Desh Ranjan

Space-filling curves and their use in the design of geometric data structures

We are given a two-dimensional square grid of size N×N, where N∶=2n and n≥0. A space filling curve (SFC) is a numbering of the cells of this grid with numbers from c+1 to c+N2, for some c≥0. We call a SFC recursive (RSFC) if it can be recursively divided into four square RSFCs of equal size. Examples of well-known RSFCs include the Hilbert curve, the z-curve, and the Gray code.

Quantifiers and approximation

We investigate the relationship between logical expressibility of NP optimization problems and their approximation properties. First such attempt was made by Papadimitrou and Yannakakis (1988), who defined the class of NPO problems MAX NP. We show that many important optimization problems do not belong to MAX NP and that, in fact, there are problems in P which are not in MAX NP. The problems that we consider fit naturally in a new complexity class that we call MAX Π1. We prove that several natural optimization problems are complete for MAX Π1 under approximation-preserving reductions. All these complete problems are not approximable unless P = NP. This motivates the definition of subclasses of MAX Π1 that only contain problems which are presumably eaiser with respect to approximation. In particular, the class that we call RMAX(2) contains approximable problems and problems like MAX CLIQUE that are not known to be nonapproximable. We prove the MAX CLIQUE and several other optimization problems are complete for RMAX(2). All the complete problems in RMAX(2) share the interesting property that they either are nonapproximable or are approximable to any degree of accuracy.

Space Bounded Computations: Review And New Separation Results

In this paper we review the key results about space bounded complexity classes, discuss the central open problems and outline the relevant proof techniques. We show that, for a slightly modified Turing machine model, the low level deterministic and nondeterministic space bounded complexity classes are different. Furthermore, for this computation model, we show that Savitch and Immerman-Szelepcsenyi theorems do not hold in the range lg lg n to lg n. We also discuss some other computation models to bring out and clarify the importance of space constructibility and establish some results about these models. We conclude by enumerating a few open problems which arise out of the discussion.

The random oracle hypothesis is false

The Random Oracle Hypothesis, attributed to Bennett and Gill, essentially states that the relationships between complexity classes which hold for almost all relativized worlds must also hold in the unrelativized case. Although this paper is not the first to provide a counterexample to the Random Oracle Hypothesis, it does provide a most compelling counterexample by showing that for almost all oracles A , IP A ≠ PSPACE A . If the Random Oracle Hypothesis were true, it would contradict Shamirs result that IP = PSPACE. In fact, it is shown that for almost all oracles A , co-NP A ⫋ IP A . These results extend to the multiprover proof systems of Ben-Or, Goldwasser, Killian, and Wigderson. In addition, this paper shows that the Random Oracle Hypothesis is sensitive to small changes in the definition. A class IPP, similar to IP, is defined. Surprisingly, the IPP = PSPACE result holds for all oracle worlds.

On the Computational Complexity of Some Classical Equivalence Relations on Boolean Functions

Abstract. The paper analyzes in terms of polynomial time many-one reductions the computational complexity of several natural equivalence relations on Boolean functions which derive from replacing variables by expressions, one of them is the Boolean isomorphism relation. Most of these computational problems turn out to be between co-NP and

Ranking valid topologies of the secondary structure elements using a constraint graph.

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Solving the secondary structure matching problem in cryo-EM de novo modeling using a constrained K-shortest path graph algorithm

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On the complexity of or-parallelism

Electron cryo-microscopy is a fast advancing biophysical technique to derive three-dimensional structures of large protein complexes. Using this technique, many density maps have been generated at intermediate resolution such as 6-10 Å resolution. Although it is challenging to derive the backbone of the protein directly from such density maps, secondary structure elements such as helices and β-sheets can be computationally detected. Our work in this paper provides an approach to enumerate the top-ranked possible topologies instead of enumerating the entire population of the topologies. This approach is particularly practical for large proteins. We developed a directed weighted graph, the topology graph, to represent the secondary structure assignment problem. We prove that the problem of finding the valid topology with the minimum cost is NP hard. We developed an O(N(2)2(N)) dynamic programming algorithm to identify the topology with the minimum cost. The test of 15 proteins suggests that our dynamic programming approach is feasible to work with proteins of much larger size than we could before. The largest protein in the test contains 18 helical sticks detected from the density map out of 33 helices in the protein.

The Temporal Precedence Problem

Electron cryomicroscopy is becoming a major experimental technique in solving the structures of large molecular assemblies. More and more three-dimensional images have been obtained at the medium resolutions between 5 and 10 Å. At this resolution range, major α-helices can be detected as cylindrical sticks and β-sheets can be detected as plain-like regions. A critical question in de novo modeling from cryo-EM images is to determine the match between the detected secondary structures from the image and those on the protein sequence. We formulate this matching problem into a constrained graph problem and present an O(Δ2N22N) algorithm to this NP-Hard problem. The algorithm incorporates the dynamic programming approach into a constrained K-shortest path algorithm. Our method, DP-TOSS, has been tested using α-proteins with maximum 33 helices and α-β proteins up to five helices and 12 β-strands. The correct match was ranked within the top 35 for 19 of the 20 α-proteins and all nine α-β proteins tested. The results demonstrate that DP-TOSS improves accuracy, time and memory space in deriving the topologies of the secondary structure elements for proteins with a large number of secondary structures and a complex skeleton.

On randomized reductions to sparse sets

AbstractWe formalize the implementation mechanisms required to support or-parallel execution of logic programs in terms of operations on dynamic data structures. Upper and lower bounds are derived, in terms of the number of operationsn performed on the data structure, for the problem of guaranteeing correct semantics during or-parallel execution. The lower bound Ω(lgn) formally proves the impossibility of achieving an ideal implementation (i.e., parallel implementation with constant time overhead per operation). We also derive an upper bound of