dataesr/scientific-paragraphs-categorization huggingface dataset:
Type Results of our experiments on GITW dataset are shown in Table  1. The conclusions are as follows. First, region proposal methods such as Deepmask  [36]  or traditional strongly-supervised object detection methods as YOLO  [55]  , fail in our problem. When the gaze does not point to a particular object but to intermediate areas, they tend to propose boxes over non-active objects, thus confusing the learning algorithms. Then, among aggregation methods, LSE achieves better results than AVG. This is due to the ap-parition of distracting elements in the scene, which guide gaze to spatial areas without active objects. Simple average including these frames decreases performance. In contrast, LSE aggregates scores considering those frames with highest values (those in which the active object is fixated), and therefore yields better performance.
The weak loss proposed in CCNN  [25]  also considers the presence of distractors, providing similar results to LSE and outperforming the strongly-supervised approaches. However, none of these weak learning techniques explicitly handle noise in the way we propose in this work, analyzing the whole video sequence and estimating the most probable area to contain the active object. Hence, we can see in the table that our two proposed techniques for noise handling improve the performance of any other compared method in both mAP and mAcc. Furthermore, although individual results show that reducing noise in gaze fixations might be preferable than using confidence measures over the original gaze points, our combined version (GDOD-COMB) takes the best of the two approaches and achieves the top performance on our dataset.
Our initial intuition was that the frames close to the moment of grasping are perhaps more stable and would provide better performance. Results are presented in Fig. 8  and show the performance of our best solution GDOD-COMB as a function of the time before grasping considered during test. The length of the fixation segments varies in our dataset from 280 ms to 3 s. Although using the whole video segments is very useful during training, as we include frames showing active objects under different viewpoints and scales, optimal active object detection results ( mAP = 83. 4 and mAcc = 77. 9 ) are obtained when considering the last 600 ms be- fore grasping. Hence, we can conclude that frames are more valuable as they are closer to the final moment at which a subject is grasping the object.
Our scenario for active object detection included weak and noisy labels. Hence, we have designed methods that estimate and reduce noise present in gaze sequences, and incorporated them to a weakly-labeled active object detection system. We showed that our methods achieve better performance than other reference approaches on a dataset specifically designed for our target application. Furthermore, we have proposed a novel loss over RNNs to perform grasping action prediction, which addresses two drawbacks of traditional losses in our scenario: 1) wrong predictions of object detection block may lead to over-fitting during training, and, 2) they do not encourage early predictions of the grasping action, a key factor to optimize system usability. Our experiments have demonstrated that our loss successfully addresses both issues and enhances the system performance by a 2-5% with respect to a baseline method trained using the cross-entropy loss function.
Acknowledgement This work was partially supported by  French National Center of Scientific research  with grant Suvipp  PEPS CNRS-Idex 215-2016 , by  French National Center of Scientific  research with  Interdisciplinary  project  CNRS RoBioVis 2017-2019 , the  Scientific Council of Labri, University of Bordeaux , and the  Spanish Ministry of Economy and Competitiveness  under the National Grants  TEC2014-53390-P  and  TEC2014-61729-EXP.
In this context, especially for 3D simulations, the use of standard FEM solvers for the damage phase-field modeling becomes very cumbersome and requires an efficient parallel implementation in order to overcome the computational limits. An example of parallel implementation based on MATLAB routines has been reported in  [28] , where the largest simulation involving about 30 million elements was parallelized over 8 processors on a single workstation and completed within 357 h. This example demonstrates the need to use massively parallel solvers involving a distributed memory implementation in order to perform large-scale simulations in reasonable computation time. As an alternative to standard FEM solvers, FFT based methods (e. g. [29] [30] [31] [32] ) consist of matrix-free iterative algorithms for solving Partial Differential Equations with periodic boundary conditions: the problem is solved with no need to assemble the FE-type sparse matrix.
