Explicit and Efficient Hash Families Suffice for Cuckoo Hashing with a Stash

It is shown that for cuckoo hashing with a stash as proposed by Kirsch et al. (Proc. 16th European Symposium on Algorithms (ESA), pp. 611–622, Springer, Berlin, 2008) families of very simple hash functions can be used, maintaining the favorable performance guarantees: with constant stash size s the probability of a rehash is O(1/n ^s+1), the lookup time and the deletion time are O(s) in the worst case, and the amortized expected insertion time is O(s) as well. Instead of the full randomness needed for the analysis of Kirsch et al. and of Kutzelnigg (Discrete Math. Theor. Comput. Sci., 12(3):81–102, 2010) (resp. Θ(logn)-wise independence for standard cuckoo hashing) the new approach even works with 2-wise independent hash families as building blocks. Both construction and analysis build upon the work of Dietzfelbinger and Woelfel (Proc. 35th ACM Symp. on Theory of Computing (STOC), pp. 629–638, 2003). The analysis, which can also be applied to the fully random case, utilizes a graph counting argument and is much simpler than previous proofs. The results can be generalized to situations where the stash size is non-constant.     

QUBLE: towards blending interactive visual subgraph search queries on large networks

In a previous paper, we laid out the vision of a novel graph query processing paradigm where instead of processing a visual query graph after its construction, it interleaves visual query formulation and processing by exploiting the latency offered by the gui to filter irrelevant matches and prefetch partial query results [8]. Our recent attempts at implementing this vision [8, 9] show significant improvement in system response time (srt) for subgraph queries. However, these efforts are designed specifically for graph databases containing a large collection of small or medium-sized graphs. In this paper, we propose a novel algorithm called quble (QUery Blender for Large nEtworks) to realize this visual subgraph querying paradigm on very large networks (e.g., protein interaction networks, social networks). First, it decomposes a large network into a set of graphlets and supergraphlets using a minimum cut-based graph partitioning technique. Next, it mines approximate frequent and small infrequent fragments (sifs) from them and identifies their occurrences in these graphlets and supergraphlets. Then, the indexing framework of [9] is enhanced so that the mined fragments can be exploited to index graphlets for efficient blending of visual subgraph query formulation and query processing. Extensive experiments on large networks demonstrate effectiveness of quble.     

The Parameterized Complexity of Unique Coverage and Its Variants

In this paper we study the parameterized complexity of the Unique Coverage problem, a variant of the classic Set Cover problem. This problem admits several parameterizations and we show that all, except the standard parameterization and a generalization of it, are unlikely to be fixed-parameter tractable. We use results from extremal combinatorics to obtain the best-known kernel for Unique Coverage and the well-known color-coding technique of Alon et al. (J. ACM 42(4), 844–856, 1995) to show that a weighted version of this problem is fixed-parameter tractable. Our application of color-coding uses an interesting variation of s-perfect hash families called (k,s)-hash families which were studied by Alon et al. (J. Comb. Theory Ser. A 104(1), 207–215, 2003) in the context of a class of codes called parent identifying codes (Barg et al. in SIAM J. Discrete Math. 14(3), 423–431, 2001). To the best of our knowledge, this is the first application of (k,s)-hash families outside the domain of coding theory. We prove the existence of such families of size smaller than the best-known s-perfect hash families using the probabilistic method (Alon and Spencer in The Probabilistic Method, Wiley, New York, 2000). Explicit constructions of such families of size promised by the probabilistic method is open.     

Image hash generation method using hierarchical histogram

Recently, web applications, such as Stock Image and Image Library, are developed to provide the integrated management for user's images. Image hashing techniques are used for the image registration, management and retrieval as the identifier also, investigations have been performed to raise the hash performance like discernment. This paper proposes GLOCAL image hashing method utilizing the hierarchical histogram which is based on histogram bin population method. So far, many studies have proven that image hashing techniques based on this histogram are robust against image processing and geometrical attacks. We modified existing image hashing method developed by our research team [20]. The main idea of the paper is that it helps generate more fluent hash string if we have specific length of histogram bin. Another operation is empowering weighting factor into hash string at each level. Thus, we can raise the magnitude of hash string generated from same context or features and also strengthen the robustness of generated hash.     

