100 research outputs found
Maintenance of Strongly Connected Component in Shared-memory Graph
In this paper, we present an on-line fully dynamic algorithm for maintaining
strongly connected component of a directed graph in a shared memory
architecture. The edges and vertices are added or deleted concurrently by fixed
number of threads. To the best of our knowledge, this is the first work to
propose using linearizable concurrent directed graph and is build using both
ordered and unordered list-based set. We provide an empirical comparison
against sequential and coarse-grained. The results show our algorithm's
throughput is increased between 3 to 6x depending on different workload
distributions and applications. We believe that there are huge applications in
the on-line graph. Finally, we show how the algorithm can be extended to
community detection in on-line graph.Comment: 29 pages, 4 figures, Accepted in the Conference NETYS-201
Algorithm Diversity for Resilient Systems
Diversity can significantly increase the resilience of systems, by reducing
the prevalence of shared vulnerabilities and making vulnerabilities harder to
exploit. Work on software diversity for security typically creates variants of
a program using low-level code transformations. This paper is the first to
study algorithm diversity for resilience. We first describe how a method based
on high-level invariants and systematic incrementalization can be used to
create algorithm variants. Executing multiple variants in parallel and
comparing their outputs provides greater resilience than executing one variant.
To prevent different parallel schedules from causing variants' behaviors to
diverge, we present a synchronized execution algorithm for DistAlgo, an
extension of Python for high-level, precise, executable specifications of
distributed algorithms. We propose static and dynamic metrics for measuring
diversity. An experimental evaluation of algorithm diversity combined with
implementation-level diversity for several sequential algorithms and
distributed algorithms shows the benefits of algorithm diversity
Improved Algorithms for Approximate String Matching (Extended Abstract)
The problem of approximate string matching is important in many different
areas such as computational biology, text processing and pattern recognition. A
great effort has been made to design efficient algorithms addressing several
variants of the problem, including comparison of two strings, approximate
pattern identification in a string or calculation of the longest common
subsequence that two strings share.
We designed an output sensitive algorithm solving the edit distance problem
between two strings of lengths n and m respectively in time
O((s-|n-m|)min(m,n,s)+m+n) and linear space, where s is the edit distance
between the two strings. This worst-case time bound sets the quadratic factor
of the algorithm independent of the longest string length and improves existing
theoretical bounds for this problem. The implementation of our algorithm excels
also in practice, especially in cases where the two strings compared differ
significantly in length. Source code of our algorithm is available at
http://www.cs.miami.edu/\~dimitris/edit_distanceComment: 10 page
An Efficient Data Structure for Dynamic Two-Dimensional Reconfiguration
In the presence of dynamic insertions and deletions into a partially
reconfigurable FPGA, fragmentation is unavoidable. This poses the challenge of
developing efficient approaches to dynamic defragmentation and reallocation.
One key aspect is to develop efficient algorithms and data structures that
exploit the two-dimensional geometry of a chip, instead of just one. We propose
a new method for this task, based on the fractal structure of a quadtree, which
allows dynamic segmentation of the chip area, along with dynamically adjusting
the necessary communication infrastructure. We describe a number of algorithmic
aspects, and present different solutions. We also provide a number of basic
simulations that indicate that the theoretical worst-case bound may be
pessimistic.Comment: 11 pages, 12 figures; full version of extended abstract that appeared
in ARCS 201
Local alignment of two-base encoded DNA sequence
<p>Abstract</p> <p>Background</p> <p>DNA sequence comparison is based on optimal local alignment of two sequences using a similarity score. However, some new DNA sequencing technologies do not directly measure the base sequence, but rather an encoded form, such as the two-base encoding considered here. In order to compare such data to a reference sequence, the data must be decoded into sequence. The decoding is deterministic, but the possibility of measurement errors requires searching among all possible error modes and resulting alignments to achieve an optimal balance of fewer errors versus greater sequence similarity.</p> <p>Results</p> <p>We present an extension of the standard dynamic programming method for local alignment, which simultaneously decodes the data and performs the alignment, maximizing a similarity score based on a weighted combination of errors and edits, and allowing an affine gap penalty. We also present simulations that demonstrate the performance characteristics of our two base encoded alignment method and contrast those with standard DNA sequence alignment under the same conditions.</p> <p>Conclusion</p> <p>The new local alignment algorithm for two-base encoded data has substantial power to properly detect and correct measurement errors while identifying underlying sequence variants, and facilitating genome re-sequencing efforts based on this form of sequence data.</p
Amyotrophic lateral sclerosis-motor neuron disease, monoclonal gammopathy, hyperparathyroidism, and B12 deficiency: case report and review of the literature
<p>Abstract</p> <p>Introduction</p> <p>Amyotrophic lateral sclerosis (the most common form of motor neuron disease) is a progressive and devastating disease involving both lower and upper motor neurons, typically following a relentless path towards death. Given the gravity of this diagnosis, all efforts must be made by the clinician to exclude alternative and more treatable entities. Frequent serology testing involves searching for treatable disorders, including vitamin B12 deficiency, parathyroid anomalies, and monoclonal gammopathies.</p> <p>Case presentation</p> <p>We present the case of a 78-year-old Caucasian man with all three of the aforementioned commonly searched for disorders during an investigation for amyotrophic lateral sclerosis.</p> <p>Conclusions</p> <p>The clinical utility of these common tests and what they ultimately mean in patients with amyotrophic lateral sclerosis is discussed, along with a review of the literature.</p
Optimality regions and fluctuations for Bernoulli last passage models
We study the sequence alignment problem and its independent version,the discrete Hammersley process with an exploration penalty.
We obtain rigorous upper bounds for the number of optimality regions in both models near the soft edge.At zero penalty the independent model becomes an exactly solvable model and we identify cases for which the law of the last passage time converges to a Tracy-Widom law
Modeling the Evolution of Regulatory Elements by Simultaneous Detection and Alignment with Phylogenetic Pair HMMs
The computational detection of regulatory elements in DNA is a difficult but important problem impacting our progress in understanding the complex nature of eukaryotic gene regulation. Attempts to utilize cross-species conservation for this task have been hampered both by evolutionary changes of functional sites and poor performance of general-purpose alignment programs when applied to non-coding sequence. We describe a new and flexible framework for modeling binding site evolution in multiple related genomes, based on phylogenetic pair hidden Markov models which explicitly model the gain and loss of binding sites along a phylogeny. We demonstrate the value of this framework for both the alignment of regulatory regions and the inference of precise binding-site locations within those regions. As the underlying formalism is a stochastic, generative model, it can also be used to simulate the evolution of regulatory elements. Our implementation is scalable in terms of numbers of species and sequence lengths and can produce alignments and binding-site predictions with accuracy rivaling or exceeding current systems that specialize in only alignment or only binding-site prediction. We demonstrate the validity and power of various model components on extensive simulations of realistic sequence data and apply a specific model to study Drosophila enhancers in as many as ten related genomes and in the presence of gain and loss of binding sites. Different models and modeling assumptions can be easily specified, thus providing an invaluable tool for the exploration of biological hypotheses that can drive improvements in our understanding of the mechanisms and evolution of gene regulation
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