pinecone/core-2020-05-10-deduplication huggingface dataset:
Morphological processing of univariate Gaussian distribution-valued images based on Poincaré upper-half plane representation

Mathematical morphology is a nonlinear image processing methodology based on the application of complete lattice theory to spatial structures. Let us consider an image model where at each pixel is given a univariate Gaussian distribution. This model is interesting to represent for each pixel the measured mean intensity as well as the variance (or uncertainty) for such measurement. The aim of this work is to formulate morphological operators for these images by embedding Gaussian distribution pixel values on the Poincaré upper-half plane. More precisely, it is explored how to endow this classical hyperbolic space with various families of partial  orderings which lead to a complete lattice structure. Properties of order invariance are explored and application to morphological processing of univariate Gaussian distribution-valued images is illustrated
Nearest Neighbor Connectivity in Two-Dimensional Multihop MANETs

A Mobile Ad Hoc Network (MANET) is characterized to be a network with free, cooperative, and dynamic nodes, self-organized in a random topology, without any kind of infrastructure, where the communication between two nodes usually occurs using multihop paths. The number of hops used in the multihop path is an important metric for the design and performance analysis of routing protocols in MANETs. In this paper, we derive the probability distribution of the hop count of a multihop path between a source node and a destination node, fixed at a known distance from each other, and when a fixed number of nodes are uniformly distributed in a region of interest. This distribution is obtained by the Poisson randomization method. To obtain the multihop path, we propose a novel routing model in which the nearest distance routing protocol (NR) is analyzed. Numerical results are obtained to evaluate the performance of the NR
Discrete Mereotopology

PublishedWhereas mereology, in the strict sense, is concerned solely with the part–whole relation, mereotopology extends mereology by including also the notion of connection, enabling one to distinguish, for example, between internal and peripheral parts, and between contact and separation. Mereotopology has been developed particularly within the Qualitative Spatial Reasoning research community, where it has been applied to, amongst other areas, geographical information science and image analysis. Most research in mereotopology has assumed that the entities being studied may be subdivided without limit, but a number of researchers have investigated mereotopological structures based on discrete spaces in which entities are built up from atomic elements that are not themselves subdivisible. This chapter presents an introductory treatment of mereotopology and its discrete variant, and
provides references for readers interested in pursuing this subject in further detail
Mathematical treatment of environmental models

Large-scale environmental models can successfully be used in different\ud
important for the modern society studies as, for example, in the investigation of the\ud
influence of the future climatic changes on pollution levels in different countries.\ud
Such models are normally described mathematically by non-linear systems of par-\ud
tial differential equations, which are defined on very large spatial domains and have\ud
to be solved numerically on very long time intervals. Moreover, very often many\ud
different scenarios have also to be developed and used in the investigations. There-\ud
fore, both the storage requirements and the computational work are enormous. The\ud
great difficulties can be overcome only if the following four tasks are successfully\ud
resolved: (a) fast and sufficiently accurate numerical methods are to be selected, (b)\ud
reliable and efficient splitting procedures are to be applied, (c) the cache memories\ud
of the available computers are to be efficiently exploited and (d) the codes are to be\ud
parallelized
Network disruption and recovery: Co-evolution of defender and attacker in a dynamic game

The evolution of interactions between individuals or organizations are a central theme of complexity research. We aim at modeling a dynamic game on a network where an attacker and a defender compete in disrupting and reconnecting a network. The choices of how to attack and defend the network are governed by a Genetic Algorithm (GA) which is used to dynamically choose among a set of available strategies. Our analysis shows that the choice of strategy is particularly important if the resources available to the defender are slightly higher than the attackers'. The best strategies found through GAs by the attackers and defenders are based on betweenness centrality. Our results agree with previous literature assessing strategies for network attack and defense in a static context. However, our paper is one of the first ones to show how a GA approach can be applied in a dynamic game on a network. This research provides a starting-point to further explore strategies as we currently apply a limited set of strategies only
Searching in Unstructured Overlays Using Local Knowledge and Gossip

This paper analyzes a class of dissemination algorithms for the discovery of
distributed contents in Peer-to-Peer unstructured overlay networks. The
algorithms are a mix of protocols employing local knowledge of peers'
neighborhood and gossip. By tuning the gossip probability and the depth k of
the k-neighborhood of which nodes have information, we obtain different
dissemination protocols employed in literature over unstructured P2P overlays.
The provided analysis and simulation results confirm that, when properly
configured, these schemes represent a viable approach to build effective P2P
resource discovery in large-scale, dynamic distributed systems.Comment: A revised version of the paper appears in Proc. of the 5th
  International Workshop on Complex Networks (CompleNet 2014) - Studies in
  Computational Intelligence Series, Springer-Verlag, Bologna (Italy), March
  201
Compositional nonblocking verification with always enabled events and selfloop-only events

