pinecone/core-2020-05-10-deduplication huggingface dataset:
Three-Wave Backward Optical Solitons

Three-wave solitons backward propagating with respect to a pump wave are generated in nonlinear optical media through stimulated Brillouin scattering (SBS) in optical fibers or through the non-degenerate three-wave interaction in quadratic (χ (2)) nonlinear media. In an optical fiber-ring cavity, nanosecond solitary wave morphogenesis takes place when it is pumped with a continuous wave (c.w.). A backward dissipative Stokes soliton is generated from the hypersound waves stimulated by electrostriction between the forward pump wave and the counterpropagating Stokes wave. Superluminous and subluminous dissipative solitons are controlled via a single parameter: the feedback or reinjection for a given pump intensity or the pump intensity for a given feedback. In a c.w. pumped optical parametric oscillator (OPO), backward picosecond soliton generation takes place for non-degenerate three-wave interaction in the quadratic medium. The resonant condition is automatically satisfied in stimulated Brillouin backscattering when the fiber-ring laser contains a large number of longitudinal modes beneath the Brillouin gain curve. However, in order to achieve quasi-phase matching between the three optical waves (the forward pump wave and the backward signal and idler waves) in the χ (2) medium, the nonlinear susceptibility should be periodically structurated by an inversion grating of sub-μm period in an optical parametric oscillator (OPO). The stability analysis of the inhomogeneous stationary solutions presents a Hopf bifurcation with a single control parameter which gives rise to temporal modulation and then to backward three-wave solitons. Above OPO threshold, the nonlinear dynamics yields self-structuration of a backward symbiotic solitary wave, which is stable for a finite temporal walk-off, i.e. different group velocities, between the backward propagating signal and idler waves. We also study the dynamics of singly backward mirrorless OPO (BMOPO) pumped by a broad bandwidth field and also with a highly incoherent pump, in line with the recent experimental demonstration of this BMOPO configuration in a KTP crystal. We show that this system is characterized, as a general rule, by the generation of a highly coherent backward field, despite the high degree of incoherence of the pump field. This remarkable property finds its origin in the convection-induced phase-locking mechanism that originates in the counter-streaming configuration: the incoherence of the pump is transferred to the co-moving field, which thus allows the backward field to evolve towards a highly coherent state. We propose other realistic experimental conditions that may be implemented with currently available technology and in which backward coherent wave generation from incoherent excitation may be observed and studied
Computing discriminating and generic words

International audienceWe study the following three problems of computing generic or discriminating words for a given collection of documents. Given a pattern P and a threshold d, we want to report (i) all longest extensions of P which occur in at least d documents, (ii) all shortest extensions of P which occur in less than d documents, and (iii) all shortest extensions of P which occur only in d selected documents. For these problems, we propose efficient algorithms based on suffix trees and using advanced data structure techniques. For problem (i), we propose an optimal solution with constant running time per output word
An algorithm to assemble gene-protein-reaction associations for genome-scale metabolic model reconstruction

The considerable growth in the number of sequenced genomes
and recent advances in Bioinformatics and Systems Biology fields have
provided several genome-scale metabolic models (GSMs) that have been
used to provide phenotype simulation methods. Given their importance
in biomedical research and biotechnology applications (e.g. in Metabolic
Engineering efforts), several workflows and computational platforms have
been proposed for GSM reconstruction. One of the challenges of these
methods is related to the assignment of gene-protein-reaction (GPR) associations
that allow to add transcriptional/ translational information
to GSMs, a task typically addressed through manual literature curation.
This work proposes a novel algorithm to create a set of GPR rules, based
on the integration of the information provided by the genome annotation
with information on protein composition and function (protein complexes,
sub-units, iso-enzymes, etc.) provided by the UniProt database.
The methods are validated by using two state-of-the-art models for E.
coli and S. cerevisiae, with competitive results.The work is partially funded by ERDF - European Regional Development Fund through the COMPETE Programme ( operational programme for competitiveness) and by National Funds through the FCT ( Portuguese Foundation for Science and Tech- nology) within projects ref. COMPETE FCOMP-01-0124-FEDER-015079 and PEst-OE/EEI/UI0752/2011
A machine learning and chemometrics assisted interpretation of spectroscopic data : a NMR-based metabolomics platform for the assessment of Brazilian propolis