In addition to this, a convergence acceleration procedure (see  [33] ) is also employed to improve the performance of the fixed-point algorithm in mechanical solution. Even though these two improvements significantly reduce the dependence of convergence rate on the choice of the reference material C 0 , we still choose to follow the suggestion of  [29] , i. e. λ 0 = (min (λ) + max (λ))/2 and µ 0 = (min (µ) + max (µ))/2, to achieve an optimal convergence rate of the mechanical solution. In practice, instead of a strain-driven method as in Eq. (  12 ), where the applied load is the average strain, it is usually more convenient to apply mixed loading conditions, i. e. average stress for some components and average strain for the others. This is made possible in the code AMITEX through a slightly modified algorithm, similar to that reported in  [48].
Parallel implementation In this FFT-based solver of the phase-field problem every step involves local operations only, except the FFT and inverse FFT. In addition, open sources of powerful FFT packages are available to complete the FFT and its inverse in a parallel fashion. Therefore, the proposed algorithm inherits the simplicity in parallel implementation of the classical FFT methods. The present implementation of the phase field problem has been introduced in a FFT-based code "AMITEX" solving mechanical problems at small and finite-strains in a massively parallel context. The code relies on the 2DECOMP&FFT library  [51]  for the distributed memory implementation and readers interested in this aspect are invited to consult the documentation found on  [52]  and some practical aspects such as the loading conditions and convergence criteria, can be found in  [53].
The growth of crack topology is shown in Fig. 19. The simulation captured the crack initiation and the influence of fibers on the crack propagation. This simulation with over 32 million voxels has been repeated three times on a cluster using one, four or eight nodes (28 cores per node). In other words, the simulation has been parallelized over 28, 112 and 224 cores. Each node consists of two Intel Xeon 6132 processors (14 cores per processor, 2. 6 GHz, Slylake R ⃝ technology). An intel OmniPath 100 network is used for inter-node communications. The code has been compiled with the intel 15 compiler together with the intel MPI library, version 5. The computation time (wall time) and efficiency are summarized in Table  4 , the efficiency being defined as E(N ) = T 1 /(N T N ), with T 1 and T N the wall time on 1 node and N nodes respectively.
Introduced in the AMITEX code and taking advantage of its distributed memory parallel implementation, a simulation with over 32 million voxels has demonstrated the attractive potential of the solver for large-scale simulations. As a conclusion, the FFT solver proposed in this paper is an efficient alternative to standard FEM solvers of phase-field models for brittle fracture, especially in the context of image-based modeling where a large number of voxels are usually involved. ε + = ( Q t • s • Q) • ε (A. 5) Thus, the projection tensors P + , P -are constructed by P + = Q t • s • Q; P -= I -P + (A. 6) where I is unitary 3 × 3 matrix. The explicit expression of the projection tensor P + is: P + = ⎛ ⎜ ⎜ ⎝ s 1 q 11 q 11 + s 2 q 21 q 21 + s 3 q 31 q 31 s 1 q 11 q 12 + s 2 q 21 q 22 + s 3 q 31 q 32 s 1 q 11 q 13 + s 2 q 21 q 23 + s 3 q 31 q 33 s 1 q 11 q 12 + s 2 q 21 q 22 + s 3 q 31 q 32 s 1 q 12 q 12 + s 2 q 22 q 22 + s 3 q 32 q 32 s 1 q 12 q 13 + s 2 q 22 q 23 + s 3 q 32 q 33 s 1 q 11 q 13 + s 2 q 21 q 23 + s 3 q 31 q 33 s 1 q 12 q 13 + s 2 q 22 q 23 + s 3 q 32 q 33 s 1 q 13 q 13 + s 2 q 23 q 23 + s 3 q 33 q 33 ⎞ ⎟ ⎟ ⎠ (A.
Acknowledgment The authors would like to acknowledge the  European Union  ( European Regional Development Fund, ERDF/FEDER) , the  French state and the Hauts-de-France Region Council  for partly funding the ELSAT2020 by  CISIT  project (POPCOM action). Our gratitude is to  Dr. Julien Derouillat  from  Maison de la Simulation  for his contribution to the improvement of AMITEX code. We also would like to thank  Prof. James Marrow  from  Oxford university  for the language proofreading of the manuscript.
