pinecone/core-2020-05-10-deduplication huggingface dataset:
(anti-Omega^x × Σ_z) based k-set agreement algorithms

International audienceThis paper considers the k-set agreement problem in a crashprone asynchronous message passing system enriched with failure detectors. Two classes of failure detectors have been previously identiﬁed as necessary to solve asynchronous k-set agreement: the class anti-leader anti−Ω k and the weak-quorum class Σk . The paper investigates the families of failure detector (anti−Ω x )1≤x≤n and (Σz )1≤z≤n . It characterizes in an n processes system equipped with failure detectors anti−Ω x and Σz for which values of k, x and z k-set-agreement can be solved. While doing so, the paper (1) disproves previous conjunctures about the weakest failure detector to solve k-set-agreement in the asynchronous message passing model and, (2) introduces the ﬁrst indulgent algorithm that tolerates a majority of processes failures
Relating L-Resilience and Wait-Freedom via Hitting Sets

The condition of t-resilience stipulates that an n-process program is only
obliged to make progress when at least n-t processes are correct. Put another
way, the live sets, the collection of process sets such that progress is
required if all the processes in one of these sets are correct, are all sets
with at least n-t processes.
  We show that the ability of arbitrary collection of live sets L to solve
distributed tasks is tightly related to the minimum hitting set of L, a minimum
cardinality subset of processes that has a non-empty intersection with every
live set. Thus, finding the computing power of L is NP-complete.
  For the special case of colorless tasks that allow participating processes to
adopt input or output values of each other, we use a simple simulation to show
that a task can be solved L-resiliently if and only if it can be solved
(h-1)-resiliently, where h is the size of the minimum hitting set of L.
  For general tasks, we characterize L-resilient solvability of tasks with
respect to a limited notion of weak solvability: in every execution where all
processes in some set in L are correct, outputs must be produced for every
process in some (possibly different) participating set in L. Given a task T, we
construct another task T_L such that T is solvable weakly L-resiliently if and
only if T_L is solvable weakly wait-free
Segmentation of Inter-neurons in Three Dimensional Brain Imagery

Segmentation of neural cells in three dimensional fluorescence microscopy  images is a challenging image processing problem. In addition to being  important to neurobiologists, accurate segmentation is a vital component of  an automated image processing system. Due to the complexity of the data, particularly  the extreme irregularity in neural cell shape, generic segmentation  techniques do not perform well. This paper presents a novel segmentation technique  for segmenting neural cells in three dimensional images. Accuracy rates  of over 90% are reported on a data set of 100 images containing over 130 neural  cells and subsequently validated using a novel data set of 64 neurons.Griffith Sciences, School of Information and Communication TechnologyFull Tex
Noise-robust method for image segmentation

Segmentation of noisy images is one of the most challenging problems in image analysis and any improvement of segmentation methods can highly influence the performance of many image processing applications. In automated image segmentation, the fuzzy c-means (FCM) clustering has been widely used because of its ability to model uncertainty within the data, applicability to multi-modal data and fairly robust behaviour. However, the standard FCM algorithm does not consider any information about the spatial linage context and is highly sensitive to noise and other imaging artefacts. Considering above mentioned problems, we developed a new FCM-based approach for the noise-robust fuzzy clustering and we present it in this paper. In this new iterative algorithm we incorporated both spatial and feature space information into the similarity measure and the membership function. We considered that spatial information depends on the relative location and features of the neighbouring pixels. The performance of the proposed algorithm is tested on synthetic image with different noise levels and real images. Experimental quantitative and qualitative segmentation results show that our method efficiently preserves the homogeneity of the regions and is more robust to noise than other FCM-based methods
Combining geometric edge detectors for feature detection

We propose a novel framework for the analysis and modeling of discrete edge filters, based on the notion of signed rays. This framework will allow us to easily deduce the geometric and localization properties of a family of first-order filters, and use this information to design custom filter banks for specific applications. As an example, a set of angle-selective corner detectors is constructed for the detection of buildings in video sequences. This clearly illustrates the merit of the theory for solving practical recognition problems
Surface reconstruction of wear in carpets by using a wavelet edge detector

Carpet manufacturers have wear labels assigned to their products by human experts who evaluate carpet samples subjected to accelerated wear in a test device. There is considerable industrial and academic interest in going from human to automated evaluation, which should be less cumbersome and more objective. In this paper, we present image analysis research on videos of carpet surfaces scanned with a 3D laser. The purpose is obtaining good depth Images for an automated system that should have a high percentage of correct assessments for a wide variety of carpets. The innovation is the use of a wavelet edge detector to obtain a more continuously defined surface shape. The evaluation is based on how well the algorithms allow a good linear ranking and a good discriminance of consecutive wear labels. The results show an improved linear ranking for most carpet types, for two carpet types the results are quite significant
Speeding up structure from motion on large scenes using parallelizable partitions