Fast Phylogeny Reconstruction Through Learning of Ancestral Sequences

Given natural limitations on the length DNA sequences, designing phylogenetic reconstruction methods which are reliable under limited information is a crucial endeavor. There have been two approaches to this problem: reconstructing partial but reliable information about the tree (Mossel in IEEE Comput. Biol. Bioinform. 4:108–116, 2007; Daskalakis et al. in SIAM J. Discrete Math. 25:872–893, 2011; Daskalakis et al. in Proc. of RECOMB 2006, pp. 281–295, 2006; Gronau et al. in Proc. of the 19th Annual SODA 2008, pp. 379–388, 2008), and reaching “deeper” in the tree through reconstruction of ancestral sequences. In the latter category, Daskalakis et al. (Proc. of the 38th Annual STOC, pp. 159–168, 2006) settled an important conjecture of M. Steel (My favourite conjecture. Preprint, 2001), showing that, under the CFN model of evolution, all trees on n leaves with edge lengths bounded by the Ising model phase transition can be recovered with high probability from genomes of length O(logn) with a polynomial time algorithm. Their methods had a running time of O(n ¹⁰). Here we enhance our methods from Daskalakis et al. (Proc. of RECOMB 2006, pp. 281–295, 2006) with the learning of ancestral sequences and provide an algorithm for reconstructing a sub-forest of the tree which is reliable given available data, without requiring a-priori known bounds on the edge lengths of the tree. Our methods are based on an intuitive minimum spanning tree approach and run in O(n ³) time. For the case of full reconstruction of trees with edges under the phase transition, we maintain the same asymptotic sequence length requirements as in Daskalakis et al. (Proc. of the 38th Annual STOC, pp. 159–168, 2006), despite the considerably faster running time.     

An Improved FPT Algorithm and a Quadratic Kernel for Pathwidth One Vertex Deletion

The Pathwidth One Vertex Deletion (POVD) problem asks whether, given an undirected graph?G and an integer k, one can delete at most k vertices from?G so that the remaining graph has pathwidth at most 1. The question can be considered as a natural variation of the extensively studied Feedback Vertex Set (FVS) problem, where the deletion of at most k vertices has to result in the remaining graph having treewidth at most 1 (i.e., being a forest). Recently Philip et?al. (WG, Lecture Notes in Computer Science, vol.?6410, pp.?196?C207, 2010) initiated the study of the parameterized complexity of POVD, showing a quartic kernel and an algorithm which runs in time 7 ^k n ^O(1). In this article we improve these results by showing a quadratic kernel and an algorithm with time complexity 4.65 ^k n ^O(1), thus obtaining almost tight kernelization bounds when compared to the general result of Dell and van Melkebeek (STOC, pp.?251?C260, ACM, New York, 2010). Techniques used in the kernelization are based on the quadratic kernel for FVS, due to Thomassé (ACM Trans. Algorithms 6(2), 2010).     

A New Nonlocal H 1 Model for Image Denoising

Following ideas of Kindermann et al. (Multiscale Model. Simul. 4(4):1091–1115, 2005) and Gilboa and Osher (Multiscale Model. Simul. 7:1005–1028, 2008) we introduce new nonlocal operators to interpret the nonlocal means filter (NLM) as a regularization of the corresponding Dirichlet functional. Then we use these nonlocal operators to propose a new nonlocal H ¹ model, which is (slightly) different from the nonlocal H ¹ model of Gilboa and Osher (Multiscale Model. Simul. 6(2):595–630, 2007; Proc. SPIE 6498:64980U, 2007). The key point is that both the fidelity and the smoothing term are derived from the same geometric principle. We compare this model with the nonlocal H ¹ model of Gilboa and Osher and the nonlocal means filter, both theoretically and in computer experiments. The experiments show that this new nonlocal H ¹ model also provides good results in image denoising and closer to the nonlocal means filter than the H ¹ model of Gilboa and Osher. This means that the new nonlocal operators yield a better interpretation of the nonlocal means filter than the nonlocal operators given in Gilboa and Osher (Multiscale Model. Simul. 7:1005–1028, 2008).     

On sunflowers and matrix multiplication

We present several variants of the sunflower conjecture of Erd?s & Rado (J Lond Math Soc 35:85–90, 1960) and discuss the relations among them. We then show that two of these conjectures (if true) imply negative answers to the questions of Coppersmith & Winograd (J Symb Comput 9:251–280, 1990) and Cohn et al. (2005) regarding possible approaches for obtaining fast matrix-multiplication algorithms. Specifically, we show that the Erd?s–Rado sunflower conjecture (if true) implies a negative answer to the “no three disjoint equivoluminous subsets” question of Coppersmith & Winograd (J Symb Comput 9:251–280, 1990); we also formulate a “multicolored” sunflower conjecture in ${\mathbb{Z}_3^n}$ and show that (if true) it implies a negative answer to the “strong USP” conjecture of Cohn et al. (2005) (although it does not seem to impact a second conjecture in Cohn et al. (2005) or the viability of the general group-theoretic approach). A surprising consequence of our results is that the Coppersmith–Winograd conjecture actually implies the Cohn et al. conjecture. The multicolored sunflower conjecture in ${\mathbb{Z}_3^n}$ is a strengthening of the well-known (ordinary) sunflower conjecture in ${\mathbb{Z}_3^n}$ , and we show via our connection that a construction from Cohn et al. (2005) yields a lower bound of (2.51 . . .) ⁿ on the size of the largest multicolored 3-sunflower-free set, which beats the current best-known lower bound of (2.21 . . . ) ⁿ Edel (2004) on the size of the largest 3-sunflower-free set in ${\mathbb{Z}_3^n}$ .     