This paper proposes to improve compositional nonblocking verification through the use of always enabled and selfloop-only events. Compositional verification involves abstraction to simplify parts of a system during verification. Normally, this abstraction is based on the set of events not used in the remainder of the system, i.e., in the part of the system not being simplified. Here, it is proposed to exploit more knowledge about the system and abstract events even though they are used in the remainder of the system. Abstraction rules from previous work are generalised, and experimental results demonstrate the applicability of the resulting algorithm to verify several industrial-scale discrete event system models, while achieving better state-space reduction than before
Parametric Schedulability Analysis of Fixed Priority Real-Time Distributed Systems

In this paper, we address the problem of parametric schedu-\ud
lability analysis of distributed real-time systems scheduled by fixed priority. We propose two different approaches to parametric analysis. The first one is a novel analytic technique that extends single-processor sensitivity analysis to the case of distributed systems. The second approach is based on model checking of Parametric Stopwatch Automata (PSA): we generate a PSA model from a high-level description of the system, and then we apply the Inverse Method to obtain all possible behaviours of the system. Both techniques have been implemented in two software\ud
tools, and they have been compared with classical holistic analysis on two meaningful test cases. The results show that the analytic method provides results similar to classical holistic analysis in a very efficient way, whereas the PSA approach is slower but covers the entire space of solutions
Certainly Unsupervisable States

This paper proposes an abstraction method for compositional synthesis. Synthesis is a method to automatically compute a control program or supervisor that restricts the behaviour of a given system to ensure safety and liveness. Compositional synthesis uses repeated abstraction and simplification to combat the state-space explosion problem for large systems. The abstraction method proposed in this paper finds and removes the so-called certainly unsupervisable states. By removing these states at an early stage, the final state space can be reduced substantially. The paper describes an algorithm with cubic time complexity to compute the largest possible set of removable states. A practical example demonstrates the feasibility of the method to solve real-world problems
Markerless Vision-Based Skeleton Tracking in Therapy of Gross Motor Skill Disorders in Children

This chapter presents a research towards implementation of a computer vision system for markerless skeleton tracking in therapy of gross motor skill disorders in children suffering from mild cognitive impairment. The proposed system is based on a low-cost 3D sensor and a skeleton tracking software. The envisioned architecture is scalable in the sense that the system may be used as a stand-alone assistive tool for tracking the effects of therapy or it may be integrated with an advanced autonomous conversational agent to maintain the spatial attention of the child and to increase her motivation to undergo a long-term therapy
The multi-mode resource-constrained project scheduling problem

This chapter reports on a new solution approach for the multi-mode resource-constrained project scheduling problem (MRCPSP, MPS | prec | C max ). This problem type aims at the selection of a single activity mode from a set of available modes in order to construct a precedence and a (renewable and nonrenewable) resource-feasible project schedule with a minimal makespan. The problem type is known to be NP -hard and has been solved using various exact as well as (meta-)heuristic procedures. The new algorithm splits the problem type into a mode assignment and a single mode project scheduling step. The mode assignment step is solved by a satisfiability (SAT) problem solver and returns a feasible mode selection to the project scheduling step. The project scheduling step is solved using an efficient meta-heuristic procedure from literature to solve the resource-constrained project scheduling problem (RCPSP). However, unlike many traditional meta-heuristic methods in literature to solve the MRCPSP, the new approach executes these two steps in one run, relying on a single priority list. Straightforward adaptations to the pure SAT solver by using pseudo boolean nonrenewable resource constraints has led to a high quality solution approach in a reasonable computational time. Computational results show that the procedure can report similar or sometimes even better solutions than found by other procedures in literature, although it often requires a higher CPU time
Open access semantic aware business intelligence

The vision of an interconnected and open Web of data is, still, a chimera far from being accomplished. Fortunately, though, one can find several evidences in this direction and despite the technical challenges behind such approach recent advances have shown its feasibility. Semantic-aware formalisms (such as RDF and ontology languages) have been successfully put in practice in approaches such as Linked Data, whereas movements like Open Data have stressed the need of a new open access paradigm to guarantee free access to Web data. In front of such promising scenario, traditional business intelligence (BI) techniques and methods have been shown not to be appropriate. BI was born to support decision making within the organizations and the data warehouse, the most popular IT construct to support BI, has been typically nurtured with data either owned or accessible within the organization. With the new linked open data paradigm BI systems must meet new requirements such as providing on-demand analysis tasks over any relevant (either internal or external) data source in right-time. In this paper we discuss the technical challenges behind such requirements, which we refer to as exploratory BI, and envision a new kind of BI system to support this scenario.Postprint (author’s final draft
Author Name Disambiguation by Using Deep Neural Network

