pinecone/core-2020-05-10-deduplication huggingface dataset:
A multi-objective optimization approach associated to climate change analysis to improve Systematic Conservation Planning

Biodiversity conservation has been since long an academic community concern, leading scientists to propose strategies to effectively meet conservation goals. In particular, Systematic Conservation Planning (SCP) aims to determine the most cost effective way of investing in conservation actions. SCP can be formalized by the Set-Covering Problem, which is NP-hard. SCP is inherently multi-objective, although it has been usually treated with a monobjective and static approach. Here, we propose a multi-objective solution for SCP, increasing its flexibility and complexity, and, at the same time, augmenting the quality of provided information, which reinforces decision-making. We used ensemble forecasting, considering future climate simulations to estimate species occurrence projected to 2080. Our method identifies sites: 1) of high priority for conservation; 2) with significant risk of investment; and, 3) that may become attractive in the future. To the best of our knowledge, this application to a real-world problem in ecology is the first attempt to apply multi-objective optimization to SCP associated to climate forecasting, in a dynamic spatial prioritization analysis for biodiversity conservation.CNPq throughout a Science without Borders scholarshipThe Royal SocietyCNPq/MCT/CAPES (projects #564717/2010-0 and #563624/2010-8)GECER (PRONEX/FAPEG/CNPq CP 07-2009)International Conference on Evolutionary Multi-criterion Optimization - EMO (8. 2015 Guimarães
Combining data mining and evolutionary computation for multi-criteria optimization of earthworks

Earthworks tasks aim at levelling the ground surface at a target construction area and precede any kind of structural construction (e.g., road and railway construction). It is comprised of sequential tasks, such as excavation, transportation, spreading and compaction, and it is strongly based on heavy mechanical equipment and repetitive processes. Under this context, it is essential to optimize the usage of all available resources under two key criteria: the costs and duration of earthwork projects. In this paper, we present an integrated system that uses two artificial intelligence based techniques: data mining and evolutionary multi-objective optimization. The former is used to build data-driven models capable of providing realistic estimates of resource productivity, while the latter is used to optimize resource allocation considering the two main earthwork objectives (duration and cost). Experiments held using real-world data, from a construction site, have shown that the proposed system is competitive when compared with current manual earthwork design
Comparison of single and multi-objective evolutionary algorithms for robust link-state routing

Traffic Engineering (TE) approaches are increasingly impor- tant in network management to allow an optimized configuration and resource allocation. In link-state routing, the task of setting appropriate weights to the links is both an important and a challenging optimization task. A number of different approaches has been put forward towards this aim, including the successful use of Evolutionary Algorithms (EAs). In this context, this work addresses the evaluation of three distinct EAs, a single and two multi-objective EAs, in two tasks related to weight setting optimization towards optimal intra-domain routing, knowing the network topology and aggregated traffic demands and seeking to mini- mize network congestion. In both tasks, the optimization considers sce- narios where there is a dynamic alteration in the state of the system, in the first considering changes in the traffic demand matrices and in the latter considering the possibility of link failures. The methods will, thus, need to simultaneously optimize for both conditions, the normal and the altered one, following a preventive TE approach towards robust configurations. Since this can be formulated as a bi-objective function, the use of multi-objective EAs, such as SPEA2 and NSGA-II, came nat- urally, being those compared to a single-objective EA. The results show a remarkable behavior of NSGA-II in all proposed tasks scaling well for harder instances, and thus presenting itself as the most promising option for TE in these scenarios
Extending CPN tools with ontologies to support the management of context-adaptive business processes

Colored Petri Nets (CPN) are a widely used graphical modeling language to manage business processes. Business processes often appear in dynamic environments; therefore, context adaptation has recently emerged as a new challenge to explicitly addressfitness between business process modeling and its execution environment. Although CPN
can introduce data by dedefining internal data records, this is not enough to capture the complexity and dynamics of the execution context data. This paper extends CPN tools to support the management of context-adaptive business processes. To achieve this challenge, CPN tools are integrated with ontology-based context models that properly represent and manage the business process context. This allows context to be appropriately modeled at design time, and queried and updated at runtime. The combination of ontologies with CPN tools presents a way to bridge business processes management with context data management while
treating data and behavior as separate concerns. In this way, system design, reuse, and maintenance are also improved.status: publishe
Generating business process recommendations with a population-based meta-heuristic