In this work, a metabolomics dataset from 1H nuclear magnetic resonance
spectroscopy of Brazilian propolis was analyzed using machine learning
algorithms, including feature selection and classification methods. Partial
least square-discriminant analysis (PLS-DA), random forest (RF), and wrapper
methods combining decision trees and rules with evolutionary algorithms (EA)
showed to be complementary approaches, allowing to obtain relevant information
as to the importance of a given set of features, mostly related to the
structural fingerprint of aliphatic and aromatic compounds typically found in
propolis, e.g., fatty acids and phenolic compounds. The feature selection and
decision tree-based algorithms used appear to be suitable tools for building
classification models for the Brazilian propolis metabolomics regarding its geographic
origin, with consistency, high accuracy, and avoiding redundant information
as to the metabolic signature of relevant compounds.The work is partially funded by ERDF -European Regional Development Fund through the COMPETE Programme (operational programme for competitiveness) and by National Funds through the FCT (Portuguese Foundation for Science and Technology) within projects ref. COMPETE FCOMP-01-0124-FEDER-015079 and PEstOE/ EEI/UI0752/2011. RC's work is funded by a PhD grant from the Portuguese FCT ( ref. SFRH/BD/66201/2009)
Application of the multi-modal relevance vector machine to the problem of protein secondary structure prediction

The aim of the paper is to experimentally examine the plausibility of Relevance Vector Machines (RVM) for protein secondary structure prediction. We restrict our attention to detecting strands which represent an especially problematic element of the secondary structure. The commonly adopted local principle of secondary structure prediction is applied, which implies comparison of a sliding window in the given polypeptide chain with a number of reference amino-acid sequences cut out of the training proteins as benchmarks representing the classes of secondary structure. As distinct from the classical RVM, the novel version applied in this paper allows for selective combination of several tentative window comparison modalities. Experiments on the RS126 data set have shown its ability to essentially decrease the number of reference fragments in the resulting decision rule and to select a subset of the most appropriate comparison modalities within the given set of the tentative ones. © 2012 Springer-Verlag
Impact of the revocation service in PKI prices

The ability to communicate securely is needed for many network\ud
applications. Public key infrastructure (PKI) is the most extended\ud
solution to verify and confirm the identity of each party involved in any\ud
secure transaction and transfer trust over the network. One of the hardest\ud
tasks of a certification infrastructure is to manage revocation. Research\ud
on this topic has focused on the trade-offs that different revocation mechanisms\ud
offer. However, less effort has been paid to understand the benefits\ud
of improving the revocation policies. In this paper, we analyze the\ud
behavior of the oligopoly of certificate providers that issue digital certificates\ud
to clients facing identical independent risks. We found the prices in\ud
the equilibrium, and we proof that certificate providers that offer better\ud
revocation information are able to impose higher prices to their certificates\ud
without sacrificing market share in favor of the other oligarchs. In\ud
addition, we show that our model is able to explain the actual tendency\ud
of the SSL market where providers with worst QoS are suffering loses.Postprint (published version
Acceleration of composite order bilinear pairing on graphics hardware

Conference Theme: Information and Communications SecurityLNCS v. 7618 entitled: Information and communications security: 14th international conference, ICICS 2012 ... : proceedingsRecently, composite-order bilinear pairing has been shown to be useful in many cryptographic constructions. However, it is time-costly to evaluate. This is because the composite order should be at least 1024bit and, hence, the elliptic curve group order n and base field become too large, rendering the bilinear pairing algorithm itself too slow to be practical (e.g., the Miller loop is Ω(n)). Thus, composite-order computation easily becomes the bottleneck of a cryptographic construction, especially, in the case where many pairings need to be evaluated at the same time. The existing solution to this problem that converts composite-order pairings to prime-order ones is only valid for certain constructions. In this paper, we leverage the huge number of threads available on Graphics Processing Units (GPUs) to speed up composite-order pairing computation. We investigate suitable SIMD algorithms for base/extension field, elliptic curve and bilinear pairing computation as well as mapping these algorithms into GPUs with careful considerations. Experimental results show that our method achieves a record of 8.7ms per pairing on a 80bit security level, which is a 20-fold speedup compared to the state-of-the-art CPU implementation. This result also opens the road to adopting higher security levels and using rich-resource parallel platforms, which for example are available in cloud computing. For example, we can achieve a record of 7 × 10 -6 USD per pairing on the Amazon cloud computing environment. © 2012 Springer-Verlag.postprin
Finding interesting contexts for explaining deviations in bus trip duration using distribution rules

Lecture Notes in Computer Science Volume 7619, 2012.In this paper we study the deviation of bus trip duration and its causes. Deviations are obtained by comparing scheduled times against actual trip duration and are either delays or early arrivals. We use distribution rules, a kind of association rules that may have continuous distributions on the consequent. Distribution rules allow the systematic identification of particular conditions, which we call contexts, under which the distribution of trip time deviations differs significantly from the overall deviation distribution. After identifying specific causes of delay the bus company operational managers can make adjustments to the timetables increasing punctuality without disrupting the service
Improving tag recommendation using few associations

Collaborative tagging services allow users to freely assign tags to resources. As the large majority of users enters only very few tags, good tag recommendation can vastly improve the usability of tags for techniques such as searching, indexing, and clustering. Previous research has shown that accurate recommendation can be achieved by using conditional probabilities computed from tag associations. The main problem, however, is that enormous amounts of associations are needed for optimal recommendation.