Author name ambiguity decreases the quality and reliability of information
retrieved from digital libraries. Existing methods have tried to solve this
problem by predefining a feature set based on expert's knowledge for a specific
dataset. In this paper, we propose a new approach which uses deep neural
network to learn features automatically from data. Additionally, we propose the
general system architecture for author name disambiguation on any dataset. In
this research, we evaluate the proposed method on a dataset containing
Vietnamese author names. The results show that this method significantly
outperforms other methods that use predefined feature set. The proposed method
achieves 99.31% in terms of accuracy. Prediction error rate decreases from
1.83% to 0.69%, i.e., it decreases by 1.14%, or 62.3% relatively compared with
other methods that use predefined feature set (Table 3)
Extensible Wind Tower

The diffusion of wind energy generators is restricted by their strong landscape impact. The PERIMA\ud
project is about the development of an extensible wind tower able to support a wind machine for several\ud
hundred kW at its optimal working height, up to more than 50 m. The wind tower has a telescopic\ud
structure, made by several tubes located inside each other with their axis in vertical direction. The lifting\ud
force is given by a jack-up system confined inside a shaft, drilled below the ground level. In the retracted\ud
tower configuration, at rest, tower tubes are hidden in the foundation of the telescopic structure, located\ud
below the ground surface, and the wind machine is the only emerging part of the system. The lifting\ud
system is based on a couple of oleodynamic cylinders that jack-up a central tube connected to the top of the\ud
tower by a spring, with a diameter smaller than the minimum tower diameter and with a length a bit greater\ud
than the length of the extended telescopic structure. The central tube works as plunger and lifts all\ud
telescopic elements. The constraint between the telescopic elements is ensured by special parts, which are\ud
kept in traction by the force of the spring and provide the resisting moment. The most evident benefit of\ud
the proposed system is attained with the use of a two-blade propeller, which can be kept horizontal in the\ud
retracted tower configuration
Semantic Context Forests for Learning-Based Knee Cartilage Segmentation
  in 3D MR Images

The automatic segmentation of human knee cartilage from 3D MR images is a
useful yet challenging task due to the thin sheet structure of the cartilage
with diffuse boundaries and inhomogeneous intensities. In this paper, we
present an iterative multi-class learning method to segment the femoral, tibial
and patellar cartilage simultaneously, which effectively exploits the spatial
contextual constraints between bone and cartilage, and also between different
cartilages. First, based on the fact that the cartilage grows in only certain
area of the corresponding bone surface, we extract the distance features of not
only to the surface of the bone, but more informatively, to the densely
registered anatomical landmarks on the bone surface. Second, we introduce a set
of iterative discriminative classifiers that at each iteration, probability
comparison features are constructed from the class confidence maps derived by
previously learned classifiers. These features automatically embed the semantic
context information between different cartilages of interest. Validated on a
total of 176 volumes from the Osteoarthritis Initiative (OAI) dataset, the
proposed approach demonstrates high robustness and accuracy of segmentation in
comparison with existing state-of-the-art MR cartilage segmentation methods.Comment: MICCAI 2013: Workshop on Medical Computer Visio
Big Data-Driven Marketing: How Machine Learning Outperforms Marketers’ Gut-Feeling

This paper shows how big data can be experimentally used at large scale for marketing purposes at a mobile network operator. We present results from a large-scale experiment in a MNO in Asia where we use machine learning to segment customers for text-based marketing. This leads to conversion rates far superior to the current best marketing practices within MNOs.
Using metadata and social network analysis, we created new metrics to identify customers that are the most likely to convert into mobile internet users. These metrics falls into three categories: discretionary income, timing, and social learning. Using historical data, a machine learning prediction model is then trained, validated, and used to select a treatment group. Experimental results with 250 000 customers show a 13 times better conversion-rate compared to the control group. The control group is selected using the current best practice marketing. The model also shows very good properties in the longer term, as 98% of the converted customers in the treatment group renew their mobile internet packages after the campaign, compared to 37% in the control group. These results show that data-driven marketing can significantly improve conversion rates over current best-practice marketing strategies.United States. Army Research Office (Cooperative Agreement Number W911NF-09-2-0053)MIT Media Lab Consortiu
Adaptive regions of interest based on HSV histograms for lane marks detection