They suggested that, if the scientific community is able to (i) overcome the production of large training datasets, (ii) more precisely identify and evaluate the error rates, (iii) scale up automated approaches, and (iv) detect novel species, it will then be possible to initiate the development of a generic automated species identification system that could open up vistas of new opportunities for theoretical and applied work in biological and related fields. Since the question raised by Gaston and O'Neill  [2] , automated species identification: why not?, a lot of work has been done on the topic (e. g. [13, 1, 17, 16, 4, 5, 11] ) and it is still attracting much research today, in particular in deep learning  [3, 6, 14]. In order to measure the progress made in a sustainable and repeatable way, the LifeCLEF 9  research platform was created in 2014 as a continuation of the plant identification task  [10]  that was run within the ImageCLEF lab 10  the three years before  [8, 9, 7].
Methodology The plant identification challenge of CLEF has been run since 2011, offering today a seven-year follow-up of the progress made in image-based plant identification. From the beginning, it mainly relied on real-world collaborative data and the evaluation protocol was defined in collaboration with biologists so as to reflect realistic usage scenarios. In particular, it considers the problem of classifying plant observations based on several images of the same individual plant rather than considering a classical image classification task. Indeed, it is usually required to observe several organs of a plant to identify it accurately (e. g. the flower, the leaf, the fruit, the stem, etc. ). As a consequence, the same individual plant is often photographed several times by the same observer resulting in contextually similar pictures and/or near-duplicates.
Methodology The bird identification challenge of LifeCLEF, initiated in 2014 in collaboration with Xeno-Canto, considerably increased the scale of the seminal challenges. The first bird challenge ICML4B  [4]  initiated in 2012 by DYNI/SABIOD had only 35 species, but received 400 runs. The next at MLSP had only 15 species, the third (NIPS4B  [5]  in 2013 by SABIOD) had 80 species. Meanwhile, Xeno-canto, launched in 2005, hosts bird sounds from all continents and daily receives new recordings from some of the remotest places on Earth. It currently archives with 379472 recordings, 9779 species of birds, making it one of the most comprehensive collections of bird sound recordings worldwide, and certainly the most comprehensive collection shared under Creative Commons licenses. For the first Bird-CLEF challenge, it was decided to not consider the whole Xeno-Canto dataset but to rather focus on a specific region, i.
e. the Amazonian rain forest because it is one of the richest in the world in terms of biodiversity but also one of the most endangered. The geographical extent and the number of species were progressively increased over the years so as to reach 1000 species in 2015/2016, and 1500 in 2017/2018. By nature, the Xeno-Canto data as well as the BirdCLEF subset has a massive class imbalance. For instance, the 2017 dataset contains 48,843 recordings in total, with a minimum of four recordings for Laniocera rufescens and a maximum of 160 recordings for Henicorhina leucophrys. In 2016, the BirdCLEF challenge was extended to soundscape recordings in addition to the classical mono-directional Xeno-Canto recordings. This enables more passive monitoring scenarios such as setting up a network of static recorders that would continuously capture the surrounding sound environment.
BirdCLEF 2019 The 2019 edition of the BirdCLEF challenge will mainly focus on the soundscape scenario that remains very challenging whereas the mono-directional identification task is now better solved. Two tasks will be evaluated, (i) the recognition of all specimens singing in a long sequence (up to one hour) of raw soundscapes that can contain tens of birds singing simultaneously, and (ii) source separation or source count estimation in complex soundscapes that were recorded using multiple microphones. Therefore, two new corpus of soundscapes will be added to the existing soundscape dataset: (i) 100+ hours of manually annotated soundscapes recorded using 30 field recorders between January and June of 2017 in Ithaca, NY, USA. (ii) 50 hours of four-channel or stereophonic binaural recordings acquired in Papa New Guinea in november 2017 at high sampling rate (96 kHz SR) and high dynamics (24 bits)  [15].