In order to provide both guidance and flexibility to users during process execution, recommendation systems have been proposed. Existing recommendation systems mainly focus on offering recommendation according to the process optimization goals (time, cost…). In this paper we offer a new approach that primarily focuses on maximizing the flexibility during execution. This means that by following the recommendations, the user retains maximal flexibility to divert from them later on. This makes it possible to handle (possibly unknown) emerging constraints during execution. The main contribution of this paper is an algorithm that uses a declarative process model to generate a set of imperative process models that can be used to generate recommendations
Generating Robust and Efficient Networks Under Targeted Attacks

Much of our commerce and traveling depend on the efficient operation of large
scale networks. Some of those, such as electric power grids, transportation
systems, communication networks, and others, must maintain their efficiency
even after several failures, or malicious attacks. We outline a procedure that
modifies any given network to enhance its robustness, defined as the size of
its largest connected component after a succession of attacks, whilst keeping a
high efficiency, described in terms of the shortest paths among nodes. We also
show that this generated set of networks is very similar to networks optimized
for robustness in several aspects such as high assortativity and the presence
of an onion-like structure
Modelling attitudes to climate change : an order effect and a test between alternatives

Quantum-like models can be fruitfully used to model attitude change in a social context. Next steps require data, and higher dimensional models. Here, we discuss an exploratory study that demonstrates an order effect when three question sets about Climate Beliefs, Political Affiliation and Attitudes Towards Science are presented in different orders within a larger study of n=533 subjects. A quantum-like model seems possible, and we propose a new experiment which could be used to test between three possible models for this scenario
Towards a quantum probability theory of similarity judgments

We review recent progress in understanding similarity judgments in cognition by means of quantum probability theory (QP) models. We begin by outlining some features of similarity judgments that have proven difficult to model by traditional approaches. We then briefly present a model of similarity judgments based on QP, and show how it can solve many of the problems faced by traditional approaches. Finally we look at some areas where the quantum model is currently less satisfactory, and discuss some open questions and areas for further work
Experiments on local search for bi-objective unconstrained binary quadratic programming

International audienceThis article reports an experimental analysis on stochastic local search for approximating the Pareto set of bi-objective unconstrained binary quadratic programming problems. First, we investigate two scalarizing strategies that iteratively identify a high-quality solution for a sequence of sub-problems. Each sub-problem is based on a static or adaptive definition of weighted-sum aggregation coefficients, and is addressed by means of a state-of-the-art single-objective tabu search procedure. Next, we design a Pareto local search that iteratively improves a set of solutions based on a neighborhood structure and on the Pareto dominance relation. At last, we hybridize both classes of algorithms by combining a scalarizing and a Pareto local search in a sequential way. A comprehensive experimental analysis reveals the high performance of the proposed approaches, which substantially improve upon previous best-known solutions. Moreover, the obtained results show the superiority of the hybrid algorithm over non-hybrid ones in terms of solution quality, while requiring a competitive computational cost. In addition, a number of structural properties of the problem instances allow us to explain the main difficulties that the different classes of local search algorithms have to face
Combining non-dominance, objective-order and spread metric to extend firefly algorithm to multi-objective optimization

In this paper, we propose an extension of the firefly algorithm (FA) to multi-objective optimization. FA is a swarm intelligence optimization algorithm inspired by the flashing behavior of fireflies at night that is capable of computing global solutions to continuous optimization problems. Our proposal relies on a fitness assignment scheme that gives lower fitness values to the positions of fireflies that correspond to non-dominated points with smaller aggregation of objective function distances to the minimum values. Furthermore, FA randomness is based on the spread metric to reduce the gaps between consecutive non-dominated solutions. The obtained results from the preliminary computational experiments show that our proposal gives a dense and well distributed approximated Pareto front with a large number of points.. The authors thank the anonymous referees for the valuable suggestions. This work has been supported by FCT (Funda¸c˜ao para a Ciˆencia e Tecnologia, Portugal) in the scope of the projects: PEst-OE/MAT/UI0013/2014 and PEstOE/EEI/UI0319/2014
Adaptive reference vector generation for inverse model based evolutionary multiobjective optimization with degenerate and disconnected pareto fronts

Inverse model based multiobjective evolutionary algorithm aims to sample candidate solutions directly in the objective space, which makes it easier to control the diversity of non-dominated solutions in multiobjective optimization. To facilitate the process of inverse modeling, the objective space is partitioned into several subregions by predefining a set of reference vectors. In the previous work, the reference vectors are uniformly distributed in the objective space. Uniformly distributed reference vectors, however, may not be efficient for problems that have nonuniform or disconnected Pareto fronts. To address this issue, an adaptive reference vector generation strategy is proposed in this work. The basic idea of the proposed strategy is to adaptively adjust the reference vectors according to the distribution of the candidate solutions in the objective space. The proposed strategy consists of two phases in the search procedure. In the first phase, the adaptive strategy promotes the population diversity for better exploration, while in the second phase, the strategy focused on convergence for better exploitation. To assess the performance of the proposed strategy, empirical simulations are carried out on two DTLZ benchmark problems, namely, DTLZ5 and DTLZ7, which have a degenerate and a disconnected Pareto front, respectively. Our results show that the proposed adaptive reference vector strategy is promising in tacking multiobjective optimization problems whose Pareto front is disconnected
Spreadsheet engineering

