Highly Scalable Algorithms for Robust String Barcoding

String barcoding is a recently introduced technique for genomic-based identification of microorganisms. In this paper we describe the engineering of highly scalable algorithms for robust string barcoding. Our methods enable distinguisher selection based on whole genomic sequences of hundreds of microorganisms of up to bacterial size on a well-equipped workstation, and can be easily parallelized to further extend the applicability range to thousands of bacterial size genomes. Experimental results on both randomly generated and NCBI genomic data show that whole-genome based selection results in a number of distinguishers nearly matching the information theoretic lower bounds for the problem

Efficient Sum of Outer Products Dictionary Learning (SOUP-DIL) - The ℓ0\ell_0 Method

The sparsity of natural signals and images in a transform domain or dictionary has been extensively exploited in several applications such as compression, denoising and inverse problems. More recently, data-driven adaptation of synthesis dictionaries has shown promise in many applications compared to fixed or analytical dictionary models. However, dictionary learning problems are typically non-convex and NP-hard, and the usual alternating minimization approaches for these problems are often computationally expensive, with the computations dominated by the NP-hard synthesis sparse coding step. In this work, we investigate an efficient method for $\ell_{0}$ "norm"-based dictionary learning by first approximating the training data set with a sum of sparse rank-one matrices and then using a block coordinate descent approach to estimate the unknowns. The proposed block coordinate descent algorithm involves efficient closed-form solutions. In particular, the sparse coding step involves a simple form of thresholding. We provide a convergence analysis for the proposed block coordinate descent approach. Our numerical experiments show the promising performance and significant speed-ups provided by our method over the classical K-SVD scheme in sparse signal representation and image denoising.Comment: This work is cited by the IEEE Transactions on Computational Imaging Paper arXiv:1511.06333 (DOI: 10.1109/TCI.2017.2697206

On the Use of Cellular Automata in Symmetric Cryptography

In this work, pseudorandom sequence generators based on finite fields have been analyzed from the point of view of their cryptographic application. In fact, a class of nonlinear sequence generators has been modelled in terms of linear cellular automata. The algorithm that converts the given generator into a linear model based on automata is very simple and is based on the concatenation of a basic structure. Once the generator has been linearized, a cryptanalytic attack that exploits the weaknesses of such a model has been developed. Linear cellular structures easily model sequence generators with application in stream cipher cryptography.Comment: 25 pages, 0 figure

Partitioning Patches into Test-equivalence Classes for Scaling Program Repair

Automated program repair is a problem of finding a transformation (called a patch) of a given incorrect program that eliminates the observable failures. It has important applications such as providing debugging aids, automatically grading assignments and patching security vulnerabilities. A common challenge faced by all existing repair techniques is scalability to large patch spaces, since there are many candidate patches that these techniques explicitly or implicitly consider. The correctness criterion for program repair is often given as a suite of tests, since a formal specification of the intended program behavior may not be available. Current repair techniques do not scale due to the large number of test executions performed by the underlying search algorithms. We address this problem by introducing a methodology of patch generation based on a test-equivalence relation (if two programs are "test-equivalent" for a given test, they produce indistinguishable results on this test). We propose two test-equivalence relations based on runtime values and dependencies respectively and present an algorithm that performs on-the-fly partitioning of patches into test-equivalence classes. Our experiments on real-world programs reveal that the proposed methodology drastically reduces the number of test executions and therefore provides an order of magnitude efficiency improvement over existing repair techniques, without sacrificing patch quality

An Algorithm for Constructing a Smallest Register with Non-Linear Update Generating a Given Binary Sequence

Registers with Non-Linear Update (RNLUs) are a generalization of Non-Linear Feedback Shift Registers (NLFSRs) in which both, feedback and feedforward, connections are allowed and no chain connection between the stages is required. In this paper, a new algorithm for constructing RNLUs generating a given binary sequence is presented. Expected size of RNLUs constructed by the presented algorithm is proved to be O(n/log(n/p)), where n is the sequence length and p is the degree of parallelization. This is asymptotically smaller than the expected size of RNLUs constructed by previous algorithms and the expected size of LFSRs and NLFSRs generating the same sequence. The presented algorithm can potentially be useful for many applications, including testing, wireless communications, and cryptography