Structure from motion based 3D reconstruction takes a lot of time for large scenes which consist of thousands of input images. We propose a method that speeds up the reconstruction of large scenes by partitioning it into smaller scenes, and then recombining those. The main benefit here is that each subscene can be optimized in parallel. We present a widely usable subdivision method, and show that the difference between the result after partitioning and recombination, and the state of the art structure from motion reconstruction on the entire scene, is negligible
A GPU-accelerated real-time NLMeans algorithm for denoising color video sequences

Abstract. The NLMeans filter, originally proposed by Buades et al., is a very popular filter for the removal of white Gaussian noise, due to its simplicity and excellent performance. The strength of this filter lies in exploiting the repetitive character of structures in images. However, to fully take advantage of the repetitivity a computationally extensive search for similar candidate blocks is indispensable. In previous work, we presented a number of algorithmic acceleration techniques for the NLMeans filter for still grayscale images. In this paper, we go one step further and incorporate both temporal information and color information into the NLMeans algorithm, in order to restore video sequences. Starting from our algorithmic acceleration techniques, we investigate how the NLMeans algorithm can be easily mapped onto recent parallel computing architectures. In particular, we consider the graphical processing unit (GPU), which is available on most recent computers. Our developments lead to a high-quality denoising filter that can process DVD-resolution video sequences in real-time on a mid-range GPU
Fire detection in color images using Markov random fields

Automatic video-based fire detection can greatly reduce fire alert delay in large industrial and commercial sites, at a minimal cost, by using the existing CCTV camera network. Most traditional computer vision methods for fire detection model the temporal dynamics of the flames, in conjunction with simple color filtering. An important drawback of these methods is that their performance degrades at lower framerates, and they cannot be applied to still images, limiting their applicability. Also, real-time operation often requires significant computational resources, which may be unfeasible for large camera networks. This paper presents a novel method for fire detection in static images, based on a Markov Random Field but with a novel potential function. The method detects 99.6% of fires in a large collection of test images, while generating less false positives then a state-of-the-art reference method. Additionally, parameters are easily trained on a 12-image training set with minimal user input
Uncertainty in climate policy: impacts on market mechanisms

In terms of geological shifts in climate, climate policy is a very young field. However, during the last two decades it has developed at a rapid pace. In 1987, the Brundtland Report first used the concept of sustainable development, followed in 1988 by the first meeting of the Intergovernmental Panel on Climate Change (IPCC) in Toronto. The establishment of the United Nations Framework Convention on Climate Change (UNFCCC) in 1992 marked the birth of global climate policy. For the first time in history governments of almost all nations gathered to discuss the effects and consequences of and measures to be taken against global warming and agreed on the principle of “common but differentiated responsibilities”. The first decade of climate policy culminated in 1997 in the signing of the Kyoto Protocol, in which industrialized countries (37 so-called “Annex B countries”), agreed to reduce anthropogenic emissions of six greenhouse gases (GHGs) by 5.2% below 1990 levels during the Kyoto commitment period, 2008–2012 (Article 3, UNFCCC 1997)
The University of Surrey Visual Concept Detection System at ImageCLEF 2010: Working Notes

Visual concept detection is one of the most important tasks in image and video indexing. This paper describes our system in the ImageCLEF@ICPR Visual Concept Detection Task which ranked first for large-scale visual concept detection tasks in terms of Equal Error Rate (EER) and Area under Curve (AUC) and ranked third in terms of hierarchical measure. The presented approach involves state-of-the-art local descriptor computation, vector quantisation via clustering, structured scene or object representation via localised histograms of vector codes, similarity measure for kernel construction and classifier learning. The main novelty is the classifier-level and kernel-level fusion using Kernel Discriminant Analysis with RBF/Power Chi-Squared kernels obtained from various image descriptors. For 32 out of 53 individual concepts, we obtain the best performance of all 12 submissions to this task
Talking about Data: Sharing Richly Structured Information through Blogs and Wikis

Several projects have brought rich data semantics to collaborative wikis, but blogging platforms remain primarily limited to text. As blogs comprise a significant portion of the web’s content, engagement of the blogging community is crucial to the development of the semantic web. We provide a study of blog content to show a latent need for better data publishing and visualization support in blogging software. We then present DataPress, an extension to the WordPress blogging platform that enables users to publish, share, aggregate, and visualize structured information using the same workflow that they already apply to text-based content. In particular, we aim to preserve those attributes that make blogs such a successful publication medium: one-click access to the information, one-click publishing of it, natural authoring interfaces, and easy copy and paste of information (and visualizations) from other sources. We reflect on how our designs make progress toward these goals with a study of how users who installed DataPress made use of various features
XtreemOS-MD: Grid computing from mobile devices