These tutorial notes present a methodology for spreadsheet engineering. First, we present data mining and database techniques to reason about spreadsheet data. These techniques are used to compute relationships between spreadsheet elements (cells/columns/rows). These relations are then used to infer a model defining the business logic of the spreadsheet. Such a model of a spreadsheet data is a visual domain specific language that we embed in a well-known spreadsheet system. The embedded model is the building block to define techniques for modeldriven spreadsheet development, where advanced techniques are used to guarantee the model-instance synchronization. In this model-driven environment, any user data update as to follow the the model-instance conformance relation, thus, guiding spreadsheet users to introduce correct data. Data refinement techniques are used to synchronize models and instances after users update/evolve the model. These notes brie y describe our model-driven spreadsheet environment, the MDSheet environment, that implements the presented methodology. To evaluate both proposed techniques and the MDSheet tool, we have conducted, in laboratory sessions, an empirical study with the summer school participants. The results of this study are presented in these notes
Securely solving classical network flow problems

We investigate how to solve several classical network flow problems using secure multi-party computation. We consider the shortest path problem, the Minimum Mean Cycle problem and the Minimum Cost Flow problem. To the best of our knowledge, this is the first time the two last problems have been addressed in a general multi-party computation setting. Furthermore, our study highlights the complexity gaps between traditional and secure implementations of the solutions, to later test its implementation. It also explores various trade-offs between performance and security. Additionally it provides protocols that can be used as building blocks to solve complex problems. Applications of our work can be found in: communication networks, routing data from rival company hubs; benchmarking, comparing several IT appliances configurations of rival companies; distribution problems, retailer/supplier selection in multi-level supply chains that want to share routes without disclosing sensible information; amongst others
Tackling Exascale Software Challenges in Molecular Dynamics Simulations
  with GROMACS

GROMACS is a widely used package for biomolecular simulation, and over the
last two decades it has evolved from small-scale efficiency to advanced
heterogeneous acceleration and multi-level parallelism targeting some of the
largest supercomputers in the world. Here, we describe some of the ways we have
been able to realize this through the use of parallelization on all levels,
combined with a constant focus on absolute performance. Release 4.6 of GROMACS
uses SIMD acceleration on a wide range of architectures, GPU offloading
acceleration, and both OpenMP and MPI parallelism within and between nodes,
respectively. The recent work on acceleration made it necessary to revisit the
fundamental algorithms of molecular simulation, including the concept of
neighborsearching, and we discuss the present and future challenges we see for
exascale simulation - in particular a very fine-grained task parallelism. We
also discuss the software management, code peer review and continuous
integration testing required for a project of this complexity.Comment: EASC 2014 conference proceedin
Color and Spectral Mixings in Printed Surfaces

International audienceThe present paper discusses the concept of subtractive color mixing widely used in color hardcopy applications and shows that a more realistic concept would be " spectral mixing " : the physical description of the coloration of light by printed surfaces comes from the mixing of light components selectively absorbed by inks or dyes during their patch within the printing materials. Some classical reflectance equations for continuous tone and halftone prints are reviewed and considered as spectral mixing laws. The challenge of extending these models to new inkless printing processes based on laser radiation is also addressed. Color mixing is a key-concept in color reproduction, either by painting, printing, or displaying. It refers to the observation that a large panel of colors (the color gamut) can be achieved by varying the amount of a limited set of base colors, called primaries. With light emitting systems, the primaries are light sources, often with red, green and blue color, that are either superposed or juxtaposed with a shorter period than the visual acuity. Since the tristimulus values of the produced colors is a linear, additive combination of the tristimulus values of the three primaries, this type of color mixing has been called additive color mixing. This concept, based on Grassman's additivity law, enabled the color matching experiments at the basis of colorimetry [1]. In opposition to the light emitting systems, paintings and printed hardcopies selectively attenuate the incident white light in different proportions according to the wavelength. Layers of primaries, paints or inks, are coated on a reflecting support and play a role of spectral filtering of light. This type of color mixing is improperly called subtractive color mixing [2], by reference to the fact that part of the incident light is removed by filtering, but the tristimulus values of paint or ink mixtures cannot be obtained by combining the tristimulus values of the primaries; it is therefore not a color mixing in the sense of colorimetry. However, the subtractive color mixing is also related to a physical experience, which consists in producing many colors by mixing nonscattering dyes, usually of cyan, magenta and yellow color. According to the Beer-Lambert-Bouguer law [1], the spectral absorption coefficient of the dye mixture, () K λ , is a linear, additive combi-The final publication is available at http://link.springer.co
Evaluation of the Colorimetric Performance of Single-Sensor Image Acquisition Systems Employing Colour and Multispectral Filter Array