GeoLifeCLEF 2019 The 2019 edition of the challenge will tackle some of the methodological weaknesses that were revealed by the pilot 2018 edition. In particular, we will rely on the top-30 accuracy instead of the Mean Average Precision as the main evaluation metric. This will allow to better take into account the fact that many species co-exist at small spatial scales (under the meter), much lower than the accuracy of the geo-coordinates in the data set. We will also produce a new dataset fixing some issues of the previous one related to the incompleteness of some environmental variables and the spatial degradation of some occurrences. More precisely, the training set will be composed of nearly one million geo-locations of plant species living on the French territory (coming from two main platforms: (i) the Global Biodiversity Information Facility and (ii), the Pl@ntNet participatory application).
Discussion and conclusion Boosting research on biodiversity informatics in the long term is crucial in terms of societal impact. Researchers are actually often opportunistic regarding the choice of a dataset and an interesting related challenge. And so are end-users regarding the use of applications emerging from that research. To fully reach its objective, an evaluation campaign such as LifeCLEF requires a long-term research effort so as to (i) encourage non-incremental contributions, (ii) measure consistent performance gaps, (iii) progressively scale-up the problem and (iv), enable the emergence of a strong community. The 2019-th edition of the lab will support this vision but will still include a set of consistent novelties: -The historical BirdCLEF subtask related to monospecies recordings will be stopped in order to concentrate all efforts on the most challenging subtask of recognizing birds in soundscapes and on a new subtask relying on polyphonic recordings.
This paper presents the architecture of an interactive scheduling decision support system (ISDSS) allowing users to find the optimal solution for fertilizer production on parallel heterogeneous processors. The proposed approach takes into account different production process constraints such as launch time, delivery date, preventive maintenance and the impact of scheduling on supply chain management. The ISDSS implemented is run by a relational database used to customize the structural data and the problem parameters. A user interface is available for ISDSS users to define the scheduling problem and design the solution based on tables and graphs for detecting possible issues. The ISDSS architecture was implemented on java using independent modules.
Literature review The following analysis reviews a number of papers published in scientific newspaper or proceedings focusing on Decision Support Systems for production scheduling. An optimization-based Decision Support System (OBDSS) generator, designed by  [9]  automatically generates a database, a solver and an interface (GUI) using PowerBuilder. They applied the OBDSS generated in the production plant. The approach, called GESCOPP, uses SML technology to represent the optimization model and generate the database. Olteanu (2011)  addresses in  [10]  production plant in the biofuel industry. His system optimizes the main activities of the biofuel value chain and supports the decisionmaking process at management level. It reduces total production cost to the lowest possible level under current technological and market constraints.
Similarly, MA is limited to the Windows platform and doesn't provide high scalability and security. In the same analytical dimension, other solutions consist in web-based approaches integrating data from different systems  [12, 17]. b) Each DSS requires problem-solving capacities. The proposed optimization processes are based on different local approaches (simulation  [11, 15, 19] , optimization  [13] , heuristics  [20] ) or general software (ERP) and their APS (Advanced Planning System,  [12] ). Some of those DSSs are implemented through such programming languages as VB (Visual Basic), VBA (Visual Basic for Application), Visual C++. Although VBA or VB are user-friendly languages, they are Windows-compatible only and exclude an object-oriented approach. c) Few DSSs propose interfaces able to transform a semi-structured problem into a structured one.
Downstream, the results processing interface is generally quite basic. The DSSs proposed in  [9, 11, 12, 13, 15, 17, 19]  use Web-based technology, Visual Basic or Visual C++ or DSS generator. d) Finally, some papers describe approaches using generic system aiming to take the three first dimensions (a, b, c) into account  [14, 9]  using technology such as IBM or DSS generators. Nowadays IBM products deliver multiple benefits especially in dealing with big data and computing capacity. Nevertheless, the most IBM packages will only work in IBM environments and deployment is very expensive. Turning to the DSS generator, this is a general approach based on structural data medialization and its implementation using a tagging system such as XML (eXtensible Markup Language). In itself, the general nature of such generators is restrictive because, for each request, one needs to formalize the new data and make fresh definitions of the constraints according to the working environment.