The lane detection is a vital component of autonomous vehicle systems. Although many di erent approaches have been proposed in the literature it is still a challenge to correctly identify road lane marks under abrupt light variations. In this work a vision-based ego-lane detection system is proposed with the capability of automatically adapting to abrupt lighting changes. The proposed method automatically adjusts the feature extraction and salient point tracking cues introduced by the GOLDIE (Geometric Overture for Lane Detection by Intersections Entirety) algorithm. The variance of the lighting conditions is measured using hue-saturation histogram and abrupt light changes on the road are detected based on the di erence between histograms. Experimental comparison with previously proposed algorithms demonstrated that this method achieved e cient lane detection in the presence of shadows and headlights. In particular, the accuracy of the algorithm applied on  the footage with highest light variation increased 12.5% on average. The overall detection rate increased 4%, which illustrated the applicability of the method.Griffith Sciences, Griffith School of EngineeringFull Tex
Multi-Directional Weighted Interpolation for Wi-Fi Localisation

The rise in popularity of unmanned autonomous vehicles (UAV) has created a need for accurate positioning systems. Due to the indoor limi- tations of the Global Positioning System (GPS), research has focused on other technologies which could be used in this landscape with Wi-Fi local- isation emerging as a popular option. When implementing such a system, it is necessary to find an equilibrium between the desired level of final pre- cision, and the time and money spent training the system. We propose Multi-Directional Weighted Interpolation (MDWI), a probabilistic-based weighting mechanism to predict unseen locations. Our results show that MDWI uses half the number of training points whilst increasing accuracy by up to 24%.Griffith Sciences, School of Information and Communication TechnologyFull Tex
Coupling of a two phase gas liquid compositional 3D Darcy flow with a 1D compositional free gas flow

International audienceA model coupling a three dimensional gas liquid compositional Darcy flow and a one dimensional compositional free gas flow is presented. The coupling conditions at the interface between the gallery and the porous media account for the molar normal fluxes continuity for each component, the gas liquid thermodynami-cal equilibrium, the gas pressure continuity and the gas and liquid molar fractions continuity. This model is applied to the simulation of the mass exchanges at the interface between the repository and the ventilation excavated gallery in a nuclear waste geological repository. The convergence of the Vertex Approximate Gradient discretization is analysed for a simplified model coupling the Richards approximation in the porous media and the gas pressure equation in the gallery
A Gradient Scheme for the Discretization of Richards Equation

International audienceWe propose a finite volume method on general meshes for the discretiza-tion of Richards equation, an elliptic-parabolic equation modeling groundwater flow. The diffusion term, which can be anisotropic and heterogeneous, is discretized in a gradient scheme framework, which can be applied to a wide range of unstruc-tured possibly non-matching polyhedral meshes in arbitrary space dimension. More precisely, we implement the SUSHI scheme which is also locally conservative. As is needed for Richards equation, the time discretization is fully implicit. We obtain a convergence result based upon energy-type estimates and the application of the Fréchet-Kolmogorov compactness theorem. We implement the scheme and present the results of a number of numerical tests
A Model Reduction Framework for Efficient Simulation of Li-Ion Batteries

In order to achieve a better understanding of degradation processes in
lithium-ion batteries, the modelling of cell dynamics at the mircometer scale
is an important focus of current mathematical research. These models lead to
large-dimensional, highly nonlinear finite volume discretizations which, due to
their complexity, cannot be solved at cell scale on current hardware. Model
order reduction strategies are therefore necessary to reduce the computational
complexity while retaining the features of the model. The application of such
strategies to specialized high performance solvers asks for new software
designs allowing flexible control of the solvers by the reduction algorithms.
In this contribution we discuss the reduction of microscale battery models with
the reduced basis method and report on our new software approach on integrating
the model order reduction software pyMOR with third-party solvers. Finally, we
present numerical results for the reduction of a 3D microscale battery model
with porous electrode geometry.Comment: 7 pages, 2 figures, 2 table
A conservative well-balanced hybrid SPH scheme for the shallow-water model

International audienceA scheme defined by a hybridization between SPH method and finite volume method is considered. The aim of the present communication is to derive a suitable discretization of the source term to enforce the required well-balanced property. To address such an issue, we adopt a relevant reformulation of the flux function by involving the free surface instead of the water height. Such an approach gives a natural discretization of the topography source term in order to preserve the lake at rest. Moreover, we prove that the scheme is in conservative form, which is, in general, a very difficult task since we do not impose restrictive assumptions on the SPH method. Several 1D numerical experiments are performed to exhibit the properties of the scheme