International audienceSingle-sensor colour imaging systems mostly employ a colour filter array (CFA). This enables the acquisition of a colour image by a single sensor at one exposure at the cost of reduced spatial resolution. The idea of CFA fit itself well with multispectral purposes by incorporating more than three types of filters into the array which results in multispectral filter array (MSFA). In comparison with a CFA, an MSFA trades spatial resolution for spectral resolution. A simulation was performed to evaluate the colorimetric performance of such CFA/MSFA imaging systems and investigate the trade-off between spatial resolution and spectral resolution by comparing CFA and MSFA systems utilising various filter characteristics and demosaicking methods including intra-and inter-channel bilinear interpolation as well as discrete wavelet transformed based techniques. In general, 4-band and 8-band MSFAs provide better or comparable performance than the CFA setup in terms of CIEDE2000 and S-CIELAB colour difference. This indicates that MSFA would be favourable for colorimetric purposes
Data Fusion of Objects Using Techniques Such as Laser Scanning,
  Structured Light and Photogrammetry for Cultural Heritage Applications

In this paper we present a semi-automatic 2D-3D local registration pipeline
capable of coloring 3D models obtained from 3D scanners by using uncalibrated
images. The proposed pipeline exploits the Structure from Motion (SfM)
technique in order to reconstruct a sparse representation of the 3D object and
obtain the camera parameters from image feature matches. We then coarsely
register the reconstructed 3D model to the scanned one through the Scale
Iterative Closest Point (SICP) algorithm. SICP provides the global scale,
rotation and translation parameters, using minimal manual user intervention. In
the final processing stage, a local registration refinement algorithm optimizes
the color projection of the aligned photos on the 3D object removing the
blurring/ghosting artefacts introduced due to small inaccuracies during the
registration. The proposed pipeline is capable of handling real world cases
with a range of characteristics from objects with low level geometric features
to complex ones
Recent Change - Marine Circulation and Stratification

This chapter describes recent change in the circulation and stratification of the Baltic Sea. A recent warming trend in sea-surface waters has been clearly demonstrated by in situ measurements, remote sensing data and numerical models. Trends in sea-surface temperature (SST) for the past three to four decades based on remote sensing data generally agree with trends determined from in situ observations. Models suggest the current warming within the Baltic Sea lies within the range experienced during the past 500 years. The salinity and stratification of the deep waters are strongly linked to the major inflows of North Sea water that occur sporadically and bring high-saline water into the deep layers of the Baltic Sea. The major inflows normally occur during winter and spring and bring cold oxygen-rich waters into the deep basins. Since 1996, large inflows have also occurred during summer, bringing in warm low-oxygen water
Quantization and Compressive Sensing

Quantization is an essential step in digitizing signals, and, therefore, an indispensable component of any modern acquisition system. This chapter explores the interaction of quantization and compressive sensing and examines practical quantization strategies for compressive acquisition systems. Specifically, we first provide a brief overview of quantization and examine fundamental performance bounds applicable to any quantization approach. Next, we consider several forms of scalar quantizers, namely uniform, non-uniform, and 1-bit. We provide performance bounds and fundamental analysis, as well as practical quantizer designs and reconstruction algorithms that account for quantization. Furthermore, we provide an overview of Sigma-Delta (Σ Δ) quantization in the compressed sensing context, and also discuss implementation issues, recovery algorithms, and performance bounds. As we demonstrate, proper accounting for quantization and careful quantizer design has significant impact in the performance of a compressive acquisition system
Exploiting Outer Loops Vectorization in High Level Synthesis

Synthesis of DoAll loops is a key aspect of High Level Synthesis since they allow to easily exploit the potential parallelism provided by programmable devices. This type of parallelism can be implemented in several ways: by duplicating the implementation of body loop, by exploiting loop pipelining or by applying vectorization.\ud
In this paper a methodology for the synthesis of complex DoAll loops based on outer vectorization is proposed. Vectorization is not limited to the innermost loops: complex constructs such as nested loops, conditional constructs and function calls are supported. Experimental results on parallel benchmarks show up to 7.35x speed-up and up to 40 % reduction of area-delay product