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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
01 Jan 2014
TL;DR: A key finding is that combining the techniques of tilting lattice, extrapolation, and fractional steps substantially increases speed and accuracy.
Abstract: This paper proposes novel lattice algorithms to compute tail conditional expectation of European calls and puts in linear time. We incorporate the technique of prefix-sum into tilting, trinomial, and extrapolation algorithms as well as some syntheses of these algorithms. Furthermore, we introduce fractional-step lattices to help reduce interpolation error in the extrapolation algorithms. We demonstrate the efficiency and accuracy of these algorithms with numerical results. A key finding is that combining the techniques of tilting lattice, extrapolation, and fractional steps substantially increases speed and accuracy.

2 citations

Proceedings ArticleDOI
27 Mar 2014
TL;DR: A deterministic online algorithm and a randomized online algorithm to search for suitable parameter values in Monte Carlo simulation for derivative pricing which are needed to achieve desired precisions are provided.
Abstract: This paper provides a deterministic online algorithm and a randomized online algorithm to search for suitable parameter values in Monte Carlo simulation for derivative pricing which are needed to achieve desired precisions. This paper also gives the competitive ratios of the two algorithms and proves the optimality of the algorithms. Experimental results on the performance of the algorithms are presented and analyzed as well.

2 citations

Journal ArticleDOI
TL;DR: This work studies how to efficiently evaluate prefix sums on positive floating-point numbers such that the worst-case roundoff error of each sum is minimized, and provides experimental comparisons of all the algorithms studied in this paper on inputs that are randomly and uniformly generated.

2 citations

Journal ArticleDOI
TL;DR: This paper gives an approximation algorithm for the STT distance for degree-d trees with arbitrary d and with generalized STT operations and some NP-hardness results related to STT Distance are presented.
Abstract: The subtree transfer (STT) distance is one of the distance metric for comparing phylogenies. Previous work on computing the STT distance considered degree-3 trees only. In this paper, we give an approximation algorithm for the STT distance for degree-d trees with arbitrary d and with generalized STT operations. Also, some NP-hardness results related to STT distance are presented.

2 citations

Posted Content
TL;DR: Deterministic polynomial-time algorithms to construct DNA words based on derandomization techniques that can construct n DNA words of shorter length and satisfy the same sets of constraints as the words constructed by the algorithms of Kao et al. (2009).
Abstract: Designing short DNA words is a problem of constructing a set (i.e., code) of n DNA strings (i.e., words) with the minimum length such that the Hamming distance between each pair of words is at least k and the n words satisfy a set of additional constraints. This problem has applications in, e.g., DNA self-assembly and DNA arrays. Previous works include those that extended results from coding theory to obtain bounds on code and word sizes for biologically motivated constraints and those that applied heuristic local searches, genetic algorithms, and randomized algorithms. In particular, Kao, Sanghi, and Schweller (2009) developed polynomial-time randomized algorithms to construct n DNA words of length within a multiplicative constant of the smallest possible word length (e.g., 9 max{log n, k}) that satisfy various sets of constraints with high probability. In this paper, we give deterministic polynomial-time algorithms to construct DNA words based on derandomization techniques. Our algorithms can construct n DNA words of shorter length (e.g., 2.1 log n + 6.28 k) and satisfy the same sets of constraints as the words constructed by the algorithms of Kao et al. Furthermore, we extend these new algorithms to construct words that satisfy a larger set of constraints for which the algorithms of Kao et al. do not work.

2 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