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Ming-Yang Kao

Bio: Ming-Yang Kao is an academic researcher from Northwestern University. The author has contributed to research in topics: Time complexity & Planar graph. The author has an hindex of 37, co-authored 202 publications receiving 4438 citations. Previous affiliations of Ming-Yang Kao include Tufts University & Indiana University.


Papers
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Journal ArticleDOI
TL;DR: Minimizing the total time spent to deliver loads L"1,...,L"n is equivalent to solving the traveling salesman problem (TSP) where the cities correspond to the loads with coordinates (@a"i,@b"i) and the distance from L"i to L"j is given by @!"@a"""i^@b^"^jf(x)dx if @b"j>[email protected]"i and by @!".

12 citations

Book ChapterDOI
10 Oct 1994
TL;DR: This paper surveys these drawing algorithms and discusses some open problems, including visibility representation, straight-line embedding, and rectangular dual problems, used in solving several planar graph drawing problems.
Abstract: The problems of nicely drawing planar graphs have received increasing attention due to their broad applications [5]. A technique, regular edge labeling, was successfully used in solving several planar graph drawing problems, including visibility representation, straight-line embedding, and rectangular dual problems. A regular edge labeling of a plane graph G labels the edges of G so that the edge labels around any vertex show certain regular pattern. The drawing of G is obtained by using the combinatorial structures resulting from the edge labeling. In this paper, we survey these drawing algorithms and discuss some open problems.

12 citations

Journal ArticleDOI
TL;DR: This paper considers a sequence of entirely arbitrary distinct values arriving in random order, and must devise strategies for selecting low values followed by high values in such a way as to maximize the expected gain in rank from low values to high values.
Abstract: In this paper we examine problems motivated by on-line financial problems and stochastic games. In particular, we consider a sequence of entirely arbitrary distinct values arriving in random order, and must devise strategies for selecting low values followed by high values in such a way as to maximize the expected gain in rank from low values to high values. First, we consider a scenario in which only one low value and one high value may be selected. We give an optimal on-line algorithm for this scenario, and analyze it to show that, surprisingly, the expected gain is n-O(1), and so differs from the best possible off-line gain by only a constant additive term (which is, in fact, fairly small---at most 15). In a second scenario, we allow multiple nonoverlapping low/high selections, where the total gain for our algorithm is the sum of the individual pair gains. We also give an optimal on-line algorithm for this problem, where the expected gain is $n^2/8-\Theta(n\log n)$. An analysis shows that the optimal expected off-line gain is $n^2/6+\Theta(1)$, so the performance of our on-line algorithm is within a factor of 3/4 of the best off-line strategy.

12 citations

Journal ArticleDOI
TL;DR: An efficient verifier is given, and based on that, a manually-checkable proof for the NP-hardness of 11-pats is established; the best previous manually- checkable proof is for 29- pats.
Abstract: Patterned self-assembly tile set synthesis (pats) aims at minimizing the number of distinct DNA tile types used to self-assemble a given rectangular color pattern. For an integer k, k-pats is the subproblem of pats that restricts input patterns to those with at most k colors. We give an efficient [InlineEquation not available: see fulltext.] verifier, and based on that, we establish a manually-checkable proof for the NP-hardness of 11-pats; the best previous manually-checkable proof is for 29-pats.

11 citations

Journal ArticleDOI
Ming-Yang Kao1
TL;DR: Using a decomposition approach, this paper establishes a fundamental correspondence between linear invariant sof a table and edge cuts of a graph induced from the table and this correspondence is employed to give a linear-time algorithm for finding animportant class oflinear invariants called therow or column linear invariants.
Abstract: To protect sensitive information in a cross tabulated table, it is acommon practice to suppress some of the cells. A linear combination of thesuppressed cells is called a linear invariant if it has a unique feasible value.Intuitively, the information contained in a linear invariant is not protectedbecause its value can be uniquely determined. Using a decomposition approach,this paper establishes a fundamental correspondence between linear invariantsof a table and edge cuts of a graph induced from the table. Thiscorrespondence is employed to give a linear-time algorithm for finding animportant class of linear invariants called therow or column linear invariants. In subsequent papers, thiscorrespondence is used to solve via graph theoretic techniques a wide varietyof problems for protecting information in a table.

