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.

Randomized Self-assembly for Approximate Shapes

Abstract: In this paper we design tile self-assembly systems which assemble arbitrarily close approximations to target squares with arbitrarily high probability This is in contrast to previous work which has only considered deterministic assemblies of a single shape Our technique takes advantage of the ability to assign tile concentrations to each tile type of a self-assembly system Such an assignment yields a probability distribution over the set of possible assembled shapes We show that by considering the assembly of close approximations to target shapes with high probability, as opposed to exact deterministic assembly, we are able to achieve significant reductions in tile complexity In fact, we restrict ourselves to constant sized tile systems, encoding all information about the target shape into the tile concentration assignment In practice, this offers a potentially useful tradeoff, as large libraries of particles may be infeasible or require substantial effort to create, while the replication of existing particles to adjust relative concentration may be much easier To illustrate our technique we focus on the assembly of n×nsquares, a special case class of shapes whose study has proven fruitful in the development of new self-assembly systems

Searching in an unknown environment: an optimal randomized algorithm for the cow-path problem

Abstract: Searching for a goal is a central and extensively studied problem in computer science. In classical searching problems, the cost of a search function is simply the number of queries made to an oracle that knows the position of the goal. In many robotics problems, as well as in problems from other areas, we want to charge a cost proportional to the distance between queries (e.g., the time required to travel between two query points). With this cost function in mind, the abstract problem known as the $w$-lane cow-path problem was designed. There are known optimal deterministic algorithms for the cow-path problem, and we give the first randomized algorithms in this paper. We show that our algorithm is optimal for two paths ($w=2$), and give evidence that it is indeed optimal for larger values of $w$. The randomized algorithms give expected performance that is almost twice as good as is possible with a deterministic algorithm.

Optimal online 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

Complexities for generalized models of self-assembly

Abstract: In this paper, we extend Rothemund and Winfree's examination of the tile complexity of tile self-assembly [6]. They provided a lower bound of Ω(log N/log log N) on the tile complexity of assembling an N × N square for almost all N. Adleman et al. [1] gave a construction which achieves this bound. We consider whether the tile complexity for self-assembly can be reduced through several natural generalizations of the model. One of our results is a tile set of size O(√log N) which assembles an N × N square in a model which allows flexible glue strength between non-equal glues (This was independently discovered in [3]). This result is matched by a lower bound dictated by Kolmogorov complexity. For three other generalizations, we show that the Ω(log N/log log N) lower bound applies to N × N squares. At the same time, we demonstrate that there are some other shapes for which these generalizations allow reduced tile sets. Specifically, for thin rectangles with length N and width k, we provide a tighter lower bound of Ω(N(1/k)/k) for the standard model, yet we also give a construction which achieves O(log N/log log N) complexity in a model in which the temperature of the tile system is adjusted during assembly. We also investigate the problem of verifying whether a given tile system uniquely assembles into a given shape, and show that this problem is NP-hard.

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