ISDSS Technical Architecture The structured problem of our ISDSS optimization modules is modeled using an AML (Algebraic Modeling Language), which allows dissociating the formulation of a generic problem from a data set, thereby enabling the instantiation of a specific problem. The cardinalities of the set of parameters and variables are totally customizable. To preserve this thorough customization capacity, it makes sense to use a relational database for the ISDSS, as this is also characterized by no-predefined cardinalities of the entities and associations and guarantees the integrity of the data in the database under a set of given rules. These characteristics lend the ISDSS an ability to adjust immediately to any changes in the number of parallel processors, inputs, outputs, routings, bills of materials (BOM) and costs.
If not go to 6. 2. Place each order using a backward scheduling with the latest due dates and including the preventive maintenance allowance. If possible go to 4, if not go to 3. 3. Place each order using a forward scheduling with the earliest due dates and including the preventive maintenance allowance. If possible go to 4, if not go to 5. 4. End: the problem is feasible. 5. End: the problem posed seems feasible. 6. End: the problem is not feasible. If the problem is feasible, a file containing all problem parameters is generated. Its structure is defined by the generic optimization model integrated into the ISDSS. The combination of this generic model and this dataset generates an instance of the fertilizer scheduling problem.
d) Process Optimizer module A generic problem  [1, 2]  based on the AML of FICO Xpress  [20]  is activated by a dataset including all the information defining the specific problem to generate a specific problem to be solved. The file of the proposed solution is then available for the solution analysis module. This model, created with the Fico Xpress-IVE modeller  [20] , imports the dataset created by the previous module. The optimization results are then exported to a file that is used by the next module.
ISDSS Implementation Obviously, the success of any information system depends on the technologies deployed and on the integrity of the data managed by the database. Our work differs from that of the papers discussed in section 2. 1. Indeed, we propose a framework relative to an ISDSS using generic and portable implementation combined with data management which ensures integrity and uses an object-oriented concept enabling proper flexibility of the cardinalities. All the ISDSS modules are grouped into a package that can run separately or following the ISDSS workflow. For the deployment environment, the ISDSS is implemented on java which is easy to learn, object-oriented, robust, safe and platform-independent. The ISDSS API implements different libraries, such as mosel. jar to interact with external tools or jfreechart.
ISDSS: Experiment A study of different datasets was used to evaluate our proposed approach and architecture. In this section, we present a scenario using the ISDSS to define a fertilizer scheduling problem, the feasibility algorithm and the solution is then generated. The case study presented in this section concerns fertilizer production at OCP. The initial setup of ISDSS consists in integration of the structural data related to our customer including the lines, the fertilizer, the inputs, BOM (Fig. 4 ) and launch time required between qualities (Fig. 5 ).
It makes use of the BIONEAT mutation operators to generate individuals of varying topologies across a range of number of templates. In one optimization run, 10% of the individuals are initialized with a random topology (i. e. , 300 individuals for an evaluation budget of 3000). This contrasts with the BIONEAT approach, where only small iterative changes in topologies are possible from mutations, and where individuals with totally new topologies are not initialized. Table  2  lists the chosen parameters for the MAP-Elites algorithm. We use our own implementation of BIONEAT and MAP-Elites. BIONEAT is open source and available from https://bitbucket. org/AubertKato/bioneat/. MAP-Elites is coded in Python using the QDpy library  [2]  and freely available as open source software at https://gitlab.
We first compiled a database of 124 large dam projects in the basin (Materials and Methods, Fig. 1 , figs. S1 and S2, and data S1). Second, we set up the CASCADE (CAtchment Sediment Connectivity And DElivery) sediment routing model for the entire basin (23) (Materials and Methods, figs. S3 and S4, and Supplementary Methods 2 and 3). We developed an approach combining CASCADE with a multiobjective evolutionary algorithm  (24)  to derive dam sequences that minimize trade-offs between power generation and sediment supply to the lower Mekong. We present this analysis for different initial conditions, i. e. , starting from a pristine basin or a basin with existing dams, and for different scales, i. e. , for the entire basin and for the lower basin countries only (Materials and Methods). We then derive optimal dam sequences by post-processing the derived PO dam portfolio (Materials and Methods, Supplementary Method 6, figs.