11 citations


Cited by
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Journal ArticleDOI

3,734 citations

Journal ArticleDOI
03 Jun 2011-Science
TL;DR: This work experimentally demonstrated several digital logic circuits, culminating in a four-bit square-root circuit that comprises 130 DNA strands, which enables fast and reliable function in large circuits with roughly constant switching time and linear signal propagation delays.
Abstract: To construct sophisticated biochemical circuits from scratch, one needs to understand how simple the building blocks can be and how robustly such circuits can scale up. Using a simple DNA reaction mechanism based on a reversible strand displacement process, we experimentally demonstrated several digital logic circuits, culminating in a four-bit square-root circuit that comprises 130 DNA strands. These multilayer circuits include thresholding and catalysis within every logical operation to perform digital signal restoration, which enables fast and reliable function in large circuits with roughly constant switching time and linear signal propagation delays. The design naturally incorporates other crucial elements for large-scale circuitry, such as general debugging tools, parallel circuit preparation, and an abstraction hierarchy supported by an automated circuit compiler.

1,249 citations

Journal ArticleDOI
TL;DR: A new de novo sequencing software package, PEAKS, is described, to extract amino acid sequence information without the use of databases, using a new model and a new algorithm to efficiently compute the best peptide sequences whose fragment ions can best interpret the peaks in the MS/MS spectrum.
Abstract: A number of different approaches have been described to identify proteins from tandem mass spectrometry (MS/MS) data. The most common approaches rely on the available databases to match experimental MS/MS data. These methods suffer from several drawbacks and cannot be used for the identification of proteins from unknown genomes. In this communication, we describe a new de novo sequencing software package, PEAKS, to extract amino acid sequence information without the use of databases. PEAKS uses a new model and a new algorithm to efficiently compute the best peptide sequences whose fragment ions can best interpret the peaks in the MS/MS spectrum. The output of the software gives amino acid sequences with confidence scores for the entire sequences, as well as an additional novel positional scoring scheme for portions of the sequences. The performance of PEAKS is compared with Lutefisk, a well-known de novo sequencing software, using quadrupole-time-of-flight (Q-TOF) data obtained for several tryptic peptides from standard proteins.

1,239 citations

Journal ArticleDOI
21 Jul 2011-Nature
TL;DR: It is suggested that DNA strand displacement cascades could be used to endow autonomous chemical systems with the capability of recognizing patterns of molecular events, making decisions and responding to the environment.
Abstract: The impressive capabilities of the mammalian brain—ranging from perception, pattern recognition and memory formation to decision making and motor activity control—have inspired their re-creation in a wide range of artificial intelligence systems for applications such as face recognition, anomaly detection, medical diagnosis and robotic vehicle control Yet before neuron-based brains evolved, complex biomolecular circuits provided individual cells with the ‘intelligent’ behaviour required for survival However, the study of how molecules can ‘think’ has not produced an equal variety of computational models and applications of artificial chemical systems Although biomolecular systems have been hypothesized to carry out neural-network-like computations in vivo and the synthesis of artificial chemical analogues has been proposed theoretically, experimental work has so far fallen short of fully implementing even a single neuron Here, building on the richness of DNA computing and strand displacement circuitry, we show how molecular systems can exhibit autonomous brain-like behaviours Using a simple DNA gate architecture that allows experimental scale-up of multilayer digital circuits, we systematically transform arbitrary linear threshold circuits (an artificial neural network model) into DNA strand displacement cascades that function as small neural networks Our approach even allows us to implement a Hopfield associative memory with four fully connected artificial neurons that, after training in silico, remembers four single-stranded DNA patterns and recalls the most similar one when presented with an incomplete pattern Our results suggest that DNA strand displacement cascades could be used to endow autonomous chemical systems with the capability of recognizing patterns of molecular events, making decisions and responding to the environment

884 citations