Sufficient conditions for sound tree and sequential hashing modes

Hash functions are usually composed of a mode of operation on top of a concrete primitive with fixed input-length and fixed output-length, such as a block cipher or a permutation. In practice, the mode is often sequential, although parallel (or tree) hashing modes are also possible. The former requires less memory, while the latter has several advantages such as its inherent parallelism and a lower cost of hash value recomputation when only a small part of the input changes. In this paper, we consider the general case of (tree or sequential) hashing modes that make use of an underlying hash function, which may in turn be sequential. We formulate a set of three simple conditions for such a (tree or sequential) hashing mode to be sound. By sound, we mean that the advantage in differentiating a hash function obtained by applying a tree hashing mode to an ideal underlying hash function from an ideal monolithic hash function is upper bounded by \(q^2/2^{n+1}\) with \(q\) the number of queries to the underlying hash function and \(n\) the length of the chaining values. We provide a proof of soundness in the indifferentiability framework. The conditions we formulate are easy to implement and to verify and can be used by the practitioner to build a tree hashing mode on top of an existing hash function. We show how to apply tree hashing modes to sequential hash functions in an optimal way, demonstrate the applicability of our conditions with two efficient and simple tree hashing modes and provide a simple method to take the union of tree hashing modes that preserves soundness. It turns out that sequential hashing modes using a compression function (i.e., a hash function with fixed input-length) can be considered as particular cases and, as a by-product, our results also apply to them. We discuss the different techniques for satisfying the three conditions, thereby shedding a new light on several published modes.     

An optimal arc consistency algorithm for a particular case of sequence constraint

The AtMostSeqCard constraint is the conjunction of a cardinality constraint on a sequence of n variables and of n???q?+?1 constraints AtMost u on each subsequence of size q. This constraint is useful in car-sequencing and crew-rostering problems. In van Hoeve et al. (Constraints 14(2):273–292, 2009), two algorithms designed for the AmongSeq constraint were adapted to this constraint with an O(2 ^q n) and O(n ³) worst case time complexity, respectively. In Maher et al. (2008), another algorithm similarly adaptable to filter the AtMostSeqCard constraint with a time complexity of O(n ²) was proposed. In this paper, we introduce an algorithm for achieving arc consistency on the AtMostSeqCard constraint with an O(n) (hence optimal) worst case time complexity. Next, we show that this algorithm can be easily modified to achieve arc consistency on some extensions of this constraint. In particular, the conjunction of a set of m AtMostSeqCard constraints sharing the same scope can be filtered in O(nm). We then empirically study the efficiency of our propagator on instances of the car-sequencing and crew-rostering problems.     

Fermat’s last theorem and chaoticity

Proving that a dynamical system is chaotic is a central problem in chaos theory (Hirsch in Chaos, fractals and dynamics, 1985]. In this note we apply the computational method developed in (Calude and Calude in Complex Syst 18:267?C285, 2009; Calude and Calude in Complex Syst 18:387?C401, 2010; Calude et al in J Multi Valued Log Soft Comput 12:285?C307, 2006) to show that Fermat??s last theorem is in the lowest complexity class ${{\mathfrak C}_{U,1}}$ . Using this result we prove the existence of a two-dimensional Hamiltonian system for which the proof that the system has a Smale horseshoe is in the class ${{\mathfrak C}_{U,1}}$ , i.e. it is not too complex.     