Grid and Cloud computing are well known topics, and currently one of the focus of many research and commercial projects. Nevertheless, the transparent access to Grid facilities from mobile devices (like PDAs or smartphones) is normally out of the scope of those initiatives, taking into account the intrinsic limitations of such devices: screen size and resolution, storage capacity, computational power, etc. To provide an integrated solution for mobile access to Grid computing, aspects like Virtual Organizations (VOs) support, graphical job management, flexible authentication and access to the file system user's volume in the Grid from a mobile device should be covered. That is the purpose\ud
of XtreemOS-MD, the mobile flavour of XtreemOS - a Linux-based operating system to support VOs for Grids -, the transparent access to Grid facilities from mobile devices.Peer ReviewedPostprint (published version
Multiscale analyses for the Shallow Water equations
 : 
The 4th Russian-German Advanced Research Workshop, Freiburg, Germany, October 12 to 16, 2009

International audienceThis paper explores several asymptotic limit regimes for shallow water flows over multiscale topography. Depending on the length and time scales considered and on the characteristic water depth and height of topography, a variety of mathematically quite different asymptotic limit systems emerges. Specifically, we recover the classical ``lake equations'' for balanced flow without gravity waves in the single time, single space scale limit (Greenspan, Cambridge Univ. Press, (1968)), discuss a weakly nonlinear and a strongly nonlinear multi-scale version of these wave-free equations involving short-range topography, and we re-derive the equations for long-wave shallow water waves passing over short-range topography by Le Maître et al., JCP (2001)
Two-fluid barotropic models for powder-snow avalanche flows

14 pages, 1 figure. Accepted to Springer series "Notes on Numerical Fluid Mechanics and Multidisciplinary Design". Other authors papers can be downloaded at http://www.lama.univ-savoie.fr/~dutykh/International audienceIn the present study we discuss several modeling issues of powder-snow avalanche flows. We take a two-fluid modeling paradigm. For the sake of simplicity, we will restrict our attention to barotropic equations. We begin the exposition by a compressible model with two velocities for each fluid. However, this model may become non-hyperbolic and thus, represents serious challenges for numerical methods. To overcome these issues, we derive a single velocity model as a result of a relaxation process. This model can be easily shown to be hyperbolic for any reasonable equation of state. Finally, an incompressible limit of this model is derived
Multi-channel key agreement using encrypted public key exchange

The original publication is available at: www.springerlink.com Copyright Springer VerlagWe present a new protocol for cryptographic key agreement between devices which have had no previous association, and which does not rely upon mutual access to a pre-existing key infrastructure. This protocol is suitable for use in mobile ad-hoc computing environments, where the only channels with high data origin authenticity have severely limited bandwidth. The protocol illustrates one use of an heretical design principle: allowing the ???same??? protocol to provide different security services in different contexts
Coalgebraic semantics for parallel derivation strategies in logic programming

Logic programming, a class of programming languages based on first-order logic, provides simple and efficient tools for goal-oriented proof-search. Logic programming supports recursive computations, and some logic programs resemble the inductive or coinductive definitions written in functional programming languages. In this paper, we give a coalgebraic semantics to logic programming. We show that ground logic programs can be modelled by either P f P f -coalgebras or P f List-coalgebras on Set. We analyse different kinds of derivation strategies and derivation trees (proof-trees, SLD-trees, and-or parallel trees) used in logic programming, and show how they can be modelled coalgebraically
StarFlow: A Script-Centric Data Analysis Environment

We introduce StarFlow, a script-centric environment for data analysis. StarFlow has four main features: (1) extraction of control and data-flow dependencies through a novel combination of static analysis, dynamic runtime analysis, and user annotations, (2) command-line tools for exploring and propagating changes through the resulting dependency network, (3) support for workflow abstractions enabling robust parallel executions of complex analysis pipelines, and (4) a seamless interface with the Python scripting language. We describe a range of real applications of StarFlow, including automatic parallelization of complex workflows in the cloud.Engineering and Applied Science
Provenance of Software Development Processes

"Why does the build fail currently?" - This and similar ques-\ud
tions arise on a daily basis in software development processes (SDP). There is no easy way to answer these questions, the required information is stored throughout different tools, the version control and continuous integration systems in this example. The tools mainly live in isolated worlds and no direct connection between their data exists. This paper proposes a solution to such problems, based on provenance technologies.\ud
After outlining the complexity of a SDP, the questions arising on a daily\ud
basis are categorized. Finally an approach to make the SDP provenance-\ud
aware is proposed based on PRiME, the Open Provenance Model and a\ud
SOA architecture using Neo4j to store the data, Gremlin to query it and\ud
REST webservices as connection to the tools
Measuring bitrate and quality trade-off in a fast region-of-interest based video coding