We argue and demonstrate that pattern selection techniques can improve tag recommendation by giving a very favourable balance between accuracy and computational demand. That is, few associations are chosen to act as information source for recommendation, providing high-quality recommendation and good scalability at the same time.

We provide a proof-of-concept using an off-the-shelf pattern selection method based on the Minimum Description Length principle. Experiments on data from Delicious, LastFM and YouTube show that our proposed methodology works well: applying pattern selection gives a very favourable trade-off between runtime and recommendation quality.status: publishe
New Avenues for Theoretical Contributions in Enterprise Architecture Principles - A Literature Review

Enterprise Architecture (EA), which has been approached by both academia and industry, is considered comprising not only architectural representations, but also principles guiding architecture's design and evolution. Even though the concept of EA principles has been defined as the integral part of EA, the number of publications on this subject is very limited and only a few organizations use EA principles to manage their EA endeavors. In order to critically assess the current state of research and identify research gaps in EA principles, we focus on four general aspects of theoretical contributions in IS. By applying these aspects to EA principles, we outline future research directions in EA principles nature, adoption, practices, and impact
Building strategic enterprise context models with i*: a pattern-based approach

Modern enterprise engineering (EE) requires deep understanding of organizations and their interaction with their context. Because of this, in early phases of the EE process, enterprise context models are often built and used to reason about organizational needs with respects to actors in their context and \ud
vice versa. However, far from simple, this task is usually cumbersome because of knowledge and communication gaps among technical personnel performing EE activities and their administrative counterparts. In this paper, we propose the use of strategic patterns expressed with the i* language aimed to help bridging this gap. Patterns emerged from several industrial applications of our \ud
DHARMA method, and synthesize knowledge about common enterprise strategies, e.g. CRM. Patterns have been constructed based on the well-known Porter’s model of the 5 market forces and built upon i* strategic dependency \ud
models. In this way technical and administrative knowledge and skills are \ud
synthesized in a commonly agreeable framework. The use of patterns is \ud
illustrated with an industrial example in the telecom field.Postprint (author’s final draft
A hierarchical image segmentation algorithm based on an observation scale

International audienceHierarchical image segmentation provides a region-oriented scale-space, i.e., a set of image segmentations at different detail levels in which the segmentations at finer levels are nested with respect to those at coarser levels. Most image segmentation algorithms, such as region merging algorithms, rely on a criterion for merging that does not lead to a hierarchy. In addition, for image segmentation, the tuning of the parameters can be difficult. In this work, we propose a hierarchical graph based image segmentation relying on a criterion popularized by Felzenszwalb and Huttenlocher. Quantitative and qualitative assessments of the method on Berkeley image database shows efficiency, ease of use and robustness of our method
A relational kernel-based framework for hierarchical image understanding

While relational representations have been popular in early work on syntactic and structural pattern recognition, they are rarely used in contemporary approaches to computer vision due to their pure symbolic nature. The recent progress and successes in combining statistical learning principles with relational representations motivates us to reinvestigate the use of such representations. More specifically, we show that statistical relational learning can be successfully used for hierarchical image understanding. We employ kLog, a new logical and relational language for learning with kernels to detect objects at different levels in the hierarchy. The key advantage of kLog is that both appearance features and rich, contextual dependencies between parts in a scene can be integrated in a principled and interpretable way to obtain a qualitative representation of the problem. At each layer, qualitative spatial structures of parts in images are detected, classified and then employed one layer up the hierarchy to obtain higher-level semantic structures. We apply a four-layer hierarchy to street view images and successfully detect corners, windows, doors, and individual houses.status: publishe
One-Sided Prototype Selection on Class Imbalanced Dissimilarity Matrices

