational complexity, as well a discussion of the major remaining open problems in the complexity of logic minimization

111 citations