Prevailing video adaptation solutions change the quality of the video uniformly throughout the whole frame in the bitrate adjustment process; while region-of-interest (ROI)-based solutions selectively retains the quality in the areas of the frame where the viewers are more likely to pay more attention to. ROI-based coding can improve perceptual quality and viewer satisfaction while trading off some bandwidth. However, there has been no comprehensive study to measure the bitrate vs. perceptual quality trade-off so far. The paper proposes an ROI detection scheme for videos, which is characterized with low computational complexity and robustness, and measures the bitrate vs. quality trade-off for ROI-based encoding using a state-of-the-art H.264/AVC encoder to justify the viability of this type of encoding method. The results from the subjective quality test reveal that ROI-based encoding achieves a significant perceptual quality improvement over the encoding with uniform quality at the cost of slightly more bits. Based on the bitrate measurements and subjective quality assessments, the bitrate and the perceptual quality estimation models for non-scalable ROI-based video coding (AVC) are developed, which are found to be similar to the models for scalable video coding (SVC)
Compressed-domain shot boundary detection for H.264/AVC using intra partitioning maps

In this paper, a novel technique for shot boundary detection operating on H.264/AVC-compressed sequences is presented. Due to new and improved coding tools in H.264/AVC, the characteristics of the obtained sequences differ from former video coding standards. Although several algorithms working on this new standard are already proposed, the presence of IDR frames can still lead to a low accuracy for abrupt transitions. To solve this issue, we present the motion-compensated intra partitioning map which relies on the intra partitioning modes and the motion vectors present in the compressed video stream. Experimental results show that this motion-compensated map achieves a high accuracy and exceeds related work
ETOMIC advanced network monitoring system for future internet experimentation

Revised Selected Papers of 6th International ICST Conference, TridentCom 2010, Berlin, Germany, May 18-20, 2010,The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-642-17851-1_20ETOMIC is a network traffic measurement platform with high precision GPS-synchronized monitoring nodes. The infrastructure is publicly available to the network research community, supporting advanced experimental techniques by providing high precision hardware equipments and a Central Management System. Researchers can deploy their own active measurement codes to perform experiments on the public Internet. Recently, the functionalities of the original system were significantly extended and new generation measurement nodes were deployed. The system now also includes well structured data repositories to archive and share raw and evaluated data. These features make ETOMIC as one of the experimental facilities that support the design, development and validation of novel experimental techniques for the future Internet. In this paper we focus on the improved capabilities of the management system, the recent extensions of the node architecture and the accompanying database solutions.The authors thank the support of the EU ICT OneLab2 Project (No.224263), the
EU ICT MOMENT Project (No.215225) and the National Office for Research
and Technology (NAP2005/KCKHA005
Spectral Finite Element Methods for solving Fractional Differential Equations with applications in Anomalous Transport

<p>Quantifying species transport rates is a main concern in chemical and petrochemical industries. In particular, the design and operation of many large-scale industrial chemical processes is as much dependent on diffusion as it is on reaction rates. However, the existing diffusion models sometimes fail to predict experimentally observed behaviors and their accuracy is usually insufficient for process optimization purposes.</p><p>Fractional diffusion models offer multiple possibilities for generalizing Fick’s law in a consistent manner in order to account for history dependence and nonlocal effects. These models have not been extensively applied to the study of real systems, mainly due to their computational cost and mathematical complexity.</p><p>A least squares spectral formulation was developed for solving fractional differential equations. The proposed method was proven particularly well-suited for dealing with the numerical difficulties inherent to fractional differential operators. The practical implementation was explained in detail in order to enhance reproducibility, and directions were specified for extending it to multiple dimensions and arbitrarily shaped domains.</p><p>A numerical framework based on the least-squares spectral element method was developed for studying and comparing anomalous diffusion models in pellets. This simulation tool is capable of solving arbitrary integro-differential equations and can be effortlessly adapted to various problems in any number of dimensions.</p><p>Simulations of the flow around a cylindrical particle were achieved by extending the functionality of the developed framework. A test case was analyzed by coupling the boundary condition yielded by the fluid model with two families of anomalous diffusion models: hyperbolic diffusion and fractional diffusion. Qualitative guidelines for determining the suitability of diffusion models can be formulated by complementing experimental data with the results obtained from this approach.</p>PhD i energi- og prosessteknikkPhD in Energy and Process Engineerin