77,673 research outputs found
Jump-sparse and sparse recovery using Potts functionals
We recover jump-sparse and sparse signals from blurred incomplete data
corrupted by (possibly non-Gaussian) noise using inverse Potts energy
functionals. We obtain analytical results (existence of minimizers, complexity)
on inverse Potts functionals and provide relations to sparsity problems. We
then propose a new optimization method for these functionals which is based on
dynamic programming and the alternating direction method of multipliers (ADMM).
A series of experiments shows that the proposed method yields very satisfactory
jump-sparse and sparse reconstructions, respectively. We highlight the
capability of the method by comparing it with classical and recent approaches
such as TV minimization (jump-sparse signals), orthogonal matching pursuit,
iterative hard thresholding, and iteratively reweighted minimization
(sparse signals)
Improved Two-Dimensional Warping
The well-known dynamic programming time warping algorithm (DTW) provides an optimal matching between 1-dimensional sequences in polynomial time. Finding an optimal 2-dimensional warping is an NP-complete problem. Hence, only approximate non-exponential time 2-dimensional warping algorithms currently exist. A polynomial time 2-dimensional approximation algorithm was proposed recently. This project provides a thorough analytical and experimental study of this algorithm. Its time complexity is improved from O(N^6) to O(N^4). An extension of the algorithm to 3D and potential higher-dimensional applications are described
HiTRACE: High-throughput robust analysis for capillary electrophoresis
Motivation: Capillary electrophoresis (CE) of nucleic acids is a workhorse
technology underlying high-throughput genome analysis and large-scale chemical
mapping for nucleic acid structural inference. Despite the wide availability of
CE-based instruments, there remain challenges in leveraging their full power
for quantitative analysis of RNA and DNA structure, thermodynamics, and
kinetics. In particular, the slow rate and poor automation of available
analysis tools have bottlenecked a new generation of studies involving hundreds
of CE profiles per experiment.
Results: We propose a computational method called high-throughput robust
analysis for capillary electrophoresis (HiTRACE) to automate the key tasks in
large-scale nucleic acid CE analysis, including the profile alignment that has
heretofore been a rate-limiting step in the highest throughput experiments. We
illustrate the application of HiTRACE on thirteen data sets representing 4
different RNAs, three chemical modification strategies, and up to 480 single
mutant variants; the largest data sets each include 87,360 bands. By applying a
series of robust dynamic programming algorithms, HiTRACE outperforms prior
tools in terms of alignment and fitting quality, as assessed by measures
including the correlation between quantified band intensities between replicate
data sets. Furthermore, while the smallest of these data sets required 7 to 10
hours of manual intervention using prior approaches, HiTRACE quantitation of
even the largest data sets herein was achieved in 3 to 12 minutes. The HiTRACE
method therefore resolves a critical barrier to the efficient and accurate
analysis of nucleic acid structure in experiments involving tens of thousands
of electrophoretic bands.Comment: Revised to include Supplement. Availability: HiTRACE is freely
available for download at http://hitrace.stanford.ed
Regulating Data Exchange in Service Oriented Applications
We define a type system for COWS, a formalism for specifying and combining services, while modelling their dynamic behaviour. Our types permit to express policies constraining data exchanges in terms of sets of service partner names attachable to each single datum. Service programmers explicitly write only the annotations necessary to specify the wanted policies for communicable data, while a type inference system (statically) derives the minimal additional annotations that ensure consistency of services initial configuration. Then, the language dynamic semantics only performs very simple checks to authorize or block communication. We prove that the type system and the operational semantics are sound. As a consequence, we have the following data protection property: services always comply with the policies regulating the exchange of data among interacting services. We illustrate our approach through a simplified but realistic scenario for a service-based electronic marketplace
Incorporating peak grouping information for alignment of multiple liquid chromatography-mass spectrometry datasets
Motivation: The combination of liquid chromatography and mass spectrometry (LC/MS) has been widely used for large-scale comparative studies in systems biology, including proteomics, glycomics and metabolomics. In almost all experimental design, it is necessary to compare chromatograms across biological or technical replicates and across sample groups. Central to this is the peak alignment step, which is one of the most important but challenging preprocessing steps. Existing alignment tools do not take into account the structural dependencies between related peaks that co-elute and are derived from the same metabolite or peptide. We propose a direct matching peak alignment method for LC/MS data that incorporates related peaks information (within each LC/MS run) and investigate its effect on alignment performance (across runs). The groupings of related peaks necessary for our method can be obtained from any peak clustering method and are built into a pairwise peak similarity score function. The similarity score matrix produced is used by an approximation algorithm for the weighted matching problem to produce the actual alignment result.<p></p>
Results:
We demonstrate that related peak information can improve alignment performance. The performance is evaluated on a set of benchmark datasets, where our method performs competitively compared to other popular alignment tools.<p></p>
Availability: The proposed alignment method has been implemented
as a stand-alone application in Python, available for download at
http://github.com/joewandy/peak-grouping-alignment.<p></p>
Service discovery and negotiation with COWS
To provide formal foundations to current (web) services technologies, we put forward using COWS, a process calculus for specifying, combining and analysing services, as a uniform formalism for modelling all the relevant phases of the life cycle of service-oriented applications, such as publication, discovery, negotiation, deployment and execution. In this paper, we show that constraints and operations on them can be smoothly incorporated in COWS, and propose a disciplined way to model multisets of constraints and to manipulate them through appropriate interaction protocols. Therefore, we demonstrate that also QoS requirement specifications and SLA achievements, and the phases of dynamic service discovery and negotiation can be comfortably modelled in COWS. We illustrate our approach through a scenario for a service-based web hosting provider
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