Unsynthesizable Cores - Minimal Explanations for Unsynthesizable High-Level Robot Behaviors

With the increasing ubiquity of multi-capable, general-purpose robots arises the need for enabling non-expert users to command these robots to perform complex high-level tasks. To this end, high-level robot control has seen the application of formal methods to automatically synthesize correct-by-construction controllers from user-defined specifications; synthesis fails if and only if there exists no controller that achieves the specified behavior. Recent work has also addressed the challenge of providing easy-to-understand feedback to users when a specification fails to yield a corresponding controller. Existing techniques provide feedback on portions of the specification that cause the failure, but do so at a coarse granularity. This work presents techniques for refining this feedback, extracting minimal explanations of unsynthesizability

Systematic Testing of Multicast Routing Protocols: Analysis of Forward and Backward Search Techniques

In this paper, we present a new methodology for developing systematic and automatic test generation algorithms for multipoint protocols. These algorithms attempt to synthesize network topologies and sequences of events that stress the protocol's correctness or performance. This problem can be viewed as a domain-specific search problem that suffers from the state space explosion problem. One goal of this work is to circumvent the state space explosion problem utilizing knowledge of network and fault modeling, and multipoint protocols. The two approaches investigated in this study are based on forward and backward search techniques. We use an extended finite state machine (FSM) model of the protocol. The first algorithm uses forward search to perform reduced reachability analysis. Using domain-specific information for multicast routing over LANs, the algorithm complexity is reduced from exponential to polynomial in the number of routers. This approach, however, does not fully automate topology synthesis. The second algorithm, the fault-oriented test generation, uses backward search for topology synthesis and uses backtracking to generate event sequences instead of searching forward from initial states. Using these algorithms, we have conducted studies for correctness of the multicast routing protocol PIM. We propose to extend these algorithms to study end-to-end multipoint protocols using a virtual LAN that represents delays of the underlying multicast distribution tree.Comment: 26 pages, 20 figure

Global Linear Complexity Analysis of Filter Keystream Generators

An efficient algorithm for computing lower bounds on the global linear complexity of nonlinearly filtered PN-sequences is presented. The technique here developed is based exclusively on the realization of bit wise logic operations, which makes it appropriate for both software simulation and hardware implementation. The present algorithm can be applied to any arbitrary nonlinear function with a unique term of maximum order. Thus, the extent of its application for different types of filter generators is quite broad. Furthermore, emphasis is on the large lower bounds obtained that confirm the exponential growth of the global linear complexity for the class of nonlinearly filtered sequences

Classifying sequences by the optimized dissimilarity space embedding approach: a case study on the solubility analysis of the E. coli proteome

We evaluate a version of the recently-proposed classification system named Optimized Dissimilarity Space Embedding (ODSE) that operates in the input space of sequences of generic objects. The ODSE system has been originally presented as a classification system for patterns represented as labeled graphs. However, since ODSE is founded on the dissimilarity space representation of the input data, the classifier can be easily adapted to any input domain where it is possible to define a meaningful dissimilarity measure. Here we demonstrate the effectiveness of the ODSE classifier for sequences by considering an application dealing with the recognition of the solubility degree of the Escherichia coli proteome. Solubility, or analogously aggregation propensity, is an important property of protein molecules, which is intimately related to the mechanisms underlying the chemico-physical process of folding. Each protein of our dataset is initially associated with a solubility degree and it is represented as a sequence of symbols, denoting the 20 amino acid residues. The herein obtained computational results, which we stress that have been achieved with no context-dependent tuning of the ODSE system, confirm the validity and generality of the ODSE-based approach for structured data classification.Comment: 10 pages, 49 reference