A Uniform Paradigm to Succinctly Encode Various Families of Trees

We propose a uniform method to encode various types of trees succinctly. These families include ordered (ordinal), k-ary (cardinal), and unordered (free) trees. We will show the approach is intrinsically suitable for obtaining entropy-based encodings of trees (such as the degree-distribution entropy). Previously-existing succinct encodings of trees use ad hoc techniques to encode each particular family of trees. Additionally, the succinct encodings obtained using the uniform approach improve upon the existing succinct encodings of each family of trees; in the case of ordered trees, it simplifies the encoding while supporting the full set of navigational operations. It also simplifies the implementation of many supported operations. The approach applied to k-ary trees yields a succinct encoding that supports both cardinal-type operations (e.g. determining the child label i) as well as the full set of ordinal-type operations (e.g. reporting the number of siblings to the left of a node). Previous work on succinct encodings of k-ary trees does not support both types of operations simultaneously (Benoit et al. in Algorithmica 43(4):275–292, 2005; Raman et al. in ACM-SIAM Symposium on Discrete Algorithms (SODA), pp. 233–242, 2002). For unordered trees, the approach achieves the first succinct encoding. The approach is based on two recursive decompositions of trees into subtrees. Recursive decomposition of a structure into substructures is a common technique in succinct encodings and has even been used to encode (ordered) trees (Geary et al. in ACM Trans. Algorithms 2(4):510–534, 2006; He et al. in ICALP, pp. 509–520, 2007) and dynamic binary trees (Munro et al. in ACM-SIAM Symposium on Discrete Algorithms (SODA), pp. 529–536, 2001; Storm in Representing dynamic binary trees succinctly, Master’s thesis, 2000). The main distinction of the approach in this paper is that a tree is decomposed into subtrees in a manner that the subtrees are maximally isolated from each other. This intermediate decomposition result is interesting in its own right and has proved useful in other applications (Farzan et al. in ICALP (1), pp. 451–462, 2009; Farzan and Munro in ICALP (1), pp. 439–450, 2009; Farzan and Kamali in ICALP, 2011).     

A Robust Mobile Target Localization Method for Cooperative Unmanned Aerial Vehicles Using Sensor Fusion Quality

One of the current unmanned systems research areas at the US Air Force Academy is finding robust methods to locate ground mobile targets using multiple, cooperative unmanned aerial vehicles (UAVs). In our previous work (Plett et al., Lect Notes Control Inf Sci 369:22–44, 2007), we showed an effective method to search, detect, and localize static ground targets. The current focus of our research is to extend the method to handle mobile ground targets. To that end, we introduced a novel concept called Sensor Fusion Quality (SFQ) in Kwon and Pack (2011). In this paper, we adapt and incorporate the SFQ principle to include both static and mobile ground targets in a modified Out-of-Order Sigma Point Kalman Filtering (O³SPKF) approach (Plett et al., Lect Notes Control Inf Sci 369:22–44, 2007). The proposed method uses augmented covariances of sigma points to compute SFQ values. This approach showed superior performance over those observed when either the SFQ method or the O³SPKF method was used alone. The added benefit of the integrated approach is in the reduction of computational complexity associated with the propagation updates of target state uncertainties. We validate the proposed method using both simulation and flight experiment results.     

Multi-player epistemic games: Guiding the enactment of classroom knowledge-building communities

Teachers and students face many challenges in shifting from traditional classroom cultures to enacting the Knowledge-Building Communities model (KBC model) supported by the CSCL environment, Knowledge Forum (Bereiter, 2002; Bereiter & Scardamalia, 1993; Scardamalia, 2002; Scardamalia & Bereiter, 2006). Enacting the model involves socializing students into knowledge work, similar to disciplinary communities. A useful construct in the field of the Learning Sciences for understanding knowledge work is “epistemic games” (Collins & Ferguson, 1993; Morrison & Collins 1995; Perkins, 1997). We propose that a powerful means for supporting classroom enactments of the KBC model entails conceptualizing Knowledge Forum as a collective space for playing multi-player epistemic games. Participation in knowledge-building communities is then scaffolded through learning the moves of such games. We have designed scaffolding tools that highlight particular knowledge-building moves for practice and reflection as a means of supporting students and teachers in coming to understand how to collectively work together toward the progressive improvement of ideas. In order to examine our design theories in practice, we present research on Ideas First, a design-based research program involving enactments of the KBC model in Singaporean primary science classrooms (Bielaczyc & Ow, 2007, 2010; Ow & Bielaczyc, 2007; 2008).     