In the dissimilarity representation paradigm, several prototype selection methods have been used to cope with the topic of how to select a small representation set for generating a low-dimensional dissimilarity space. In addition, these methods have also been used to reduce the size of the dissimilarity matrix. However, these approaches assume a relatively balanced class distribution, which is grossly violated in many real-life problems. Often, the ratios of prior probabilities between classes are extremely skewed. In this paper, we study the use of renowned prototype selection methods adapted to the case of learning from an imbalanced dissimilarity matrix. More specifically, we propose the use of these methods to under-sample the majority class in the dissimilarity space. The experimental results demonstrate that the one-sided selection strategy performs better than the classical prototype selection methods applied over all classes
An Incremental Structured Part Model for Image Classification

The state-of-the-art image classification methods usually require many training samples to achieve good performance. To tackle this problem, we present a novel incremental method in this paper, which learns a part model to classify objects using only a small number of training samples. Our model captures the inherent connections of the semantic parts of objects and builds structural relationship between them. In the incremental learning stage, we use high entropy images that have been accepted by users to update the learned model. The proposed approach is evaluated on two datasets, which demonstrates its advantages over several alternative classification methods in the literature.Full Tex
A Heuristic Based on the Intrinsic Dimensionality for Reducing the Number of Cyclic DTW Comparisons in Shape Classification and Retrieval Using AESA

Cyclic Dynamic Time Warping (CDTW) is a good dissimilarity of shape descriptors of high dimensionality based on contours, but it is computationally expensive. For this reason, to perform recognition tasks, a method to reduce the number of comparisons and avoid an exhaustive search is convenient. The Approximate and Eliminate Search Algorithm (AESA) is a relevant indexing method because of its drastic reduction of comparisons, however, this algorithm requires a metric distance and that is not the case of CDTW. In this paper, we introduce a heuristic based on the intrinsic dimensionality that allows to use CDTW and AESA together in classification and retrieval tasks over these shape descriptors. Experimental results show that, for descriptors of high dimensionality, our proposal is optimal in practice and significantly outperforms an exhaustive search, which is the only alternative for them and CDTW in these tasks
Reusable graph transformation templates

The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-642-34176-2_5Revised Selected and Invited Papers, 4th International Symposium, AGTIVE 2011, Budapest, Hungary, October 4-7, 2011Model-Driven Engineering promotes models as the principal artefacts of the development, hence model transformation techniques – like graph transformation – become key enablers for this development paradigm. In order to increase the adoption of Model-Driven Engineering in industrial practice, techniques aimed at raising the quality and productivity in model transformation development are needed.
In this paper we bring elements from generic programming into graph transformation in order to define generic graph transformations that can be reused in different contexts. In particular, we propose the definition and instantiation of graph transformation templates whose requirements from generic types are specified through so-called concepts, as well as mixin layers that extend meta-models with the extra auxiliary elements needed by templates.Work funded by the Spanish Ministry of Economy and Competitivity (project TIN2011-24139) and the Region of Madrid (project S2009/TIC-1650
Toward a Motor Theory of Sign Language Perception

12 pages Partiellement financé par le projet ANR SignComInternational audienceResearches on signed languages still strongly dissociate lin- guistic issues related on phonological and phonetic aspects, and gesture studies for recognition and synthesis purposes. This paper focuses on the imbrication of motion and meaning for the analysis, synthesis and evaluation of sign language gestures. We discuss the relevance and interest of a motor theory of perception in sign language communication. According to this theory, we consider that linguistic knowledge is mapped on sensory-motor processes, and propose a methodology based on the principle of a synthesis-by-analysis approach, guided by an evaluation process that aims to validate some hypothesis and concepts of this theory. Examples from existing studies illustrate the di erent concepts and provide avenues for future work
Connecting Red Cells in a Bicolour Voronoi Diagram

Let S be a set of n + m sites, of which n are red and have weight wR, and m are blue and weigh wB. The objective of this paper\ud
is to calculate the minimum value of the red sites’ weight such that the union of the red Voronoi cells in the weighted Voronoi diagram of S is a connected region. This problem is solved for the multiplicativelyweighted\ud
Voronoi diagram in O((n+m)2 log(nm)) time and for both the additively-weighted and power Voronoi diagram in O(nmlog(nm)) timePostprint (published version
Meshes Preserving Minimum Feature Size

The minimum feature size of a planar straight-line graph is the minimum distance between a vertex and a nonincident edge. When such a graph is partitioned into a mesh, the degradation is the ratio of original to final minimum feature size. For an n-vertex input, we give a triangulation (meshing) algorithm that limits degradation to only a constant factor, as long as Steiner points are allowed on the sides of triangles. If such Steiner points are not allowed, our algorithm realizes \ensuremathO(lgn) degradation. This addresses a 14-year-old open problem by Bern, Dobkin, and Eppstein