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.

Optimal Broadcast in All-Port Wormhole-Routed Hypercubes

Abstract: We give an optimal algorithm that broadcasts on an n-dimensional hypercube in Theta(n/ log_{2}(n+ 1)) routing steps with wormhole, e-cube routing and all-port communication. Previously, the best algorithm of McKinley and Trefftz requires [n/2] routing steps. We also give routing algorithms that achieve tight time bounds for n leqslant 7.

Optimal On-Line Scheduling of Parallel Jobs with Dependencies

Abstract: We study the following general on-line scheduling problem. Paralleljobs arrive on a parallel machine dynamically according to thedependencies between them. Each job requests a certain number ofprocessors in a specific communication configuration, but its runningtime is not known until it is completed. We present optimal on-linealgorithms for PRAMs and one-dimensional meshes, and efficientalgorithms for hypercubes and general meshes. For PRAMs we obtainoptimal tradeoffs between the competitive ratio and the largestnumber of processors requested by any job. Our results demonstrate that on-line scheduling with dependenciesdiffers from scheduling without dependencies in several crucialaspects. First, it is essential to use virtualization, i.e., toschedule parallel jobs on fewer processors than requested. Second,the maximal number of processors requested by a job has significantinfluence on the performance. Third, the geometric structure of thenetwork topology is an even more important factor than in the absenceof dependencies.

Tree Contractions and Evolutionary Trees

Abstract: An evolutionary tree is a rooted tree where each internal vertex has at least two children and where the leaves are labeled with distinct symbols representing species. Evolutionary trees are useful for modeling the evolutionary history of species. An agreement subtree of two evolutionary trees is an evolutionary tree which is also a topological subtree of the two given trees. We give an algorithm to determine the largest possible number of leaves in any agreement subtree of two trees T1 and T2 with n leaves each. If the maximum degree d of these trees is bounded by a constant, the time complexity is O(n log2n) and is within a log n factor of optimal. For general d, this algorithm runs in O(nd2 log d log2 n) time or alternatively in $O(nd\sqrt{d}\log^3{n})$ time.

Optimal Buy-and-Hold Strategies for Financial Markets with Bounded Daily Returns

Abstract: In the context of investment analysis, we formulate an abstract online computing problem called a planning game and develop general tools for solving such a game. We then use the tools to investigate a practical buy-and-hold trading problem faced by long-term investors in stocks. We obtain the unique optimal static online algorithm for the problem and determine its exact competitive ratio. We also compare this algorithm with the popular dollar averaging strategy using actual market data.

Scaling Up Digital Circuit Computation with DNA Strand Displacement Cascades

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.

PEAKS: powerful software for peptide de novo sequencing by tandem mass spectrometry

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.

Neural network computation with DNA strand displacement cascades

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