Dual Norm Based Iterative Methods for Image Restoration

A convergent iterative regularization procedure based on the square of a dual norm is introduced for image restoration models with general (quadratic or non-quadratic) convex fidelity terms. Iterative regularization methods have been previously employed for image deblurring or denoising in the presence of Gaussian noise, which use L ² (Tadmor et?al. in Multiscale Model. Simul. 2:554?C579, 2004; Osher et?al. in Multiscale Model. Simul. 4:460?C489, 2005; Tadmor et?al. in Commun. Math. Sci. 6(2):281?C307, 2008), and L ¹ (He et?al. in J.?Math. Imaging Vis. 26:167?C184, 2005) data fidelity terms, with rigorous convergence results. Recently, Iusem and Resmerita (Set-Valued Var. Anal. 18(1):109?C120, 2010) proposed a proximal point method using inexact Bregman distance for minimizing a convex function defined on a non-reflexive Banach space (e.g. BV(??)), which is the dual of a separable Banach space. Based on this method, we investigate several approaches for image restoration such as image deblurring in the presence of noise or image deblurring via (cartoon+texture) decomposition. We show that the resulting proximal point algorithms approximate stably a true image. For image denoising-deblurring we consider Gaussian, Laplace, and Poisson noise models with the corresponding convex fidelity terms as in the Bayesian approach. We test the behavior of proposed algorithms on synthetic and real images in several numerical experiments and compare the results with other state-of-the-art iterative procedures based on the total variation penalization as well as the corresponding existing one-step gradient descent implementations. The numerical experiments indicate that the iterative procedure yields high quality reconstructions and superior results to those obtained by one-step standard gradient descent, with faster computational time.     

Better Size Estimation for Sparse Matrix Products

We consider the problem of doing fast and reliable estimation of the number z of non-zero entries in a sparse boolean matrix product. This problem has applications in databases and computer algebra. Let n denote the total number of non-zero entries in the input matrices. We show how to compute a 1±ε approximation of z (with small probability of error) in expected time for any $\varepsilon> 4/\sqrt[4]{z}$ . The previously best estimation algorithm, due to Cohen (J. Comput. Syst. Sci. 53(3):441–453, 1997), uses time . We also present a variant using I/Os in expectation in the cache-oblivious model. In contrast to these results, the currently best algorithms for computing a sparse boolean matrix product use time ω(n ^4/3) (resp. ω(n ^4/3/B) I/Os), even if the result matrix is restricted to nonzero entries. Our algorithm combines the size estimation technique of Bar-Yossef et al. (Proceedings of the 6th International Workshop on Randomization and Approximation Techniques (RANDOM ’02), pp. 1–10, 2002) with a particular class of pairwise independent hash functions that allows the sketch of a set of the form to be computed in expected time and I/Os. We then describe how sampling can be used to maintain (independent) sketches of matrices that allow estimation to be performed in time o(n) if z is sufficiently large. This gives a simpler alternative to the sketching technique of Ganguly et al. (Proceedings of the 24th ACM Symposium on Principles of Database Systems (PODS ’05), pp. 259–270, 2005), and matches a space lower bound shown in that paper. Finally, we present experiments on real-world data sets that show the accuracy of both our methods to be significantly better than the worst-case analysis predicts.     

Comments and further improvements on “pth moment stability of stochastic neural networks with Markov volatilities”

In a very recent paper, Peng and Liu (Neural Comput Appl 20:543–547, 2011) investigated the pth moment stability of the stochastic Grossberg–Hopfield neural networks with Markov volatilities by Mao et al. (Bernoulli 6:73–90, 2000, Theorem 4.1). We should point out that Mao et al. (Bernoulli 6:73–90, 2000, Theorem 4.1) investigated the pth moment exponentially stable for a class of stochastic dynamical systems with constant delay; however, this theorem cannot apply to the case of variable time delays. It is also worthy to emphasize that Peng and Liu (Neural Comput Appl 20:543–547, 2011) discussed by Mao et al. (Bernoulli 6:73–90, 2000, Theorem 4.1) the pth moment exponentially stable for the Grossberg–Hopfield neural networks with variable delays, and therefore, there are some gaps between Peng and Liu (Neural Comput Appl 20:543–547, 2011, Theorem 1) and Mao et al. (Bernoulli 6:73–90, 2000, Theorem 4.1). In this paper, we fill up this gap. Moreover, a numerical example is also provided to demonstrate the effectiveness and applicability of the theoretical results.     

On the Complexity of Time-Dependent Shortest Paths

We investigate the complexity of shortest paths in time-dependent graphs where the costs of edges (that is, edge travel times) vary as a function of time, and as a result the shortest path between two nodes s and d can change over time. Our main result is that when the edge cost functions are (polynomial-size) piecewise linear, the shortest path from s to d can change n ^Θ(logn) times, settling a several-year-old conjecture of Dean (Technical Reports, 1999, 2004). However, despite the fact that the arrival time function may have superpolynomial complexity, we show that a minimum delay path for any departure time interval can be computed in polynomial time. We also show that the complexity is polynomial if the slopes of the linear function come from a restricted class and describe an efficient scheme for computing a (1+?)-approximation of the travel time function.