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 Augmentation for Bipartite Componentwise Biconnectivity in Linear Time

Abstract: A graph is componentwise biconnected if every connected component either is an isolated vertex or is biconnected. We present a linear-time algorithm for the problem of adding the smallest number of edges to make a bipartite graph componentwise biconnected while preserving its bipartiteness. This algorithm has immediate applications for protecting sensitive information in statistical tables.

Address generation for nanowire decoders

Abstract: Nanoscale crossbars built from nanowires can form high density memories and programmable logic devices. To integrate such nanoscale devices with CMOS circuits, nanowire decoders were invented. Due to the stochastic nature of the nanoscale fabrication, the decoder addresses that address the nanowires selectively must be generated after fabrication. In this paper, we develop a mathematical model of the nanowire decoders for the generation of the proper addresses. Assuming a simple testing approach calledon-off measurement, we prove that the maximum number of the proper addresses can be generated in finite time. We design the algorithms to generate the required number of the proper addresses. Experimental results confirm the efficiency of our algorithms.

Average case analysis for tree labelling schemes

Abstract: We study how to label the vertices of a tree in such a way that we can decide the distance of two vertices in the tree given only their labels. For trees, Gavoille et al. [7] proved that for any such distance labelling scheme, the maximum label length is at least ${1 \over 8} {\rm log}^{2} n - O({\rm log} n)$ bits. They also gave a separator-based labelling scheme that has the optimal label length ${\it \Theta}({\rm log} {n} \cdot {\rm log}(H_{n}(T)))$, where Hn(T) is the height of the tree. In this paper, we present two new distance labelling schemes that not only achieve the optimal label length ${\it \Theta}({\rm log} n \cdot {\rm log} (H_{n}(T)))$, but also have a much smaller expected label length under certain tree distributions. With these new schemes, we also can efficiently find the least common ancestor of any two vertices based on their labels only.

Linear-time succinct encodings of planar graphs via canonical orderings

Abstract: Let G be an embedded planar undirected graph that has n vertices, m edges, and f faces but has no self-loop or multiple edge. If G is triangulated, we can encode it using 4/3m-1 bits, improving on the best previous bound of about 1.53m bits. In case exponential time is acceptable, roughly 1.08m bits have been known to suffice. If G is triconnected, we use at most $(2.5+2\log{3})\min\{n,f\}-7$ bits, which is at most 2.835m bits and smaller than the best previous bound of 3m bits. Both of our schemes take O(n) time for encoding and decoding.

Nonplanar topological inference and political-map graphs

Abstract: Given a political map M, we define a mixed graph G where the vertices correspond to the districts on M, the arcs encode the inclusion relation between the districts, and the edges indicate the sharing of boundary points of the districts. We prove that such graphs can be recognized in nondeterministic polynomial time. We then prove that a natural subclass of such graphs can be recognized in O(na(n) log n) time, where n is the number of vertices in the input graph and a(n) is an inverse of Ackermann’s function. The main result is an O(ncr(n)logn)-time algorithm for the following problem II: Given a planar graph G = (V,E) and a partition Vl,Vz,..., V, of V, decide whether G has a planar embedding E such that for every Vi, there is a face Fi in & whose boundary intersects every connected component of the subgraph induced by Vi:,. This result also has an application in printed circuit board design.

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